未验证 提交 fb82692a 编写于 作者: X xinyingxinying 提交者: GitHub

Add dcn on fcos head and backbone (#562)

* #add dcn on  FCOS_head and backbone
上级 4a7cba60
......@@ -30,6 +30,7 @@
| CornerNet-Squeeze-dcn-mixup-cosine* | ResNet50-vd | 14 | [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar) | 38.2 | 40.05 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.pdparams) |
| FCOS | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 39.8 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_1x.pdparams) |
| FCOS+multiscale_train | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 42.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_multiscale_2x.pdparams) |
| FCOS+DCN | ResNet50 | 2 | [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar) | 44.4 | - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_dcn_r50_fpn_1x.pdparams) |
**注意:**
......
architecture: FCOS
max_iters: 90000
use_gpu: true
snapshot_iter: 5000
log_smooth_window: 20
log_iter: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
metric: COCO
weights: output/fcos_dcn_r50_fpn_1x/model_final
num_classes: 81
FCOS:
backbone: ResNet
fpn: FPN
fcos_head: FCOSHead
ResNet:
norm_type: affine_channel
norm_decay: 0.
depth: 50
feature_maps: [3, 4, 5]
freeze_at: 2
dcn_v2_stages: [3, 4, 5]
FPN:
min_level: 3
max_level: 7
num_chan: 256
use_c5: false
spatial_scale: [0.03125, 0.0625, 0.125]
has_extra_convs: true
FCOSHead:
num_classes: 81
fpn_stride: [8, 16, 32, 64, 128]
num_convs: 4
norm_type: "gn"
fcos_loss: FCOSLoss
norm_reg_targets: True
centerness_on_reg: True
use_dcn_in_tower: True
nms: MultiClassNMS
MultiClassNMS:
score_threshold: 0.025
nms_top_k: 1000
keep_top_k: 100
nms_threshold: 0.6
background_label: -1
FCOSLoss:
loss_alpha: 0.25
loss_gamma: 2.0
iou_loss_type: "giou"
reg_weights: 1.0
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [60000, 80000]
- !LinearWarmup
start_factor: 0.3333333333333333
steps: 500
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
TrainReader:
inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score', 'im_info']
dataset:
!COCODataSet
image_dir: train2017
anno_path: annotations/instances_train2017.json
dataset_dir: dataset/coco
with_background: true
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: 128
use_padded_im_info: false
- !Gt2FCOSTarget
object_sizes_boundary: [64, 128, 256, 512]
center_sampling_radius: 1.5
downsample_ratios: [8, 16, 32, 64, 128]
norm_reg_targets: True
batch_size: 2
shuffle: true
worker_num: 16
use_process: false
EvalReader:
inputs_def:
fields: ['image', 'im_id', 'im_shape', 'im_info']
dataset:
!COCODataSet
image_dir: val2017
anno_path: annotations/instances_val2017.json
dataset_dir: dataset/coco
with_background: false
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
target_size: 800
max_size: 1333
interp: 1
use_cv2: true
- !Permute
channel_first: true
to_bgr: false
batch_transforms:
- !PadBatch
pad_to_stride: 128
use_padded_im_info: true
batch_size: 8
shuffle: false
worker_num: 2
use_process: false
TestReader:
inputs_def:
# set image_shape if needed
fields: ['image', 'im_id', 'im_shape', 'im_info']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
with_background: false
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: 128
use_padded_im_info: true
batch_size: 1
shuffle: false
......@@ -22,7 +22,7 @@ import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.initializer import Normal, Constant, NumpyArrayInitializer
from paddle.fluid.regularizer import L2Decay
from ppdet.modeling.ops import ConvNorm
from ppdet.modeling.ops import ConvNorm, DeformConvNorm
from ppdet.modeling.ops import MultiClassNMS
from ppdet.core.workspace import register
......@@ -89,9 +89,13 @@ class FCOSHead(object):
subnet_blob_cls = features
subnet_blob_reg = features
in_channles = features.shape[1]
if self.use_dcn_in_tower:
conv_norm = DeformConvNorm
else:
conv_norm = ConvNorm
for lvl in range(0, self.num_convs):
conv_cls_name = 'fcos_head_cls_tower_conv_{}'.format(lvl)
subnet_blob_cls = ConvNorm(
subnet_blob_cls = conv_norm(
input=subnet_blob_cls,
num_filters=in_channles,
filter_size=3,
......@@ -104,7 +108,7 @@ class FCOSHead(object):
norm_name=conv_cls_name + "_norm",
name=conv_cls_name)
conv_reg_name = 'fcos_head_reg_tower_conv_{}'.format(lvl)
subnet_blob_reg = ConvNorm(
subnet_blob_reg = conv_norm(
input=subnet_blob_reg,
num_filters=in_channles,
filter_size=3,
......
......@@ -27,11 +27,139 @@ __all__ = [
'AnchorGenerator', 'DropBlock', 'RPNTargetAssign', 'GenerateProposals',
'MultiClassNMS', 'BBoxAssigner', 'MaskAssigner', 'RoIAlign', 'RoIPool',
'MultiBoxHead', 'SSDLiteMultiBoxHead', 'SSDOutputDecoder',
'RetinaTargetAssign', 'RetinaOutputDecoder', 'ConvNorm',
'RetinaTargetAssign', 'RetinaOutputDecoder', 'ConvNorm', 'DeformConvNorm',
'MultiClassSoftNMS', 'LibraBBoxAssigner'
]
def _conv_offset(input, filter_size, stride, padding, act=None, name=None):
out_channel = filter_size * filter_size * 3
out = fluid.layers.conv2d(
input,
num_filters=out_channel,
filter_size=filter_size,
stride=stride,
padding=padding,
param_attr=ParamAttr(
initializer=fluid.initializer.Constant(value=0),
name=name + ".w_0"),
bias_attr=ParamAttr(
initializer=fluid.initializer.Constant(value=0),
name=name + ".b_0"),
act=act,
name=name)
return out
def DeformConvNorm(input,
num_filters,
filter_size,
stride=1,
groups=1,
norm_decay=0.,
norm_type='affine_channel',
norm_groups=32,
dilation=1,
lr_scale=1,
freeze_norm=False,
act=None,
norm_name=None,
initializer=None,
bias_attr=False,
name=None):
if bias_attr:
bias_para = ParamAttr(
name=name + "_bias",
initializer=fluid.initializer.Constant(value=0),
learning_rate=lr_scale * 2)
else:
bias_para = False
offset_mask = _conv_offset(
input=input,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
act=None,
name=name + "_conv_offset")
offset_channel = filter_size**2 * 2
mask_channel = filter_size**2
offset, mask = fluid.layers.split(
input=offset_mask,
num_or_sections=[offset_channel, mask_channel],
dim=1)
mask = fluid.layers.sigmoid(mask)
conv = fluid.layers.deformable_conv(
input=input,
offset=offset,
mask=mask,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2 * dilation,
dilation=dilation,
groups=groups,
deformable_groups=1,
im2col_step=1,
param_attr=ParamAttr(
name=name + "_weights",
initializer=initializer,
learning_rate=lr_scale),
bias_attr=bias_para,
name=name + ".conv2d.output.1")
norm_lr = 0. if freeze_norm else 1.
pattr = ParamAttr(
name=norm_name + '_scale',
learning_rate=norm_lr * lr_scale,
regularizer=L2Decay(norm_decay))
battr = ParamAttr(
name=norm_name + '_offset',
learning_rate=norm_lr * lr_scale,
regularizer=L2Decay(norm_decay))
if norm_type in ['bn', 'sync_bn']:
global_stats = True if freeze_norm else False
out = fluid.layers.batch_norm(
input=conv,
act=act,
name=norm_name + '.output.1',
param_attr=pattr,
bias_attr=battr,
moving_mean_name=norm_name + '_mean',
moving_variance_name=norm_name + '_variance',
use_global_stats=global_stats)
scale = fluid.framework._get_var(pattr.name)
bias = fluid.framework._get_var(battr.name)
elif norm_type == 'gn':
out = fluid.layers.group_norm(
input=conv,
act=act,
name=norm_name + '.output.1',
groups=norm_groups,
param_attr=pattr,
bias_attr=battr)
scale = fluid.framework._get_var(pattr.name)
bias = fluid.framework._get_var(battr.name)
elif norm_type == 'affine_channel':
scale = fluid.layers.create_parameter(
shape=[conv.shape[1]],
dtype=conv.dtype,
attr=pattr,
default_initializer=fluid.initializer.Constant(1.))
bias = fluid.layers.create_parameter(
shape=[conv.shape[1]],
dtype=conv.dtype,
attr=battr,
default_initializer=fluid.initializer.Constant(0.))
out = fluid.layers.affine_channel(
x=conv, scale=scale, bias=bias, act=act)
if freeze_norm:
scale.stop_gradient = True
bias.stop_gradient = True
return out
def ConvNorm(input,
num_filters,
filter_size,
......
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