341 lines
14 KiB
Python
341 lines
14 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class FCCNetwork(nn.Module):
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def __init__(self, input_shape, num_output_classes, num_filters, num_layers, use_bias=False):
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"""
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Initializes a fully connected network similar to the ones implemented previously in the MLP package.
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:param input_shape: The shape of the inputs going in to the network.
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:param num_output_classes: The number of outputs the network should have (for classification those would be the number of classes)
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:param num_filters: Number of filters used in every fcc layer.
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:param num_layers: Number of fcc layers (excluding dim reduction stages)
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:param use_bias: Whether our fcc layers will use a bias.
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"""
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super(FCCNetwork, self).__init__()
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# set up class attributes useful in building the network and inference
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self.input_shape = input_shape
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self.num_filters = num_filters
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self.num_output_classes = num_output_classes
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self.use_bias = use_bias
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self.num_layers = num_layers
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# initialize a module dict, which is effectively a dictionary that can collect layers and integrate them into pytorch
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self.layer_dict = nn.ModuleDict()
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# build the network
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self.build_module()
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def build_module(self):
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print("Building basic block of FCCNetwork using input shape", self.input_shape)
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x = torch.zeros((self.input_shape))
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out = x
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out = out.view(out.shape[0], -1)
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# flatten inputs to shape (b, -1) where -1 is the dim resulting from multiplying the
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# shapes of all dimensions after the 0th dim
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for i in range(self.num_layers):
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self.layer_dict['fcc_{}'.format(i)] = nn.Linear(in_features=out.shape[1], # initialize a fcc layer
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out_features=self.num_filters,
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bias=self.use_bias)
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out = self.layer_dict['fcc_{}'.format(i)](out) # apply ith fcc layer to the previous layers outputs
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out = F.relu(out) # apply a ReLU on the outputs
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self.logits_linear_layer = nn.Linear(in_features=out.shape[1], # initialize the prediction output linear layer
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out_features=self.num_output_classes,
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bias=self.use_bias)
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out = self.logits_linear_layer(out) # apply the layer to the previous layer's outputs
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print("Block is built, output volume is", out.shape)
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return out
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def forward(self, x):
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"""
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Forward prop data through the network and return the preds
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:param x: Input batch x a batch of shape batch number of samples, each of any dimensionality.
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:return: preds of shape (b, num_classes)
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"""
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out = x
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out = out.view(out.shape[0], -1)
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# flatten inputs to shape (b, -1) where -1 is the dim resulting from multiplying the
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# shapes of all dimensions after the 0th dim
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for i in range(self.num_layers):
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out = self.layer_dict['fcc_{}'.format(i)](out) # apply ith fcc layer to the previous layers outputs
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out = F.relu(out) # apply a ReLU on the outputs
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out = self.logits_linear_layer(out) # apply the layer to the previous layer's outputs
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return out
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def reset_parameters(self):
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"""
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Re-initializes the networks parameters
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"""
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for item in self.layer_dict.children():
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item.reset_parameters()
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self.logits_linear_layer.reset_parameters()
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class EmptyBlock(nn.Module):
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def __init__(self, input_shape=None, num_filters=None, kernel_size=None, padding=None, bias=None, dilation=None,
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reduction_factor=None):
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super(EmptyBlock, self).__init__()
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self.num_filters = num_filters
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self.kernel_size = kernel_size
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self.input_shape = input_shape
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self.padding = padding
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self.bias = bias
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self.dilation = dilation
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self.build_module()
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def build_module(self):
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self.layer_dict = nn.ModuleDict()
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x = torch.zeros(self.input_shape)
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self.layer_dict['Identity'] = nn.Identity()
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def forward(self, x):
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out = x
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out = self.layer_dict['Identity'].forward(out)
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return out
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class EntryConvolutionalBlock(nn.Module):
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def __init__(self, input_shape, num_filters, kernel_size, padding, bias, dilation):
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super(EntryConvolutionalBlock, self).__init__()
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self.num_filters = num_filters
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self.kernel_size = kernel_size
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self.input_shape = input_shape
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self.padding = padding
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self.bias = bias
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self.dilation = dilation
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self.build_module()
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def build_module(self):
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self.layer_dict = nn.ModuleDict()
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x = torch.zeros(self.input_shape)
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out = x
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self.layer_dict['conv_0'] = nn.Conv2d(in_channels=out.shape[1], out_channels=self.num_filters, bias=self.bias,
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kernel_size=self.kernel_size, dilation=self.dilation,
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padding=self.padding, stride=1)
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out = self.layer_dict['conv_0'].forward(out)
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self.layer_dict['bn_0'] = nn.BatchNorm2d(num_features=out.shape[1])
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out = F.leaky_relu(self.layer_dict['bn_0'].forward(out))
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print(out.shape)
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def forward(self, x):
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out = x
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out = self.layer_dict['conv_0'].forward(out)
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out = F.leaky_relu(self.layer_dict['bn_0'].forward(out))
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return out
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class ConvolutionalProcessingBlock(nn.Module):
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def __init__(self, input_shape, num_filters, kernel_size, padding, bias, dilation):
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super(ConvolutionalProcessingBlock, self).__init__()
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self.num_filters = num_filters
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self.kernel_size = kernel_size
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self.input_shape = input_shape
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self.padding = padding
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self.bias = bias
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self.dilation = dilation
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self.build_module()
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def build_module(self):
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self.layer_dict = nn.ModuleDict()
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x = torch.zeros(self.input_shape)
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out = x
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self.layer_dict['conv_0'] = nn.Conv2d(in_channels=out.shape[1], out_channels=self.num_filters, bias=self.bias,
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kernel_size=self.kernel_size, dilation=self.dilation,
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padding=self.padding, stride=1)
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out = self.layer_dict['conv_0'].forward(out)
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out = F.leaky_relu(out)
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self.layer_dict['conv_1'] = nn.Conv2d(in_channels=out.shape[1], out_channels=self.num_filters, bias=self.bias,
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kernel_size=self.kernel_size, dilation=self.dilation,
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padding=self.padding, stride=1)
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out = self.layer_dict['conv_1'].forward(out)
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out = F.leaky_relu(out)
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print(out.shape)
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def forward(self, x):
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out = x
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out = self.layer_dict['conv_0'].forward(out)
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out = F.leaky_relu(out)
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out = self.layer_dict['conv_1'].forward(out)
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out = F.leaky_relu(out)
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return out
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class ConvolutionalDimensionalityReductionBlock(nn.Module):
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def __init__(self, input_shape, num_filters, kernel_size, padding, bias, dilation, reduction_factor):
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super(ConvolutionalDimensionalityReductionBlock, self).__init__()
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self.num_filters = num_filters
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self.kernel_size = kernel_size
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self.input_shape = input_shape
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self.padding = padding
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self.bias = bias
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self.dilation = dilation
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self.reduction_factor = reduction_factor
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self.build_module()
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def build_module(self):
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self.layer_dict = nn.ModuleDict()
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x = torch.zeros(self.input_shape)
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out = x
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self.layer_dict['conv_0'] = nn.Conv2d(in_channels=out.shape[1], out_channels=self.num_filters, bias=self.bias,
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kernel_size=self.kernel_size, dilation=self.dilation,
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padding=self.padding, stride=1)
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out = self.layer_dict['conv_0'].forward(out)
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out = F.leaky_relu(out)
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out = F.avg_pool2d(out, self.reduction_factor)
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self.layer_dict['conv_1'] = nn.Conv2d(in_channels=out.shape[1], out_channels=self.num_filters, bias=self.bias,
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kernel_size=self.kernel_size, dilation=self.dilation,
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padding=self.padding, stride=1)
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out = self.layer_dict['conv_1'].forward(out)
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out = F.leaky_relu(out)
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print(out.shape)
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def forward(self, x):
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out = x
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out = self.layer_dict['conv_0'].forward(out)
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out = F.leaky_relu(out)
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out = F.avg_pool2d(out, self.reduction_factor)
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out = self.layer_dict['conv_1'].forward(out)
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out = F.leaky_relu(out)
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return out
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class ConvolutionalNetwork(nn.Module):
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def __init__(self, input_shape, num_output_classes, num_filters,
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num_blocks_per_stage, num_stages, use_bias=False, processing_block_type=ConvolutionalProcessingBlock,
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dimensionality_reduction_block_type=ConvolutionalDimensionalityReductionBlock):
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"""
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Initializes a convolutional network module
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:param input_shape: The shape of the tensor to be passed into this network
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:param num_output_classes: Number of output classes
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:param num_filters: Number of filters per convolutional layer
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:param num_blocks_per_stage: Number of blocks per "stage". Each block is composed of 2 convolutional layers.
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:param num_stages: Number of stages in a network. A stage is defined as a sequence of layers within which the
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data dimensionality remains constant in the spacial axis (h, w) and can change in the channel axis. After each stage
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there exists a dimensionality reduction stage, composed of two convolutional layers and an avg pooling layer.
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:param use_bias: Whether to use biases in our convolutional layers
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:param processing_block_type: Type of processing block to use within our stages
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:param dimensionality_reduction_block_type: Type of dimensionality reduction block to use after each stage in our network
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"""
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super(ConvolutionalNetwork, self).__init__()
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# set up class attributes useful in building the network and inference
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self.input_shape = input_shape
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self.num_filters = num_filters
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self.num_output_classes = num_output_classes
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self.use_bias = use_bias
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self.num_blocks_per_stage = num_blocks_per_stage
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self.num_stages = num_stages
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self.processing_block_type = processing_block_type
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self.dimensionality_reduction_block_type = dimensionality_reduction_block_type
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# build the network
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self.build_module()
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def build_module(self):
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"""
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Builds network whilst automatically inferring shapes of layers.
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"""
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self.layer_dict = nn.ModuleDict()
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# initialize a module dict, which is effectively a dictionary that can collect layers and integrate them into pytorch
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print("Building basic block of ConvolutionalNetwork using input shape", self.input_shape)
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x = torch.zeros((self.input_shape)) # create dummy inputs to be used to infer shapes of layers
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out = x
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self.layer_dict['input_conv'] = EntryConvolutionalBlock(input_shape=out.shape, num_filters=self.num_filters,
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kernel_size=3, padding=1, bias=self.use_bias,
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dilation=1)
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out = self.layer_dict['input_conv'].forward(out)
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# torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)
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for i in range(self.num_stages): # for number of layers times
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for j in range(self.num_blocks_per_stage):
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self.layer_dict['block_{}_{}'.format(i, j)] = self.processing_block_type(input_shape=out.shape,
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num_filters=self.num_filters,
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bias=self.use_bias,
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kernel_size=3, dilation=1,
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padding=1)
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out = self.layer_dict['block_{}_{}'.format(i, j)].forward(out)
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self.layer_dict['reduction_block_{}'.format(i)] = self.dimensionality_reduction_block_type(
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input_shape=out.shape,
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num_filters=self.num_filters, bias=True,
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kernel_size=3, dilation=1,
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padding=1,
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reduction_factor=2)
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out = self.layer_dict['reduction_block_{}'.format(i)].forward(out)
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out = F.avg_pool2d(out, out.shape[-1])
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print('shape before final linear layer', out.shape)
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out = out.view(out.shape[0], -1)
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self.logit_linear_layer = nn.Linear(in_features=out.shape[1], # add a linear layer
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out_features=self.num_output_classes,
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bias=True)
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out = self.logit_linear_layer(out) # apply linear layer on flattened inputs
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print("Block is built, output volume is", out.shape)
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return out
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def forward(self, x):
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"""
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Forward propages the network given an input batch
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:param x: Inputs x (b, c, h, w)
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:return: preds (b, num_classes)
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"""
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out = x
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out = self.layer_dict['input_conv'].forward(out)
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for i in range(self.num_stages): # for number of layers times
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for j in range(self.num_blocks_per_stage):
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out = self.layer_dict['block_{}_{}'.format(i, j)].forward(out)
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out = self.layer_dict['reduction_block_{}'.format(i)].forward(out)
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out = F.avg_pool2d(out, out.shape[-1])
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out = out.view(out.shape[0], -1) # flatten outputs from (b, c, h, w) to (b, c*h*w)
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out = self.logit_linear_layer(out) # pass through a linear layer to get logits/preds
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return out
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def reset_parameters(self):
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"""
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Re-initialize the network parameters.
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"""
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for item in self.layer_dict.children():
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try:
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item.reset_parameters()
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except:
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pass
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self.logit_linear_layer.reset_parameters()
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