提交 1ea2a74b 编写于 作者: W wangguanzhong 提交者: GitHub

Add full config (#3095)

* Add full configs example & refine doc

* refine docs
上级 6b1c4dfa
...@@ -85,6 +85,8 @@ python tools/infer.py -c configs/mask_rcnn_r50_1x.yml \ ...@@ -85,6 +85,8 @@ python tools/infer.py -c configs/mask_rcnn_r50_1x.yml \
For detailed training and evaluation workflow, please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md). For detailed training and evaluation workflow, please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md).
For detailed configuration and parameter description, please refer to [Complete config files](docs/config_example/)
We also recommend users to take a look at the [IPython Notebook demo](demo/mask_rcnn_demo.ipynb) We also recommend users to take a look at the [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
Further information can be found in these documentations: Further information can be found in these documentations:
......
...@@ -72,6 +72,8 @@ python tools/infer.py -c configs/mask_rcnn_r50_1x.yml \ ...@@ -72,6 +72,8 @@ python tools/infer.py -c configs/mask_rcnn_r50_1x.yml \
更多训练及评估流程,请参考[GETTING_STARTED_cn.md](docs/GETTING_STARTED_cn.md). 更多训练及评估流程,请参考[GETTING_STARTED_cn.md](docs/GETTING_STARTED_cn.md).
详细的配置信息和参数说明,请参考[示例配置文件](docs/config_example/).
同时推荐用户参考[IPython Notebook demo](demo/mask_rcnn_demo.ipynb) 同时推荐用户参考[IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
其他更多信息可参考以下文档内容: 其他更多信息可参考以下文档内容:
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...@@ -157,6 +157,7 @@ LearningRate: ...@@ -157,6 +157,7 @@ LearningRate:
steps: 500 steps: 500
``` ```
[Complete config files](config_example/) of multiple detection architectures are given and brief description of each parameter.
## Requirements ## Requirements
......
...@@ -149,6 +149,7 @@ LearningRate: ...@@ -149,6 +149,7 @@ LearningRate:
steps: 500 steps: 500
``` ```
[示例配置文件](config_example/)中给出了多种检测结构的完整配置文件,以及其中各个超参的简要说明。
## 安装依赖 ## 安装依赖
......
# Architecture of detection, which is also the prefix of data feed module
architecture: MaskRCNN
# Data feed module
train_feed: MaskRCNNTrainFeed
eval_feed: MaskRCNNEvalFeed
test_feed: MaskRCNNTestFeed
# Use GPU or CPU, true by default
use_gpu: true
# Maximum number of iteration.
# In rcnn models, max_iters is 180000 if lr schedule is 1x and batch_size is 1.
max_iters: 180000
# Snapshot period. If training and test at same time, evaluate model at each snapshot_iter. 10000 by default.
snapshot_iter: 10000
# Smooth the log output in specified iterations, 20 by default.
log_smooth_window: 20
# The number of iteration interval to display in training log.
log_iter: 20
# The directory to save models.
save_dir: output
# The path of oretrained wegiths. If url is provided, it will download the pretrain_weights and decompress automatically.
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
# Evalution method, COCO and VOC are available.
metric: COCO
# The path of final model for evaluation and test.
weights: output/mask_rcnn_r50_fpn_1x/model_final/
# Number of classes, 81 for COCO and 21 for VOC
num_classes: 81
# Mask RCNN architecture, see https://arxiv.org/abs/1703.06870
MaskRCNN:
backbone: ResNet
fpn: FPN
roi_extractor: FPNRoIAlign
rpn_head: FPNRPNHead
bbox_assigner: BBoxAssigner
bbox_head: BBoxHead
mask_assigner: MaskAssigner
mask_head: MaskHead
rpn_only: false
# Backbone module
ResNet:
# Index of stages using deformable conv v2, [] by default
dcn_v2_stages: []
# ResNet depth, 50 by default
depth: 50
# Stage index of returned feature map, [2,3,4,5] by default
feature_maps:
- 2
- 3
- 4
- 5
# Stage Index of backbone to freeze, 2 by default
freeze_at: 2
# Whether freeze normalization layers, true by default
freeze_norm: true
# Weight decay for normalization layer weights, 0. by default
norm_decay: 0.0
# Normalization type, bn/sync_bn/affine_channel, affine_channel by default
norm_type: affine_channel
# ResNet variant, supports 'a', 'b', 'c', 'd' currently, b by default
variant: b
# FPN module
FPN:
# Whether has extra conv in higher levels, false by default
has_extra_convs: false
# Highest level of the backbone feature map to use, 6 by default
max_level: 6
# Lowest level of the backbone feature map to use, 6 by default
min_level: 2
# FPN normalization type, bn/sync_bn/affine_channel, null by default
norm_type: null
# Number of feature channels, 256 by default
num_chan: 256
# Feature map scaling factors, [0.03125, 0.0625, 0.125, 0.25] by default
spatial_scale:
- 0.03125
- 0.0625
- 0.125
- 0.25
# RPN module, if use non-FPN architecture, use RPNHead instead
# Extract proposals according to anchors and assign box targets and
# score targets to selected proposals to compute RPN loss. For FPN
# architecture, RPN is computed from each levels and collect proposals
# together.
FPNRPNHead:
# fluid.layers.anchor_generator
# Generate anchors for RCNN models. Each position of input produces
# N anchors. N = anchor_sizes * aspect_ratios. In FPNRPNHead, aspect_ratios
# is provided and anchor_sizes depends on FPN levels and anchor_start_size.
anchor_generator:
aspect_ratios:
- 0.5
- 1.0
- 2.0
variance:
- 1.0
- 1.0
- 1.0
- 1.0
# fluid.layers.rpn_target_assign
# Assign classification and regression targets to each anchor according
# to Intersection-over-Union(IoU) overlap between anchors and ground
# truth boxes. The classification targets is binary class labels. the
# positive labels are two kinds of anchors: the anchors with the highest
# IoU overlap with a ground-truth box, or an anchor that has an IoU overlap
# higher than rpn_positive_overlap with any ground-truth box.
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
# fluid.layers.generate_proposals in training
# Generate RoIs according to each box with probability to be a foreground
# object. The operation performs following steps: Transposes and resizes
# scores and bbox_deltas; Calculate box locations as proposal candidates;
# Clip boxes to image; Remove predicted boxes with small area; Apply NMS to
# get final proposals as output.
train_proposal:
min_size: 0.0
nms_thresh: 0.7
post_nms_top_n: 2000
pre_nms_top_n: 2000
# fluid.layers.generate_proposals in test
test_proposal:
min_size: 0.0
nms_thresh: 0.7
post_nms_top_n: 1000
pre_nms_top_n: 1000
# Size of anchor at the first scale, 32 by default
anchor_start_size: 32
# highest level of FPN output, 6 by default
max_level: 6
# Lowest level of FPN output, 2 by default
min_level: 2
# Number of FPN output channels, 256 by default
num_chan: 256
# Number of classes in RPN output, 1 by default
num_classes: 1
# RoI extractor module, if use non-FPN architecture, use RoIAlign instead
# For FPN architecture, proposals are distributed to different levels and
# apply roi align at each level. Then concat the outputs.
FPNRoIAlign:
# The canconical FPN feature map level, 4 by default
canconical_level: 4
# The canconical FPN feature map size, 224 by default
canonical_size: 224
# The highest level of FPN layer, 5 by default
max_level: 5
# The lowest level of FPN layer, 2 by default
min_level: 2
# Number of sampling points, 0 by default
sampling_ratio: 2
# Box resolution, 7 by default
box_resolution: 7
# Mask RoI resolution, 14 by default
mask_resolution: 14
# Mask head module
# Generate mask output and compute loss mask.
MaskHead:
# Number of convolutions, 4 for FPN, 0 otherwise. 0 by default
num_convs: 4
# size of the output mask, 14 by default
resolution: 28
# Dilation rate, 1 by default
dilation: 1
# Number of channels after first conv, 256 by default
num_chan_reduced: 256
# Number of output classes, 81 by default
num_classes: 81
# fluid.layers.generate_proposal_labels
# Combine boxes and gt_boxes, and sample foreground proposals and background
# prosals.Then assign classification and regression targets to selected RoIs.
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
num_classes: 81
shuffle_before_sample: true
# fluid.layers.generate_mask_labels
# For given the RoIs and corresponding labels, sample foreground RoIs.
# Assign mask targets to selected RoIs which are encoded to K binary masks
# of resolution M x M.
MaskAssigner:
resolution: 28
num_classes: 81
# BBox head module
# Faster bbox head following the RoI extractor, and apply post process, such as
# NMS and box coder..
BBoxHead:
# Head after RoI extractor, ResNetC5/TwoFCHead
head: TwoFCHead
# fluid.layers.multiclass_nms
# Select a subset of detection bounding boxes that have high scores larger
# than score_threshold. Then prune away boxes that have high IoU overlap
# with already selected boxes by nms_threshold.
keep_top_k: 100
nms_threshold: 0.5
score_threshold: 0.05
# fluid.layers.box_coder
box_coder:
axis: 1
box_normalized: false
code_type: decode_center_size
prior_box_var:
- 0.1
- 0.1
- 0.2
- 0.2
num_classes: 81
# RCNN head with two Fully Connected layers
TwoFCHead:
# The number of output channels, 1024 by default
num_chan: 1024
# Learning rate configuration
LearningRate:
# Base learning rate, 0.01 by default
base_lr: 0.01
# Learning rate schedulers, PiecewiseDecay and LinearWarmup by default
schedulers:
# fluid.layers.piecewise_decay
# Values has higher priority and if values is null, learning rate is multipled by gamma at each stage
- !PiecewiseDecay
gamma: 0.1
milestones:
- 120000
- 160000
values: null
# fluid.layers.linear_lr_warmup
# Start learning rate equals to base_lr * start_factor
- !LinearWarmup
start_factor: 0.3333333333333333
steps: 500
# Optimizer module
OptimizerBuilder:
# fluid.optimizer
optimizer:
momentum: 0.9
type: Momentum
# fluid.regularizer
regularizer:
factor: 0.0001
type: L2
# Data feed module for training
MaskRCNNTrainFeed:
# Batch size per device, 1 by default
batch_size: 1
# Dataset module
dataset:
# Annotation file path
annotation: annotations/instances_train2017.json
# Dataset directory
dataset_dir: dataset/coco
# Directory where image files are stored
image_dir: train2017
# List of data fields needed
fields:
- image
- im_info
- im_id
- gt_box
- gt_label
- is_crowd
- gt_mask
# list of image dims
image_shape:
- 3
- 800
- 1333
# List of sample transformations to use
sample_transforms:
# Transform the image data to numpy format.
- !DecodeImage
to_rgb: true # default: true
with_mixup: false # default: false
# Flip images randomly
# Transform the x coordinates of bboxes and segmentations
- !RandomFlipImage
is_mask_flip: true # default: false
# Whether bbox is normalized
is_normalized: false # default: false
prob: 0.5 # default: 0.5
# Normalize the image
- !NormalizeImage
# The format of image, [H, W, C]/[C, H, W], true by default
is_channel_first: false
# Whether divide by 255, true by default
is_scale: true
# default: [0.485, 0.456, 0.406]
mean:
- 0.485
- 0.456
- 0.406
# default: [1, 1, 1]
std:
- 0.229
- 0.224
- 0.225
# Rescale image to the specified target size, and capped at max_size
- !ResizeImage
# Resize method, cv2.INTER_LINEAR(1) by default
interp: 1
max_size: 1333
target_size: 800
use_cv2: true # default: true
# Change the channel
- !Permute
# The format of image, [H, W, C]/[C, H, W], true by default
channel_first: true
to_bgr: false # default: true
# List of batch transformations to use
batch_transforms:
# Pad a batch of samples to same dimensions
- !PadBatch
pad_to_stride: 32 # default: 32
# Drop last batch if size is uneven, false by default
drop_last: false
# Number of workers processes(or threads), 2 by default
num_workers: 2
# Number of samples, -1 represents all samples. -1 by default
samples: -1
# If samples should be shuffled, true by default
shuffle: true
# If update im_info after padding, false by default
use_padded_im_info: false
# If use multi-process, false by default
use_process: false
# Data feed module for test
MaskRCNNEvalFeed:
# Batch size per device, 1 by default
batch_size: 1
# Dataset module
dataset:
# Annotation file path
annotation: annotations/instances_val2017.json
# Dataset directory
dataset_dir: dataset/coco
# Directory where image files are stored
image_dir: val2017
# List of data fields needed
fields:
- image
- im_info
- im_id
- im_shape
# list of image dims
image_shape:
- 3
- 800
- 1333
# List of sample transformations to use
sample_transforms:
# Transform the image data to numpy format.
- !DecodeImage
to_rgb: true # default: true
with_mixup: false # default: false
# Normalize the image
- !NormalizeImage
# The format of image, [H, W, C]/[C, H, W], true by default
is_channel_first: false
# Whether divide by 255, true by default
is_scale: true
# default: [0.485, 0.456, 0.406]
mean:
- 0.485
- 0.456
- 0.406
# default: [1, 1, 1]
std:
- 0.229
- 0.224
- 0.225
# Rescale image to the specified target size, and capped at max_size
- !ResizeImage
# Resize method, cv2.INTER_LINEAR(1) by default
interp: 1
max_size: 1333
target_size: 800
use_cv2: true # default: true
# Change the channel
- !Permute
# The format of image, [H, W, C]/[C, H, W], true by default
channel_first: true
to_bgr: false # default: true
# List of batch transformations to use
batch_transforms:
# Pad a batch of samples to same dimensions
- !PadBatch
pad_to_stride: 32 # default: 32
# Drop last batch if size is uneven, false by default
drop_last: false
# Number of workers processes(or threads), 2 by default
num_workers: 2
# Number of samples, -1 represents all samples. -1 by default
samples: -1
# If samples should be shuffled, true by default
shuffle: false
# If update im_info after padding, false by default
use_padded_im_info: true
# If use multi-process, false by default
use_process: false
# Data feed module for test
MaskRCNNTestFeed:
# Batch size per device, 1 by default
batch_size: 1
# Dataset module
dataset:
# Annotation file path
annotation: dataset/coco/annotations/instances_val2017.json
# List of data fields needed
fields:
- image
- im_info
- im_id
- im_shape
# list of image dims
image_shape:
- 3
- 800
- 1333
# List of sample transformations to use
sample_transforms:
# Transform the image data to numpy format.
- !DecodeImage
to_rgb: true # default: true
with_mixup: false # default: false
# Normalize the image
- !NormalizeImage
# The format of image, [H, W, C]/[C, H, W], true by default
is_channel_first: false
# Whether divide by 255, true by default
is_scale: true
# default: [0.485, 0.456, 0.406]
mean:
- 0.485
- 0.456
- 0.406
# default: [1, 1, 1]
std:
- 0.229
- 0.224
- 0.225
# Change the channel
- !Permute
# The format of image, [H, W, C]/[C, H, W], true by default
channel_first: true
to_bgr: false # default: true
# List of batch transformations to use
batch_transforms:
# Pad a batch of samples to same dimensions
- !PadBatch
pad_to_stride: 32 # default: 32
# Drop last batch if size is uneven, false by default
drop_last: false
# Number of workers processes(or threads), 2 by default
num_workers: 2
# Number of samples, -1 represents all samples. -1 by default
samples: -1
# If samples should be shuffled, true by default
shuffle: false
# If update im_info after padding, false by default
use_padded_im_info: true
# If use multi-process, false by default
use_process: false
...@@ -154,7 +154,7 @@ class ResizeImage(BaseOperator): ...@@ -154,7 +154,7 @@ class ResizeImage(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):
""" Resise the image numpy. """ Resize the image numpy.
""" """
im = sample['image'] im = sample['image']
if not isinstance(im, np.ndarray): if not isinstance(im, np.ndarray):
......
...@@ -38,6 +38,7 @@ class RPNHead(object): ...@@ -38,6 +38,7 @@ class RPNHead(object):
rpn_target_assign (object): `RPNTargetAssign` instance rpn_target_assign (object): `RPNTargetAssign` instance
train_proposal (object): `GenerateProposals` instance for training train_proposal (object): `GenerateProposals` instance for training
test_proposal (object): `GenerateProposals` instance for testing test_proposal (object): `GenerateProposals` instance for testing
num_classes (int): number of classes in rpn output
""" """
__inject__ = [ __inject__ = [
'anchor_generator', 'rpn_target_assign', 'train_proposal', 'anchor_generator', 'rpn_target_assign', 'train_proposal',
...@@ -281,6 +282,7 @@ class FPNRPNHead(RPNHead): ...@@ -281,6 +282,7 @@ class FPNRPNHead(RPNHead):
num_chan (int): number of FPN output channels num_chan (int): number of FPN output channels
min_level (int): lowest level of FPN output min_level (int): lowest level of FPN output
max_level (int): highest level of FPN output max_level (int): highest level of FPN output
num_classes (int): number of classes in rpn output
""" """
__inject__ = [ __inject__ = [
......
...@@ -29,13 +29,13 @@ class FPNRoIAlign(object): ...@@ -29,13 +29,13 @@ class FPNRoIAlign(object):
""" """
RoI align pooling for FPN feature maps RoI align pooling for FPN feature maps
Args: Args:
pooled_height (int): output height
pooled_height (int): output width
sampling_ratio (int): number of sampling points sampling_ratio (int): number of sampling points
min_level (int): lowest level of FPN layer min_level (int): lowest level of FPN layer
max_level (int): highest level of FPN layer max_level (int): highest level of FPN layer
canconical_level (int): the canconical FPN feature map level canconical_level (int): the canconical FPN feature map level
canonical_size (int): the canconical FPN feature map size canonical_size (int): the canconical FPN feature map size
box_resolution (int): box resolution
mask_resolution (int): mask roi resolution
""" """
def __init__(self, def __init__(self,
......
...@@ -31,7 +31,7 @@ class MaskHead(object): ...@@ -31,7 +31,7 @@ class MaskHead(object):
""" """
RCNN mask head RCNN mask head
Args: Args:
num_convs (int): num of convolutions, 4 for FPN, 1 otherwise num_convs (int): num of convolutions, 4 for FPN, 0 otherwise
num_chan_reduced (int): num of channels after first convolution num_chan_reduced (int): num of channels after first convolution
resolution (int): size of the output mask resolution (int): size of the output mask
dilation (int): dilation rate dilation (int): dilation rate
......
...@@ -40,7 +40,7 @@ class PiecewiseDecay(object): ...@@ -40,7 +40,7 @@ class PiecewiseDecay(object):
milestones (list): steps at which to decay learning rate milestones (list): steps at which to decay learning rate
""" """
def __init__(self, gamma=0.1, milestones=[6000, 8000], values=None): def __init__(self, gamma=0.1, milestones=[60000, 80000], values=None):
super(PiecewiseDecay, self).__init__() super(PiecewiseDecay, self).__init__()
self.gamma = gamma self.gamma = gamma
self.milestones = milestones self.milestones = milestones
......
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