680 lines
25 KiB
Python
680 lines
25 KiB
Python
from functools import partial
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Upsample1D(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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Parameters:
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channels: channels in the inputs and outputs.
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use_conv: a bool determining if a convolution is applied.
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use_conv_transpose:
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out_channels:
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"""
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def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.use_conv_transpose = use_conv_transpose
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self.name = name
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self.conv = None
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if use_conv_transpose:
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self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
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elif use_conv:
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self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
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def forward(self, x):
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assert x.shape[1] == self.channels
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if self.use_conv_transpose:
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return self.conv(x)
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x = F.interpolate(x, scale_factor=2.0, mode="nearest")
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if self.use_conv:
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x = self.conv(x)
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return x
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class Downsample1D(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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Parameters:
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channels: channels in the inputs and outputs.
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use_conv: a bool determining if a convolution is applied.
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out_channels:
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padding:
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"""
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def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.padding = padding
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stride = 2
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self.name = name
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if use_conv:
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self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
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else:
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assert self.channels == self.out_channels
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self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)
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def forward(self, x):
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assert x.shape[1] == self.channels
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return self.conv(x)
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class Upsample2D(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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Parameters:
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channels: channels in the inputs and outputs.
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use_conv: a bool determining if a convolution is applied.
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use_conv_transpose:
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out_channels:
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"""
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def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.use_conv_transpose = use_conv_transpose
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self.name = name
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conv = None
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if use_conv_transpose:
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conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
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elif use_conv:
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conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)
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# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
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if name == "conv":
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self.conv = conv
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else:
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self.Conv2d_0 = conv
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def forward(self, hidden_states, output_size=None):
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assert hidden_states.shape[1] == self.channels
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if self.use_conv_transpose:
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return self.conv(hidden_states)
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# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
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# TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
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# https://github.com/pytorch/pytorch/issues/86679
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dtype = hidden_states.dtype
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if dtype == torch.bfloat16:
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hidden_states = hidden_states.to(torch.float32)
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# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
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if hidden_states.shape[0] >= 64:
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hidden_states = hidden_states.contiguous()
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# if `output_size` is passed we force the interpolation output
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# size and do not make use of `scale_factor=2`
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if output_size is None:
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hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
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else:
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hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
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# If the input is bfloat16, we cast back to bfloat16
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if dtype == torch.bfloat16:
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hidden_states = hidden_states.to(dtype)
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# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
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if self.use_conv:
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if self.name == "conv":
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hidden_states = self.conv(hidden_states)
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else:
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hidden_states = self.Conv2d_0(hidden_states)
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return hidden_states
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class Downsample2D(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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Parameters:
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channels: channels in the inputs and outputs.
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use_conv: a bool determining if a convolution is applied.
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out_channels:
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padding:
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"""
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def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.padding = padding
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stride = 2
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self.name = name
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if use_conv:
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conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
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else:
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assert self.channels == self.out_channels
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conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
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# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
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if name == "conv":
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self.Conv2d_0 = conv
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self.conv = conv
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elif name == "Conv2d_0":
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self.conv = conv
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else:
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self.conv = conv
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def forward(self, hidden_states):
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assert hidden_states.shape[1] == self.channels
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if self.use_conv and self.padding == 0:
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pad = (0, 1, 0, 1)
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hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
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assert hidden_states.shape[1] == self.channels
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hidden_states = self.conv(hidden_states)
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return hidden_states
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class FirUpsample2D(nn.Module):
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def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
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super().__init__()
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out_channels = out_channels if out_channels else channels
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if use_conv:
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self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.use_conv = use_conv
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self.fir_kernel = fir_kernel
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self.out_channels = out_channels
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def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
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"""Fused `upsample_2d()` followed by `Conv2d()`.
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Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
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efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
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arbitrary order.
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Args:
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hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
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weight: Weight tensor of the shape `[filterH, filterW, inChannels,
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outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
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kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
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(separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
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factor: Integer upsampling factor (default: 2).
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gain: Scaling factor for signal magnitude (default: 1.0).
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Returns:
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output: Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same
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datatype as `hidden_states`.
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"""
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assert isinstance(factor, int) and factor >= 1
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# Setup filter kernel.
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if kernel is None:
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kernel = [1] * factor
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# setup kernel
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kernel = torch.tensor(kernel, dtype=torch.float32)
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if kernel.ndim == 1:
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kernel = torch.outer(kernel, kernel)
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kernel /= torch.sum(kernel)
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kernel = kernel * (gain * (factor**2))
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if self.use_conv:
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convH = weight.shape[2]
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convW = weight.shape[3]
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inC = weight.shape[1]
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pad_value = (kernel.shape[0] - factor) - (convW - 1)
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stride = (factor, factor)
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# Determine data dimensions.
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output_shape = (
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(hidden_states.shape[2] - 1) * factor + convH,
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(hidden_states.shape[3] - 1) * factor + convW,
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)
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output_padding = (
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output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,
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output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
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)
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assert output_padding[0] >= 0 and output_padding[1] >= 0
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num_groups = hidden_states.shape[1] // inC
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# Transpose weights.
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weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
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weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)
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weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
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inverse_conv = F.conv_transpose2d(
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hidden_states, weight, stride=stride, output_padding=output_padding, padding=0
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)
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output = upfirdn2d_native(
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inverse_conv,
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torch.tensor(kernel, device=inverse_conv.device),
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pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),
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)
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else:
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pad_value = kernel.shape[0] - factor
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output = upfirdn2d_native(
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hidden_states,
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torch.tensor(kernel, device=hidden_states.device),
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up=factor,
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pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
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)
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return output
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def forward(self, hidden_states):
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if self.use_conv:
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height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
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height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
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else:
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height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
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return height
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class FirDownsample2D(nn.Module):
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def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
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super().__init__()
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out_channels = out_channels if out_channels else channels
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if use_conv:
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self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.fir_kernel = fir_kernel
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self.use_conv = use_conv
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self.out_channels = out_channels
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def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
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"""Fused `Conv2d()` followed by `downsample_2d()`.
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Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
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efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
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arbitrary order.
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Args:
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hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
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weight:
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Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
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performed by `inChannels = x.shape[0] // numGroups`.
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kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] *
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factor`, which corresponds to average pooling.
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factor: Integer downsampling factor (default: 2).
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gain: Scaling factor for signal magnitude (default: 1.0).
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Returns:
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output: Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and
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same datatype as `x`.
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"""
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assert isinstance(factor, int) and factor >= 1
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if kernel is None:
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kernel = [1] * factor
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# setup kernel
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kernel = torch.tensor(kernel, dtype=torch.float32)
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if kernel.ndim == 1:
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kernel = torch.outer(kernel, kernel)
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kernel /= torch.sum(kernel)
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kernel = kernel * gain
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if self.use_conv:
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_, _, convH, convW = weight.shape
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pad_value = (kernel.shape[0] - factor) + (convW - 1)
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stride_value = [factor, factor]
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upfirdn_input = upfirdn2d_native(
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hidden_states,
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torch.tensor(kernel, device=hidden_states.device),
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pad=((pad_value + 1) // 2, pad_value // 2),
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)
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output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
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else:
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pad_value = kernel.shape[0] - factor
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output = upfirdn2d_native(
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hidden_states,
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torch.tensor(kernel, device=hidden_states.device),
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down=factor,
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pad=((pad_value + 1) // 2, pad_value // 2),
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)
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return output
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def forward(self, hidden_states):
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if self.use_conv:
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downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
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hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
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else:
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hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
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return hidden_states
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class ResnetBlock2D(nn.Module):
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def __init__(
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self,
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*,
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in_channels,
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out_channels=None,
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conv_shortcut=False,
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dropout=0.0,
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temb_channels=512,
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groups=32,
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groups_out=None,
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pre_norm=True,
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eps=1e-6,
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non_linearity="swish",
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time_embedding_norm="default",
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kernel=None,
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output_scale_factor=1.0,
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use_in_shortcut=None,
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up=False,
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down=False,
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):
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super().__init__()
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self.pre_norm = pre_norm
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self.pre_norm = True
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.use_conv_shortcut = conv_shortcut
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self.time_embedding_norm = time_embedding_norm
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self.up = up
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self.down = down
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self.output_scale_factor = output_scale_factor
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if groups_out is None:
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groups_out = groups
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self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
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self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if temb_channels is not None:
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if self.time_embedding_norm == "default":
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time_emb_proj_out_channels = out_channels
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elif self.time_embedding_norm == "scale_shift":
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time_emb_proj_out_channels = out_channels * 2
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else:
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raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
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self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
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else:
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self.time_emb_proj = None
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self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if non_linearity == "swish":
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self.nonlinearity = lambda x: F.silu(x)
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elif non_linearity == "mish":
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self.nonlinearity = Mish()
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elif non_linearity == "silu":
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self.nonlinearity = nn.SiLU()
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self.upsample = self.downsample = None
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if self.up:
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if kernel == "fir":
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fir_kernel = (1, 3, 3, 1)
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self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
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elif kernel == "sde_vp":
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self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
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else:
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self.upsample = Upsample2D(in_channels, use_conv=False)
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elif self.down:
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if kernel == "fir":
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fir_kernel = (1, 3, 3, 1)
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self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
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elif kernel == "sde_vp":
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self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
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else:
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self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
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self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
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self.conv_shortcut = None
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if self.use_in_shortcut:
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self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, input_tensor, temb):
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hidden_states = input_tensor
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hidden_states = self.norm1(hidden_states)
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hidden_states = self.nonlinearity(hidden_states)
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if self.upsample is not None:
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# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
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if hidden_states.shape[0] >= 64:
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input_tensor = input_tensor.contiguous()
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hidden_states = hidden_states.contiguous()
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input_tensor = self.upsample(input_tensor)
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hidden_states = self.upsample(hidden_states)
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elif self.downsample is not None:
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input_tensor = self.downsample(input_tensor)
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hidden_states = self.downsample(hidden_states)
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hidden_states = self.conv1(hidden_states)
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if temb is not None:
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temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
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|
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|
if temb is not None and self.time_embedding_norm == "default":
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|
hidden_states = hidden_states + temb
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|
|
|
hidden_states = self.norm2(hidden_states)
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|
|
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if temb is not None and self.time_embedding_norm == "scale_shift":
|
|
scale, shift = torch.chunk(temb, 2, dim=1)
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|
hidden_states = hidden_states * (1 + scale) + shift
|
|
|
|
hidden_states = self.nonlinearity(hidden_states)
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|
|
|
hidden_states = self.dropout(hidden_states)
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|
hidden_states = self.conv2(hidden_states)
|
|
|
|
if self.conv_shortcut is not None:
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|
input_tensor = self.conv_shortcut(input_tensor)
|
|
|
|
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
|
|
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|
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)
|