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classDecoAlex_onlyDepth.py
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classDecoAlex_onlyDepth.py
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from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import numpy as np
# import matplotlib.pyplot as plt
from torch.nn.modules.module import _addindent
import Alex
def torch_summarize(model, show_weights=True, show_parameters=True):
"""Summarizes torch model by showing trainable parameters and weights."""
tmpstr = model.__class__.__name__ + ' (\n'
total_params = 0
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = torch_summarize(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
total_params += params
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
# get_output_shape(model,module)
# if hasattr(module,'output'):
# tmpstr += ' output_shape={}'.format(module.output.size())
# tmpstr += '\n'
tmpstr = tmpstr + ')'
tmpstr = tmpstr + '\n total number of parameters={}'.format(total_params)
return tmpstr
class ResidualBlock(nn.Module):
def __init__(self, filters, pad=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(filters, filters, kernel_size=3, stride=1, padding=pad, bias=True)
self.bn1 = nn.BatchNorm2d(filters, eps=0.0001)
self.Lrelu = nn.LeakyReLU(negative_slope=0.02)
self.conv2 = nn.Conv2d(filters, filters, kernel_size=3, stride=1, padding=pad, bias=True)
self.bn2 = nn.BatchNorm2d(filters, eps=0.0001)
self.downsample = None
self.filters = filters
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.Lrelu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.Lrelu(out)
return out
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, reduction),
nn.ReLU(inplace=True),
nn.Linear(reduction, channel),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class SEBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
super(SEBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, 1)
self.bn2 = nn.BatchNorm2d(planes)
self.se = SELayer(planes, reduction)
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.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class DECO(nn.Module):
def __init__(self):
super(DECO, self).__init__()
# 1 input image channel, 6 output channels, 5x5 square convolution
# kernel
# convoluzione1
self.conv1 = nn.Conv2d(1, 64, 7, stride=2, padding=3)
# BN e Leacky ReLU
self.bn1 = nn.BatchNorm1d(64)
self.Lrelu = nn.LeakyReLU(negative_slope=0.01)
# maxPooling
self.pool = nn.MaxPool2d(3, stride=2) # 64x57x57
# 8 blocchi di residual
self.res1 = ResidualBlock(64)
self.res2 = ResidualBlock(64)
self.res3 = ResidualBlock(64)
self.res4 = ResidualBlock(64)
self.res5 = ResidualBlock(64)
self.res6 = ResidualBlock(64)
self.res7 = ResidualBlock(64)
self.res8 = ResidualBlock(64)
# convoluzione2
self.conv2 = nn.Conv2d(64, 3, 1, stride=1) # 1 canale, 3 kernels,
# deconvolution-upsampling porta a 3x228x228
self.deconv = nn.ConvTranspose2d(3, 3, 8, stride=4, padding=2, groups=3, bias=False)
def forward(self, x):
# import code
# code.interact(local=locals())
# import ipdb; ipdb.set_trace()
x = x.view(x.size(0), 1, 228, 228)
x = self.conv1(x)
x = self.bn1(x)
x = self.Lrelu(x)
x = self.pool(x)
x = self.res1(x)
x = self.res2(x)
x = self.res3(x)
x = self.res4(x)
x = self.res5(x)
x = self.res6(x)
x = self.res7(x)
x = self.res8(x)
x = self.conv2(x)
x = self.deconv(x)
# import code
# code.interact(local=locals())
return x
class DecoAlexNet(nn.Module):
def __init__(self, num_classes):
super(DecoAlexNet, self).__init__()
self.Deco = DECO()
self.Alex = Alex.alexnet(pretrained=True)
num_feats = self.Alex.classifier[6].in_features
class_model = list(self.Alex.classifier.children())
class_model.pop()
class_model.append(nn.Linear(num_feats, num_classes))
self.Alex.classifier = nn.Sequential(*class_model)
def forward(self, x):
x = self.Deco(x)
# import code
# code.interact(local=locals())
# vision.utils.save_image(x, './images/output_deco.jpg')
# sm.imsave('./images/output_deco.jpg', x)
# import ipdb; ipdb.set_trace()
# x = Image.cpu().numpy()
# import code
# code.interact(local=locals())
x = self.Alex(x)
return x