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HamsterMimi
108
pycls/models/common.py
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108
pycls/models/common.py
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#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn as nn
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from pycls.core.config import cfg
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def Preprocess(x):
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if cfg.TASK == 'jig':
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assert len(x.shape) == 5, 'Wrong tensor dimension for jigsaw'
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assert x.shape[1] == cfg.JIGSAW_GRID ** 2, 'Wrong grid for jigsaw'
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x = x.view([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4]])
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return x
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class Classifier(nn.Module):
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def __init__(self, channels, num_classes):
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super(Classifier, self).__init__()
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if cfg.TASK == 'jig':
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self.jig_sq = cfg.JIGSAW_GRID ** 2
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self.pooling = nn.AdaptiveAvgPool2d(1)
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self.classifier = nn.Linear(channels * self.jig_sq, num_classes)
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elif cfg.TASK == 'col':
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self.classifier = nn.Conv2d(channels, num_classes, kernel_size=1, stride=1)
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elif cfg.TASK == 'seg':
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self.classifier = ASPP(channels, cfg.MODEL.ASPP_CHANNELS, num_classes, cfg.MODEL.ASPP_RATES)
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else:
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self.pooling = nn.AdaptiveAvgPool2d(1)
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self.classifier = nn.Linear(channels, num_classes)
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def forward(self, x, shape):
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if cfg.TASK == 'jig':
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x = self.pooling(x)
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x = x.view([x.shape[0] // self.jig_sq, x.shape[1] * self.jig_sq, x.shape[2], x.shape[3]])
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x = self.classifier(x.view(x.size(0), -1))
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elif cfg.TASK in ['col', 'seg']:
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x = self.classifier(x)
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x = nn.Upsample(shape, mode='bilinear', align_corners=True)(x)
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else:
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x = self.pooling(x)
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x = self.classifier(x.view(x.size(0), -1))
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return x
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class ASPP(nn.Module):
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def __init__(self, in_channels, out_channels, num_classes, rates):
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super(ASPP, self).__init__()
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assert len(rates) in [1, 3]
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self.rates = rates
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self.global_pooling = nn.AdaptiveAvgPool2d(1)
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self.aspp1 = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True)
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)
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self.aspp2 = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 3, dilation=rates[0],
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padding=rates[0], bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True)
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)
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if len(self.rates) == 3:
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self.aspp3 = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 3, dilation=rates[1],
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padding=rates[1], bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True)
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)
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self.aspp4 = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 3, dilation=rates[2],
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padding=rates[2], bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True)
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)
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self.aspp5 = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True)
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)
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self.classifier = nn.Sequential(
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nn.Conv2d(out_channels * (len(rates) + 2), out_channels, 1,
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bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, num_classes, 1)
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)
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def forward(self, x):
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x1 = self.aspp1(x)
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x2 = self.aspp2(x)
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x5 = self.global_pooling(x)
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x5 = self.aspp5(x5)
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x5 = nn.Upsample((x.shape[2], x.shape[3]), mode='bilinear',
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align_corners=True)(x5)
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if len(self.rates) == 3:
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x3 = self.aspp3(x)
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x4 = self.aspp4(x)
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x = torch.cat((x1, x2, x3, x4, x5), 1)
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else:
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x = torch.cat((x1, x2, x5), 1)
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x = self.classifier(x)
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return x
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634
pycls/models/nas/genotypes.py
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634
pycls/models/nas/genotypes.py
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#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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"""NAS genotypes (adopted from DARTS)."""
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from collections import namedtuple
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Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
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# NASNet ops
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NASNET_OPS = [
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'skip_connect',
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'conv_3x1_1x3',
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'conv_7x1_1x7',
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'dil_conv_3x3',
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'avg_pool_3x3',
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'max_pool_3x3',
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'max_pool_5x5',
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'max_pool_7x7',
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'conv_1x1',
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'conv_3x3',
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'sep_conv_3x3',
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'sep_conv_5x5',
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'sep_conv_7x7',
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]
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# ENAS ops
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ENAS_OPS = [
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'skip_connect',
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'sep_conv_3x3',
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'sep_conv_5x5',
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'avg_pool_3x3',
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'max_pool_3x3',
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]
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# AmoebaNet ops
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AMOEBA_OPS = [
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'skip_connect',
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'sep_conv_3x3',
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'sep_conv_5x5',
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'sep_conv_7x7',
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'avg_pool_3x3',
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'max_pool_3x3',
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'dil_sep_conv_3x3',
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'conv_7x1_1x7',
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]
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# NAO ops
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NAO_OPS = [
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'skip_connect',
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'conv_1x1',
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'conv_3x3',
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'conv_3x1_1x3',
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'conv_7x1_1x7',
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'max_pool_2x2',
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'max_pool_3x3',
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'max_pool_5x5',
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'avg_pool_2x2',
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'avg_pool_3x3',
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'avg_pool_5x5',
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]
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# PNAS ops
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PNAS_OPS = [
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'sep_conv_3x3',
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'sep_conv_5x5',
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'sep_conv_7x7',
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'conv_7x1_1x7',
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'skip_connect',
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'avg_pool_3x3',
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'max_pool_3x3',
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'dil_conv_3x3',
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]
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# DARTS ops
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DARTS_OPS = [
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'none',
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'max_pool_3x3',
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'avg_pool_3x3',
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'skip_connect',
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'sep_conv_3x3',
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'sep_conv_5x5',
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'dil_conv_3x3',
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'dil_conv_5x5',
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]
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NASNet = Genotype(
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normal=[
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('sep_conv_5x5', 1),
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('sep_conv_3x3', 0),
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('sep_conv_5x5', 0),
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('sep_conv_3x3', 0),
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('avg_pool_3x3', 1),
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('skip_connect', 0),
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('avg_pool_3x3', 0),
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('avg_pool_3x3', 0),
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('sep_conv_3x3', 1),
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('skip_connect', 1),
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],
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normal_concat=[2, 3, 4, 5, 6],
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reduce=[
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('sep_conv_5x5', 1),
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('sep_conv_7x7', 0),
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('max_pool_3x3', 1),
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('sep_conv_7x7', 0),
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('avg_pool_3x3', 1),
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('sep_conv_5x5', 0),
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('skip_connect', 3),
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('avg_pool_3x3', 2),
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('sep_conv_3x3', 2),
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('max_pool_3x3', 1),
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],
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reduce_concat=[4, 5, 6],
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)
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PNASNet = Genotype(
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normal=[
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('sep_conv_5x5', 0),
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('max_pool_3x3', 0),
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('sep_conv_7x7', 1),
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('max_pool_3x3', 1),
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('sep_conv_5x5', 1),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 4),
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('max_pool_3x3', 1),
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('sep_conv_3x3', 0),
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('skip_connect', 1),
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],
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normal_concat=[2, 3, 4, 5, 6],
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reduce=[
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('sep_conv_5x5', 0),
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('max_pool_3x3', 0),
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('sep_conv_7x7', 1),
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('max_pool_3x3', 1),
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('sep_conv_5x5', 1),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 4),
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('max_pool_3x3', 1),
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('sep_conv_3x3', 0),
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('skip_connect', 1),
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],
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reduce_concat=[2, 3, 4, 5, 6],
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)
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AmoebaNet = Genotype(
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normal=[
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('avg_pool_3x3', 0),
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('max_pool_3x3', 1),
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('sep_conv_3x3', 0),
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('sep_conv_5x5', 2),
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('sep_conv_3x3', 0),
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('avg_pool_3x3', 3),
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('sep_conv_3x3', 1),
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('skip_connect', 1),
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('skip_connect', 0),
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('avg_pool_3x3', 1),
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],
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normal_concat=[4, 5, 6],
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reduce=[
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('avg_pool_3x3', 0),
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('sep_conv_3x3', 1),
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('max_pool_3x3', 0),
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('sep_conv_7x7', 2),
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('sep_conv_7x7', 0),
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('avg_pool_3x3', 1),
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('max_pool_3x3', 0),
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('max_pool_3x3', 1),
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('conv_7x1_1x7', 0),
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('sep_conv_3x3', 5),
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],
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reduce_concat=[3, 4, 6]
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)
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DARTS_V1 = Genotype(
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normal=[
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 0),
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('skip_connect', 0),
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('sep_conv_3x3', 1),
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('skip_connect', 0),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 0),
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('skip_connect', 2)
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],
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normal_concat=[2, 3, 4, 5],
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reduce=[
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('max_pool_3x3', 0),
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('max_pool_3x3', 1),
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('skip_connect', 2),
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('max_pool_3x3', 0),
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('max_pool_3x3', 0),
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('skip_connect', 2),
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('skip_connect', 2),
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('avg_pool_3x3', 0)
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],
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reduce_concat=[2, 3, 4, 5]
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)
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DARTS_V2 = Genotype(
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normal=[
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('sep_conv_3x3', 0),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 0),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 1),
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('skip_connect', 0),
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('skip_connect', 0),
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('dil_conv_3x3', 2)
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],
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normal_concat=[2, 3, 4, 5],
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reduce=[
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('max_pool_3x3', 0),
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('max_pool_3x3', 1),
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('skip_connect', 2),
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('max_pool_3x3', 1),
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('max_pool_3x3', 0),
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('skip_connect', 2),
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('skip_connect', 2),
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('max_pool_3x3', 1)
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],
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reduce_concat=[2, 3, 4, 5]
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)
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PDARTS = Genotype(
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normal=[
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('skip_connect', 0),
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('dil_conv_3x3', 1),
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('skip_connect', 0),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 3),
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('sep_conv_3x3', 0),
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('dil_conv_5x5', 4)
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],
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normal_concat=range(2, 6),
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reduce=[
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('avg_pool_3x3', 0),
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('sep_conv_5x5', 1),
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('sep_conv_3x3', 0),
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('dil_conv_5x5', 2),
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('max_pool_3x3', 0),
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('dil_conv_3x3', 1),
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('dil_conv_3x3', 1),
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('dil_conv_5x5', 3)
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],
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reduce_concat=range(2, 6)
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)
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PCDARTS_C10 = Genotype(
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normal=[
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('sep_conv_3x3', 1),
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('skip_connect', 0),
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('sep_conv_3x3', 0),
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('dil_conv_3x3', 1),
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('sep_conv_5x5', 0),
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('sep_conv_3x3', 1),
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('avg_pool_3x3', 0),
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('dil_conv_3x3', 1)
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],
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normal_concat=range(2, 6),
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reduce=[
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('sep_conv_5x5', 1),
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('max_pool_3x3', 0),
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('sep_conv_5x5', 1),
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('sep_conv_5x5', 2),
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('sep_conv_3x3', 0),
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('sep_conv_3x3', 3),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 2)
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],
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reduce_concat=range(2, 6)
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)
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PCDARTS_IN1K = Genotype(
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normal=[
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('skip_connect', 1),
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('sep_conv_3x3', 0),
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('sep_conv_3x3', 0),
|
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('skip_connect', 1),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 3),
|
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('sep_conv_3x3', 1),
|
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('dil_conv_5x5', 4)
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],
|
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normal_concat=range(2, 6),
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reduce=[
|
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('sep_conv_3x3', 0),
|
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('skip_connect', 1),
|
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('dil_conv_5x5', 2),
|
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('max_pool_3x3', 1),
|
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('sep_conv_3x3', 2),
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('sep_conv_3x3', 1),
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('sep_conv_5x5', 0),
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('sep_conv_3x3', 3)
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],
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reduce_concat=range(2, 6)
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)
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UNNAS_IMAGENET_CLS = Genotype(
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normal=[
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 0),
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 0),
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('sep_conv_3x3', 0),
|
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('sep_conv_3x3', 2),
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('sep_conv_5x5', 1),
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('sep_conv_3x3', 0)
|
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],
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normal_concat=range(2, 6),
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reduce=[
|
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('max_pool_3x3', 0),
|
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('skip_connect', 1),
|
||||
('max_pool_3x3', 0),
|
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('dil_conv_5x5', 2),
|
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('max_pool_3x3', 0),
|
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('sep_conv_3x3', 2),
|
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('sep_conv_3x3', 4),
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('dil_conv_5x5', 3)
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],
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reduce_concat=range(2, 6)
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)
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UNNAS_IMAGENET_ROT = Genotype(
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normal=[
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('sep_conv_3x3', 0),
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('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 1),
|
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('sep_conv_3x3', 0),
|
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('sep_conv_3x3', 0),
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||||
('sep_conv_3x3', 1)
|
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],
|
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normal_concat=range(2, 6),
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reduce=[
|
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('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 2),
|
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('sep_conv_3x3', 1),
|
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('sep_conv_3x3', 1),
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('sep_conv_3x3', 2),
|
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('sep_conv_3x3', 4),
|
||||
('sep_conv_5x5', 2)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_IMAGENET_COL = Genotype(
|
||||
normal=[
|
||||
('skip_connect', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 1),
|
||||
('skip_connect', 0),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 3),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 2)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('max_pool_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('max_pool_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('max_pool_3x3', 0),
|
||||
('sep_conv_5x5', 3),
|
||||
('max_pool_3x3', 0),
|
||||
('sep_conv_3x3', 4)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_IMAGENET_JIG = Genotype(
|
||||
normal=[
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 3),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_5x5', 0)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_3x3', 1)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_IMAGENET22K_CLS = Genotype(
|
||||
normal=[
|
||||
('sep_conv_3x3', 1),
|
||||
('skip_connect', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('max_pool_3x3', 0),
|
||||
('max_pool_3x3', 1),
|
||||
('dil_conv_5x5', 2),
|
||||
('max_pool_3x3', 0),
|
||||
('dil_conv_5x5', 3),
|
||||
('dil_conv_5x5', 2),
|
||||
('dil_conv_5x5', 4),
|
||||
('dil_conv_5x5', 3)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_IMAGENET22K_ROT = Genotype(
|
||||
normal=[
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('max_pool_3x3', 0),
|
||||
('sep_conv_5x5', 1),
|
||||
('dil_conv_5x5', 2),
|
||||
('sep_conv_5x5', 0),
|
||||
('dil_conv_5x5', 3),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_3x3', 4),
|
||||
('sep_conv_3x3', 3)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_IMAGENET22K_COL = Genotype(
|
||||
normal=[
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 3),
|
||||
('sep_conv_3x3', 0)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('max_pool_3x3', 0),
|
||||
('skip_connect', 1),
|
||||
('dil_conv_5x5', 2),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 3),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 4),
|
||||
('sep_conv_5x5', 1)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_IMAGENET22K_JIG = Genotype(
|
||||
normal=[
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 4)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('sep_conv_5x5', 0),
|
||||
('skip_connect', 1),
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_5x5', 3),
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_5x5', 4)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_CITYSCAPES_SEG = Genotype(
|
||||
normal=[
|
||||
('skip_connect', 0),
|
||||
('sep_conv_5x5', 1),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('sep_conv_3x3', 0),
|
||||
('avg_pool_3x3', 1),
|
||||
('avg_pool_3x3', 1),
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_3x3', 4),
|
||||
('sep_conv_5x5', 2)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_CITYSCAPES_ROT = Genotype(
|
||||
normal=[
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 3),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('max_pool_3x3', 0),
|
||||
('sep_conv_5x5', 1),
|
||||
('sep_conv_5x5', 2),
|
||||
('sep_conv_5x5', 1),
|
||||
('sep_conv_5x5', 3),
|
||||
('dil_conv_5x5', 2),
|
||||
('sep_conv_5x5', 2),
|
||||
('sep_conv_5x5', 0)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_CITYSCAPES_COL = Genotype(
|
||||
normal=[
|
||||
('dil_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('skip_connect', 0),
|
||||
('sep_conv_5x5', 2),
|
||||
('dil_conv_3x3', 3),
|
||||
('skip_connect', 0),
|
||||
('skip_connect', 0),
|
||||
('sep_conv_3x3', 1)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('avg_pool_3x3', 1),
|
||||
('avg_pool_3x3', 0),
|
||||
('avg_pool_3x3', 1),
|
||||
('avg_pool_3x3', 0),
|
||||
('avg_pool_3x3', 1),
|
||||
('avg_pool_3x3', 0),
|
||||
('avg_pool_3x3', 1),
|
||||
('skip_connect', 4)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
UNNAS_CITYSCAPES_JIG = Genotype(
|
||||
normal=[
|
||||
('dil_conv_5x5', 1),
|
||||
('sep_conv_5x5', 0),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 1),
|
||||
('sep_conv_3x3', 0),
|
||||
('sep_conv_3x3', 2),
|
||||
('sep_conv_3x3', 0),
|
||||
('dil_conv_5x5', 1)
|
||||
],
|
||||
normal_concat=range(2, 6),
|
||||
reduce=[
|
||||
('avg_pool_3x3', 0),
|
||||
('skip_connect', 1),
|
||||
('dil_conv_5x5', 1),
|
||||
('dil_conv_5x5', 2),
|
||||
('dil_conv_5x5', 2),
|
||||
('dil_conv_5x5', 0),
|
||||
('dil_conv_5x5', 3),
|
||||
('dil_conv_5x5', 2)
|
||||
],
|
||||
reduce_concat=range(2, 6)
|
||||
)
|
||||
|
||||
|
||||
# Supported genotypes
|
||||
GENOTYPES = {
|
||||
'nas': NASNet,
|
||||
'pnas': PNASNet,
|
||||
'amoeba': AmoebaNet,
|
||||
'darts_v1': DARTS_V1,
|
||||
'darts_v2': DARTS_V2,
|
||||
'pdarts': PDARTS,
|
||||
'pcdarts_c10': PCDARTS_C10,
|
||||
'pcdarts_in1k': PCDARTS_IN1K,
|
||||
'unnas_imagenet_cls': UNNAS_IMAGENET_CLS,
|
||||
'unnas_imagenet_rot': UNNAS_IMAGENET_ROT,
|
||||
'unnas_imagenet_col': UNNAS_IMAGENET_COL,
|
||||
'unnas_imagenet_jig': UNNAS_IMAGENET_JIG,
|
||||
'unnas_imagenet22k_cls': UNNAS_IMAGENET22K_CLS,
|
||||
'unnas_imagenet22k_rot': UNNAS_IMAGENET22K_ROT,
|
||||
'unnas_imagenet22k_col': UNNAS_IMAGENET22K_COL,
|
||||
'unnas_imagenet22k_jig': UNNAS_IMAGENET22K_JIG,
|
||||
'unnas_cityscapes_seg': UNNAS_CITYSCAPES_SEG,
|
||||
'unnas_cityscapes_rot': UNNAS_CITYSCAPES_ROT,
|
||||
'unnas_cityscapes_col': UNNAS_CITYSCAPES_COL,
|
||||
'unnas_cityscapes_jig': UNNAS_CITYSCAPES_JIG,
|
||||
'custom': None,
|
||||
}
|
||||
337
pycls/models/nas/nas.py
Normal file
337
pycls/models/nas/nas.py
Normal file
@@ -0,0 +1,337 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
#
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
"""NAS network (adopted from DARTS)."""
|
||||
|
||||
from torch.autograd import Variable
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import pycls.core.logging as logging
|
||||
|
||||
from pycls.core.config import cfg
|
||||
from pycls.models.common import Preprocess
|
||||
from pycls.models.common import Classifier
|
||||
from pycls.models.nas.genotypes import GENOTYPES
|
||||
from pycls.models.nas.genotypes import Genotype
|
||||
from pycls.models.nas.operations import FactorizedReduce
|
||||
from pycls.models.nas.operations import OPS
|
||||
from pycls.models.nas.operations import ReLUConvBN
|
||||
from pycls.models.nas.operations import Identity
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
def drop_path(x, drop_prob):
|
||||
"""Drop path (ported from DARTS)."""
|
||||
if drop_prob > 0.:
|
||||
keep_prob = 1.-drop_prob
|
||||
mask = Variable(
|
||||
torch.cuda.FloatTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob)
|
||||
)
|
||||
x.div_(keep_prob)
|
||||
x.mul_(mask)
|
||||
return x
|
||||
|
||||
|
||||
class Cell(nn.Module):
|
||||
"""NAS cell (ported from DARTS)."""
|
||||
|
||||
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
|
||||
super(Cell, self).__init__()
|
||||
logger.info('{}, {}, {}'.format(C_prev_prev, C_prev, C))
|
||||
|
||||
if reduction_prev:
|
||||
self.preprocess0 = FactorizedReduce(C_prev_prev, C)
|
||||
else:
|
||||
self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
|
||||
self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)
|
||||
|
||||
if reduction:
|
||||
op_names, indices = zip(*genotype.reduce)
|
||||
concat = genotype.reduce_concat
|
||||
else:
|
||||
op_names, indices = zip(*genotype.normal)
|
||||
concat = genotype.normal_concat
|
||||
self._compile(C, op_names, indices, concat, reduction)
|
||||
|
||||
def _compile(self, C, op_names, indices, concat, reduction):
|
||||
assert len(op_names) == len(indices)
|
||||
self._steps = len(op_names) // 2
|
||||
self._concat = concat
|
||||
self.multiplier = len(concat)
|
||||
|
||||
self._ops = nn.ModuleList()
|
||||
for name, index in zip(op_names, indices):
|
||||
stride = 2 if reduction and index < 2 else 1
|
||||
op = OPS[name](C, stride, True)
|
||||
self._ops += [op]
|
||||
self._indices = indices
|
||||
|
||||
def forward(self, s0, s1, drop_prob):
|
||||
s0 = self.preprocess0(s0)
|
||||
s1 = self.preprocess1(s1)
|
||||
|
||||
states = [s0, s1]
|
||||
for i in range(self._steps):
|
||||
h1 = states[self._indices[2*i]]
|
||||
h2 = states[self._indices[2*i+1]]
|
||||
op1 = self._ops[2*i]
|
||||
op2 = self._ops[2*i+1]
|
||||
h1 = op1(h1)
|
||||
h2 = op2(h2)
|
||||
if self.training and drop_prob > 0.:
|
||||
if not isinstance(op1, Identity):
|
||||
h1 = drop_path(h1, drop_prob)
|
||||
if not isinstance(op2, Identity):
|
||||
h2 = drop_path(h2, drop_prob)
|
||||
s = h1 + h2
|
||||
states += [s]
|
||||
return torch.cat([states[i] for i in self._concat], dim=1)
|
||||
|
||||
|
||||
class AuxiliaryHeadCIFAR(nn.Module):
|
||||
|
||||
def __init__(self, C, num_classes):
|
||||
"""assuming input size 8x8"""
|
||||
super(AuxiliaryHeadCIFAR, self).__init__()
|
||||
self.features = nn.Sequential(
|
||||
nn.ReLU(inplace=True),
|
||||
nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False), # image size = 2 x 2
|
||||
nn.Conv2d(C, 128, 1, bias=False),
|
||||
nn.BatchNorm2d(128),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(128, 768, 2, bias=False),
|
||||
nn.BatchNorm2d(768),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
self.classifier = nn.Linear(768, num_classes)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.features(x)
|
||||
x = self.classifier(x.view(x.size(0),-1))
|
||||
return x
|
||||
|
||||
|
||||
class AuxiliaryHeadImageNet(nn.Module):
|
||||
|
||||
def __init__(self, C, num_classes):
|
||||
"""assuming input size 14x14"""
|
||||
super(AuxiliaryHeadImageNet, self).__init__()
|
||||
self.features = nn.Sequential(
|
||||
nn.ReLU(inplace=True),
|
||||
nn.AvgPool2d(5, stride=2, padding=0, count_include_pad=False),
|
||||
nn.Conv2d(C, 128, 1, bias=False),
|
||||
nn.BatchNorm2d(128),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(128, 768, 2, bias=False),
|
||||
# NOTE: This batchnorm was omitted in my earlier implementation due to a typo.
|
||||
# Commenting it out for consistency with the experiments in the paper.
|
||||
# nn.BatchNorm2d(768),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
self.classifier = nn.Linear(768, num_classes)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.features(x)
|
||||
x = self.classifier(x.view(x.size(0),-1))
|
||||
return x
|
||||
|
||||
|
||||
class NetworkCIFAR(nn.Module):
|
||||
"""CIFAR network (ported from DARTS)."""
|
||||
|
||||
def __init__(self, C, num_classes, layers, auxiliary, genotype):
|
||||
super(NetworkCIFAR, self).__init__()
|
||||
self._layers = layers
|
||||
self._auxiliary = auxiliary
|
||||
|
||||
stem_multiplier = 3
|
||||
C_curr = stem_multiplier*C
|
||||
self.stem = nn.Sequential(
|
||||
nn.Conv2d(cfg.MODEL.INPUT_CHANNELS, C_curr, 3, padding=1, bias=False),
|
||||
nn.BatchNorm2d(C_curr)
|
||||
)
|
||||
|
||||
C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
|
||||
self.cells = nn.ModuleList()
|
||||
reduction_prev = False
|
||||
for i in range(layers):
|
||||
if i in [layers//3, 2*layers//3]:
|
||||
C_curr *= 2
|
||||
reduction = True
|
||||
else:
|
||||
reduction = False
|
||||
cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
|
||||
reduction_prev = reduction
|
||||
self.cells += [cell]
|
||||
C_prev_prev, C_prev = C_prev, cell.multiplier*C_curr
|
||||
if i == 2*layers//3:
|
||||
C_to_auxiliary = C_prev
|
||||
|
||||
if auxiliary:
|
||||
self.auxiliary_head = AuxiliaryHeadCIFAR(C_to_auxiliary, num_classes)
|
||||
self.classifier = Classifier(C_prev, num_classes)
|
||||
|
||||
def forward(self, input):
|
||||
input = Preprocess(input)
|
||||
logits_aux = None
|
||||
s0 = s1 = self.stem(input)
|
||||
for i, cell in enumerate(self.cells):
|
||||
s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
|
||||
if i == 2*self._layers//3:
|
||||
if self._auxiliary and self.training:
|
||||
logits_aux = self.auxiliary_head(s1)
|
||||
logits = self.classifier(s1, input.shape[2:])
|
||||
if self._auxiliary and self.training:
|
||||
return logits, logits_aux
|
||||
return logits
|
||||
|
||||
def _loss(self, input, target, return_logits=False):
|
||||
logits = self(input)
|
||||
loss = self._criterion(logits, target)
|
||||
|
||||
return (loss, logits) if return_logits else loss
|
||||
|
||||
def step(self, input, target, args, shared=None, return_grad=False):
|
||||
Lt, logit_t = self._loss(input, target, return_logits=True)
|
||||
Lt.backward()
|
||||
if args.grad_clip != 0:
|
||||
nn.utils.clip_grad_norm_(self.get_weights(), args.grad_clip)
|
||||
self.optimizer.step()
|
||||
|
||||
if return_grad:
|
||||
grad = torch.nn.utils.parameters_to_vector([p.grad for p in self.get_weights()])
|
||||
return logit_t, Lt, grad
|
||||
else:
|
||||
return logit_t, Lt
|
||||
|
||||
|
||||
class NetworkImageNet(nn.Module):
|
||||
"""ImageNet network (ported from DARTS)."""
|
||||
|
||||
def __init__(self, C, num_classes, layers, auxiliary, genotype):
|
||||
super(NetworkImageNet, self).__init__()
|
||||
self._layers = layers
|
||||
self._auxiliary = auxiliary
|
||||
|
||||
self.stem0 = nn.Sequential(
|
||||
nn.Conv2d(cfg.MODEL.INPUT_CHANNELS, C // 2, kernel_size=3, stride=2, padding=1, bias=False),
|
||||
nn.BatchNorm2d(C // 2),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
|
||||
nn.BatchNorm2d(C),
|
||||
)
|
||||
|
||||
self.stem1 = nn.Sequential(
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
|
||||
nn.BatchNorm2d(C),
|
||||
)
|
||||
|
||||
C_prev_prev, C_prev, C_curr = C, C, C
|
||||
|
||||
self.cells = nn.ModuleList()
|
||||
reduction_prev = True
|
||||
reduction_layers = [layers//3] if cfg.TASK == 'seg' else [layers//3, 2*layers//3]
|
||||
for i in range(layers):
|
||||
if i in reduction_layers:
|
||||
C_curr *= 2
|
||||
reduction = True
|
||||
else:
|
||||
reduction = False
|
||||
cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
|
||||
reduction_prev = reduction
|
||||
self.cells += [cell]
|
||||
C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
|
||||
if i == 2 * layers // 3:
|
||||
C_to_auxiliary = C_prev
|
||||
|
||||
if auxiliary:
|
||||
self.auxiliary_head = AuxiliaryHeadImageNet(C_to_auxiliary, num_classes)
|
||||
self.classifier = Classifier(C_prev, num_classes)
|
||||
|
||||
def forward(self, input):
|
||||
input = Preprocess(input)
|
||||
logits_aux = None
|
||||
s0 = self.stem0(input)
|
||||
s1 = self.stem1(s0)
|
||||
for i, cell in enumerate(self.cells):
|
||||
s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
|
||||
if i == 2 * self._layers // 3:
|
||||
if self._auxiliary and self.training:
|
||||
logits_aux = self.auxiliary_head(s1)
|
||||
logits = self.classifier(s1, input.shape[2:])
|
||||
if self._auxiliary and self.training:
|
||||
return logits, logits_aux
|
||||
return logits
|
||||
|
||||
def _loss(self, input, target, return_logits=False):
|
||||
logits = self(input)
|
||||
loss = self._criterion(logits, target)
|
||||
|
||||
return (loss, logits) if return_logits else loss
|
||||
|
||||
def step(self, input, target, args, shared=None, return_grad=False):
|
||||
Lt, logit_t = self._loss(input, target, return_logits=True)
|
||||
Lt.backward()
|
||||
if args.grad_clip != 0:
|
||||
nn.utils.clip_grad_norm_(self.get_weights(), args.grad_clip)
|
||||
self.optimizer.step()
|
||||
|
||||
if return_grad:
|
||||
grad = torch.nn.utils.parameters_to_vector([p.grad for p in self.get_weights()])
|
||||
return logit_t, Lt, grad
|
||||
else:
|
||||
return logit_t, Lt
|
||||
|
||||
|
||||
class NAS(nn.Module):
|
||||
"""NAS net wrapper (delegates to nets from DARTS)."""
|
||||
|
||||
def __init__(self):
|
||||
assert cfg.TRAIN.DATASET in ['cifar10', 'imagenet', 'cityscapes'], \
|
||||
'Training on {} is not supported'.format(cfg.TRAIN.DATASET)
|
||||
assert cfg.TEST.DATASET in ['cifar10', 'imagenet', 'cityscapes'], \
|
||||
'Testing on {} is not supported'.format(cfg.TEST.DATASET)
|
||||
assert cfg.NAS.GENOTYPE in GENOTYPES, \
|
||||
'Genotype {} not supported'.format(cfg.NAS.GENOTYPE)
|
||||
super(NAS, self).__init__()
|
||||
logger.info('Constructing NAS: {}'.format(cfg.NAS))
|
||||
# Use a custom or predefined genotype
|
||||
if cfg.NAS.GENOTYPE == 'custom':
|
||||
genotype = Genotype(
|
||||
normal=cfg.NAS.CUSTOM_GENOTYPE[0],
|
||||
normal_concat=cfg.NAS.CUSTOM_GENOTYPE[1],
|
||||
reduce=cfg.NAS.CUSTOM_GENOTYPE[2],
|
||||
reduce_concat=cfg.NAS.CUSTOM_GENOTYPE[3],
|
||||
)
|
||||
else:
|
||||
genotype = GENOTYPES[cfg.NAS.GENOTYPE]
|
||||
# Determine the network constructor for dataset
|
||||
if 'cifar' in cfg.TRAIN.DATASET:
|
||||
net_ctor = NetworkCIFAR
|
||||
else:
|
||||
net_ctor = NetworkImageNet
|
||||
# Construct the network
|
||||
self.net_ = net_ctor(
|
||||
C=cfg.NAS.WIDTH,
|
||||
num_classes=cfg.MODEL.NUM_CLASSES,
|
||||
layers=cfg.NAS.DEPTH,
|
||||
auxiliary=cfg.NAS.AUX,
|
||||
genotype=genotype
|
||||
)
|
||||
# Drop path probability (set / annealed based on epoch)
|
||||
self.net_.drop_path_prob = 0.0
|
||||
|
||||
def set_drop_path_prob(self, drop_path_prob):
|
||||
self.net_.drop_path_prob = drop_path_prob
|
||||
|
||||
def forward(self, x):
|
||||
return self.net_.forward(x)
|
||||
219
pycls/models/nas/operations.py
Normal file
219
pycls/models/nas/operations.py
Normal file
@@ -0,0 +1,219 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# Copyright (c) Facebook, Inc. and its affiliates.
|
||||
#
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
|
||||
"""NAS ops (adopted from DARTS)."""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.autograd import Variable
|
||||
|
||||
OPS = {
|
||||
'none': lambda C, stride, affine:
|
||||
Zero(stride),
|
||||
'noise': lambda C, stride, affine: NoiseOp(stride, 0., 1.),
|
||||
'avg_pool_2x2': lambda C, stride, affine:
|
||||
nn.AvgPool2d(2, stride=stride, padding=0, count_include_pad=False),
|
||||
'avg_pool_3x3': lambda C, stride, affine:
|
||||
nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
|
||||
'avg_pool_5x5': lambda C, stride, affine:
|
||||
nn.AvgPool2d(5, stride=stride, padding=2, count_include_pad=False),
|
||||
'max_pool_2x2': lambda C, stride, affine:
|
||||
nn.MaxPool2d(2, stride=stride, padding=0),
|
||||
'max_pool_3x3': lambda C, stride, affine:
|
||||
nn.MaxPool2d(3, stride=stride, padding=1),
|
||||
'max_pool_5x5': lambda C, stride, affine:
|
||||
nn.MaxPool2d(5, stride=stride, padding=2),
|
||||
'max_pool_7x7': lambda C, stride, affine:
|
||||
nn.MaxPool2d(7, stride=stride, padding=3),
|
||||
'skip_connect': lambda C, stride, affine:
|
||||
Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
|
||||
'conv_1x1': lambda C, stride, affine:
|
||||
nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(C, C, 1, stride=stride, padding=0, bias=False),
|
||||
nn.BatchNorm2d(C, affine=affine)
|
||||
),
|
||||
'conv_3x3': lambda C, stride, affine:
|
||||
nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(C, C, 3, stride=stride, padding=1, bias=False),
|
||||
nn.BatchNorm2d(C, affine=affine)
|
||||
),
|
||||
'sep_conv_3x3': lambda C, stride, affine:
|
||||
SepConv(C, C, 3, stride, 1, affine=affine),
|
||||
'sep_conv_5x5': lambda C, stride, affine:
|
||||
SepConv(C, C, 5, stride, 2, affine=affine),
|
||||
'sep_conv_7x7': lambda C, stride, affine:
|
||||
SepConv(C, C, 7, stride, 3, affine=affine),
|
||||
'dil_conv_3x3': lambda C, stride, affine:
|
||||
DilConv(C, C, 3, stride, 2, 2, affine=affine),
|
||||
'dil_conv_5x5': lambda C, stride, affine:
|
||||
DilConv(C, C, 5, stride, 4, 2, affine=affine),
|
||||
'dil_sep_conv_3x3': lambda C, stride, affine:
|
||||
DilSepConv(C, C, 3, stride, 2, 2, affine=affine),
|
||||
'conv_3x1_1x3': lambda C, stride, affine:
|
||||
nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(C, C, (1,3), stride=(1, stride), padding=(0, 1), bias=False),
|
||||
nn.Conv2d(C, C, (3,1), stride=(stride, 1), padding=(1, 0), bias=False),
|
||||
nn.BatchNorm2d(C, affine=affine)
|
||||
),
|
||||
'conv_7x1_1x7': lambda C, stride, affine:
|
||||
nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(C, C, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
|
||||
nn.Conv2d(C, C, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
|
||||
nn.BatchNorm2d(C, affine=affine)
|
||||
),
|
||||
}
|
||||
|
||||
class NoiseOp(nn.Module):
|
||||
def __init__(self, stride, mean, std):
|
||||
super(NoiseOp, self).__init__()
|
||||
self.stride = stride
|
||||
self.mean = mean
|
||||
self.std = std
|
||||
|
||||
def forward(self, x, block_input=False):
|
||||
if block_input:
|
||||
x = x*0
|
||||
if self.stride != 1:
|
||||
x_new = x[:,:,::self.stride,::self.stride]
|
||||
else:
|
||||
x_new = x
|
||||
noise = Variable(x_new.data.new(x_new.size()).normal_(self.mean, self.std))
|
||||
|
||||
return noise
|
||||
|
||||
class ReLUConvBN(nn.Module):
|
||||
|
||||
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
|
||||
super(ReLUConvBN, self).__init__()
|
||||
self.op = nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(
|
||||
C_in, C_out, kernel_size, stride=stride,
|
||||
padding=padding, bias=False
|
||||
),
|
||||
nn.BatchNorm2d(C_out, affine=affine)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.op(x)
|
||||
|
||||
|
||||
class DilConv(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True
|
||||
):
|
||||
super(DilConv, self).__init__()
|
||||
self.op = nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(
|
||||
C_in, C_in, kernel_size=kernel_size, stride=stride,
|
||||
padding=padding, dilation=dilation, groups=C_in, bias=False
|
||||
),
|
||||
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
|
||||
nn.BatchNorm2d(C_out, affine=affine),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.op(x)
|
||||
|
||||
|
||||
class SepConv(nn.Module):
|
||||
|
||||
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
|
||||
super(SepConv, self).__init__()
|
||||
self.op = nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(
|
||||
C_in, C_in, kernel_size=kernel_size, stride=stride,
|
||||
padding=padding, groups=C_in, bias=False
|
||||
),
|
||||
nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
|
||||
nn.BatchNorm2d(C_in, affine=affine),
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(
|
||||
C_in, C_in, kernel_size=kernel_size, stride=1,
|
||||
padding=padding, groups=C_in, bias=False
|
||||
),
|
||||
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
|
||||
nn.BatchNorm2d(C_out, affine=affine),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.op(x)
|
||||
|
||||
|
||||
class DilSepConv(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True
|
||||
):
|
||||
super(DilSepConv, self).__init__()
|
||||
self.op = nn.Sequential(
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(
|
||||
C_in, C_in, kernel_size=kernel_size, stride=stride,
|
||||
padding=padding, dilation=dilation, groups=C_in, bias=False
|
||||
),
|
||||
nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
|
||||
nn.BatchNorm2d(C_in, affine=affine),
|
||||
nn.ReLU(inplace=False),
|
||||
nn.Conv2d(
|
||||
C_in, C_in, kernel_size=kernel_size, stride=1,
|
||||
padding=padding, dilation=dilation, groups=C_in, bias=False
|
||||
),
|
||||
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
|
||||
nn.BatchNorm2d(C_out, affine=affine),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.op(x)
|
||||
|
||||
|
||||
class Identity(nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super(Identity, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x
|
||||
|
||||
|
||||
class Zero(nn.Module):
|
||||
|
||||
def __init__(self, stride):
|
||||
super(Zero, self).__init__()
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
if self.stride == 1:
|
||||
return x.mul(0.)
|
||||
return x[:,:,::self.stride,::self.stride].mul(0.)
|
||||
|
||||
|
||||
class FactorizedReduce(nn.Module):
|
||||
|
||||
def __init__(self, C_in, C_out, affine=True):
|
||||
super(FactorizedReduce, self).__init__()
|
||||
assert C_out % 2 == 0
|
||||
self.relu = nn.ReLU(inplace=False)
|
||||
self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
|
||||
self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
|
||||
self.bn = nn.BatchNorm2d(C_out, affine=affine)
|
||||
self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.relu(x)
|
||||
y = self.pad(x)
|
||||
out = torch.cat([self.conv_1(x), self.conv_2(y[:,:,1:,1:])], dim=1)
|
||||
out = self.bn(out)
|
||||
return out
|
||||
Reference in New Issue
Block a user