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resnets.py
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resnets.py
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"""RefineNet-LightWeight
RefineNet-LigthWeight PyTorch for non-commercial purposes
Copyright (c) 2018, Vladimir Nekrasov ([email protected])
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import torch.nn as nn
import torch.nn.functional as F
import torch
from configs import MODEL_PATH
import numpy as np
from aux_utils import maybe_download
from layers import conv1x1, conv3x3, CRPBlock
data_info = {
7: 'Person',
21: 'VOC',
40: 'NYU',
60: 'Context'
}
models_urls = {
'50_person': 'https://cloudstor.aarnet.edu.au/plus/s/mLA7NxVSPjNL7Oo/download',
'101_person': 'https://cloudstor.aarnet.edu.au/plus/s/f1tGGpwdCnYS3xu/download',
'152_person': 'https://cloudstor.aarnet.edu.au/plus/s/Ql64rWqiTvWGAA0/download',
'50_voc': 'https://cloudstor.aarnet.edu.au/plus/s/2E1KrdF2Rfc5khB/download',
'101_voc': 'https://cloudstor.aarnet.edu.au/plus/s/CPRKWiaCIDRdOwF/download',
'152_voc': 'https://cloudstor.aarnet.edu.au/plus/s/2w8bFOd45JtPqbD/download',
'50_nyu': 'https://cloudstor.aarnet.edu.au/plus/s/gE8dnQmHr9svpfu/download',
'101_nyu': 'https://cloudstor.aarnet.edu.au/plus/s/VnsaSUHNZkuIqeB/download',
'152_nyu': 'https://cloudstor.aarnet.edu.au/plus/s/EkPQzB2KtrrDnKf/download',
'101_context': 'https://cloudstor.aarnet.edu.au/plus/s/hqmplxWOBbOYYjN/download',
'152_context': 'https://cloudstor.aarnet.edu.au/plus/s/O84NszlYlsu00fW/download',
'50_imagenet': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'101_imagenet': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'152_imagenet': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
stages_suffixes = {0: '_conv',
1: '_conv_relu_varout_dimred'}
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNetLW(nn.Module):
def __init__(self, block, layers, num_classes=21):
self.inplanes = 64
super(ResNetLW, self).__init__()
self.do = nn.Dropout(p=0.5)
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.p_ims1d2_outl1_dimred = conv1x1(2048, 512, bias=False)
self.mflow_conv_g1_pool = self._make_crp(512, 512, 4)
self.mflow_conv_g1_b3_joint_varout_dimred = conv1x1(512, 256, bias=False)
self.p_ims1d2_outl2_dimred = conv1x1(1024, 256, bias=False)
self.adapt_stage2_b2_joint_varout_dimred = conv1x1(256, 256, bias=False)
self.mflow_conv_g2_pool = self._make_crp(256, 256, 4)
self.mflow_conv_g2_b3_joint_varout_dimred = conv1x1(256, 256, bias=False)
self.p_ims1d2_outl3_dimred = conv1x1(512, 256, bias=False)
self.adapt_stage3_b2_joint_varout_dimred = conv1x1(256, 256, bias=False)
self.mflow_conv_g3_pool = self._make_crp(256, 256, 4)
self.mflow_conv_g3_b3_joint_varout_dimred = conv1x1(256, 256, bias=False)
self.p_ims1d2_outl4_dimred = conv1x1(256, 256, bias=False)
self.adapt_stage4_b2_joint_varout_dimred = conv1x1(256, 256, bias=False)
self.mflow_conv_g4_pool = self._make_crp(256, 256, 4)
self.clf_conv = nn.Conv2d(256, num_classes, kernel_size=3, stride=1,
padding=1, bias=True)
def _make_crp(self, in_planes, out_planes, stages):
layers = [CRPBlock(in_planes, out_planes, stages)]
return nn.Sequential(*layers)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
l1 = self.layer1(x)
l2 = self.layer2(l1)
l3 = self.layer3(l2)
l4 = self.layer4(l3)
l4 = self.do(l4)
l3 = self.do(l3)
x4 = self.p_ims1d2_outl1_dimred(l4)
x4 = self.relu(x4)
x4 = self.mflow_conv_g1_pool(x4)
x4 = self.mflow_conv_g1_b3_joint_varout_dimred(x4)
x4 = nn.Upsample(size=l3.size()[2:], mode='bilinear', align_corners=True)(x4)
x3 = self.p_ims1d2_outl2_dimred(l3)
x3 = self.adapt_stage2_b2_joint_varout_dimred(x3)
x3 = x3 + x4
x3 = F.relu(x3)
x3 = self.mflow_conv_g2_pool(x3)
x3 = self.mflow_conv_g2_b3_joint_varout_dimred(x3)
x3 = nn.Upsample(size=l2.size()[2:], mode='bilinear', align_corners=True)(x3)
x2 = self.p_ims1d2_outl3_dimred(l2)
x2 = self.adapt_stage3_b2_joint_varout_dimred(x2)
x2 = x2 + x3
x2 = F.relu(x2)
x2 = self.mflow_conv_g3_pool(x2)
x2 = self.mflow_conv_g3_b3_joint_varout_dimred(x2)
x2 = nn.Upsample(size=l1.size()[2:], mode='bilinear', align_corners=True)(x2)
x1 = self.p_ims1d2_outl4_dimred(l1)
x1 = self.adapt_stage4_b2_joint_varout_dimred(x1)
x1 = x1 + x2
x1 = F.relu(x1)
x1 = self.mflow_conv_g4_pool(x1)
out = self.clf_conv(x1)
return out
def rf_lw50(num_classes, model_path, pretrained=False, **kwargs):
model = ResNetLW(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, **kwargs)
if pretrained:
print("loading model_file")
model = torch.load(model_path)
# if imagenet:
# key = '50_imagenet'
# url = models_urls[key]
# model.load_state_dict(maybe_download(key, url), strict=False)
# elif pretrained:
# dataset = data_info.get(num_classes, None)
# if dataset:
# bname = '50_' + dataset.lower()
# key = 'rf_lw' + bname
# url = models_urls[bname]
# model.load_state_dict(maybe_download(key, url), strict=False)
return model
def rf_lw101(num_classes, model_path, pretrained=False, **kwargs):
model = ResNetLW(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, **kwargs)
if pretrained:
print("loading model_file")
model = torch.load(model_path)
# if imagenet:
# key = '101_imagenet'
# url = models_urls[key]
# model.load_state_dict(maybe_download(key, url), strict=False)
# elif pretrained:
# dataset = data_info.get(num_classes, None)
# if dataset:
# bname = '101_' + dataset.lower()
# key = 'rf_lw' + bname
# url = models_urls[bname]
# model.load_state_dict(maybe_download(key, url), strict=False)
return model
def rf_lw152(num_classes, model_path, pretrained=False, **kwargs):
model = ResNetLW(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, **kwargs)
if pretrained:
print("loading model_file")
model = torch.load(model_path)
# if imagenet:
# key = '152_imagenet'
# url = models_urls[key]
# model.load_state_dict(maybe_download(key, url), strict=False)
# elif pretrained:
# dataset = data_info.get(num_classes, None)
# if dataset:
# bname = '152_' + dataset.lower()
# key = 'rf_lw' + bname
# url = models_urls[bname]
# model.load_state_dict(maybe_download(key, url), strict=False)
return model