Utils.py

import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(os.path.dirname(__file__))

import torch
import torch.nn as nn


def conv(batchNorm, in_planes, out_planes, kernel_size=3, stride=1):
    if batchNorm:
        return nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size,
                      stride=stride, padding=(kernel_size-1)//2, bias=False),
            nn.BatchNorm2d(out_planes),
            nn.LeakyReLU(0.05, inplace=True)
        )
    else:
        return nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size,
                      stride=stride, padding=(kernel_size-1)//2, bias=True),
            nn.LeakyReLU(0.05, inplace=True)
        )


def conv_no_lrelu(batchNorm, in_planes, out_planes, kernel_size=3, stride=1):
    if batchNorm:
        return nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size,
                      stride=stride, padding=(kernel_size-1)//2, bias=False),
            nn.BatchNorm2d(out_planes)
        )
    else:
        return nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size,
                      stride=stride, padding=(kernel_size-1)//2, bias=True),
        )


def predict_image(in_planes):
    return nn.Conv2d(in_planes, 3, kernel_size=3, stride=1, padding=1, bias=False)


def deconv(in_planes, out_planes):
    return nn.Sequential(
        nn.ConvTranspose2d(in_planes, out_planes, kernel_size=4,
                           stride=2, padding=1, bias=False),
        nn.LeakyReLU(0.05, inplace=True)
    )


def crop_like(input, target):
    if input.size()[2:] == target.size()[2:]:
        return input
    else:
        return input[:, :, :target.size(2), :target.size(3)]