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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import SyncBatchNorm as BatchNorm2d
import re
import os, sys
# from six import moves
class Exchange(nn.Module):
def __init__(self):
super(Exchange, self).__init__()
def forward(self, x, bn, bn_threshold):
bn1, bn2 = bn[0].weight.abs(), bn[1].weight.abs()
x1, x2 = torch.zeros_like(x[0]), torch.zeros_like(x[1])
x1[:, bn1 >= bn_threshold] = x[0][:, bn1 >= bn_threshold]
x1[:, bn1 < bn_threshold] = x[1][:, bn1 < bn_threshold]
x2[:, bn2 >= bn_threshold] = x[1][:, bn2 >= bn_threshold]
x2[:, bn2 < bn_threshold] = x[0][:, bn2 < bn_threshold]
return [x1, x2]
class ModuleParallel(nn.Module):
def __init__(self, module):
super(ModuleParallel, self).__init__()
self.module = module
def forward(self, x_parallel):
return [self.module(x) for x in x_parallel]
class BatchNorm2dParallel(nn.Module):
def __init__(self, num_features, num_parallel):
super(BatchNorm2dParallel, self).__init__()
for i in range(num_parallel):
setattr(self, 'bn_' + str(i), BatchNorm2d(num_features))
def forward(self, x_parallel):
return [getattr(self, 'bn_' + str(i))(x) for i, x in enumerate(x_parallel)]
class ChannelExchangingNetwork(nn.Module):
def __init__(self, num_layers, num_classes, num_parallel, l1_lambda, bn_threshold):
super(ChannelExchangingNetwork, self).__init__()
self.model = refinenet(num_layers, num_classes, num_parallel, bn_threshold)
self.model = model_init(self.model, num_layers, num_parallel, imagenet=True) #Only initializes the encoder
self.l1_lambda = l1_lambda
def get_slim_params(self):
slim_params = []
for name, param in self.model.named_parameters():
if param.requires_grad and name.endswith('weight') and 'bn2' in name:
if len(slim_params) % 2 == 0:
slim_params.append(param[:len(param) // 2])
else:
slim_params.append(param[len(param) // 2:])
return slim_params
def forward(self, data, get_sup_loss = False, gt = None, criterion = None):
b, c, h, w = data[0].shape #rgb is the 0th element
pred = self.model(data)
for i in range(len(pred)):
pred[i] = F.interpolate(pred[i], size=(h, w), mode='bilinear', align_corners=True)
if not self.training:
return pred
else: # training
if get_sup_loss:
l1_loss = self.l1_lambda * self.get_l1_loss(data[0].get_device())
sup_loss = self.get_sup_loss(pred, gt, criterion)
return pred, sup_loss + l1_loss
else:
return pred
def get_sup_loss(self, pred, gt, criterion):
sup_loss = 0
for p in pred:
p = p[:gt.shape[0]] #Getting loss for only those examples in batch where gt exists. Won't get sup loss for unlabeled data.
sup_loss += criterion(p, gt)
return sup_loss / len(pred)
def get_params(self):
self.slim_params = self.get_slim_params() #Doing it here and not in __init__ because first the model should be put in appropriate device before accumulating slim_params
# enc_params, dec_params = [], []
# for name, param in self.model.named_parameters():
# if bool(re.match('.*conv1.*|.*bn1.*|.*layer.*', name)):
# enc_params.append(param)
# else:
# dec_params.append(param)
# return enc_params, dec_params
param_groups = [[], [], []]
for name, param in self.model.named_parameters():
if "norm" in name:
param_groups[1].append(param)
elif bool(re.match('.*conv1.*|.*bn1.*|.*layer.*', name)):
param_groups[0].append(param)
else:
param_groups[2].append(param)
return param_groups
def get_l1_loss(self, device):
L1_norm = sum([L1_penalty(m, device) for m in self.slim_params])
if L1_norm > 0:
return L1_norm.to(device)
else:
return torch.tensor(0).to(device)
"""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.
"""
models_urls = {
'101_voc' : 'https://cloudstor.aarnet.edu.au/plus/s/Owmttk9bdPROwc6/download',
'18_imagenet' : 'https://download.pytorch.org/models/resnet18-f37072fd.pth',
'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',
}
bottleneck_idx = 0
save_idx = 0
def conv3x3(in_planes, out_planes, stride=1, bias=False):
"3x3 convolution with padding"
return ModuleParallel(nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=stride, padding=1, bias=bias))
def conv1x1(in_planes, out_planes, stride=1, bias=False):
"1x1 convolution"
return ModuleParallel(nn.Conv2d(in_planes, out_planes, kernel_size=1,
stride=stride, padding=0, bias=bias))
class CRPBlock(nn.Module):
def __init__(self, in_planes, out_planes, num_stages, num_parallel):
super(CRPBlock, self).__init__()
for i in range(num_stages):
setattr(self, '{}_{}'.format(i + 1, 'outvar_dimred'),
conv3x3(in_planes if (i == 0) else out_planes, out_planes))
self.stride = 1
self.num_stages = num_stages
self.num_parallel = num_parallel
self.maxpool = ModuleParallel(nn.MaxPool2d(kernel_size=5, stride=1, padding=2))
def forward(self, x):
top = x
for i in range(self.num_stages):
top = self.maxpool(top)
top = getattr(self, '{}_{}'.format(i + 1, 'outvar_dimred'))(top)
x = [x[l] + top[l] for l in range(self.num_parallel)]
return x
stages_suffixes = {0 : '_conv', 1 : '_conv_relu_varout_dimred'}
class RCUBlock(nn.Module):
def __init__(self, in_planes, out_planes, num_blocks, num_stages, num_parallel):
super(RCUBlock, self).__init__()
for i in range(num_blocks):
for j in range(num_stages):
setattr(self, '{}{}'.format(i + 1, stages_suffixes[j]),
conv3x3(in_planes if (i == 0) and (j == 0) else out_planes,
out_planes, bias=(j == 0)))
self.stride = 1
self.num_blocks = num_blocks
self.num_stages = num_stages
self.num_parallel = num_parallel
self.relu = ModuleParallel(nn.ReLU(inplace=True))
def forward(self, x):
for i in range(self.num_blocks):
residual = x
for j in range(self.num_stages):
x = self.relu(x)
x = getattr(self, '{}{}'.format(i + 1, stages_suffixes[j]))(x)
x = [x[l] + residual[l] for l in range(self.num_parallel)]
return x
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, num_parallel, bn_threshold, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = BatchNorm2dParallel(planes, num_parallel)
self.relu = ModuleParallel(nn.ReLU(inplace=True))
self.conv2 = conv3x3(planes, planes)
self.bn2 = BatchNorm2dParallel(planes, num_parallel)
self.num_parallel = num_parallel
self.downsample = downsample
self.stride = stride
self.exchange = Exchange()
self.bn_threshold = bn_threshold
self.bn2_list = []
for module in self.bn2.modules():
if isinstance(module, BatchNorm2d):
self.bn2_list.append(module)
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 len(x) > 1:
out = self.exchange(out, self.bn2_list, self.bn_threshold)
if self.downsample is not None:
residual = self.downsample(x)
out = [out[l] + residual[l] for l in range(self.num_parallel)]
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, num_parallel, bn_threshold, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = BatchNorm2dParallel(planes, num_parallel)
self.conv2 = conv3x3(planes, planes, stride=stride)
self.bn2 = BatchNorm2dParallel(planes, num_parallel)
self.conv3 = conv1x1(planes, planes * 4)
self.bn3 = BatchNorm2dParallel(planes * 4, num_parallel)
self.relu = ModuleParallel(nn.ReLU(inplace=True))
self.num_parallel = num_parallel
self.downsample = downsample
self.stride = stride
self.exchange = Exchange()
self.bn_threshold = bn_threshold
self.bn2_list = []
for module in self.bn2.modules():
if isinstance(module, BatchNorm2d):
self.bn2_list.append(module)
def forward(self, x):
residual = x
out = x
out = self.conv1(out)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if len(x) > 1:
out = self.exchange(out, self.bn2_list, self.bn_threshold)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out = [out[l] + residual[l] for l in range(self.num_parallel)]
out = self.relu(out)
return out
class RefineNet(nn.Module):
def __init__(self, block, layers, num_parallel, num_classes=21, bn_threshold=2e-2):
self.inplanes = 64
self.num_parallel = num_parallel
super(RefineNet, self).__init__()
self.dropout = ModuleParallel(nn.Dropout(p=0.5))
self.conv1 = ModuleParallel(nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False))
self.bn1 = BatchNorm2dParallel(64, num_parallel)
self.relu = ModuleParallel(nn.ReLU(inplace=True))
self.maxpool = ModuleParallel(nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
self.layer1 = self._make_layer(block, 64, layers[0], bn_threshold)
self.layer2 = self._make_layer(block, 128, layers[1], bn_threshold, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], bn_threshold, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], bn_threshold, stride=2)
self.p_ims1d2_outl1_dimred = conv3x3(2048, 512)
self.adapt_stage1_b = self._make_rcu(512, 512, 2, 2)
self.mflow_conv_g1_pool = self._make_crp(512, 512, 4)
self.mflow_conv_g1_b = self._make_rcu(512, 512, 3, 2)
self.mflow_conv_g1_b3_joint_varout_dimred = conv3x3(512, 256)
self.p_ims1d2_outl2_dimred = conv3x3(1024, 256)
self.adapt_stage2_b = self._make_rcu(256, 256, 2, 2)
self.adapt_stage2_b2_joint_varout_dimred = conv3x3(256, 256)
self.mflow_conv_g2_pool = self._make_crp(256, 256, 4)
self.mflow_conv_g2_b = self._make_rcu(256, 256, 3, 2)
self.mflow_conv_g2_b3_joint_varout_dimred = conv3x3(256, 256)
self.p_ims1d2_outl3_dimred = conv3x3(512, 256)
self.adapt_stage3_b = self._make_rcu(256, 256, 2, 2)
self.adapt_stage3_b2_joint_varout_dimred = conv3x3(256, 256)
self.mflow_conv_g3_pool = self._make_crp(256, 256, 4)
self.mflow_conv_g3_b = self._make_rcu(256, 256, 3, 2)
self.mflow_conv_g3_b3_joint_varout_dimred = conv3x3(256, 256)
self.p_ims1d2_outl4_dimred = conv3x3(256, 256)
self.adapt_stage4_b = self._make_rcu(256, 256, 2, 2)
self.adapt_stage4_b2_joint_varout_dimred = conv3x3(256, 256)
self.mflow_conv_g4_pool = self._make_crp(256, 256, 4)
self.mflow_conv_g4_b = self._make_rcu(256, 256, 3, 2)
self.clf_conv = conv3x3(256, num_classes, bias=True)
self.alpha = nn.Parameter(torch.ones(num_parallel, requires_grad=True))
# self.alpha = nn.Parameter(torch.ones([1, num_parallel, 157, 157], requires_grad=True))
self.register_parameter('alpha', self.alpha)
def _make_crp(self, in_planes, out_planes, num_stages):
layers = [CRPBlock(in_planes, out_planes, num_stages, self.num_parallel)]
return nn.Sequential(*layers)
def _make_rcu(self, in_planes, out_planes, num_blocks, num_stages):
layers = [RCUBlock(in_planes, out_planes, num_blocks, num_stages, self.num_parallel)]
return nn.Sequential(*layers)
def _make_layer(self, block, planes, num_blocks, bn_threshold, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride=stride),
BatchNorm2dParallel(planes * block.expansion, self.num_parallel)
)
layers = []
layers.append(block(self.inplanes, planes, self.num_parallel, bn_threshold, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, num_blocks):
layers.append(block(self.inplanes, planes, self.num_parallel, bn_threshold))
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.dropout(l4)
l3 = self.dropout(l3)
x4 = self.p_ims1d2_outl1_dimred(l4)
x4 = self.adapt_stage1_b(x4)
x4 = self.relu(x4)
x4 = self.mflow_conv_g1_pool(x4)
x4 = self.mflow_conv_g1_b(x4)
x4 = self.mflow_conv_g1_b3_joint_varout_dimred(x4)
x4 = [nn.Upsample(size=l3[0].size()[2:], mode='bilinear', align_corners=True)(x4_) for x4_ in x4]
x3 = self.p_ims1d2_outl2_dimred(l3)
x3 = self.adapt_stage2_b(x3)
x3 = self.adapt_stage2_b2_joint_varout_dimred(x3)
x3 = [x3[l] + x4[l] for l in range(self.num_parallel)]
x3 = self.relu(x3)
x3 = self.mflow_conv_g2_pool(x3)
x3 = self.mflow_conv_g2_b(x3)
x3 = self.mflow_conv_g2_b3_joint_varout_dimred(x3)
x3 = [nn.Upsample(size=l2[0].size()[2:], mode='bilinear', align_corners=True)(x3_) for x3_ in x3]
x2 = self.p_ims1d2_outl3_dimred(l2)
x2 = self.adapt_stage3_b(x2)
x2 = self.adapt_stage3_b2_joint_varout_dimred(x2)
x2 = [x2[l] + x3[l] for l in range(self.num_parallel)]
x2 = self.relu(x2)
x2 = self.mflow_conv_g3_pool(x2)
x2 = self.mflow_conv_g3_b(x2)
x2 = self.mflow_conv_g3_b3_joint_varout_dimred(x2)
x2 = [nn.Upsample(size=l1[0].size()[2:], mode='bilinear', align_corners=True)(x2_) for x2_ in x2]
x1 = self.p_ims1d2_outl4_dimred(l1)
x1 = self.adapt_stage4_b(x1)
x1 = self.adapt_stage4_b2_joint_varout_dimred(x1)
x1 = [x1[l] + x2[l] for l in range(self.num_parallel)]
x1 = self.relu(x1)
x1 = self.mflow_conv_g4_pool(x1)
x1 = self.mflow_conv_g4_b(x1)
x1 = self.dropout(x1)
out = self.clf_conv(x1)
ens = 0
alpha_soft = F.softmax(self.alpha, dim = 0)
for l in range(self.num_parallel):
ens += alpha_soft[l] * out[l].detach()
# alpha_soft = F.softmax(self.alpha, dim=1)
# for l in range(self.num_parallel):
# print(out[l].shape, l)
# ens += alpha_soft[:, l].unsqueeze(1) * out[l].detach()
out.append(ens)
# return out, alpha_soft
return out
class RefineNet_Resnet18(nn.Module):
def __init__(self, block, layers, num_parallel, num_classes=21, bn_threshold=2e-2):
self.inplanes = 64
self.num_parallel = num_parallel
super(RefineNet_Resnet18, self).__init__()
self.dropout = ModuleParallel(nn.Dropout(p=0.5))
self.conv1 = ModuleParallel(nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False))
self.bn1 = BatchNorm2dParallel(64, num_parallel)
self.relu = ModuleParallel(nn.ReLU(inplace=True))
self.maxpool = ModuleParallel(nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
self.layer1 = self._make_layer(block, 64, layers[0], bn_threshold)
self.layer2 = self._make_layer(block, 128, layers[1], bn_threshold, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], bn_threshold, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], bn_threshold, stride=2)
self.p_ims1d2_outl1_dimred = conv3x3(512, 256)
self.adapt_stage1_b = self._make_rcu(256, 256, 2, 2)
self.mflow_conv_g1_pool = self._make_crp(256, 256, 4)
self.mflow_conv_g1_b = self._make_rcu(256, 256, 3, 2)
self.mflow_conv_g1_b3_joint_varout_dimred = conv3x3(256, 64)
self.p_ims1d2_outl2_dimred = conv3x3(256, 64)
self.adapt_stage2_b = self._make_rcu(64, 64, 2, 2)
self.adapt_stage2_b2_joint_varout_dimred = conv3x3(64, 64)
self.mflow_conv_g2_pool = self._make_crp(64, 64, 4)
self.mflow_conv_g2_b = self._make_rcu(64, 64, 3, 2)
self.mflow_conv_g2_b3_joint_varout_dimred = conv3x3(64, 64)
self.p_ims1d2_outl3_dimred = conv3x3(128, 64)
self.adapt_stage3_b = self._make_rcu(64, 64, 2, 2)
self.adapt_stage3_b2_joint_varout_dimred = conv3x3(64, 64)
self.mflow_conv_g3_pool = self._make_crp(64, 64, 4)
self.mflow_conv_g3_b = self._make_rcu(64, 64, 3, 2)
self.mflow_conv_g3_b3_joint_varout_dimred = conv3x3(64, 64)
self.p_ims1d2_outl4_dimred = conv3x3(64, 64)
self.adapt_stage4_b = self._make_rcu(64, 64, 2, 2)
self.adapt_stage4_b2_joint_varout_dimred = conv3x3(64, 64)
self.mflow_conv_g4_pool = self._make_crp(64, 64, 4)
self.mflow_conv_g4_b = self._make_rcu(64, 64, 3, 2)
self.clf_conv = conv3x3(64, num_classes, bias=True)
self.alpha = nn.Parameter(torch.ones(num_parallel, requires_grad=True))
# self.alpha = nn.Parameter(torch.ones([1, num_parallel, 157, 157], requires_grad=True))
self.register_parameter('alpha', self.alpha)
def _make_crp(self, in_planes, out_planes, num_stages):
layers = [CRPBlock(in_planes, out_planes, num_stages, self.num_parallel)]
return nn.Sequential(*layers)
def _make_rcu(self, in_planes, out_planes, num_blocks, num_stages):
layers = [RCUBlock(in_planes, out_planes, num_blocks, num_stages, self.num_parallel)]
return nn.Sequential(*layers)
def _make_layer(self, block, planes, num_blocks, bn_threshold, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride=stride),
BatchNorm2dParallel(planes * block.expansion, self.num_parallel)
)
layers = []
layers.append(block(self.inplanes, planes, self.num_parallel, bn_threshold, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, num_blocks):
layers.append(block(self.inplanes, planes, self.num_parallel, bn_threshold))
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.dropout(l4)
l3 = self.dropout(l3)
x4 = self.p_ims1d2_outl1_dimred(l4)
x4 = self.adapt_stage1_b(x4)
x4 = self.relu(x4)
x4 = self.mflow_conv_g1_pool(x4)
x4 = self.mflow_conv_g1_b(x4)
x4 = self.mflow_conv_g1_b3_joint_varout_dimred(x4)
x4 = [nn.Upsample(size=l3[0].size()[2:], mode='bilinear', align_corners=True)(x4_) for x4_ in x4]
x3 = self.p_ims1d2_outl2_dimred(l3)
x3 = self.adapt_stage2_b(x3)
x3 = self.adapt_stage2_b2_joint_varout_dimred(x3)
x3 = [x3[l] + x4[l] for l in range(self.num_parallel)]
x3 = self.relu(x3)
x3 = self.mflow_conv_g2_pool(x3)
x3 = self.mflow_conv_g2_b(x3)
x3 = self.mflow_conv_g2_b3_joint_varout_dimred(x3)
x3 = [nn.Upsample(size=l2[0].size()[2:], mode='bilinear', align_corners=True)(x3_) for x3_ in x3]
x2 = self.p_ims1d2_outl3_dimred(l2)
x2 = self.adapt_stage3_b(x2)
x2 = self.adapt_stage3_b2_joint_varout_dimred(x2)
x2 = [x2[l] + x3[l] for l in range(self.num_parallel)]
x2 = self.relu(x2)
x2 = self.mflow_conv_g3_pool(x2)
x2 = self.mflow_conv_g3_b(x2)
x2 = self.mflow_conv_g3_b3_joint_varout_dimred(x2)
x2 = [nn.Upsample(size=l1[0].size()[2:], mode='bilinear', align_corners=True)(x2_) for x2_ in x2]
x1 = self.p_ims1d2_outl4_dimred(l1)
x1 = self.adapt_stage4_b(x1)
x1 = self.adapt_stage4_b2_joint_varout_dimred(x1)
x1 = [x1[l] + x2[l] for l in range(self.num_parallel)]
x1 = self.relu(x1)
x1 = self.mflow_conv_g4_pool(x1)
x1 = self.mflow_conv_g4_b(x1)
x1 = self.dropout(x1)
out = self.clf_conv(x1)
ens = 0
alpha_soft = F.softmax(self.alpha, dim = 0)
for l in range(self.num_parallel):
ens += alpha_soft[l] * out[l].detach()
# alpha_soft = F.softmax(self.alpha, dim=1)
# for l in range(self.num_parallel):
# print(out[l].shape, l)
# ens += alpha_soft[:, l].unsqueeze(1) * out[l].detach()
out.append(ens)
return out, alpha_soft
def refinenet(num_layers, num_classes, num_parallel, bn_threshold):
refinnetClass = RefineNet
if int(num_layers) == 18:
layers = [2, 2, 2, 2]
block = BasicBlock
refinnetClass = RefineNet_Resnet18
elif int(num_layers) == 50:
layers = [3, 4, 6, 3]
block = Bottleneck
elif int(num_layers) == 101:
layers = [3, 4, 23, 3]
block = Bottleneck
elif int(num_layers) == 152:
layers = [3, 8, 36, 3]
block = Bottleneck
else:
print('invalid num_layers')
model = refinnetClass(block, layers, num_parallel, num_classes, bn_threshold)
return model
def maybe_download(model_name, model_url, model_dir=None, map_location=None):
if model_dir is None:
torch_home = os.path.expanduser(os.getenv('TORCH_HOME', '~/.torch'))
model_dir = os.getenv('TORCH_MODEL_ZOO', os.path.join(torch_home, 'models'))
if not os.path.exists(model_dir):
os.makedirs(model_dir)
filename = '{}.pth.tar'.format(model_name)
cached_file = os.path.join(model_dir, filename)
if not os.path.exists(cached_file):
# url = model_url
# sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
# moves.urllib.request.urlretrieve(url, cached_file)
raise Exception('cached file not found, maybe_download failed')
return torch.load(cached_file, map_location=map_location)
def model_init(model, num_layers, num_parallel, imagenet=False):
if imagenet:
key = str(num_layers) + '_imagenet'
url = models_urls[key]
state_dict = maybe_download(key, url)
model_dict = expand_model_dict(model.state_dict(), state_dict, num_parallel)
model.load_state_dict(model_dict, strict=True)
return model
def expand_model_dict(model_dict, state_dict, num_parallel):
model_dict_keys = model_dict.keys()
state_dict_keys = state_dict.keys()
for model_dict_key in model_dict_keys:
model_dict_key_re = model_dict_key.replace('module.', '')
if model_dict_key_re in state_dict_keys:
model_dict[model_dict_key] = state_dict[model_dict_key_re]
for i in range(num_parallel):
bn = '.bn_%d' % i
replace = True if bn in model_dict_key_re else False
model_dict_key_re = model_dict_key_re.replace(bn, '')
if replace and model_dict_key_re in state_dict_keys:
model_dict[model_dict_key] = state_dict[model_dict_key_re]
return model_dict
def L1_penalty(var, device):
return torch.abs(var).sum().to(device)