283 lines
8.7 KiB
Python
283 lines
8.7 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""ResNet."""
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import numpy as np
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore.common.tensor import Tensor
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def _weight_variable(shape, factor=0.01):
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init_value = np.random.randn(*shape).astype(np.float32) * factor
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return Tensor(init_value)
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def _conv3x3(in_channel, out_channel, stride=1):
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weight_shape = (out_channel, in_channel, 3, 3)
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weight = _weight_variable(weight_shape)
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return nn.Conv2d(in_channel, out_channel,
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kernel_size=3, stride=stride, padding=0, pad_mode='same', weight_init=weight)
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def _conv1x1(in_channel, out_channel, stride=1):
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weight_shape = (out_channel, in_channel, 1, 1)
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weight = _weight_variable(weight_shape)
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return nn.Conv2d(in_channel, out_channel,
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kernel_size=1, stride=stride, padding=0, pad_mode='same', weight_init=weight)
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def _conv7x7(in_channel, out_channel, stride=1):
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weight_shape = (out_channel, in_channel, 7, 7)
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weight = _weight_variable(weight_shape)
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return nn.Conv2d(in_channel, out_channel,
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kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight)
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def _bn(channel):
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return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
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gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)
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def _bn_last(channel):
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return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
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gamma_init=0, beta_init=0, moving_mean_init=0, moving_var_init=1)
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def _fc(in_channel, out_channel):
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weight_shape = (out_channel, in_channel)
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weight = _weight_variable(weight_shape)
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return nn.Dense(in_channel, out_channel, has_bias=True, weight_init=weight, bias_init=0)
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class ResidualBlock(nn.Cell):
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"""
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ResNet V1 residual block definition.
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Args:
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in_channel (int): Input channel.
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out_channel (int): Output channel.
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stride (int): Stride size for the first convolutional layer. Default: 1.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> ResidualBlock(3, 256, stride=2)
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"""
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expansion = 4
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def __init__(self,
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in_channel,
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out_channel,
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stride=1):
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super(ResidualBlock, self).__init__()
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channel = out_channel // self.expansion
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self.conv1 = _conv1x1(in_channel, channel, stride=1)
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self.bn1 = _bn(channel)
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self.conv2 = _conv3x3(channel, channel, stride=stride)
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self.bn2 = _bn(channel)
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self.conv3 = _conv1x1(channel, out_channel, stride=1)
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self.bn3 = _bn_last(out_channel)
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self.relu = nn.ReLU()
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self.down_sample = False
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if stride != 1 or in_channel != out_channel:
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self.down_sample = True
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self.down_sample_layer = None
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if self.down_sample:
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self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride),
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_bn(out_channel)])
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self.add = P.Add()
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def construct(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.down_sample:
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identity = self.down_sample_layer(identity)
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out = self.add(out, identity)
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out = self.relu(out)
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return out
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class ResNet(nn.Cell):
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"""
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ResNet architecture.
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Args:
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block (Cell): Block for network.
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layer_nums (list): Numbers of block in different layers.
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in_channels (list): Input channel in each layer.
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out_channels (list): Output channel in each layer.
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strides (list): Stride size in each layer.
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num_classes (int): The number of classes that the training images are belonging to.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> ResNet(ResidualBlock,
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>>> [3, 4, 6, 3],
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>>> [64, 256, 512, 1024],
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>>> [256, 512, 1024, 2048],
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>>> [1, 2, 2, 2],
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>>> 10)
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"""
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def __init__(self,
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block,
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layer_nums,
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in_channels,
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out_channels,
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strides,
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num_classes):
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super(ResNet, self).__init__()
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if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
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raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!")
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self.conv1 = _conv7x7(3, 64, stride=2)
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self.bn1 = _bn(64)
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self.relu = P.ReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
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self.layer1 = self._make_layer(block,
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layer_nums[0],
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in_channel=in_channels[0],
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out_channel=out_channels[0],
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stride=strides[0])
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self.layer2 = self._make_layer(block,
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layer_nums[1],
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in_channel=in_channels[1],
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out_channel=out_channels[1],
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stride=strides[1])
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self.layer3 = self._make_layer(block,
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layer_nums[2],
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in_channel=in_channels[2],
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out_channel=out_channels[2],
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stride=strides[2])
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self.layer4 = self._make_layer(block,
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layer_nums[3],
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in_channel=in_channels[3],
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out_channel=out_channels[3],
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stride=strides[3])
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self.mean = P.ReduceMean(keep_dims=True)
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self.flatten = nn.Flatten()
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self.end_point = _fc(out_channels[3], num_classes)
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def _make_layer(self, block, layer_num, in_channel, out_channel, stride):
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"""
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Make stage network of ResNet.
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Args:
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block (Cell): Resnet block.
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layer_num (int): Layer number.
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in_channel (int): Input channel.
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out_channel (int): Output channel.
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stride (int): Stride size for the first convolutional layer.
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Returns:
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SequentialCell, the output layer.
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Examples:
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>>> _make_layer(ResidualBlock, 3, 128, 256, 2)
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"""
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layers = []
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resnet_block = block(in_channel, out_channel, stride=stride)
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layers.append(resnet_block)
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for _ in range(1, layer_num):
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resnet_block = block(out_channel, out_channel, stride=1)
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layers.append(resnet_block)
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return nn.SequentialCell(layers)
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def construct(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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c1 = self.maxpool(x)
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c2 = self.layer1(c1)
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c3 = self.layer2(c2)
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c4 = self.layer3(c3)
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c5 = self.layer4(c4)
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out = self.mean(c5, (2, 3))
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out = self.flatten(out)
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out = self.end_point(out)
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return out
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def resnet50(class_num=10):
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"""
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Get ResNet50 neural network.
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Args:
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class_num (int): Class number.
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Returns:
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Cell, cell instance of ResNet50 neural network.
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Examples:
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>>> net = resnet50(10)
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"""
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return ResNet(ResidualBlock,
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[3, 4, 6, 3],
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[64, 256, 512, 1024],
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[256, 512, 1024, 2048],
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[1, 2, 2, 2],
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class_num)
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def resnet101(class_num=1001):
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"""
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Get ResNet101 neural network.
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Args:
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class_num (int): Class number.
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Returns:
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Cell, cell instance of ResNet101 neural network.
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Examples:
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>>> net = resnet101(1001)
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"""
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return ResNet(ResidualBlock,
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[3, 4, 23, 3],
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[64, 256, 512, 1024],
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[256, 512, 1024, 2048],
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[1, 2, 2, 2],
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class_num)
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