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https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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183 lines
6.5 KiB
Python
183 lines
6.5 KiB
Python
#!/bin/python
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import torch
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import torch.nn as nn
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import torchvision
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import torchvision.transforms as transforms
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import torch.optim as optim
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from torchsummary import summary
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#custom import
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import numpy as np
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import time
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import os
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# ResBlock
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class ResBlock(nn.Module):
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def __init__(self, num_features, use_batch_norm=False):
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super(ResBlock, self).__init__()
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self.num_features = num_features
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self.conv_layer1 = nn.Conv2d(num_features, num_features, kernel_size=3, stride=1, padding=1)
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self.relu_layer = nn.ReLU()
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self.conv_layer2 = nn.Conv2d(num_features, num_features, kernel_size=3, stride=1, padding=1)
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self.use_batch_norm = use_batch_norm
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if self.use_batch_norm:
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self.batch_norm_layer1 = nn.BatchNorm2d(self.num_features)
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self.batch_norm_layer2 = nn.BatchNorm2d(self.num_features)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight)
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# nn.init.xavier_uniform_(m.weight)
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def forward(self, x):
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residual = x
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x = self.conv_layer1(x)
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if self.use_batch_norm:
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x = self.batch_norm_layer1(x)
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x = self.relu_layer(x)
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x = self.conv_layer2(x)
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if self.use_batch_norm:
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x = self.batch_norm_layer2(x)
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x += residual
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x = self.relu_layer(x)
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return x
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# ResNet
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class ResNet(nn.Module):
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def __init__(self, in_features, num_class, feature_channel_list, batch_norm= False, num_stacks=1, zero_init_residual=True):
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super(ResNet, self).__init__()
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self.in_features = in_features
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self.num_in_channel = in_features[2]
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self.num_class = num_class
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self.feature_channel_list = feature_channel_list
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self.num_residual_blocks = len(self.feature_channel_list)
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self.num_stacks = num_stacks
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self.batch_norm = batch_norm
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self.shape_list = []
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self.shape_list.append(in_features)
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self.module_list = nn.ModuleList()
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self.zero_init_residual= zero_init_residual
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self.build_()
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def build_(self):
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#track filter shape
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cur_shape = self.GetCurShape()
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cur_shape = self.CalcConvOutShape(cur_shape, kernel_size=7, padding=1, stride=2, out_filters= self.feature_channel_list[0])
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self.shape_list.append(cur_shape)
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if len(self.in_features) == 2:
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in_channels = 1
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else:
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in_channels = self.in_features[2]
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# First Conv layer 7x7 stride=2, pad =1
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self.module_list.append(nn.Conv2d(in_channels= in_channels,
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out_channels= self.feature_channel_list[0],
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kernel_size=7,
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stride=2,
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padding=3))
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#batch norm
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if self.batch_norm: #batch_norm
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self.module_list.append(nn.BatchNorm2d(self.feature_channel_list[0]))
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# ReLU()
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self.module_list.append(nn.ReLU())
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for i in range(self.num_residual_blocks-1):
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in_size = self.feature_channel_list[i]
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out_size = self.feature_channel_list[i+1]
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res_block = ResBlock(in_size, use_batch_norm=True)
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# #Stacking Residual blocks
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for num in range(self.num_stacks):
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self.module_list.append(res_block)
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# # Intermediate Conv and ReLU()
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self.module_list.append(nn.Conv2d(in_channels=in_size,
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out_channels= out_size,
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kernel_size=3,
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padding=1,
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stride=2))
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# track filter shape
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cur_shape = self.CalcConvOutShape(cur_shape, kernel_size=3, padding=1,
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stride=2, out_filters=out_size)
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self.shape_list.append(cur_shape)
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# # batch norm
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if self.batch_norm: # batch_norm
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self.module_list.append(nn.BatchNorm2d(out_size))
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self.module_list.append(nn.ReLU())
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# print("shape list", self.shape_list)
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#TODO include in the main loop
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#Last Residual block
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res_block = ResBlock(out_size, use_batch_norm=True)
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for num in range(self.num_stacks):
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self.module_list.append(res_block)
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#Last AvgPool layer
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# self.module_list.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
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self.module_list.append(nn.MaxPool2d(kernel_size=2, stride=2, padding=0))
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# track filter shape
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cur_shape = self.CalcConvOutShape(cur_shape, kernel_size=2, padding=0, stride=2, out_filters=out_size)
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self.shape_list.append(cur_shape)
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s = self.GetCurShape()
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in_features = s[0] * s[1] * s[2]
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# Initialization
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight)
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# nn.init.xavier_uniform_(m.weight)
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# if self.zero_init_residual:
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# for m in self.modules():
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# if isinstance(m, ResBlock):
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# nn.init.constant_(m.batch_norm_layer1.weight, 0)
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# nn.init.constant_(m.batch_norm_layer2.weight, 0)
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def GetCurShape(self):
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return self.shape_list[-1]
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def CalcConvFormula(self, W, K, P, S):
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return int(np.floor(((W - K + 2 * P) / S) + 1))
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# https://stackoverflow.com/questions/53580088/calculate-the-output-size-in-convolution-layer
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# Calculate the output shape after applying a convolution
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def CalcConvOutShape(self, in_shape, kernel_size, padding, stride, out_filters):
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# Multiple options for different kernel shapes
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if type(kernel_size) == int:
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out_shape = [self.CalcConvFormula(in_shape[i], kernel_size, padding, stride) for i in range(2)]
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else:
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out_shape = [self.CalcConvFormula(in_shape[i], kernel_size[i], padding, stride) for i in range(2)]
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return (out_shape[0], out_shape[1], out_filters) # , batch_size... but not necessary.
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def AddMLP(self, MLP):
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if MLP:
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self.module_list.append(MLP)
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# def MLP(self, in_features, num_classes, use_batch_norm=False, use_dropout=False, use_softmax=False):
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# return nn.ReLU(nn.Linear(in_features, num_classes))
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def forward(self, x):
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for mod_name in self.module_list:
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x = mod_name(x)
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x = x.view(x.size(0), -1) # flat #TODO check if it works
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return x
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