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Resolve PEP8 Issues #4

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40 changes: 20 additions & 20 deletions training/deeplab_resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,10 +23,10 @@
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import numpy as np
import torch.nn as nn
import torch
import numpy as np

affine_par = True


Expand Down Expand Up @@ -77,10 +77,10 @@ def forward(self, x):
class Bottleneck(nn.Module):
expansion = 4

def __init__(self, inplanes, planes, stride=1, dilation_ = 1, downsample=None):
def __init__(self, inplanes, planes, stride=1, dilation_=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
self.bn1 = nn.BatchNorm2d(planes,affine = affine_par)
self.bn1 = nn.BatchNorm2d(planes,affine=affine_par)
for i in self.bn1.parameters():
i.requires_grad = False
padding = 1
Expand All @@ -89,12 +89,12 @@ def __init__(self, inplanes, planes, stride=1, dilation_ = 1, downsample=None):
elif dilation_ == 4:
padding = 4
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
padding=padding, bias=False, dilation = dilation_)
self.bn2 = nn.BatchNorm2d(planes,affine = affine_par)
padding=padding, bias=False, dilation=dilation_)
self.bn2 = nn.BatchNorm2d(planes, affine=affine_par)
for i in self.bn2.parameters():
i.requires_grad = False
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4, affine = affine_par)
self.bn3 = nn.BatchNorm2d(planes * 4, affine=affine_par)
for i in self.bn3.parameters():
i.requires_grad = False
self.relu = nn.ReLU(inplace=True)
Expand Down Expand Up @@ -130,7 +130,7 @@ def __init__(self,dilation_series,padding_series,NoLabels):
super(Classifier_Module, self).__init__()
self.conv2d_list = nn.ModuleList()
for dilation,padding in zip(dilation_series,padding_series):
self.conv2d_list.append(nn.Conv2d(2048,NoLabels,kernel_size=3,stride=1, padding =padding, dilation = dilation,bias = True))
self.conv2d_list.append(nn.Conv2d(2048, NoLabels, kernel_size=3, stride=1, padding=padding, dilation=dilation, bias=True))

for m in self.conv2d_list:
m.weight.data.normal_(0, 0.01)
Expand All @@ -155,9 +155,9 @@ def __init__(self, block, layers,NoLabels):
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
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=1, dilation__ = 2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__ = 4)
self.layer5 = self._make_pred_layer(Classifier_Module, [6,12,18,24],[6,12,18,24],NoLabels)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation__=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__=4)
self.layer5 = self._make_pred_layer(Classifier_Module, [6,12,18,24], [6,12,18,24], NoLabels)

for m in self.modules():
if isinstance(m, nn.Conv2d):
Expand All @@ -169,21 +169,21 @@ def __init__(self, block, layers,NoLabels):
# for i in m.parameters():
# i.requires_grad = False

def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):
def _make_layer(self, block, planes, blocks, stride=1,dilation__=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion,affine = affine_par),
nn.BatchNorm2d(planes * block.expansion, affine=affine_par),
)
for i in downsample._modules['1'].parameters():
i.requires_grad = False
layers = []
layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample = downsample ))
layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample=downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,dilation_=dilation__))
layers.append(block(self.inplanes, planes, dilation_=dilation__))

return nn.Sequential(*layers)

Expand Down Expand Up @@ -213,16 +213,16 @@ def forward(self, x):
class MS_Deeplab(nn.Module):
def __init__(self,block,NoLabels):
super(MS_Deeplab,self).__init__()
self.Scale = ResNet(block,[3, 4, 23, 3],NoLabels) #changed to fix #4
self.Scale = ResNet(block, [3, 4, 23, 3], NoLabels) #changed to fix #4

def forward(self,x):
input_size_1 = x.size()[2]
input_size_2 = x.size()[3]
#print(x.size())

self.interp1 = nn.UpsamplingBilinear2d(size = (int(input_size_1*0.75)+1, int(input_size_2*0.75)+1))
self.interp2 = nn.UpsamplingBilinear2d(size = (int(input_size_1*0.5)+1, int(input_size_2*0.5)+1))
self.interp3 = nn.UpsamplingBilinear2d(size = (outS(input_size_1), outS(input_size_2)))
self.interp1 = nn.UpsamplingBilinear2d(size=(int(input_size_1*0.75)+1, int(input_size_2*0.75)+1))
self.interp2 = nn.UpsamplingBilinear2d(size=(int(input_size_1*0.5)+1, int(input_size_2*0.5)+1))
self.interp3 = nn.UpsamplingBilinear2d(size=(outS(input_size_1), outS(input_size_2)))

out = []
x2 = self.interp1(x)
Expand All @@ -244,7 +244,7 @@ def forward(self,x):
class Deeplab(nn.Module):
def __init__(self,block,NoLabels):
super(Deeplab,self).__init__()
self.Scale = ResNet(block,[3, 4, 23, 3],NoLabels) #changed to fix #4
self.Scale = ResNet(block, [3, 4, 23, 3], NoLabels) #changed to fix #4

def forward(self,x):
return self.Scale(x)
Expand Down