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提交 bdad0cef 编写于 作者: W WenmuZhou
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检测和识别的resnet使用paddleclass里的

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ppocr/modeling/backbones/det_resnet_vd.py
浏览文件 @ bdad0cef
......@@ -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
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 ConvBNLayerNew(nn.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
stride,
shortcut=True,
if_first=False,
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
class BottleneckBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first):
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")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
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)
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(
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=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
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)
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=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)
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))
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
ppocr/modeling/backbones/rec_resnet_vd.py
浏览文件 @ bdad0cef
......@@ -16,144 +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):
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=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=stride,
stride=1 if is_vd_mode else stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
......@@ -162,88 +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
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 ConvBNLayerNew(nn.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
stride,
shortcut=True,
if_first=False,
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,
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[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
class BottleneckBlock(nn.Layer):
def __init__(self, in_channels, out_channels, stride, name, if_first):
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")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
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)
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(
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=out_channels,
stride=stride,
if_first=if_first,
name=name + "_branch1")
self.out_channels = out_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)
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = y + self.short(x)
return F.relu(y)
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
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