wehub-resource-sync
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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from typing import Callable
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.nn.utils.spectral_norm import SpectralNorm
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class ResidualBlock(nn.Module):
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def __init__(self, fn: Callable):
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super().__init__()
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self.fn = fn
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return (self.fn(x) + x) / np.sqrt(2)
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class SpectralConv1d(nn.Conv1d):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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SpectralNorm.apply(self, name="weight", n_power_iterations=1, dim=0, eps=1e-12)
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class BatchNormLocal(nn.Module):
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def __init__(self, num_features: int, affine: bool = True, virtual_bs: int = 8, eps: float = 1e-5):
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super().__init__()
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self.virtual_bs = virtual_bs
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self.eps = eps
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self.affine = affine
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if self.affine:
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self.weight = nn.Parameter(torch.ones(num_features))
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self.bias = nn.Parameter(torch.zeros(num_features))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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shape = x.size()
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# Reshape batch into groups.
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G = np.ceil(x.size(0) / self.virtual_bs).astype(int)
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x = x.view(G, -1, x.size(-2), x.size(-1))
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# Calculate stats.
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mean = x.mean([1, 3], keepdim=True)
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var = x.var([1, 3], keepdim=True, unbiased=False)
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x = (x - mean) / (torch.sqrt(var + self.eps))
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if self.affine:
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x = x * self.weight[None, :, None] + self.bias[None, :, None]
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return x.view(shape)
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def make_block(channels: int, kernel_size: int) -> nn.Module:
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return nn.Sequential(
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SpectralConv1d(
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channels,
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channels,
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kernel_size=kernel_size,
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padding=kernel_size // 2,
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padding_mode="circular",
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),
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BatchNormLocal(channels),
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nn.LeakyReLU(0.2, True),
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)
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# Adapted from https://github.com/autonomousvision/stylegan-t/blob/main/networks/discriminator.py
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class DiscHead(nn.Module):
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def __init__(self, channels: int, c_dim: int, cmap_dim: int = 64):
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super().__init__()
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self.channels = channels
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self.c_dim = c_dim
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self.cmap_dim = cmap_dim
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self.main = nn.Sequential(
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make_block(channels, kernel_size=1), ResidualBlock(make_block(channels, kernel_size=9))
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)
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if self.c_dim > 0:
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self.cmapper = nn.Linear(self.c_dim, cmap_dim)
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self.cls = SpectralConv1d(channels, cmap_dim, kernel_size=1, padding=0)
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else:
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self.cls = SpectralConv1d(channels, 1, kernel_size=1, padding=0)
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def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
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h = self.main(x)
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out = self.cls(h)
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if self.c_dim > 0:
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cmap = self.cmapper(c).unsqueeze(-1)
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out = (out * cmap).sum(1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
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return out
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