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import torch
import torch.nn as nn
import numpy as np
from torch.utils.checkpoint import checkpoint
class ReductionKernel(nn.Module):
# Tensorflow
def __init__(self, in_channels, kernel_size=2, dtype=torch.float32, device=None):
if device is None:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
super(ReductionKernel, self).__init__()
self.kernel_size = kernel_size
self.in_channels = in_channels
numpy_kernel = self.build_kernel()
self.kernel = torch.from_numpy(numpy_kernel).to(device=device, dtype=dtype)
def build_kernel(self):
# tensorflow kernel is (height, width, in_channels, out_channels)
# pytorch kernel is (out_channels, in_channels, height, width)
kernel_size = self.kernel_size
channels = self.in_channels
kernel_shape = [channels, channels, kernel_size, kernel_size]
kernel = np.zeros(kernel_shape, np.float32)
kernel_value = 1.0 / (kernel_size * kernel_size)
for i in range(0, channels):
kernel[i, i, :, :] = kernel_value
return kernel
def forward(self, x):
return nn.functional.conv2d(x, self.kernel, stride=self.kernel_size, padding=0, groups=1)
class CheckpointGradients(nn.Module):
def __init__(self, is_gradient_checkpointing=True):
super(CheckpointGradients, self).__init__()
self.is_gradient_checkpointing = is_gradient_checkpointing
def forward(self, module, *args, num_chunks=1):
if self.is_gradient_checkpointing:
return checkpoint(module, *args, num_chunks=self.num_chunks)
else:
return module(*args)
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