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Merge pull request #952 from WenmuZhou/dygraph 

使用PaddleClass的resnet_vd
This commit is contained in:
zhoujun 2020-10-16 20:21:45 +08:00 committed by GitHub
parent 7d3ba1f13d bdad0cefe6
commit 52b40f36e5
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GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 540 additions and 517 deletions
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14
configs/det/det_mv3_db.yml
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@ -3,7 +3,7 @@ Global:
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/20201010/
save_model_dir: ./output/db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 8
@ -66,9 +66,9 @@ Metric:
TRAIN:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/detection/
data_dir: ./detection/
file_list:
- /home/zhoujun20/detection/train_icdar2015_label.txt # dataset1
- ./detection/train_icdar2015_label.txt # dataset1
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
@ -103,14 +103,14 @@ TRAIN:
shuffle: True
drop_last: False
batch_size: 16
num_workers: 6
num_workers: 8
EVAL:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/detection/
data_dir: ./detection/
file_list:
- /home/zhoujun20/detection/test_icdar2015_label.txt
- ./detection/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
@ -130,4 +130,4 @@ EVAL:
shuffle: False
drop_last: False
batch_size: 1 # must be 1
num_workers: 6
num_workers: 8

8
configs/det/det_r50_vd_db.yml
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@ -3,14 +3,14 @@ Global:
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/20201010/
save_model_dir: ./output/20201015_r50/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 8
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: False
pretrained_model: /home/zhoujun20/pretrain_models/MobileNetV3_large_x0_5_pretrained
pretrained_model: /home/zhoujun20/pretrain_models/ResNet50_vd_ssld_pretrained/
checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
save_inference_dir:
use_visualdl: True
@ -102,7 +102,7 @@ TRAIN:
shuffle: True
drop_last: False
batch_size: 16
num_workers: 6
num_workers: 8
EVAL:
dataset:
@ -129,4 +129,4 @@ EVAL:
shuffle: False
drop_last: False
batch_size: 1 # must be 1
num_workers: 6
num_workers: 8

4
configs/rec/rec_mv3_none_bilstm_ctc.yml
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@ -84,7 +84,7 @@ TRAIN:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 6
num_workers: 8
EVAL:
dataset:
@ -105,4 +105,4 @@ EVAL:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 6
num_workers: 8

4
configs/rec/rec_mv3_none_bilstm_ctc_lmdb.yml
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@ -83,7 +83,7 @@ TRAIN:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 6
num_workers: 8
EVAL:
dataset:
@ -103,4 +103,4 @@ EVAL:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 6
num_workers: 8

105
configs/rec/rec_mv3_none_none_ctc_lmdb.yml Normal file
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@ -0,0 +1,105 @@
Global:
use_gpu: false
epoch_num: 500
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/rec/test/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 1016
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
checkpoints: #output/rec/rec_crnn/best_accuracy
save_inference_dir:
use_visualdl: True
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
max_text_length: 80
character_dict_path: /home/zhoujun20/rec/lmdb/dict.txt
character_type: 'en'
use_space_char: True
infer_mode: False
use_tps: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
name: Cosine
lr: 0.0005
warmup_epoch: 1
regularizer:
name: 'L2'
factor: 0.00001
Architecture:
type: rec
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride: [ 1, 2, 2, 2 ]
Neck:
name: SequenceEncoder
encoder_type: reshape
Head:
name: CTC
fc_decay: 0.00001
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
TRAIN:
dataset:
name: LMDBDateSet
file_list:
- /Users/zhoujun20/Downloads/evaluation_new # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecAug:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
loader:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 8
EVAL:
dataset:
name: LMDBDateSet
file_list:
- /home/zhoujun20/rec/lmdb/val
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
loader:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 8

6
configs/rec/rec_r34_vd_none_bilstm_ctc.yml
View File

@ -42,7 +42,7 @@ Architecture:
Transform:
Backbone:
name: ResNet
layers: 200
layers: 34
Neck:
name: SequenceEncoder
encoder_type: fc
@ -82,7 +82,7 @@ TRAIN:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 6
num_workers: 8
EVAL:
dataset:
@ -103,4 +103,4 @@ EVAL:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 6
num_workers: 8

20
ppocr/modeling/architectures/model.py
View File

@ -94,13 +94,11 @@ def check_static():
from ppocr.utils.logging import get_logger
from tools import program
config = program.load_config('configs/det/det_r50_vd_db.yml')
config = program.load_config('configs/rec/rec_r34_vd_none_bilstm_ctc.yml')
# import cv2
# data = cv2.imread('doc/imgs/1.jpg')
# data = normalize(data)
logger = get_logger()
data = np.zeros((1, 3, 640, 640), dtype=np.float32)
np.random.seed(0)
data = np.random.rand(1, 3, 32, 320).astype(np.float32)
paddle.disable_static()
config['Architecture']['in_channels'] = 3
@ -110,17 +108,15 @@ def check_static():
load_dygraph_pretrain(
model,
logger,
'/Users/zhoujun20/Desktop/code/PaddleOCR/db/db',
'/Users/zhoujun20/Desktop/code/PaddleOCR/cnn_ctc/cnn_ctc',
load_static_weights=True)
x = paddle.to_variable(data)
x = paddle.to_tensor(data)
y = model(x)
for y1 in y:
print(y1.shape)
#
# # from matplotlib import pyplot as plt
# # plt.imshow(y.numpy())
# # plt.show()
static_out = np.load('/Users/zhoujun20/Desktop/code/PaddleOCR/db/db.npy')
static_out = np.load(
'/Users/zhoujun20/Desktop/code/PaddleOCR/output/conv.npy')
diff = y.numpy() - static_out
print(y.shape, static_out.shape, diff.mean())

438
ppocr/modeling/backbones/det_resnet_vd.py
View File

@ -16,143 +16,30 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle import nn
from paddle.nn import functional as F
import paddle
from paddle import ParamAttr
import paddle.nn as nn
__all__ = ["ResNet"]
class ResNet(nn.Layer):
def __init__(self, in_channels=3, layers=50, **kwargs):
"""
the Resnet backbone network for detection module.
Args:
params(dict): the super parameters for network build
"""
super(ResNet, self).__init__()
supported_layers = {
18: {
'depth': [2, 2, 2, 2],
'block_class': BasicBlock
},
34: {
'depth': [3, 4, 6, 3],
'block_class': BasicBlock
},
50: {
'depth': [3, 4, 6, 3],
'block_class': BottleneckBlock
},
101: {
'depth': [3, 4, 23, 3],
'block_class': BottleneckBlock
},
152: {
'depth': [3, 8, 36, 3],
'block_class': BottleneckBlock
},
200: {
'depth': [3, 12, 48, 3],
'block_class': BottleneckBlock
}
}
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers.keys(), layers)
is_3x3 = True
depth = supported_layers[layers]['depth']
block_class = supported_layers[layers]['block_class']
num_filters = [64, 128, 256, 512]
conv = []
if is_3x3 == False:
conv.append(
ConvBNLayer(
in_channels=in_channels,
out_channels=64,
kernel_size=7,
stride=2,
act='relu'))
else:
conv.append(
ConvBNLayer(
in_channels=3,
out_channels=32,
kernel_size=3,
stride=2,
act='relu',
name='conv1_1'))
conv.append(
ConvBNLayer(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
name='conv1_2'))
conv.append(
ConvBNLayer(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
act='relu',
name='conv1_3'))
self.conv1 = nn.Sequential(*conv)
self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.stages = []
self.out_channels = []
in_ch = 64
for block_index in range(len(depth)):
block_list = []
for i in range(depth[block_index]):
if layers >= 50:
if layers in [101, 152, 200] and block_index == 2:
if i == 0:
conv_name = "res" + str(block_index + 2) + "a"
else:
conv_name = "res" + str(block_index +
2) + "b" + str(i)
else:
conv_name = "res" + str(block_index + 2) + chr(97 + i)
else:
conv_name = "res" + str(block_index + 2) + chr(97 + i)
block_list.append(
block_class(
in_channels=in_ch,
out_channels=num_filters[block_index],
stride=2 if i == 0 and block_index != 0 else 1,
if_first=block_index == i == 0,
name=conv_name))
in_ch = block_list[-1].out_channels
self.out_channels.append(in_ch)
self.stages.append(nn.Sequential(*block_list))
for i, stage in enumerate(self.stages):
self.add_sublayer(sublayer=stage, name="stage{}".format(i))
def forward(self, x):
x = self.conv1(x)
x = self.pool(x)
out_list = []
for stage in self.stages:
x = stage(x)
out_list.append(x)
return out_list
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
name=None):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
name=None, ):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2d(
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2d(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@ -165,87 +52,32 @@ class ConvBNLayer(nn.Layer):
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self.bn = nn.BatchNorm(
num_channels=out_channels,
self._batch_norm = nn.BatchNorm(
out_channels,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance")
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def __call__(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class ConvBNLayerNew(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayerNew, self).__init__()
self.pool = nn.AvgPool2d(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self.bn = nn.BatchNorm(
num_channels=out_channels,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance")
def __call__(self, x):
x = self.pool(x)
x = self.conv(x)
x = self.bn(x)
return x
class ShortCut(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first=False):
super(ShortCut, self).__init__()
self.use_conv = True
if in_channels != out_channels or stride != 1:
if if_first:
self.conv = ConvBNLayer(
in_channels, out_channels, 1, stride, name=name)
else:
self.conv = ConvBNLayerNew(
in_channels, out_channels, 1, stride, name=name)
elif if_first:
self.conv = ConvBNLayer(
in_channels, out_channels, 1, stride, name=name)
else:
self.use_conv = False
def forward(self, x):
if self.use_conv:
x = self.conv(x)
return x
def forward(self, inputs):
if self.is_vd_mode:
inputs = self._pool2d_avg(inputs)
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
@ -266,32 +98,46 @@ class BottleneckBlock(nn.Layer):
act=None,
name=name + "_branch2c")
self.short = ShortCut(
in_channels=in_channels,
out_channels=out_channels * 4,
stride=stride,
if_first=if_first,
name=name + "_branch1")
self.out_channels = out_channels * 4
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels * 4,
kernel_size=1,
stride=1,
is_vd_mode=False if if_first else True,
name=name + "_branch1")
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = self.conv2(y)
y = y + self.short(x)
y = F.relu(y)
self.shortcut = shortcut
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.elementwise_add(x=short, y=conv2, act='relu')
return y
class BasicBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
act='relu',
stride=stride,
act='relu',
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
in_channels=out_channels,
@ -299,31 +145,133 @@ class BasicBlock(nn.Layer):
kernel_size=3,
act=None,
name=name + "_branch2b")
self.short = ShortCut(
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
is_vd_mode=False if if_first else True,
name=name + "_branch1")
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.elementwise_add(x=short, y=conv1, act='relu')
return y
class ResNet(nn.Layer):
def __init__(self, in_channels=3, layers=50, **kwargs):
super(ResNet, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
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]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512,
1024] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
self.conv1_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
if_first=if_first,
name=name + "_branch1")
self.out_channels = out_channels
out_channels=32,
kernel_size=3,
stride=2,
act='relu',
name="conv1_1")
self.conv1_2 = ConvBNLayer(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
name="conv1_2")
self.conv1_3 = ConvBNLayer(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
act='relu',
name="conv1_3")
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = y + self.short(x)
return F.relu(y)
self.stages = []
self.out_channels = []
if layers >= 50:
for block in range(len(depth)):
block_list = []
shortcut = False
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(
'bb_%d_%d' % (block, i),
BottleneckBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
shortcut = True
block_list.append(bottleneck_block)
self.out_channels.append(num_filters[block] * 4)
self.stages.append(nn.Sequential(*block_list))
else:
for block in range(len(depth)):
block_list = []
shortcut = False
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
basic_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BasicBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block],
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
shortcut = True
block_list.append(basic_block)
self.out_channels.append(num_filters[block])
self.stages.append(nn.Sequential(*block_list))
if __name__ == '__main__':
import paddle
paddle.disable_static()
x = paddle.zeros([1, 3, 640, 640])
x = paddle.to_variable(x)
print(x.shape)
net = ResNet(layers=18)
y = net(x)
for stage in y:
print(stage.shape)
# paddle.save(net.state_dict(),'1.pth')
def forward(self, inputs):
y = self.conv1_1(inputs)
y = self.conv1_2(y)
y = self.conv1_3(y)
y = self.pool2d_max(y)
out = []
for block in self.stages:
y = block(y)
out.append(y)
return out

441
ppocr/modeling/backbones/rec_resnet_vd.py
View File

@ -16,184 +16,34 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import paddle
from paddle import ParamAttr
import paddle.nn as nn
__all__ = ["ResNet"]
class ResNet(nn.Layer):
def __init__(self, in_channels=3, layers=34):
super(ResNet, self).__init__()
supported_layers = {
18: {
'depth': [2, 2, 2, 2],
'block_class': BasicBlock
},
34: {
'depth': [3, 4, 6, 3],
'block_class': BasicBlock
},
50: {
'depth': [3, 4, 6, 3],
'block_class': BottleneckBlock
},
101: {
'depth': [3, 4, 23, 3],
'block_class': BottleneckBlock
},
152: {
'depth': [3, 8, 36, 3],
'block_class': BottleneckBlock
},
200: {
'depth': [3, 12, 48, 3],
'block_class': BottleneckBlock
}
}
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers.keys(), layers)
is_3x3 = True
num_filters = [64, 128, 256, 512]
depth = supported_layers[layers]['depth']
block_class = supported_layers[layers]['block_class']
conv = []
if is_3x3 == False:
conv.append(
ConvBNLayer(
in_channels=in_channels,
out_channels=64,
kernel_size=7,
stride=1,
act='relu'))
else:
conv.append(
ConvBNLayer(
in_channels=in_channels,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
name='conv1_1'))
conv.append(
ConvBNLayer(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
name='conv1_2'))
conv.append(
ConvBNLayer(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
act='relu',
name='conv1_3'))
self.conv1 = nn.Sequential(*conv)
self.pool = nn.MaxPool2d(
kernel_size=3,
stride=2,
padding=1, )
block_list = []
in_ch = 64
for block_index in range(len(depth)):
for i in range(depth[block_index]):
if layers >= 50:
if layers in [101, 152, 200] and block_index == 2:
if i == 0:
conv_name = "res" + str(block_index + 2) + "a"
else:
conv_name = "res" + str(block_index +
2) + "b" + str(i)
else:
conv_name = "res" + str(block_index + 2) + chr(97 + i)
else:
conv_name = "res" + str(block_index + 2) + chr(97 + i)
if i == 0 and block_index != 0:
stride = (2, 1)
else:
stride = (1, 1)
block_list.append(
block_class(
in_channels=in_ch,
out_channels=num_filters[block_index],
stride=stride,
if_first=block_index == i == 0,
name=conv_name))
in_ch = block_list[-1].out_channels
self.block_list = nn.Sequential(*block_list)
self.add_sublayer(sublayer=self.block_list, name="block_list")
self.pool_out = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.out_channels = in_ch
def forward(self, x):
x = self.conv1(x)
x = self.pool(x)
x = self.block_list(x)
x = self.pool_out(x)
return x
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self.bn = nn.BatchNorm(
num_channels=out_channels,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance")
def __call__(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class ConvBNLayerNew(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayerNew, self).__init__()
self.pool = nn.AvgPool2d(
kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
name=None, ):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2d(
kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
self._conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1 if is_vd_mode else stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
@ -202,48 +52,32 @@ class ConvBNLayerNew(nn.Layer):
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self.bn = nn.BatchNorm(
num_channels=out_channels,
self._batch_norm = nn.BatchNorm(
out_channels,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance")
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def __call__(self, x):
x = self.pool(x)
x = self.conv(x)
x = self.bn(x)
return x
class ShortCut(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first=False):
super(ShortCut, self).__init__()
self.use_conv = True
if in_channels != out_channels or stride[0] != 1:
if if_first:
self.conv = ConvBNLayer(
in_channels, out_channels, 1, stride, name=name)
else:
self.conv = ConvBNLayerNew(
in_channels, out_channels, 1, stride, name=name)
elif if_first:
self.conv = ConvBNLayer(
in_channels, out_channels, 1, stride, name=name)
else:
self.use_conv = False
def forward(self, x):
if self.use_conv:
x = self.conv(x)
return x
def forward(self, inputs):
if self.is_vd_mode:
inputs = self._pool2d_avg(inputs)
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
@ -264,32 +98,47 @@ class BottleneckBlock(nn.Layer):
act=None,
name=name + "_branch2c")
self.short = ShortCut(
in_channels=in_channels,
out_channels=out_channels * 4,
stride=stride,
if_first=if_first,
name=name + "_branch1")
self.out_channels = out_channels * 4
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels * 4,
kernel_size=1,
stride=stride,
is_vd_mode=not if_first and stride[0] != 1,
name=name + "_branch1")
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = self.conv2(y)
y = y + self.short(x)
y = F.relu(y)
self.shortcut = shortcut
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.elementwise_add(x=short, y=conv2, act='relu')
return y
class BasicBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
act='relu',
stride=stride,
act='relu',
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
in_channels=out_channels,
@ -297,16 +146,138 @@ class BasicBlock(nn.Layer):
kernel_size=3,
act=None,
name=name + "_branch2b")
self.short = ShortCut(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
if_first=if_first,
name=name + "_branch1")
self.out_channels = out_channels
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = y + self.short(x)
return F.relu(y)
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=stride,
is_vd_mode=not if_first and stride[0] != 1,
name=name + "_branch1")
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.elementwise_add(x=short, y=conv1, act='relu')
return y
class ResNet(nn.Layer):
def __init__(self, in_channels=3, layers=50, **kwargs):
super(ResNet, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
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]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512,
1024] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
self.conv1_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
name="conv1_1")
self.conv1_2 = ConvBNLayer(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
name="conv1_2")
self.conv1_3 = ConvBNLayer(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
act='relu',
name="conv1_3")
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
if layers in [101, 152, 200] 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)
if i == 0 and block != 0:
stride = (2, 1)
else:
stride = (1, 1)
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
out_channels=num_filters[block],
stride=stride,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
shortcut = True
self.block_list.append(bottleneck_block)
self.out_channels = num_filters[block]
else:
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
if i == 0 and block != 0:
stride = (2, 1)
else:
stride = (1, 1)
basic_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BasicBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block],
out_channels=num_filters[block],
stride=stride,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
shortcut = True
self.block_list.append(basic_block)
self.out_channels = num_filters[block]
self.out_pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
def forward(self, inputs):
y = self.conv1_1(inputs)
y = self.conv1_2(y)
y = self.conv1_3(y)
y = self.pool2d_max(y)
for block in self.block_list:
y = block(y)
y = self.out_pool(y)
return y

2
ppocr/modeling/necks/rnn.py
View File

@ -116,7 +116,7 @@ class EncoderWithFC(nn.Layer):
class SequenceEncoder(nn.Layer):
def __init__(self, in_channels, encoder_type, hidden_size, **kwargs):
def __init__(self, in_channels, encoder_type, hidden_size=48, **kwargs):
super(SequenceEncoder, self).__init__()
self.encoder_reshape = EncoderWithReshape(in_channels)
self.out_channels = self.encoder_reshape.out_channels

15
tools/train.py
View File

@ -88,20 +88,23 @@ def main(config, device, logger, vdl_writer):
best_model_dict, logger, vdl_writer)
def test_reader(config, place, logger):
train_loader = build_dataloader(config['TRAIN'], place)
def test_reader(config, place, logger, global_config):
train_loader, _ = build_dataloader(
config['TRAIN'], place, global_config=global_config)
import time
starttime = time.time()
count = 0
try:
for data in train_loader():
for data in train_loader:
count += 1
if count % 1 == 0:
batch_time = time.time() - starttime
starttime = time.time()
logger.info("reader: {}, {}, {}".format(count,
len(data), batch_time))
logger.info("reader: {}, {}, {}".format(
count, len(data[0]), batch_time))
except Exception as e:
import traceback
traceback.print_exc()
logger.info(e)
logger.info("finish reader: {}, Success!".format(count))
@ -130,7 +133,7 @@ def dis_main():
device))
main(config, device, logger, vdl_writer)
# test_reader(config, place, logger)
# test_reader(config, device, logger, config['Global'])
if __name__ == '__main__':