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| from functools import partial | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class Upsample1D(nn.Module): | |
| """ | |
| An upsampling layer with an optional convolution. | |
| Parameters: | |
| channels: channels in the inputs and outputs. | |
| use_conv: a bool determining if a convolution is applied. | |
| use_conv_transpose: | |
| out_channels: | |
| """ | |
| def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): | |
| super().__init__() | |
| self.channels = channels | |
| self.out_channels = out_channels or channels | |
| self.use_conv = use_conv | |
| self.use_conv_transpose = use_conv_transpose | |
| self.name = name | |
| self.conv = None | |
| if use_conv_transpose: | |
| self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1) | |
| elif use_conv: | |
| self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1) | |
| def forward(self, x): | |
| assert x.shape[1] == self.channels | |
| if self.use_conv_transpose: | |
| return self.conv(x) | |
| x = F.interpolate(x, scale_factor=2.0, mode="nearest") | |
| if self.use_conv: | |
| x = self.conv(x) | |
| return x | |
| class Downsample1D(nn.Module): | |
| """ | |
| A downsampling layer with an optional convolution. | |
| Parameters: | |
| channels: channels in the inputs and outputs. | |
| use_conv: a bool determining if a convolution is applied. | |
| out_channels: | |
| padding: | |
| """ | |
| def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): | |
| super().__init__() | |
| self.channels = channels | |
| self.out_channels = out_channels or channels | |
| self.use_conv = use_conv | |
| self.padding = padding | |
| stride = 2 | |
| self.name = name | |
| if use_conv: | |
| self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding) | |
| else: | |
| assert self.channels == self.out_channels | |
| self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride) | |
| def forward(self, x): | |
| assert x.shape[1] == self.channels | |
| return self.conv(x) | |
| class Upsample2D(nn.Module): | |
| """ | |
| An upsampling layer with an optional convolution. | |
| Parameters: | |
| channels: channels in the inputs and outputs. | |
| use_conv: a bool determining if a convolution is applied. | |
| use_conv_transpose: | |
| out_channels: | |
| """ | |
| def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): | |
| super().__init__() | |
| self.channels = channels | |
| self.out_channels = out_channels or channels | |
| self.use_conv = use_conv | |
| self.use_conv_transpose = use_conv_transpose | |
| self.name = name | |
| conv = None | |
| if use_conv_transpose: | |
| conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1) | |
| elif use_conv: | |
| conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) | |
| # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed | |
| if name == "conv": | |
| self.conv = conv | |
| else: | |
| self.Conv2d_0 = conv | |
| def forward(self, hidden_states, output_size=None): | |
| assert hidden_states.shape[1] == self.channels | |
| if self.use_conv_transpose: | |
| return self.conv(hidden_states) | |
| # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 | |
| # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch | |
| # https://github.com/pytorch/pytorch/issues/86679 | |
| dtype = hidden_states.dtype | |
| if dtype == torch.bfloat16: | |
| hidden_states = hidden_states.to(torch.float32) | |
| # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 | |
| if hidden_states.shape[0] >= 64: | |
| hidden_states = hidden_states.contiguous() | |
| # if `output_size` is passed we force the interpolation output | |
| # size and do not make use of `scale_factor=2` | |
| if output_size is None: | |
| hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest") | |
| else: | |
| hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") | |
| # If the input is bfloat16, we cast back to bfloat16 | |
| if dtype == torch.bfloat16: | |
| hidden_states = hidden_states.to(dtype) | |
| # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed | |
| if self.use_conv: | |
| if self.name == "conv": | |
| hidden_states = self.conv(hidden_states) | |
| else: | |
| hidden_states = self.Conv2d_0(hidden_states) | |
| return hidden_states | |
| class Downsample2D(nn.Module): | |
| """ | |
| A downsampling layer with an optional convolution. | |
| Parameters: | |
| channels: channels in the inputs and outputs. | |
| use_conv: a bool determining if a convolution is applied. | |
| out_channels: | |
| padding: | |
| """ | |
| def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): | |
| super().__init__() | |
| self.channels = channels | |
| self.out_channels = out_channels or channels | |
| self.use_conv = use_conv | |
| self.padding = padding | |
| stride = 2 | |
| self.name = name | |
| if use_conv: | |
| conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) | |
| else: | |
| assert self.channels == self.out_channels | |
| conv = nn.AvgPool2d(kernel_size=stride, stride=stride) | |
| # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed | |
| if name == "conv": | |
| self.Conv2d_0 = conv | |
| self.conv = conv | |
| elif name == "Conv2d_0": | |
| self.conv = conv | |
| else: | |
| self.conv = conv | |
| def forward(self, hidden_states): | |
| assert hidden_states.shape[1] == self.channels | |
| if self.use_conv and self.padding == 0: | |
| pad = (0, 1, 0, 1) | |
| hidden_states = F.pad(hidden_states, pad, mode="constant", value=0) | |
| assert hidden_states.shape[1] == self.channels | |
| hidden_states = self.conv(hidden_states) | |
| return hidden_states | |
| class FirUpsample2D(nn.Module): | |
| def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)): | |
| super().__init__() | |
| out_channels = out_channels if out_channels else channels | |
| if use_conv: | |
| self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.use_conv = use_conv | |
| self.fir_kernel = fir_kernel | |
| self.out_channels = out_channels | |
| def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1): | |
| """Fused `upsample_2d()` followed by `Conv2d()`. | |
| Padding is performed only once at the beginning, not between the operations. The fused op is considerably more | |
| efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of | |
| arbitrary order. | |
| Args: | |
| hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. | |
| weight: Weight tensor of the shape `[filterH, filterW, inChannels, | |
| outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`. | |
| kernel: FIR filter of the shape `[firH, firW]` or `[firN]` | |
| (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling. | |
| factor: Integer upsampling factor (default: 2). | |
| gain: Scaling factor for signal magnitude (default: 1.0). | |
| Returns: | |
| output: Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same | |
| datatype as `hidden_states`. | |
| """ | |
| assert isinstance(factor, int) and factor >= 1 | |
| # Setup filter kernel. | |
| if kernel is None: | |
| kernel = [1] * factor | |
| # setup kernel | |
| kernel = torch.tensor(kernel, dtype=torch.float32) | |
| if kernel.ndim == 1: | |
| kernel = torch.outer(kernel, kernel) | |
| kernel /= torch.sum(kernel) | |
| kernel = kernel * (gain * (factor**2)) | |
| if self.use_conv: | |
| convH = weight.shape[2] | |
| convW = weight.shape[3] | |
| inC = weight.shape[1] | |
| pad_value = (kernel.shape[0] - factor) - (convW - 1) | |
| stride = (factor, factor) | |
| # Determine data dimensions. | |
| output_shape = ( | |
| (hidden_states.shape[2] - 1) * factor + convH, | |
| (hidden_states.shape[3] - 1) * factor + convW, | |
| ) | |
| output_padding = ( | |
| output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH, | |
| output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW, | |
| ) | |
| assert output_padding[0] >= 0 and output_padding[1] >= 0 | |
| num_groups = hidden_states.shape[1] // inC | |
| # Transpose weights. | |
| weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW)) | |
| weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4) | |
| weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW)) | |
| inverse_conv = F.conv_transpose2d( | |
| hidden_states, weight, stride=stride, output_padding=output_padding, padding=0 | |
| ) | |
| output = upfirdn2d_native( | |
| inverse_conv, | |
| torch.tensor(kernel, device=inverse_conv.device), | |
| pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1), | |
| ) | |
| else: | |
| pad_value = kernel.shape[0] - factor | |
| output = upfirdn2d_native( | |
| hidden_states, | |
| torch.tensor(kernel, device=hidden_states.device), | |
| up=factor, | |
| pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2), | |
| ) | |
| return output | |
| def forward(self, hidden_states): | |
| if self.use_conv: | |
| height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel) | |
| height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1) | |
| else: | |
| height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2) | |
| return height | |
| class FirDownsample2D(nn.Module): | |
| def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)): | |
| super().__init__() | |
| out_channels = out_channels if out_channels else channels | |
| if use_conv: | |
| self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.fir_kernel = fir_kernel | |
| self.use_conv = use_conv | |
| self.out_channels = out_channels | |
| def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1): | |
| """Fused `Conv2d()` followed by `downsample_2d()`. | |
| Padding is performed only once at the beginning, not between the operations. The fused op is considerably more | |
| efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of | |
| arbitrary order. | |
| Args: | |
| hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. | |
| weight: | |
| Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be | |
| performed by `inChannels = x.shape[0] // numGroups`. | |
| kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * | |
| factor`, which corresponds to average pooling. | |
| factor: Integer downsampling factor (default: 2). | |
| gain: Scaling factor for signal magnitude (default: 1.0). | |
| Returns: | |
| output: Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and | |
| same datatype as `x`. | |
| """ | |
| assert isinstance(factor, int) and factor >= 1 | |
| if kernel is None: | |
| kernel = [1] * factor | |
| # setup kernel | |
| kernel = torch.tensor(kernel, dtype=torch.float32) | |
| if kernel.ndim == 1: | |
| kernel = torch.outer(kernel, kernel) | |
| kernel /= torch.sum(kernel) | |
| kernel = kernel * gain | |
| if self.use_conv: | |
| _, _, convH, convW = weight.shape | |
| pad_value = (kernel.shape[0] - factor) + (convW - 1) | |
| stride_value = [factor, factor] | |
| upfirdn_input = upfirdn2d_native( | |
| hidden_states, | |
| torch.tensor(kernel, device=hidden_states.device), | |
| pad=((pad_value + 1) // 2, pad_value // 2), | |
| ) | |
| output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0) | |
| else: | |
| pad_value = kernel.shape[0] - factor | |
| output = upfirdn2d_native( | |
| hidden_states, | |
| torch.tensor(kernel, device=hidden_states.device), | |
| down=factor, | |
| pad=((pad_value + 1) // 2, pad_value // 2), | |
| ) | |
| return output | |
| def forward(self, hidden_states): | |
| if self.use_conv: | |
| downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel) | |
| hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1) | |
| else: | |
| hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2) | |
| return hidden_states | |
| class ResnetBlock2D(nn.Module): | |
| def __init__( | |
| self, | |
| *, | |
| in_channels, | |
| out_channels=None, | |
| conv_shortcut=False, | |
| dropout=0.0, | |
| temb_channels=512, | |
| groups=32, | |
| groups_out=None, | |
| pre_norm=True, | |
| eps=1e-6, | |
| non_linearity="swish", | |
| time_embedding_norm="default", | |
| kernel=None, | |
| output_scale_factor=1.0, | |
| use_in_shortcut=None, | |
| up=False, | |
| down=False, | |
| ): | |
| super().__init__() | |
| self.pre_norm = pre_norm | |
| self.pre_norm = True | |
| self.in_channels = in_channels | |
| out_channels = in_channels if out_channels is None else out_channels | |
| self.out_channels = out_channels | |
| self.use_conv_shortcut = conv_shortcut | |
| self.time_embedding_norm = time_embedding_norm | |
| self.up = up | |
| self.down = down | |
| self.output_scale_factor = output_scale_factor | |
| if groups_out is None: | |
| groups_out = groups | |
| self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) | |
| self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| if temb_channels is not None: | |
| self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) | |
| else: | |
| self.time_emb_proj = None | |
| self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) | |
| self.dropout = torch.nn.Dropout(dropout) | |
| self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| if non_linearity == "swish": | |
| self.nonlinearity = lambda x: F.silu(x) | |
| elif non_linearity == "mish": | |
| self.nonlinearity = Mish() | |
| elif non_linearity == "silu": | |
| self.nonlinearity = nn.SiLU() | |
| self.upsample = self.downsample = None | |
| if self.up: | |
| if kernel == "fir": | |
| fir_kernel = (1, 3, 3, 1) | |
| self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel) | |
| elif kernel == "sde_vp": | |
| self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest") | |
| else: | |
| self.upsample = Upsample2D(in_channels, use_conv=False) | |
| elif self.down: | |
| if kernel == "fir": | |
| fir_kernel = (1, 3, 3, 1) | |
| self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel) | |
| elif kernel == "sde_vp": | |
| self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2) | |
| else: | |
| self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op") | |
| self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut | |
| self.conv_shortcut = None | |
| if self.use_in_shortcut: | |
| self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) | |
| def forward(self, input_tensor, temb): | |
| hidden_states = input_tensor | |
| hidden_states = self.norm1(hidden_states) | |
| hidden_states = self.nonlinearity(hidden_states) | |
| if self.upsample is not None: | |
| # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 | |
| if hidden_states.shape[0] >= 64: | |
| input_tensor = input_tensor.contiguous() | |
| hidden_states = hidden_states.contiguous() | |
| input_tensor = self.upsample(input_tensor) | |
| hidden_states = self.upsample(hidden_states) | |
| elif self.downsample is not None: | |
| input_tensor = self.downsample(input_tensor) | |
| hidden_states = self.downsample(hidden_states) | |
| hidden_states = self.conv1(hidden_states) | |
| if temb is not None: | |
| temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None] | |
| hidden_states = hidden_states + temb | |
| hidden_states = self.norm2(hidden_states) | |
| hidden_states = self.nonlinearity(hidden_states) | |
| hidden_states = self.dropout(hidden_states) | |
| hidden_states = self.conv2(hidden_states) | |
| if self.conv_shortcut is not None: | |
| input_tensor = self.conv_shortcut(input_tensor) | |
| output_tensor = (input_tensor + hidden_states) / self.output_scale_factor | |
| return output_tensor | |
| class Mish(torch.nn.Module): | |
| def forward(self, hidden_states): | |
| return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) | |
| # unet_rl.py | |
| def rearrange_dims(tensor): | |
| if len(tensor.shape) == 2: | |
| return tensor[:, :, None] | |
| if len(tensor.shape) == 3: | |
| return tensor[:, :, None, :] | |
| elif len(tensor.shape) == 4: | |
| return tensor[:, :, 0, :] | |
| else: | |
| raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.") | |
| class Conv1dBlock(nn.Module): | |
| """ | |
| Conv1d --> GroupNorm --> Mish | |
| """ | |
| def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8): | |
| super().__init__() | |
| self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2) | |
| self.group_norm = nn.GroupNorm(n_groups, out_channels) | |
| self.mish = nn.Mish() | |
| def forward(self, x): | |
| x = self.conv1d(x) | |
| x = rearrange_dims(x) | |
| x = self.group_norm(x) | |
| x = rearrange_dims(x) | |
| x = self.mish(x) | |
| return x | |
| # unet_rl.py | |
| class ResidualTemporalBlock1D(nn.Module): | |
| def __init__(self, inp_channels, out_channels, embed_dim, kernel_size=5): | |
| super().__init__() | |
| self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size) | |
| self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size) | |
| self.time_emb_act = nn.Mish() | |
| self.time_emb = nn.Linear(embed_dim, out_channels) | |
| self.residual_conv = ( | |
| nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity() | |
| ) | |
| def forward(self, x, t): | |
| """ | |
| Args: | |
| x : [ batch_size x inp_channels x horizon ] | |
| t : [ batch_size x embed_dim ] | |
| returns: | |
| out : [ batch_size x out_channels x horizon ] | |
| """ | |
| t = self.time_emb_act(t) | |
| t = self.time_emb(t) | |
| out = self.conv_in(x) + rearrange_dims(t) | |
| out = self.conv_out(out) | |
| return out + self.residual_conv(x) | |
| def upsample_2d(hidden_states, kernel=None, factor=2, gain=1): | |
| r"""Upsample2D a batch of 2D images with the given filter. | |
| Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given | |
| filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified | |
| `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is | |
| a: multiple of the upsampling factor. | |
| Args: | |
| hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. | |
| kernel: FIR filter of the shape `[firH, firW]` or `[firN]` | |
| (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling. | |
| factor: Integer upsampling factor (default: 2). | |
| gain: Scaling factor for signal magnitude (default: 1.0). | |
| Returns: | |
| output: Tensor of the shape `[N, C, H * factor, W * factor]` | |
| """ | |
| assert isinstance(factor, int) and factor >= 1 | |
| if kernel is None: | |
| kernel = [1] * factor | |
| kernel = torch.tensor(kernel, dtype=torch.float32) | |
| if kernel.ndim == 1: | |
| kernel = torch.outer(kernel, kernel) | |
| kernel /= torch.sum(kernel) | |
| kernel = kernel * (gain * (factor**2)) | |
| pad_value = kernel.shape[0] - factor | |
| output = upfirdn2d_native( | |
| hidden_states, | |
| kernel.to(device=hidden_states.device), | |
| up=factor, | |
| pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2), | |
| ) | |
| return output | |
| def downsample_2d(hidden_states, kernel=None, factor=2, gain=1): | |
| r"""Downsample2D a batch of 2D images with the given filter. | |
| Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the | |
| given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the | |
| specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its | |
| shape is a multiple of the downsampling factor. | |
| Args: | |
| hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. | |
| kernel: FIR filter of the shape `[firH, firW]` or `[firN]` | |
| (separable). The default is `[1] * factor`, which corresponds to average pooling. | |
| factor: Integer downsampling factor (default: 2). | |
| gain: Scaling factor for signal magnitude (default: 1.0). | |
| Returns: | |
| output: Tensor of the shape `[N, C, H // factor, W // factor]` | |
| """ | |
| assert isinstance(factor, int) and factor >= 1 | |
| if kernel is None: | |
| kernel = [1] * factor | |
| kernel = torch.tensor(kernel, dtype=torch.float32) | |
| if kernel.ndim == 1: | |
| kernel = torch.outer(kernel, kernel) | |
| kernel /= torch.sum(kernel) | |
| kernel = kernel * gain | |
| pad_value = kernel.shape[0] - factor | |
| output = upfirdn2d_native( | |
| hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2) | |
| ) | |
| return output | |
| def upfirdn2d_native(tensor, kernel, up=1, down=1, pad=(0, 0)): | |
| up_x = up_y = up | |
| down_x = down_y = down | |
| pad_x0 = pad_y0 = pad[0] | |
| pad_x1 = pad_y1 = pad[1] | |
| _, channel, in_h, in_w = tensor.shape | |
| tensor = tensor.reshape(-1, in_h, in_w, 1) | |
| _, in_h, in_w, minor = tensor.shape | |
| kernel_h, kernel_w = kernel.shape | |
| out = tensor.view(-1, in_h, 1, in_w, 1, minor) | |
| out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) | |
| out = out.view(-1, in_h * up_y, in_w * up_x, minor) | |
| out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) | |
| out = out.to(tensor.device) # Move back to mps if necessary | |
| out = out[ | |
| :, | |
| max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), | |
| max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), | |
| :, | |
| ] | |
| out = out.permute(0, 3, 1, 2) | |
| out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) | |
| w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) | |
| out = F.conv2d(out, w) | |
| out = out.reshape( | |
| -1, | |
| minor, | |
| in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, | |
| in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, | |
| ) | |
| out = out.permute(0, 2, 3, 1) | |
| out = out[:, ::down_y, ::down_x, :] | |
| out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 | |
| out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 | |
| return out.view(-1, channel, out_h, out_w) | |