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|
|
|
| import torch
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| from torch import nn
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| from torch.autograd import Function
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|
|
| try:
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| from . import fused_act_ext
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| except ImportError:
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| import os
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| BASICSR_JIT = os.getenv('BASICSR_JIT')
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| if BASICSR_JIT == 'True':
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| from torch.utils.cpp_extension import load
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| module_path = os.path.dirname(__file__)
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| fused_act_ext = load(
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| 'fused',
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| sources=[
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| os.path.join(module_path, 'src', 'fused_bias_act.cpp'),
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| os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'),
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| ],
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| )
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|
|
|
|
| class FusedLeakyReLUFunctionBackward(Function):
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|
|
| @staticmethod
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| def forward(ctx, grad_output, out, negative_slope, scale):
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| ctx.save_for_backward(out)
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| ctx.negative_slope = negative_slope
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| ctx.scale = scale
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|
|
| empty = grad_output.new_empty(0)
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|
|
| grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale)
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|
|
| dim = [0]
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|
|
| if grad_input.ndim > 2:
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| dim += list(range(2, grad_input.ndim))
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|
|
| grad_bias = grad_input.sum(dim).detach()
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|
|
| return grad_input, grad_bias
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|
|
| @staticmethod
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| def backward(ctx, gradgrad_input, gradgrad_bias):
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| out, = ctx.saved_tensors
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| gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope,
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| ctx.scale)
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|
|
| return gradgrad_out, None, None, None
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|
|
|
|
| class FusedLeakyReLUFunction(Function):
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|
|
| @staticmethod
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| def forward(ctx, input, bias, negative_slope, scale):
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| empty = input.new_empty(0)
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| out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
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| ctx.save_for_backward(out)
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| ctx.negative_slope = negative_slope
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| ctx.scale = scale
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|
|
| return out
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|
|
| @staticmethod
|
| def backward(ctx, grad_output):
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| out, = ctx.saved_tensors
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|
|
| grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale)
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|
|
| return grad_input, grad_bias, None, None
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|
|
|
|
| class FusedLeakyReLU(nn.Module):
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|
|
| def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
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| super().__init__()
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|
|
| self.bias = nn.Parameter(torch.zeros(channel))
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| self.negative_slope = negative_slope
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| self.scale = scale
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|
|
| def forward(self, input):
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| return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
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|
|
|
|
| def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
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| return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
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|
|
