检测和识别的resnet使用paddleclass里的
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WenmuZhou
parent
8f1b9c8b0d
commit
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@ -16,143 +16,30 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from paddle import nn
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from paddle.nn import functional as F
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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__all__ = ["ResNet"]
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class ResNet(nn.Layer):
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def __init__(self, in_channels=3, layers=50, **kwargs):
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"""
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the Resnet backbone network for detection module.
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Args:
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params(dict): the super parameters for network build
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"""
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super(ResNet, self).__init__()
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supported_layers = {
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18: {
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'depth': [2, 2, 2, 2],
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'block_class': BasicBlock
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},
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34: {
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'depth': [3, 4, 6, 3],
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'block_class': BasicBlock
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},
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50: {
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'depth': [3, 4, 6, 3],
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'block_class': BottleneckBlock
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},
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101: {
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'depth': [3, 4, 23, 3],
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'block_class': BottleneckBlock
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},
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152: {
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'depth': [3, 8, 36, 3],
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'block_class': BottleneckBlock
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},
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200: {
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'depth': [3, 12, 48, 3],
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'block_class': BottleneckBlock
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}
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}
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assert layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(supported_layers.keys(), layers)
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is_3x3 = True
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depth = supported_layers[layers]['depth']
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block_class = supported_layers[layers]['block_class']
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num_filters = [64, 128, 256, 512]
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conv = []
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if is_3x3 == False:
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conv.append(
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ConvBNLayer(
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in_channels=in_channels,
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out_channels=64,
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kernel_size=7,
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stride=2,
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act='relu'))
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else:
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conv.append(
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ConvBNLayer(
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in_channels=3,
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out_channels=32,
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kernel_size=3,
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stride=2,
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act='relu',
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name='conv1_1'))
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conv.append(
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ConvBNLayer(
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in_channels=32,
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out_channels=32,
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kernel_size=3,
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stride=1,
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act='relu',
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name='conv1_2'))
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conv.append(
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ConvBNLayer(
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in_channels=32,
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out_channels=64,
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kernel_size=3,
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stride=1,
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act='relu',
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name='conv1_3'))
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self.conv1 = nn.Sequential(*conv)
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self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.stages = []
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self.out_channels = []
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in_ch = 64
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for block_index in range(len(depth)):
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block_list = []
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for i in range(depth[block_index]):
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if layers >= 50:
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if layers in [101, 152, 200] and block_index == 2:
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if i == 0:
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conv_name = "res" + str(block_index + 2) + "a"
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else:
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conv_name = "res" + str(block_index +
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2) + "b" + str(i)
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else:
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conv_name = "res" + str(block_index + 2) + chr(97 + i)
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else:
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conv_name = "res" + str(block_index + 2) + chr(97 + i)
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block_list.append(
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block_class(
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in_channels=in_ch,
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out_channels=num_filters[block_index],
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stride=2 if i == 0 and block_index != 0 else 1,
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if_first=block_index == i == 0,
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name=conv_name))
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in_ch = block_list[-1].out_channels
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self.out_channels.append(in_ch)
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self.stages.append(nn.Sequential(*block_list))
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for i, stage in enumerate(self.stages):
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self.add_sublayer(sublayer=stage, name="stage{}".format(i))
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def forward(self, x):
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x = self.conv1(x)
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x = self.pool(x)
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out_list = []
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for stage in self.stages:
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x = stage(x)
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out_list.append(x)
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return out_list
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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groups=1,
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is_vd_mode=False,
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act=None,
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name=None, ):
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super(ConvBNLayer, self).__init__()
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self.conv = nn.Conv2d(
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self.is_vd_mode = is_vd_mode
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self._pool2d_avg = nn.AvgPool2d(
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kernel_size=2, stride=2, padding=0, ceil_mode=True)
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self._conv = nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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@ -165,87 +52,32 @@ class ConvBNLayer(nn.Layer):
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bn_name = "bn_" + name
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else:
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bn_name = "bn" + name[3:]
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self.bn = nn.BatchNorm(
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num_channels=out_channels,
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self._batch_norm = nn.BatchNorm(
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out_channels,
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act=act,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(name=bn_name + "_offset"),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance")
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def __call__(self, x):
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x = self.conv(x)
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x = self.bn(x)
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return x
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class ConvBNLayerNew(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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super(ConvBNLayerNew, self).__init__()
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self.pool = nn.AvgPool2d(
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kernel_size=2, stride=2, padding=0, ceil_mode=True)
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self.conv = nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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if name == "conv1":
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bn_name = "bn_" + name
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else:
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bn_name = "bn" + name[3:]
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self.bn = nn.BatchNorm(
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num_channels=out_channels,
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act=act,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(name=bn_name + "_offset"),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance")
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def __call__(self, x):
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x = self.pool(x)
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x = self.conv(x)
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x = self.bn(x)
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return x
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class ShortCut(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, name, if_first=False):
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super(ShortCut, self).__init__()
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self.use_conv = True
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if in_channels != out_channels or stride != 1:
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if if_first:
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self.conv = ConvBNLayer(
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in_channels, out_channels, 1, stride, name=name)
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else:
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self.conv = ConvBNLayerNew(
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in_channels, out_channels, 1, stride, name=name)
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elif if_first:
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self.conv = ConvBNLayer(
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in_channels, out_channels, 1, stride, name=name)
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else:
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self.use_conv = False
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def forward(self, x):
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if self.use_conv:
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x = self.conv(x)
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return x
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def forward(self, inputs):
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if self.is_vd_mode:
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inputs = self._pool2d_avg(inputs)
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class BottleneckBlock(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, name, if_first):
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def __init__(self,
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in_channels,
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out_channels,
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stride,
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shortcut=True,
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if_first=False,
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name=None):
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super(BottleneckBlock, self).__init__()
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self.conv0 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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@ -266,32 +98,46 @@ class BottleneckBlock(nn.Layer):
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act=None,
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name=name + "_branch2c")
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self.short = ShortCut(
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in_channels=in_channels,
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out_channels=out_channels * 4,
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stride=stride,
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if_first=if_first,
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name=name + "_branch1")
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self.out_channels = out_channels * 4
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if not shortcut:
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self.short = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels * 4,
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kernel_size=1,
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stride=1,
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is_vd_mode=False if if_first else True,
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name=name + "_branch1")
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def forward(self, x):
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y = self.conv0(x)
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y = self.conv1(y)
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y = self.conv2(y)
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y = y + self.short(x)
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y = F.relu(y)
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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conv2 = self.conv2(conv1)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.elementwise_add(x=short, y=conv2, act='relu')
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return y
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class BasicBlock(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, name, if_first):
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def __init__(self,
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in_channels,
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out_channels,
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stride,
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shortcut=True,
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if_first=False,
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name=None):
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super(BasicBlock, self).__init__()
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self.stride = stride
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self.conv0 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=3,
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act='relu',
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stride=stride,
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act='relu',
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name=name + "_branch2a")
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self.conv1 = ConvBNLayer(
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in_channels=out_channels,
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@ -299,31 +145,133 @@ class BasicBlock(nn.Layer):
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kernel_size=3,
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act=None,
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name=name + "_branch2b")
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self.short = ShortCut(
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if not shortcut:
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self.short = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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is_vd_mode=False if if_first else True,
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name=name + "_branch1")
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.elementwise_add(x=short, y=conv1, act='relu')
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return y
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class ResNet(nn.Layer):
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def __init__(self, in_channels=3, layers=50, **kwargs):
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super(ResNet, self).__init__()
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self.layers = layers
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supported_layers = [18, 34, 50, 101, 152, 200]
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assert layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(
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supported_layers, layers)
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if layers == 18:
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depth = [2, 2, 2, 2]
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elif layers == 34 or layers == 50:
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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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elif layers == 200:
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depth = [3, 12, 48, 3]
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num_channels = [64, 256, 512,
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1024] if layers >= 50 else [64, 64, 128, 256]
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num_filters = [64, 128, 256, 512]
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self.conv1_1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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stride=stride,
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if_first=if_first,
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name=name + "_branch1")
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self.out_channels = out_channels
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out_channels=32,
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kernel_size=3,
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stride=2,
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act='relu',
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name="conv1_1")
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self.conv1_2 = ConvBNLayer(
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in_channels=32,
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out_channels=32,
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kernel_size=3,
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stride=1,
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act='relu',
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name="conv1_2")
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self.conv1_3 = ConvBNLayer(
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in_channels=32,
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out_channels=64,
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kernel_size=3,
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stride=1,
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act='relu',
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name="conv1_3")
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self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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def forward(self, x):
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y = self.conv0(x)
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y = self.conv1(y)
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y = y + self.short(x)
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return F.relu(y)
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self.stages = []
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self.out_channels = []
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if layers >= 50:
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for block in range(len(depth)):
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block_list = []
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shortcut = False
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for i in range(depth[block]):
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if layers in [101, 152] and block == 2:
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if i == 0:
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conv_name = "res" + str(block + 2) + "a"
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else:
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conv_name = "res" + str(block + 2) + "b" + str(i)
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else:
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conv_name = "res" + str(block + 2) + chr(97 + i)
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bottleneck_block = self.add_sublayer(
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'bb_%d_%d' % (block, i),
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BottleneckBlock(
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in_channels=num_channels[block]
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if i == 0 else num_filters[block] * 4,
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out_channels=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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shortcut=shortcut,
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if_first=block == i == 0,
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name=conv_name))
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shortcut = True
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block_list.append(bottleneck_block)
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self.out_channels.append(num_filters[block] * 4)
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self.stages.append(nn.Sequential(*block_list))
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else:
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for block in range(len(depth)):
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block_list = []
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shortcut = False
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for i in range(depth[block]):
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conv_name = "res" + str(block + 2) + chr(97 + i)
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basic_block = self.add_sublayer(
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'bb_%d_%d' % (block, i),
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BasicBlock(
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in_channels=num_channels[block]
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if i == 0 else num_filters[block],
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out_channels=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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shortcut=shortcut,
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if_first=block == i == 0,
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name=conv_name))
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shortcut = True
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block_list.append(basic_block)
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self.out_channels.append(num_filters[block])
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self.stages.append(nn.Sequential(*block_list))
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if __name__ == '__main__':
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import paddle
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paddle.disable_static()
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x = paddle.zeros([1, 3, 640, 640])
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x = paddle.to_variable(x)
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print(x.shape)
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net = ResNet(layers=18)
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y = net(x)
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for stage in y:
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print(stage.shape)
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# paddle.save(net.state_dict(),'1.pth')
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def forward(self, inputs):
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y = self.conv1_1(inputs)
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y = self.conv1_2(y)
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y = self.conv1_3(y)
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y = self.pool2d_max(y)
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out = []
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for block in self.stages:
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y = block(y)
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out.append(y)
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return out
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|
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@ -16,184 +16,34 @@ from __future__ import absolute_import
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from __future__ import division
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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
|
||||
|
|
|
|||
Loading…
Reference in New Issue