mirror of
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277 lines
8.5 KiB
Python
277 lines
8.5 KiB
Python
# Modified from https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppocr/modeling/backbones/det_resnet_vd.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from easycv.models.registry import BACKBONES
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from .det_mobilenet_v3 import Activation
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class ConvBNLayer(nn.Module):
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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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):
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super(ConvBNLayer, self).__init__()
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self.is_vd_mode = is_vd_mode
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self.act = act
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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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stride=stride,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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bias=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._batch_norm = nn.BatchNorm2d(
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out_channels,
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track_running_stats=True,
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)
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if act is not None:
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self._act = Activation(act_type=act, inplace=True)
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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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if self.act is not None:
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y = self._act(y)
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return y
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class BottleneckBlock(nn.Module):
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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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kernel_size=1,
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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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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(
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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(
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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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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 = torch.add(short, conv2)
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y = F.relu(y)
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return y
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class BasicBlock(nn.Module):
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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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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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out_channels=out_channels,
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kernel_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(
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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 = short + conv1
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y = F.relu(y)
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return y
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@BACKBONES.register_module()
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class OCRDetResNet(nn.Module):
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def __init__(self, in_channels=3, layers=50, **kwargs):
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super(OCRDetResNet, 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, 1024
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] 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=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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self.stages = nn.ModuleList()
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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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block_list = nn.Sequential()
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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 = 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.add_module('bb_%d_%d' % (block, i),
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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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self.stages.append(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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block_list = nn.Sequential()
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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 = 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.add_module('bb_%d_%d' % (block, i), basic_block)
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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(block_list)
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# self.stages.append(nn.Sequential(*block_list))
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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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