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PaddleDetection
未验证 提交 8192c758 编写于 作者: Q qingqing01 提交者： GitHub
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Polish reader to simplify preprocessing logic. (#112) 

* Polish Reader to simplify preprocessing logic.
#  sample_transforms-> make batch -> batch_transforms in Reader.
* Clean some code
* Imporve yolov3_r50vd_dcn_obj365_pretrained_coco 41.4 to 41.8.
* Update all configs.
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Showing with 4022 addition and 6918 deletion
.pre-commit-config.yaml
浏览文件 @ 8192c758
...@@ -11,17 +11,17 @@ ...@@ -11,17 +11,17 @@
- id: detect-private-key - id: detect-private-key
files: (?!.*paddle)^.*$ files: (?!.*paddle)^.*$
- id: end-of-file-fixer - id: end-of-file-fixer
files: \.md$ files: \.(md|yml)$
- id: trailing-whitespace - id: trailing-whitespace
files: \.md$ files: \.(md|yml)$
- repo: https://github.com/Lucas-C/pre-commit-hooks - repo: https://github.com/Lucas-C/pre-commit-hooks
sha: v1.0.1 sha: v1.0.1
hooks: hooks:
- id: forbid-crlf - id: forbid-crlf
files: \.md$ files: \.(md|yml)$
- id: remove-crlf - id: remove-crlf
files: \.md$ files: \.(md|yml)$
- id: forbid-tabs - id: forbid-tabs
files: \.md$ files: \.(md|yml)$
- id: remove-tabs - id: remove-tabs
files: \.md$ files: \.(md|yml)$
configs/cascade_mask_rcnn_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: CascadeMaskRCNN architecture: CascadeMaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -86,7 +83,7 @@ MaskAssigner: ...@@ -86,7 +83,7 @@ MaskAssigner:
resolution: 28 resolution: 28
CascadeBBoxHead: CascadeBBoxHead:
head: CascadeTwoFCHead head: CascadeTwoFCHead
nms: nms:
keep_top_k: 100 keep_top_k: 100
nms_threshold: 0.5 nms_threshold: 0.5
...@@ -113,33 +110,4 @@ OptimizerBuilder: ...@@ -113,33 +110,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/cascade_rcnn_cls_aware_r101_vd_fpn_1x_softnms.yml
浏览文件 @ 8192c758
architecture: CascadeRCNNClsAware architecture: CascadeRCNNClsAware
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -107,80 +104,6 @@ OptimizerBuilder: ...@@ -107,80 +104,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
sample_transforms:
- !DecodeImage
to_rgb: True
with_mixup: False
- !NormalizeImage
is_channel_first: false
is_scale: True
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
- !ResizeImage
interp: 1
target_size:
- 800
max_size: 1333
use_cv2: true
- !Permute
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: True
with_mixup: False
- !NormalizeImage
is_channel_first: false
is_scale: True
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
- !ResizeImage
interp: 1
target_size:
- 800
max_size: 1333
use_cv2: true
- !Permute
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
\ No newline at end of file
configs/cascade_rcnn_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: CascadeRCNN architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -104,34 +101,6 @@ OptimizerBuilder: ...@@ -104,34 +101,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/cascade_rcnn_r50_fpn_1x_ms_test.yml
浏览文件 @ 8192c758
architecture: CascadeRCNN architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -111,23 +108,22 @@ OptimizerBuilder: ...@@ -111,23 +108,22 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed:
_READER_: 'faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed: EvalReader:
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
multi_scale: true
num_scales: 18
use_flip: true
dataset: dataset:
!COCODataSet
dataset_dir: dataset/coco dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json anno_path: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
...@@ -160,18 +156,6 @@ FasterRCNNEvalFeed: ...@@ -160,18 +156,6 @@ FasterRCNNEvalFeed:
- !Permute - !Permute
channel_first: true channel_first: true
to_bgr: false to_bgr: false
batch_transforms: - !PadMultiScaleTest
- !PadMSTest
pad_to_stride: 32
num_scale: 18
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32 pad_to_stride: 32
drop_last: false worker_num: 2
num_workers: 2
configs/dcn/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_s1x.yml
浏览文件 @ 8192c758
architecture: CascadeMaskRCNN architecture: CascadeMaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 300000 max_iters: 300000
snapshot_iter: 10 snapshot_iter: 10
use_gpu: true use_gpu: true
log_iter: 20 log_iter: 20
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_caffe_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_caffe_pretrained.tar
weights: output/cascade_mask_rcnn_dcn_se154_vd_fpn_gn_s1x/model_final/ weights: output/cascade_mask_rcnn_dcn_se154_vd_fpn_gn_s1x/model_final/
metric: COCO metric: COCO
num_classes: 81 num_classes: 81
...@@ -96,7 +93,7 @@ MaskAssigner: ...@@ -96,7 +93,7 @@ MaskAssigner:
resolution: 28 resolution: 28
CascadeBBoxHead: CascadeBBoxHead:
head: CascadeXConvNormHead head: CascadeXConvNormHead
nms: nms:
keep_top_k: 100 keep_top_k: 100
nms_threshold: 0.5 nms_threshold: 0.5
...@@ -123,17 +120,19 @@ OptimizerBuilder: ...@@ -123,17 +120,19 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: TrainReader:
# batch size per device # batch size per device
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_mask']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/coco dataset_dir: dataset/coco
image_dir: train2017 image_dir: train2017
annotation: annotations/instances_train2017.json anno_path: annotations/instances_train2017.json
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: false
with_mixup: False
- !RandomFlipImage - !RandomFlipImage
is_mask_flip: true is_mask_flip: true
is_normalized: false is_normalized: false
...@@ -142,13 +141,13 @@ MaskRCNNTrainFeed: ...@@ -142,13 +141,13 @@ MaskRCNNTrainFeed:
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
mean: mean:
- 102.9801 - 102.9801
- 115.9465 - 115.9465
- 122.7717 - 122.7717
std: std:
- 1.0 - 1.0
- 1.0 - 1.0
- 1.0 - 1.0
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -161,7 +160,7 @@ MaskRCNNTrainFeed: ...@@ -161,7 +160,7 @@ MaskRCNNTrainFeed:
- 608 - 608
- 640 - 640
- 672 - 672
- 704 - 704
- 736 - 736
- 768 - 768
- 800 - 800
...@@ -192,29 +191,32 @@ MaskRCNNTrainFeed: ...@@ -192,29 +191,32 @@ MaskRCNNTrainFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
num_workers: 8 worker_num: 8
shuffle: true
MaskRCNNEvalFeed: EvalReader:
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/coco dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json anno_path: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
with_mixup: False
- !NormalizeImage - !NormalizeImage
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
mean: mean:
- 102.9801 - 102.9801
- 115.9465 - 115.9465
- 122.7717 - 122.7717
std: std:
- 1.0 - 1.0
- 1.0 - 1.0
- 1.0 - 1.0
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -227,16 +229,20 @@ MaskRCNNEvalFeed: ...@@ -227,16 +229,20 @@ MaskRCNNEvalFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
num_workers: 2 worker_num: 2
drop_empty: false
MaskRCNNTestFeed: TestReader:
batch_size: 1 batch_size: 1
inputs_def:
image_shape: [3,800,1333]
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/coco/annotations/instances_val2017.json !ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
with_mixup: False
- !NormalizeImage - !NormalizeImage
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
...@@ -254,4 +260,4 @@ MaskRCNNTestFeed: ...@@ -254,4 +260,4 @@ MaskRCNNTestFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
num_workers: 2 worker_num: 2
configs/dcn/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_s1x_ms_test.yml
浏览文件 @ 8192c758
architecture: CascadeMaskRCNN architecture: CascadeMaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 300000 max_iters: 300000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
log_iter: 20 log_iter: 20
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_caffe_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_caffe_pretrained.tar
weights: output/cascade_mask_rcnn_dcn_se154_vd_fpn_gn_s1x/model_final/ weights: output/cascade_mask_rcnn_dcn_se154_vd_fpn_gn_s1x/model_final/
metric: COCO metric: COCO
num_classes: 81 num_classes: 81
...@@ -96,7 +93,7 @@ MaskAssigner: ...@@ -96,7 +93,7 @@ MaskAssigner:
resolution: 28 resolution: 28
CascadeBBoxHead: CascadeBBoxHead:
head: CascadeXConvNormHead head: CascadeXConvNormHead
nms: nms:
keep_top_k: 100 keep_top_k: 100
nms_threshold: 0.5 nms_threshold: 0.5
...@@ -130,14 +127,17 @@ OptimizerBuilder: ...@@ -130,14 +127,17 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: TrainReader:
# batch size per device # batch size per device
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_mask']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/coco dataset_dir: dataset/coco
image_dir: train2017 image_dir: train2017
annotation: annotations/instances_train2017.json anno_path: annotations/instances_train2017.json
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
with_mixup: False with_mixup: False
...@@ -149,13 +149,13 @@ MaskRCNNTrainFeed: ...@@ -149,13 +149,13 @@ MaskRCNNTrainFeed:
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
mean: mean:
- 102.9801 - 102.9801
- 115.9465 - 115.9465
- 122.7717 - 122.7717
std: std:
- 1.0 - 1.0
- 1.0 - 1.0
- 1.0 - 1.0
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -168,7 +168,7 @@ MaskRCNNTrainFeed: ...@@ -168,7 +168,7 @@ MaskRCNNTrainFeed:
- 608 - 608
- 640 - 640
- 672 - 672
- 704 - 704
- 736 - 736
- 768 - 768
- 800 - 800
...@@ -199,28 +199,36 @@ MaskRCNNTrainFeed: ...@@ -199,28 +199,36 @@ MaskRCNNTrainFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
num_workers: 8 worker_num: 8
shuffle: true
MaskRCNNEvalFeed: EvalReader:
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
multi_scale: true
# num_scale = (len(target_size) + 1) * (1 + use_flip)
num_scales: 18
use_flip: true
dataset: dataset:
dataset_dir: dataset/coco !COCODataSet
annotation: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
sample_transforms: anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
- !NormalizeImage - !NormalizeImage
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
mean: mean:
- 102.9801 - 102.9801
- 115.9465 - 115.9465
- 122.7717 - 122.7717
std: std:
- 1.0 - 1.0
- 1.0 - 1.0
- 1.0 - 1.0
- !MultiscaleTestResize - !MultiscaleTestResize
origin_target_size: 800 origin_target_size: 800
origin_max_size: 1333 origin_max_size: 1333
...@@ -239,16 +247,17 @@ MaskRCNNEvalFeed: ...@@ -239,16 +247,17 @@ MaskRCNNEvalFeed:
channel_first: true channel_first: true
to_bgr: false to_bgr: false
batch_transforms: batch_transforms:
- !PadMSTest - !PadMultiScaleTest
pad_to_stride: 32 pad_to_stride: 32
# num_scale = (len(target_size) + 1) * (1 + use_flip) worker_num: 2
num_scale: 18
num_workers: 2
MaskRCNNTestFeed: TestReader:
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/coco/annotations/instances_val2017.json !ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
...@@ -269,4 +278,3 @@ MaskRCNNTestFeed: ...@@ -269,4 +278,3 @@ MaskRCNNTestFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
num_workers: 2
configs/dcn/cascade_rcnn_cbr200_vd_fpn_dcnv2_nonlocal_softnms.yml
浏览文件 @ 8192c758
architecture: CascadeRCNN architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 460000 max_iters: 460000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -109,16 +106,18 @@ OptimizerBuilder: ...@@ -109,16 +106,18 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: TrainReader:
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset: dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json sample_transforms:
image_dir: train2017
sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: true
with_mixup: False
- !RandomFlipImage - !RandomFlipImage
prob: 0.5 prob: 0.5
- !NormalizeImage - !NormalizeImage
...@@ -131,7 +130,7 @@ FasterRCNNTrainFeed: ...@@ -131,7 +130,7 @@ FasterRCNNTrainFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408] target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408]
...@@ -142,16 +141,19 @@ FasterRCNNTrainFeed: ...@@ -142,16 +141,19 @@ FasterRCNNTrainFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
drop_last: false worker_num: 2
num_workers: 2 shuffle: true
FasterRCNNEvalFeed: EvalReader:
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
dataset_dir: dataset/coco !COCODataSet
annotation: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
sample_transforms: anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False with_mixup: False
...@@ -165,7 +167,7 @@ FasterRCNNEvalFeed: ...@@ -165,7 +167,7 @@ FasterRCNNEvalFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -177,13 +179,34 @@ FasterRCNNEvalFeed: ...@@ -177,13 +179,34 @@ FasterRCNNEvalFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
worker_num: 2
FasterRCNNTestFeed: TestReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/coco/annotations/instances_val2017.json !ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
drop_last: false use_padded_im_info: true
num_workers: 2 batch_size: 1
worker_num: 2
configs/dcn/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml
浏览文件 @ 8192c758
architecture: CascadeRCNNClsAware architecture: CascadeRCNNClsAware
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 460000 max_iters: 460000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -109,16 +106,17 @@ OptimizerBuilder: ...@@ -109,16 +106,17 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: TrainReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset: dataset:
dataset_dir: dataset/coco !COCODataSet
annotation: annotations/instances_train2017.json
image_dir: train2017 image_dir: train2017
sample_transforms: anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: true
with_mixup: False
- !RandomFlipImage - !RandomFlipImage
prob: 0.5 prob: 0.5
- !NormalizeImage - !NormalizeImage
...@@ -131,7 +129,7 @@ FasterRCNNTrainFeed: ...@@ -131,7 +129,7 @@ FasterRCNNTrainFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408] target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408]
...@@ -142,16 +140,20 @@ FasterRCNNTrainFeed: ...@@ -142,16 +140,20 @@ FasterRCNNTrainFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
batch_size: 1
shuffle: true
drop_last: false drop_last: false
num_workers: 2 worker_num: 2
FasterRCNNEvalFeed: EvalReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
dataset_dir: dataset/coco !COCODataSet
annotation: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
sample_transforms: anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False with_mixup: False
...@@ -165,7 +167,7 @@ FasterRCNNEvalFeed: ...@@ -165,7 +167,7 @@ FasterRCNNEvalFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -177,13 +179,37 @@ FasterRCNNEvalFeed: ...@@ -177,13 +179,37 @@ FasterRCNNEvalFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1 batch_size: 1
worker_num: 2
drop_empty: false
TestReader:
inputs_def:
image_shape: [3,800,1333]
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/coco/annotations/instances_val2017.json !ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
drop_last: false use_padded_im_info: true
num_workers: 2 batch_size: 1
worker_num: 2
configs/dcn/cascade_rcnn_dcn_r101_vd_fpn_1x.yml
浏览文件 @ 8192c758
architecture: CascadeRCNN architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,34 +103,6 @@ OptimizerBuilder: ...@@ -106,34 +103,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: '../faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/dcn/cascade_rcnn_dcn_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: CascadeRCNN architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,34 +103,6 @@ OptimizerBuilder: ...@@ -106,34 +103,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: '../faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/dcn/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x.yml
浏览文件 @ 8192c758
architecture: CascadeRCNN architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -26,7 +23,7 @@ ResNeXt: ...@@ -26,7 +23,7 @@ ResNeXt:
depth: 101 depth: 101
feature_maps: [2, 3, 4, 5] feature_maps: [2, 3, 4, 5]
freeze_at: 2 freeze_at: 2
group_width: 4 group_width: 4
groups: 64 groups: 64
variant: d variant: d
dcn_v2_stages: [3, 4, 5] dcn_v2_stages: [3, 4, 5]
...@@ -108,34 +105,6 @@ OptimizerBuilder: ...@@ -108,34 +105,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: '../faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/dcn/faster_rcnn_dcn_r101_vd_fpn_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,34 +103,7 @@ OptimizerBuilder: ...@@ -106,34 +103,7 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: '../faster_fpn_reader.yml'
TrainReader:
# batch size per device # batch size per device
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/dcn/faster_rcnn_dcn_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
use_gpu: true use_gpu: true
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -105,34 +102,8 @@ OptimizerBuilder: ...@@ -105,34 +102,8 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed:
batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed: _READER_: '../faster_fpn_reader.yml'
batch_size: 1 TrainReader:
dataset: # batch size per device
annotation: dataset/coco/annotations/instances_val2017.json batch_size: 2
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/dcn/faster_rcnn_dcn_r50_vd_fpn_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,34 +103,7 @@ OptimizerBuilder: ...@@ -106,34 +103,7 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: '../faster_fpn_reader.yml'
TrainReader:
# batch size per device # batch size per device
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/dcn/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -108,36 +105,5 @@ OptimizerBuilder: ...@@ -108,36 +105,5 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed:
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: true
FasterRCNNEvalFeed: _READER_: '../faster_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: false
configs/dcn/mask_rcnn_dcn_r101_vd_fpn_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
log_smooth_window: 20 log_smooth_window: 20
log_iter: 20 log_iter: 20
save_dir: output save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
weights: output/mask_rcnn_dcn_r101_vd_fpn_1x/model_final weights: output/mask_rcnn_dcn_r101_vd_fpn_1x/model_final
metric: COCO metric: COCO
num_classes: 81 num_classes: 81
...@@ -114,33 +111,4 @@ OptimizerBuilder: ...@@ -114,33 +111,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: '../mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/dcn/mask_rcnn_dcn_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -113,33 +110,5 @@ OptimizerBuilder: ...@@ -113,33 +110,5 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed: _READER_: '../mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -114,34 +111,4 @@ OptimizerBuilder: ...@@ -114,34 +111,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: '../mask_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
log_smooth_window: 20 log_smooth_window: 20
log_iter: 20 log_iter: 20
save_dir: output save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
weights: output/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x/model_final weights: output/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x/model_final
metric: COCO metric: COCO
num_classes: 81 num_classes: 81
...@@ -116,33 +113,4 @@ OptimizerBuilder: ...@@ -116,33 +113,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: '../mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/dcn/yolov3_r50vd_dcn.yml
浏览文件 @ 8192c758
architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 500000 max_iters: 500000
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
snapshot_iter: 20000 snapshot_iter: 20000
metric: COCO metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
weights: output/yolov3_r50vd_dcn/model_final weights: output/yolov3_r50vd_dcn/model_final
num_classes: 80 num_classes: 80
...@@ -62,67 +59,4 @@ OptimizerBuilder: ...@@ -62,67 +59,4 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: '../yolov3_reader.yml'
batch_size: 8
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
sample_transforms:
- !DecodeImage
to_rgb: True
with_mixup: True
- !MixupImage
alpha: 1.5
beta: 1.5
- !NormalizeBox {}
- !RandomDistort {}
- !ExpandImage
max_ratio: 4
prob: 0.5
mean:
- 123.675
- 116.28
- 103.53
- !CropImage
batch_sampler: [[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
- !RandomInterpImage
target_size: 608
- !RandomFlipImage
is_normalized: True
- !NormalizeImage
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
is_scale: True
is_channel_first: False
- !Permute
to_bgr: False
num_workers: 8
bufsize: 128
use_process: true
YoloEvalFeed:
batch_size: 8
image_shape: [3, 608, 608]
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
YoloTestFeed:
batch_size: 1
image_shape: [3, 608, 608]
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/dcn/yolov3_r50vd_dcn_obj365_pretrained_coco.yml
浏览文件 @ 8192c758
architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 55000 max_iters: 55000
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
snapshot_iter: 10000 snapshot_iter: 10000
metric: COCO metric: COCO
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_obj365_pretrained.tar pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_obj365_pretrained.tar
weights: output/yolov3_r50vd_dcn_obj365_pretrained_coco/model_final weights: output/yolov3_r50vd_dcn_obj365_pretrained_coco/model_final
num_classes: 80 num_classes: 80
...@@ -62,98 +59,97 @@ OptimizerBuilder: ...@@ -62,98 +59,97 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: TrainReader:
batch_size: 8 inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
num_max_boxes: 50
dataset: dataset:
dataset_dir: dataset/coco !COCODataSet
annotation: annotations/instances_train2017.json
image_dir: train2017 image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
with_background: false
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False - !RandomCrop {}
- !NormalizeBox {}
- !CropImage
batch_sampler: [[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
- !RandomInterpImage
target_size: 608
- !RandomFlipImage - !RandomFlipImage
is_normalized: True is_normalized: false
- !NormalizeBox {}
- !PadBox
num_max_boxes: 50
- !BboxXYXY2XYWH {}
batch_transforms:
- !RandomShape
sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
random_inter: True
- !NormalizeImage - !NormalizeImage
mean: mean: [0.485, 0.456, 0.406]
- 0.485 std: [0.229, 0.224, 0.225]
- 0.456 is_scale: True
- 0.406 is_channel_first: false
std:
- 0.229
- 0.224
- 0.225
is_scale: False
is_channel_first: False
- !Permute - !Permute
to_bgr: False to_bgr: false
num_workers: 8 channel_first: True
bufsize: 128 batch_size: 8
shuffle: true
drop_last: true
worker_num: 8
bufsize: 32
use_process: true use_process: true
YoloEvalFeed: EvalReader:
batch_size: 8 inputs_def:
image_shape: [3, 608, 608] image_shape: [3, 608, 608]
fields: ['image', 'im_size', 'im_id']
num_max_boxes: 50
dataset: dataset:
!COCODataSet
dataset_dir: dataset/coco dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json anno_path: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
with_background: false
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False with_mixup: false
- !ResizeImage - !ResizeImage
interp: 2 interp: 2
target_size: 608 target_size: 608
- !NormalizeImage - !NormalizeImage
mean: mean: [0.485, 0.456, 0.406]
- 0.485 std: [0.229, 0.224, 0.225]
- 0.456 is_scale: True
- 0.406 is_channel_first: false
std:
- 0.229
- 0.224
- 0.225
is_scale: False
is_channel_first: False
- !Permute - !Permute
to_bgr: False to_bgr: false
channel_first: True
batch_size: 8
drop_empty: false
worker_num: 8
bufsize: 32
YoloTestFeed: TestReader:
batch_size: 1 inputs_def:
image_shape: [3, 608, 608] image_shape: [3, 608, 608]
fields: ['image', 'im_size', 'im_id']
dataset: dataset:
annotation: dataset/coco/annotations/instances_val2017.json !ImageFolder
anno_path: annotations/instances_val2017.json
with_background: false
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False with_mixup: false
- !ResizeImage - !ResizeImage
interp: 2 interp: 2
target_size: 608 target_size: 608
- !NormalizeImage - !NormalizeImage
mean: mean: [0.485, 0.456, 0.406]
- 0.485 std: [0.229, 0.224, 0.225]
- 0.456 is_scale: True
- 0.406 is_channel_first: false
std:
- 0.229
- 0.224
- 0.225
is_scale: False
is_channel_first: False
- !Permute - !Permute
to_bgr: False to_bgr: false
channel_first: True
batch_size: 1
configs/face_detection/blazeface.yml
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architecture: BlazeFace architecture: BlazeFace
max_iters: 320000 max_iters: 320000
train_feed: SSDTrainFeed pretrain_weights:
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
pretrain_weights:
use_gpu: true use_gpu: true
snapshot_iter: 10000 snapshot_iter: 10000
log_smooth_window: 20 log_smooth_window: 20
...@@ -43,18 +40,18 @@ OptimizerBuilder: ...@@ -43,18 +40,18 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 inputs_def:
use_process: True image_shape: [3, 640, 640]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_train_bbx_gt.txt anno_path: wider_face_split/wider_face_train_bbx_gt.txt
image_dir: WIDER_train/images image_dir: WIDER_train/images
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {} - !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
...@@ -82,49 +79,41 @@ SSDTrainFeed: ...@@ -82,49 +79,41 @@ SSDTrainFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
batch_size: 8
use_process: true
shuffle: true
SSDEvalFeed: EvalReader:
batch_size: 1 inputs_def:
use_process: false fields: ['image', 'im_id']
fields: ['image', 'im_id', 'gt_box']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_val_bbx_gt.txt anno_path: wider_face_split/wider_face_val_bbx_gt.txt
image_dir: WIDER_val/images image_dir: WIDER_val/images
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {} - !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 640
use_cv2: false
- !Permute {} - !Permute {}
- !NormalizeImage - !NormalizeImage
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
SSDTestFeed:
batch_size: 1 batch_size: 1
use_process: false
TestReader:
inputs_def:
fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
!ImageFolder
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !ResizeImage
interp: 1
target_size: 640
use_cv2: false
- !Permute {} - !Permute {}
- !NormalizeImage - !NormalizeImage
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
batch_size: 1
configs/face_detection/blazeface_nas.yml
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architecture: BlazeFace architecture: BlazeFace
max_iters: 320000 max_iters: 320000
train_feed: SSDTrainFeed pretrain_weights:
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
pretrain_weights:
use_gpu: true use_gpu: true
snapshot_iter: 10000 snapshot_iter: 10000
log_smooth_window: 20 log_smooth_window: 20
...@@ -45,18 +42,18 @@ OptimizerBuilder: ...@@ -45,18 +42,18 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 inputs_def:
use_process: True image_shape: [3, 640, 640]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_train_bbx_gt.txt anno_path: wider_face_split/wider_face_train_bbx_gt.txt
image_dir: WIDER_train/images image_dir: WIDER_train/images
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {} - !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
...@@ -84,21 +81,21 @@ SSDTrainFeed: ...@@ -84,21 +81,21 @@ SSDTrainFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
batch_size: 8
use_process: true
shuffle: true
SSDEvalFeed: EvalReader:
batch_size: 1 inputs_def:
use_process: false fields: ['image', 'im_id']
fields: ['image', 'im_id', 'gt_box']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_val_bbx_gt.txt anno_path: wider_face_split/wider_face_val_bbx_gt.txt
image_dir: WIDER_val/images image_dir: WIDER_val/images
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {} - !NormalizeBox {}
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
...@@ -109,18 +106,18 @@ SSDEvalFeed: ...@@ -109,18 +106,18 @@ SSDEvalFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
SSDTestFeed:
batch_size: 1 batch_size: 1
use_process: false
TestReader:
inputs_def:
image_shape: [3,640,640]
fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
!ImageFolder
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: 640 target_size: 640
...@@ -130,3 +127,4 @@ SSDTestFeed: ...@@ -130,3 +127,4 @@ SSDTestFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
batch_size: 1
configs/face_detection/faceboxes.yml
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architecture: FaceBoxes architecture: FaceBoxes
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
pretrain_weights: pretrain_weights:
use_gpu: true use_gpu: true
max_iters: 320000 max_iters: 320000
...@@ -43,18 +40,21 @@ OptimizerBuilder: ...@@ -43,18 +40,21 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 batch_size: 8
use_process: True use_process: True
shuffle: true
inputs_def:
image_shape: [3, 640, 640]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_train_bbx_gt.txt anno_path: wider_face_split/wider_face_train_bbx_gt.txt
image_dir: WIDER_train/images image_dir: WIDER_train/images
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {} - !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
...@@ -83,48 +83,37 @@ SSDTrainFeed: ...@@ -83,48 +83,37 @@ SSDTrainFeed:
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
SSDEvalFeed: EvalReader:
batch_size: 1 batch_size: 1
use_process: false use_process: false
fields: ['image', 'im_id', 'gt_box'] inputs_def:
fields: ['image', 'im_id']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_val_bbx_gt.txt anno_path: wider_face_split/wider_face_val_bbx_gt.txt
image_dir: WIDER_val/images image_dir: WIDER_val/images
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 640
use_cv2: false
- !Permute {} - !Permute {}
- !NormalizeImage - !NormalizeImage
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
SSDTestFeed: TestReader:
batch_size: 1 inputs_def:
use_process: false fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
!ImageFolder
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !ResizeImage
interp: 1
target_size: 640
use_cv2: false
- !Permute {} - !Permute {}
- !NormalizeImage - !NormalizeImage
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
batch_size: 1
configs/face_detection/faceboxes_lite.yml
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architecture: FaceBoxes architecture: FaceBoxes
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
pretrain_weights: pretrain_weights:
use_gpu: true use_gpu: true
max_iters: 320000 max_iters: 320000
...@@ -43,18 +40,21 @@ OptimizerBuilder: ...@@ -43,18 +40,21 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 batch_size: 8
use_process: True use_process: True
shuffle: true
inputs_def:
image_shape: [3, 640, 640]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_train_bbx_gt.txt anno_path: wider_face_split/wider_face_train_bbx_gt.txt
image_dir: WIDER_train/images image_dir: WIDER_train/images
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {} - !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
...@@ -83,48 +83,38 @@ SSDTrainFeed: ...@@ -83,48 +83,38 @@ SSDTrainFeed:
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
SSDEvalFeed: EvalReader:
batch_size: 1 batch_size: 1
use_process: false use_process: false
fields: ['image', 'im_id', 'gt_box'] inputs_def:
fields: ['image', 'im_id']
dataset: dataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face dataset_dir: dataset/wider_face
annotation: wider_face_split/wider_face_val_bbx_gt.txt anno_path: wider_face_split/wider_face_val_bbx_gt.txt
image_dir: WIDER_val/images image_dir: WIDER_val/images
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {} - !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 640
use_cv2: false
- !Permute {} - !Permute {}
- !NormalizeImage - !NormalizeImage
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
SSDTestFeed: TestReader:
batch_size: 1 inputs_def:
use_process: false fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
!ImageFolder
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 640, 640]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !ResizeImage
interp: 1
target_size: 640
use_cv2: false
- !Permute {} - !Permute {}
- !NormalizeImage - !NormalizeImage
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [127.502231, 127.502231, 127.502231] std: [127.502231, 127.502231, 127.502231]
batch_size: 1
configs/faster_fpn_reader.yml 0 → 100644
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TrainReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !RandomFlipImage
prob: 0.5
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
target_size: 800
max_size: 1333
interp: 1
use_cv2: true
- !Permute
to_bgr: false
channel_first: true
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: false
batch_size: 1
shuffle: true
worker_num: 2
use_process: false
EvalReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
# for voc
#fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: true
batch_size: 1
shuffle: false
drop_empty: false
worker_num: 2
TestReader:
inputs_def:
# set image_shape if needed
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: true
batch_size: 1
shuffle: false
configs/faster_rcnn_cbr101_vd_dual_fpn_1x.yml
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architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,34 +103,6 @@ OptimizerBuilder: ...@@ -106,34 +103,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_cbr50_vd_dual_fpn_1x.yml
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architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,34 +103,7 @@ OptimizerBuilder: ...@@ -106,34 +103,7 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
TrainReader:
# batch size per device # batch size per device
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_r101_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
log_smooth_window: 20 log_smooth_window: 20
...@@ -91,25 +88,4 @@ OptimizerBuilder: ...@@ -91,25 +88,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/faster_rcnn_r101_fpn_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -103,34 +100,4 @@ OptimizerBuilder: ...@@ -103,34 +100,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_r101_fpn_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -103,34 +100,4 @@ OptimizerBuilder: ...@@ -103,34 +100,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_r101_vd_fpn_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -104,34 +101,4 @@ OptimizerBuilder: ...@@ -104,34 +101,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_r101_vd_fpn_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -104,34 +101,4 @@ OptimizerBuilder: ...@@ -104,34 +101,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_r50_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
log_smooth_window: 20 log_smooth_window: 20
...@@ -91,25 +88,4 @@ OptimizerBuilder: ...@@ -91,25 +88,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/faster_rcnn_r50_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 360000 max_iters: 360000
log_smooth_window: 20 log_smooth_window: 20
...@@ -91,25 +88,4 @@ OptimizerBuilder: ...@@ -91,25 +88,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/faster_rcnn_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
use_gpu: true use_gpu: true
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -104,34 +101,6 @@ OptimizerBuilder: ...@@ -104,34 +101,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/faster_rcnn_r50_fpn_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
use_gpu: true use_gpu: true
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -104,34 +101,6 @@ OptimizerBuilder: ...@@ -104,34 +101,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/faster_rcnn_r50_vd_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
log_smooth_window: 20 log_smooth_window: 20
...@@ -93,25 +90,4 @@ OptimizerBuilder: ...@@ -93,25 +90,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
drop_last: false
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/faster_rcnn_r50_vd_fpn_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -104,34 +101,6 @@ OptimizerBuilder: ...@@ -104,34 +101,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_se154_vd_fpn_s1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 260000 max_iters: 260000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,34 +103,4 @@ OptimizerBuilder: ...@@ -106,34 +103,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/faster_rcnn_x101_vd_64x4d_fpn_1x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -107,36 +104,4 @@ OptimizerBuilder: ...@@ -107,36 +104,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: true
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: false
configs/faster_rcnn_x101_vd_64x4d_fpn_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -106,36 +103,4 @@ OptimizerBuilder: ...@@ -106,36 +103,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: true
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: false
configs/faster_reader.yml 0 → 100644
浏览文件 @ 8192c758
TrainReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !RandomFlipImage
prob: 0.5
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
target_size: 800
max_size: 1333
interp: 1
use_cv2: true
- !Permute
to_bgr: false
channel_first: true
batch_size: 1
shuffle: true
worker_num: 2
use_process: false
EvalReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
# for voc
#fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_size: 1
shuffle: false
drop_empty: false
worker_num: 2
TestReader:
inputs_def:
image_shape: [3,800,1333]
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_size: 1
shuffle: false
configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x.yml
浏览文件 @ 8192c758
architecture: CascadeMaskRCNN architecture: CascadeMaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -88,7 +85,7 @@ MaskAssigner: ...@@ -88,7 +85,7 @@ MaskAssigner:
resolution: 28 resolution: 28
CascadeBBoxHead: CascadeBBoxHead:
head: CascadeXConvNormHead head: CascadeXConvNormHead
nms: nms:
keep_top_k: 100 keep_top_k: 100
nms_threshold: 0.5 nms_threshold: 0.5
...@@ -115,33 +112,6 @@ OptimizerBuilder: ...@@ -115,33 +112,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: '../mask_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/gn/faster_rcnn_r50_fpn_gn_2x.yml
浏览文件 @ 8192c758
architecture: FasterRCNN architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -104,34 +101,6 @@ OptimizerBuilder: ...@@ -104,34 +101,6 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: '../faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 16
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
drop_last: false
num_workers: 2
configs/gn/mask_rcnn_r50_fpn_gn_2x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -113,33 +110,4 @@ OptimizerBuilder: ...@@ -113,33 +110,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: '../mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_fpn_reader.yml 0 → 100644
浏览文件 @ 8192c758
TrainReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_mask']
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !RandomFlipImage
prob: 0.5
is_mask_flip: true
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
target_size: 800
max_size: 1333
interp: 1
use_cv2: true
- !Permute
to_bgr: false
channel_first: true
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: false
batch_size: 1
shuffle: true
worker_num: 2
drop_last: false
use_process: false
EvalReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
# for voc
#fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: true
batch_size: 1
shuffle: false
drop_last: false
drop_empty: false
worker_num: 2
TestReader:
inputs_def:
image_shape: [3,800,1333]
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: true
batch_size: 1
shuffle: false
drop_last: false
configs/mask_rcnn_r101_fpn_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -111,33 +108,4 @@ OptimizerBuilder: ...@@ -111,33 +108,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_r101_vd_fpn_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
weights: output/mask_rcnn_r101_vd_fpn_1x/model_final weights: output/mask_rcnn_r101_vd_fpn_1x/model_final
metric: COCO metric: COCO
num_classes: 81 num_classes: 81
...@@ -112,33 +109,4 @@ OptimizerBuilder: ...@@ -112,33 +109,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_r50_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -102,22 +99,4 @@ OptimizerBuilder: ...@@ -102,22 +99,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/mask_rcnn_r50_2x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -104,22 +101,4 @@ OptimizerBuilder: ...@@ -104,22 +101,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/mask_rcnn_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -111,33 +108,4 @@ OptimizerBuilder: ...@@ -111,33 +108,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_r50_fpn_2x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -111,33 +108,4 @@ OptimizerBuilder: ...@@ -111,33 +108,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_r50_vd_fpn_2x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
use_gpu: true use_gpu: true
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -112,34 +109,4 @@ OptimizerBuilder: ...@@ -112,34 +109,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_se154_vd_fpn_s1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 260000 max_iters: 260000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -114,34 +111,4 @@ OptimizerBuilder: ...@@ -114,34 +111,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_x101_vd_64x4d_fpn_1x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 180000 max_iters: 180000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
weights: output/mask_rcnn_x101_vd_64x4d_fpn_1x/model_final weights: output/mask_rcnn_x101_vd_64x4d_fpn_1x/model_final
metric: COCO metric: COCO
num_classes: 81 num_classes: 81
...@@ -114,33 +111,4 @@ OptimizerBuilder: ...@@ -114,33 +111,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_rcnn_x101_vd_64x4d_fpn_2x.yml
浏览文件 @ 8192c758
architecture: MaskRCNN architecture: MaskRCNN
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
max_iters: 360000 max_iters: 360000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
weights: output/mask_rcnn_x101_vd_64x4d_fpn_2x/model_final weights: output/mask_rcnn_x101_vd_64x4d_fpn_2x/model_final
metric: COCO metric: COCO
num_classes: 81 num_classes: 81
...@@ -114,33 +111,4 @@ OptimizerBuilder: ...@@ -114,33 +111,4 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
MaskRCNNTrainFeed: _READER_: 'mask_fpn_reader.yml'
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
MaskRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
configs/mask_reader.yml 0 → 100644
浏览文件 @ 8192c758
TrainReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_mask']
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !RandomFlipImage
prob: 0.5
is_mask_flip: true
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
target_size: 800
max_size: 1333
interp: 1
use_cv2: true
- !Permute
to_bgr: false
channel_first: true
batch_size: 1
shuffle: true
worker_num: 2
drop_last: false
use_process: false
EvalReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
# for voc
#fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_size: 1
shuffle: false
drop_last: false
drop_empty: false
worker_num: 2
TestReader:
inputs_def:
image_shape: [3,800,1333]
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_size: 1
shuffle: false
drop_last: false
configs/obj365/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml
浏览文件 @ 8192c758
architecture: CascadeRCNNClsAware architecture: CascadeRCNNClsAware
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 800000 max_iters: 800000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -110,16 +107,17 @@ OptimizerBuilder: ...@@ -110,16 +107,17 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: TrainReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/obj365 dataset_dir: dataset/obj365
annotation: train.json anno_path: train.json
image_dir: train image_dir: train
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False
- !RandomFlipImage - !RandomFlipImage
prob: 0.5 prob: 0.5
- !NormalizeImage - !NormalizeImage
...@@ -132,7 +130,7 @@ FasterRCNNTrainFeed: ...@@ -132,7 +130,7 @@ FasterRCNNTrainFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408] target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408]
...@@ -143,16 +141,20 @@ FasterRCNNTrainFeed: ...@@ -143,16 +141,20 @@ FasterRCNNTrainFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
batch_size: 1
shuffle: true
drop_last: false drop_last: false
num_workers: 2 worker_num: 2
FasterRCNNEvalFeed: EvalReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/obj365 dataset_dir: dataset/obj365
annotation: val.json anno_path: val.json
image_dir: val image_dir: val
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False with_mixup: False
...@@ -166,7 +168,7 @@ FasterRCNNEvalFeed: ...@@ -166,7 +168,7 @@ FasterRCNNEvalFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -178,13 +180,36 @@ FasterRCNNEvalFeed: ...@@ -178,13 +180,36 @@ FasterRCNNEvalFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
FasterRCNNTestFeed:
batch_size: 1 batch_size: 1
worker_num: 2
drop_empty: false
TestReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/obj365/val.json !ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
drop_last: false use_padded_im_info: true
num_workers: 2 batch_size: 1
worker_num: 2
configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml
浏览文件 @ 8192c758
architecture: CascadeRCNN architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 500000 max_iters: 500000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -85,7 +82,7 @@ CascadeBBoxAssigner: ...@@ -85,7 +82,7 @@ CascadeBBoxAssigner:
fg_fraction: 0.25 fg_fraction: 0.25
CascadeBBoxHead: CascadeBBoxHead:
head: CascadeXConvNormHead head: CascadeXConvNormHead
nms: nms:
keep_top_k: 100 keep_top_k: 100
nms_threshold: 0.5 nms_threshold: 0.5
...@@ -115,16 +112,17 @@ OptimizerBuilder: ...@@ -115,16 +112,17 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: TrainReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/objects365 dataset_dir: dataset/objects365
annotation: annotations/train.json anno_path: annotations/train.json
image_dir: train image_dir: train
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
with_mixup: False
- !RandomFlipImage - !RandomFlipImage
is_mask_flip: true is_mask_flip: true
is_normalized: false is_normalized: false
...@@ -133,13 +131,13 @@ FasterRCNNTrainFeed: ...@@ -133,13 +131,13 @@ FasterRCNNTrainFeed:
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
mean: mean:
- 102.9801 - 102.9801
- 115.9465 - 115.9465
- 122.7717 - 122.7717
std: std:
- 1.0 - 1.0
- 1.0 - 1.0
- 1.0 - 1.0
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -152,7 +150,7 @@ FasterRCNNTrainFeed: ...@@ -152,7 +150,7 @@ FasterRCNNTrainFeed:
- 608 - 608
- 640 - 640
- 672 - 672
- 704 - 704
- 736 - 736
- 768 - 768
- 800 - 800
...@@ -183,30 +181,34 @@ FasterRCNNTrainFeed: ...@@ -183,30 +181,34 @@ FasterRCNNTrainFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
num_workers: 4 batch_size: 1
worker_num: 4
shuffle: true
class_aware_sampling: true class_aware_sampling: true
use_process: false
FasterRCNNEvalFeed: EvalReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/objects365 dataset_dir: dataset/objects365
annotation: annotations/val.json anno_path: annotations/val.json
image_dir: val image_dir: val
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
with_mixup: False
- !NormalizeImage - !NormalizeImage
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
mean: mean:
- 102.9801 - 102.9801
- 115.9465 - 115.9465
- 122.7717 - 122.7717
std: std:
- 1.0 - 1.0
- 1.0 - 1.0
- 1.0 - 1.0
- !ResizeImage - !ResizeImage
target_size: 800 target_size: 800
max_size: 1333 max_size: 1333
...@@ -217,31 +219,33 @@ FasterRCNNEvalFeed: ...@@ -217,31 +219,33 @@ FasterRCNNEvalFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
batch_size: 1
drop_empty: false
worker_num: 2
FasterRCNNTestFeed: TestReader:
batch_size: 1 batch_size: 1
dataset: dataset:
annotation: dataset/obj365/annotations/val.json !ImageFolder
sample_transforms: anno_path: dataset/obj365/annotations/val.json
sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: False to_rgb: False
with_mixup: False
- !NormalizeImage - !NormalizeImage
is_channel_first: false is_channel_first: false
is_scale: False is_scale: False
mean: mean:
- 102.9801 - 102.9801
- 115.9465 - 115.9465
- 122.7717 - 122.7717
std: std:
- 1.0 - 1.0
- 1.0 - 1.0
- 1.0 - 1.0
- !Permute - !Permute
channel_first: true channel_first: true
to_bgr: false to_bgr: false
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
drop_last: false worker_num: 2
num_workers: 2
configs/oidv5/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml
浏览文件 @ 8192c758
architecture: CascadeRCNNClsAware architecture: CascadeRCNNClsAware
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 1500000 max_iters: 1500000
snapshot_iter: 10000 snapshot_iter: 10000
use_gpu: true use_gpu: true
...@@ -109,16 +106,17 @@ OptimizerBuilder: ...@@ -109,16 +106,17 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: TrainReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/oid dataset_dir: dataset/oid
annotation: train.json anno_path: train.json
image_dir: train image_dir: train
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False
- !RandomFlipImage - !RandomFlipImage
prob: 0.5 prob: 0.5
- !NormalizeImage - !NormalizeImage
...@@ -131,7 +129,7 @@ FasterRCNNTrainFeed: ...@@ -131,7 +129,7 @@ FasterRCNNTrainFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408] target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408]
...@@ -142,16 +140,20 @@ FasterRCNNTrainFeed: ...@@ -142,16 +140,20 @@ FasterRCNNTrainFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
batch_size: 1
drop_last: false drop_last: false
num_workers: 2 shuffle: true
worker_num: 2
FasterRCNNEvalFeed: EvalReader:
batch_size: 1 inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
!COCODataSet
dataset_dir: dataset/oidv5 dataset_dir: dataset/oidv5
annotation: val.json anno_path: val.json
image_dir: val image_dir: val
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: True to_rgb: True
with_mixup: False with_mixup: False
...@@ -165,7 +167,7 @@ FasterRCNNEvalFeed: ...@@ -165,7 +167,7 @@ FasterRCNNEvalFeed:
std: std:
- 0.229 - 0.229
- 0.224 - 0.224
- 0.225 - 0.225
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: target_size:
...@@ -177,13 +179,34 @@ FasterRCNNEvalFeed: ...@@ -177,13 +179,34 @@ FasterRCNNEvalFeed:
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
batch_size: 1
worker_num: 2
drop_empty: false
FasterRCNNTestFeed: TestReader:
batch_size: 1 batch_size: 1
inputs_def:
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/oidv5/val.json !ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 32 pad_to_stride: 32
drop_last: false worker_num: 2
num_workers: 2
configs/retinanet_r101_fpn_1x.yml
浏览文件 @ 8192c758
architecture: RetinaNet architecture: RetinaNet
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
use_gpu: true use_gpu: true
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
...@@ -73,33 +70,21 @@ OptimizerBuilder: ...@@ -73,33 +70,21 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
num_workers: 2
FasterRCNNEvalFeed: EvalReader:
batch_size: 2 batch_size: 2
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
num_workers: 2
FasterRCNNTestFeed: TestReader:
batch_size: 1 batch_size: 1
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
num_workers: 2
configs/retinanet_r50_fpn_1x.yml
浏览文件 @ 8192c758
architecture: RetinaNet architecture: RetinaNet
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000 max_iters: 90000
use_gpu: true use_gpu: true
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
...@@ -73,33 +70,21 @@ OptimizerBuilder: ...@@ -73,33 +70,21 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
TrainReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
num_workers: 2
FasterRCNNEvalFeed: EvalReader:
batch_size: 2 batch_size: 2
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
num_workers: 2
FasterRCNNTestFeed: TestReader:
batch_size: 1 batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
num_workers: 2
configs/retinanet_x101_vd_64x4d_fpn_1x.yml
浏览文件 @ 8192c758
architecture: RetinaNet architecture: RetinaNet
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000 max_iters: 180000
use_gpu: true use_gpu: true
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
...@@ -76,33 +73,18 @@ OptimizerBuilder: ...@@ -76,33 +73,18 @@ OptimizerBuilder:
factor: 0.0001 factor: 0.0001
type: L2 type: L2
FasterRCNNTrainFeed: _READER_: 'faster_fpn_reader.yml'
batch_size: 1 TrainReader:
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
num_workers: 2
FasterRCNNEvalFeed: EvalReader:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
num_workers: 2
FasterRCNNTestFeed: TestReader:
batch_size: 1
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
batch_transforms: batch_transforms:
- !PadBatch - !PadBatch
pad_to_stride: 128 pad_to_stride: 128
num_workers: 2
configs/ssd/ssd_mobilenet_v1_voc.yml
浏览文件 @ 8192c758
architecture: SSD architecture: SSD
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_coco_pretrained.tar pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_coco_pretrained.tar
use_gpu: true use_gpu: true
max_iters: 28000 max_iters: 28000
...@@ -56,25 +53,91 @@ OptimizerBuilder: ...@@ -56,25 +53,91 @@ OptimizerBuilder:
factor: 0.00005 factor: 0.00005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 32 inputs_def:
use_process: true image_shape: [3, 300, 300]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!VOCDataSet
anno_path: trainval.txt
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: trainval.txt
use_default_label: true use_default_label: true
sample_transforms:
SSDEvalFeed: - !DecodeImage
batch_size: 64 to_rgb: true
- !RandomDistort
brightness_lower: 0.875
brightness_upper: 1.125
is_order: true
- !RandomExpand
fill_value: [127.5, 127.5, 127.5]
- !RandomCrop
allow_no_crop: false
- !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 300
use_cv2: false
- !RandomFlipImage
is_normalized: true
- !Permute {}
- !NormalizeImage
is_scale: false
mean: [127.5, 127.5, 127.5]
std: [127.502231, 127.502231, 127.502231]
batch_size: 32
shuffle: true
drop_last: true
worker_num: 8
bufsize: 16
use_process: true use_process: true
EvalReader:
inputs_def:
image_shape: [3, 300, 300]
fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id', 'is_difficult']
dataset: dataset:
!VOCDataSet
anno_path: test.txt
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: test.txt
use_default_label: true use_default_label: true
drop_last: false sample_transforms:
- !DecodeImage
to_rgb: true
- !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 300
use_cv2: false
- !Permute {}
- !NormalizeImage
is_scale: false
mean: [127.5, 127.5, 127.5]
std: [127.502231, 127.502231, 127.502231]
batch_size: 32
worker_num: 8
bufsize: 32
use_process: false
SSDTestFeed: TestReader:
batch_size: 1 inputs_def:
image_shape: [3,300,300]
fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
!ImageFolder
anno_path: test.txt
use_default_label: true use_default_label: true
drop_last: false sample_transforms:
- !DecodeImage
to_rgb: true
- !ResizeImage
interp: 1
max_size: 0
target_size: 300
use_cv2: false
- !Permute {}
- !NormalizeImage
is_scale: false
mean: [127.5, 127.5, 127.5]
std: [127.502231, 127.502231, 127.502231]
batch_size: 1
configs/ssd/ssd_vgg16_300.yml
浏览文件 @ 8192c758
architecture: SSD architecture: SSD
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
use_gpu: true use_gpu: true
max_iters: 400000 max_iters: 400000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -60,37 +57,27 @@ OptimizerBuilder: ...@@ -60,37 +57,27 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 inputs_def:
image_shape: [3, 300, 300]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
image_shape: [3, 300, 300]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
brightness_upper: 1.125 brightness_upper: 1.125
is_order: true is_order: true
- !ExpandImage - !RandomExpand
max_ratio: 4 fill_value: [104, 117, 123]
mean: [104, 117, 123] - !RandomCrop
prob: 0.5 allow_no_crop: true
- !CropImage - !NormalizeBox {}
avoid_no_bbox: true
batch_sampler:
- [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
satisfy_all: false
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: 300 target_size: 300
...@@ -103,19 +90,27 @@ SSDTrainFeed: ...@@ -103,19 +90,27 @@ SSDTrainFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 8
shuffle: true
worker_num: 8
bufsize: 32
use_process: true
drop_empty: true
SSDEvalFeed: EvalReader:
batch_size: 16 inputs_def:
image_shape: [3, 300, 300]
fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id', 'is_difficult']
dataset: dataset:
dataset_dir: dataset/coco !COCODataSet
annotation: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
drop_last: false anno_path: annotations/instances_val2017.json
image_shape: [3, 300, 300] dataset_dir: dataset/coco
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false with_mixup: false
- !NormalizeBox {}
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: 300 target_size: 300
...@@ -126,12 +121,17 @@ SSDEvalFeed: ...@@ -126,12 +121,17 @@ SSDEvalFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 16
worker_num: 8
bufsize: 32
SSDTestFeed: TestReader:
batch_size: 1 inputs_def:
image_shape: [3,300,300]
fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/coco/annotations/instances_val2017.json !ImageFolder
image_shape: [3, 300, 300] anno_path: annotations/instances_val2017.json
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -147,3 +147,4 @@ SSDTestFeed: ...@@ -147,3 +147,4 @@ SSDTestFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 1
configs/ssd/ssd_vgg16_300_voc.yml
浏览文件 @ 8192c758
architecture: SSD architecture: SSD
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
use_gpu: true use_gpu: true
max_iters: 120001 max_iters: 120001
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -60,41 +57,31 @@ OptimizerBuilder: ...@@ -60,41 +57,31 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 inputs_def:
image_shape: [3, 300, 300]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: trainval.txt anno_path: trainval.txt
use_default_label: true use_default_label: true
image_shape: [3, 300, 300]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
brightness_upper: 1.125 brightness_upper: 1.125
is_order: true is_order: true
- !ExpandImage - !RandomExpand
max_ratio: 4 fill_value: [104, 117, 123]
mean: [104, 117, 123] - !RandomCrop
prob: 0.5 allow_no_crop: true
- !CropImage - !NormalizeBox {}
avoid_no_bbox: true
batch_sampler:
- [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
satisfy_all: false
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: 300 target_size: 300
use_cv2: False use_cv2: false
- !RandomFlipImage - !RandomFlipImage
is_normalized: true is_normalized: true
- !Permute - !Permute
...@@ -103,15 +90,21 @@ SSDTrainFeed: ...@@ -103,15 +90,21 @@ SSDTrainFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 8
shuffle: true
worker_num: 8
bufsize: 32
use_process: 8
SSDEvalFeed: EvalReader:
batch_size: 32 inputs_def:
image_shape: [3, 300, 300]
fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id', 'is_difficult']
dataset: dataset:
!VOCDataSet
anno_path: test.txt
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: test.txt
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 300, 300]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -125,15 +118,20 @@ SSDEvalFeed: ...@@ -125,15 +118,20 @@ SSDEvalFeed:
to_bgr: false to_bgr: false
- !NormalizeImage - !NormalizeImage
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 32
worker_num: 8
bufsize: 32
SSDTestFeed: TestReader:
batch_size: 1 inputs_def:
image_shape: [3,300,300]
fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
!ImageFolder
anno_path: test.txt
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 300, 300]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -149,3 +147,4 @@ SSDTestFeed: ...@@ -149,3 +147,4 @@ SSDTestFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 1
configs/ssd/ssd_vgg16_512.yml
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architecture: SSD architecture: SSD
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
use_gpu: true use_gpu: true
max_iters: 400000 max_iters: 400000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -62,37 +59,28 @@ OptimizerBuilder: ...@@ -62,37 +59,28 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 inputs_def:
image_shape: [3, 512, 512]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
image_shape: [3, 512, 512]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false with_mixup: false
- !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
brightness_upper: 1.125 brightness_upper: 1.125
is_order: true is_order: true
- !ExpandImage - !RandomExpand
max_ratio: 4 fill_value: [104, 117, 123]
mean: [104, 117, 123] - !RandomCrop
prob: 0.5 allow_no_crop: true
- !CropImage - !NormalizeBox {}
avoid_no_bbox: true
batch_sampler:
- [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
satisfy_all: false
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: 512 target_size: 512
...@@ -105,15 +93,21 @@ SSDTrainFeed: ...@@ -105,15 +93,21 @@ SSDTrainFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
SSDEvalFeed:
batch_size: 8 batch_size: 8
shuffle: true
worker_num: 8
bufsize: 32
use_process: 8
EvalReader:
inputs_def:
image_shape: [3,512,512]
fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id', 'is_difficult']
dataset: dataset:
dataset_dir: dataset/coco !COCODataSet
annotation: annotations/instances_val2017.json
image_dir: val2017 image_dir: val2017
drop_last: false anno_path: annotations/instances_val2017.json
image_shape: [3, 512, 512] dataset_dir: dataset/coco
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -128,12 +122,18 @@ SSDEvalFeed: ...@@ -128,12 +122,18 @@ SSDEvalFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 8
worker_num: 8
bufsize: 32
drop_empty: false
SSDTestFeed: TestReader:
batch_size: 1 inputs_def:
image_shape: [3,512,512]
fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
annotation: dataset/coco/annotations/instances_val2017.json !ImageFolder
image_shape: [3, 512, 512] anno_path: annotations/instances_val2017.json
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -149,3 +149,4 @@ SSDTestFeed: ...@@ -149,3 +149,4 @@ SSDTestFeed:
is_scale: false is_scale: false
mean: [104, 117, 123] mean: [104, 117, 123]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 1
configs/ssd/ssd_vgg16_512_voc.yml
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architecture: SSD architecture: SSD
train_feed: SSDTrainFeed
eval_feed: SSDEvalFeed
test_feed: SSDTestFeed
use_gpu: true use_gpu: true
max_iters: 120000 max_iters: 120000
snapshot_iter: 10000 snapshot_iter: 10000
...@@ -64,37 +61,27 @@ OptimizerBuilder: ...@@ -64,37 +61,27 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
SSDTrainFeed: TrainReader:
batch_size: 8 inputs_def:
image_shape: [3, 512, 512]
fields: ['image', 'gt_bbox', 'gt_class']
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: trainval.txt anno_path: trainval.txt
use_default_label: true use_default_label: true
image_shape: [3, 512, 512]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
with_mixup: false
- !NormalizeBox {}
- !RandomDistort - !RandomDistort
brightness_lower: 0.875 brightness_lower: 0.875
brightness_upper: 1.125 brightness_upper: 1.125
is_order: true is_order: true
- !ExpandImage - !RandomExpand
max_ratio: 4 fill_value: [123, 117, 104]
mean: [123, 117, 104] - !RandomCrop
prob: 0.5 allow_no_crop: true
- !CropImage - !NormalizeBox {}
avoid_no_bbox: true
batch_sampler:
- [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
- [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
satisfy_all: false
- !ResizeImage - !ResizeImage
interp: 1 interp: 1
target_size: 512 target_size: 512
...@@ -107,15 +94,21 @@ SSDTrainFeed: ...@@ -107,15 +94,21 @@ SSDTrainFeed:
is_scale: false is_scale: false
mean: [123, 117, 104] mean: [123, 117, 104]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 8
shuffle: true
worker_num: 8
bufsize: 32
use_process: 8
SSDEvalFeed: EvalReader:
batch_size: 32 inputs_def:
image_shape: [3, 512, 512]
fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id', 'is_difficult']
dataset: dataset:
!VOCDataSet
anno_path: test.txt
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: test.txt
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 512, 512]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -131,13 +124,18 @@ SSDEvalFeed: ...@@ -131,13 +124,18 @@ SSDEvalFeed:
is_scale: false is_scale: false
mean: [123, 117, 104] mean: [123, 117, 104]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 32
worker_num: 8
bufsize: 32
SSDTestFeed: TestReader:
batch_size: 1 inputs_def:
image_shape: [3,512,512]
fields: ['image', 'im_id', 'im_shape']
dataset: dataset:
!ImageFolder
anno_path: test.txt
use_default_label: true use_default_label: true
drop_last: false
image_shape: [3, 512, 512]
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -153,3 +151,4 @@ SSDTestFeed: ...@@ -153,3 +151,4 @@ SSDTestFeed:
is_scale: false is_scale: false
mean: [123, 117, 104] mean: [123, 117, 104]
std: [1, 1, 1] std: [1, 1, 1]
batch_size: 1
configs/yolov3_darknet.yml
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architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 500200 max_iters: 500200
log_smooth_window: 20 log_smooth_window: 20
...@@ -57,26 +54,4 @@ OptimizerBuilder: ...@@ -57,26 +54,4 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: 'yolov3_reader.yml'
batch_size: 8
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
num_workers: 8
bufsize: 128
use_process: true
YoloEvalFeed:
batch_size: 8
image_shape: [3, 608, 608]
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
YoloTestFeed:
batch_size: 1
image_shape: [3, 608, 608]
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/yolov3_darknet_voc.yml
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architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 70000 max_iters: 70000
log_smooth_window: 20 log_smooth_window: 20
...@@ -58,27 +55,31 @@ OptimizerBuilder: ...@@ -58,27 +55,31 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: 'yolov3_reader.yml'
batch_size: 8 TrainReader:
inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
num_max_boxes: 50
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: trainval.txt anno_path: trainval.txt
use_default_label: true use_default_label: true
num_workers: 8 with_background: false
bufsize: 128
use_process: true
mixup_epoch: 250
YoloEvalFeed: EvalReader:
batch_size: 8 inputs_def:
image_shape: [3, 608, 608] fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
num_max_boxes: 50
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: test.txt anno_path: test.txt
use_default_label: true use_default_label: true
with_background: false
YoloTestFeed: TestReader:
batch_size: 1
image_shape: [3, 608, 608]
dataset: dataset:
!ImageFolder
use_default_label: true use_default_label: true
with_background: false
configs/yolov3_mobilenet_v1.yml
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architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 500200 max_iters: 500200
log_smooth_window: 20 log_smooth_window: 20
...@@ -58,26 +55,4 @@ OptimizerBuilder: ...@@ -58,26 +55,4 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: 'yolov3_reader.yml'
batch_size: 8
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
num_workers: 8
bufsize: 128
use_process: true
YoloEvalFeed:
batch_size: 8
image_shape: [3, 608, 608]
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
YoloTestFeed:
batch_size: 1
image_shape: [3, 608, 608]
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/yolov3_mobilenet_v1_fruit.yml
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architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 20000 max_iters: 20000
log_smooth_window: 20 log_smooth_window: 20
save_dir: output save_dir: output
snapshot_iter: 1000 snapshot_iter: 200
metric: VOC metric: VOC
map_type: 11point map_type: 11point
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
...@@ -60,16 +57,19 @@ OptimizerBuilder: ...@@ -60,16 +57,19 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: 'yolov3_reader.yml'
batch_size: 1 # will merge TrainReader into yolov3_reader.yml
TrainReader:
inputs_def:
image_shape: [3, 608, 608]
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
num_max_boxes: 50
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/fruit dataset_dir: dataset/fruit
annotation: fruit-detection/train.txt anno_path: train.txt
with_background: false
use_default_label: false use_default_label: false
num_workers: 16
bufsize: 128
use_process: true
mixup_epoch: -1
sample_transforms: sample_transforms:
- !DecodeImage - !DecodeImage
to_rgb: true to_rgb: true
...@@ -83,40 +83,45 @@ YoloTrainFeed: ...@@ -83,40 +83,45 @@ YoloTrainFeed:
max_size: 0 max_size: 0
target_size: 608 target_size: 608
- !RandomFlipImage - !RandomFlipImage
is_mask_flip: false
is_normalized: true is_normalized: true
prob: 0.5 prob: 0.5
- !NormalizeImage - !NormalizeImage
is_channel_first: false mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: true is_scale: true
mean: is_channel_first: false
- 0.485 - !PadBox
- 0.456 num_max_boxes: 50
- 0.406 - !BboxXYXY2XYWH {}
std: batch_transforms:
- 0.229 - !RandomShape
- 0.224 sizes: [608]
- 0.225
- !Permute - !Permute
channel_first: true channel_first: true
to_bgr: false to_bgr: false
batch_transforms: batch_size: 1
- !RandomShape shuffle: true
sizes: [608] mixup_epoch: -1
with_background: false worker_num: 4
bufsize: 64
use_process: true
YoloEvalFeed: EvalReader:
batch_size: 1 batch_size: 1
image_shape: [3, 608, 608] inputs_def:
image_shape: [3, 608, 608]
fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
num_max_boxes: 50
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/fruit dataset_dir: dataset/fruit
annotation: fruit-detection/val.txt anno_path: val.txt
use_default_label: false use_default_label: false
with_background: false
YoloTestFeed: TestReader:
batch_size: 1 batch_size: 1
image_shape: [3, 608, 608]
dataset: dataset:
dataset_dir: dataset/fruit !ImageFolder
use_default_label: false use_default_label: false
with_background: false
configs/yolov3_mobilenet_v1_voc.yml
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architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 70000 max_iters: 70000
log_smooth_window: 20 log_smooth_window: 20
...@@ -59,27 +56,29 @@ OptimizerBuilder: ...@@ -59,27 +56,29 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: 'yolov3_reader.yml'
batch_size: 8 TrainReader:
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: trainval.txt anno_path: trainval.txt
use_default_label: true use_default_label: true
num_workers: 8 with_background: false
bufsize: 128
use_process: true
mixup_epoch: 250
YoloEvalFeed: EvalReader:
batch_size: 8 inputs_def:
image_shape: [3, 608, 608] image_shape: [3, 608, 608]
fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
num_max_boxes: 50
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: test.txt anno_path: test.txt
use_default_label: true use_default_label: true
with_background: false
YoloTestFeed: TestReader:
batch_size: 1
image_shape: [3, 608, 608]
dataset: dataset:
!ImageFolder
use_default_label: true use_default_label: true
with_background: false
configs/yolov3_r34.yml
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architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 500200 max_iters: 500200
log_smooth_window: 20 log_smooth_window: 20
...@@ -60,26 +57,4 @@ OptimizerBuilder: ...@@ -60,26 +57,4 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: 'yolov3_reader.yml'
batch_size: 8
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_train2017.json
image_dir: train2017
num_workers: 8
bufsize: 128
use_process: true
YoloEvalFeed:
batch_size: 8
image_shape: [3, 608, 608]
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
YoloTestFeed:
batch_size: 1
image_shape: [3, 608, 608]
dataset:
annotation: dataset/coco/annotations/instances_val2017.json
configs/yolov3_r34_voc.yml
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architecture: YOLOv3 architecture: YOLOv3
train_feed: YoloTrainFeed
eval_feed: YoloEvalFeed
test_feed: YoloTestFeed
use_gpu: true use_gpu: true
max_iters: 70000 max_iters: 70000
log_smooth_window: 20 log_smooth_window: 20
...@@ -61,27 +58,29 @@ OptimizerBuilder: ...@@ -61,27 +58,29 @@ OptimizerBuilder:
factor: 0.0005 factor: 0.0005
type: L2 type: L2
YoloTrainFeed: _READER_: 'yolov3_reader.yml'
batch_size: 8 TrainReader:
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: trainval.txt anno_path: trainval.txt
use_default_label: true use_default_label: true
num_workers: 8 with_background: false
bufsize: 128
use_process: true
mixup_epoch: 250
YoloEvalFeed: EvalReader:
batch_size: 8 inputs_def:
image_shape: [3, 608, 608] image_shape: [3, 608, 608]
fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
num_max_boxes: 50
dataset: dataset:
!VOCDataSet
dataset_dir: dataset/voc dataset_dir: dataset/voc
annotation: test.txt anno_path: test.txt
use_default_label: true use_default_label: true
with_background: false
YoloTestFeed: TestReader:
batch_size: 1
image_shape: [3, 608, 608]
dataset: dataset:
!ImageFolder
use_default_label: true use_default_label: true
with_background: false
configs/yolov3_reader.yml 0 → 100644
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TrainReader:
inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
num_max_boxes: 50
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
with_background: false
sample_transforms:
- !DecodeImage
to_rgb: True
with_mixup: True
- !MixupImage
alpha: 1.5
beta: 1.5
- !ColorDistort {}
- !RandomExpand
fill_value: [123.675, 116.28, 103.53]
- !RandomCrop {}
- !RandomFlipImage
is_normalized: false
- !NormalizeBox {}
- !PadBox
num_max_boxes: 50
- !BboxXYXY2XYWH {}
batch_transforms:
- !RandomShape
sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
random_inter: True
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: True
is_channel_first: false
- !Permute
to_bgr: false
channel_first: True
batch_size: 8
shuffle: true
mixup_epoch: 250
drop_last: true
worker_num: 8
bufsize: 32
use_process: true
EvalReader:
inputs_def:
fields: ['image', 'im_size', 'im_id']
num_max_boxes: 50
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
with_background: false
sample_transforms:
- !DecodeImage
to_rgb: True
- !ResizeImage
target_size: 608
interp: 2
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: True
is_channel_first: false
- !PadBox
num_max_boxes: 50
- !Permute
to_bgr: false
channel_first: True
batch_size: 8
drop_empty: false
worker_num: 8
bufsize: 32
TestReader:
inputs_def:
image_shape: [3, 608, 608]
fields: ['image', 'im_size', 'im_id']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
with_background: false
sample_transforms:
- !DecodeImage
to_rgb: True
- !ResizeImage
target_size: 608
interp: 2
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
is_scale: True
is_channel_first: false
- !Permute
to_bgr: false
channel_first: True
batch_size: 1
configs2/faster_rcnn_r50_1x.yml 0 → 100644
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architecture: FasterRCNN
use_gpu: true
max_iters: 180000
log_smooth_window: 20
save_dir: output
snapshot_iter: 10000
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
metric: COCO
weights: output/faster_rcnn_r50_1x/model_final
num_classes: 81
FasterRCNN:
backbone: ResNet
rpn_head: RPNHead
roi_extractor: RoIAlign
bbox_head: BBoxHead
bbox_assigner: BBoxAssigner
ResNet:
norm_type: affine_channel
depth: 50
feature_maps: 4
freeze_at: 2
ResNetC5:
depth: 50
norm_type: affine_channel
RPNHead:
anchor_generator:
anchor_sizes: [32, 64, 128, 256, 512]
aspect_ratios: [0.5, 1.0, 2.0]
stride: [16.0, 16.0]
variance: [1.0, 1.0, 1.0, 1.0]
rpn_target_assign:
rpn_batch_size_per_im: 256
rpn_fg_fraction: 0.5
rpn_negative_overlap: 0.3
rpn_positive_overlap: 0.7
rpn_straddle_thresh: 0.0
use_random: true
train_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 12000
post_nms_top_n: 2000
test_proposal:
min_size: 0.0
nms_thresh: 0.7
pre_nms_top_n: 6000
post_nms_top_n: 1000
RoIAlign:
resolution: 14
sampling_ratio: 0
spatial_scale: 0.0625
BBoxAssigner:
batch_size_per_im: 512
bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
bg_thresh_hi: 0.5
bg_thresh_lo: 0.0
fg_fraction: 0.25
fg_thresh: 0.5
BBoxHead:
head: ResNetC5
nms:
keep_top_k: 100
nms_threshold: 0.5
score_threshold: 0.05
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [120000, 160000]
- !LinearWarmup
start_factor: 0.3333333333333333
steps: 500
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
_LOADER_: 'faster_reader.yml'
TrainLoader:
inputs_def:
image_shape: [3,800,800]
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
batch_size: 3
configs2/faster_reader.yml 0 → 100644
浏览文件 @ 8192c758
TrainReader:
inputs_def:
image_shape: [3,NULL,NULL]
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_transforms:
- !DecodeImage
to_rgb: true
- !RandomFlipImage
prob: 0.5
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
target_size: 800
max_size: 1333
interp: 1
use_cv2: true
- !Permute
to_bgr: false
channel_first: true
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: false
batch_size: 1
shuffle: true
worker_num: 2
drop_last: false
use_multi_process: false
EvalReader:
inputs_def:
image_shape: [3,800,1333]
fields: ['image', 'im_info', 'im_id', 'im_shape']
# for voc
#fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
#sample_num: 100
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: true
batch_size: 1
shuffle: false
drop_last: false
# worker_num: 2
TestReader:
inputs_def:
image_shape: [3,800,1333]
fields: ['image', 'im_info', 'im_id', 'im_shape']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: true
batch_size: 1
shuffle: false
drop_last: false
docs/GETTING_STARTED.md
浏览文件 @ 8192c758
...@@ -34,7 +34,6 @@ list below can be viewed by `--help` ...@@ -34,7 +34,6 @@ list below can be viewed by `--help`
| -r/--resume_checkpoint | train | Checkpoint path for resuming training | None | `-r output/faster_rcnn_r50_1x/10000` | | -r/--resume_checkpoint | train | Checkpoint path for resuming training | None | `-r output/faster_rcnn_r50_1x/10000` |
| --eval | train | Whether to perform evaluation in training | False | | | --eval | train | Whether to perform evaluation in training | False | |
| --output_eval | train/eval | json path in evalution | current path | `--output_eval ./json_result` | | --output_eval | train/eval | json path in evalution | current path | `--output_eval ./json_result` |
| -d/--dataset_dir | train/eval | path for dataset, same as dataset_dir in configs | None | `-d dataset/coco` |
| --fp16 | train | Whether to enable mixed precision training | False | GPU training is required | | --fp16 | train | Whether to enable mixed precision training | False | GPU training is required |
| --loss_scale | train | Loss scaling factor for mixed precision training | 8.0 | enable when `--fp16` is True | | --loss_scale | train | Loss scaling factor for mixed precision training | 8.0 | enable when `--fp16` is True |
| --json_eval | eval | Whether to evaluate with already existed bbox.json or mask.json | False | json path is set in `--output_eval` | | --json_eval | eval | Whether to evaluate with already existed bbox.json or mask.json | False | json path is set in `--output_eval` |
......
docs/GETTING_STARTED_cn.md
浏览文件 @ 8192c758
...@@ -31,7 +31,6 @@ python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/0000005 ...@@ -31,7 +31,6 @@ python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/0000005
| -r/--resume_checkpoint | train | 从某一检查点恢复训练 | None | `-r output/faster_rcnn_r50_1x/10000` | | -r/--resume_checkpoint | train | 从某一检查点恢复训练 | None | `-r output/faster_rcnn_r50_1x/10000` |
| --eval | train | 是否边训练边测试 | False | | | --eval | train | 是否边训练边测试 | False | |
| --output_eval | train/eval | 编辑评测保存json路径 | 当前路径 | `--output_eval ./json_result` | | --output_eval | train/eval | 编辑评测保存json路径 | 当前路径 | `--output_eval ./json_result` |
| -d/--dataset_dir | train/eval | 数据集路径, 同配置文件里的dataset_dir | None | `-d dataset/coco` |
| --fp16 | train | 是否使用混合精度训练模式 | False | 需使用GPU训练 | | --fp16 | train | 是否使用混合精度训练模式 | False | 需使用GPU训练 |
| --loss_scale | train | 设置混合精度训练模式中损失值的缩放比例 | 8.0 | 需先开启`--fp16`后使用 | | --loss_scale | train | 设置混合精度训练模式中损失值的缩放比例 | 8.0 | 需先开启`--fp16`后使用 |
| --json_eval | eval | 是否通过已存在的bbox.json或者mask.json进行评估 | False | json文件路径在`--output_eval`中设置 | | --json_eval | eval | 是否通过已存在的bbox.json或者mask.json进行评估 | False | json文件路径在`--output_eval`中设置 |
......
ppdet/core/workspace.py
浏览文件 @ 8192c758
...@@ -22,6 +22,7 @@ import sys ...@@ -22,6 +22,7 @@ import sys
import yaml import yaml
import copy import copy
import collections
from .config.schema import SchemaDict, SharedConfig, extract_schema from .config.schema import SchemaDict, SharedConfig, extract_schema
from .config.yaml_helpers import serializable from .config.yaml_helpers import serializable
...@@ -65,6 +66,8 @@ class AttrDict(dict): ...@@ -65,6 +66,8 @@ class AttrDict(dict):
global_config = AttrDict() global_config = AttrDict()
READER_KEY = '_READER_'
def load_config(file_path): def load_config(file_path):
""" """
...@@ -77,25 +80,59 @@ def load_config(file_path): ...@@ -77,25 +80,59 @@ def load_config(file_path):
""" """
_, ext = os.path.splitext(file_path) _, ext = os.path.splitext(file_path)
assert ext in ['.yml', '.yaml'], "only support yaml files for now" assert ext in ['.yml', '.yaml'], "only support yaml files for now"
cfg = AttrDict()
with open(file_path) as f: with open(file_path) as f:
merge_config(yaml.load(f, Loader=yaml.Loader)) cfg = merge_config(yaml.load(f, Loader=yaml.Loader), cfg)
if READER_KEY in cfg:
reader_cfg = cfg[READER_KEY]
if reader_cfg.startswith("~"):
reader_cfg = os.path.expanduser(reader_cfg)
if not reader_cfg.startswith('/'):
reader_cfg = os.path.join(os.path.dirname(file_path), reader_cfg)
with open(reader_cfg) as f:
merge_config(yaml.load(f, Loader=yaml.Loader))
del cfg[READER_KEY]
merge_config(cfg)
return global_config return global_config
def merge_config(config): def dict_merge(dct, merge_dct):
""" Recursive dict merge. Inspired by :meth:``dict.update()``, instead of
updating only top-level keys, dict_merge recurses down into dicts nested
to an arbitrary depth, updating keys. The ``merge_dct`` is merged into
``dct``.
Args:
dct: dict onto which the merge is executed
merge_dct: dct merged into dct
Returns: dct
"""
for k, v in merge_dct.items():
if (k in dct and isinstance(dct[k], dict) and
isinstance(merge_dct[k], collections.Mapping)):
dict_merge(dct[k], merge_dct[k])
else:
dct[k] = merge_dct[k]
return dct
def merge_config(config, another_cfg=None):
""" """
Merge config into global config. Merge config into global config or another_cfg.
Args: Args:
config (dict): Config to be merged. config (dict): Config to be merged.
Returns: global config Returns: global config
""" """
for key, value in config.items(): global global_config
if isinstance(value, dict) and key in global_config: dct = another_cfg if another_cfg is not None else global_config
global_config[key].update(value) return dict_merge(dct, config)
else:
global_config[key] = value
def get_registered_modules(): def get_registered_modules():
......
ppdet/data/README.md 已删除 120000 → 0
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docs/DATA.md
\ No newline at end of file
ppdet/data/README_cn.md 已删除 120000 → 0
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docs/DATA_cn.md
\ No newline at end of file
ppdet/data/__init__.py
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...@@ -12,35 +12,8 @@ ...@@ -12,35 +12,8 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# function:
# module to prepare data for detection model training
#
# implementation notes:
# - Dateset
# basic interface to accessing data samples in stream mode
#
# - xxxSource (RoiDbSource)
# * subclass of 'Dataset'
# * load data from local files and other source data
#
# - xxxOperator (DecodeImage)
# * subclass of 'BaseOperator'
# * each op can transform a sample, eg: decode/resize/crop image
# * each op must obey basic rules defined in transform.operator.base
#
# - transformer
# * subclass of 'Dataset'
# * 'MappedDataset' accept a 'xxxSource' and a list of 'xxxOperator'
# to build a transformed 'Dataset'
from __future__ import absolute_import from __future__ import absolute_import
from .dataset import Dataset from .reader import *
from .reader import Reader from .source import *
import traceback from .transform import *
if traceback.extract_stack()[0][
0] == 'ppdet/data/tools/generate_data_for_training.py':
__all__ = ['Dataset', 'Reader']
else:
from .data_feed import create_reader
__all__ = ['Dataset', 'Reader', 'create_reader']
ppdet/data/data_feed.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import inspect
from ppdet.core.workspace import register, serializable
from ppdet.utils.download import get_dataset_path
from ppdet.data.reader import Reader
# XXX these are for triggering the decorator
from ppdet.data.transform.operators import (
DecodeImage, MixupImage, NormalizeBox, NormalizeImage, RandomDistort,
RandomFlipImage, RandomInterpImage, ResizeImage, ExpandImage, CropImage,
Permute, MultiscaleTestResize, Resize, ColorDistort, NormalizePermute,
RandomExpand, RandomCrop)
from ppdet.data.transform.arrange_sample import (
ArrangeRCNN, ArrangeEvalRCNN, ArrangeTestRCNN, ArrangeSSD, ArrangeEvalSSD,
ArrangeTestSSD, ArrangeYOLO, ArrangeEvalYOLO, ArrangeTestYOLO)
__all__ = [
'PadBatch', 'MultiScale', 'RandomShape', 'PadMSTest', 'DataSet',
'CocoDataSet', 'DataFeed', 'TrainFeed', 'EvalFeed', 'FasterRCNNTrainFeed',
'MaskRCNNTrainFeed', 'FasterRCNNEvalFeed', 'MaskRCNNEvalFeed',
'FasterRCNNTestFeed', 'MaskRCNNTestFeed', 'SSDTrainFeed', 'SSDEvalFeed',
'SSDTestFeed', 'YoloTrainFeed', 'YoloEvalFeed', 'YoloTestFeed',
'create_reader'
]
def _prepare_data_config(feed, args_path):
# if `DATASET_DIR` does not exists, search ~/.paddle/dataset for a directory
# named `DATASET_DIR` (e.g., coco, pascal), if not present either, download
dataset_home = args_path if args_path else feed.dataset.dataset_dir
if dataset_home:
annotation = getattr(feed.dataset, 'annotation', None)
image_dir = getattr(feed.dataset, 'image_dir', None)
dataset_dir = get_dataset_path(dataset_home, annotation, image_dir)
if annotation:
feed.dataset.annotation = os.path.join(dataset_dir, annotation)
if image_dir:
feed.dataset.image_dir = os.path.join(dataset_dir, image_dir)
mixup_epoch = -1
if getattr(feed, 'mixup_epoch', None) is not None:
mixup_epoch = feed.mixup_epoch
data_config = {
'ANNO_FILE': feed.dataset.annotation,
'IMAGE_DIR': feed.dataset.image_dir,
'USE_DEFAULT_LABEL': feed.dataset.use_default_label,
'IS_SHUFFLE': feed.shuffle,
'SAMPLES': feed.samples,
'WITH_BACKGROUND': feed.with_background,
'MIXUP_EPOCH': mixup_epoch,
'TYPE': type(feed.dataset).__source__
}
if feed.mode == 'TRAIN':
data_config['CLASS_AWARE_SAMPLING'] = getattr(
feed, 'class_aware_sampling', False)
if len(getattr(feed.dataset, 'images', [])) > 0:
data_config['IMAGES'] = feed.dataset.images
return data_config
def create_reader(feed, max_iter=0, args_path=None, my_source=None):
"""
Return iterable data reader.
Args:
max_iter (int): number of iterations.
my_source (callable): callable function to create a source iterator
which is used to provide source data in 'ppdet.data.reader'
"""
# if `DATASET_DIR` does not exists, search ~/.paddle/dataset for a directory
# named `DATASET_DIR` (e.g., coco, pascal), if not present either, download
data_config = _prepare_data_config(feed, args_path)
bufsize = getattr(feed, 'bufsize', 10)
use_process = getattr(feed, 'use_process', False)
memsize = getattr(feed, 'memsize', '3G')
transform_config = {
'WORKER_CONF': {
'bufsize': bufsize,
'worker_num': feed.num_workers,
'use_process': use_process,
'memsize': memsize
},
'BATCH_SIZE': feed.batch_size,
'DROP_LAST': feed.drop_last,
'USE_PADDED_IM_INFO': feed.use_padded_im_info,
}
batch_transforms = feed.batch_transforms
pad = [t for t in batch_transforms if isinstance(t, PadBatch)]
rand_shape = [t for t in batch_transforms if isinstance(t, RandomShape)]
multi_scale = [t for t in batch_transforms if isinstance(t, MultiScale)]
pad_ms_test = [t for t in batch_transforms if isinstance(t, PadMSTest)]
if any(pad):
transform_config['IS_PADDING'] = True
if pad[0].pad_to_stride != 0:
transform_config['COARSEST_STRIDE'] = pad[0].pad_to_stride
if any(rand_shape):
transform_config['RANDOM_SHAPES'] = rand_shape[0].sizes
if any(multi_scale):
transform_config['MULTI_SCALES'] = multi_scale[0].scales
if any(pad_ms_test):
transform_config['ENABLE_MULTISCALE_TEST'] = True
transform_config['NUM_SCALE'] = feed.num_scale
transform_config['COARSEST_STRIDE'] = pad_ms_test[0].pad_to_stride
if hasattr(inspect, 'getfullargspec'):
argspec = inspect.getfullargspec
else:
argspec = inspect.getargspec
ops = []
for op in feed.sample_transforms:
op_dict = op.__dict__.copy()
argnames = [
arg for arg in argspec(type(op).__init__).args if arg != 'self'
]
op_dict = {k: v for k, v in op_dict.items() if k in argnames}
op_dict['op'] = op.__class__.__name__
ops.append(op_dict)
transform_config['OPS'] = ops
return Reader.create(feed.mode, data_config, transform_config, max_iter,
my_source)
# XXX batch transforms are only stubs for now, actually handled by `post_map`
@serializable
class PadBatch(object):
"""
Pad a batch of samples to same dimensions
Args:
pad_to_stride (int): pad to multiple of strides, e.g., 32
"""
def __init__(self, pad_to_stride=0):
super(PadBatch, self).__init__()
self.pad_to_stride = pad_to_stride
@serializable
class MultiScale(object):
"""
Randomly resize image by scale
Args:
scales (list): list of int, randomly resize to one of these scales
"""
def __init__(self, scales=[]):
super(MultiScale, self).__init__()
self.scales = scales
@serializable
class RandomShape(object):
"""
Randomly reshape a batch
Args:
sizes (list): list of int, random choose a size from these
"""
def __init__(self, sizes=[]):
super(RandomShape, self).__init__()
self.sizes = sizes
@serializable
class PadMSTest(object):
"""
Padding for multi-scale test
Args:
pad_to_stride (int): pad to multiple of strides, e.g., 32
"""
def __init__(self, pad_to_stride=0):
super(PadMSTest, self).__init__()
self.pad_to_stride = pad_to_stride
@serializable
class DataSet(object):
"""
Dataset, e.g., coco, pascal voc
Args:
annotation (str): annotation file path
image_dir (str): directory where image files are stored
shuffle (bool): shuffle samples
"""
__source__ = 'RoiDbSource'
def __init__(self,
annotation,
image_dir=None,
dataset_dir=None,
use_default_label=None):
super(DataSet, self).__init__()
self.dataset_dir = dataset_dir
self.annotation = annotation
self.image_dir = image_dir
self.use_default_label = use_default_label
COCO_DATASET_DIR = 'dataset/coco'
COCO_TRAIN_ANNOTATION = 'annotations/instances_train2017.json'
COCO_TRAIN_IMAGE_DIR = 'train2017'
COCO_VAL_ANNOTATION = 'annotations/instances_val2017.json'
COCO_VAL_IMAGE_DIR = 'val2017'
@serializable
class CocoDataSet(DataSet):
def __init__(self,
dataset_dir=COCO_DATASET_DIR,
annotation=COCO_TRAIN_ANNOTATION,
image_dir=COCO_TRAIN_IMAGE_DIR):
super(CocoDataSet, self).__init__(
dataset_dir=dataset_dir, annotation=annotation, image_dir=image_dir)
VOC_DATASET_DIR = 'dataset/voc'
VOC_TRAIN_ANNOTATION = 'train.txt'
VOC_VAL_ANNOTATION = 'val.txt'
VOC_IMAGE_DIR = None
VOC_USE_DEFAULT_LABEL = True
@serializable
class VocDataSet(DataSet):
__source__ = 'VOCSource'
def __init__(self,
dataset_dir=VOC_DATASET_DIR,
annotation=VOC_TRAIN_ANNOTATION,
image_dir=VOC_IMAGE_DIR,
use_default_label=VOC_USE_DEFAULT_LABEL):
super(VocDataSet, self).__init__(
dataset_dir=dataset_dir,
annotation=annotation,
image_dir=image_dir,
use_default_label=use_default_label)
@serializable
class SimpleDataSet(DataSet):
__source__ = 'SimpleSource'
def __init__(self,
dataset_dir=None,
annotation=None,
image_dir=None,
use_default_label=None):
super(SimpleDataSet, self).__init__(
dataset_dir=dataset_dir, annotation=annotation, image_dir=image_dir)
self.images = []
def add_images(self, images):
self.images.extend(images)
@serializable
class DataFeed(object):
"""
DataFeed encompasses all data loading related settings
Args:
dataset (object): a `Dataset` instance
fields (list): list of data fields needed
image_shape (list): list of image dims (C, MAX_DIM, MIN_DIM)
sample_transforms (list): list of sample transformations to use
batch_transforms (list): list of batch transformations to use
batch_size (int): number of images per device
shuffle (bool): if samples should be shuffled
drop_last (bool): drop last batch if size is uneven
num_workers (int): number of workers processes (or threads)
bufsize (int): size of queue used to buffer results from workers
use_process (bool): use process or thread as workers
memsize (str): size of shared memory used in result queue
when 'use_process' is True, default to '3G'
"""
__category__ = 'data'
def __init__(self,
dataset,
fields,
image_shape,
sample_transforms=None,
batch_transforms=None,
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
with_background=True,
num_workers=2,
bufsize=10,
use_process=False,
memsize=None,
use_padded_im_info=False,
class_aware_sampling=False):
super(DataFeed, self).__init__()
self.fields = fields
self.image_shape = image_shape
self.sample_transforms = sample_transforms
self.batch_transforms = batch_transforms
self.batch_size = batch_size
self.shuffle = shuffle
self.samples = samples
self.drop_last = drop_last
self.with_background = with_background
self.num_workers = num_workers
self.bufsize = bufsize
self.use_process = use_process
self.memsize = memsize
self.dataset = dataset
self.use_padded_im_info = use_padded_im_info
self.class_aware_sampling = class_aware_sampling
if isinstance(dataset, dict):
self.dataset = DataSet(**dataset)
# for custom (i.e., Non-preset) datasets
@register
class TrainFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset,
fields,
image_shape,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=True,
samples=-1,
drop_last=False,
with_background=True,
num_workers=2,
bufsize=10,
use_process=True,
memsize=None):
super(TrainFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
with_background=with_background,
num_workers=num_workers,
bufsize=bufsize,
use_process=use_process,
memsize=memsize)
@register
class EvalFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset,
fields,
image_shape,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
with_background=True,
num_workers=2):
super(EvalFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
with_background=with_background,
num_workers=num_workers)
@register
class TestFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset,
fields,
image_shape,
sample_transforms=[],
batch_transforms=[],
batch_size=1,
shuffle=False,
drop_last=False,
with_background=True,
num_workers=2):
super(TestFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
drop_last=drop_last,
with_background=with_background,
num_workers=num_workers)
# yapf: disable
@register
class FasterRCNNTrainFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=CocoDataSet().__dict__,
fields=[
'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
'is_crowd'
],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
RandomFlipImage(prob=0.5),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800, max_size=1333, interp=1),
Permute(to_bgr=False)
],
batch_transforms=[PadBatch()],
batch_size=1,
shuffle=True,
samples=-1,
drop_last=False,
bufsize=10,
num_workers=2,
use_process=False,
memsize=None,
class_aware_sampling=False):
# XXX this should be handled by the data loader, since `fields` is
# given, just collect them
sample_transforms.append(ArrangeRCNN())
super(FasterRCNNTrainFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
bufsize=bufsize,
num_workers=num_workers,
use_process=use_process,
memsize=memsize,
class_aware_sampling=class_aware_sampling)
# XXX these modes should be unified
self.mode = 'TRAIN'
@register
class FasterRCNNEvalFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=CocoDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape', 'gt_box',
'gt_label', 'is_difficult'],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800, max_size=1333, interp=1),
Permute(to_bgr=False)
],
batch_transforms=[PadBatch()],
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
num_workers=2,
use_padded_im_info=True,
enable_multiscale=False,
num_scale=1,
enable_aug_flip=False):
sample_transforms.append(ArrangeEvalRCNN())
super(FasterRCNNEvalFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
use_padded_im_info=use_padded_im_info)
self.mode = 'VAL'
self.enable_multiscale = enable_multiscale
self.num_scale = num_scale
self.enable_aug_flip = enable_aug_flip
@register
class FasterRCNNTestFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800, max_size=1333, interp=1),
Permute(to_bgr=False)
],
batch_transforms=[PadBatch()],
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
num_workers=2,
use_padded_im_info=True):
sample_transforms.append(ArrangeTestRCNN())
if isinstance(dataset, dict):
dataset = SimpleDataSet(**dataset)
super(FasterRCNNTestFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
use_padded_im_info=use_padded_im_info)
self.mode = 'TEST'
# XXX currently use two presets, in the future, these should be combined into a
# single `RCNNTrainFeed`. Mask (and keypoint) should be processed
# automatically if `gt_mask` (or `gt_keypoints`) is in the required fields
@register
class MaskRCNNTrainFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=CocoDataSet().__dict__,
fields=[
'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
'is_crowd', 'gt_mask'
],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
RandomFlipImage(prob=0.5, is_mask_flip=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800,
max_size=1333,
interp=1,
use_cv2=True),
Permute(to_bgr=False, channel_first=True)
],
batch_transforms=[PadBatch()],
batch_size=1,
shuffle=True,
samples=-1,
drop_last=False,
num_workers=2,
use_process=False,
use_padded_im_info=False):
sample_transforms.append(ArrangeRCNN(is_mask=True))
super(MaskRCNNTrainFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
use_process=use_process)
self.mode = 'TRAIN'
@register
class MaskRCNNEvalFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=CocoDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800,
max_size=1333,
interp=1,
use_cv2=True),
Permute(to_bgr=False, channel_first=True)
],
batch_transforms=[PadBatch()],
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
num_workers=2,
use_process=False,
use_padded_im_info=True,
enable_multiscale=False,
num_scale=1,
enable_aug_flip=False):
sample_transforms.append(ArrangeTestRCNN())
super(MaskRCNNEvalFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
use_process=use_process,
use_padded_im_info=use_padded_im_info)
self.mode = 'VAL'
self.enable_multiscale = enable_multiscale
self.num_scale = num_scale
self.enable_aug_flip = enable_aug_flip
@register
class MaskRCNNTestFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800, max_size=1333, interp=1),
Permute(to_bgr=False, channel_first=True)
],
batch_transforms=[PadBatch()],
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
num_workers=2,
use_process=False,
use_padded_im_info=True):
sample_transforms.append(ArrangeTestRCNN())
if isinstance(dataset, dict):
dataset = SimpleDataSet(**dataset)
super(MaskRCNNTestFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
use_process=use_process,
use_padded_im_info=use_padded_im_info)
self.mode = 'TEST'
@register
class SSDTrainFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=VocDataSet().__dict__,
fields=['image', 'gt_box', 'gt_label'],
image_shape=[3, 300, 300],
sample_transforms=[
DecodeImage(to_rgb=True, with_mixup=False),
NormalizeBox(),
RandomDistort(brightness_lower=0.875,
brightness_upper=1.125,
is_order=True),
ExpandImage(max_ratio=4, prob=0.5),
CropImage(batch_sampler=[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]],
satisfy_all=False, avoid_no_bbox=False),
ResizeImage(target_size=300, use_cv2=False, interp=1),
RandomFlipImage(is_normalized=True),
Permute(),
NormalizeImage(mean=[127.5, 127.5, 127.5],
std=[127.502231, 127.502231, 127.502231],
is_scale=False)
],
batch_transforms=[],
batch_size=32,
shuffle=True,
samples=-1,
drop_last=True,
num_workers=8,
bufsize=10,
use_process=True,
memsize=None):
sample_transforms.append(ArrangeSSD())
super(SSDTrainFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
bufsize=bufsize,
use_process=use_process,
memsize=None)
self.mode = 'TRAIN'
@register
class SSDEvalFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(
self,
dataset=VocDataSet(VOC_VAL_ANNOTATION).__dict__,
fields=['image', 'im_shape', 'im_id', 'gt_box',
'gt_label', 'is_difficult'],
image_shape=[3, 300, 300],
sample_transforms=[
DecodeImage(to_rgb=True, with_mixup=False),
NormalizeBox(),
ResizeImage(target_size=300, use_cv2=False, interp=1),
Permute(),
NormalizeImage(
mean=[127.5, 127.5, 127.5],
std=[127.502231, 127.502231, 127.502231],
is_scale=False)
],
batch_transforms=[],
batch_size=64,
shuffle=False,
samples=-1,
drop_last=True,
num_workers=8,
bufsize=10,
use_process=False,
memsize=None):
sample_transforms.append(ArrangeEvalSSD(fields))
super(SSDEvalFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
bufsize=bufsize,
use_process=use_process,
memsize=memsize)
self.mode = 'VAL'
@register
class SSDTestFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=SimpleDataSet(VOC_VAL_ANNOTATION).__dict__,
fields=['image', 'im_id', 'im_shape'],
image_shape=[3, 300, 300],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(target_size=300, use_cv2=False, interp=1),
Permute(),
NormalizeImage(
mean=[127.5, 127.5, 127.5],
std=[127.502231, 127.502231, 127.502231],
is_scale=False)
],
batch_transforms=[],
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
num_workers=8,
bufsize=10,
use_process=False,
memsize=None):
sample_transforms.append(ArrangeTestSSD())
if isinstance(dataset, dict):
dataset = SimpleDataSet(**dataset)
super(SSDTestFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
num_workers=num_workers,
bufsize=bufsize,
use_process=use_process,
memsize=memsize)
self.mode = 'TEST'
@register
class YoloTrainFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=CocoDataSet().__dict__,
fields=['image', 'gt_box', 'gt_label', 'gt_score'],
image_shape=[3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True, with_mixup=True),
MixupImage(alpha=1.5, beta=1.5),
ColorDistort(),
RandomExpand(fill_value=[123.675, 116.28, 103.53]),
RandomCrop(),
RandomFlipImage(is_normalized=False),
Resize(target_dim=608, interp='random'),
NormalizePermute(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.120, 57.375]),
NormalizeBox(),
],
batch_transforms=[
RandomShape(sizes=[
320, 352, 384, 416, 448, 480, 512, 544, 576, 608
])
],
batch_size=8,
shuffle=True,
samples=-1,
drop_last=True,
with_background=False,
num_workers=8,
bufsize=128,
use_process=True,
memsize=None,
num_max_boxes=50,
mixup_epoch=250,
class_aware_sampling=False):
sample_transforms.append(ArrangeYOLO())
super(YoloTrainFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
with_background=with_background,
num_workers=num_workers,
bufsize=bufsize,
use_process=use_process,
memsize=memsize,
class_aware_sampling=class_aware_sampling)
self.num_max_boxes = num_max_boxes
self.mixup_epoch = mixup_epoch
self.mode = 'TRAIN'
@register
class YoloEvalFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=CocoDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_size', 'im_id', 'gt_box',
'gt_label', 'is_difficult'],
image_shape=[3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(target_size=608, interp=2),
NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
Permute(to_bgr=False),
],
batch_transforms=[],
batch_size=8,
shuffle=False,
samples=-1,
drop_last=False,
with_background=False,
num_workers=8,
num_max_boxes=50,
use_process=False,
memsize=None):
sample_transforms.append(ArrangeEvalYOLO())
super(YoloEvalFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
with_background=with_background,
num_workers=num_workers,
use_process=use_process,
memsize=memsize)
self.num_max_boxes = num_max_boxes
self.mode = 'VAL'
self.bufsize = 128
# support image shape config, resize image with image_shape
for i, trans in enumerate(sample_transforms):
if isinstance(trans, ResizeImage):
sample_transforms[i] = ResizeImage(
target_size=self.image_shape[-1],
interp=trans.interp)
if isinstance(trans, Resize):
sample_transforms[i].target_dim = self.image_shape[-1]
@register
class YoloTestFeed(DataFeed):
__doc__ = DataFeed.__doc__
def __init__(self,
dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_size', 'im_id'],
image_shape=[3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(target_size=608, interp=2),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
Permute(to_bgr=False),
],
batch_transforms=[],
batch_size=1,
shuffle=False,
samples=-1,
drop_last=False,
with_background=False,
num_workers=8,
num_max_boxes=50,
use_process=False,
memsize=None):
sample_transforms.append(ArrangeTestYOLO())
if isinstance(dataset, dict):
dataset = SimpleDataSet(**dataset)
super(YoloTestFeed, self).__init__(
dataset,
fields,
image_shape,
sample_transforms,
batch_transforms,
batch_size=batch_size,
shuffle=shuffle,
samples=samples,
drop_last=drop_last,
with_background=with_background,
num_workers=num_workers,
use_process=use_process,
memsize=memsize)
self.mode = 'TEST'
self.bufsize = 128
# support image shape config, resize image with image_shape
for i, trans in enumerate(sample_transforms):
if isinstance(trans, ResizeImage):
sample_transforms[i] = ResizeImage(
target_size=self.image_shape[-1],
interp=trans.interp)
if isinstance(trans, Resize):
sample_transforms[i].target_dim = self.image_shape[-1]
# yapf: enable
ppdet/data/dataset.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# function:
# interface for accessing data samples in stream
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
class Dataset(object):
"""interface to access a stream of data samples"""
def __init__(self):
self._epoch = -1
self._pos = 0
def __next__(self):
return self.next()
def __iter__(self):
return self
def __str__(self):
return "{}(epoch:{:d}, size:{:d}, pos:{:d})".format(
type(self).__name__, self._epoch,
self.size(), self._pos)
def next(self):
"""get next sample"""
raise NotImplementedError('%s.next not available' %
(self.__class__.__name__))
def reset(self):
"""reset to initial status and begins a new epoch"""
raise NotImplementedError('%s.reset not available' %
(self.__class__.__name__))
def size(self):
"""get number of samples in this dataset"""
raise NotImplementedError('%s.size not available' %
(self.__class__.__name__))
def drained(self):
"""whether all sampled has been readed out for this epoch"""
raise NotImplementedError('%s.drained not available' %
(self.__class__.__name__))
def epoch_id(self):
"""return epoch id for latest sample"""
raise NotImplementedError('%s.epoch_id not available' %
(self.__class__.__name__))
ppdet/data/transform/parallel_map.py → ppdet/data/parallel_map.py
浏览文件 @ 8192c758
...@@ -13,7 +13,7 @@ ...@@ -13,7 +13,7 @@
# limitations under the License. # limitations under the License.
# function: # function:
# transform samples in 'source' using 'mapper' # transform samples in 'source' using 'worker'
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division
...@@ -30,7 +30,7 @@ import uuid ...@@ -30,7 +30,7 @@ import uuid
import logging import logging
import signal import signal
import threading import threading
from .transformer import ProxiedDataset import traceback
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
...@@ -38,13 +38,14 @@ logger = logging.getLogger(__name__) ...@@ -38,13 +38,14 @@ logger = logging.getLogger(__name__)
class EndSignal(object): class EndSignal(object):
""" signal used to notify worker to exit """ signal used to notify worker to exit
""" """
def __init__(self, id, errno=0, errmsg=''): def __init__(self, id, errno=0, errmsg=''):
self.id = id self.id = id
self.errno = errno self.errno = errno
self.errmsg = errmsg self.errmsg = errmsg
class ParallelMappedDataset(ProxiedDataset): class ParallelMap(object):
""" """
Transform samples to mapped samples which is similar to Transform samples to mapped samples which is similar to
'basic.MappedDataset', but multiple workers (threads or processes) 'basic.MappedDataset', but multiple workers (threads or processes)
...@@ -54,45 +55,48 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -54,45 +55,48 @@ class ParallelMappedDataset(ProxiedDataset):
this class is not thread-safe this class is not thread-safe
""" """
def __init__(self, source, mapper, worker_args): def __init__(self,
super(ParallelMappedDataset, self).__init__(source) source,
worker_args = {k.lower(): v for k, v in worker_args.items()} worker,
worker_num,
args = { bufsize=100,
'bufsize': 100, use_process=False,
'worker_num': 8, memsize='3G'):
'use_process': False, self._worker_num = worker_num
'memsize': '3G' self._bufsize = bufsize
} self._use_process = use_process
args.update(worker_args) if self._use_process and type(memsize) is str:
if args['use_process'] and type(args['memsize']) is str: assert memsize[-1].lower() == 'g', \
assert args['memsize'][-1].lower() == 'g', \ "invalid param for memsize[%s], should be ended with 'G' or 'g'" % (memsize)
"invalid param for memsize[{}], should " \ gb = memsize[:-1]
"be ended with 'G' or 'g'".format(args['memsize']) self._memsize = int(gb) * 1024**3
gb = args['memsize'][:-1]
args['memsize'] = int(gb) * 1024 ** 3
self._worker_args = args
self._started = False self._started = False
self._source = source self._source = source
self._mapper = mapper self._worker = worker
self._exit = False self._exit = False
self._setup() self._setup()
self._souce_drained = False
def __iter__(self):
return self
def __next__(self):
return self.next()
def _setup(self): def _setup(self):
"""setup input/output queues and workers """ """setup input/output queues and workers """
use_process = self._worker_args.get('use_process', False) use_process = self._use_process
if use_process and sys.platform == "win32": if use_process and sys.platform == "win32":
logger.info("Use multi-thread reader instead of " logger.info("Use multi-thread reader instead of "
"multi-process reader on Windows.") "multi-process reader on Windows.")
use_process = False use_process = False
bufsize = self._worker_args['bufsize'] bufsize = self._bufsize
if use_process: if use_process:
from .shared_queue import SharedQueue as Queue from .shared_queue import SharedQueue as Queue
from multiprocessing import Process as Worker from multiprocessing import Process as Worker
from multiprocessing import Event from multiprocessing import Event
memsize = self._worker_args['memsize'] memsize = self._memsize
self._inq = Queue(bufsize, memsize=memsize) self._inq = Queue(bufsize, memsize=memsize)
self._outq = Queue(bufsize, memsize=memsize) self._outq = Queue(bufsize, memsize=memsize)
else: else:
...@@ -105,7 +109,7 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -105,7 +109,7 @@ class ParallelMappedDataset(ProxiedDataset):
self._inq = Queue(bufsize) self._inq = Queue(bufsize)
self._outq = Queue(bufsize) self._outq = Queue(bufsize)
consumer_num = self._worker_args['worker_num'] consumer_num = self._worker_num
id = str(uuid.uuid4())[-3:] id = str(uuid.uuid4())[-3:]
self._producer = threading.Thread( self._producer = threading.Thread(
target=self._produce, target=self._produce,
...@@ -118,8 +122,7 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -118,8 +122,7 @@ class ParallelMappedDataset(ProxiedDataset):
consumer_id = 'consumer-' + id + '-' + str(i) consumer_id = 'consumer-' + id + '-' + str(i)
p = Worker( p = Worker(
target=self._consume, target=self._consume,
args=(consumer_id, self._inq, self._outq, args=(consumer_id, self._inq, self._outq, self._worker))
self._mapper))
self._consumers.append(p) self._consumers.append(p)
p.daemon = True p.daemon = True
setattr(p, 'id', consumer_id) setattr(p, 'id', consumer_id)
...@@ -137,9 +140,11 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -137,9 +140,11 @@ class ParallelMappedDataset(ProxiedDataset):
if self._exit: if self._exit:
break break
try: try:
inq.put(source.next()) s = source.next()
inq.put(s)
self._produced += 1 self._produced += 1
except StopIteration: except StopIteration:
self._souce_drained = True
self._feeding_ev.clear() self._feeding_ev.clear()
self._feeding_ev.wait() self._feeding_ev.wait()
except Exception as e: except Exception as e:
...@@ -149,11 +154,11 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -149,11 +154,11 @@ class ParallelMappedDataset(ProxiedDataset):
inq.put(endsig) inq.put(endsig)
break break
def _consume(self, id, inq, outq, mapper): def _consume(self, id, inq, outq, worker):
"""Fetch data from 'inq', process it and put result to 'outq'""" """Fetch data from 'inq', process it and put result to 'outq'"""
if self._worker_args['use_process']: if self._use_process:
# handle SIGTERM signal to exit to prevent print stack frame # handle SIGTERM signal to exit to prevent print stack frame
signal.signal(signal.SIGTERM, lambda signum, frame : sys.exit()) signal.signal(signal.SIGTERM, lambda signum, frame: sys.exit())
endsig = EndSignal(id) endsig = EndSignal(id)
while True: while True:
...@@ -166,7 +171,7 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -166,7 +171,7 @@ class ParallelMappedDataset(ProxiedDataset):
break break
try: try:
result = mapper(sample) result = worker(sample)
outq.put(result) outq.put(result)
except Exception as e: except Exception as e:
endsig.errno = -2 endsig.errno = -2
...@@ -192,12 +197,12 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -192,12 +197,12 @@ class ParallelMappedDataset(ProxiedDataset):
for w in self._consumers: for w in self._consumers:
if not w.is_alive() and w.id not in self._consumer_endsig: if not w.is_alive() and w.id not in self._consumer_endsig:
abnormal_num += 1 abnormal_num += 1
if self._worker_args['use_process']: if self._use_process:
errmsg = "consumer[{}] exit abnormally with exitcode[{}]" \ errmsg = "consumer[{}] exit abnormally with exitcode[{}]" \
.format(w.pid, w.exitcode) .format(w.pid, w.exitcode)
else: else:
errmsg = "consumer[{}] exit abnormally".format(w.ident) errmsg = "consumer[{}] exit abnormally".format(w.ident)
logger.warn(errmsg) logger.warn(errmsg)
if abnormal_num > 0: if abnormal_num > 0:
...@@ -255,7 +260,8 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -255,7 +260,8 @@ class ParallelMappedDataset(ProxiedDataset):
" for some consumers exited abnormally before!!!" " for some consumers exited abnormally before!!!"
if not self.drained(): if not self.drained():
logger.warn("reset before epoch[{}] finishes".format(self._epoch)) logger.warn("reset before epoch[{}] finishes".format(
self._epoch))
self._produced = self._produced - self._consumed self._produced = self._produced - self._consumed
else: else:
self._produced = 0 self._produced = 0
...@@ -266,10 +272,11 @@ class ParallelMappedDataset(ProxiedDataset): ...@@ -266,10 +272,11 @@ class ParallelMappedDataset(ProxiedDataset):
+ " cannot start another epoch" + " cannot start another epoch"
self._source.reset() self._source.reset()
self._souce_drained = False
self._consumed = 0 self._consumed = 0
self._feeding_ev.set() self._feeding_ev.set()
# FIXME(dengkaipeng): fix me if you have better impliment # FIXME(dengkaipeng): fix me if you have better impliment
# handle terminate reader process, do not print stack frame # handle terminate reader process, do not print stack frame
signal.signal(signal.SIGTERM, lambda signum, frame : sys.exit()) signal.signal(signal.SIGTERM, lambda signum, frame: sys.exit())
ppdet/data/reader.py
浏览文件 @ 8192c758
...@@ -12,131 +12,393 @@ ...@@ -12,131 +12,393 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# function:
# Interface to build readers for detection data like COCO or VOC
#
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
from __future__ import unicode_literals
from numbers import Integral
import copy
import functools
import collections
import traceback
import numpy as np
import logging import logging
from .source import build_source
from .transform import build_mapper, map, batch, batch_map from ppdet.core.workspace import register, serializable
from .parallel_map import ParallelMap
__all__ = ['Reader', 'create_reader']
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
class Compose(object):
def __init__(self, transforms, ctx=None):
self.transforms = transforms
self.ctx = ctx
def __call__(self, data):
ctx = self.ctx if self.ctx else {}
for f in self.transforms:
try:
data = f(data, ctx)
except Exception as e:
stack_info = traceback.format_exc()
logger.info("fail to map op [{}] with error: {} and stack:\n{}".
format(f, e, str(stack_info)))
raise e
return data
def _calc_img_weights(roidbs):
""" calculate the probabilities of each sample
"""
imgs_cls = []
num_per_cls = {}
img_weights = []
for i, roidb in enumerate(roidbs):
img_cls = set([k for cls in roidbs[i]['gt_class'] for k in cls])
imgs_cls.append(img_cls)
for c in img_cls:
if c not in num_per_cls:
num_per_cls[c] = 1
else:
num_per_cls[c] += 1
for i in range(len(roidbs)):
weights = 0
for c in imgs_cls[i]:
weights += 1 / num_per_cls[c]
img_weights.append(weights)
# probabilities sum to 1
img_weights = img_weights / np.sum(img_weights)
return img_weights
def _has_empty(item):
def empty(x):
if isinstance(x, np.ndarray) and x.size == 0:
return True
elif isinstance(x, collections.Sequence) and len(x) == 0:
return True
else:
return False
if isinstance(item, collections.Sequence) and len(item) == 0:
return True
if item is None:
return True
if empty(item):
return True
return False
def _segm(samples):
assert 'gt_poly' in samples
segms = samples['gt_poly']
if 'is_crowd' in samples:
is_crowd = samples['is_crowd']
if len(segms) != 0:
assert len(segms) == is_crowd.shape[0]
gt_masks = []
valid = True
for i in range(len(segms)):
segm = segms[i]
gt_segm = []
if 'is_crowd' in samples and is_crowd[i]:
gt_segm.append([[0, 0]])
else:
for poly in segm:
if len(poly) == 0:
valid = False
break
gt_segm.append(np.array(poly).reshape(-1, 2))
if (not valid) or len(gt_segm) == 0:
break
gt_masks.append(gt_segm)
return gt_masks
def batch_arrange(batch_samples, fields):
def im_shape(samples, dim=3):
# hard code
assert 'h' in samples
assert 'w' in samples
if dim == 3: # RCNN, ..
return np.array((samples['h'], samples['w'], 1), dtype=np.float32)
else: # YOLOv3, ..
return np.array((samples['h'], samples['w']), dtype=np.int32)
arrange_batch = []
for samples in batch_samples:
one_ins = ()
for i, field in enumerate(fields):
if field == 'gt_mask':
one_ins += (_segm(samples), )
elif field == 'im_shape':
one_ins += (im_shape(samples), )
elif field == 'im_size':
one_ins += (im_shape(samples, 2), )
else:
if field == 'is_difficult':
field = 'difficult'
assert field in samples, '{} not in samples'.format(field)
one_ins += (samples[field], )
arrange_batch.append(one_ins)
return arrange_batch
@register
@serializable
class Reader(object): class Reader(object):
"""Interface to make readers for training or evaluation""" """
Args:
dataset (DataSet): DataSet object
sample_transforms (list of BaseOperator): a list of sample transforms
operators.
batch_transforms (list of BaseOperator): a list of batch transforms
operators.
batch_size (int): batch size.
shuffle (bool): whether shuffle dataset or not. Default False.
drop_last (bool): whether drop last batch or not. Default False.
drop_empty (bool): whether drop sample when it's gt is empty or not.
Default True.
mixup_epoch (int): mixup epoc number. Default is -1, meaning
not use mixup.
class_aware_sampling (bool): whether use class-aware sampling or not.
Default False.
worker_num (int): number of working threads/processes.
Default -1, meaning not use multi-threads/multi-processes.
use_process (bool): whether use multi-processes or not.
It only works when worker_num > 1. Default False.
bufsize (int): buffer size for multi-threads/multi-processes,
please note, one instance in buffer is one batch data.
memsize (str): size of shared memory used in result queue when
use_process is true. Default 3G.
inputs_def (dict): network input definition use to get input fields,
which is used to determine the order of returned data.
"""
def __init__(self,
dataset=None,
sample_transforms=None,
batch_transforms=None,
batch_size=None,
shuffle=False,
drop_last=False,
drop_empty=True,
mixup_epoch=-1,
class_aware_sampling=False,
worker_num=-1,
use_process=False,
bufsize=100,
memsize='3G',
inputs_def=None):
self._dataset = dataset
self._roidbs = self._dataset.get_roidb()
self._fields = copy.deepcopy(inputs_def[
'fields']) if inputs_def else None
# transform
self._sample_transforms = Compose(sample_transforms,
{'fields': self._fields})
self._batch_transforms = None
if batch_transforms:
self._batch_transforms = Compose(batch_transforms,
{'fields': self._fields})
# data
if inputs_def and inputs_def.get('multi_scale', False):
from ppdet.modeling.architectures.input_helper import multiscale_def
im_shape = inputs_def[
'image_shape'] if 'image_shape' in inputs_def else [
3, None, None
]
_, ms_fields = multiscale_def(im_shape, inputs_def['num_scales'],
inputs_def['use_flip'])
self._fields += ms_fields
self._batch_size = batch_size
self._shuffle = shuffle
self._drop_last = drop_last
self._drop_empty = drop_empty
# sampling
self._mixup_epoch = mixup_epoch
self._class_aware_sampling = class_aware_sampling
def __init__(self, data_cf, trans_conf, maxiter=-1): self._load_img = False
self._data_cf = data_cf self._sample_num = len(self._roidbs)
self._trans_conf = trans_conf
self._maxiter = maxiter
self._cname2cid = None
assert isinstance(self._maxiter, Integral), "maxiter should be int"
def _make_reader(self, mode, my_source=None): if self._class_aware_sampling:
"""Build reader for training or validation""" self.img_weights = _calc_img_weights(self._roidbs)
if my_source is None: self._indexes = None
file_conf = self._data_cf[mode]
# 1, Build data source self._pos = -1
self._epoch = -1
self._drained = False
sc_conf = {'data_cf': file_conf, 'cname2cid': self._cname2cid} # multi-process
sc = build_source(sc_conf) self._worker_num = worker_num
self._parallel = None
if self._worker_num > -1:
task = functools.partial(self.worker, self._drop_empty)
self._parallel = ParallelMap(self, task, worker_num, bufsize,
use_process, memsize)
def __call__(self):
if self._worker_num > -1:
return self._parallel
else:
return self
def __iter__(self):
return self
def reset(self):
"""implementation of Dataset.reset
"""
self.indexes = [i for i in range(self.size())]
if self._class_aware_sampling:
self.indexes = np.random.choice(
self._sample_num,
self._sample_num,
replace=False,
p=self.img_weights)
if self._shuffle:
np.random.shuffle(self.indexes)
if self._mixup_epoch > 0 and len(self.indexes) < 2:
logger.info("Disable mixup for dataset samples "
"less than 2 samples")
self._mixup_epoch = -1
if self._epoch < 0:
self._epoch = 0
else: else:
sc = my_source self._epoch += 1
# 2, Buid a transformed dataset self._pos = 0
ops = self._trans_conf[mode]['OPS'] self._drained = False
batchsize = self._trans_conf[mode]['BATCH_SIZE']
drop_last = False if 'DROP_LAST' not in \ def __next__(self):
self._trans_conf[mode] else self._trans_conf[mode]['DROP_LAST'] return self.next()
mapper = build_mapper(ops, {'is_train': mode == 'TRAIN'}) def next(self):
if self._epoch < 0:
worker_args = None self.reset()
if 'WORKER_CONF' in self._trans_conf[mode]: if self.drained():
worker_args = self._trans_conf[mode]['WORKER_CONF'] raise StopIteration
worker_args = {k.lower(): v for k, v in worker_args.items()} batch = self._load_batch()
if self._drop_last and len(batch) < self._batch_size:
mapped_ds = map(sc, mapper, worker_args) raise StopIteration
# In VAL mode, gt_bbox, gt_label can be empty, and should if self._worker_num > -1:
# not be dropped return batch
batched_ds = batch( else:
mapped_ds, batchsize, drop_last, drop_empty=(mode != "VAL")) return self.worker(self._drop_empty, batch)
trans_conf = {k.lower(): v for k, v in self._trans_conf[mode].items()} def _load_batch(self):
need_keys = { batch = []
'is_padding', bs = 0
'coarsest_stride', while bs != self._batch_size:
'random_shapes', if self._pos >= self.size():
'multi_scales', break
'use_padded_im_info', pos = self.indexes[self._pos]
'enable_multiscale_test', sample = copy.deepcopy(self._roidbs[pos])
'num_scale', self._pos += 1
}
bm_config = { if self._drop_empty and self._fields and 'gt_mask' in self._fields:
key: value if _has_empty(_segm(sample)):
for key, value in trans_conf.items() if key in need_keys #logger.warn('gt_mask is empty or not valid in {}'.format(
} # sample['im_file']))
continue
batched_ds = batch_map(batched_ds, bm_config) if self._drop_empty and self._fields and 'gt_bbox' in self._fields:
if _has_empty(sample['gt_bbox']):
batched_ds.reset() #logger.warn('gt_bbox {} is empty or not valid in {}, '
if mode.lower() == 'train': # 'drop this sample'.format(
if self._cname2cid is not None: # sample['im_file'], sample['gt_bbox']))
logger.warn('cname2cid already set, it will be overridden') continue
self._cname2cid = getattr(sc, 'cname2cid', None)
if self._load_img:
# 3, Build a reader sample['image'] = self._load_image(sample['im_file'])
maxit = -1 if self._maxiter <= 0 else self._maxiter
if self._epoch < self._mixup_epoch:
def _reader(): num = len(self.indexes)
n = 0 mix_idx = np.random.randint(1, num)
while True: mix_idx = self.indexes[(mix_idx + self._pos - 1) % num]
for _batch in batched_ds: sample['mixup'] = copy.deepcopy(self._roidbs[mix_idx])
if self._load_img:
sample['mixup']['image'] = self._load_image(sample['mixup'][
'im_file'])
batch.append(sample)
bs += 1
return batch
def worker(self, drop_empty=True, batch_samples=None):
"""
sample transform and batch transform.
"""
batch = []
for sample in batch_samples:
sample = self._sample_transforms(sample)
if drop_empty and 'gt_bbox' in sample:
if _has_empty(sample['gt_bbox']):
#logger.warn('gt_bbox {} is empty or not valid in {}, '
# 'drop this sample'.format(
# sample['im_file'], sample['gt_bbox']))
continue
batch.append(sample)
if len(batch) > 0 and self._batch_transforms:
batch = self._batch_transforms(batch)
if len(batch) > 0 and self._fields:
batch = batch_arrange(batch, self._fields)
return batch
def _load_image(self, filename):
with open(filename, 'rb') as f:
return f.read()
def size(self):
""" implementation of Dataset.size
"""
return self._sample_num
def drained(self):
""" implementation of Dataset.drained
"""
assert self._epoch >= 0, 'The first epoch has not begin!'
return self._pos >= self.size()
def stop(self):
if self._parallel:
self._parallel.stop()
def create_reader(cfg, max_iter=0):
"""
Return iterable data reader.
Args:
max_iter (int): number of iterations.
"""
if not isinstance(cfg, dict):
raise TypeError("The config should be a dict when creating reader.")
reader = Reader(**cfg)()
def _reader():
n = 0
while True:
for _batch in reader:
if len(_batch) > 0:
yield _batch yield _batch
n += 1 n += 1
if maxit > 0 and n == maxit: if max_iter > 0 and n == max_iter:
return
batched_ds.reset()
if maxit <= 0:
return return
reader.reset()
if max_iter <= 0:
return
if hasattr(sc, 'get_imid2path'): return _reader
_reader.imid2path = sc.get_imid2path()
return _reader
def train(self):
"""Build reader for training"""
return self._make_reader('TRAIN')
def val(self):
"""Build reader for validation"""
return self._make_reader('VAL')
def test(self):
"""Build reader for inference"""
return self._make_reader('TEST')
@classmethod
def create(cls,
mode,
data_config,
transform_config,
max_iter=-1,
my_source=None,
ret_iter=True):
""" create a specific reader """
reader = Reader({mode: data_config}, {mode: transform_config}, max_iter)
if ret_iter:
return reader._make_reader(mode, my_source)
else:
return reader
ppdet/data/source/__init__.py
浏览文件 @ 8192c758
...@@ -12,62 +12,10 @@ ...@@ -12,62 +12,10 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from __future__ import absolute_import from . import coco
from __future__ import division from . import voc
from __future__ import print_function from . import widerface
import copy from .coco import *
from .voc import *
from .roidb_source import RoiDbSource from .widerface import *
from .simple_source import SimpleSource
from .iterator_source import IteratorSource
from .class_aware_sampling_roidb_source import ClassAwareSamplingRoiDbSource
def build_source(config):
"""
Build dataset from source data, default source type is 'RoiDbSource'
Args:
config (dict): should have following structure:
{
data_cf (dict):
anno_file (str): label file or image list file path
image_dir (str): root directory for images
samples (int): number of samples to load, -1 means all
is_shuffle (bool): should samples be shuffled
load_img (bool): should images be loaded
mixup_epoch (int): parse mixup in first n epoch
with_background (bool): whether load background as a class
cname2cid (dict): the label name to id dictionary
}
"""
if 'data_cf' in config:
data_cf = config['data_cf']
data_cf['cname2cid'] = config['cname2cid']
else:
data_cf = config
data_cf = {k.lower(): v for k, v in data_cf.items()}
args = copy.deepcopy(data_cf)
# defaut type is 'RoiDbSource'
source_type = 'RoiDbSource'
if 'type' in data_cf:
if data_cf['type'] in ['VOCSource', 'COCOSource', 'RoiDbSource']:
if 'class_aware_sampling' in args and args['class_aware_sampling']:
source_type = 'ClassAwareSamplingRoiDbSource'
else:
source_type = 'RoiDbSource'
if 'class_aware_sampling' in args:
del args['class_aware_sampling']
else:
source_type = data_cf['type']
del args['type']
if source_type == 'RoiDbSource':
return RoiDbSource(**args)
elif source_type == 'SimpleSource':
return SimpleSource(**args)
elif source_type == 'ClassAwareSamplingRoiDbSource':
return ClassAwareSamplingRoiDbSource(**args)
else:
raise ValueError('source type not supported: ' + source_type)
ppdet/data/source/class_aware_sampling_roidb_source.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#function:
# interface to load data from local files and parse it for samples,
# eg: roidb data in pickled files
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import random
import copy
import collections
import pickle as pkl
import numpy as np
from .roidb_source import RoiDbSource
class ClassAwareSamplingRoiDbSource(RoiDbSource):
""" interface to load class aware sampling roidb data from files
"""
def __init__(self,
anno_file,
image_dir=None,
samples=-1,
is_shuffle=True,
load_img=False,
cname2cid=None,
use_default_label=None,
mixup_epoch=-1,
with_background=True):
""" Init
Args:
fname (str): label file path
image_dir (str): root dir for images
samples (int): samples to load, -1 means all
is_shuffle (bool): whether to shuffle samples
load_img (bool): whether load data in this class
cname2cid (dict): the label name to id dictionary
use_default_label (bool):whether use the default mapping of label to id
mixup_epoch (int): parse mixup in first n epoch
with_background (bool): whether load background
as a class
"""
super(ClassAwareSamplingRoiDbSource, self).__init__(
anno_file=anno_file,
image_dir=image_dir,
samples=samples,
is_shuffle=is_shuffle,
load_img=load_img,
cname2cid=cname2cid,
use_default_label=use_default_label,
mixup_epoch=mixup_epoch,
with_background=with_background)
self._img_weights = None
def __str__(self):
return 'ClassAwareSamplingRoidbSource(fname:%s,epoch:%d,size:%d)' \
% (self._fname, self._epoch, self.size())
def next(self):
""" load next sample
"""
if self._epoch < 0:
self.reset()
_pos = np.random.choice(
self._samples, 1, replace=False, p=self._img_weights)[0]
sample = copy.deepcopy(self._roidb[_pos])
if self._load_img:
sample['image'] = self._load_image(sample['im_file'])
else:
sample['im_file'] = os.path.join(self._image_dir, sample['im_file'])
return sample
def _calc_img_weights(self):
""" calculate the probabilities of each sample
"""
imgs_cls = []
num_per_cls = {}
img_weights = []
for i, roidb in enumerate(self._roidb):
img_cls = set(
[k for cls in self._roidb[i]['gt_class'] for k in cls])
imgs_cls.append(img_cls)
for c in img_cls:
if c not in num_per_cls:
num_per_cls[c] = 1
else:
num_per_cls[c] += 1
for i in range(len(self._roidb)):
weights = 0
for c in imgs_cls[i]:
weights += 1 / num_per_cls[c]
img_weights.append(weights)
# Probabilities sum to 1
img_weights = img_weights / np.sum(img_weights)
return img_weights
def reset(self):
""" implementation of Dataset.reset
"""
if self._roidb is None:
self._roidb = self._load()
if self._img_weights is None:
self._img_weights = self._calc_img_weights()
self._samples = len(self._roidb)
if self._epoch < 0:
self._epoch = 0
ppdet/data/source/coco.py 0 → 100644
浏览文件 @ 8192c758
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
from pycocotools.coco import COCO
from .dataset import DataSet
from ppdet.core.workspace import register, serializable
import logging
logger = logging.getLogger(__name__)
@register
@serializable
class COCODataSet(DataSet):
"""
Load COCO records with annotations in json file 'anno_path'
Args:
dataset_dir (str): root directory for dataset.
image_dir (str): directory for images.
anno_path (str): json file path.
sample_num (int): number of samples to load, -1 means all.
with_background (bool): whether load background as a class.
if True, total class number will be 81. default True.
"""
def __init__(self,
image_dir=None,
anno_path=None,
dataset_dir=None,
sample_num=-1,
with_background=True):
super(COCODataSet, self).__init__(
image_dir=image_dir,
anno_path=anno_path,
dataset_dir=dataset_dir,
sample_num=sample_num,
with_background=with_background)
self.anno_path = anno_path
self.sample_num = sample_num
self.with_background = with_background
# `roidbs` is list of dict whose structure is:
# {
# 'im_file': im_fname, # image file name
# 'im_id': img_id, # image id
# 'h': im_h, # height of image
# 'w': im_w, # width
# 'is_crowd': is_crowd,
# 'gt_score': gt_score,
# 'gt_class': gt_class,
# 'gt_bbox': gt_bbox,
# 'gt_poly': gt_poly,
# }
self.roidbs = None
# a dict used to map category name to class id
self.cname2cid = None
def load_roidb_and_cname2cid(self):
anno_path = os.path.join(self.dataset_dir, self.anno_path)
image_dir = os.path.join(self.dataset_dir, self.image_dir)
assert anno_path.endswith('.json'), \
'invalid coco annotation file: ' + anno_path
coco = COCO(anno_path)
img_ids = coco.getImgIds()
cat_ids = coco.getCatIds()
records = []
ct = 0
# when with_background = True, mapping category to classid, like:
# background:0, first_class:1, second_class:2, ...
catid2clsid = dict({
catid: i + int(self.with_background)
for i, catid in enumerate(cat_ids)
})
cname2cid = dict({
coco.loadCats(catid)[0]['name']: clsid
for catid, clsid in catid2clsid.items()
})
for img_id in img_ids:
img_anno = coco.loadImgs(img_id)[0]
im_fname = img_anno['file_name']
im_w = float(img_anno['width'])
im_h = float(img_anno['height'])
ins_anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=False)
instances = coco.loadAnns(ins_anno_ids)
bboxes = []
for inst in instances:
x, y, box_w, box_h = inst['bbox']
x1 = max(0, x)
y1 = max(0, y)
x2 = min(im_w - 1, x1 + max(0, box_w - 1))
y2 = min(im_h - 1, y1 + max(0, box_h - 1))
if inst['area'] > 0 and x2 >= x1 and y2 >= y1:
inst['clean_bbox'] = [x1, y1, x2, y2]
bboxes.append(inst)
else:
logger.warn(
'Found an invalid bbox in annotations: im_id: {}, '
'area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.format(
img_id, float(inst['area']), x1, y1, x2, y2))
num_bbox = len(bboxes)
gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
gt_score = np.ones((num_bbox, 1), dtype=np.float32)
is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
difficult = np.zeros((num_bbox, 1), dtype=np.int32)
gt_poly = [None] * num_bbox
for i, box in enumerate(bboxes):
catid = box['category_id']
gt_class[i][0] = catid2clsid[catid]
gt_bbox[i, :] = box['clean_bbox']
is_crowd[i][0] = box['iscrowd']
if 'segmentation' in box:
gt_poly[i] = box['segmentation']
im_fname = os.path.join(image_dir,
im_fname) if image_dir else im_fname
coco_rec = {
'im_file': im_fname,
'im_id': np.array([img_id]),
'h': im_h,
'w': im_w,
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_score': gt_score,
'gt_poly': gt_poly,
'difficult': difficult
}
logger.debug('Load file: {}, im_id: {}, h: {}, w: {}.'.format(
im_fname, img_id, im_h, im_w))
records.append(coco_rec)
ct += 1
if self.sample_num > 0 and ct >= self.sample_num:
break
assert len(records) > 0, 'not found any coco record in %s' % (anno_path)
logger.info('{} samples in file {}'.format(ct, anno_path))
self.roidbs, self.cname2cid = records, cname2cid
ppdet/data/source/coco_loader.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from pycocotools.coco import COCO
import logging
logger = logging.getLogger(__name__)
def load(anno_path, sample_num=-1, with_background=True):
"""
Load COCO records with annotations in json file 'anno_path'
Args:
anno_path (str): json file path
sample_num (int): number of samples to load, -1 means all
with_background (bool): whether load background as a class.
if True, total class number will
be 81. default True
Returns:
(records, cname2cid)
'records' is list of dict whose structure is:
{
'im_file': im_fname, # image file name
'im_id': img_id, # image id
'h': im_h, # height of image
'w': im_w, # width
'is_crowd': is_crowd,
'gt_score': gt_score,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_poly': gt_poly,
}
'cname2cid' is a dict used to map category name to class id
"""
assert anno_path.endswith('.json'), 'invalid coco annotation file: ' \
+ anno_path
coco = COCO(anno_path)
img_ids = coco.getImgIds()
cat_ids = coco.getCatIds()
records = []
ct = 0
# when with_background = True, mapping category to classid, like:
# background:0, first_class:1, second_class:2, ...
catid2clsid = dict(
{catid: i + int(with_background)
for i, catid in enumerate(cat_ids)})
cname2cid = dict({
coco.loadCats(catid)[0]['name']: clsid
for catid, clsid in catid2clsid.items()
})
for img_id in img_ids:
img_anno = coco.loadImgs(img_id)[0]
im_fname = img_anno['file_name']
im_w = float(img_anno['width'])
im_h = float(img_anno['height'])
ins_anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=False)
instances = coco.loadAnns(ins_anno_ids)
bboxes = []
for inst in instances:
x, y, box_w, box_h = inst['bbox']
x1 = max(0, x)
y1 = max(0, y)
x2 = min(im_w - 1, x1 + max(0, box_w - 1))
y2 = min(im_h - 1, y1 + max(0, box_h - 1))
if inst['area'] > 0 and x2 >= x1 and y2 >= y1:
inst['clean_bbox'] = [x1, y1, x2, y2]
bboxes.append(inst)
else:
logger.warn(
'Found an invalid bbox in annotations: im_id: {}, area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.
format(img_id, float(inst['area']), x1, y1, x2, y2))
num_bbox = len(bboxes)
gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
gt_score = np.ones((num_bbox, 1), dtype=np.float32)
is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
difficult = np.zeros((num_bbox, 1), dtype=np.int32)
gt_poly = [None] * num_bbox
for i, box in enumerate(bboxes):
catid = box['category_id']
gt_class[i][0] = catid2clsid[catid]
gt_bbox[i, :] = box['clean_bbox']
is_crowd[i][0] = box['iscrowd']
if 'segmentation' in box:
gt_poly[i] = box['segmentation']
coco_rec = {
'im_file': im_fname,
'im_id': np.array([img_id]),
'h': im_h,
'w': im_w,
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_score': gt_score,
'gt_poly': gt_poly,
'difficult': difficult
}
logger.debug('Load file: {}, im_id: {}, h: {}, w: {}.'.format(
im_fname, img_id, im_h, im_w))
records.append(coco_rec)
ct += 1
if sample_num > 0 and ct >= sample_num:
break
assert len(records) > 0, 'not found any coco record in %s' % (anno_path)
logger.info('{} samples in file {}'.format(ct, anno_path))
return records, cname2cid
ppdet/data/source/dataset.py 0 → 100644
浏览文件 @ 8192c758
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
from ppdet.core.workspace import register, serializable
from ppdet.utils.download import get_dataset_path
@serializable
class DataSet(object):
"""
Dataset, e.g., coco, pascal voc
Args:
annotation (str): annotation file path
image_dir (str): directory where image files are stored
shuffle (bool): shuffle samples
"""
def __init__(self,
dataset_dir=None,
image_dir=None,
anno_path=None,
sample_num=-1,
with_background=True,
use_default_label=None,
**kwargs):
super(DataSet, self).__init__()
self.anno_path = anno_path
self.image_dir = image_dir if image_dir is not None else ''
self.dataset_dir = dataset_dir if dataset_dir is not None else ''
self.sample_num = sample_num
self.with_background = with_background
self.use_default_label = use_default_label
self.cname2cid = None
self._imid2path = None
def load_roidb_and_cname2cid(self):
"""load dataset"""
raise NotImplementedError('%s.load_roidb_and_cname2cid not available' %
(self.__class__.__name__))
def get_roidb(self):
if not self.roidbs:
data_dir = get_dataset_path(self.dataset_dir, self.anno_path,
self.image_dir)
if data_dir:
self.dataset_dir = data_dir
self.load_roidb_and_cname2cid()
return self.roidbs
def get_cname2cid(self):
if not self.cname2cid:
self.load_roidb_and_cname2cid()
return self.cname2cid
def get_anno(self):
return os.path.join(self.dataset_dir, self.anno_path)
def get_imid2path(self):
return self._imid2path
def get_cname2cid(self):
return self.cname2cid
def _is_valid_file(f, extensions=('.jpg', '.jpeg', '.png', '.bmp')):
return f.lower().endswith(extensions)
def _make_dataset(dir):
dir = os.path.expanduser(dir)
if not os.path.isdir(d):
raise ('{} should be a dir'.format(dir))
images = []
for root, _, fnames in sorted(os.walk(dir, followlinks=True)):
for fname in sorted(fnames):
path = os.path.join(root, fname)
if is_valid_file(path):
images.append(path)
return images
@register
@serializable
class ImageFolder(DataSet):
"""
Args:
dataset_dir (str): root directory for dataset.
image_dir(list|str): list of image folders or list of image files
anno_path (str): annotation file path.
samples (int): number of samples to load, -1 means all
"""
def __init__(self,
dataset_dir=None,
image_dir=None,
anno_path=None,
sample_num=-1,
with_background=True,
**kwargs):
super(ImageFolder, self).__init__(image_dir, anno_path, sample_num,
dataset_dir, with_background)
self.anno_path = anno_path
self.sample_num = sample_num
self.with_background = with_background
self.roidbs = None
self._imid2path = {}
def get_roidb(self):
if not self.roidbs:
self.roidbs = self._load_images()
return self.roidbs
def set_images(self, images):
self.image_dir = images
self.roidbs = self._load_images()
def _parse(self):
image_dir = self.image_dir
if not isinstance(image_dir, Sequence):
image_dir = [image_dir]
images = []
for im_dir in image_dir:
if os.path.isdir(im_dir):
im_dir = os.path.join(self.dataset_dir, im_dir)
images.extend(_make_dataset(im_dir))
elif os.path.isfile(im_dir) and _is_valid_file(im_dir):
images.append(im_dir)
return images
def _load_images(self):
images = self._parse()
ct = 0
records = []
for image in images:
assert image != '' and os.path.isfile(image), \
"Image {} not found".format(image)
if self.sample_num > 0 and ct >= self.sample_num:
break
rec = {'im_id': np.array([ct]), 'im_file': image}
self._imid2path[ct] = image
ct += 1
records.append(rec)
assert len(records) > 0, "No image file found"
return records
ppdet/data/source/iterator_source.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import copy
import logging
logger = logging.getLogger(__name__)
from ..dataset import Dataset
class IteratorSource(Dataset):
"""
Load data samples from iterator in stream mode
Args:
iter_maker (callable): callable function to generate a iter
samples (int): number of samples to load, -1 means all
"""
def __init__(self, iter_maker, samples=-1, **kwargs):
super(IteratorSource, self).__init__()
self._epoch = -1
self._iter_maker = iter_maker
self._data_iter = None
self._pos = -1
self._drained = False
self._samples = samples
self._sample_num = -1
def next(self):
if self._epoch < 0:
self.reset()
if self._data_iter is not None:
try:
sample = next(self._data_iter)
self._pos += 1
ret = sample
except StopIteration as e:
if self._sample_num <= 0:
self._sample_num = self._pos
elif self._sample_num != self._pos:
logger.info('num of loaded samples is different '
'with previouse setting[prev:%d,now:%d]' %
(self._sample_num, self._pos))
self._sample_num = self._pos
self._data_iter = None
self._drained = True
raise e
else:
raise StopIteration("no more data in " + str(self))
if self._samples > 0 and self._pos >= self._samples:
self._data_iter = None
self._drained = True
raise StopIteration("no more data in " + str(self))
else:
return ret
def reset(self):
if self._data_iter is None:
self._data_iter = self._iter_maker()
if self._epoch < 0:
self._epoch = 0
else:
self._epoch += 1
self._pos = 0
self._drained = False
def size(self):
return self._sample_num
def drained(self):
assert self._epoch >= 0, "the first epoch has not started yet"
return self._pos >= self.size()
def epoch_id(self):
return self._epoch
ppdet/data/source/loader.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# function:
# load data records from local files(maybe in COCO or VOC data formats)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import numpy as np
import logging
import pickle as pkl
logger = logging.getLogger(__name__)
def check_records(records):
""" check the fields of 'records' must contains some keys
"""
needed_fields = [
'im_file', 'im_id', 'h', 'w', 'is_crowd', 'gt_class', 'gt_bbox',
'gt_poly'
]
for i, rec in enumerate(records):
for k in needed_fields:
assert k in rec, 'not found field[%s] in record[%d]' % (k, i)
def load_roidb(anno_file, sample_num=-1):
""" load normalized data records from file
'anno_file' which is a pickled file.
And the records should has a structure:
{
'im_file': str, # image file name
'im_id': int, # image id
'h': int, # height of image
'w': int, # width of image
'is_crowd': bool,
'gt_class': list of np.ndarray, # classids info
'gt_bbox': list of np.ndarray, # bounding box info
'gt_poly': list of int, # poly info
}
Args:
anno_file (str): file name for picked records
sample_num (int): number of samples to load
Returns:
list of records for detection model training
"""
assert anno_file.endswith('.roidb'), 'invalid roidb file[%s]' % (anno_file)
with open(anno_file, 'rb') as f:
roidb = f.read()
# for support python3 and python2
try:
records, cname2cid = pkl.loads(roidb, encoding='bytes')
except:
records, cname2cid = pkl.loads(roidb)
assert type(records) is list, 'invalid data type from roidb'
if sample_num > 0 and sample_num < len(records):
records = records[:sample_num]
return records, cname2cid
def load(fname,
samples=-1,
with_background=True,
with_cat2id=False,
use_default_label=None,
cname2cid=None):
""" Load data records from 'fnames'
Args:
fnames (str): file name for data record, eg:
instances_val2017.json or COCO17_val2017.roidb
samples (int): number of samples to load, default to all
with_background (bool): whether load background as a class.
default True.
with_cat2id (bool): whether return cname2cid info out
use_default_label (bool): whether use the default mapping of label to id
cname2cid (dict): the mapping of category name to id
Returns:
list of loaded records whose structure is:
{
'im_file': str, # image file name
'im_id': int, # image id
'h': int, # height of image
'w': int, # width of image
'is_crowd': bool,
'gt_class': list of np.ndarray, # classids info
'gt_bbox': list of np.ndarray, # bounding box info
'gt_poly': list of int, # poly info
}
"""
if fname.endswith('.roidb'):
records, cname2cid = load_roidb(fname, samples)
elif fname.endswith('.json'):
from . import coco_loader
records, cname2cid = coco_loader.load(fname, samples, with_background)
elif "wider_face" in fname:
from . import widerface_loader
records = widerface_loader.load(fname, samples)
return records
elif os.path.isfile(fname):
from . import voc_loader
if use_default_label is None or cname2cid is not None:
records, cname2cid = voc_loader.get_roidb(
fname, samples, cname2cid, with_background=with_background)
else:
records, cname2cid = voc_loader.load(
fname,
samples,
use_default_label,
with_background=with_background)
else:
raise ValueError('invalid file type when load data from file[%s]' %
(fname))
check_records(records)
if with_cat2id:
return records, cname2cid
else:
return records
ppdet/data/source/roidb_source.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#function:
# interface to load data from local files and parse it for samples,
# eg: roidb data in pickled files
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import random
import copy
import pickle as pkl
from ..dataset import Dataset
import logging
logger = logging.getLogger(__name__)
class RoiDbSource(Dataset):
""" interface to load roidb data from files
"""
def __init__(self,
anno_file,
image_dir=None,
samples=-1,
is_shuffle=True,
load_img=False,
cname2cid=None,
use_default_label=None,
mixup_epoch=-1,
with_background=True):
""" Init
Args:
fname (str): label file path
image_dir (str): root dir for images
samples (int): samples to load, -1 means all
is_shuffle (bool): whether to shuffle samples
load_img (bool): whether load data in this class
cname2cid (dict): the label name to id dictionary
use_default_label (bool):whether use the default mapping of label to id
mixup_epoch (int): parse mixup in first n epoch
with_background (bool): whether load background
as a class
"""
super(RoiDbSource, self).__init__()
self._epoch = -1
assert os.path.isfile(anno_file) or os.path.isdir(anno_file), \
'anno_file {} is not a file or a directory'.format(anno_file)
self._fname = anno_file
self._image_dir = image_dir if image_dir is not None else ''
if image_dir is not None:
assert os.path.isdir(image_dir), \
'image_dir {} is not a directory'.format(image_dir)
self._roidb = None
self._pos = -1
self._drained = False
self._samples = samples
self._is_shuffle = is_shuffle
self._load_img = load_img
self.use_default_label = use_default_label
self._mixup_epoch = mixup_epoch
self._with_background = with_background
self.cname2cid = cname2cid
self._imid2path = None
def __str__(self):
return 'RoiDbSource(epoch:%d,size:%d,pos:%d,fname:%s)' \
% (self._epoch, self.size(), self._pos, self._fname)
def next(self):
""" load next sample
"""
if self._epoch < 0:
self.reset()
if self._pos >= self._samples:
self._drained = True
raise StopIteration('%s no more data' % (str(self)))
sample = copy.deepcopy(self._roidb[self._pos])
if self._load_img:
sample['image'] = self._load_image(sample['im_file'])
else:
sample['im_file'] = os.path.join(self._image_dir, sample['im_file'])
if self._epoch < self._mixup_epoch:
mix_idx = random.randint(1, self._samples - 1)
mix_pos = (mix_idx + self._pos) % self._samples
sample['mixup'] = copy.deepcopy(self._roidb[mix_pos])
if self._load_img:
sample['mixup']['image'] = \
self._load_image(sample['mixup']['im_file'])
else:
sample['mixup']['im_file'] = \
os.path.join(self._image_dir, sample['mixup']['im_file'])
self._pos += 1
return sample
def _load(self):
""" load data from file
"""
from . import loader
records, cname2cid = loader.load(self._fname, self._samples,
self._with_background, True,
self.use_default_label, self.cname2cid)
self.cname2cid = cname2cid
return records
def _load_image(self, where):
fn = os.path.join(self._image_dir, where)
with open(fn, 'rb') as f:
return f.read()
def reset(self):
""" implementation of Dataset.reset
"""
if self._roidb is None:
self._roidb = self._load()
self._samples = len(self._roidb)
if self._is_shuffle:
random.shuffle(self._roidb)
if self._mixup_epoch > 0 and self._samples < 2:
logger.info("Disable mixup for dataset samples "
"less than 2 samples")
self._mixup_epoch = -1
if self._epoch < 0:
self._epoch = 0
else:
self._epoch += 1
self._pos = 0
self._drained = False
def size(self):
""" implementation of Dataset.size
"""
return len(self._roidb)
def drained(self):
""" implementation of Dataset.drained
"""
assert self._epoch >= 0, 'The first epoch has not begin!'
return self._pos >= self.size()
def epoch_id(self):
""" return epoch id for latest sample
"""
return self._epoch
def get_imid2path(self):
"""return image id to image path map"""
if self._imid2path is None:
self._imid2path = {}
for record in self._roidb:
im_id = record['im_id']
im_id = im_id if isinstance(im_id, int) else im_id[0]
im_path = os.path.join(self._image_dir, record['im_file'])
self._imid2path[im_id] = im_path
return self._imid2path
ppdet/data/source/simple_source.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# function:
# interface to load data from txt file.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import copy
from ..dataset import Dataset
class SimpleSource(Dataset):
"""
Load image files for testing purpose
Args:
images (list): list of path of images
samples (int): number of samples to load, -1 means all
load_img (bool): should images be loaded
"""
def __init__(self, images=[], samples=-1, load_img=True, **kwargs):
super(SimpleSource, self).__init__()
self._epoch = -1
for image in images:
assert image != '' and os.path.isfile(image), \
"Image {} not found".format(image)
self._images = images
self._fname = None
self._simple = None
self._pos = -1
self._drained = False
self._samples = samples
self._load_img = load_img
self._imid2path = {}
def next(self):
if self._epoch < 0:
self.reset()
if self._pos >= self.size():
self._drained = True
raise StopIteration("no more data in " + str(self))
else:
sample = copy.deepcopy(self._simple[self._pos])
if self._load_img:
sample['image'] = self._load_image(sample['im_file'])
self._pos += 1
return sample
def _load(self):
ct = 0
records = []
for image in self._images:
if self._samples > 0 and ct >= self._samples:
break
rec = {'im_id': np.array([ct]), 'im_file': image}
self._imid2path[ct] = image
ct += 1
records.append(rec)
assert len(records) > 0, "no image file found"
return records
def _load_image(self, where):
with open(where, 'rb') as f:
return f.read()
def reset(self):
if self._simple is None:
self._simple = self._load()
if self._epoch < 0:
self._epoch = 0
else:
self._epoch += 1
self._pos = 0
self._drained = False
def size(self):
return len(self._simple)
def drained(self):
assert self._epoch >= 0, "the first epoch has not started yet"
return self._pos >= self.size()
def epoch_id(self):
return self._epoch
def get_imid2path(self):
"""return image id to image path map"""
return self._imid2path
ppdet/data/source/voc.py 0 → 100644
浏览文件 @ 8192c758
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import xml.etree.ElementTree as ET
from ppdet.core.workspace import register, serializable
from .dataset import DataSet
@register
@serializable
class VOCDataSet(DataSet):
"""
Load dataset with PascalVOC format.
Notes:
`anno_path` must contains xml file and image file path for annotations.
Args:
dataset_dir (str): root directory for dataset.
image_dir (str): directory for images.
anno_path (str): voc annotation file path.
sample_num (int): number of samples to load, -1 means all.
use_default_label (bool): whether use the default mapping of
label to integer index. Default True.
with_background (bool): whether load background as a class,
default True.
label_list (str): if use_default_label is False, will load
mapping between category and class index.
"""
def __init__(self,
dataset_dir=None,
image_dir=None,
anno_path=None,
sample_num=-1,
use_default_label=True,
with_background=True,
label_list='label_list.txt'):
super(VOCDataSet, self).__init__(
image_dir=image_dir,
anno_path=anno_path,
sample_num=sample_num,
dataset_dir=dataset_dir,
with_background=with_background)
# roidbs is list of dict whose structure is:
# {
# 'im_file': im_fname, # image file name
# 'im_id': im_id, # image id
# 'h': im_h, # height of image
# 'w': im_w, # width
# 'is_crowd': is_crowd,
# 'gt_class': gt_class,
# 'gt_bbox': gt_bbox,
# 'gt_poly': gt_poly,
# }
self.roidbs = None
# 'cname2id' is a dict to map category name to class id
self.cname2cid = None
self.label_list = label_list
def load_roidb_and_cname2cid(self):
anno_path = os.path.join(self.dataset_dir, self.anno_path)
image_dir = os.path.join(self.dataset_dir, self.image_dir)
# mapping category name to class id
# if with_background is True:
# background:0, first_class:1, second_class:2, ...
# if with_background is False:
# first_class:0, second_class:1, ...
records = []
ct = 0
cname2cid = {}
if not self.use_default_label:
label_path = os.path.join(self.dataset_dir, self.label_list)
if not os.path.exists(label_path):
raise ValueError("label_list {} does not exists".format(
label_path))
with open(label_path, 'r') as fr:
label_id = int(self.with_background)
for line in fr.readlines():
cname2cid[line.strip()] = label_id
label_id += 1
else:
cname2cid = pascalvoc_label(self.with_background)
with open(anno_path, 'r') as fr:
while True:
line = fr.readline()
if not line:
break
img_file, xml_file = [os.path.join(image_dir, x) \
for x in line.strip().split()[:2]]
if not os.path.isfile(xml_file):
continue
tree = ET.parse(xml_file)
if tree.find('id') is None:
im_id = np.array([ct])
else:
im_id = np.array([int(tree.find('id').text)])
objs = tree.findall('object')
im_w = float(tree.find('size').find('width').text)
im_h = float(tree.find('size').find('height').text)
gt_bbox = np.zeros((len(objs), 4), dtype=np.float32)
gt_class = np.zeros((len(objs), 1), dtype=np.int32)
gt_score = np.ones((len(objs), 1), dtype=np.float32)
is_crowd = np.zeros((len(objs), 1), dtype=np.int32)
difficult = np.zeros((len(objs), 1), dtype=np.int32)
for i, obj in enumerate(objs):
cname = obj.find('name').text
gt_class[i][0] = cname2cid[cname]
_difficult = int(obj.find('difficult').text)
x1 = float(obj.find('bndbox').find('xmin').text)
y1 = float(obj.find('bndbox').find('ymin').text)
x2 = float(obj.find('bndbox').find('xmax').text)
y2 = float(obj.find('bndbox').find('ymax').text)
x1 = max(0, x1)
y1 = max(0, y1)
x2 = min(im_w - 1, x2)
y2 = min(im_h - 1, y2)
gt_bbox[i] = [x1, y1, x2, y2]
is_crowd[i][0] = 0
difficult[i][0] = _difficult
voc_rec = {
'im_file': img_file,
'im_id': im_id,
'h': im_h,
'w': im_w,
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_score': gt_score,
'gt_bbox': gt_bbox,
'gt_poly': [],
'difficult': difficult
}
if len(objs) != 0:
records.append(voc_rec)
ct += 1
if self.sample_num > 0 and ct >= self.sample_num:
break
assert len(records) > 0, 'not found any voc record in %s' % (
self.anno_path)
self.roidbs, self.cname2cid = records, cname2cid
def pascalvoc_label(with_background=True):
labels_map = {
'aeroplane': 1,
'bicycle': 2,
'bird': 3,
'boat': 4,
'bottle': 5,
'bus': 6,
'car': 7,
'cat': 8,
'chair': 9,
'cow': 10,
'diningtable': 11,
'dog': 12,
'horse': 13,
'motorbike': 14,
'person': 15,
'pottedplant': 16,
'sheep': 17,
'sofa': 18,
'train': 19,
'tvmonitor': 20
}
if not with_background:
labels_map = {k: v - 1 for k, v in labels_map.items()}
return labels_map
ppdet/data/source/voc_loader.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import xml.etree.ElementTree as ET
def get_roidb(anno_path, sample_num=-1, cname2cid=None, with_background=True):
"""
Load VOC records with annotations in xml directory 'anno_path'
Notes:
${anno_path} must contains xml file and image file path for annotations
Args:
anno_path (str): root directory for voc annotation data
sample_num (int): number of samples to load, -1 means all
cname2cid (dict): the label name to id dictionary
with_background (bool): whether load background as a class.
if True, total class number will
be 81. default True
Returns:
(records, catname2clsid)
'records' is list of dict whose structure is:
{
'im_file': im_fname, # image file name
'im_id': im_id, # image id
'h': im_h, # height of image
'w': im_w, # width
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_poly': gt_poly,
}
'cname2id' is a dict to map category name to class id
"""
data_dir = os.path.dirname(anno_path)
records = []
ct = 0
existence = False if cname2cid is None else True
if cname2cid is None:
cname2cid = {}
# mapping category name to class id
# background:0, first_class:1, second_class:2, ...
with open(anno_path, 'r') as fr:
while True:
line = fr.readline()
if not line:
break
img_file, xml_file = [os.path.join(data_dir, x) \
for x in line.strip().split()[:2]]
if not os.path.isfile(xml_file):
continue
tree = ET.parse(xml_file)
if tree.find('id') is None:
im_id = np.array([ct])
else:
im_id = np.array([int(tree.find('id').text)])
objs = tree.findall('object')
im_w = float(tree.find('size').find('width').text)
im_h = float(tree.find('size').find('height').text)
gt_bbox = np.zeros((len(objs), 4), dtype=np.float32)
gt_class = np.zeros((len(objs), 1), dtype=np.int32)
gt_score = np.ones((len(objs), 1), dtype=np.float32)
is_crowd = np.zeros((len(objs), 1), dtype=np.int32)
difficult = np.zeros((len(objs), 1), dtype=np.int32)
for i, obj in enumerate(objs):
cname = obj.find('name').text
if not existence and cname not in cname2cid:
# the background's id is 0, so need to add 1.
cname2cid[cname] = len(cname2cid) + int(with_background)
elif existence and cname not in cname2cid:
raise KeyError(
'Not found cname[%s] in cname2cid when map it to cid.' %
(cname))
gt_class[i][0] = cname2cid[cname]
_difficult = int(obj.find('difficult').text)
x1 = float(obj.find('bndbox').find('xmin').text)
y1 = float(obj.find('bndbox').find('ymin').text)
x2 = float(obj.find('bndbox').find('xmax').text)
y2 = float(obj.find('bndbox').find('ymax').text)
x1 = max(0, x1)
y1 = max(0, y1)
x2 = min(im_w - 1, x2)
y2 = min(im_h - 1, y2)
gt_bbox[i] = [x1, y1, x2, y2]
is_crowd[i][0] = 0
difficult[i][0] = _difficult
voc_rec = {
'im_file': img_file,
'im_id': im_id,
'h': im_h,
'w': im_w,
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_score': gt_score,
'gt_bbox': gt_bbox,
'gt_poly': [],
'difficult': difficult
}
if len(objs) != 0:
records.append(voc_rec)
ct += 1
if sample_num > 0 and ct >= sample_num:
break
assert len(records) > 0, 'not found any voc record in %s' % (anno_path)
return [records, cname2cid]
def load(anno_path, sample_num=-1, use_default_label=True,
with_background=True):
"""
Load VOC records with annotations in
xml directory 'anno_path'
Notes:
${anno_path} must contains xml file and image file path for annotations
Args:
@anno_path (str): root directory for voc annotation data
@sample_num (int): number of samples to load, -1 means all
@use_default_label (bool): whether use the default mapping of label to id
@with_background (bool): whether load background as a class.
if True, total class number will
be 81. default True
Returns:
(records, catname2clsid)
'records' is list of dict whose structure is:
{
'im_file': im_fname, # image file name
'im_id': im_id, # image id
'h': im_h, # height of image
'w': im_w, # width
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_poly': gt_poly,
}
'cname2id' is a dict to map category name to class id
"""
data_dir = os.path.dirname(anno_path)
# mapping category name to class id
# if with_background is True:
# background:0, first_class:1, second_class:2, ...
# if with_background is False:
# first_class:0, second_class:1, ...
records = []
ct = 0
cname2cid = {}
if not use_default_label:
label_path = os.path.join(data_dir, 'label_list.txt')
with open(label_path, 'r') as fr:
label_id = int(with_background)
for line in fr.readlines():
cname2cid[line.strip()] = label_id
label_id += 1
else:
cname2cid = pascalvoc_label(with_background)
with open(anno_path, 'r') as fr:
while True:
line = fr.readline()
if not line:
break
img_file, xml_file = [os.path.join(data_dir, x) \
for x in line.strip().split()[:2]]
if not os.path.isfile(xml_file):
continue
tree = ET.parse(xml_file)
if tree.find('id') is None:
im_id = np.array([ct])
else:
im_id = np.array([int(tree.find('id').text)])
objs = tree.findall('object')
im_w = float(tree.find('size').find('width').text)
im_h = float(tree.find('size').find('height').text)
gt_bbox = np.zeros((len(objs), 4), dtype=np.float32)
gt_class = np.zeros((len(objs), 1), dtype=np.int32)
gt_score = np.ones((len(objs), 1), dtype=np.float32)
is_crowd = np.zeros((len(objs), 1), dtype=np.int32)
difficult = np.zeros((len(objs), 1), dtype=np.int32)
for i, obj in enumerate(objs):
cname = obj.find('name').text
gt_class[i][0] = cname2cid[cname]
_difficult = int(obj.find('difficult').text)
x1 = float(obj.find('bndbox').find('xmin').text)
y1 = float(obj.find('bndbox').find('ymin').text)
x2 = float(obj.find('bndbox').find('xmax').text)
y2 = float(obj.find('bndbox').find('ymax').text)
x1 = max(0, x1)
y1 = max(0, y1)
x2 = min(im_w - 1, x2)
y2 = min(im_h - 1, y2)
gt_bbox[i] = [x1, y1, x2, y2]
is_crowd[i][0] = 0
difficult[i][0] = _difficult
voc_rec = {
'im_file': img_file,
'im_id': im_id,
'h': im_h,
'w': im_w,
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_score': gt_score,
'gt_bbox': gt_bbox,
'gt_poly': [],
'difficult': difficult
}
if len(objs) != 0:
records.append(voc_rec)
ct += 1
if sample_num > 0 and ct >= sample_num:
break
assert len(records) > 0, 'not found any voc record in %s' % (anno_path)
return [records, cname2cid]
def pascalvoc_label(with_background=True):
labels_map = {
'aeroplane': 1,
'bicycle': 2,
'bird': 3,
'boat': 4,
'bottle': 5,
'bus': 6,
'car': 7,
'cat': 8,
'chair': 9,
'cow': 10,
'diningtable': 11,
'dog': 12,
'horse': 13,
'motorbike': 14,
'person': 15,
'pottedplant': 16,
'sheep': 17,
'sofa': 18,
'train': 19,
'tvmonitor': 20
}
if not with_background:
labels_map = {k: v - 1 for k, v in labels_map.items()}
return labels_map
ppdet/data/source/widerface_loader.py → ppdet/data/source/widerface.py
浏览文件 @ 8192c758
...@@ -17,74 +17,93 @@ import numpy as np ...@@ -17,74 +17,93 @@ import numpy as np
import logging import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
from ppdet.core.workspace import register, serializable
from .dataset import DataSet
def load(anno_path, sample_num=-1, cname2cid=None, with_background=True):
@register
@serializable
class WIDERFaceDataSet(DataSet):
""" """
Load WiderFace records with 'anno_path' Load WiderFace records with 'anno_path'
Args: Args:
dataset_dir (str): root directory for dataset.
image_dir (str): directory for images.
anno_path (str): root directory for voc annotation data anno_path (str): root directory for voc annotation data
sample_num (int): number of samples to load, -1 means all sample_num (int): number of samples to load, -1 means all
with_background (bool): whether load background as a class. with_background (bool): whether load background as a class.
if True, total class number will if True, total class number will be 2. default True.
be 2. default True
Returns:
(records, catname2clsid)
'records' is list of dict whose structure is:
{
'im_file': im_fname, # image file name
'im_id': im_id, # image id
'gt_class': gt_class,
'gt_bbox': gt_bbox,
}
'cname2id' is a dict to map category name to class id
""" """
txt_file = anno_path def __init__(self,
dataset_dir=None,
records = [] image_dir=None,
ct = 0 anno_path=None,
file_lists = _load_file_list(txt_file) sample_num=-1,
cname2cid = widerface_label(with_background) with_background=True):
super(WIDERFaceDataSet, self).__init__(
for item in file_lists: image_dir=image_dir,
im_fname = item[0] anno_path=anno_path,
im_id = np.array([ct]) sample_num=sample_num,
gt_bbox = np.zeros((len(item) - 2, 4), dtype=np.float32) dataset_dir=dataset_dir,
gt_class = np.ones((len(item) - 2, 1), dtype=np.int32) with_background=with_background)
for index_box in range(len(item)): self.anno_path = anno_path
if index_box >= 2: self.sample_num = sample_num
temp_info_box = item[index_box].split(' ') self.with_background = with_background
xmin = float(temp_info_box[0]) self.roidbs = None
ymin = float(temp_info_box[1]) self.cname2cid = None
w = float(temp_info_box[2])
h = float(temp_info_box[3]) def load_roidb_and_cname2cid(self):
# Filter out wrong labels anno_path = os.path.join(self.dataset_dir, self.anno_path)
if w < 0 or h < 0: image_dir = os.path.join(self.dataset_dir, self.image_dir)
continue
xmin = max(0, xmin) txt_file = anno_path
ymin = max(0, ymin)
xmax = xmin + w records = []
ymax = ymin + h ct = 0
gt_bbox[index_box - 2] = [xmin, ymin, xmax, ymax] file_lists = _load_file_list(txt_file)
cname2cid = widerface_label(self.with_background)
widerface_rec = {
'im_file': im_fname, for item in file_lists:
'im_id': im_id, im_fname = item[0]
'gt_bbox': gt_bbox, im_id = np.array([ct])
'gt_class': gt_class, gt_bbox = np.zeros((len(item) - 2, 4), dtype=np.float32)
} gt_class = np.ones((len(item) - 2, 1), dtype=np.int32)
# logger.debug for index_box in range(len(item)):
if len(item) != 0: if index_box >= 2:
records.append(widerface_rec) temp_info_box = item[index_box].split(' ')
xmin = float(temp_info_box[0])
ct += 1 ymin = float(temp_info_box[1])
if sample_num > 0 and ct >= sample_num: w = float(temp_info_box[2])
break h = float(temp_info_box[3])
assert len(records) > 0, 'not found any widerface in %s' % (anno_path) # Filter out wrong labels
logger.info('{} samples in file {}'.format(ct, anno_path)) if w < 0 or h < 0:
return records, cname2cid continue
xmin = max(0, xmin)
ymin = max(0, ymin)
xmax = xmin + w
ymax = ymin + h
gt_bbox[index_box - 2] = [xmin, ymin, xmax, ymax]
im_fname = os.path.join(image_dir,
im_fname) if image_dir else im_fname
widerface_rec = {
'im_file': im_fname,
'im_id': im_id,
'gt_bbox': gt_bbox,
'gt_class': gt_class,
}
# logger.debug
if len(item) != 0:
records.append(widerface_rec)
ct += 1
if self.sample_num > 0 and ct >= self.sample_num:
break
assert len(records) > 0, 'not found any widerface in %s' % (anno_path)
logger.info('{} samples in file {}'.format(ct, anno_path))
self.roidbs, self.cname2cid = records, cname2cid
def _load_file_list(input_txt): def _load_file_list(input_txt):
......
ppdet/data/tests/coco.yml 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
DATA:
TRAIN:
ANNO_FILE: data/coco.test/train2017.roidb
IMAGE_DIR: data/coco.test/train2017
SAMPLES: 10
TYPE: RoiDbSource
VAL:
ANNO_FILE: data/coco.test/val2017.roidb
IMAGE_DIR: data/coco.test/val2017
SAMPLES: 10
TYPE: RoiDbSource
TRANSFORM:
TRAIN:
OPS:
- OP: DecodeImage
TO_RGB: False
- OP: RandomFlipImage
PROB: 0.5
- OP: NormalizeImage
MEAN: [102.9801, 115.9465, 122.7717]
IS_SCALE: False
IS_CHANNEL_FIRST: False
- OP: ResizeImage
TARGET_SIZE: 800
MAX_SIZE: 1333
- OP: Permute
TO_BGR: False
- OP: ArrangeRCNN
BATCH_SIZE: 1
IS_PADDING: True
DROP_LAST: False
WORKER_CONF:
BUFSIZE: 100
WORKER_NUM: 4
USE_PROCESS: True
MEMSIZE: 2G
VAL:
OPS:
- OP: DecodeImage
TO_RGB: True
- OP: ResizeImage
TARGET_SIZE: 224
- OP: ArrangeSSD
BATCH_SIZE: 1
WORKER_CONF:
BUFSIZE: 100
WORKER_NUM: 4
USE_PROCESS: True
ppdet/data/tests/data/prepare_data.sh 已删除 100755 → 0
浏览文件 @ 9cd0d1ee
#!/bin/bash
#function:
# prepare coco data for testing
root=$(dirname `readlink -f ${BASH_SOURCE}[0]`)
cwd=`pwd`
if [[ $cwd != $root ]];then
pushd $root 2>&1 1>/dev/null
fi
test_coco_python2_url="http://filecenter.matrix.baidu.com/api/v1/file/wanglong03/coco.test.python2.zip/20190603095315/download"
test_coco_python3_url="http://filecenter.matrix.baidu.com/api/v1/file/wanglong03/coco.test.python3.zip/20190603095447/download"
if [[ $1 = "python2" ]];then
test_coco_data_url=${test_coco_python2_url}
coco_zip_file="coco.test.python2.zip"
else
test_coco_data_url=${test_coco_python3_url}
coco_zip_file="coco.test.python3.zip"
fi
echo "download testing coco from url[${test_coco_data_url}]"
coco_root_dir=${coco_zip_file/.zip/}
# clear already exist file or directory
rm -rf ${coco_root_dir} ${coco_zip_file}
wget ${test_coco_data_url} -O ${coco_zip_file}
if [ -e $coco_zip_file ];then
echo "succeed to download ${coco_zip_file}, so unzip it"
unzip ${coco_zip_file} >/dev/null 2>&1
fi
if [ -e ${coco_root_dir} ];then
rm -rf coco.test
ln -s ${coco_root_dir} coco.test
echo "succeed to generate coco data in[${coco_root_dir}] for testing"
exit 0
else
echo "failed to generate coco data"
exit 1
fi
ppdet/data/tests/rcnn_dataset.yml 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
DATA:
TRAIN:
ANNO_FILE: data/coco.test/train2017.roidb
IMAGE_DIR: data/coco.test/train2017
SAMPLES: 10
IS_SHUFFLE: True
TYPE: RoiDbSource
TRANSFORM:
TRAIN:
OPS:
- OP: DecodeImage
TO_RGB: False
- OP: RandomFlipImage
PROB: 0.5
- OP: NormalizeImage
MEAN: [102.9801, 115.9465, 122.7717]
IS_SCALE: False
IS_CHANNEL_FIRST: False
- OP: ResizeImage
TARGET_SIZE: 800
MAX_SIZE: 1333
- OP: Permute
TO_BGR: False
- OP: ArrangeRCNN
BATCH_SIZE: 1
IS_PADDING: True
DROP_LAST: False
WORKER_CONF:
BUFSIZE: 100
WORKER_NUM: 4
MEMSIZE: 2G
USE_PROCESS: True
ppdet/data/tests/set_env.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import os
import six
import logging
import matplotlib
matplotlib.use('Agg', force=False)
prefix = os.path.dirname(os.path.abspath(__file__))
#coco data for testing
if six.PY3:
version = 'python3'
else:
version = 'python2'
data_root = os.path.join(prefix, 'data/coco.test.%s' % (version))
# coco data for testing
coco_data = {
'TRAIN': {
'ANNO_FILE': os.path.join(data_root, 'train2017.roidb'),
'IMAGE_DIR': os.path.join(data_root, 'train2017')
},
'VAL': {
'ANNO_FILE': os.path.join(data_root, 'val2017.roidb'),
'IMAGE_DIR': os.path.join(data_root, 'val2017')
}
}
script = os.path.join(os.path.dirname(__file__), 'data/prepare_data.sh')
if not os.path.exists(data_root):
ret = os.system('bash %s %s' % (script, version))
if ret != 0:
logging.error('not found file[%s], you should manually prepare '
'your data using "data/prepare_data.sh"' % (data_root))
sys.exit(1)
ppdet/data/tests/test.yml 0 → 100644
浏览文件 @ 8192c758
TrainReader:
inputs_def:
fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_mask']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_num: 10
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !RandomFlipImage
is_mask_flip: true
is_normalized: false
prob: 0.5
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: false
batch_size: 1
shuffle: true
worker_num: 2
drop_last: false
use_process: false
EvalReader:
inputs_def:
fields: ['image', 'im_info', 'im_id']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
sample_num: 10
sample_transforms:
- !DecodeImage
to_rgb: true
with_mixup: false
- !NormalizeImage
is_channel_first: false
is_scale: true
mean: [0.485,0.456,0.406]
std: [0.229, 0.224,0.225]
- !ResizeImage
interp: 1
max_size: 1333
target_size: 800
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 32
use_padded_im_info: true
batch_size: 1
shuffle: false
drop_last: false
ppdet/data/tests/test_dataset.py
浏览文件 @ 8192c758
...@@ -19,22 +19,26 @@ import logging ...@@ -19,22 +19,26 @@ import logging
import random import random
import copy import copy
import set_env from ppdet.data.parallel_map import ParallelMap
import ppdet.data.transform as tf
from ppdet.data.dataset import Dataset
class MemorySource(Dataset): class MemorySource(object):
""" memory data source for testing """ memory data source for testing
""" """
def __init__(self, samples): def __init__(self, samples):
super(MemorySource, self).__init__()
self._epoch = -1 self._epoch = -1
self._pos = -1 self._pos = -1
self._drained = False self._drained = False
self._samples = samples self._samples = samples
def __iter__(self):
return self
def __next__(self):
return self.next()
def next(self): def next(self):
if self._epoch < 0: if self._epoch < 0:
self.reset() self.reset()
...@@ -95,20 +99,20 @@ class TestDataset(unittest.TestCase): ...@@ -95,20 +99,20 @@ class TestDataset(unittest.TestCase):
def test_transform_with_abnormal_worker(self): def test_transform_with_abnormal_worker(self):
""" test dataset transform with abnormally exit process """ test dataset transform with abnormally exit process
""" """
samples = list(range(1000)) samples = list(range(20))
ds = MemorySource(samples) mem_sc = MemorySource(samples)
def _mapper(sample): def _worker(sample):
if sample == 3: if sample == 3:
sys.exit(1) sys.exit(1)
return 2 * sample return 2 * sample
worker_conf = {'WORKER_NUM': 2, 'use_process': True} test_worker = ParallelMap(
mapped = tf.map(ds, _mapper, worker_conf) mem_sc, _worker, worker_num=2, use_process=True)
ct = 0 ct = 0
for i, d in enumerate(mapped): for i, d in enumerate(test_worker):
ct += 1 ct += 1
self.assertTrue(d / 2 in samples) self.assertTrue(d / 2 in samples)
...@@ -117,20 +121,20 @@ class TestDataset(unittest.TestCase): ...@@ -117,20 +121,20 @@ class TestDataset(unittest.TestCase):
def test_transform_with_delay_worker(self): def test_transform_with_delay_worker(self):
""" test dataset transform with delayed process """ test dataset transform with delayed process
""" """
samples = list(range(1000)) samples = list(range(20))
ds = MemorySource(samples) mem_sc = MemorySource(samples)
def _mapper(sample): def _worker(sample):
if sample == 3: if sample == 3:
time.sleep(30) time.sleep(30)
return 2 * sample return 2 * sample
worker_conf = {'WORKER_NUM': 2, 'use_process': True} test_worker = ParallelMap(
mapped = tf.map(ds, _mapper, worker_conf) mem_sc, _worker, worker_num=2, use_process=True)
ct = 0 ct = 0
for i, d in enumerate(mapped): for i, d in enumerate(test_worker):
ct += 1 ct += 1
self.assertTrue(d / 2 in samples) self.assertTrue(d / 2 in samples)
...@@ -140,4 +144,3 @@ class TestDataset(unittest.TestCase): ...@@ -140,4 +144,3 @@ class TestDataset(unittest.TestCase):
if __name__ == '__main__': if __name__ == '__main__':
logging.basicConfig() logging.basicConfig()
unittest.main() unittest.main()
ppdet/data/tests/test_iterator_source.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import unittest
import sys
import logging
import set_env
from ppdet.data.source import IteratorSource
def _generate_iter_maker(num=10):
def _reader():
for i in range(num):
yield {'image': 'image_' + str(i), 'label': i}
return _reader
class TestIteratorSource(unittest.TestCase):
"""Test cases for dataset.source.roidb_source
"""
@classmethod
def setUpClass(cls):
""" setup
"""
pass
@classmethod
def tearDownClass(cls):
""" tearDownClass """
pass
def test_basic(self):
""" test basic apis 'next/size/drained'
"""
iter_maker = _generate_iter_maker()
iter_source = IteratorSource(iter_maker)
for i, sample in enumerate(iter_source):
self.assertTrue('image' in sample)
self.assertGreater(len(sample['image']), 0)
self.assertTrue(iter_source.drained())
self.assertEqual(i + 1, iter_source.size())
def test_reset(self):
""" test functions 'reset/epoch_id'
"""
iter_maker = _generate_iter_maker()
iter_source = IteratorSource(iter_maker)
self.assertTrue(iter_source.next() is not None)
self.assertEqual(iter_source.epoch_id(), 0)
iter_source.reset()
self.assertEqual(iter_source.epoch_id(), 1)
self.assertTrue(iter_source.next() is not None)
if __name__ == '__main__':
unittest.main()
ppdet/data/tests/test_loader.py
浏览文件 @ 8192c758
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -12,96 +12,154 @@ ...@@ -12,96 +12,154 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import time
import unittest import unittest
import sys import os
import logging
import numpy as np from ppdet.data.source.coco import COCODataSet
from ppdet.data.reader import Reader
from ppdet.utils.download import get_path
from ppdet.utils.download import DATASET_HOME
import set_env from ppdet.data.transform.operators import DecodeImage, ResizeImage, Permute
from ppdet.data.transform.batch_operators import PadBatch
COCO_VAL_URL = 'http://images.cocodataset.org/zips/val2017.zip'
COCO_VAL_MD5SUM = '442b8da7639aecaf257c1dceb8ba8c80'
COCO_ANNO_URL = 'http://images.cocodataset.org/annotations/annotations_trainval2017.zip'
COCO_ANNO_MD5SUM = 'f4bbac642086de4f52a3fdda2de5fa2c'
class TestLoader(unittest.TestCase):
"""Test cases for dataset.source.loader
"""
class TestReader(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
""" setup """ setup
""" """
cls.prefix = os.path.dirname(os.path.abspath(__file__)) root_path = os.path.join(DATASET_HOME, 'coco')
# json data _, _ = get_path(COCO_VAL_URL, root_path, COCO_VAL_MD5SUM)
cls.anno_path = os.path.join(cls.prefix, _, _ = get_path(COCO_ANNO_URL, root_path, COCO_ANNO_MD5SUM)
'data/coco/instances_val2017.json') cls.anno_path = 'annotations/instances_val2017.json'
cls.image_dir = os.path.join(cls.prefix, 'data/coco/val2017') cls.image_dir = 'val2017'
cls.anno_path1 = os.path.join(cls.prefix, cls.root_path = root_path
"data/voc/ImageSets/Main/train.txt")
cls.image_dir1 = os.path.join(cls.prefix, "data/voc/JPEGImages")
@classmethod @classmethod
def tearDownClass(cls): def tearDownClass(cls):
""" tearDownClass """ """ tearDownClass """
pass pass
def test_load_coco_in_json(self): def test_loader(self):
""" test loading COCO data in json file coco_loader = COCODataSet(
""" dataset_dir=self.root_path,
from ppdet.data.source.coco_loader import load image_dir=self.image_dir,
if not os.path.exists(self.anno_path): anno_path=self.anno_path,
logging.warn('not found %s, so skip this test' % (self.anno_path)) sample_num=10)
return sample_trans = [
samples = 10 DecodeImage(to_rgb=True), ResizeImage(
records, cname2id = load(self.anno_path, samples) target_size=800, max_size=1333, interp=1), Permute(to_bgr=False)
self.assertEqual(len(records), samples) ]
self.assertGreater(len(cname2id), 0) batch_trans = [PadBatch(pad_to_stride=32, use_padded_im_info=True), ]
def test_load_coco_in_roidb(self): inputs_def = {
""" test loading COCO data in pickled records 'fields': [
""" 'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd',
anno_path = os.path.join(self.prefix, 'gt_mask'
'data/roidbs/instances_val2017.roidb') ],
}
if not os.path.exists(anno_path): data_loader = Reader(
logging.warn('not found %s, so skip this test' % (anno_path)) coco_loader,
return sample_transforms=sample_trans,
batch_transforms=batch_trans,
samples = 10 batch_size=2,
from ppdet.data.source.loader import load_roidb shuffle=True,
records, cname2cid = load_roidb(anno_path, samples) drop_empty=True,
self.assertEqual(len(records), samples) inputs_def=inputs_def)()
self.assertGreater(len(cname2cid), 0) for i in range(2):
for samples in data_loader:
def test_load_voc_in_xml(self): for sample in samples:
""" test loading VOC data in xml files im_shape = sample[0].shape
""" self.assertEqual(im_shape[0], 3)
from ppdet.data.source.voc_loader import load self.assertEqual(im_shape[1] % 32, 0)
if not os.path.exists(self.anno_path1): self.assertEqual(im_shape[2] % 32, 0)
logging.warn('not found %s, so skip this test' % (self.anno_path1))
return im_info_shape = sample[1].shape
samples = 3 self.assertEqual(im_info_shape[-1], 3)
records, cname2cid = load(self.anno_path1, samples)
self.assertEqual(len(records), samples) im_id_shape = sample[2].shape
self.assertGreater(len(cname2cid), 0) self.assertEqual(im_id_shape[-1], 1)
def test_load_voc_in_roidb(self): gt_bbox_shape = sample[3].shape
""" test loading VOC data in pickled records self.assertEqual(gt_bbox_shape[-1], 4)
"""
anno_path = os.path.join(self.prefix, 'data/roidbs/train.roidb') gt_class_shape = sample[4].shape
self.assertEqual(gt_class_shape[-1], 1)
if not os.path.exists(anno_path): self.assertEqual(gt_class_shape[0], gt_bbox_shape[0])
logging.warn('not found %s, so skip this test' % (anno_path))
return is_crowd_shape = sample[5].shape
self.assertEqual(is_crowd_shape[-1], 1)
samples = 3 self.assertEqual(is_crowd_shape[0], gt_bbox_shape[0])
from ppdet.data.source.loader import load_roidb
records, cname2cid = load_roidb(anno_path, samples) mask = sample[6]
self.assertEqual(len(records), samples) self.assertEqual(len(mask), gt_bbox_shape[0])
self.assertGreater(len(cname2cid), 0) self.assertEqual(mask[0][0].shape[-1], 2)
data_loader.reset()
def test_loader_multi_threads(self):
coco_loader = COCODataSet(
dataset_dir=self.root_path,
image_dir=self.image_dir,
anno_path=self.anno_path,
sample_num=10)
sample_trans = [
DecodeImage(to_rgb=True), ResizeImage(
target_size=800, max_size=1333, interp=1), Permute(to_bgr=False)
]
batch_trans = [PadBatch(pad_to_stride=32, use_padded_im_info=True), ]
inputs_def = {
'fields': [
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd',
'gt_mask'
],
}
data_loader = Reader(
coco_loader,
sample_transforms=sample_trans,
batch_transforms=batch_trans,
batch_size=2,
shuffle=True,
drop_empty=True,
worker_num=2,
use_process=False,
bufsize=8,
inputs_def=inputs_def)()
for i in range(2):
for samples in data_loader:
for sample in samples:
im_shape = sample[0].shape
self.assertEqual(im_shape[0], 3)
self.assertEqual(im_shape[1] % 32, 0)
self.assertEqual(im_shape[2] % 32, 0)
im_info_shape = sample[1].shape
self.assertEqual(im_info_shape[-1], 3)
im_id_shape = sample[2].shape
self.assertEqual(im_id_shape[-1], 1)
gt_bbox_shape = sample[3].shape
self.assertEqual(gt_bbox_shape[-1], 4)
gt_class_shape = sample[4].shape
self.assertEqual(gt_class_shape[-1], 1)
self.assertEqual(gt_class_shape[0], gt_bbox_shape[0])
is_crowd_shape = sample[5].shape
self.assertEqual(is_crowd_shape[-1], 1)
self.assertEqual(is_crowd_shape[0], gt_bbox_shape[0])
mask = sample[6]
self.assertEqual(len(mask), gt_bbox_shape[0])
self.assertEqual(mask[0][0].shape[-1], 2)
data_loader.reset()
if __name__ == '__main__': if __name__ == '__main__':
......
ppdet/data/tests/test_loader_yaml.py 0 → 100644
浏览文件 @ 8192c758
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import os
import yaml
import logging
from ppdet.utils.download import get_path
from ppdet.utils.download import DATASET_HOME
from ppdet.core.workspace import load_config, merge_config
from ppdet.data.reader import create_reader
COCO_VAL_URL = 'http://images.cocodataset.org/zips/val2017.zip'
COCO_VAL_MD5SUM = '442b8da7639aecaf257c1dceb8ba8c80'
COCO_ANNO_URL = 'http://images.cocodataset.org/annotations/annotations_trainval2017.zip'
COCO_ANNO_MD5SUM = 'f4bbac642086de4f52a3fdda2de5fa2c'
FORMAT = '[%(asctime)s-%(filename)s-%(levelname)s:%(message)s]'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
class TestReaderYAML(unittest.TestCase):
@classmethod
def setUpClass(cls):
""" setup
"""
root_path = os.path.join(DATASET_HOME, 'coco')
_, _ = get_path(COCO_VAL_URL, root_path, COCO_VAL_MD5SUM)
_, _ = get_path(COCO_ANNO_URL, root_path, COCO_ANNO_MD5SUM)
cls.anno_path = 'annotations/instances_val2017.json'
cls.image_dir = 'val2017'
cls.root_path = root_path
@classmethod
def tearDownClass(cls):
""" tearDownClass """
pass
def test_loader_yaml(self):
cfg_file = 'ppdet/data/tests/test.yml'
cfg = load_config(cfg_file)
data_cfg = '[!COCODataSet {{image_dir: {0}, dataset_dir: {1}, ' \
'anno_path: {2}, sample_num: 10}}]'.format(
self.image_dir, self.root_path, self.anno_path)
dataset_ins = yaml.load(data_cfg, Loader=yaml.Loader)
update_train_cfg = {'TrainReader': {'dataset': dataset_ins[0]}}
update_test_cfg = {'EvalReader': {'dataset': dataset_ins[0]}}
merge_config(update_train_cfg)
merge_config(update_test_cfg)
reader = create_reader(cfg['TrainReader'], 10)()
for samples in reader:
for sample in samples:
im_shape = sample[0].shape
self.assertEqual(im_shape[0], 3)
self.assertEqual(im_shape[1] % 32, 0)
self.assertEqual(im_shape[2] % 32, 0)
im_info_shape = sample[1].shape
self.assertEqual(im_info_shape[-1], 3)
im_id_shape = sample[2].shape
self.assertEqual(im_id_shape[-1], 1)
gt_bbox_shape = sample[3].shape
self.assertEqual(gt_bbox_shape[-1], 4)
gt_class_shape = sample[4].shape
self.assertEqual(gt_class_shape[-1], 1)
self.assertEqual(gt_class_shape[0], gt_bbox_shape[0])
is_crowd_shape = sample[5].shape
self.assertEqual(is_crowd_shape[-1], 1)
self.assertEqual(is_crowd_shape[0], gt_bbox_shape[0])
mask = sample[6]
self.assertEqual(len(mask), gt_bbox_shape[0])
self.assertEqual(mask[0][0].shape[-1], 2)
reader = create_reader(cfg['EvalReader'], 10)()
for samples in reader:
for sample in samples:
im_shape = sample[0].shape
self.assertEqual(im_shape[0], 3)
self.assertEqual(im_shape[1] % 32, 0)
self.assertEqual(im_shape[2] % 32, 0)
im_info_shape = sample[1].shape
self.assertEqual(im_info_shape[-1], 3)
im_id_shape = sample[2].shape
self.assertEqual(im_id_shape[-1], 1)
if __name__ == '__main__':
unittest.main()
ppdet/data/tests/test_operator.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
import logging
import numpy as np
import set_env
import ppdet.data.transform as tf
logging.basicConfig(level=logging.INFO)
class TestBase(unittest.TestCase):
"""Test cases for dataset.transform.operator
"""
@classmethod
def setUpClass(cls, with_mixup=False):
""" setup
"""
roidb_fname = set_env.coco_data['TRAIN']['ANNO_FILE']
image_dir = set_env.coco_data['TRAIN']['IMAGE_DIR']
import pickle as pkl
with open(roidb_fname, 'rb') as f:
roidb = f.read()
roidb = pkl.loads(roidb)
fn = os.path.join(image_dir, roidb[0][0]['im_file'])
with open(fn, 'rb') as f:
roidb[0][0]['image'] = f.read()
if with_mixup:
mixup_fn = os.path.join(image_dir, roidb[0][1]['im_file'])
roidb[0][0]['mixup'] = roidb[0][1]
with open(fn, 'rb') as f:
roidb[0][0]['mixup']['image'] = f.read()
cls.sample = roidb[0][0]
@classmethod
def tearDownClass(cls):
""" tearDownClass """
pass
def test_ops_all(self):
""" test operators
"""
# ResizeImage
ops_conf = [{
'op': 'DecodeImage'
}, {
'op': 'ResizeImage',
'target_size': 300,
'max_size': 1333
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result0 = mapper(data)
self.assertIsNotNone(result0['image'])
self.assertEqual(len(result0['image'].shape), 3)
# RandFlipImage
ops_conf = [{'op': 'RandomFlipImage'}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
result1 = mapper(result0)
self.assertEqual(result1['image'].shape, result0['image'].shape)
self.assertEqual(result1['gt_bbox'].shape, result0['gt_bbox'].shape)
# NormalizeImage
ops_conf = [{'op': 'NormalizeImage', 'is_channel_first': False}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
result2 = mapper(result1)
im1 = result1['image']
count = np.where(im1 <= 1)[0]
if im1.dtype == 'float64':
self.assertEqual(count, im1.shape[0] * im1.shape[1], im1.shape[2])
# ArrangeSample
ops_conf = [{'op': 'ArrangeRCNN'}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
result3 = mapper(result2)
self.assertEqual(type(result3), tuple)
def test_ops_part1(self):
"""test Crop and Resize
"""
ops_conf = [{
'op': 'DecodeImage'
}, {
'op': 'NormalizeBox'
}, {
'op': 'CropImage',
'batch_sampler': [[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result = mapper(data)
self.assertEqual(len(result['image'].shape), 3)
def test_ops_part2(self):
"""test Expand and RandomDistort
"""
ops_conf = [{
'op': 'DecodeImage'
}, {
'op': 'NormalizeBox'
}, {
'op': 'ExpandImage',
'max_ratio': 1.5,
'prob': 1
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result = mapper(data)
self.assertEqual(len(result['image'].shape), 3)
self.assertGreater(result['gt_bbox'].shape[0], 0)
def test_ops_part3(self):
"""test Mixup and RandomInterp
"""
ops_conf = [{
'op': 'DecodeImage',
'with_mixup': True,
}, {
'op': 'MixupImage',
}, {
'op': 'RandomInterpImage',
'target_size': 608
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result = mapper(data)
self.assertEqual(len(result['image'].shape), 3)
self.assertGreater(result['gt_bbox'].shape[0], 0)
#self.assertGreater(result['gt_score'].shape[0], 0)
if __name__ == '__main__':
unittest.main()
ppdet/data/tests/test_reader.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import unittest
import sys
import logging
import numpy as np
import yaml
import set_env
from ppdet.data.reader import Reader
from ppdet.data.source import build_source
from ppdet.data.source import IteratorSource
class TestReader(unittest.TestCase):
"""Test cases for dataset.reader
"""
@classmethod
def setUpClass(cls):
""" setup
"""
prefix = os.path.dirname(os.path.abspath(__file__))
coco_yml = os.path.join(prefix, 'coco.yml')
with open(coco_yml, 'rb') as f:
cls.coco_conf = yaml.load(f.read())
cls.coco_conf['DATA']['TRAIN'] = set_env.coco_data['TRAIN']
cls.coco_conf['DATA']['VAL'] = set_env.coco_data['VAL']
rcnn_yml = os.path.join(prefix, 'rcnn_dataset.yml')
with open(rcnn_yml, 'rb') as f:
cls.rcnn_conf = yaml.load(f.read())
cls.rcnn_conf['DATA']['TRAIN'] = set_env.coco_data['TRAIN']
cls.rcnn_conf['DATA']['VAL'] = set_env.coco_data['VAL']
@classmethod
def tearDownClass(cls):
""" tearDownClass """
pass
def test_train(self):
""" Test reader for training
"""
coco = Reader(
self.coco_conf['DATA'], self.coco_conf['TRANSFORM'], maxiter=1000)
train_rd = coco.train()
self.assertTrue(train_rd is not None)
ct = 0
total = 0
bytes = 0
prev_ts = None
for sample in train_rd():
if prev_ts is None:
start_ts = time.time()
prev_ts = start_ts
ct += 1
bytes += 4 * sample[0][0].size * len(sample[0])
self.assertTrue(sample is not None)
cost = time.time() - prev_ts
if cost >= 1.0:
total += ct
qps = total / (time.time() - start_ts)
bps = bytes / (time.time() - start_ts)
logging.info('got %d/%d samples in %.3fsec with qps:%d bps:%d' %
(ct, total, cost, qps, bps))
bytes = 0
ct = 0
prev_ts = time.time()
total += ct
self.assertEqual(total, coco._maxiter)
def test_val(self):
""" Test reader for validation
"""
coco = Reader(self.coco_conf['DATA'], self.coco_conf['TRANSFORM'], 10)
val_rd = coco.val()
self.assertTrue(val_rd is not None)
# test 3 epoches
for _ in range(3):
ct = 0
for sample in val_rd():
ct += 1
self.assertTrue(sample is not None)
self.assertGreaterEqual(ct, coco._maxiter)
def test_rcnn_train(self):
""" Test reader for training
"""
anno = self.rcnn_conf['DATA']['TRAIN']['ANNO_FILE']
if not os.path.exists(anno):
logging.error('exit test_rcnn for not found file[%s]' % (anno))
return
rcnn = Reader(self.rcnn_conf['DATA'], self.rcnn_conf['TRANSFORM'], 10)
rcnn_rd = rcnn.train()
self.assertTrue(rcnn_rd is not None)
ct = 0
out = None
for sample in rcnn_rd():
out = sample
ct += 1
self.assertTrue(sample is not None)
self.assertEqual(out[0][0].shape[0], 3)
self.assertEqual(out[0][1].shape[0], 3)
self.assertEqual(out[0][3].shape[1], 4)
self.assertEqual(out[0][4].shape[1], 1)
self.assertEqual(out[0][5].shape[1], 1)
self.assertGreaterEqual(ct, rcnn._maxiter)
def test_create(self):
""" Test create a reader using my source
"""
def _my_data_reader():
mydata = build_source(self.rcnn_conf['DATA']['TRAIN'])
for i, sample in enumerate(mydata):
yield sample
my_source = IteratorSource(_my_data_reader)
mode = 'TRAIN'
train_rd = Reader.create(
mode,
self.rcnn_conf['DATA'][mode],
self.rcnn_conf['TRANSFORM'][mode],
max_iter=10,
my_source=my_source)
out = None
for sample in train_rd():
out = sample
self.assertTrue(sample is not None)
self.assertEqual(out[0][0].shape[0], 3)
self.assertEqual(out[0][1].shape[0], 3)
self.assertEqual(out[0][3].shape[1], 4)
self.assertEqual(out[0][4].shape[1], 1)
self.assertEqual(out[0][5].shape[1], 1)
if __name__ == '__main__':
unittest.main()
ppdet/data/tests/test_roidb_source.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import unittest
import sys
import logging
import set_env
from ppdet.data.source import build_source
class TestRoiDbSource(unittest.TestCase):
"""Test cases for dataset.source.roidb_source
"""
@classmethod
def setUpClass(cls):
""" setup
"""
anno_path = set_env.coco_data['TRAIN']['ANNO_FILE']
image_dir = set_env.coco_data['TRAIN']['IMAGE_DIR']
cls.config = {
'data_cf': {
'anno_file': anno_path,
'image_dir': image_dir,
'samples': 100,
'load_img': True
},
'cname2cid': None
}
@classmethod
def tearDownClass(cls):
""" tearDownClass """
pass
def test_basic(self):
""" test basic apis 'next/size/drained'
"""
roi_source = build_source(self.config)
for i, sample in enumerate(roi_source):
self.assertTrue('image' in sample)
self.assertGreater(len(sample['image']), 0)
self.assertTrue(roi_source.drained())
self.assertEqual(i + 1, roi_source.size())
def test_reset(self):
""" test functions 'reset/epoch_id'
"""
roi_source = build_source(self.config)
self.assertTrue(roi_source.next() is not None)
self.assertEqual(roi_source.epoch_id(), 0)
roi_source.reset()
self.assertEqual(roi_source.epoch_id(), 1)
self.assertTrue(roi_source.next() is not None)
if __name__ == '__main__':
unittest.main()
ppdet/data/tests/test_transformer.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import unittest
import sys
import logging
import numpy as np
import set_env
import ppdet.data.transform as tf
from ppdet.data.source import build_source
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
class TestTransformer(unittest.TestCase):
"""Test cases for dataset.transform.transformer
"""
@classmethod
def setUpClass(cls):
""" setup
"""
prefix = os.path.dirname(os.path.abspath(__file__))
# json data
anno_path = set_env.coco_data['TRAIN']['ANNO_FILE']
image_dir = set_env.coco_data['TRAIN']['IMAGE_DIR']
cls.sc_config = {
'anno_file': anno_path,
'image_dir': image_dir,
'samples': 200
}
cls.ops = [{
'op': 'DecodeImage',
'to_rgb': True
}, {
'op': 'ResizeImage',
'target_size': 800,
'max_size': 1333
}, {
'op': 'ArrangeRCNN',
'is_mask': False
}]
@classmethod
def tearDownClass(cls):
""" tearDownClass """
pass
def test_map(self):
""" test transformer.map
"""
mapper = tf.build_mapper(self.ops)
ds = build_source(self.sc_config)
mapped_ds = tf.map(ds, mapper)
ct = 0
for sample in mapped_ds:
self.assertTrue(type(sample[0]) is np.ndarray)
ct += 1
self.assertEqual(ct, mapped_ds.size())
def test_parallel_map(self):
""" test transformer.map with concurrent workers
"""
mapper = tf.build_mapper(self.ops)
ds = build_source(self.sc_config)
worker_conf = {'WORKER_NUM': 2, 'use_process': True}
mapped_ds = tf.map(ds, mapper, worker_conf)
ct = 0
for sample in mapped_ds:
self.assertTrue(type(sample[0]) is np.ndarray)
ct += 1
self.assertTrue(mapped_ds.drained())
self.assertEqual(ct, mapped_ds.size())
mapped_ds.reset()
ct = 0
for sample in mapped_ds:
self.assertTrue(type(sample[0]) is np.ndarray)
ct += 1
self.assertEqual(ct, mapped_ds.size())
def test_batch(self):
""" test batched dataset
"""
batchsize = 2
mapper = tf.build_mapper(self.ops)
ds = build_source(self.sc_config)
mapped_ds = tf.map(ds, mapper)
batched_ds = tf.batch(mapped_ds, batchsize, True)
for sample in batched_ds:
out = sample
self.assertEqual(len(out), batchsize)
if __name__ == '__main__':
unittest.main()
ppdet/data/tools/generate_data_for_training.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# function:
# tool used convert COCO or VOC data to a pickled file whose
# schema for each sample is the same.
#
# notes:
# Original data format of COCO or VOC can also be directly
# used by 'PPdetection' to train.
# This tool just convert data to a unified schema,
# and it's useful when debuging with small dataset.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import os
import sys
import logging
import pickle as pkl
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../')
if path not in sys.path:
sys.path.insert(0, path)
from data.source import loader
def parse_args():
""" parse arguments
"""
parser = argparse.ArgumentParser(
description='Generate Standard Dataset for PPdetection')
parser.add_argument(
'--type',
type=str,
default='json',
help='file format of label file, eg: json for COCO and xml for VOC')
parser.add_argument(
'--annotation',
type=str,
help='label file name for COCO or VOC dataset, '
'eg: instances_val2017.json or train.txt')
parser.add_argument(
'--save-dir',
type=str,
default='roidb',
help='directory to save roidb file which contains pickled samples')
parser.add_argument(
'--samples',
type=int,
default=-1,
help='number of samples to dump, default to all')
args = parser.parse_args()
return args
def dump_coco_as_pickle(args):
""" Load COCO data, and then save it as pickled file.
Notes:
label file of COCO contains a json which consists
of label info for each sample
"""
samples = args.samples
save_dir = args.save_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
anno_path = args.annotation
roidb, cat2id = loader.load(anno_path, samples, with_cat2id=True)
samples = len(roidb)
dsname = os.path.basename(anno_path).rstrip('.json')
roidb_fname = save_dir + "/%s.roidb" % (dsname)
with open(roidb_fname, "wb") as fout:
pkl.dump((roidb, cat2id), fout)
#for rec in roidb:
# sys.stderr.write('%s\n' % (rec['im_file']))
logging.info('dumped %d samples to file[%s]' % (samples, roidb_fname))
def dump_voc_as_pickle(args):
""" Load VOC data, and then save it as pickled file.
Notes:
we assume label file of VOC contains lines
each of which corresponds to a xml file
that contains it's label info
"""
samples = args.samples
save_dir = args.save_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_dir = args.save_dir
anno_path = os.path.expanduser(args.annotation)
roidb, cat2id = loader.load(
anno_path, samples, with_cat2id=True, use_default_label=None)
samples = len(roidb)
part = anno_path.split('/')
dsname = part[-4]
roidb_fname = save_dir + "/%s.roidb" % (dsname)
with open(roidb_fname, "wb") as fout:
pkl.dump((roidb, cat2id), fout)
anno_path = os.path.join(anno_path.split('/train.txt')[0], 'label_list.txt')
with open(anno_path, 'w') as fw:
for key in cat2id.keys():
fw.write(key + '\n')
logging.info('dumped %d samples to file[%s]' % (samples, roidb_fname))
if __name__ == "__main__":
""" Make sure you have already downloaded original COCO or VOC data,
then you can convert it using this tool.
Usage:
python generate_data_for_training.py --type=json
--annotation=./annotations/instances_val2017.json
--save-dir=./roidb --samples=100
"""
args = parse_args()
# VOC data are organized in xml files
if args.type == 'xml':
dump_voc_as_pickle(args)
# COCO data are organized in json file
elif args.type == 'json':
dump_coco_as_pickle(args)
else:
TypeError('Can\'t deal with {} type. '\
'Only xml or json file format supported'.format(args.type))
ppdet/data/transform/__init__.py
浏览文件 @ 8192c758
...@@ -12,132 +12,11 @@ ...@@ -12,132 +12,11 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from __future__ import absolute_import from . import operators
from __future__ import print_function from . import batch_operators
import copy from .operators import *
import logging from .batch_operators import *
import traceback
from .transformer import MappedDataset, BatchedDataset
from .post_map import build_post_map
from .parallel_map import ParallelMappedDataset
from .operators import BaseOperator, registered_ops
__all__ = ['build_mapper', 'map', 'batch', 'batch_map']
logger = logging.getLogger(__name__)
def build_mapper(ops, context=None):
"""
Build a mapper for operators in 'ops'
Args:
ops (list of operator.BaseOperator or list of op dict):
configs for oprators, eg:
[{'name': 'DecodeImage', 'params': {'to_rgb': True}}, {xxx}]
context (dict): a context object for mapper
Returns:
a mapper function which accept one argument 'sample' and
return the processed result
"""
new_ops = []
for _dict in ops:
new_dict = {}
for i, j in _dict.items():
new_dict[i.lower()] = j
new_ops.append(new_dict)
ops = new_ops
op_funcs = []
op_repr = []
for op in ops:
if type(op) is dict and 'op' in op:
op_func = getattr(BaseOperator, op['op'])
params = copy.deepcopy(op)
del params['op']
o = op_func(**params)
elif not isinstance(op, BaseOperator):
op_func = getattr(BaseOperator, op['name'])
params = {} if 'params' not in op else op['params']
o = op_func(**params)
else:
assert isinstance(op, BaseOperator), \
"invalid operator when build ops"
o = op
op_funcs.append(o)
op_repr.append('{{{}}}'.format(str(o)))
op_repr = '[{}]'.format(','.join(op_repr))
def _mapper(sample):
ctx = {} if context is None else copy.deepcopy(context)
for f in op_funcs:
try:
out = f(sample, ctx)
sample = out
except Exception as e:
stack_info = traceback.format_exc()
logger.warn("fail to map op [{}] with error: {} and stack:\n{}".
format(f, e, str(stack_info)))
raise e
return out
_mapper.ops = op_repr
return _mapper
def map(ds, mapper, worker_args=None):
"""
Apply 'mapper' to 'ds'
Args:
ds (instance of Dataset): dataset to be mapped
mapper (function): action to be executed for every data sample
worker_args (dict): configs for concurrent mapper
Returns:
a mapped dataset
"""
if worker_args is not None:
return ParallelMappedDataset(ds, mapper, worker_args)
else:
return MappedDataset(ds, mapper)
def batch(ds, batchsize, drop_last=False, drop_empty=True):
"""
Batch data samples to batches
Args:
batchsize (int): number of samples for a batch
drop_last (bool): drop last few samples if not enough for a batch
Returns:
a batched dataset
"""
return BatchedDataset(
ds, batchsize, drop_last=drop_last, drop_empty=drop_empty)
def batch_map(ds, config):
"""
Post process the batches.
Args:
ds (instance of Dataset): dataset to be mapped
mapper (function): action to be executed for every batch
Returns:
a batched dataset which is processed
"""
mapper = build_post_map(**config)
return MappedDataset(ds, mapper)
for nm in registered_ops:
op = getattr(BaseOperator, nm)
locals()[nm] = op
__all__ = []
__all__ += registered_ops __all__ += registered_ops
ppdet/data/transform/arrange_sample.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# function:
# operators to process sample,
# eg: decode/resize/crop image
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import numpy as np
from .operators import BaseOperator, register_op
logger = logging.getLogger(__name__)
@register_op
class ArrangeRCNN(BaseOperator):
"""
Transform dict to tuple format needed for training.
Args:
is_mask (bool): whether to use include mask data
"""
def __init__(self, is_mask=False):
super(ArrangeRCNN, self).__init__()
self.is_mask = is_mask
assert isinstance(self.is_mask, bool), "wrong type for is_mask"
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing following items
(image, im_info, im_id, gt_bbox, gt_class, is_crowd, gt_masks)
"""
im = sample['image']
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
keys = list(sample.keys())
if 'is_crowd' in keys:
is_crowd = sample['is_crowd']
else:
raise KeyError("The dataset doesn't have 'is_crowd' key.")
if 'im_info' in keys:
im_info = sample['im_info']
else:
raise KeyError("The dataset doesn't have 'im_info' key.")
im_id = sample['im_id']
outs = (im, im_info, im_id, gt_bbox, gt_class, is_crowd)
gt_masks = []
if self.is_mask and len(sample['gt_poly']) != 0 \
and 'is_crowd' in keys:
valid = True
segms = sample['gt_poly']
assert len(segms) == is_crowd.shape[0]
for i in range(len(sample['gt_poly'])):
segm, iscrowd = segms[i], is_crowd[i]
gt_segm = []
if iscrowd:
gt_segm.append([[0, 0]])
else:
for poly in segm:
if len(poly) == 0:
valid = False
break
gt_segm.append(np.array(poly).reshape(-1, 2))
if (not valid) or len(gt_segm) == 0:
break
gt_masks.append(gt_segm)
outs = outs + (gt_masks, )
return outs
@register_op
class ArrangeEvalRCNN(BaseOperator):
"""
Transform dict to the tuple format needed for evaluation.
"""
def __init__(self):
super(ArrangeEvalRCNN, self).__init__()
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items:
(image, im_info, im_id, im_shape, gt_bbox,
gt_class, difficult)
"""
ims = []
keys = sorted(list(sample.keys()))
for k in keys:
if 'image' in k:
ims.append(sample[k])
if 'im_info' in keys:
im_info = sample['im_info']
else:
raise KeyError("The dataset doesn't have 'im_info' key.")
im_id = sample['im_id']
h = sample['h']
w = sample['w']
# For rcnn models in eval and infer stage, original image size
# is needed to clip the bounding boxes. And box clip op in
# bbox prediction needs im_info as input in format of [N, 3],
# so im_shape is appended by 1 to match dimension.
im_shape = np.array((h, w, 1), dtype=np.float32)
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
difficult = sample['difficult']
remain_list = [im_info, im_id, im_shape, gt_bbox, gt_class, difficult]
ims.extend(remain_list)
outs = tuple(ims)
return outs
@register_op
class ArrangeTestRCNN(BaseOperator):
"""
Transform dict to the tuple format needed for training.
"""
def __init__(self):
super(ArrangeTestRCNN, self).__init__()
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items:
(image, im_info, im_id, im_shape)
"""
ims = []
keys = sorted(list(sample.keys()))
for k in keys:
if 'image' in k:
ims.append(sample[k])
if 'im_info' in keys:
im_info = sample['im_info']
else:
raise KeyError("The dataset doesn't have 'im_info' key.")
im_id = sample['im_id']
h = sample['h']
w = sample['w']
# For rcnn models in eval and infer stage, original image size
# is needed to clip the bounding boxes. And box clip op in
# bbox prediction needs im_info as input in format of [N, 3],
# so im_shape is appended by 1 to match dimension.
im_shape = np.array((h, w, 1), dtype=np.float32)
remain_list = [im_info, im_id, im_shape]
ims.extend(remain_list)
outs = tuple(ims)
return outs
@register_op
class ArrangeSSD(BaseOperator):
"""
Transform dict to tuple format needed for training.
"""
def __init__(self):
super(ArrangeSSD, self).__init__()
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items:
(image, gt_bbox, gt_class, difficult)
"""
im = sample['image']
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
outs = (im, gt_bbox, gt_class)
return outs
@register_op
class ArrangeEvalSSD(BaseOperator):
"""
Transform dict to tuple format needed for training.
"""
def __init__(self, fields):
super(ArrangeEvalSSD, self).__init__()
self.fields = fields
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items: (image)
"""
outs = []
if len(sample['gt_bbox']) != len(sample['gt_class']):
raise ValueError("gt num mismatch: bbox and class.")
for field in self.fields:
if field == 'im_shape':
h = sample['h']
w = sample['w']
im_shape = np.array((h, w))
outs.append(im_shape)
elif field == 'is_difficult':
outs.append(sample['difficult'])
elif field == 'gt_box':
outs.append(sample['gt_bbox'])
elif field == 'gt_label':
outs.append(sample['gt_class'])
else:
outs.append(sample[field])
outs = tuple(outs)
return outs
@register_op
class ArrangeTestSSD(BaseOperator):
"""
Transform dict to tuple format needed for training.
Args:
is_mask (bool): whether to use include mask data
"""
def __init__(self):
super(ArrangeTestSSD, self).__init__()
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items: (image)
"""
im = sample['image']
im_id = sample['im_id']
h = sample['h']
w = sample['w']
im_shape = np.array((h, w))
outs = (im, im_id, im_shape)
return outs
@register_op
class ArrangeYOLO(BaseOperator):
"""
Transform dict to the tuple format needed for training.
"""
def __init__(self):
super(ArrangeYOLO, self).__init__()
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items:
(image, gt_bbox, gt_class, gt_score,
is_crowd, im_info, gt_masks)
"""
im = sample['image']
if len(sample['gt_bbox']) != len(sample['gt_class']):
raise ValueError("gt num mismatch: bbox and class.")
if len(sample['gt_bbox']) != len(sample['gt_score']):
raise ValueError("gt num mismatch: bbox and score.")
gt_bbox = np.zeros((50, 4), dtype=im.dtype)
gt_class = np.zeros((50, ), dtype=np.int32)
gt_score = np.zeros((50, ), dtype=im.dtype)
gt_num = min(50, len(sample['gt_bbox']))
if gt_num > 0:
gt_bbox[:gt_num, :] = sample['gt_bbox'][:gt_num, :]
gt_class[:gt_num] = sample['gt_class'][:gt_num, 0]
gt_score[:gt_num] = sample['gt_score'][:gt_num, 0]
# parse [x1, y1, x2, y2] to [x, y, w, h]
gt_bbox[:, 2:4] = gt_bbox[:, 2:4] - gt_bbox[:, :2]
gt_bbox[:, :2] = gt_bbox[:, :2] + gt_bbox[:, 2:4] / 2.
outs = (im, gt_bbox, gt_class, gt_score)
return outs
@register_op
class ArrangeEvalYOLO(BaseOperator):
"""
Transform dict to the tuple format needed for evaluation.
"""
def __init__(self):
super(ArrangeEvalYOLO, self).__init__()
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items:
(image, im_shape, im_id, gt_bbox, gt_class,
difficult)
"""
im = sample['image']
if len(sample['gt_bbox']) != len(sample['gt_class']):
raise ValueError("gt num mismatch: bbox and class.")
im_id = sample['im_id']
h = sample['h']
w = sample['w']
im_shape = np.array((h, w))
gt_bbox = np.zeros((50, 4), dtype=im.dtype)
gt_class = np.zeros((50, ), dtype=np.int32)
difficult = np.zeros((50, ), dtype=np.int32)
gt_num = min(50, len(sample['gt_bbox']))
if gt_num > 0:
gt_bbox[:gt_num, :] = sample['gt_bbox'][:gt_num, :]
gt_class[:gt_num] = sample['gt_class'][:gt_num, 0]
difficult[:gt_num] = sample['difficult'][:gt_num, 0]
outs = (im, im_shape, im_id, gt_bbox, gt_class, difficult)
return outs
@register_op
class ArrangeTestYOLO(BaseOperator):
"""
Transform dict to the tuple format needed for inference.
"""
def __init__(self):
super(ArrangeTestYOLO, self).__init__()
def __call__(self, sample, context=None):
"""
Args:
sample: a dict which contains image
info and annotation info.
context: a dict which contains additional info.
Returns:
sample: a tuple containing the following items:
(image, gt_bbox, gt_class, gt_score, is_crowd,
im_info, gt_masks)
"""
im = sample['image']
im_id = sample['im_id']
h = sample['h']
w = sample['w']
im_shape = np.array((h, w))
outs = (im, im_shape, im_id)
return outs
ppdet/data/transform/batch_operators.py 0 → 100644
浏览文件 @ 8192c758
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
import logging
import cv2
import numpy as np
from .operators import register_op, BaseOperator
logger = logging.getLogger(__name__)
@register_op
class PadBatch(BaseOperator):
"""
Pad a batch of samples so they can be divisible by a stride.
The layout of each image should be 'CHW'.
Args:
pad_to_stride (int): If `pad_to_stride > 0`, pad zeros to ensure
height and width is divisible by `pad_to_stride`.
"""
def __init__(self, pad_to_stride=0, use_padded_im_info=True):
super(PadBatch, self).__init__()
self.pad_to_stride = pad_to_stride
self.use_padded_im_info = use_padded_im_info
def __call__(self, samples, context=None):
"""
Args:
samples (list): a batch of sample, each is dict.
"""
coarsest_stride = self.pad_to_stride
if coarsest_stride == 0:
return samples
max_shape = np.array([data['image'].shape for data in samples]).max(
axis=0)
if coarsest_stride > 0:
max_shape[1] = int(
np.ceil(max_shape[1] / coarsest_stride) * coarsest_stride)
max_shape[2] = int(
np.ceil(max_shape[2] / coarsest_stride) * coarsest_stride)
padding_batch = []
for data in samples:
im = data['image']
im_c, im_h, im_w = im.shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
data['image'] = padding_im
if self.use_padded_im_info:
data['im_info'][:2] = max_shape[1:3]
return samples
@register_op
class RandomShape(BaseOperator):
"""
Randomly reshape a batch. If random_inter is True, also randomly
select one an interpolation algorithm [cv2.INTER_NEAREST, cv2.INTER_LINEAR,
cv2.INTER_AREA, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4]. If random_inter is
False, use cv2.INTER_NEAREST.
Args:
sizes (list): list of int, random choose a size from these
random_inter (bool): whether to randomly interpolation, defalut true.
"""
def __init__(self, sizes=[], random_inter=False):
super(RandomShape, self).__init__()
self.sizes = sizes
self.random_inter = random_inter
self.interps = [
cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_AREA,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4,
] if random_inter else []
def __call__(self, samples, context=None):
shape = np.random.choice(self.sizes)
method = np.random.choice(self.interps) if self.random_inter \
else cv2.INTER_NEAREST
for i in range(len(samples)):
im = samples[i]['image']
h, w = im.shape[:2]
scale_x = float(shape) / w
scale_y = float(shape) / h
im = cv2.resize(
im, None, None, fx=scale_x, fy=scale_y, interpolation=method)
samples[i]['image'] = im
return samples
@register_op
class PadMultiScaleTest(BaseOperator):
"""
Pad the image so they can be divisible by a stride for multi-scale testing.
Args:
pad_to_stride (int): If `pad_to_stride > 0`, pad zeros to ensure
height and width is divisible by `pad_to_stride`.
"""
def __init__(self, pad_to_stride=0):
super(PadMultiScaleTest, self).__init__()
self.pad_to_stride = pad_to_stride
def __call__(self, samples, context=None):
coarsest_stride = self.pad_to_stride
if coarsest_stride == 0:
return samples
batch_input = True
if not isinstance(samples, Sequence):
batch_input = False
samples = [samples]
if len(samples) != 1:
raise ValueError("Batch size must be 1 when using multiscale test, "
"but now batch size is {}".format(len(samples)))
for i in range(len(samples)):
sample = samples[i]
for k in sample.keys():
# hard code
if k.startswith('image'):
im = sample[k]
im_c, im_h, im_w = im.shape
max_h = int(
np.ceil(im_h / coarsest_stride) * coarsest_stride)
max_w = int(
np.ceil(im_w / coarsest_stride) * coarsest_stride)
padding_im = np.zeros(
(im_c, max_h, max_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
sample[k] = padding_im
info_name = 'im_info' if k == 'image' else 'im_info_' + k
# update im_info
sample[info_name][:2] = [max_h, max_w]
if not batch_input:
samples = samples[0]
return samples
ppdet/data/transform/operators.py
浏览文件 @ 8192c758
...@@ -32,6 +32,7 @@ import logging ...@@ -32,6 +32,7 @@ import logging
import random import random
import math import math
import numpy as np import numpy as np
import cv2 import cv2
from PIL import Image, ImageEnhance from PIL import Image, ImageEnhance
...@@ -182,10 +183,10 @@ class MultiscaleTestResize(BaseOperator): ...@@ -182,10 +183,10 @@ class MultiscaleTestResize(BaseOperator):
base_name_list = ['image'] base_name_list = ['image']
origin_ims['image'] = im origin_ims['image'] = im
if self.use_flip: if self.use_flip:
sample['flip_image'] = im[:, ::-1, :] sample['image_flip'] = im[:, ::-1, :]
base_name_list.append('flip_image') base_name_list.append('image_flip')
origin_ims['flip_image'] = sample['flip_image'] origin_ims['image_flip'] = sample['image_flip']
im_info = []
for base_name in base_name_list: for base_name in base_name_list:
im_scale = float(self.origin_target_size) / float(im_size_min) im_scale = float(self.origin_target_size) / float(im_size_min)
# Prevent the biggest axis from being more than max_size # Prevent the biggest axis from being more than max_size
...@@ -203,8 +204,12 @@ class MultiscaleTestResize(BaseOperator): ...@@ -203,8 +204,12 @@ class MultiscaleTestResize(BaseOperator):
fx=im_scale_x, fx=im_scale_x,
fy=im_scale_y, fy=im_scale_y,
interpolation=self.interp) interpolation=self.interp)
im_info.extend([resize_h, resize_w, im_scale])
sample[base_name] = im_resize sample[base_name] = im_resize
info_name = 'im_info' if base_name == 'image' else 'im_info_image_flip'
sample[base_name] = im_resize
sample[info_name] = np.array(
[resize_h, resize_w, im_scale], dtype=np.float32)
for i, size in enumerate(self.target_size): for i, size in enumerate(self.target_size):
im_scale = float(size) / float(im_size_min) im_scale = float(size) / float(im_size_min)
if np.round(im_scale * im_size_max) > self.max_size: if np.round(im_scale * im_size_max) > self.max_size:
...@@ -220,10 +225,15 @@ class MultiscaleTestResize(BaseOperator): ...@@ -220,10 +225,15 @@ class MultiscaleTestResize(BaseOperator):
fx=im_scale_x, fx=im_scale_x,
fy=im_scale_y, fy=im_scale_y,
interpolation=self.interp) interpolation=self.interp)
im_info.extend([resize_h, resize_w, im_scale])
im_info = [resize_h, resize_w, im_scale]
# hard-code here, must be consistent with
# ppdet/modeling/architectures/input_helper.py
name = base_name + '_scale_' + str(i) name = base_name + '_scale_' + str(i)
info_name = 'im_info_' + name
sample[name] = im_resize sample[name] = im_resize
sample['im_info'] = np.array(im_info, dtype=np.float32) sample[info_name] = np.array(
[resize_h, resize_w, im_scale], dtype=np.float32)
return sample return sample
...@@ -315,6 +325,7 @@ class ResizeImage(BaseOperator): ...@@ -315,6 +325,7 @@ class ResizeImage(BaseOperator):
raise TypeError( raise TypeError(
'If you set max_size to cap the maximum size of image,' 'If you set max_size to cap the maximum size of image,'
'please set use_cv2 to True to resize the image.') 'please set use_cv2 to True to resize the image.')
im = im.astype('uint8')
im = Image.fromarray(im) im = Image.fromarray(im)
im = im.resize((int(resize_w), int(resize_h)), self.interp) im = im.resize((int(resize_w), int(resize_h)), self.interp)
im = np.array(im) im = np.array(im)
...@@ -383,34 +394,44 @@ class RandomFlipImage(BaseOperator): ...@@ -383,34 +394,44 @@ class RandomFlipImage(BaseOperator):
sample: the image, bounding box and segmentation part sample: the image, bounding box and segmentation part
in sample are flipped. in sample are flipped.
""" """
gt_bbox = sample['gt_bbox']
im = sample['image'] samples = sample
if not isinstance(im, np.ndarray): batch_input = True
raise TypeError("{}: image is not a numpy array.".format(self)) if not isinstance(samples, Sequence):
if len(im.shape) != 3: batch_input = False
raise ImageError("{}: image is not 3-dimensional.".format(self)) samples = [samples]
height, width, _ = im.shape for sample in samples:
if np.random.uniform(0, 1) < self.prob: gt_bbox = sample['gt_bbox']
im = im[:, ::-1, :] im = sample['image']
if gt_bbox.shape[0] == 0: if not isinstance(im, np.ndarray):
return sample raise TypeError("{}: image is not a numpy array.".format(self))
oldx1 = gt_bbox[:, 0].copy() if len(im.shape) != 3:
oldx2 = gt_bbox[:, 2].copy() raise ImageError("{}: image is not 3-dimensional.".format(self))
if self.is_normalized: height, width, _ = im.shape
gt_bbox[:, 0] = 1 - oldx2 if np.random.uniform(0, 1) < self.prob:
gt_bbox[:, 2] = 1 - oldx1 im = im[:, ::-1, :]
else: if gt_bbox.shape[0] == 0:
gt_bbox[:, 0] = width - oldx2 - 1 return sample
gt_bbox[:, 2] = width - oldx1 - 1 oldx1 = gt_bbox[:, 0].copy()
if gt_bbox.shape[0] != 0 and (gt_bbox[:, 2] < gt_bbox[:, 0]).all(): oldx2 = gt_bbox[:, 2].copy()
m = "{}: invalid box, x2 should be greater than x1".format(self) if self.is_normalized:
raise BboxError(m) gt_bbox[:, 0] = 1 - oldx2
sample['gt_bbox'] = gt_bbox gt_bbox[:, 2] = 1 - oldx1
if self.is_mask_flip and len(sample['gt_poly']) != 0: else:
sample['gt_poly'] = self.flip_segms(sample['gt_poly'], height, gt_bbox[:, 0] = width - oldx2 - 1
width) gt_bbox[:, 2] = width - oldx1 - 1
sample['flipped'] = True if gt_bbox.shape[0] != 0 and (
sample['image'] = im gt_bbox[:, 2] < gt_bbox[:, 0]).all():
m = "{}: invalid box, x2 should be greater than x1".format(
self)
raise BboxError(m)
sample['gt_bbox'] = gt_bbox
if self.is_mask_flip and len(sample['gt_poly']) != 0:
sample['gt_poly'] = self.flip_segms(sample['gt_poly'],
height, width)
sample['flipped'] = True
sample['image'] = im
sample = samples if batch_input else samples[0]
return sample return sample
...@@ -444,22 +465,31 @@ class NormalizeImage(BaseOperator): ...@@ -444,22 +465,31 @@ class NormalizeImage(BaseOperator):
1.(optional) Scale the image to [0,1] 1.(optional) Scale the image to [0,1]
2. Each pixel minus mean and is divided by std 2. Each pixel minus mean and is divided by std
""" """
for k in sample.keys(): samples = sample
if 'image' in k: batch_input = True
im = sample[k] if not isinstance(samples, Sequence):
im = im.astype(np.float32, copy=False) batch_input = False
if self.is_channel_first: samples = [samples]
mean = np.array(self.mean)[:, np.newaxis, np.newaxis] for sample in samples:
std = np.array(self.std)[:, np.newaxis, np.newaxis] for k in sample.keys():
else: # hard code
mean = np.array(self.mean)[np.newaxis, np.newaxis, :] if k.startswith('image'):
std = np.array(self.std)[np.newaxis, np.newaxis, :] im = sample[k]
if self.is_scale: im = im.astype(np.float32, copy=False)
im = im / 255.0 if self.is_channel_first:
im -= mean mean = np.array(self.mean)[:, np.newaxis, np.newaxis]
im /= std std = np.array(self.std)[:, np.newaxis, np.newaxis]
sample[k] = im else:
return sample mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
if self.is_scale:
im = im / 255.0
im -= mean
im /= std
sample[k] = im
if not batch_input:
samples = samples[0]
return samples
@register_op @register_op
...@@ -899,17 +929,26 @@ class Permute(BaseOperator): ...@@ -899,17 +929,26 @@ class Permute(BaseOperator):
raise TypeError("{}: input type is invalid.".format(self)) raise TypeError("{}: input type is invalid.".format(self))
def __call__(self, sample, context=None): def __call__(self, sample, context=None):
assert 'image' in sample, "image data not found" samples = sample
for k in sample.keys(): batch_input = True
if 'image' in k: if not isinstance(samples, Sequence):
im = sample[k] batch_input = False
if self.channel_first: samples = [samples]
im = np.swapaxes(im, 1, 2) for sample in samples:
im = np.swapaxes(im, 1, 0) assert 'image' in sample, "image data not found"
if self.to_bgr: for k in sample.keys():
im = im[[2, 1, 0], :, :] # hard code
sample[k] = im if k.startswith('image'):
return sample im = sample[k]
if self.channel_first:
im = np.swapaxes(im, 1, 2)
im = np.swapaxes(im, 1, 0)
if self.to_bgr:
im = im[[2, 1, 0], :, :]
sample[k] = im
if not batch_input:
samples = samples[0]
return samples
@register_op @register_op
...@@ -1233,6 +1272,7 @@ class RandomExpand(BaseOperator): ...@@ -1233,6 +1272,7 @@ class RandomExpand(BaseOperator):
sample['image'] = canvas sample['image'] = canvas
if 'gt_bbox' in sample and len(sample['gt_bbox']) > 0: if 'gt_bbox' in sample and len(sample['gt_bbox']) > 0:
sample['gt_bbox'] += np.array([x, y] * 2, dtype=np.float32) sample['gt_bbox'] += np.array([x, y] * 2, dtype=np.float32)
return sample return sample
...@@ -1361,3 +1401,63 @@ class RandomCrop(BaseOperator): ...@@ -1361,3 +1401,63 @@ class RandomCrop(BaseOperator):
def _crop_image(self, img, crop): def _crop_image(self, img, crop):
x1, y1, x2, y2 = crop x1, y1, x2, y2 = crop
return img[y1:y2, x1:x2, :] return img[y1:y2, x1:x2, :]
@register_op
class PadBox(BaseOperator):
def __init__(self, num_max_boxes=50):
"""
Pad zeros to bboxes if number of bboxes is less than num_max_boxes.
Args:
num_max_boxes (int): the max number of bboxes
"""
self.num_max_boxes = num_max_boxes
super(PadBox, self).__init__()
def __call__(self, sample, context=None):
assert 'gt_bbox' in sample
bbox = sample['gt_bbox']
gt_num = min(self.num_max_boxes, len(bbox))
num_max = self.num_max_boxes
fields = context['fields'] if context else []
pad_bbox = np.zeros((num_max, 4), dtype=np.float32)
if gt_num > 0:
pad_bbox[:gt_num, :] = bbox[:gt_num, :]
sample['gt_bbox'] = pad_bbox
if 'gt_class' in fields:
pad_class = np.zeros((num_max), dtype=np.int32)
if gt_num > 0:
pad_class[:gt_num] = sample['gt_class'][:gt_num, 0]
sample['gt_class'] = pad_class
if 'gt_score' in fields:
pad_score = np.zeros((num_max), dtype=np.float32)
if gt_num > 0:
pad_score[:gt_num] = sample['gt_score'][:gt_num, 0]
sample['gt_score'] = pad_score
# in training, for example in op ExpandImage,
# the bbox and gt_class is expandded, but the difficult is not,
# so, judging by it's length
if 'is_difficult' in fields:
pad_diff = np.zeros((num_max), dtype=np.int32)
if gt_num > 0:
pad_diff[:gt_num] = sample['difficult'][:gt_num, 0]
sample['difficult'] = pad_diff
return sample
@register_op
class BboxXYXY2XYWH(BaseOperator):
"""
Convert bbox XYXY format to XYWH format.
"""
def __init__(self):
super(BboxXYXY2XYWH, self).__init__()
def __call__(self, sample, context=None):
assert 'gt_bbox' in sample
bbox = sample['gt_bbox']
bbox[:, 2:4] = bbox[:, 2:4] - bbox[:, :2]
bbox[:, :2] = bbox[:, :2] + bbox[:, 2:4] / 2.
sample['gt_bbox'] = bbox
return sample
ppdet/data/transform/post_map.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import cv2
import numpy as np
logger = logging.getLogger(__name__)
def build_post_map(coarsest_stride=1,
is_padding=False,
random_shapes=[],
multi_scales=[],
use_padded_im_info=False,
enable_multiscale_test=False,
num_scale=1):
"""
Build a mapper for post-processing batches
Args:
config (dict of parameters):
{
coarsest_stride (int): stride of the coarsest FPN level
is_padding (bool): whether to padding in minibatch
random_shapes (list of int): resize to image to random shapes,
[] for not resize.
multi_scales (list of int): resize image by random scales,
[] for not resize.
use_padded_im_info (bool): whether to update im_info after padding
enable_multiscale_test (bool): whether to use multiscale test.
num_scale (int) : the number of scales for multiscale test.
}
Returns:
a mapper function which accept one argument 'batch' and
return the processed result
"""
def padding_minibatch(batch_data):
if len(batch_data) == 1 and coarsest_stride == 1:
return batch_data
max_shape = np.array([data[0].shape for data in batch_data]).max(axis=0)
if coarsest_stride > 1:
max_shape[1] = int(
np.ceil(max_shape[1] / coarsest_stride) * coarsest_stride)
max_shape[2] = int(
np.ceil(max_shape[2] / coarsest_stride) * coarsest_stride)
padding_batch = []
for data in batch_data:
im_c, im_h, im_w = data[0].shape[:]
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = data[0]
if use_padded_im_info:
data[1][:2] = max_shape[1:3]
padding_batch.append((padding_im, ) + data[1:])
return padding_batch
def padding_multiscale_test(batch_data):
if len(batch_data) != 1:
raise NotImplementedError(
"Batch size must be 1 when using multiscale test, but now batch size is {}".
format(len(batch_data)))
if coarsest_stride > 1:
padding_batch = []
padding_images = []
data = batch_data[0]
for i, input in enumerate(data):
if i < num_scale:
im_c, im_h, im_w = input.shape
max_h = int(
np.ceil(im_h / coarsest_stride) * coarsest_stride)
max_w = int(
np.ceil(im_w / coarsest_stride) * coarsest_stride)
padding_im = np.zeros(
(im_c, max_h, max_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = input
data[num_scale][3 * i:3 * i + 2] = [max_h, max_w]
padding_batch.append(padding_im)
else:
padding_batch.append(input)
return [tuple(padding_batch)]
# no need to padding
return batch_data
def random_shape(batch_data):
# For YOLO: gt_bbox is normalized, is scale invariant.
shape = np.random.choice(random_shapes)
scaled_batch = []
h, w = batch_data[0][0].shape[1:3]
scale_x = float(shape) / w
scale_y = float(shape) / h
for data in batch_data:
im = cv2.resize(
data[0].transpose((1, 2, 0)),
None,
None,
fx=scale_x,
fy=scale_y,
interpolation=cv2.INTER_NEAREST)
scaled_batch.append((im.transpose(2, 0, 1), ) + data[1:])
return scaled_batch
def multi_scale_resize(batch_data):
# For RCNN: image shape in record in im_info.
scale = np.random.choice(multi_scales)
scaled_batch = []
for data in batch_data:
im = cv2.resize(
data[0].transpose((1, 2, 0)),
None,
None,
fx=scale,
fy=scale,
interpolation=cv2.INTER_NEAREST)
im_info = [im.shape[:2], scale]
scaled_batch.append((im.transpose(2, 0, 1), im_info) + data[2:])
return scaled_batch
def _mapper(batch_data):
try:
if is_padding:
batch_data = padding_minibatch(batch_data)
if len(random_shapes) > 0:
batch_data = random_shape(batch_data)
if len(multi_scales) > 0:
batch_data = multi_scale_resize(batch_data)
if enable_multiscale_test:
batch_data = padding_multiscale_test(batch_data)
except Exception as e:
errmsg = "post-process failed with error: " + str(e)
logger.warn(errmsg)
raise e
return batch_data
return _mapper
ppdet/data/transform/transformer.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import functools
import collections
from ..dataset import Dataset
class ProxiedDataset(Dataset):
"""proxy method called to 'self._ds' when if not defined"""
def __init__(self, ds):
super(ProxiedDataset, self).__init__()
self._ds = ds
methods = filter(lambda k: not k.startswith('_'),
Dataset.__dict__.keys())
for m in methods:
func = functools.partial(self._proxy_method, getattr(self, m))
setattr(self, m, func)
def _proxy_method(self, func, *args, **kwargs):
"""
proxy call to 'func', if not available then call self._ds.xxx
whose name is the same with func.__name__
"""
method = func.__name__
try:
return func(*args, **kwargs)
except NotImplementedError:
ds_func = getattr(self._ds, method)
return ds_func(*args, **kwargs)
class MappedDataset(ProxiedDataset):
def __init__(self, ds, mapper):
super(MappedDataset, self).__init__(ds)
self._ds = ds
self._mapper = mapper
def next(self):
sample = self._ds.next()
return self._mapper(sample)
class BatchedDataset(ProxiedDataset):
"""
Batching samples
Args:
ds (instance of Dataset): dataset to be batched
batchsize (int): sample number for each batch
drop_last (bool): drop last samples when not enough for one batch
drop_empty (bool): drop samples which have empty field
"""
def __init__(self, ds, batchsize, drop_last=False, drop_empty=True):
super(BatchedDataset, self).__init__(ds)
self._batchsz = batchsize
self._drop_last = drop_last
self._drop_empty = drop_empty
def next(self):
"""proxy to self._ds.next"""
def empty(x):
if isinstance(x, np.ndarray) and x.size == 0:
return True
elif isinstance(x, collections.Sequence) and len(x) == 0:
return True
else:
return False
def has_empty(items):
if any(x is None for x in items):
return True
if any(empty(x) for x in items):
return True
return False
batch = []
for _ in range(self._batchsz):
try:
out = self._ds.next()
while self._drop_empty and has_empty(out):
out = self._ds.next()
batch.append(out)
except StopIteration:
if not self._drop_last and len(batch) > 0:
return batch
else:
raise StopIteration
return batch
ppdet/modeling/architectures/blazeface.py
浏览文件 @ 8192c758
...@@ -17,8 +17,9 @@ from __future__ import division ...@@ -17,8 +17,9 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import numpy as np import numpy as np
from paddle import fluid from collections import OrderedDict
from paddle import fluid
from paddle.fluid.param_attr import ParamAttr from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.regularizer import L2Decay from paddle.fluid.regularizer import L2Decay
...@@ -74,8 +75,8 @@ class BlazeFace(object): ...@@ -74,8 +75,8 @@ class BlazeFace(object):
def build(self, feed_vars, mode='train'): def build(self, feed_vars, mode='train'):
im = feed_vars['image'] im = feed_vars['image']
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
gt_label = feed_vars['gt_label'] gt_class = feed_vars['gt_class']
body_feats = self.backbone(im) body_feats = self.backbone(im)
locs, confs, box, box_var = self._multi_box_head( locs, confs, box, box_var = self._multi_box_head(
...@@ -88,8 +89,8 @@ class BlazeFace(object): ...@@ -88,8 +89,8 @@ class BlazeFace(object):
loss = fluid.layers.ssd_loss( loss = fluid.layers.ssd_loss(
locs, locs,
confs, confs,
gt_box, gt_bbox,
gt_label, gt_class,
box, box,
box_var, box_var,
overlap_threshold=0.35, overlap_threshold=0.35,
...@@ -169,6 +170,38 @@ class BlazeFace(object): ...@@ -169,6 +170,38 @@ class BlazeFace(object):
box_vars = fluid.layers.concat(vars) box_vars = fluid.layers.concat(vars)
return face_mbox_loc, face_mbox_conf, prior_boxes, box_vars return face_mbox_loc, face_mbox_conf, prior_boxes, box_vars
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'im_shape': {'shape': [None, 3], 'dtype': 'int32', 'lod_level': 0},
}
# yapf: enable
return inputs_def
def build_inputs(
self,
image_shape=[3, None, None],
fields=['image', 'im_id', 'gt_bbox', 'gt_class'], # for train
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape)
feed_vars = OrderedDict([(key, fluid.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/cascade_mask_rcnn.py
浏览文件 @ 8192c758
...@@ -17,12 +17,15 @@ from __future__ import division ...@@ -17,12 +17,15 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
from collections import OrderedDict from collections import OrderedDict
import copy
import paddle.fluid as fluid import paddle.fluid as fluid
from ppdet.experimental import mixed_precision_global_state from ppdet.experimental import mixed_precision_global_state
from ppdet.core.workspace import register from ppdet.core.workspace import register
from .input_helper import multiscale_def
__all__ = ['CascadeMaskRCNN'] __all__ = ['CascadeMaskRCNN']
...@@ -82,7 +85,7 @@ class CascadeMaskRCNN(object): ...@@ -82,7 +85,7 @@ class CascadeMaskRCNN(object):
def build(self, feed_vars, mode='train'): def build(self, feed_vars, mode='train'):
if mode == 'train': if mode == 'train':
required_fields = [ required_fields = [
'gt_label', 'gt_box', 'gt_mask', 'is_crowd', 'im_info' 'gt_class', 'gt_bbox', 'gt_mask', 'is_crowd', 'im_info'
] ]
else: else:
required_fields = ['im_shape', 'im_info'] required_fields = ['im_shape', 'im_info']
...@@ -90,7 +93,7 @@ class CascadeMaskRCNN(object): ...@@ -90,7 +93,7 @@ class CascadeMaskRCNN(object):
im = feed_vars['image'] im = feed_vars['image']
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
is_crowd = feed_vars['is_crowd'] is_crowd = feed_vars['is_crowd']
im_info = feed_vars['im_info'] im_info = feed_vars['im_info']
...@@ -116,7 +119,7 @@ class CascadeMaskRCNN(object): ...@@ -116,7 +119,7 @@ class CascadeMaskRCNN(object):
rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)
if mode == 'train': if mode == 'train':
rpn_loss = self.rpn_head.get_loss(im_info, gt_box, is_crowd) rpn_loss = self.rpn_head.get_loss(im_info, gt_bbox, is_crowd)
else: else:
if self.rpn_only: if self.rpn_only:
im_scale = fluid.layers.slice( im_scale = fluid.layers.slice(
...@@ -174,7 +177,7 @@ class CascadeMaskRCNN(object): ...@@ -174,7 +177,7 @@ class CascadeMaskRCNN(object):
mask_rois, roi_has_mask_int32, mask_int32 = self.mask_assigner( mask_rois, roi_has_mask_int32, mask_int32 = self.mask_assigner(
rois=rois, rois=rois,
gt_classes=feed_vars['gt_label'], gt_classes=feed_vars['gt_class'],
is_crowd=feed_vars['is_crowd'], is_crowd=feed_vars['is_crowd'],
gt_segms=feed_vars['gt_mask'], gt_segms=feed_vars['gt_mask'],
im_info=feed_vars['im_info'], im_info=feed_vars['im_info'],
...@@ -204,25 +207,16 @@ class CascadeMaskRCNN(object): ...@@ -204,25 +207,16 @@ class CascadeMaskRCNN(object):
required_fields = ['image', 'im_info'] required_fields = ['image', 'im_info']
self._input_check(required_fields, feed_vars) self._input_check(required_fields, feed_vars)
ims = []
for k in feed_vars.keys():
if 'image' in k:
ims.append(feed_vars[k])
result = {} result = {}
if not mask_branch: if not mask_branch:
assert 'im_shape' in feed_vars, \ assert 'im_shape' in feed_vars, \
"{} has no im_shape field".format(feed_vars) "{} has no im_shape field".format(feed_vars)
result.update(feed_vars) result.update(feed_vars)
for i, im in enumerate(ims): for i in range(len(self.im_info_names) // 2):
im_info = fluid.layers.slice( im = feed_vars[self.im_info_names[2 * i]]
input=feed_vars['im_info'], im_info = feed_vars[self.im_info_names[2 * i + 1]]
axes=[1],
starts=[3 * i],
ends=[3 * i + 3])
body_feats = self.backbone(im) body_feats = self.backbone(im)
result.update(body_feats)
# FPN # FPN
if self.fpn is not None: if self.fpn is not None:
...@@ -282,7 +276,6 @@ class CascadeMaskRCNN(object): ...@@ -282,7 +276,6 @@ class CascadeMaskRCNN(object):
else: else:
mask_name = 'mask_pred_' + str(i) mask_name = 'mask_pred_' + str(i)
bbox_pred = feed_vars['bbox'] bbox_pred = feed_vars['bbox']
result.update({im.name: im})
if 'flip' in im.name: if 'flip' in im.name:
mask_name += '_flip' mask_name += '_flip'
bbox_pred = feed_vars['bbox_flip'] bbox_pred = feed_vars['bbox_flip']
...@@ -372,6 +365,65 @@ class CascadeMaskRCNN(object): ...@@ -372,6 +365,65 @@ class CascadeMaskRCNN(object):
return refined_bbox return refined_bbox
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'gt_mask': {'shape': [None, 2], 'dtype': 'float32', 'lod_level': 3}, # polygon coordinates
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
}
# yapf: enable
return inputs_def
def build_inputs(self,
image_shape=[3, None, None],
fields=[
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class',
'is_crowd', 'gt_mask'
],
multi_scale=False,
num_scales=-1,
use_flip=None,
use_dataloader=True,
iterable=False,
mask_branch=False):
inputs_def = self._inputs_def(image_shape)
fields = copy.deepcopy(fields)
if multi_scale:
ms_def, ms_fields = multiscale_def(image_shape, num_scales,
use_flip)
inputs_def.update(ms_def)
fields += ms_fields
self.im_info_names = ['image', 'im_info'] + ms_fields
if mask_branch:
box_fields = ['bbox', 'bbox_flip'] if use_flip else ['bbox']
for key in box_fields:
inputs_def[key] = {
'shape': [6],
'dtype': 'float32',
'lod_level': 1
}
fields += box_fields
feed_vars = OrderedDict([(key, fluid.layers.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
use_dataloader = use_dataloader and not mask_branch
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/cascade_rcnn.py
浏览文件 @ 8192c758
...@@ -16,12 +16,14 @@ from __future__ import absolute_import ...@@ -16,12 +16,14 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import copy
from collections import OrderedDict from collections import OrderedDict
import paddle.fluid as fluid import paddle.fluid as fluid
from ppdet.experimental import mixed_precision_global_state from ppdet.experimental import mixed_precision_global_state
from ppdet.core.workspace import register from ppdet.core.workspace import register
from .input_helper import multiscale_def
__all__ = ['CascadeRCNN'] __all__ = ['CascadeRCNN']
...@@ -75,7 +77,7 @@ class CascadeRCNN(object): ...@@ -75,7 +77,7 @@ class CascadeRCNN(object):
def build(self, feed_vars, mode='train'): def build(self, feed_vars, mode='train'):
if mode == 'train': if mode == 'train':
required_fields = ['gt_label', 'gt_box', 'is_crowd', 'im_info'] required_fields = ['gt_class', 'gt_bbox', 'is_crowd', 'im_info']
else: else:
required_fields = ['im_shape', 'im_info'] required_fields = ['im_shape', 'im_info']
self._input_check(required_fields, feed_vars) self._input_check(required_fields, feed_vars)
...@@ -84,7 +86,7 @@ class CascadeRCNN(object): ...@@ -84,7 +86,7 @@ class CascadeRCNN(object):
im_info = feed_vars['im_info'] im_info = feed_vars['im_info']
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
is_crowd = feed_vars['is_crowd'] is_crowd = feed_vars['is_crowd']
mixed_precision_enabled = mixed_precision_global_state() is not None mixed_precision_enabled = mixed_precision_global_state() is not None
...@@ -108,7 +110,9 @@ class CascadeRCNN(object): ...@@ -108,7 +110,9 @@ class CascadeRCNN(object):
rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)
if mode == 'train': if mode == 'train':
rpn_loss = self.rpn_head.get_loss(im_info, gt_box, is_crowd) #fluid.layers.Print(gt_bbox)
#fluid.layers.Print(is_crowd)
rpn_loss = self.rpn_head.get_loss(im_info, gt_bbox, is_crowd)
else: else:
if self.rpn_only: if self.rpn_only:
im_scale = fluid.layers.slice( im_scale = fluid.layers.slice(
...@@ -171,19 +175,14 @@ class CascadeRCNN(object): ...@@ -171,19 +175,14 @@ class CascadeRCNN(object):
def build_multi_scale(self, feed_vars): def build_multi_scale(self, feed_vars):
required_fields = ['image', 'im_shape', 'im_info'] required_fields = ['image', 'im_shape', 'im_info']
self._input_check(required_fields, feed_vars) self._input_check(required_fields, feed_vars)
ims = []
for k in feed_vars.keys():
if 'image' in k:
ims.append(feed_vars[k])
result = {} result = {}
result.update(feed_vars) im_shape = feed_vars['im_shape']
for i, im in enumerate(ims): result['im_shape'] = im_shape
im_info = fluid.layers.slice(
input=feed_vars['im_info'], for i in range(len(self.im_info_names) // 2):
axes=[1], im = feed_vars[self.im_info_names[2 * i]]
starts=[3 * i], im_info = feed_vars[self.im_info_names[2 * i + 1]]
ends=[3 * i + 3])
im_shape = feed_vars['im_shape']
# backbone # backbone
body_feats = self.backbone(im) body_feats = self.backbone(im)
...@@ -277,6 +276,54 @@ class CascadeRCNN(object): ...@@ -277,6 +276,54 @@ class CascadeRCNN(object):
return refined_bbox return refined_bbox
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
}
# yapf: enable
return inputs_def
def build_inputs(self,
image_shape=[3, None, None],
fields=[
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class',
'is_crowd'
],
multi_scale=False,
num_scales=-1,
use_flip=None,
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape)
fields = copy.deepcopy(fields)
if multi_scale:
ms_def, ms_fields = multiscale_def(image_shape, num_scales,
use_flip)
inputs_def.update(ms_def)
fields += ms_fields
self.im_info_names = ['image', 'im_info'] + ms_fields
feed_vars = OrderedDict([(key, fluid.layers.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/cascade_rcnn_cls_aware.py
浏览文件 @ 8192c758
...@@ -19,6 +19,9 @@ from __future__ import print_function ...@@ -19,6 +19,9 @@ from __future__ import print_function
import numpy as np import numpy as np
import sys import sys
from collections import OrderedDict
import copy
import paddle.fluid as fluid import paddle.fluid as fluid
from ppdet.core.workspace import register from ppdet.core.workspace import register
...@@ -48,14 +51,14 @@ class CascadeRCNNClsAware(object): ...@@ -48,14 +51,14 @@ class CascadeRCNNClsAware(object):
'bbox_head' 'bbox_head'
] ]
def __init__(self, def __init__(
backbone, self,
rpn_head, backbone,
roi_extractor='FPNRoIAlign', rpn_head,
bbox_head='CascadeBBoxHead', roi_extractor='FPNRoIAlign',
bbox_assigner='CascadeBBoxAssigner', bbox_head='CascadeBBoxHead',
fpn='FPN', bbox_assigner='CascadeBBoxAssigner',
): fpn='FPN', ):
super(CascadeRCNNClsAware, self).__init__() super(CascadeRCNNClsAware, self).__init__()
assert fpn is not None, "cascade RCNN requires FPN" assert fpn is not None, "cascade RCNN requires FPN"
self.backbone = backbone self.backbone = backbone
...@@ -78,9 +81,9 @@ class CascadeRCNNClsAware(object): ...@@ -78,9 +81,9 @@ class CascadeRCNNClsAware(object):
im = feed_vars['image'] im = feed_vars['image']
im_info = feed_vars['im_info'] im_info = feed_vars['im_info']
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
is_crowd = feed_vars['is_crowd'] is_crowd = feed_vars['is_crowd']
gt_label = feed_vars['gt_label'] gt_class = feed_vars['gt_class']
else: else:
im_shape = feed_vars['im_shape'] im_shape = feed_vars['im_shape']
...@@ -95,7 +98,7 @@ class CascadeRCNNClsAware(object): ...@@ -95,7 +98,7 @@ class CascadeRCNNClsAware(object):
rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)
if mode == 'train': if mode == 'train':
rpn_loss = self.rpn_head.get_loss(im_info, gt_box, is_crowd) rpn_loss = self.rpn_head.get_loss(im_info, gt_bbox, is_crowd)
proposal_list = [] proposal_list = []
roi_feat_list = [] roi_feat_list = []
...@@ -103,10 +106,11 @@ class CascadeRCNNClsAware(object): ...@@ -103,10 +106,11 @@ class CascadeRCNNClsAware(object):
rcnn_target_list = [] rcnn_target_list = []
bbox_pred = None bbox_pred = None
self.cascade_var_v = [] self.cascade_var_v = []
for stage in range(3): for stage in range(3):
var_v = np.array(self.cascade_bbox_reg_weights[stage], dtype="float32") var_v = np.array(
self.cascade_bbox_reg_weights[stage], dtype="float32")
prior_box_var = fluid.layers.create_tensor(dtype="float32") prior_box_var = fluid.layers.create_tensor(dtype="float32")
fluid.layers.assign(input=var_v, output=prior_box_var) fluid.layers.assign(input=var_v, output=prior_box_var)
self.cascade_var_v.append(prior_box_var) self.cascade_var_v.append(prior_box_var)
...@@ -124,42 +128,37 @@ class CascadeRCNNClsAware(object): ...@@ -124,42 +128,37 @@ class CascadeRCNNClsAware(object):
outs = self.bbox_assigner( outs = self.bbox_assigner(
input_rois=pool_rois, feed_vars=feed_vars, curr_stage=stage) input_rois=pool_rois, feed_vars=feed_vars, curr_stage=stage)
pool_rois = outs[0] pool_rois = outs[0]
rcnn_target_list.append( outs ) rcnn_target_list.append(outs)
# extract roi features # extract roi features
roi_feat = self.roi_extractor(body_feats, pool_rois, spatial_scale) roi_feat = self.roi_extractor(body_feats, pool_rois, spatial_scale)
roi_feat_list.append(roi_feat) roi_feat_list.append(roi_feat)
# bbox head # bbox head
cls_score, bbox_pred = self.bbox_head.get_output( cls_score, bbox_pred = self.bbox_head.get_output(
roi_feat, roi_feat,
cls_agnostic_bbox_reg=self.bbox_head.num_classes, cls_agnostic_bbox_reg=self.bbox_head.num_classes,
wb_scalar=1.0 / self.cascade_rcnn_loss_weight[stage], wb_scalar=1.0 / self.cascade_rcnn_loss_weight[stage],
name='_' + str(stage + 1) ) name='_' + str(stage + 1))
cls_prob = fluid.layers.softmax(cls_score, use_cudnn=False) cls_prob = fluid.layers.softmax(cls_score, use_cudnn=False)
decoded_box, decoded_assign_box = fluid.layers.box_decoder_and_assign( decoded_box, decoded_assign_box = fluid.layers.box_decoder_and_assign(
pool_rois, pool_rois, self.cascade_var_v[stage], bbox_pred, cls_prob,
self.cascade_var_v[stage], self.bbox_clip)
bbox_pred,
cls_prob,
self.bbox_clip)
if mode == "train": if mode == "train":
decoded_box.stop_gradient = True decoded_box.stop_gradient = True
decoded_assign_box.stop_gradient = True decoded_assign_box.stop_gradient = True
else: else:
self.cascade_cls_prob.append( cls_prob ) self.cascade_cls_prob.append(cls_prob)
self.cascade_decoded_box.append(decoded_box) self.cascade_decoded_box.append(decoded_box)
rcnn_pred_list.append((cls_score, bbox_pred)) rcnn_pred_list.append((cls_score, bbox_pred))
# out loop # out loop
if mode == 'train': if mode == 'train':
loss = self.bbox_head.get_loss(rcnn_pred_list, loss = self.bbox_head.get_loss(rcnn_pred_list, rcnn_target_list,
rcnn_target_list,
self.cascade_rcnn_loss_weight) self.cascade_rcnn_loss_weight)
loss.update(rpn_loss) loss.update(rpn_loss)
total_loss = fluid.layers.sum(list(loss.values())) total_loss = fluid.layers.sum(list(loss.values()))
...@@ -167,12 +166,46 @@ class CascadeRCNNClsAware(object): ...@@ -167,12 +166,46 @@ class CascadeRCNNClsAware(object):
return loss return loss
else: else:
pred = self.bbox_head.get_prediction_cls_aware( pred = self.bbox_head.get_prediction_cls_aware(
im_info, im_shape, im_info, im_shape, self.cascade_cls_prob,
self.cascade_cls_prob, self.cascade_decoded_box, self.cascade_bbox_reg_weights)
self.cascade_decoded_box,
self.cascade_bbox_reg_weights)
return pred return pred
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
}
# yapf: enable
return inputs_def
def build_inputs(self,
image_shape=[3, None, None],
fields=[
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class',
'is_crowd', 'gt_mask'
],
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape)
feed_vars = OrderedDict([(key, fluid.layers.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/faceboxes.py
浏览文件 @ 8192c758
...@@ -17,8 +17,9 @@ from __future__ import division ...@@ -17,8 +17,9 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import numpy as np import numpy as np
from paddle import fluid from collections import OrderedDict
from paddle import fluid
from paddle.fluid.param_attr import ParamAttr from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.regularizer import L2Decay from paddle.fluid.regularizer import L2Decay
...@@ -66,8 +67,8 @@ class FaceBoxes(object): ...@@ -66,8 +67,8 @@ class FaceBoxes(object):
def build(self, feed_vars, mode='train'): def build(self, feed_vars, mode='train'):
im = feed_vars['image'] im = feed_vars['image']
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
gt_label = feed_vars['gt_label'] gt_class = feed_vars['gt_class']
body_feats = self.backbone(im) body_feats = self.backbone(im)
locs, confs, box, box_var = self._multi_box_head( locs, confs, box, box_var = self._multi_box_head(
...@@ -77,8 +78,8 @@ class FaceBoxes(object): ...@@ -77,8 +78,8 @@ class FaceBoxes(object):
loss = fluid.layers.ssd_loss( loss = fluid.layers.ssd_loss(
locs, locs,
confs, confs,
gt_box, gt_bbox,
gt_label, gt_class,
box, box,
box_var, box_var,
overlap_threshold=0.35, overlap_threshold=0.35,
...@@ -141,6 +142,38 @@ class FaceBoxes(object): ...@@ -141,6 +142,38 @@ class FaceBoxes(object):
box_vars = fluid.layers.concat(vars) box_vars = fluid.layers.concat(vars)
return face_mbox_loc, face_mbox_conf, prior_boxes, box_vars return face_mbox_loc, face_mbox_conf, prior_boxes, box_vars
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'im_shape': {'shape': [None, 3], 'dtype': 'int32', 'lod_level': 0},
}
# yapf: enable
return inputs_def
def build_inputs(
self,
image_shape=[3, None, None],
fields=['image', 'im_id', 'gt_bbox', 'gt_class'], # for train
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape)
feed_vars = OrderedDict([(key, fluid.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/faster_rcnn.py
浏览文件 @ 8192c758
...@@ -17,12 +17,15 @@ from __future__ import division ...@@ -17,12 +17,15 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
from collections import OrderedDict from collections import OrderedDict
import copy
from paddle import fluid from paddle import fluid
from ppdet.experimental import mixed_precision_global_state from ppdet.experimental import mixed_precision_global_state
from ppdet.core.workspace import register from ppdet.core.workspace import register
from .input_helper import multiscale_def
__all__ = ['FasterRCNN'] __all__ = ['FasterRCNN']
...@@ -64,7 +67,7 @@ class FasterRCNN(object): ...@@ -64,7 +67,7 @@ class FasterRCNN(object):
def build(self, feed_vars, mode='train'): def build(self, feed_vars, mode='train'):
if mode == 'train': if mode == 'train':
required_fields = ['gt_label', 'gt_box', 'is_crowd', 'im_info'] required_fields = ['gt_class', 'gt_bbox', 'is_crowd', 'im_info']
else: else:
required_fields = ['im_shape', 'im_info'] required_fields = ['im_shape', 'im_info']
self._input_check(required_fields, feed_vars) self._input_check(required_fields, feed_vars)
...@@ -72,7 +75,7 @@ class FasterRCNN(object): ...@@ -72,7 +75,7 @@ class FasterRCNN(object):
im = feed_vars['image'] im = feed_vars['image']
im_info = feed_vars['im_info'] im_info = feed_vars['im_info']
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
is_crowd = feed_vars['is_crowd'] is_crowd = feed_vars['is_crowd']
else: else:
im_shape = feed_vars['im_shape'] im_shape = feed_vars['im_shape']
...@@ -97,15 +100,15 @@ class FasterRCNN(object): ...@@ -97,15 +100,15 @@ class FasterRCNN(object):
rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)
if mode == 'train': if mode == 'train':
rpn_loss = self.rpn_head.get_loss(im_info, gt_box, is_crowd) rpn_loss = self.rpn_head.get_loss(im_info, gt_bbox, is_crowd)
# sampled rpn proposals # sampled rpn proposals
for var in ['gt_label', 'is_crowd', 'gt_box', 'im_info']: for var in ['gt_class', 'is_crowd', 'gt_bbox', 'im_info']:
assert var in feed_vars, "{} has no {}".format(feed_vars, var) assert var in feed_vars, "{} has no {}".format(feed_vars, var)
outs = self.bbox_assigner( outs = self.bbox_assigner(
rpn_rois=rois, rpn_rois=rois,
gt_classes=feed_vars['gt_label'], gt_classes=feed_vars['gt_class'],
is_crowd=feed_vars['is_crowd'], is_crowd=feed_vars['is_crowd'],
gt_boxes=feed_vars['gt_box'], gt_boxes=feed_vars['gt_bbox'],
im_info=feed_vars['im_info']) im_info=feed_vars['im_info'])
rois = outs[0] rois = outs[0]
...@@ -145,21 +148,14 @@ class FasterRCNN(object): ...@@ -145,21 +148,14 @@ class FasterRCNN(object):
def build_multi_scale(self, feed_vars): def build_multi_scale(self, feed_vars):
required_fields = ['image', 'im_info', 'im_shape'] required_fields = ['image', 'im_info', 'im_shape']
self._input_check(required_fields, feed_vars) self._input_check(required_fields, feed_vars)
ims = []
for k in feed_vars.keys():
if 'image' in k:
ims.append(feed_vars[k])
result = {} result = {}
result.update(feed_vars) im_shape = feed_vars['im_shape']
for i, im in enumerate(ims): result['im_shape'] = im_shape
im_info = fluid.layers.slice( for i in range(len(self.im_info_names) // 2):
input=feed_vars['im_info'], im = feed_vars[self.im_info_names[2 * i]]
axes=[1], im_info = feed_vars[self.im_info_names[2 * i + 1]]
starts=[3 * i],
ends=[3 * i + 3])
im_shape = feed_vars['im_shape']
body_feats = self.backbone(im) body_feats = self.backbone(im)
result.update(body_feats)
body_feat_names = list(body_feats.keys()) body_feat_names = list(body_feats.keys())
if self.fpn is not None: if self.fpn is not None:
...@@ -192,6 +188,54 @@ class FasterRCNN(object): ...@@ -192,6 +188,54 @@ class FasterRCNN(object):
assert var in feed_vars, \ assert var in feed_vars, \
"{} has no {} field".format(feed_vars, var) "{} has no {} field".format(feed_vars, var)
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
}
# yapf: enable
return inputs_def
def build_inputs(
self,
image_shape=[3, None, None],
fields=[
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd'
], # for train
multi_scale=False,
num_scales=-1,
use_flip=None,
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape)
fields = copy.deepcopy(fields)
if multi_scale:
ms_def, ms_fields = multiscale_def(image_shape, num_scales,
use_flip)
inputs_def.update(ms_def)
fields += ms_fields
self.im_info_names = ['image', 'im_info'] + ms_fields
feed_vars = OrderedDict([(key, fluid.layers.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/data/tests/run_all_tests.py → ppdet/modeling/architectures/input_helper.py
浏览文件 @ 8192c758
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -11,35 +11,34 @@ ...@@ -11,35 +11,34 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
#!/usr/bin/python
#-*-coding:utf-8-*-
"""Run all tests
"""
import unittest
import test_loader
import test_operator
import test_roidb_source
import test_iterator_source
import test_transformer
import test_reader
if __name__ == '__main__': def multiscale_def(image_shape, num_scale, use_flip=True):
alltests = unittest.TestSuite([ base_name_list = ['image']
unittest.TestLoader().loadTestsFromTestCase(t) \ multiscale_def = {}
for t in [ ms_def_names = []
test_loader.TestLoader, if use_flip:
test_operator.TestBase, num_scale //= 2
test_roidb_source.TestRoiDbSource, base_name_list.append('image_flip')
test_iterator_source.TestIteratorSource, multiscale_def['im_info_image_flip'] = {
test_transformer.TestTransformer, 'shape': [None, 3],
test_reader.TestReader, 'dtype': 'float32',
] 'lod_level': 0
]) }
for base_name in base_name_list:
was_succ = unittest\ for i in range(0, num_scale - 1):
.TextTestRunner(verbosity=2)\ name = base_name + '_scale_' + str(i)
.run(alltests)\ multiscale_def[name] = {
.wasSuccessful() 'shape': [None] + image_shape,
'dtype': 'float32',
exit(0 if was_succ else 1) 'lod_level': 0
}
im_info_name = 'im_info_' + name
multiscale_def[im_info_name] = {
'shape': [None, 3],
'dtype': 'float32',
'lod_level': 0
}
ms_def_names.append(name)
ms_def_names.append(im_info_name)
return multiscale_def, ms_def_names
ppdet/modeling/architectures/mask_rcnn.py
浏览文件 @ 8192c758
...@@ -17,12 +17,15 @@ from __future__ import division ...@@ -17,12 +17,15 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
from collections import OrderedDict from collections import OrderedDict
import copy
import paddle.fluid as fluid import paddle.fluid as fluid
from ppdet.experimental import mixed_precision_global_state from ppdet.experimental import mixed_precision_global_state
from ppdet.core.workspace import register from ppdet.core.workspace import register
from .input_helper import multiscale_def
__all__ = ['MaskRCNN'] __all__ = ['MaskRCNN']
...@@ -71,7 +74,7 @@ class MaskRCNN(object): ...@@ -71,7 +74,7 @@ class MaskRCNN(object):
def build(self, feed_vars, mode='train'): def build(self, feed_vars, mode='train'):
if mode == 'train': if mode == 'train':
required_fields = [ required_fields = [
'gt_label', 'gt_box', 'gt_mask', 'is_crowd', 'im_info' 'gt_class', 'gt_bbox', 'gt_mask', 'is_crowd', 'im_info'
] ]
else: else:
required_fields = ['im_shape', 'im_info'] required_fields = ['im_shape', 'im_info']
...@@ -101,14 +104,14 @@ class MaskRCNN(object): ...@@ -101,14 +104,14 @@ class MaskRCNN(object):
rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)
if mode == 'train': if mode == 'train':
rpn_loss = self.rpn_head.get_loss(im_info, feed_vars['gt_box'], rpn_loss = self.rpn_head.get_loss(im_info, feed_vars['gt_bbox'],
feed_vars['is_crowd']) feed_vars['is_crowd'])
outs = self.bbox_assigner( outs = self.bbox_assigner(
rpn_rois=rois, rpn_rois=rois,
gt_classes=feed_vars['gt_label'], gt_classes=feed_vars['gt_class'],
is_crowd=feed_vars['is_crowd'], is_crowd=feed_vars['is_crowd'],
gt_boxes=feed_vars['gt_box'], gt_boxes=feed_vars['gt_bbox'],
im_info=feed_vars['im_info']) im_info=feed_vars['im_info'])
rois = outs[0] rois = outs[0]
labels_int32 = outs[1] labels_int32 = outs[1]
...@@ -124,7 +127,7 @@ class MaskRCNN(object): ...@@ -124,7 +127,7 @@ class MaskRCNN(object):
mask_rois, roi_has_mask_int32, mask_int32 = self.mask_assigner( mask_rois, roi_has_mask_int32, mask_int32 = self.mask_assigner(
rois=rois, rois=rois,
gt_classes=feed_vars['gt_label'], gt_classes=feed_vars['gt_class'],
is_crowd=feed_vars['is_crowd'], is_crowd=feed_vars['is_crowd'],
gt_segms=feed_vars['gt_mask'], gt_segms=feed_vars['gt_mask'],
im_info=feed_vars['im_info'], im_info=feed_vars['im_info'],
...@@ -160,25 +163,16 @@ class MaskRCNN(object): ...@@ -160,25 +163,16 @@ class MaskRCNN(object):
required_fields = ['image', 'im_info'] required_fields = ['image', 'im_info']
self._input_check(required_fields, feed_vars) self._input_check(required_fields, feed_vars)
ims = []
for k in feed_vars.keys():
if 'image' in k:
ims.append(feed_vars[k])
result = {} result = {}
if not mask_branch: if not mask_branch:
assert 'im_shape' in feed_vars, \ assert 'im_shape' in feed_vars, \
"{} has no im_shape field".format(feed_vars) "{} has no im_shape field".format(feed_vars)
result.update(feed_vars) result.update(feed_vars)
for i, im in enumerate(ims): for i in range(len(self.im_info_names) // 2):
im_info = fluid.layers.slice( im = feed_vars[self.im_info_names[2 * i]]
input=feed_vars['im_info'], im_info = feed_vars[self.im_info_names[2 * i + 1]]
axes=[1],
starts=[3 * i],
ends=[3 * i + 3])
body_feats = self.backbone(im) body_feats = self.backbone(im)
result.update(body_feats)
# FPN # FPN
if self.fpn is not None: if self.fpn is not None:
...@@ -205,7 +199,7 @@ class MaskRCNN(object): ...@@ -205,7 +199,7 @@ class MaskRCNN(object):
else: else:
mask_name = 'mask_pred_' + str(i) mask_name = 'mask_pred_' + str(i)
bbox_pred = feed_vars['bbox'] bbox_pred = feed_vars['bbox']
result.update({im.name: im}) #result.update({im.name: im})
if 'flip' in im.name: if 'flip' in im.name:
mask_name += '_flip' mask_name += '_flip'
bbox_pred = feed_vars['bbox_flip'] bbox_pred = feed_vars['bbox_flip']
...@@ -223,12 +217,12 @@ class MaskRCNN(object): ...@@ -223,12 +217,12 @@ class MaskRCNN(object):
im_shape, im_shape,
spatial_scale, spatial_scale,
bbox_pred=None): bbox_pred=None):
if self.fpn is None:
last_feat = body_feats[list(body_feats.keys())[-1]]
roi_feat = self.roi_extractor(last_feat, rois)
else:
roi_feat = self.roi_extractor(body_feats, rois, spatial_scale)
if not bbox_pred: if not bbox_pred:
if self.fpn is None:
last_feat = body_feats[list(body_feats.keys())[-1]]
roi_feat = self.roi_extractor(last_feat, rois)
else:
roi_feat = self.roi_extractor(body_feats, rois, spatial_scale)
bbox_pred = self.bbox_head.get_prediction(roi_feat, rois, im_info, bbox_pred = self.bbox_head.get_prediction(roi_feat, rois, im_info,
im_shape) im_shape)
bbox_pred = bbox_pred['bbox'] bbox_pred = bbox_pred['bbox']
...@@ -258,6 +252,7 @@ class MaskRCNN(object): ...@@ -258,6 +252,7 @@ class MaskRCNN(object):
mask_rois = bbox * im_scale mask_rois = bbox * im_scale
if self.fpn is None: if self.fpn is None:
last_feat = body_feats[list(body_feats.keys())[-1]]
mask_feat = self.roi_extractor(last_feat, mask_rois) mask_feat = self.roi_extractor(last_feat, mask_rois)
mask_feat = self.bbox_head.get_head_feat(mask_feat) mask_feat = self.bbox_head.get_head_feat(mask_feat)
else: else:
...@@ -273,6 +268,65 @@ class MaskRCNN(object): ...@@ -273,6 +268,65 @@ class MaskRCNN(object):
assert var in feed_vars, \ assert var in feed_vars, \
"{} has no {} field".format(feed_vars, var) "{} has no {} field".format(feed_vars, var)
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'gt_mask': {'shape': [None, 2], 'dtype': 'float32', 'lod_level': 3}, # polygon coordinates
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
}
# yapf: enable
return inputs_def
def build_inputs(self,
image_shape=[3, None, None],
fields=[
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class',
'is_crowd', 'gt_mask'
],
multi_scale=False,
num_scales=-1,
use_flip=None,
use_dataloader=True,
iterable=False,
mask_branch=False):
inputs_def = self._inputs_def(image_shape)
fields = copy.deepcopy(fields)
if multi_scale:
ms_def, ms_fields = multiscale_def(image_shape, num_scales,
use_flip)
inputs_def.update(ms_def)
fields += ms_fields
self.im_info_names = ['image', 'im_info'] + ms_fields
if mask_branch:
box_fields = ['bbox', 'bbox_flip'] if use_flip else ['bbox']
for key in box_fields:
inputs_def[key] = {
'shape': [6],
'dtype': 'float32',
'lod_level': 1
}
fields += box_fields
feed_vars = OrderedDict([(key, fluid.layers.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
use_dataloader = use_dataloader and not mask_branch
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/retinanet.py
浏览文件 @ 8192c758
...@@ -50,8 +50,8 @@ class RetinaNet(object): ...@@ -50,8 +50,8 @@ class RetinaNet(object):
im = feed_vars['image'] im = feed_vars['image']
im_info = feed_vars['im_info'] im_info = feed_vars['im_info']
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
gt_label = feed_vars['gt_label'] gt_class = feed_vars['gt_class']
is_crowd = feed_vars['is_crowd'] is_crowd = feed_vars['is_crowd']
mixed_precision_enabled = mixed_precision_global_state() is not None mixed_precision_enabled = mixed_precision_global_state() is not None
...@@ -73,7 +73,7 @@ class RetinaNet(object): ...@@ -73,7 +73,7 @@ class RetinaNet(object):
# retinanet head # retinanet head
if mode == 'train': if mode == 'train':
loss = self.retina_head.get_loss(body_feats, spatial_scale, im_info, loss = self.retina_head.get_loss(body_feats, spatial_scale, im_info,
gt_box, gt_label, is_crowd) gt_bbox, gt_class, is_crowd)
total_loss = fluid.layers.sum(list(loss.values())) total_loss = fluid.layers.sum(list(loss.values()))
loss.update({'loss': total_loss}) loss.update({'loss': total_loss})
return loss return loss
...@@ -82,6 +82,43 @@ class RetinaNet(object): ...@@ -82,6 +82,43 @@ class RetinaNet(object):
im_info) im_info)
return pred return pred
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
}
# yapf: enable
return inputs_def
def build_inputs(
self,
image_shape=[3, None, None],
fields=[
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd'
], # for-train
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape)
feed_vars = OrderedDict([(key, fluid.layers.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/ssd.py
浏览文件 @ 8192c758
...@@ -59,8 +59,8 @@ class SSD(object): ...@@ -59,8 +59,8 @@ class SSD(object):
def build(self, feed_vars, mode='train'): def build(self, feed_vars, mode='train'):
im = feed_vars['image'] im = feed_vars['image']
if mode == 'train' or mode == 'eval': if mode == 'train' or mode == 'eval':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
gt_label = feed_vars['gt_label'] gt_class = feed_vars['gt_class']
mixed_precision_enabled = mixed_precision_global_state() is not None mixed_precision_enabled = mixed_precision_global_state() is not None
# cast inputs to FP16 # cast inputs to FP16
...@@ -82,7 +82,7 @@ class SSD(object): ...@@ -82,7 +82,7 @@ class SSD(object):
inputs=body_feats, image=im, num_classes=self.num_classes) inputs=body_feats, image=im, num_classes=self.num_classes)
if mode == 'train': if mode == 'train':
loss = fluid.layers.ssd_loss(locs, confs, gt_box, gt_label, box, loss = fluid.layers.ssd_loss(locs, confs, gt_bbox, gt_class, box,
box_var) box_var)
loss = fluid.layers.reduce_sum(loss) loss = fluid.layers.reduce_sum(loss)
return {'loss': loss} return {'loss': loss}
...@@ -90,6 +90,39 @@ class SSD(object): ...@@ -90,6 +90,39 @@ class SSD(object):
pred = self.output_decoder(locs, confs, box, box_var) pred = self.output_decoder(locs, confs, box, box_var)
return {'bbox': pred} return {'bbox': pred}
def _inputs_def(self, image_shape):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
'im_shape': {'shape': [None, 3], 'dtype': 'int32', 'lod_level': 0},
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
}
# yapf: enable
return inputs_def
def build_inputs(
self,
image_shape=[3, None, None],
fields=['image', 'im_id', 'gt_bbox', 'gt_class'], # for train
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape)
feed_vars = OrderedDict([(key, fluid.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, 'train') return self.build(feed_vars, 'train')
......
ppdet/modeling/architectures/yolov3.py
浏览文件 @ 8192c758
...@@ -64,18 +64,53 @@ class YOLOv3(object): ...@@ -64,18 +64,53 @@ class YOLOv3(object):
body_feats = [fluid.layers.cast(v, 'float32') for v in body_feats] body_feats = [fluid.layers.cast(v, 'float32') for v in body_feats]
if mode == 'train': if mode == 'train':
gt_box = feed_vars['gt_box'] gt_bbox = feed_vars['gt_bbox']
gt_label = feed_vars['gt_label'] gt_class = feed_vars['gt_class']
gt_score = feed_vars['gt_score'] gt_score = feed_vars['gt_score']
return { return {
'loss': self.yolo_head.get_loss(body_feats, gt_box, gt_label, 'loss': self.yolo_head.get_loss(body_feats, gt_bbox, gt_class,
gt_score) gt_score)
} }
else: else:
im_size = feed_vars['im_size'] im_size = feed_vars['im_size']
return self.yolo_head.get_prediction(body_feats, im_size) return self.yolo_head.get_prediction(body_feats, im_size)
def _inputs_def(self, image_shape, num_max_boxes):
im_shape = [None] + image_shape
# yapf: disable
inputs_def = {
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
'im_size': {'shape': [None, 2], 'dtype': 'int32', 'lod_level': 0},
'im_id': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
'gt_bbox': {'shape': [None, num_max_boxes, 4], 'dtype': 'float32', 'lod_level': 0},
'gt_class': {'shape': [None, num_max_boxes], 'dtype': 'int32', 'lod_level': 0},
'gt_score': {'shape': [None, num_max_boxes], 'dtype': 'float32', 'lod_level': 0},
'is_difficult': {'shape': [None, num_max_boxes],'dtype': 'int32', 'lod_level': 0},
}
# yapf: enable
return inputs_def
def build_inputs(
self,
image_shape=[3, None, None],
fields=['image', 'gt_bbox', 'gt_class', 'gt_score'], # for train
num_max_boxes=50,
use_dataloader=True,
iterable=False):
inputs_def = self._inputs_def(image_shape, num_max_boxes)
feed_vars = OrderedDict([(key, fluid.data(
name=key,
shape=inputs_def[key]['shape'],
dtype=inputs_def[key]['dtype'],
lod_level=inputs_def[key]['lod_level'])) for key in fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if use_dataloader else None
return feed_vars, loader
def train(self, feed_vars): def train(self, feed_vars):
return self.build(feed_vars, mode='train') return self.build(feed_vars, mode='train')
......
ppdet/modeling/model_input.py 已删除 100644 → 0
浏览文件 @ 9cd0d1ee
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from collections import OrderedDict
from ppdet.data.transform.operators import *
from paddle import fluid
__all__ = ['create_feed']
# yapf: disable
feed_var_def = [
{'name': 'im_info', 'shape': [3], 'dtype': 'float32', 'lod_level': 0},
{'name': 'im_id', 'shape': [1], 'dtype': 'int32', 'lod_level': 0},
{'name': 'gt_box', 'shape': [4], 'dtype': 'float32', 'lod_level': 1},
{'name': 'gt_label', 'shape': [1], 'dtype': 'int32', 'lod_level': 1},
{'name': 'is_crowd', 'shape': [1], 'dtype': 'int32', 'lod_level': 1},
{'name': 'gt_mask', 'shape': [2], 'dtype': 'float32', 'lod_level': 3},
{'name': 'is_difficult', 'shape': [1], 'dtype': 'int32', 'lod_level': 1},
{'name': 'gt_score', 'shape': [1], 'dtype': 'float32', 'lod_level': 0},
{'name': 'im_shape', 'shape': [3], 'dtype': 'float32', 'lod_level': 0},
{'name': 'im_size', 'shape': [2], 'dtype': 'int32', 'lod_level': 0},
]
# yapf: enable
def create_feed(feed, iterable=False, sub_prog_feed=False):
image_shape = feed.image_shape
feed_var_map = {var['name']: var for var in feed_var_def}
feed_var_map['image'] = {
'name': 'image',
'shape': image_shape,
'dtype': 'float32',
'lod_level': 0
}
# tensor padding with 0 is used instead of LoD tensor when
# num_max_boxes is set
if getattr(feed, 'num_max_boxes', None) is not None:
feed_var_map['gt_label']['shape'] = [feed.num_max_boxes]
feed_var_map['gt_score']['shape'] = [feed.num_max_boxes]
feed_var_map['gt_box']['shape'] = [feed.num_max_boxes, 4]
feed_var_map['is_difficult']['shape'] = [feed.num_max_boxes]
feed_var_map['gt_label']['lod_level'] = 0
feed_var_map['gt_score']['lod_level'] = 0
feed_var_map['gt_box']['lod_level'] = 0
feed_var_map['is_difficult']['lod_level'] = 0
base_name_list = ['image']
num_scale = getattr(feed, 'num_scale', 1)
sample_transform = feed.sample_transforms
multiscale_test = False
aug_flip = False
for t in sample_transform:
if isinstance(t, MultiscaleTestResize):
multiscale_test = True
aug_flip = t.use_flip
assert (len(t.target_size)+1)*(aug_flip+1) == num_scale, \
"num_scale: {} is not equal to the actual number of scale: {}."\
.format(num_scale, (len(t.target_size)+1)*(aug_flip+1))
break
if aug_flip:
num_scale //= 2
base_name_list.insert(0, 'flip_image')
feed_var_map['flip_image'] = {
'name': 'flip_image',
'shape': image_shape,
'dtype': 'float32',
'lod_level': 0
}
image_name_list = []
if multiscale_test:
for base_name in base_name_list:
for i in range(0, num_scale):
name = base_name if i == 0 else base_name + '_scale_' + str(i -
1)
feed_var_map[name] = {
'name': name,
'shape': image_shape,
'dtype': 'float32',
'lod_level': 0
}
image_name_list.append(name)
feed_var_map['im_info']['shape'] = [feed.num_scale * 3]
feed.fields = image_name_list + feed.fields[1:]
if sub_prog_feed:
box_names = ['bbox', 'bbox_flip']
for box_name in box_names:
sub_prog_feed = {
'name': box_name,
'shape': [6],
'dtype': 'float32',
'lod_level': 1
}
feed.fields = feed.fields + [box_name]
feed_var_map[box_name] = sub_prog_feed
feed_vars = OrderedDict([(key, fluid.layers.data(
name=feed_var_map[key]['name'],
shape=feed_var_map[key]['shape'],
dtype=feed_var_map[key]['dtype'],
lod_level=feed_var_map[key]['lod_level'])) for key in feed.fields])
loader = fluid.io.DataLoader.from_generator(
feed_list=list(feed_vars.values()),
capacity=64,
use_double_buffer=True,
iterable=iterable) if not sub_prog_feed else None
return loader, feed_vars
ppdet/modeling/target_assigners.py
浏览文件 @ 8192c758
...@@ -59,9 +59,9 @@ class CascadeBBoxAssigner(object): ...@@ -59,9 +59,9 @@ class CascadeBBoxAssigner(object):
] ]
outs = fluid.layers.generate_proposal_labels( outs = fluid.layers.generate_proposal_labels(
rpn_rois=input_rois, rpn_rois=input_rois,
gt_classes=feed_vars['gt_label'], gt_classes=feed_vars['gt_class'],
is_crowd=feed_vars['is_crowd'], is_crowd=feed_vars['is_crowd'],
gt_boxes=feed_vars['gt_box'], gt_boxes=feed_vars['gt_bbox'],
im_info=feed_vars['im_info'], im_info=feed_vars['im_info'],
batch_size_per_im=self.batch_size_per_im, batch_size_per_im=self.batch_size_per_im,
fg_thresh=self.fg_thresh[curr_stage], fg_thresh=self.fg_thresh[curr_stage],
...@@ -71,5 +71,6 @@ class CascadeBBoxAssigner(object): ...@@ -71,5 +71,6 @@ class CascadeBBoxAssigner(object):
use_random=self.use_random, use_random=self.use_random,
class_nums=self.class_nums if self.class_aware else 2, class_nums=self.class_nums if self.class_aware else 2,
is_cls_agnostic=not self.class_aware, is_cls_agnostic=not self.class_aware,
is_cascade_rcnn=True if curr_stage > 0 and not self.class_aware else False) is_cascade_rcnn=True
if curr_stage > 0 and not self.class_aware else False)
return outs return outs
ppdet/modeling/tests/test_architectures.py
浏览文件 @ 8192c758
...@@ -37,16 +37,18 @@ class TestFasterRCNN(unittest.TestCase): ...@@ -37,16 +37,18 @@ class TestFasterRCNN(unittest.TestCase):
@prog_scope() @prog_scope()
def test_train(self): def test_train(self):
train_feed = create(self.cfg['train_feed'])
model = create(self.detector_type) model = create(self.detector_type)
_, feed_vars = create_feed(train_feed) inputs_def = self.cfg['TrainReader']['inputs_def']
inputs_def['image_shape'] = [3, None, None]
feed_vars, _ = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars) train_fetches = model.train(feed_vars)
@prog_scope() @prog_scope()
def test_test(self): def test_test(self):
test_feed = create(self.cfg['eval_feed']) inputs_def = self.cfg['EvalReader']['inputs_def']
inputs_def['image_shape'] = [3, None, None]
model = create(self.detector_type) model = create(self.detector_type)
_, feed_vars = create_feed(test_feed) feed_vars, _ = model.build_inputs(**inputs_def)
test_fetches = model.eval(feed_vars) test_fetches = model.eval(feed_vars)
......
ppdet/utils/coco_eval.py
浏览文件 @ 8192c758
...@@ -22,8 +22,6 @@ import sys ...@@ -22,8 +22,6 @@ import sys
import json import json
import cv2 import cv2
import numpy as np import numpy as np
import matplotlib
matplotlib.use('Agg')
import logging import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
...@@ -221,7 +219,8 @@ def bbox2out(results, clsid2catid, is_bbox_normalized=False): ...@@ -221,7 +219,8 @@ def bbox2out(results, clsid2catid, is_bbox_normalized=False):
clip_bbox([xmin, ymin, xmax, ymax]) clip_bbox([xmin, ymin, xmax, ymax])
w = xmax - xmin w = xmax - xmin
h = ymax - ymin h = ymax - ymin
im_height, im_width = t['im_shape'][0][i].tolist() im_shape = t['im_shape'][0][i].tolist()
im_height, im_width = int(im_shape[0]), int(im_shape[1])
xmin *= im_width xmin *= im_width
ymin *= im_height ymin *= im_height
w *= im_width w *= im_width
......
ppdet/utils/download.py
浏览文件 @ 8192c758
...@@ -30,7 +30,10 @@ from .voc_utils import create_list ...@@ -30,7 +30,10 @@ from .voc_utils import create_list
import logging import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
__all__ = ['get_weights_path', 'get_dataset_path', 'download_dataset', 'create_voc_list'] __all__ = [
'get_weights_path', 'get_dataset_path', 'download_dataset',
'create_voc_list'
]
WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/weights") WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/weights")
DATASET_HOME = osp.expanduser("~/.cache/paddle/dataset") DATASET_HOME = osp.expanduser("~/.cache/paddle/dataset")
...@@ -72,8 +75,9 @@ DATASETS = { ...@@ -72,8 +75,9 @@ DATASETS = {
'a4a898d6193db4b9ef3260a68bad0dc7', ), 'a4a898d6193db4b9ef3260a68bad0dc7', ),
], ["WIDER_train", "WIDER_val", "wider_face_split"]), ], ["WIDER_train", "WIDER_val", "wider_face_split"]),
'fruit': ([( 'fruit': ([(
'https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar', 'https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit.tar',
'ee4a1bf2e321b75b0850cc6e063f79d7', ), ], ["fruit-detection"]), 'baa8806617a54ccf3685fa7153388ae6', ), ],
['Annotations', 'JPEGImages']),
'objects365': (), 'objects365': (),
} }
...@@ -101,17 +105,19 @@ def get_dataset_path(path, annotation, image_dir): ...@@ -101,17 +105,19 @@ def get_dataset_path(path, annotation, image_dir):
"downloading dataset...".format( "downloading dataset...".format(
osp.realpath(path), DATASET_HOME)) osp.realpath(path), DATASET_HOME))
data_name = os.path.split(path.strip().lower())[-1]
for name, dataset in DATASETS.items(): for name, dataset in DATASETS.items():
if os.path.split(path.strip().lower())[-1] == name: if data_name == name:
logger.info("Parse dataset_dir {} as dataset " logger.info("Parse dataset_dir {} as dataset "
"{}".format(path, name)) "{}".format(path, name))
if name == 'objects365': if name == 'objects365':
raise NotImplementedError( raise NotImplementedError(
"Dataset {} is not valid for download automatically. Please apply and download the dataset from https://www.objects365.org/download.html". "Dataset {} is not valid for download automatically. "
format(name)) "Please apply and download the dataset from "
"https://www.objects365.org/download.html".format(name))
data_dir = osp.join(DATASET_HOME, name) data_dir = osp.join(DATASET_HOME, name)
# For voc, only check dir VOCdevkit/VOC2012, VOCdevkit/VOC2007 # For voc, only check dir VOCdevkit/VOC2012, VOCdevkit/VOC2007
if name == 'voc': if name == 'voc' or name == 'fruit':
exists = True exists = True
for sub_dir in dataset[1]: for sub_dir in dataset[1]:
check_dir = osp.join(data_dir, sub_dir) check_dir = osp.join(data_dir, sub_dir)
...@@ -123,7 +129,7 @@ def get_dataset_path(path, annotation, image_dir): ...@@ -123,7 +129,7 @@ def get_dataset_path(path, annotation, image_dir):
return data_dir return data_dir
# voc exist is checked above, voc is not exist here # voc exist is checked above, voc is not exist here
check_exist = name != 'voc' check_exist = name != 'voc' and name != 'fruit'
for url, md5sum in dataset[0]: for url, md5sum in dataset[0]:
get_path(url, data_dir, md5sum, check_exist) get_path(url, data_dir, md5sum, check_exist)
...@@ -147,7 +153,7 @@ def create_voc_list(data_dir, devkit_subdir='VOCdevkit'): ...@@ -147,7 +153,7 @@ def create_voc_list(data_dir, devkit_subdir='VOCdevkit'):
# NOTE: since using auto download VOC # NOTE: since using auto download VOC
# dataset, VOC default label list should be used, # dataset, VOC default label list should be used,
# do not generate label_list.txt here. For default # do not generate label_list.txt here. For default
# label, see ../data/source/voc_loader.py # label, see ../data/source/voc.py
create_list(devkit_dir, years, data_dir) create_list(devkit_dir, years, data_dir)
logger.info("Create voc file list finished") logger.info("Create voc file list finished")
...@@ -345,6 +351,8 @@ def _move_and_merge_tree(src, dst): ...@@ -345,6 +351,8 @@ def _move_and_merge_tree(src, dst):
""" """
if not osp.exists(dst): if not osp.exists(dst):
shutil.move(src, dst) shutil.move(src, dst)
elif osp.isfile(src):
shutil.move(src, dst)
else: else:
for fp in os.listdir(src): for fp in os.listdir(src):
src_fp = osp.join(src, fp) src_fp = osp.join(src, fp)
......
ppdet/utils/eval_utils.py
浏览文件 @ 8192c758
...@@ -23,8 +23,8 @@ import time ...@@ -23,8 +23,8 @@ import time
import paddle.fluid as fluid import paddle.fluid as fluid
from ppdet.utils.voc_eval import bbox_eval as voc_bbox_eval from .voc_eval import bbox_eval as voc_bbox_eval
from ppdet.utils.post_process import mstest_box_post_process, mstest_mask_post_process, box_flip from .post_process import mstest_box_post_process, mstest_mask_post_process, box_flip
__all__ = ['parse_fetches', 'eval_run', 'eval_results', 'json_eval_results'] __all__ = ['parse_fetches', 'eval_run', 'eval_results', 'json_eval_results']
...@@ -41,7 +41,7 @@ def parse_fetches(fetches, prog=None, extra_keys=None): ...@@ -41,7 +41,7 @@ def parse_fetches(fetches, prog=None, extra_keys=None):
for k, v in fetches.items(): for k, v in fetches.items():
if hasattr(v, 'name'): if hasattr(v, 'name'):
keys.append(k) keys.append(k)
v.persistable = True #v.persistable = True
values.append(v.name) values.append(v.name)
else: else:
cls.append(v) cls.append(v)
...@@ -174,19 +174,19 @@ def eval_run(exe, ...@@ -174,19 +174,19 @@ def eval_run(exe,
def eval_results(results, def eval_results(results,
feed,
metric, metric,
num_classes, num_classes,
resolution=None, resolution=None,
is_bbox_normalized=False, is_bbox_normalized=False,
output_directory=None, output_directory=None,
map_type='11point'): map_type='11point',
dataset=None):
"""Evaluation for evaluation program results""" """Evaluation for evaluation program results"""
box_ap_stats = [] box_ap_stats = []
if metric == 'COCO': if metric == 'COCO':
from ppdet.utils.coco_eval import proposal_eval, bbox_eval, mask_eval from ppdet.utils.coco_eval import proposal_eval, bbox_eval, mask_eval
anno_file = getattr(feed.dataset, 'annotation', None) anno_file = dataset.get_anno()
with_background = getattr(feed, 'with_background', True) with_background = dataset.with_background
if 'proposal' in results[0]: if 'proposal' in results[0]:
output = 'proposal.json' output = 'proposal.json'
if output_directory: if output_directory:
...@@ -224,13 +224,13 @@ def eval_results(results, ...@@ -224,13 +224,13 @@ def eval_results(results,
return box_ap_stats return box_ap_stats
def json_eval_results(feed, metric, json_directory=None): def json_eval_results(metric, json_directory=None, dataset=None):
""" """
cocoapi eval with already exists proposal.json, bbox.json or mask.json cocoapi eval with already exists proposal.json, bbox.json or mask.json
""" """
assert metric == 'COCO' assert metric == 'COCO'
from ppdet.utils.coco_eval import cocoapi_eval from ppdet.utils.coco_eval import cocoapi_eval
anno_file = getattr(feed.dataset, 'annotation', None) anno_file = dataset.get_anno()
json_file_list = ['proposal.json', 'bbox.json', 'mask.json'] json_file_list = ['proposal.json', 'bbox.json', 'mask.json']
if json_directory: if json_directory:
assert os.path.exists( assert os.path.exists(
......
ppdet/utils/voc_eval.py
浏览文件 @ 8192c758
...@@ -21,7 +21,7 @@ import os ...@@ -21,7 +21,7 @@ import os
import sys import sys
import numpy as np import numpy as np
from ..data.source.voc_loader import pascalvoc_label from ..data.source.voc import pascalvoc_label
from .map_utils import DetectionMAP from .map_utils import DetectionMAP
from .coco_eval import bbox2out from .coco_eval import bbox2out
...@@ -69,13 +69,13 @@ def bbox_eval(results, ...@@ -69,13 +69,13 @@ def bbox_eval(results,
if bboxes.shape == (1, 1) or bboxes is None: if bboxes.shape == (1, 1) or bboxes is None:
continue continue
gt_boxes = t['gt_box'][0] gt_boxes = t['gt_bbox'][0]
gt_labels = t['gt_label'][0] gt_labels = t['gt_class'][0]
difficults = t['is_difficult'][0] if not evaluate_difficult \ difficults = t['is_difficult'][0] if not evaluate_difficult \
else None else None
if len(t['gt_box'][1]) == 0: if len(t['gt_bbox'][1]) == 0:
# gt_box, gt_label, difficult read as zero padded Tensor # gt_bbox, gt_class, difficult read as zero padded Tensor
bbox_idx = 0 bbox_idx = 0
for i in range(len(gt_boxes)): for i in range(len(gt_boxes)):
gt_box = gt_boxes[i] gt_box = gt_boxes[i]
...@@ -90,7 +90,7 @@ def bbox_eval(results, ...@@ -90,7 +90,7 @@ def bbox_eval(results,
bbox_idx += bbox_num bbox_idx += bbox_num
else: else:
# gt_box, gt_label, difficult read as LoDTensor # gt_box, gt_label, difficult read as LoDTensor
gt_box_lengths = t['gt_box'][1][0] gt_box_lengths = t['gt_bbox'][1][0]
bbox_idx = 0 bbox_idx = 0
gt_box_idx = 0 gt_box_idx = 0
for i in range(len(bbox_lengths)): for i in range(len(bbox_lengths)):
......
ppdet/utils/widerface_eval_utils.py
浏览文件 @ 8192c758
...@@ -19,7 +19,7 @@ from __future__ import print_function ...@@ -19,7 +19,7 @@ from __future__ import print_function
import os import os
import numpy as np import numpy as np
from ppdet.data.source.widerface_loader import widerface_label from ppdet.data.source.widerface import widerface_label
from ppdet.utils.coco_eval import bbox2out from ppdet.utils.coco_eval import bbox2out
import logging import logging
......
requirements.txt
浏览文件 @ 8192c758
...@@ -3,3 +3,4 @@ docstring_parser @ http://github.com/willthefrog/docstring_parser/tarball/master ...@@ -3,3 +3,4 @@ docstring_parser @ http://github.com/willthefrog/docstring_parser/tarball/master
typeguard ; python_version >= '3.4' typeguard ; python_version >= '3.4'
tb-paddle tb-paddle
tensorboard >= 1.15 tensorboard >= 1.15
pycocotools
tools/eval.py
浏览文件 @ 8192c758
...@@ -36,8 +36,9 @@ import paddle.fluid as fluid ...@@ -36,8 +36,9 @@ import paddle.fluid as fluid
from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results, json_eval_results from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results, json_eval_results
import ppdet.utils.checkpoint as checkpoint import ppdet.utils.checkpoint as checkpoint
from ppdet.utils.check import check_gpu, check_version from ppdet.utils.check import check_gpu, check_version
from ppdet.modeling.model_input import create_feed
from ppdet.data.data_feed import create_reader from ppdet.data.reader import create_reader
from ppdet.core.workspace import load_config, merge_config, create from ppdet.core.workspace import load_config, merge_config, create
from ppdet.utils.cli import ArgsParser from ppdet.utils.cli import ArgsParser
...@@ -63,11 +64,6 @@ def main(): ...@@ -63,11 +64,6 @@ def main():
# check if paddlepaddle version is satisfied # check if paddlepaddle version is satisfied
check_version() check_version()
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
multi_scale_test = getattr(cfg, 'MultiScaleTEST', None) multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
# define executor # define executor
...@@ -80,13 +76,15 @@ def main(): ...@@ -80,13 +76,15 @@ def main():
eval_prog = fluid.Program() eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog): with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard(): with fluid.unique_name.guard():
loader, feed_vars = create_feed(eval_feed) inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, loader = model.build_inputs(**inputs_def)
if multi_scale_test is None: if multi_scale_test is None:
fetches = model.eval(feed_vars) fetches = model.eval(feed_vars)
else: else:
fetches = model.eval(feed_vars, multi_scale_test) fetches = model.eval(feed_vars, multi_scale_test)
eval_prog = eval_prog.clone(True) eval_prog = eval_prog.clone(True)
reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
reader = create_reader(cfg.EvalReader)
loader.set_sample_list_generator(reader, place) loader.set_sample_list_generator(reader, place)
# eval already exists json file # eval already exists json file
...@@ -96,30 +94,26 @@ def main(): ...@@ -96,30 +94,26 @@ def main():
"output_eval directly. And proposal.json, bbox.json and mask.json " "output_eval directly. And proposal.json, bbox.json and mask.json "
"will be detected by default.") "will be detected by default.")
json_eval_results( json_eval_results(
eval_feed, cfg.metric, json_directory=FLAGS.output_eval) cfg.metric, json_directory=FLAGS.output_eval, dataset=dataset)
return return
compile_program = fluid.compiler.CompiledProgram( compile_program = fluid.compiler.CompiledProgram(
eval_prog).with_data_parallel() eval_prog).with_data_parallel()
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, eval_prog, cfg.weights)
assert cfg.metric != 'OID', "eval process of OID dataset \ assert cfg.metric != 'OID', "eval process of OID dataset \
is not supported." is not supported."
if cfg.metric == "WIDERFACE": if cfg.metric == "WIDERFACE":
raise ValueError("metric type {} does not support in tools/eval.py, " raise ValueError("metric type {} does not support in tools/eval.py, "
"please use tools/face_eval.py".format(cfg.metric)) "please use tools/face_eval.py".format(cfg.metric))
assert cfg.metric in ['COCO', 'VOC'], \ assert cfg.metric in ['COCO', 'VOC'], \
"unknown metric type {}".format(cfg.metric) "unknown metric type {}".format(cfg.metric)
extra_keys = [] extra_keys = []
if cfg.metric == 'COCO': if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape'] extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC': if cfg.metric == 'VOC':
extra_keys = ['gt_box', 'gt_label', 'is_difficult'] extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys) keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
...@@ -129,6 +123,8 @@ def main(): ...@@ -129,6 +123,8 @@ def main():
callable(model.is_bbox_normalized): callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized() is_bbox_normalized = model.is_bbox_normalized()
dataset = cfg['EvalReader']['dataset']
sub_eval_prog = None sub_eval_prog = None
sub_keys = None sub_keys = None
sub_values = None sub_values = None
...@@ -137,32 +133,44 @@ def main(): ...@@ -137,32 +133,44 @@ def main():
sub_eval_prog = fluid.Program() sub_eval_prog = fluid.Program()
with fluid.program_guard(sub_eval_prog, startup_prog): with fluid.program_guard(sub_eval_prog, startup_prog):
with fluid.unique_name.guard(): with fluid.unique_name.guard():
_, feed_vars = create_feed(eval_feed, False, sub_prog_feed=True) inputs_def = cfg['EvalReader']['inputs_def']
inputs_def['mask_branch'] = True
feed_vars, eval_loader = model.build_inputs(**inputs_def)
sub_fetches = model.eval( sub_fetches = model.eval(
feed_vars, multi_scale_test, mask_branch=True) feed_vars, multi_scale_test, mask_branch=True)
extra_keys = [] assert cfg.metric == 'COCO'
if cfg.metric == 'COCO': extra_keys = ['im_id', 'im_shape']
extra_keys = ['im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_box', 'gt_label', 'is_difficult']
sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog, sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
extra_keys) extra_keys)
sub_eval_prog = sub_eval_prog.clone(True) sub_eval_prog = sub_eval_prog.clone(True)
if 'weights' in cfg: #if 'weights' in cfg:
checkpoint.load_params(exe, sub_eval_prog, cfg.weights) # checkpoint.load_params(exe, sub_eval_prog, cfg.weights)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, startup_prog, cfg.weights)
results = eval_run(exe, compile_program, loader, keys, values, cls, cfg, results = eval_run(exe, compile_program, loader, keys, values, cls, cfg,
sub_eval_prog, sub_keys, sub_values) sub_eval_prog, sub_keys, sub_values)
#print(cfg['EvalReader']['dataset'].__dict__)
# evaluation # evaluation
resolution = None resolution = None
if 'mask' in results[0]: if 'mask' in results[0]:
resolution = model.mask_head.resolution resolution = model.mask_head.resolution
# if map_type not set, use default 11point, only use in VOC eval # if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point' map_type = cfg.map_type if 'map_type' in cfg else '11point'
eval_results(results, eval_feed, cfg.metric, cfg.num_classes, resolution, eval_results(
is_bbox_normalized, FLAGS.output_eval, map_type) results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset)
if __name__ == '__main__': if __name__ == '__main__':
...@@ -172,12 +180,6 @@ if __name__ == '__main__': ...@@ -172,12 +180,6 @@ if __name__ == '__main__':
action='store_true', action='store_true',
default=False, default=False,
help="Whether to re eval with already exists bbox.json or mask.json") help="Whether to re eval with already exists bbox.json or mask.json")
parser.add_argument(
"-d",
"--dataset_dir",
default=None,
type=str,
help="Dataset path, same as DataFeed.dataset.dataset_dir")
parser.add_argument( parser.add_argument(
"-f", "-f",
"--output_eval", "--output_eval",
......
tools/export_model.py
浏览文件 @ 8192c758
...@@ -82,11 +82,6 @@ def main(): ...@@ -82,11 +82,6 @@ def main():
merge_config(FLAGS.opt) merge_config(FLAGS.opt)
if 'test_feed' not in cfg:
test_feed = create(main_arch + 'TestFeed')
else:
test_feed = create(cfg.test_feed)
# Use CPU for exporting inference model instead of GPU # Use CPU for exporting inference model instead of GPU
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
...@@ -97,7 +92,9 @@ def main(): ...@@ -97,7 +92,9 @@ def main():
infer_prog = fluid.Program() infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog): with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard(): with fluid.unique_name.guard():
_, feed_vars = create_feed(test_feed, iterable=True) inputs_def = cfg['TestReader']['inputs_def']
inputs_def['use_dataloader'] = False
feed_vars, _ = model.build_inputs(**inputs_def)
test_fetches = model.test(feed_vars) test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True) infer_prog = infer_prog.clone(True)
......
tools/face_eval.py
浏览文件 @ 8192c758
...@@ -29,7 +29,6 @@ from ppdet.utils.check import check_gpu ...@@ -29,7 +29,6 @@ from ppdet.utils.check import check_gpu
from ppdet.utils.widerface_eval_utils import get_shrink, bbox_vote, \ from ppdet.utils.widerface_eval_utils import get_shrink, bbox_vote, \
save_widerface_bboxes, save_fddb_bboxes, to_chw_bgr save_widerface_bboxes, save_fddb_bboxes, to_chw_bgr
from ppdet.core.workspace import load_config, merge_config, create from ppdet.core.workspace import load_config, merge_config, create
from ppdet.modeling.model_input import create_feed
import logging import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s' FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
...@@ -53,7 +52,7 @@ def face_img_process(image, ...@@ -53,7 +52,7 @@ def face_img_process(image,
def face_eval_run(exe, def face_eval_run(exe,
compile_program, compile_program,
fetches, fetches,
img_root_dir, image_dir,
gt_file, gt_file,
pred_dir='output/pred', pred_dir='output/pred',
eval_mode='widerface', eval_mode='widerface',
...@@ -73,9 +72,10 @@ def face_eval_run(exe, ...@@ -73,9 +72,10 @@ def face_eval_run(exe,
dets_dist = OrderedDict() dets_dist = OrderedDict()
for iter_id, im_path in enumerate(imid2path): for iter_id, im_path in enumerate(imid2path):
image_path = os.path.join(img_root_dir, im_path) image_path = os.path.join(image_dir, im_path)
if eval_mode == 'fddb': if eval_mode == 'fddb':
image_path += '.jpg' image_path += '.jpg'
assert os.path.exists(image_path)
image = Image.open(image_path).convert('RGB') image = Image.open(image_path).convert('RGB')
if multi_scale: if multi_scale:
shrink, max_shrink = get_shrink(image.size[1], image.size[0]) shrink, max_shrink = get_shrink(image.size[1], image.size[0])
...@@ -220,11 +220,6 @@ def main(): ...@@ -220,11 +220,6 @@ def main():
# check if set use_gpu=True in paddlepaddle cpu version # check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu) check_gpu(cfg.use_gpu)
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
# define executor # define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
...@@ -235,7 +230,9 @@ def main(): ...@@ -235,7 +230,9 @@ def main():
eval_prog = fluid.Program() eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog): with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard(): with fluid.unique_name.guard():
_, feed_vars = create_feed(eval_feed, iterable=True) inputs_def = cfg['EvalReader']['inputs_def']
inputs_def['use_dataloader'] = False
feed_vars, _ = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars) fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True) eval_prog = eval_prog.clone(True)
...@@ -248,21 +245,19 @@ def main(): ...@@ -248,21 +245,19 @@ def main():
assert cfg.metric in ['WIDERFACE'], \ assert cfg.metric in ['WIDERFACE'], \
"unknown metric type {}".format(cfg.metric) "unknown metric type {}".format(cfg.metric)
annotation_file = getattr(eval_feed.dataset, 'annotation', None) dataset = cfg['EvalReader']['dataset']
dataset_dir = FLAGS.dataset_dir if FLAGS.dataset_dir else \
getattr(eval_feed.dataset, 'dataset_dir', None) annotation_file = dataset.get_anno()
img_root_dir = dataset_dir dataset_dir = dataset.dataset_dir
if FLAGS.eval_mode == "widerface": image_dir = dataset.image_dir
image_dir = getattr(eval_feed.dataset, 'image_dir', None)
img_root_dir = os.path.join(dataset_dir, image_dir)
gt_file = os.path.join(dataset_dir, annotation_file)
pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred' pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred'
face_eval_run( face_eval_run(
exe, exe,
eval_prog, eval_prog,
fetches, fetches,
img_root_dir, image_dir,
gt_file, annotation_file,
pred_dir=pred_dir, pred_dir=pred_dir,
eval_mode=FLAGS.eval_mode, eval_mode=FLAGS.eval_mode,
multi_scale=FLAGS.multi_scale) multi_scale=FLAGS.multi_scale)
......
tools/infer.py
浏览文件 @ 8192c758
...@@ -38,8 +38,6 @@ set_paddle_flags( ...@@ -38,8 +38,6 @@ set_paddle_flags(
from paddle import fluid from paddle import fluid
from ppdet.core.workspace import load_config, merge_config, create from ppdet.core.workspace import load_config, merge_config, create
from ppdet.modeling.model_input import create_feed
from ppdet.data.data_feed import create_reader
from ppdet.utils.eval_utils import parse_fetches from ppdet.utils.eval_utils import parse_fetches
from ppdet.utils.cli import ArgsParser from ppdet.utils.cli import ArgsParser
...@@ -47,6 +45,8 @@ from ppdet.utils.check import check_gpu, check_version ...@@ -47,6 +45,8 @@ from ppdet.utils.check import check_gpu, check_version
from ppdet.utils.visualizer import visualize_results from ppdet.utils.visualizer import visualize_results
import ppdet.utils.checkpoint as checkpoint import ppdet.utils.checkpoint as checkpoint
from ppdet.data.reader import create_reader
import logging import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s' FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT) logging.basicConfig(level=logging.INFO, format=FORMAT)
...@@ -110,13 +110,10 @@ def main(): ...@@ -110,13 +110,10 @@ def main():
# check if paddlepaddle version is satisfied # check if paddlepaddle version is satisfied
check_version() check_version()
if 'test_feed' not in cfg: dataset = cfg.TestReader['dataset']
test_feed = create(main_arch + 'TestFeed')
else:
test_feed = create(cfg.test_feed)
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img) test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
test_feed.dataset.add_images(test_images) dataset.set_images(test_images)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
...@@ -127,11 +124,13 @@ def main(): ...@@ -127,11 +124,13 @@ def main():
infer_prog = fluid.Program() infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog): with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard(): with fluid.unique_name.guard():
loader, feed_vars = create_feed(test_feed, iterable=True) inputs_def = cfg['TestReader']['inputs_def']
inputs_def['iterable'] = True
feed_vars, loader = model.build_inputs(**inputs_def)
test_fetches = model.test(feed_vars) test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True) infer_prog = infer_prog.clone(True)
reader = create_reader(test_feed) reader = create_reader(cfg.TestReader)
loader.set_sample_list_generator(reader, place) loader.set_sample_list_generator(reader, place)
exe.run(startup_prog) exe.run(startup_prog)
...@@ -158,9 +157,10 @@ def main(): ...@@ -158,9 +157,10 @@ def main():
if cfg.metric == "WIDERFACE": if cfg.metric == "WIDERFACE":
from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info
anno_file = getattr(test_feed.dataset, 'annotation', None) anno_file = dataset.get_anno()
with_background = getattr(test_feed, 'with_background', True) with_background = dataset.with_background
use_default_label = getattr(test_feed, 'use_default_label', False) use_default_label = dataset.use_default_label
clsid2catid, catid2name = get_category_info(anno_file, with_background, clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label) use_default_label)
...@@ -177,7 +177,7 @@ def main(): ...@@ -177,7 +177,7 @@ def main():
tb_image_step = 0 tb_image_step = 0
tb_image_frame = 0 # each frame can display ten pictures at most. tb_image_frame = 0 # each frame can display ten pictures at most.
imid2path = reader.imid2path imid2path = dataset.get_imid2path()
for iter_id, data in enumerate(loader()): for iter_id, data in enumerate(loader()):
outs = exe.run(infer_prog, outs = exe.run(infer_prog,
feed=data, feed=data,
......
tools/train.py
浏览文件 @ 8192c758
...@@ -39,7 +39,7 @@ from paddle import fluid ...@@ -39,7 +39,7 @@ from paddle import fluid
from ppdet.experimental import mixed_precision_context from ppdet.experimental import mixed_precision_context
from ppdet.core.workspace import load_config, merge_config, create from ppdet.core.workspace import load_config, merge_config, create
from ppdet.data.data_feed import create_reader from ppdet.data.reader import create_reader
from ppdet.utils.cli import print_total_cfg from ppdet.utils.cli import print_total_cfg
from ppdet.utils import dist_utils from ppdet.utils import dist_utils
...@@ -48,7 +48,6 @@ from ppdet.utils.stats import TrainingStats ...@@ -48,7 +48,6 @@ from ppdet.utils.stats import TrainingStats
from ppdet.utils.cli import ArgsParser from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu, check_version from ppdet.utils.check import check_gpu, check_version
import ppdet.utils.checkpoint as checkpoint import ppdet.utils.checkpoint as checkpoint
from ppdet.modeling.model_input import create_feed
import logging import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s' FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
...@@ -89,17 +88,6 @@ def main(): ...@@ -89,17 +88,6 @@ def main():
else: else:
devices_num = int(os.environ.get('CPU_NUM', 1)) devices_num = int(os.environ.get('CPU_NUM', 1))
if 'train_feed' not in cfg:
train_feed = create(main_arch + 'TrainFeed')
else:
train_feed = create(cfg.train_feed)
if FLAGS.eval:
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
if 'FLAGS_selected_gpus' in env: if 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus']) device_id = int(env['FLAGS_selected_gpus'])
else: else:
...@@ -116,8 +104,6 @@ def main(): ...@@ -116,8 +104,6 @@ def main():
with fluid.program_guard(train_prog, startup_prog): with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard(): with fluid.unique_name.guard():
model = create(main_arch) model = create(main_arch)
train_loader, feed_vars = create_feed(train_feed)
if FLAGS.fp16: if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None) assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \ != 'affine_channel'), \
...@@ -125,8 +111,9 @@ def main(): ...@@ -125,8 +111,9 @@ def main():
' please modify backbone settings to use batch norm' ' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx: with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars) train_fetches = model.train(feed_vars)
loss = train_fetches['loss'] loss = train_fetches['loss']
if FLAGS.fp16: if FLAGS.fp16:
loss *= ctx.get_loss_scale_var() loss *= ctx.get_loss_scale_var()
...@@ -145,11 +132,12 @@ def main(): ...@@ -145,11 +132,12 @@ def main():
with fluid.program_guard(eval_prog, startup_prog): with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard(): with fluid.unique_name.guard():
model = create(main_arch) model = create(main_arch)
eval_loader, feed_vars = create_feed(eval_feed) inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars) fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True) eval_prog = eval_prog.clone(True)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir) eval_reader = create_reader(cfg.EvalReader)
eval_loader.set_sample_list_generator(eval_reader, place) eval_loader.set_sample_list_generator(eval_reader, place)
# parse eval fetches # parse eval fetches
...@@ -157,9 +145,9 @@ def main(): ...@@ -157,9 +145,9 @@ def main():
if cfg.metric == 'COCO': if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape'] extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC': if cfg.metric == 'VOC':
extra_keys = ['gt_box', 'gt_label', 'is_difficult'] extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
if cfg.metric == 'WIDERFACE': if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_box'] extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog, eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys) extra_keys)
...@@ -206,8 +194,8 @@ def main(): ...@@ -206,8 +194,8 @@ def main():
checkpoint.load_params( checkpoint.load_params(
exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params) exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
train_reader = create_reader(train_feed, (cfg.max_iters - start_iter) * train_reader = create_reader(cfg.TrainReader,
devices_num, FLAGS.dataset_dir) (cfg.max_iters - start_iter) * devices_num)
train_loader.set_sample_list_generator(train_reader, place) train_loader.set_sample_list_generator(train_reader, place)
# whether output bbox is normalized in model output layer # whether output bbox is normalized in model output layer
...@@ -273,8 +261,9 @@ def main(): ...@@ -273,8 +261,9 @@ def main():
if 'mask' in results[0]: if 'mask' in results[0]:
resolution = model.mask_head.resolution resolution = model.mask_head.resolution
box_ap_stats = eval_results( box_ap_stats = eval_results(
results, eval_feed, cfg.metric, cfg.num_classes, resolution, results, cfg.metric, cfg.num_classes, resolution,
is_bbox_normalized, FLAGS.output_eval, map_type) is_bbox_normalized, FLAGS.output_eval, map_type,
cfg['EvalReader']['dataset'])
# use tb_paddle to log mAP # use tb_paddle to log mAP
if FLAGS.use_tb: if FLAGS.use_tb:
...@@ -320,12 +309,6 @@ if __name__ == '__main__': ...@@ -320,12 +309,6 @@ if __name__ == '__main__':
default=None, default=None,
type=str, type=str,
help="Evaluation directory, default is current directory.") help="Evaluation directory, default is current directory.")
parser.add_argument(
"-d",
"--dataset_dir",
default=None,
type=str,
help="Dataset path, same as DataFeed.dataset.dataset_dir")
parser.add_argument( parser.add_argument(
"--use_tb", "--use_tb",
type=bool, type=bool,
......
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