Take some code from chainner to implement ESRGAN and other upscale models.
This commit is contained in:
comfyanonymous
698
comfy_extras/chainner_models/architecture/mat/utils.py
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698
comfy_extras/chainner_models/architecture/mat/utils.py
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"""Code used for this implementation of the MAT helper utils is modified from
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lama-cleaner, copyright of Sanster: https://github.com/fenglinglwb/MAT"""
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import collections
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from itertools import repeat
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from typing import Any
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import numpy as np
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import torch
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from torch import conv2d, conv_transpose2d
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def normalize_2nd_moment(x, dim=1, eps=1e-8):
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return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
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class EasyDict(dict):
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"""Convenience class that behaves like a dict but allows access with the attribute syntax."""
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def __getattr__(self, name: str) -> Any:
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try:
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return self[name]
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except KeyError:
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raise AttributeError(name)
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def __setattr__(self, name: str, value: Any) -> None:
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self[name] = value
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def __delattr__(self, name: str) -> None:
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del self[name]
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activation_funcs = {
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"linear": EasyDict(
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func=lambda x, **_: x,
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def_alpha=0,
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def_gain=1,
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cuda_idx=1,
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ref="",
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has_2nd_grad=False,
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),
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"relu": EasyDict(
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func=lambda x, **_: torch.nn.functional.relu(x),
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def_alpha=0,
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def_gain=np.sqrt(2),
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cuda_idx=2,
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ref="y",
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has_2nd_grad=False,
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),
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"lrelu": EasyDict(
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func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha),
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def_alpha=0.2,
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def_gain=np.sqrt(2),
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cuda_idx=3,
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ref="y",
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has_2nd_grad=False,
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),
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"tanh": EasyDict(
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func=lambda x, **_: torch.tanh(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=4,
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ref="y",
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has_2nd_grad=True,
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),
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"sigmoid": EasyDict(
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func=lambda x, **_: torch.sigmoid(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=5,
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ref="y",
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has_2nd_grad=True,
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),
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"elu": EasyDict(
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func=lambda x, **_: torch.nn.functional.elu(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=6,
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ref="y",
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has_2nd_grad=True,
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),
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"selu": EasyDict(
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func=lambda x, **_: torch.nn.functional.selu(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=7,
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ref="y",
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has_2nd_grad=True,
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),
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"softplus": EasyDict(
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func=lambda x, **_: torch.nn.functional.softplus(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=8,
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ref="y",
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has_2nd_grad=True,
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),
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"swish": EasyDict(
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func=lambda x, **_: torch.sigmoid(x) * x,
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def_alpha=0,
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def_gain=np.sqrt(2),
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cuda_idx=9,
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ref="x",
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has_2nd_grad=True,
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),
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}
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def _bias_act_ref(x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None):
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"""Slow reference implementation of `bias_act()` using standard TensorFlow ops."""
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assert isinstance(x, torch.Tensor)
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assert clamp is None or clamp >= 0
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spec = activation_funcs[act]
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alpha = float(alpha if alpha is not None else spec.def_alpha)
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gain = float(gain if gain is not None else spec.def_gain)
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clamp = float(clamp if clamp is not None else -1)
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# Add bias.
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if b is not None:
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assert isinstance(b, torch.Tensor) and b.ndim == 1
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assert 0 <= dim < x.ndim
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assert b.shape[0] == x.shape[dim]
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x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]).to(x.device)
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# Evaluate activation function.
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alpha = float(alpha)
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x = spec.func(x, alpha=alpha)
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# Scale by gain.
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gain = float(gain)
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if gain != 1:
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x = x * gain
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# Clamp.
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if clamp >= 0:
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x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
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return x
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def bias_act(
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x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None, impl="ref"
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):
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r"""Fused bias and activation function.
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Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
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and scales the result by `gain`. Each of the steps is optional. In most cases,
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the fused op is considerably more efficient than performing the same calculation
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using standard PyTorch ops. It supports first and second order gradients,
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but not third order gradients.
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Args:
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x: Input activation tensor. Can be of any shape.
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b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
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as `x`. The shape must be known, and it must match the dimension of `x`
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corresponding to `dim`.
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dim: The dimension in `x` corresponding to the elements of `b`.
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The value of `dim` is ignored if `b` is not specified.
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act: Name of the activation function to evaluate, or `"linear"` to disable.
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Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
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See `activation_funcs` for a full list. `None` is not allowed.
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alpha: Shape parameter for the activation function, or `None` to use the default.
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gain: Scaling factor for the output tensor, or `None` to use default.
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See `activation_funcs` for the default scaling of each activation function.
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If unsure, consider specifying 1.
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clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
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the clamping (default).
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impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
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Returns:
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Tensor of the same shape and datatype as `x`.
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"""
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assert isinstance(x, torch.Tensor)
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assert impl in ["ref", "cuda"]
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return _bias_act_ref(
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x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp
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)
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def setup_filter(
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f,
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device=torch.device("cpu"),
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normalize=True,
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flip_filter=False,
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gain=1,
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separable=None,
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):
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r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
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Args:
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f: Torch tensor, numpy array, or python list of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable),
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`[]` (impulse), or
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`None` (identity).
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device: Result device (default: cpu).
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normalize: Normalize the filter so that it retains the magnitude
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for constant input signal (DC)? (default: True).
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flip_filter: Flip the filter? (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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separable: Return a separable filter? (default: select automatically).
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Returns:
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Float32 tensor of the shape
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`[filter_height, filter_width]` (non-separable) or
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`[filter_taps]` (separable).
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"""
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# Validate.
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if f is None:
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f = 1
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f = torch.as_tensor(f, dtype=torch.float32)
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assert f.ndim in [0, 1, 2]
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assert f.numel() > 0
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if f.ndim == 0:
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f = f[np.newaxis]
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# Separable?
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if separable is None:
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separable = f.ndim == 1 and f.numel() >= 8
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if f.ndim == 1 and not separable:
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f = f.ger(f)
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assert f.ndim == (1 if separable else 2)
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# Apply normalize, flip, gain, and device.
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if normalize:
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f /= f.sum()
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if flip_filter:
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f = f.flip(list(range(f.ndim)))
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f = f * (gain ** (f.ndim / 2))
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f = f.to(device=device)
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return f
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def _get_filter_size(f):
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if f is None:
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return 1, 1
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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fw = f.shape[-1]
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fh = f.shape[0]
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fw = int(fw)
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fh = int(fh)
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assert fw >= 1 and fh >= 1
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return fw, fh
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def _get_weight_shape(w):
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shape = [int(sz) for sz in w.shape]
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return shape
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def _parse_scaling(scaling):
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if isinstance(scaling, int):
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scaling = [scaling, scaling]
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assert isinstance(scaling, (list, tuple))
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assert all(isinstance(x, int) for x in scaling)
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sx, sy = scaling
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assert sx >= 1 and sy >= 1
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return sx, sy
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def _parse_padding(padding):
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if isinstance(padding, int):
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padding = [padding, padding]
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assert isinstance(padding, (list, tuple))
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assert all(isinstance(x, int) for x in padding)
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if len(padding) == 2:
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padx, pady = padding
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padding = [padx, padx, pady, pady]
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padx0, padx1, pady0, pady1 = padding
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return padx0, padx1, pady0, pady1
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def _ntuple(n):
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def parse(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return tuple(repeat(x, n))
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return parse
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to_2tuple = _ntuple(2)
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def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
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"""Slow reference implementation of `upfirdn2d()` using standard PyTorch ops."""
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# Validate arguments.
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assert isinstance(x, torch.Tensor) and x.ndim == 4
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if f is None:
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f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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assert f.dtype == torch.float32 and not f.requires_grad
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batch_size, num_channels, in_height, in_width = x.shape
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# upx, upy = _parse_scaling(up)
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# downx, downy = _parse_scaling(down)
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upx, upy = up, up
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downx, downy = down, down
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# padx0, padx1, pady0, pady1 = _parse_padding(padding)
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padx0, padx1, pady0, pady1 = padding[0], padding[1], padding[2], padding[3]
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# Upsample by inserting zeros.
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x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
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x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
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x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
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# Pad or crop.
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x = torch.nn.functional.pad(
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x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]
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)
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x = x[
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:,
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:,
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max(-pady0, 0) : x.shape[2] - max(-pady1, 0),
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max(-padx0, 0) : x.shape[3] - max(-padx1, 0),
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]
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# Setup filter.
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f = f * (gain ** (f.ndim / 2))
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f = f.to(x.dtype)
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if not flip_filter:
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f = f.flip(list(range(f.ndim)))
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# Convolve with the filter.
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f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
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if f.ndim == 4:
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x = conv2d(input=x, weight=f, groups=num_channels)
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else:
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x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
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x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
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# Downsample by throwing away pixels.
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x = x[:, :, ::downy, ::downx]
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return x
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def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl="cuda"):
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r"""Pad, upsample, filter, and downsample a batch of 2D images.
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Performs the following sequence of operations for each channel:
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1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
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2. Pad the image with the specified number of zeros on each side (`padding`).
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Negative padding corresponds to cropping the image.
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3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
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so that the footprint of all output pixels lies within the input image.
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4. Downsample the image by keeping every Nth pixel (`down`).
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This sequence of operations bears close resemblance to scipy.signal.upfirdn().
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The fused op is considerably more efficient than performing the same calculation
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using standard PyTorch ops. It supports gradients of arbitrary order.
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Args:
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x: Float32/float64/float16 input tensor of the shape
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`[batch_size, num_channels, in_height, in_width]`.
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f: Float32 FIR filter of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable), or
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`None` (identity).
|
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up: Integer upsampling factor. Can be a single int or a list/tuple
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`[x, y]` (default: 1).
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down: Integer downsampling factor. Can be a single int or a list/tuple
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`[x, y]` (default: 1).
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padding: Padding with respect to the upsampled image. Can be a single number
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or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
|
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(default: 0).
|
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flip_filter: False = convolution, True = correlation (default: False).
|
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gain: Overall scaling factor for signal magnitude (default: 1).
|
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impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
|
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Returns:
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Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
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"""
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# assert isinstance(x, torch.Tensor)
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# assert impl in ['ref', 'cuda']
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return _upfirdn2d_ref(
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x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain
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||||
)
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||||
|
||||
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def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl="cuda"):
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r"""Upsample a batch of 2D images using the given 2D FIR filter.
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||||
By default, the result is padded so that its shape is a multiple of the input.
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||||
User-specified padding is applied on top of that, with negative values
|
||||
indicating cropping. Pixels outside the image are assumed to be zero.
|
||||
Args:
|
||||
x: Float32/float64/float16 input tensor of the shape
|
||||
`[batch_size, num_channels, in_height, in_width]`.
|
||||
f: Float32 FIR filter of the shape
|
||||
`[filter_height, filter_width]` (non-separable),
|
||||
`[filter_taps]` (separable), or
|
||||
`None` (identity).
|
||||
up: Integer upsampling factor. Can be a single int or a list/tuple
|
||||
`[x, y]` (default: 1).
|
||||
padding: Padding with respect to the output. Can be a single number or a
|
||||
list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
|
||||
(default: 0).
|
||||
flip_filter: False = convolution, True = correlation (default: False).
|
||||
gain: Overall scaling factor for signal magnitude (default: 1).
|
||||
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
|
||||
Returns:
|
||||
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
|
||||
"""
|
||||
upx, upy = _parse_scaling(up)
|
||||
# upx, upy = up, up
|
||||
padx0, padx1, pady0, pady1 = _parse_padding(padding)
|
||||
# padx0, padx1, pady0, pady1 = padding, padding, padding, padding
|
||||
fw, fh = _get_filter_size(f)
|
||||
p = [
|
||||
padx0 + (fw + upx - 1) // 2,
|
||||
padx1 + (fw - upx) // 2,
|
||||
pady0 + (fh + upy - 1) // 2,
|
||||
pady1 + (fh - upy) // 2,
|
||||
]
|
||||
return upfirdn2d(
|
||||
x,
|
||||
f,
|
||||
up=up,
|
||||
padding=p,
|
||||
flip_filter=flip_filter,
|
||||
gain=gain * upx * upy,
|
||||
impl=impl,
|
||||
)
|
||||
|
||||
|
||||
class FullyConnectedLayer(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_features, # Number of input features.
|
||||
out_features, # Number of output features.
|
||||
bias=True, # Apply additive bias before the activation function?
|
||||
activation="linear", # Activation function: 'relu', 'lrelu', etc.
|
||||
lr_multiplier=1, # Learning rate multiplier.
|
||||
bias_init=0, # Initial value for the additive bias.
|
||||
):
|
||||
super().__init__()
|
||||
self.weight = torch.nn.Parameter(
|
||||
torch.randn([out_features, in_features]) / lr_multiplier
|
||||
)
|
||||
self.bias = (
|
||||
torch.nn.Parameter(torch.full([out_features], np.float32(bias_init)))
|
||||
if bias
|
||||
else None
|
||||
)
|
||||
self.activation = activation
|
||||
|
||||
self.weight_gain = lr_multiplier / np.sqrt(in_features)
|
||||
self.bias_gain = lr_multiplier
|
||||
|
||||
def forward(self, x):
|
||||
w = self.weight * self.weight_gain
|
||||
b = self.bias
|
||||
if b is not None and self.bias_gain != 1:
|
||||
b = b * self.bias_gain
|
||||
|
||||
if self.activation == "linear" and b is not None:
|
||||
# out = torch.addmm(b.unsqueeze(0), x, w.t())
|
||||
x = x.matmul(w.t().to(x.device))
|
||||
out = x + b.reshape(
|
||||
[-1 if i == x.ndim - 1 else 1 for i in range(x.ndim)]
|
||||
).to(x.device)
|
||||
else:
|
||||
x = x.matmul(w.t().to(x.device))
|
||||
out = bias_act(x, b, act=self.activation, dim=x.ndim - 1).to(x.device)
|
||||
return out
|
||||
|
||||
|
||||
def _conv2d_wrapper(
|
||||
x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True
|
||||
):
|
||||
"""Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations."""
|
||||
out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
|
||||
|
||||
# Flip weight if requested.
|
||||
if (
|
||||
not flip_weight
|
||||
): # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
|
||||
w = w.flip([2, 3])
|
||||
|
||||
# Workaround performance pitfall in cuDNN 8.0.5, triggered when using
|
||||
# 1x1 kernel + memory_format=channels_last + less than 64 channels.
|
||||
if (
|
||||
kw == 1
|
||||
and kh == 1
|
||||
and stride == 1
|
||||
and padding in [0, [0, 0], (0, 0)]
|
||||
and not transpose
|
||||
):
|
||||
if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
|
||||
if out_channels <= 4 and groups == 1:
|
||||
in_shape = x.shape
|
||||
x = w.squeeze(3).squeeze(2) @ x.reshape(
|
||||
[in_shape[0], in_channels_per_group, -1]
|
||||
)
|
||||
x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
|
||||
else:
|
||||
x = x.to(memory_format=torch.contiguous_format)
|
||||
w = w.to(memory_format=torch.contiguous_format)
|
||||
x = conv2d(x, w, groups=groups)
|
||||
return x.to(memory_format=torch.channels_last)
|
||||
|
||||
# Otherwise => execute using conv2d_gradfix.
|
||||
op = conv_transpose2d if transpose else conv2d
|
||||
return op(x, w, stride=stride, padding=padding, groups=groups)
|
||||
|
||||
|
||||
def conv2d_resample(
|
||||
x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False
|
||||
):
|
||||
r"""2D convolution with optional up/downsampling.
|
||||
Padding is performed only once at the beginning, not between the operations.
|
||||
Args:
|
||||
x: Input tensor of shape
|
||||
`[batch_size, in_channels, in_height, in_width]`.
|
||||
w: Weight tensor of shape
|
||||
`[out_channels, in_channels//groups, kernel_height, kernel_width]`.
|
||||
f: Low-pass filter for up/downsampling. Must be prepared beforehand by
|
||||
calling setup_filter(). None = identity (default).
|
||||
up: Integer upsampling factor (default: 1).
|
||||
down: Integer downsampling factor (default: 1).
|
||||
padding: Padding with respect to the upsampled image. Can be a single number
|
||||
or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
|
||||
(default: 0).
|
||||
groups: Split input channels into N groups (default: 1).
|
||||
flip_weight: False = convolution, True = correlation (default: True).
|
||||
flip_filter: False = convolution, True = correlation (default: False).
|
||||
Returns:
|
||||
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
|
||||
"""
|
||||
# Validate arguments.
|
||||
assert isinstance(x, torch.Tensor) and (x.ndim == 4)
|
||||
assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
|
||||
assert f is None or (
|
||||
isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32
|
||||
)
|
||||
assert isinstance(up, int) and (up >= 1)
|
||||
assert isinstance(down, int) and (down >= 1)
|
||||
# assert isinstance(groups, int) and (groups >= 1), f"!!!!!! groups: {groups} isinstance(groups, int) {isinstance(groups, int)} {type(groups)}"
|
||||
out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
|
||||
fw, fh = _get_filter_size(f)
|
||||
# px0, px1, py0, py1 = _parse_padding(padding)
|
||||
px0, px1, py0, py1 = padding, padding, padding, padding
|
||||
|
||||
# Adjust padding to account for up/downsampling.
|
||||
if up > 1:
|
||||
px0 += (fw + up - 1) // 2
|
||||
px1 += (fw - up) // 2
|
||||
py0 += (fh + up - 1) // 2
|
||||
py1 += (fh - up) // 2
|
||||
if down > 1:
|
||||
px0 += (fw - down + 1) // 2
|
||||
px1 += (fw - down) // 2
|
||||
py0 += (fh - down + 1) // 2
|
||||
py1 += (fh - down) // 2
|
||||
|
||||
# Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
|
||||
if kw == 1 and kh == 1 and (down > 1 and up == 1):
|
||||
x = upfirdn2d(
|
||||
x=x, f=f, down=down, padding=[px0, px1, py0, py1], flip_filter=flip_filter
|
||||
)
|
||||
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
|
||||
return x
|
||||
|
||||
# Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
|
||||
if kw == 1 and kh == 1 and (up > 1 and down == 1):
|
||||
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
|
||||
x = upfirdn2d(
|
||||
x=x,
|
||||
f=f,
|
||||
up=up,
|
||||
padding=[px0, px1, py0, py1],
|
||||
gain=up**2,
|
||||
flip_filter=flip_filter,
|
||||
)
|
||||
return x
|
||||
|
||||
# Fast path: downsampling only => use strided convolution.
|
||||
if down > 1 and up == 1:
|
||||
x = upfirdn2d(x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter)
|
||||
x = _conv2d_wrapper(
|
||||
x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight
|
||||
)
|
||||
return x
|
||||
|
||||
# Fast path: upsampling with optional downsampling => use transpose strided convolution.
|
||||
if up > 1:
|
||||
if groups == 1:
|
||||
w = w.transpose(0, 1)
|
||||
else:
|
||||
w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
|
||||
w = w.transpose(1, 2)
|
||||
w = w.reshape(
|
||||
groups * in_channels_per_group, out_channels // groups, kh, kw
|
||||
)
|
||||
px0 -= kw - 1
|
||||
px1 -= kw - up
|
||||
py0 -= kh - 1
|
||||
py1 -= kh - up
|
||||
pxt = max(min(-px0, -px1), 0)
|
||||
pyt = max(min(-py0, -py1), 0)
|
||||
x = _conv2d_wrapper(
|
||||
x=x,
|
||||
w=w,
|
||||
stride=up,
|
||||
padding=[pyt, pxt],
|
||||
groups=groups,
|
||||
transpose=True,
|
||||
flip_weight=(not flip_weight),
|
||||
)
|
||||
x = upfirdn2d(
|
||||
x=x,
|
||||
f=f,
|
||||
padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt],
|
||||
gain=up**2,
|
||||
flip_filter=flip_filter,
|
||||
)
|
||||
if down > 1:
|
||||
x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
|
||||
return x
|
||||
|
||||
# Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
|
||||
if up == 1 and down == 1:
|
||||
if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
|
||||
return _conv2d_wrapper(
|
||||
x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight
|
||||
)
|
||||
|
||||
# Fallback: Generic reference implementation.
|
||||
x = upfirdn2d(
|
||||
x=x,
|
||||
f=(f if up > 1 else None),
|
||||
up=up,
|
||||
padding=[px0, px1, py0, py1],
|
||||
gain=up**2,
|
||||
flip_filter=flip_filter,
|
||||
)
|
||||
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
|
||||
if down > 1:
|
||||
x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
|
||||
return x
|
||||
|
||||
|
||||
class Conv2dLayer(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels, # Number of input channels.
|
||||
out_channels, # Number of output channels.
|
||||
kernel_size, # Width and height of the convolution kernel.
|
||||
bias=True, # Apply additive bias before the activation function?
|
||||
activation="linear", # Activation function: 'relu', 'lrelu', etc.
|
||||
up=1, # Integer upsampling factor.
|
||||
down=1, # Integer downsampling factor.
|
||||
resample_filter=[
|
||||
1,
|
||||
3,
|
||||
3,
|
||||
1,
|
||||
], # Low-pass filter to apply when resampling activations.
|
||||
conv_clamp=None, # Clamp the output to +-X, None = disable clamping.
|
||||
channels_last=False, # Expect the input to have memory_format=channels_last?
|
||||
trainable=True, # Update the weights of this layer during training?
|
||||
):
|
||||
super().__init__()
|
||||
self.activation = activation
|
||||
self.up = up
|
||||
self.down = down
|
||||
self.register_buffer("resample_filter", setup_filter(resample_filter))
|
||||
self.conv_clamp = conv_clamp
|
||||
self.padding = kernel_size // 2
|
||||
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
|
||||
self.act_gain = activation_funcs[activation].def_gain
|
||||
|
||||
memory_format = (
|
||||
torch.channels_last if channels_last else torch.contiguous_format
|
||||
)
|
||||
weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
|
||||
memory_format=memory_format
|
||||
)
|
||||
bias = torch.zeros([out_channels]) if bias else None
|
||||
if trainable:
|
||||
self.weight = torch.nn.Parameter(weight)
|
||||
self.bias = torch.nn.Parameter(bias) if bias is not None else None
|
||||
else:
|
||||
self.register_buffer("weight", weight)
|
||||
if bias is not None:
|
||||
self.register_buffer("bias", bias)
|
||||
else:
|
||||
self.bias = None
|
||||
|
||||
def forward(self, x, gain=1):
|
||||
w = self.weight * self.weight_gain
|
||||
x = conv2d_resample(
|
||||
x=x,
|
||||
w=w,
|
||||
f=self.resample_filter,
|
||||
up=self.up,
|
||||
down=self.down,
|
||||
padding=self.padding,
|
||||
)
|
||||
|
||||
act_gain = self.act_gain * gain
|
||||
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
|
||||
out = bias_act(
|
||||
x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp
|
||||
)
|
||||
return out
|
||||
Reference in New Issue
Block a user