Pytorch_CNN-RNN/utils/CNN_Layers.py

from torch import device, cuda
import torch
from torch import add
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import copy

class Conv_elu_maxpool_drop(nn.Module):
    def __init__(self, input_size, output_size, kernel_size, prps, stride=(1,1,1), pool = False, sep_conv = False, padding = 0):
        super(Conv_elu_maxpool_drop, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.pool_status = pool
        self.sep_conv_status = sep_conv

        # LAYERS
        # TODO Check here, how many groups? just 2? or groups=input_size?
        if(self.sep_conv_status):
            self.sepConvDepthwise = nn.Conv3d(input_size, output_size, kernel_size=kernel_size, stride=stride,
                                              padding=padding, dilation=prps['dilation'], groups=2, bias=prps["bias"], padding_mode=prps["padding_mode"])

        self.conv = nn.Conv3d(input_size, output_size, kernel_size=kernel_size, stride=stride,
                                          padding=padding, groups=1, bias=prps["bias"], padding_mode=prps["padding_mode"])
        self.normalization = nn.BatchNorm3d(output_size)
        self.elu = nn.ELU()
        self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=0)
        self.dropout = nn.Dropout(p=prps['drop_rate'])

        self.weight = nn.Parameter(torch.randn(input_size, output_size))
        self.bias = nn.Parameter(torch.randn(output_size))


    def forward(self, x):
        # print(f"Forward Input: {x.size()}")
        if(self.sep_conv_status): x = self.sepConvDepthwise(x)
        else: x = self.conv(x)
        x = self.normalization(x)
        x = self.elu(x)
        if(self.pool_status): self.maxpool(x)
        x = self.dropout(x)

        # return torch.matmul(x, self.weight) + self.bias
        return x        # TODO WHAT??? WEIGHT & BIAS YES OR NO?



class Mid_flow(nn.Module):
    def __init__(self, input_size, output_size, prps):
        super(Mid_flow, self).__init__()
        self.input_size = input_size
        self.output_size = output_size

        # LAYERS
        self.conv = Conv_elu_maxpool_drop(input_size, output_size, kernel_size=(3,3,3), stride=(1,1,1), sep_conv=True, padding='same', prps=prps)
        self.elu = nn.ELU()

        self.weight = nn.Parameter(torch.randn(input_size, output_size))
        self.bias = nn.Parameter(torch.randn(output_size))


    def forward(self, x):
        # print("AT MIDFLOW!")
        residual = x.clone()

        # print(f"Input: {x.size()}")
        x = self.conv(x)
        x = self.conv(x)
        x = self.conv(x)
        # print(f"Output: {x.size()}")

        x = add(x, residual)
        x = self.elu(x)

        # return torch.matmul(x, self.weight) + self.bias       # TODO WHAT??? WEIGHT & BIAS YES OR NO?
        return x



class Fc_elu_drop(nn.Module):
    def __init__(self, input_size, output_size, softmax, prps):
        super(Fc_elu_drop, self).__init__()
        self.input_size = input_size
        self.output_size = output_size

        # LAYERS
        self.linear = nn.Linear(input_size, output_size)
        self.normalization = nn.BatchNorm1d(output_size)
        self.elu = nn.ELU()
        self.dropout = nn.Dropout(p=prps['drop_rate'])
        self.softmax_status = softmax
        if(softmax): self.softmax = nn.Softmax()

        self.weight = nn.Parameter(torch.randn(input_size, output_size))
        self.bias = nn.Parameter(torch.randn(output_size))


    def forward(self, x):
        # print("AT FC")
        # print(f"Forward Input: {x.size()}")
        x = self.linear(x)
        # print(f"After Linear: {x.size()}")
        x = self.normalization(x)
        x = self.elu(x)
        x = self.dropout(x)
        if(self.softmax_status): x = self.softmax(x)

        # return torch.matmul(x, self.weight) + self.bias
        return x        # TODO WHAT??? WEIGHT & BIAS YES OR NO?