diff --git a/ppocr/modeling/backbones/det_resnet_vd.py b/ppocr/modeling/backbones/det_resnet_vd.py
index b501bec8..6fa52716 100644
--- a/ppocr/modeling/backbones/det_resnet_vd.py
+++ b/ppocr/modeling/backbones/det_resnet_vd.py
@@ -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
diff --git a/ppocr/modeling/backbones/rec_resnet_vd.py b/ppocr/modeling/backbones/rec_resnet_vd.py
index d8602498..20b03c3d 100644
--- a/ppocr/modeling/backbones/rec_resnet_vd.py
+++ b/ppocr/modeling/backbones/rec_resnet_vd.py
@@ -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