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| """Gradient interface.""" | |
| import torch | |
| from .modules.utils import _single, _pair, _triple | |
| def conv1d_input(input_size, weight, grad_output, stride=1, padding=0, dilation=1, groups=1): | |
| r"""Compute the gradient of conv1d with respect to the input of the convolution. | |
| This is same as the 1D transposed convolution operator under the hood but requires | |
| the shape of the gradient w.r.t. input to be specified explicitly. | |
| Args: | |
| input_size : Shape of the input gradient tensor | |
| weight: weight tensor (out_channels x in_channels/groups x kW) | |
| grad_output : output gradient tensor (minibatch x out_channels x oW) | |
| stride (int or tuple, optional): Stride of the convolution. Default: 1 | |
| padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 | |
| dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 | |
| groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 | |
| Examples:: | |
| >>> input = torch.randn(1, 1, 3, requires_grad=True) | |
| >>> weight = torch.randn(1, 1, 1, requires_grad=True) | |
| >>> output = F.conv1d(input, weight) | |
| >>> grad_output = torch.randn(output.shape) | |
| >>> grad_input = torch.autograd.grad(output, input, grad_output) | |
| >>> F.grad.conv1d_input(input.shape, weight, grad_output) | |
| """ | |
| input = grad_output.new_empty(1).expand(input_size) | |
| return torch.ops.aten.convolution_backward(grad_output, input, weight, None, | |
| _single(stride), _single(padding), _single(dilation), | |
| False, [0], groups, (True, False, False))[0] | |
| def conv1d_weight(input, weight_size, grad_output, stride=1, padding=0, dilation=1, groups=1): | |
| r"""Compute the gradient of conv1d with respect to the weight of the convolution. | |
| Args: | |
| input: input tensor of shape (minibatch x in_channels x iW) | |
| weight_size : Shape of the weight gradient tensor | |
| grad_output : output gradient tensor (minibatch x out_channels x oW) | |
| stride (int or tuple, optional): Stride of the convolution. Default: 1 | |
| padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 | |
| dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 | |
| groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 | |
| Examples:: | |
| >>> input = torch.randn(1, 1, 3, requires_grad=True) | |
| >>> weight = torch.randn(1, 1, 1, requires_grad=True) | |
| >>> output = F.conv1d(input, weight) | |
| >>> grad_output = torch.randn(output.shape) | |
| >>> # xdoctest: +SKIP | |
| >>> grad_weight = torch.autograd.grad(output, filter, grad_output) | |
| >>> F.grad.conv1d_weight(input, weight.shape, grad_output) | |
| """ | |
| weight = grad_output.new_empty(1).expand(weight_size) | |
| return torch.ops.aten.convolution_backward(grad_output, input, weight, None, | |
| _single(stride), _single(padding), _single(dilation), | |
| False, [0], groups, (False, True, False))[1] | |
| def conv2d_input(input_size, weight, grad_output, stride=1, padding=0, dilation=1, groups=1): | |
| r"""Compute the gradient of conv2d with respect to the input of the convolution. | |
| This is same as the 2D transposed convolution operator under the hood but requires | |
| the shape of the gradient w.r.t. input to be specified explicitly. | |
| Args: | |
| input_size : Shape of the input gradient tensor | |
| weight: weight tensor (out_channels x in_channels/groups x kH x kW) | |
| grad_output : output gradient tensor (minibatch x out_channels x oH x oW) | |
| stride (int or tuple, optional): Stride of the convolution. Default: 1 | |
| padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 | |
| dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 | |
| groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 | |
| Examples:: | |
| >>> input = torch.randn(1, 1, 3, 3, requires_grad=True) | |
| >>> weight = torch.randn(1, 1, 1, 2, requires_grad=True) | |
| >>> output = F.conv2d(input, weight) | |
| >>> grad_output = torch.randn(output.shape) | |
| >>> grad_input = torch.autograd.grad(output, input, grad_output) | |
| >>> F.grad.conv2d_input(input.shape, weight, grad_output) | |
| """ | |
| input = grad_output.new_empty(1).expand(input_size) | |
| return torch.ops.aten.convolution_backward(grad_output, input, weight, None, | |
| _pair(stride), _pair(padding), _pair(dilation), | |
| False, [0], groups, (True, False, False))[0] | |
| def conv2d_weight(input, weight_size, grad_output, stride=1, padding=0, dilation=1, groups=1): | |
| r"""Compute the gradient of conv2d with respect to the weight of the convolution. | |
| Args: | |
| input: input tensor of shape (minibatch x in_channels x iH x iW) | |
| weight_size : Shape of the weight gradient tensor | |
| grad_output : output gradient tensor (minibatch x out_channels x oH x oW) | |
| stride (int or tuple, optional): Stride of the convolution. Default: 1 | |
| padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 | |
| dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 | |
| groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 | |
| Examples:: | |
| >>> input = torch.randn(1, 1, 3, 3, requires_grad=True) | |
| >>> weight = torch.randn(1, 1, 1, 2, requires_grad=True) | |
| >>> output = F.conv2d(input, weight) | |
| >>> grad_output = torch.randn(output.shape) | |
| >>> # xdoctest: +SKIP | |
| >>> grad_weight = torch.autograd.grad(output, filter, grad_output) | |
| >>> F.grad.conv2d_weight(input, weight.shape, grad_output) | |
| """ | |
| weight = grad_output.new_empty(1).expand(weight_size) | |
| return torch.ops.aten.convolution_backward(grad_output, input, weight, None, | |
| _pair(stride), _pair(padding), _pair(dilation), | |
| False, [0], groups, (False, True, False))[1] | |
| def conv3d_input(input_size, weight, grad_output, stride=1, padding=0, dilation=1, groups=1): | |
| r"""Compute the gradient of conv3d with respect to the input of the convolution. | |
| This is same as the 3D transposed convolution operator under the hood but requires | |
| the shape of the gradient w.r.t. input to be specified explicitly. | |
| Args: | |
| input_size : Shape of the input gradient tensor | |
| weight: weights tensor (out_channels x in_channels/groups x kT x kH x kW) | |
| grad_output : output gradient tensor (minibatch x out_channels x oT x oH x oW) | |
| stride (int or tuple, optional): Stride of the convolution. Default: 1 | |
| padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 | |
| dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 | |
| groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 | |
| Examples:: | |
| >>> input = torch.randn(2, 8, 10, 10, 20, requires_grad=True) | |
| >>> weight = torch.randn(4, 8, 2, 3, 3, requires_grad=True) | |
| >>> output = F.conv3d(input, weight) | |
| >>> grad_output = torch.randn(output.shape) | |
| >>> grad_input = torch.autograd.grad(output, input, grad_output) | |
| >>> F.grad.conv3d_input(input.shape, weight, grad_output) | |
| """ | |
| input = grad_output.new_empty(1).expand(input_size) | |
| return torch.ops.aten.convolution_backward(grad_output, input, weight, None, | |
| _triple(stride), _triple(padding), _triple(dilation), | |
| False, [0], groups, (True, False, False))[0] | |
| def conv3d_weight(input, weight_size, grad_output, stride=1, padding=0, dilation=1, groups=1): | |
| r"""Compute the gradient of conv3d with respect to the weight of the convolution. | |
| Args: | |
| input: input tensor of shape (minibatch x in_channels x iT x iH x iW) | |
| weight_size : Shape of the weight gradient tensor | |
| grad_output : output gradient tensor (minibatch x out_channels x oT x oH x oW) | |
| stride (int or tuple, optional): Stride of the convolution. Default: 1 | |
| padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 | |
| dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 | |
| groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 | |
| Examples:: | |
| >>> input = torch.randn(2, 8, 10, 10, 20, requires_grad=True) | |
| >>> weight = torch.randn(4, 8, 2, 3, 3, requires_grad=True) | |
| >>> output = F.conv3d(input, weight) | |
| >>> grad_output = torch.randn(output.shape) | |
| >>> grad_weight = torch.autograd.grad(output, weight, grad_output) | |
| >>> F.grad.conv3d_weight(input, weight.shape, grad_output) | |
| """ | |
| weight = grad_output.new_empty(1).expand(weight_size) | |
| return torch.ops.aten.convolution_backward(grad_output, input, weight, None, | |
| _triple(stride), _triple(padding), _triple(dilation), | |
| False, [0], groups, (False, True, False))[1] | |