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284 lines
11 KiB
Python
284 lines
11 KiB
Python
2 years ago
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import math
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import paddle
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import paddle.nn as nn
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from paddle.nn import (Conv2D, BatchNorm2D, Linear, Dropout, MaxPool2D,
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AvgPool2D)
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from paddle import ParamAttr
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import paddle.nn.functional as F
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from ..registry import BACKBONES
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from ..weight_init import weight_init_
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from ...utils import load_ckpt
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class ConvBNLayer(nn.Layer):
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"""Conv2D and BatchNorm2D layer.
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Args:
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in_channels (int): Number of channels for the input.
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out_channels (int): Number of channels for the output.
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kernel_size (int): Kernel size.
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stride (int): Stride in the Conv2D layer. Default: 1.
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groups (int): Groups in the Conv2D, Default: 1.
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act (str): Indicate activation after BatchNorm2D layer.
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name (str): the name of an instance of ConvBNLayer.
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Note: weight and bias initialization include initialize values and name the restored parameters, values initialization are explicit declared in the ```init_weights``` method.
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"""
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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(ConvBNLayer, self).__init__()
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self._conv = Conv2D(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=stride,
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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._act = act
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self._batch_norm = BatchNorm2D(out_channels,
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weight_attr=ParamAttr(name=bn_name +
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"_scale"),
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bias_attr=ParamAttr(bn_name + "_offset"))
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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if self._act:
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y = getattr(paddle.nn.functional, self._act)(y)
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return y
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class BottleneckBlock(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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stride,
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shortcut=True,
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name=None):
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super(BottleneckBlock, self).__init__()
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self.conv0 = ConvBNLayer(in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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act="relu",
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name=name + "_branch2a")
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self.conv1 = ConvBNLayer(in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=3,
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stride=stride,
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act="relu",
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name=name + "_branch2b")
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self.conv2 = ConvBNLayer(in_channels=out_channels,
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out_channels=out_channels * 4,
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kernel_size=1,
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act=None,
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name=name + "_branch2c")
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if not shortcut:
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self.short = ConvBNLayer(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=stride,
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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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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.add(x=short, y=conv2)
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return F.relu(y)
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class BasicBlock(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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stride,
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shortcut=True,
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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(in_channels=in_channels,
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out_channels=out_channels,
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filter_size=3,
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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(in_channels=out_channels,
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out_channels=out_channels,
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filter_size=3,
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act=None,
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name=name + "_branch2b")
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if not shortcut:
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self.short = ConvBNLayer(in_channels=in_channels,
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out_channels=out_channels,
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filter_size=1,
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stride=stride,
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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.add(short, conv1)
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y = F.relu(y)
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return y
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@BACKBONES.register()
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class ResNet(nn.Layer):
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"""ResNet backbone.
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Args:
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depth (int): Depth of resnet model.
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pretrained (str): pretrained model. Default: None.
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"""
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def __init__(self, depth, pretrained=None):
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super(ResNet, self).__init__()
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self.pretrained = pretrained
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self.layers = depth
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supported_layers = [18, 34, 50, 101, 152]
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assert self.layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(
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supported_layers, self.layers)
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if self.layers == 18:
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depth = [2, 2, 2, 2]
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elif self.layers == 34 or self.layers == 50:
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depth = [3, 4, 6, 3]
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elif self.layers == 101:
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depth = [3, 4, 23, 3]
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elif self.layers == 152:
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depth = [3, 8, 36, 3]
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in_channels = [64, 256, 512, 1024]
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out_channels = [64, 128, 256, 512]
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self.conv = ConvBNLayer(in_channels=3,
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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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name="conv1")
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self.pool2D_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
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self.block_list = []
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if self.layers >= 50:
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for block in range(len(depth)):
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shortcut = False
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for i in range(depth[block]):
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if self.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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conv_name,
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BottleneckBlock(
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# NOTE: Be careful! Here is different from TSM model.
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in_channels=in_channels[block]
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if i == 0 else out_channels[block] * 4,
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out_channels=out_channels[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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name=conv_name))
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self.block_list.append(bottleneck_block)
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shortcut = True
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else:
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for block in range(len(depth)):
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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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conv_name,
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BasicBlock(in_channels=in_channels[block]
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if i == 0 else out_channels[block],
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out_channels=out_channels[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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name=conv_name))
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self.block_list.append(basic_block)
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shortcut = True
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def init_weights(self):
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"""Initiate the parameters.
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Note:
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1. when indicate pretrained loading path, will load it to initiate backbone.
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2. when not indicating pretrained loading path, will follow specific initialization initiate backbone. Always, Conv2D layer will be initiated by KaimingNormal function, and BatchNorm2d will be initiated by Constant function.
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Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/api/paddle/nn/initializer/kaiming/KaimingNormal_en.html
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"""
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#XXX: check bias!!! check pretrained!!!
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if isinstance(self.pretrained, str) and self.pretrained.strip() != "":
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load_ckpt(self, self.pretrained)
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elif self.pretrained is None or self.pretrained.strip() == "":
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for layer in self.sublayers():
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if isinstance(layer, nn.Conv2D):
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#XXX: no bias
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weight_init_(layer, 'KaimingNormal')
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elif isinstance(layer, nn.BatchNorm2D):
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weight_init_(layer, 'Constant', value=1)
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def forward(self, inputs):
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"""Define how the backbone is going to run.
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"""
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#NOTE: Already merge axis 0(batches) and axis 1(channels) before extracting feature phase,
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# please refer to paddlevideo/modeling/framework/recognizers/recognizer2d.py#L27
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#y = paddle.reshape(
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# inputs, [-1, inputs.shape[2], inputs.shape[3], inputs.shape[4]])
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y = self.conv(inputs)
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y = self.pool2D_max(y)
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for block in self.block_list:
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y = block(y)
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return y
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