mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2026-09-24 23:33:08 +08:00
add resnet
This commit is contained in:
wangjingyeye
@@ -0,0 +1,166 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 1000
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/det_r50_td_tr/
|
||||
save_epoch_step: 200
|
||||
eval_batch_step:
|
||||
- 0
|
||||
- 2000
|
||||
cal_metric_during_train: false
|
||||
pretrained_model: ./pretrain_models/synthtext_pretrained_res50_dcn_asf_spatial
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./checkpoints/det_db/predicts_db.txt
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: ResNet
|
||||
layers: 50
|
||||
dcn_stage: [False, True, True, True]
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
use_asf: True
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: BCELoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: DecayLearningRate
|
||||
learning_rate: 0.007
|
||||
epochs: 1000
|
||||
factor: 0.9
|
||||
end_lr: 0
|
||||
weight_decay: 0.0001
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.5
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/TD_TR/TD500/train_gt_labels.txt
|
||||
- ./train_data/TD_TR/TR400/gt_labels.txt
|
||||
ratio_list:
|
||||
- 1.0
|
||||
- 1.0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- IaaAugment:
|
||||
augmenter_args:
|
||||
- type: Fliplr
|
||||
args:
|
||||
p: 0.5
|
||||
- type: Affine
|
||||
args:
|
||||
rotate:
|
||||
- -10
|
||||
- 10
|
||||
- type: Resize
|
||||
args:
|
||||
size:
|
||||
- 0.5
|
||||
- 3
|
||||
- EastRandomCropData:
|
||||
size:
|
||||
- 640
|
||||
- 640
|
||||
max_tries: 10
|
||||
keep_ratio: true
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.48109378172549
|
||||
- 0.45752457890196
|
||||
- 0.40787054090196
|
||||
std:
|
||||
- 1.0
|
||||
- 1.0
|
||||
- 1.0
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- threshold_map
|
||||
- threshold_mask
|
||||
- shrink_map
|
||||
- shrink_mask
|
||||
loader:
|
||||
shuffle: true
|
||||
drop_last: false
|
||||
batch_size_per_card: 4
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
label_file_list:
|
||||
- ./train_data/TD_TR/TD500/test_gt_labels.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- DetLabelEncode: null
|
||||
- DetResizeForTest:
|
||||
image_shape:
|
||||
- 736
|
||||
- 736
|
||||
keep_ratio: True
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean:
|
||||
- 0.48109378172549
|
||||
- 0.45752457890196
|
||||
- 0.40787054090196
|
||||
std:
|
||||
- 1.0
|
||||
- 1.0
|
||||
- 1.0
|
||||
order: hwc
|
||||
- ToCHWImage: null
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- shape
|
||||
- polys
|
||||
- ignore_tags
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 1
|
||||
num_workers: 2
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,237 @@
|
||||
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# 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 paddle
|
||||
from paddle import ParamAttr
|
||||
import paddle.nn as nn
|
||||
import paddle.nn.functional as F
|
||||
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
|
||||
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
|
||||
from paddle.nn.initializer import Uniform
|
||||
|
||||
import math
|
||||
|
||||
from paddle.vision.ops import DeformConv2D
|
||||
from paddle.regularizer import L2Decay
|
||||
from paddle.nn.initializer import Normal, Constant, XavierUniform
|
||||
from .det_resnet_vd import DeformableConvV2, ConvBNLayer
|
||||
|
||||
|
||||
class BottleneckBlock(nn.Layer):
|
||||
def __init__(self,
|
||||
num_channels,
|
||||
num_filters,
|
||||
stride,
|
||||
shortcut=True,
|
||||
is_dcn=False):
|
||||
super(BottleneckBlock, self).__init__()
|
||||
|
||||
self.conv0 = ConvBNLayer(
|
||||
in_channels=num_channels,
|
||||
out_channels=num_filters,
|
||||
kernel_size=1,
|
||||
act="relu", )
|
||||
self.conv1 = ConvBNLayer(
|
||||
in_channels=num_filters,
|
||||
out_channels=num_filters,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
act="relu",
|
||||
is_dcn=is_dcn,
|
||||
dcn_groups=1, )
|
||||
self.conv2 = ConvBNLayer(
|
||||
in_channels=num_filters,
|
||||
out_channels=num_filters * 4,
|
||||
kernel_size=1,
|
||||
act=None, )
|
||||
|
||||
if not shortcut:
|
||||
self.short = ConvBNLayer(
|
||||
in_channels=num_channels,
|
||||
out_channels=num_filters * 4,
|
||||
kernel_size=1,
|
||||
stride=stride, )
|
||||
|
||||
self.shortcut = shortcut
|
||||
|
||||
self._num_channels_out = num_filters * 4
|
||||
|
||||
def forward(self, inputs):
|
||||
y = self.conv0(inputs)
|
||||
conv1 = self.conv1(y)
|
||||
conv2 = self.conv2(conv1)
|
||||
|
||||
if self.shortcut:
|
||||
short = inputs
|
||||
else:
|
||||
short = self.short(inputs)
|
||||
|
||||
y = paddle.add(x=short, y=conv2)
|
||||
y = F.relu(y)
|
||||
return y
|
||||
|
||||
|
||||
class BasicBlock(nn.Layer):
|
||||
def __init__(self,
|
||||
num_channels,
|
||||
num_filters,
|
||||
stride,
|
||||
shortcut=True,
|
||||
name=None):
|
||||
super(BasicBlock, self).__init__()
|
||||
self.stride = stride
|
||||
self.conv0 = ConvBNLayer(
|
||||
in_channels=num_channels,
|
||||
out_channels=num_filters,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
act="relu")
|
||||
self.conv1 = ConvBNLayer(
|
||||
in_channels=num_filters,
|
||||
out_channels=num_filters,
|
||||
kernel_size=3,
|
||||
act=None)
|
||||
|
||||
if not shortcut:
|
||||
self.short = ConvBNLayer(
|
||||
in_channels=num_channels,
|
||||
out_channels=num_filters,
|
||||
kernel_size=1,
|
||||
stride=stride)
|
||||
|
||||
self.shortcut = shortcut
|
||||
|
||||
def forward(self, inputs):
|
||||
y = self.conv0(inputs)
|
||||
conv1 = self.conv1(y)
|
||||
|
||||
if self.shortcut:
|
||||
short = inputs
|
||||
else:
|
||||
short = self.short(inputs)
|
||||
y = paddle.add(x=short, y=conv1)
|
||||
y = F.relu(y)
|
||||
return y
|
||||
|
||||
|
||||
class ResNet(nn.Layer):
|
||||
def __init__(self,
|
||||
in_channels=3,
|
||||
layers=50,
|
||||
out_indices=None,
|
||||
dcn_stage=None):
|
||||
super(ResNet, self).__init__()
|
||||
|
||||
self.layers = layers
|
||||
self.input_image_channel = in_channels
|
||||
|
||||
supported_layers = [18, 34, 50, 101, 152]
|
||||
assert layers in supported_layers, \
|
||||
"supported layers are {} but input layer is {}".format(
|
||||
supported_layers, layers)
|
||||
|
||||
if layers == 18:
|
||||
depth = [2, 2, 2, 2]
|
||||
elif layers == 34 or layers == 50:
|
||||
depth = [3, 4, 6, 3]
|
||||
elif layers == 101:
|
||||
depth = [3, 4, 23, 3]
|
||||
elif layers == 152:
|
||||
depth = [3, 8, 36, 3]
|
||||
num_channels = [64, 256, 512,
|
||||
1024] if layers >= 50 else [64, 64, 128, 256]
|
||||
num_filters = [64, 128, 256, 512]
|
||||
|
||||
self.dcn_stage = dcn_stage if dcn_stage is not None else [
|
||||
False, False, False, False
|
||||
]
|
||||
self.out_indices = out_indices if out_indices is not None else [
|
||||
0, 1, 2, 3
|
||||
]
|
||||
|
||||
self.conv = ConvBNLayer(
|
||||
in_channels=self.input_image_channel,
|
||||
out_channels=64,
|
||||
kernel_size=7,
|
||||
stride=2,
|
||||
act="relu", )
|
||||
self.pool2d_max = MaxPool2D(
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1, )
|
||||
|
||||
self.stages = []
|
||||
self.out_channels = []
|
||||
if layers >= 50:
|
||||
for block in range(len(depth)):
|
||||
shortcut = False
|
||||
block_list = []
|
||||
is_dcn = self.dcn_stage[block]
|
||||
for i in range(depth[block]):
|
||||
if layers in [101, 152] and block == 2:
|
||||
if i == 0:
|
||||
conv_name = "res" + str(block + 2) + "a"
|
||||
else:
|
||||
conv_name = "res" + str(block + 2) + "b" + str(i)
|
||||
else:
|
||||
conv_name = "res" + str(block + 2) + chr(97 + i)
|
||||
bottleneck_block = self.add_sublayer(
|
||||
conv_name,
|
||||
BottleneckBlock(
|
||||
num_channels=num_channels[block]
|
||||
if i == 0 else num_filters[block] * 4,
|
||||
num_filters=num_filters[block],
|
||||
stride=2 if i == 0 and block != 0 else 1,
|
||||
shortcut=shortcut,
|
||||
is_dcn=is_dcn))
|
||||
block_list.append(bottleneck_block)
|
||||
shortcut = True
|
||||
if block in self.out_indices:
|
||||
self.out_channels.append(num_filters[block] * 4)
|
||||
self.stages.append(nn.Sequential(*block_list))
|
||||
else:
|
||||
for block in range(len(depth)):
|
||||
shortcut = False
|
||||
block_list = []
|
||||
# is_dcn = self.dcn_stage[block]
|
||||
for i in range(depth[block]):
|
||||
conv_name = "res" + str(block + 2) + chr(97 + i)
|
||||
basic_block = self.add_sublayer(
|
||||
conv_name,
|
||||
BasicBlock(
|
||||
num_channels=num_channels[block]
|
||||
if i == 0 else num_filters[block],
|
||||
num_filters=num_filters[block],
|
||||
stride=2 if i == 0 and block != 0 else 1,
|
||||
shortcut=shortcut))
|
||||
block_list.append(basic_block)
|
||||
shortcut = True
|
||||
if block in self.out_indices:
|
||||
self.out_channels.append(num_filters[block])
|
||||
self.stages.append(nn.Sequential(*block_list))
|
||||
|
||||
def forward(self, inputs):
|
||||
y = self.conv(inputs)
|
||||
y = self.pool2d_max(y)
|
||||
out = []
|
||||
for i, block in enumerate(self.stages):
|
||||
y = block(y)
|
||||
if i in self.out_indices:
|
||||
out.append(y)
|
||||
return out
|
||||
Reference in New Issue
Block a user