Conv4_CL.py

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from torch import nn
import math

def init_layer(L):
    # Initialization using fan-in
    if isinstance(L, nn.Conv2d):
        n = L.kernel_size[0]*L.kernel_size[1]*L.out_channels
        L.weight.data.normal_(0,math.sqrt(2.0/float(n)))
    elif isinstance(L, nn.BatchNorm2d):
        L.weight.data.fill_(1)
        L.bias.data.fill_(0)
        
class Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()
        
    def forward(self, x):        
        return x.view(x.size(0), -1)
    
# Simple Conv Block
class ConvBlock(nn.Module):
    maml = False #Default
    def __init__(self, indim, outdim, pool = True, padding = 1):
        super(ConvBlock, self).__init__()
        self.indim  = indim
        self.outdim = outdim
        if self.maml:
            self.C      = Conv2d_fw(indim, outdim, 3, padding = padding)
            self.BN     = BatchNorm2d_fw(outdim)
        else:
            self.C      = nn.Conv2d(indim, outdim, 3, padding= padding)
            self.BN     = nn.BatchNorm2d(outdim)
        self.relu   = nn.ReLU(inplace=True)

        self.parametrized_layers = [self.C, self.BN, self.relu]
        if pool:
            self.pool   = nn.MaxPool2d(2)
            self.parametrized_layers.append(self.pool)

        for layer in self.parametrized_layers:
            init_layer(layer)

        self.trunk = nn.Sequential(*self.parametrized_layers)


    def forward(self,x):
        out = self.trunk(x)
        return out
    
class ConvNet(nn.Module):
    def __init__(self, depth, flatten = True):
        super(ConvNet,self).__init__()
        trunk = []
        for i in range(depth):
            indim = 3 if i == 0 else 64
            outdim = 64
            B = ConvBlock(indim, outdim, pool = ( i <4 ) ) #only pooling for fist 4 layers
            trunk.append(B)

        if flatten:
            trunk.append(Flatten())

        self.trunk = nn.Sequential(*trunk)
#         self.final_feat_dim = 1600

    def forward(self,x):
        out = self.trunk(x)
        return out
    
if __name__ == '__main__':
    m = ConvNet(4)
    print(m)