209 lines
11 KiB
Python
209 lines
11 KiB
Python
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import numpy as np
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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 ConvolutionalNetwork(nn.Module):
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def __init__(self, input_shape, dim_reduction_type, num_output_classes, num_filters, num_layers, use_bias=False):
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"""
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Initializes a convolutional network module object.
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:param input_shape: The shape of the inputs going in to the network.
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:param dim_reduction_type: The type of dimensionality reduction to apply after each convolutional stage, should be one of ['max_pooling', 'avg_pooling', 'strided_convolution', 'dilated_convolution']
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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 conv layer, except dim reduction stages, where those are automatically infered.
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:param num_layers: Number of conv layers (excluding dim reduction stages)
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:param use_bias: Whether our convolutions will use a bias.
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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_layers = num_layers
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self.dim_reduction_type = dim_reduction_type
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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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"""
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Builds network whilst automatically inferring shapes of layers.
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"""
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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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# 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_layers): # for number of layers times
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self.layer_dict['conv_{}'.format(i)] = nn.Conv2d(in_channels=out.shape[1],
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# add a conv layer in the module dict
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kernel_size=3,
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out_channels=self.num_filters, padding=1,
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bias=self.use_bias)
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out = self.layer_dict['conv_{}'.format(i)](out) # use layer on inputs to get an output
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out = F.relu(out) # apply relu
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print(out.shape)
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if self.dim_reduction_type == 'strided_convolution': # if dim reduction is strided conv, then add a strided conv
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self.layer_dict['dim_reduction_strided_conv_{}'.format(i)] = nn.Conv2d(in_channels=out.shape[1],
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kernel_size=3,
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out_channels=out.shape[1],
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padding=1,
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bias=self.use_bias, stride=2,
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dilation=1)
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out = self.layer_dict['dim_reduction_strided_conv_{}'.format(i)](
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out) # use strided conv to get an output
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out = F.relu(out) # apply relu to the output
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elif self.dim_reduction_type == 'dilated_convolution': # if dim reduction is dilated conv, then add a dilated conv, using an arbitrary dilation rate of i + 2 (so it gets smaller as we go, you can choose other dilation rates should you wish to do it.)
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self.layer_dict['dim_reduction_dilated_conv_{}'.format(i)] = nn.Conv2d(in_channels=out.shape[1],
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kernel_size=3,
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out_channels=out.shape[1],
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padding=1,
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bias=self.use_bias, stride=1,
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dilation=i + 2)
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out = self.layer_dict['dim_reduction_dilated_conv_{}'.format(i)](
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out) # run dilated conv on input to get output
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out = F.relu(out) # apply relu on output
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elif self.dim_reduction_type == 'max_pooling':
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self.layer_dict['dim_reduction_max_pool_{}'.format(i)] = nn.MaxPool2d(2, padding=1)
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out = self.layer_dict['dim_reduction_max_pool_{}'.format(i)](out)
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elif self.dim_reduction_type == 'avg_pooling':
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self.layer_dict['dim_reduction_avg_pool_{}'.format(i)] = nn.AvgPool2d(2, padding=1)
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out = self.layer_dict['dim_reduction_avg_pool_{}'.format(i)](out)
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print(out.shape)
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if out.shape[-1] != 2:
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out = F.adaptive_avg_pool2d(out,
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2) # apply adaptive pooling to make sure output of conv layers is always (2, 2) spacially (helps with comparisons).
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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=self.use_bias)
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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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for i in range(self.num_layers): # for number of layers
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out = self.layer_dict['conv_{}'.format(i)](out) # pass through conv layer indexed at i
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out = F.relu(out) # pass conv outputs through ReLU
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if self.dim_reduction_type == 'strided_convolution': # if strided convolution dim reduction then
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out = self.layer_dict['dim_reduction_strided_conv_{}'.format(i)](
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out) # pass previous outputs through a strided convolution indexed i
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out = F.relu(out) # pass strided conv outputs through ReLU
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elif self.dim_reduction_type == 'dilated_convolution':
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out = self.layer_dict['dim_reduction_dilated_conv_{}'.format(i)](out)
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out = F.relu(out)
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elif self.dim_reduction_type == 'max_pooling':
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out = self.layer_dict['dim_reduction_max_pool_{}'.format(i)](out)
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elif self.dim_reduction_type == 'avg_pooling':
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out = self.layer_dict['dim_reduction_avg_pool_{}'.format(i)](out)
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if out.shape[-1] != 2:
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out = F.adaptive_avg_pool2d(out, 2)
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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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