167 lines
6.5 KiB
Python
167 lines
6.5 KiB
Python
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# Copyright 2021 Samsung Electronics 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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# http://www.apache.org/licenses/LICENSE-2.0
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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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import os
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import argparse
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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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class ReLUConvBN(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, affine, track_running_stats=True, use_bn=True, name='ReLUConvBN'):
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super(ReLUConvBN, self).__init__()
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self.name = name
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if use_bn:
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine),
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nn.BatchNorm2d(out_channels, affine=affine, track_running_stats=track_running_stats)
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)
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else:
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine)
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)
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def forward(self, x):
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return self.op(x)
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class Identity(nn.Module):
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def __init__(self, name='Identity'):
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self.name = name
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super(Identity, self).__init__()
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def forward(self, x):
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return x
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class Zero(nn.Module):
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def __init__(self, stride, name='Zero'):
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self.name = name
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super(Zero, self).__init__()
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self.stride = stride
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def forward(self, x):
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if self.stride == 1:
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return x.mul(0.)
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return x[:,:,::self.stride,::self.stride].mul(0.)
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class POOLING(nn.Module):
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def __init__(self, kernel_size, stride, padding, name='POOLING'):
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super(POOLING, self).__init__()
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self.name = name
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self.avgpool = nn.AvgPool2d(kernel_size=kernel_size, stride=1, padding=1, count_include_pad=False)
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def forward(self, x):
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return self.avgpool(x)
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class reduction(nn.Module):
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def __init__(self, in_channels, out_channels):
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super(reduction, self).__init__()
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self.residual = nn.Sequential(
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nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
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nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, bias=False))
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self.conv_a = ReLUConvBN(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1, dilation=1, affine=True, track_running_stats=True)
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self.conv_b = ReLUConvBN(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, dilation=1, affine=True, track_running_stats=True)
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def forward(self, x):
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basicblock = self.conv_a(x)
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basicblock = self.conv_b(basicblock)
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residual = self.residual(x)
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return residual + basicblock
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class stem(nn.Module):
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def __init__(self, out_channels, use_bn=True):
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super(stem, self).__init__()
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if use_bn:
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self.net = nn.Sequential(
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nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels))
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else:
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self.net = nn.Sequential(
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nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False)
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)
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def forward(self, x):
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return self.net(x)
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class top(nn.Module):
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# def __init__(self, in_dims, num_classes, use_bn=True):
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def __init__(self, in_dims, use_bn=True):
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super(top, self).__init__()
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if use_bn:
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self.lastact = nn.Sequential(nn.BatchNorm2d(in_dims), nn.ReLU(inplace=True))
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else:
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self.lastact = nn.ReLU(inplace=True)
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self.global_pooling = nn.AdaptiveAvgPool2d(1)
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# self.classifier = nn.Linear(in_dims, num_classes)
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def forward(self, x):
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x = self.lastact(x)
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x = self.global_pooling(x)
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x = x.view(x.size(0), -1)
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# logits = self.classifier(x)
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# return logits
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return x
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class SearchCell(nn.Module):
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def __init__(self, in_channels, out_channels, stride, affine, track_running_stats, use_bn=True, num_nodes=4, keep_mask=None):
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super(SearchCell, self).__init__()
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self.num_nodes = num_nodes
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self.options = nn.ModuleList()
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for curr_node in range(self.num_nodes-1):
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for prev_node in range(curr_node+1):
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for _op_name in OPS.keys():
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op = OPS[_op_name](in_channels, out_channels, stride, affine, track_running_stats, use_bn)
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self.options.append(op)
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if keep_mask is not None:
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self.keep_mask = keep_mask
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else:
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self.keep_mask = [True]*len(self.options)
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def forward(self, x):
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outs = [x]
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idx = 0
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for curr_node in range(self.num_nodes-1):
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edges_in = []
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for prev_node in range(curr_node+1): # n-1 prev nodes
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for op_idx in range(len(OPS.keys())):
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if self.keep_mask[idx]:
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edges_in.append(self.options[idx](outs[prev_node]))
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idx += 1
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node_output = sum(edges_in)
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outs.append(node_output)
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return outs[-1]
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OPS = {
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'none' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Zero(stride, name='none'),
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'avg_pool_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: POOLING(3, 1, 1, name='avg_pool_3x3'),
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'nor_conv_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 3, 1, 1, 1, affine, track_running_stats, use_bn, name='nor_conv_3x3'),
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'nor_conv_1x1' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 1, 1, 0, 1, affine, track_running_stats, use_bn, name='nor_conv_1x1'),
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'skip_connect' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Identity(name='skip_connect'),
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}
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