Add FcF
This commit is contained in:
Qing
parent
b6d71c4733
commit
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@ -4,6 +4,7 @@ from typing import Optional
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import cv2
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import cv2
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import torch
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import torch
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from loguru import logger
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from loguru import logger
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import numpy as np
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from lama_cleaner.helper import boxes_from_mask, resize_max_size, pad_img_to_modulo
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from lama_cleaner.helper import boxes_from_mask, resize_max_size, pad_img_to_modulo
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from lama_cleaner.schema import Config, HDStrategy
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from lama_cleaner.schema import Config, HDStrategy
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@ -51,7 +52,7 @@ class InpaintModel:
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result = self.forward(pad_image, pad_mask, config)
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result = self.forward(pad_image, pad_mask, config)
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result = result[0:origin_height, 0:origin_width, :]
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result = result[0:origin_height, 0:origin_width, :]
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original_pixel_indices = mask != 255
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original_pixel_indices = mask < 127
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result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
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result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
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return result
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return result
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1180
lama_cleaner/model/fcf.py
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1180
lama_cleaner/model/fcf.py
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File diff suppressed because it is too large
Load Diff
@ -1,615 +1,20 @@
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import collections
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import os
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import os
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from itertools import repeat
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from typing import Any
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import random
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import random
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import cv2
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import cv2
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import numpy as np
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img
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from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img
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from lama_cleaner.model.base import InpaintModel
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from lama_cleaner.model.base import InpaintModel
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from torch.nn.functional import conv2d, conv_transpose2d
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from lama_cleaner.model.utils import setup_filter, Conv2dLayer, FullyConnectedLayer, conv2d_resample, bias_act, \
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import torch.utils.checkpoint as checkpoint
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upsample2d, activation_funcs, MinibatchStdLayer, to_2tuple, normalize_2nd_moment
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import numpy as np
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from lama_cleaner.schema import Config
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from lama_cleaner.schema import Config
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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(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False),
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'relu': EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2,
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ref='y', has_2nd_grad=False),
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'lrelu': EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2,
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def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False),
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'tanh': EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y',
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has_2nd_grad=True),
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'sigmoid': EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y',
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has_2nd_grad=True),
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'elu': EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y',
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has_2nd_grad=True),
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'selu': EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y',
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has_2nd_grad=True),
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'softplus': EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8,
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ref='y', has_2nd_grad=True),
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'swish': EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x',
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has_2nd_grad=True),
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}
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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 _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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"""
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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)])
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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(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='ref'):
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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(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
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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 setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
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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 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(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
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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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"""
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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(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
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x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0), max(-padx0, 0): x.shape[3] - max(-padx1, 0)]
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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.
|
|
||||||
x = x[:, :, ::downy, ::downx]
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
|
|
||||||
r"""Upsample a batch of 2D images using the given 2D FIR filter.
|
|
||||||
|
|
||||||
By default, the result is padded so that its shape is a multiple of the input.
|
|
||||||
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)
|
|
||||||
|
|
||||||
|
|
||||||
def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
|
|
||||||
r"""Downsample a batch of 2D images using the given 2D FIR filter.
|
|
||||||
|
|
||||||
By default, the result is padded so that its shape is a fraction of the input.
|
|
||||||
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).
|
|
||||||
down: Integer downsampling factor. Can be a single int or a list/tuple
|
|
||||||
`[x, y]` (default: 1).
|
|
||||||
padding: Padding with respect to the input. 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]`.
|
|
||||||
"""
|
|
||||||
downx, downy = _parse_scaling(down)
|
|
||||||
# padx0, padx1, pady0, pady1 = _parse_padding(padding)
|
|
||||||
padx0, padx1, pady0, pady1 = padding, padding, padding, padding
|
|
||||||
|
|
||||||
fw, fh = _get_filter_size(f)
|
|
||||||
p = [
|
|
||||||
padx0 + (fw - downx + 1) // 2,
|
|
||||||
padx1 + (fw - downx) // 2,
|
|
||||||
pady0 + (fh - downy + 1) // 2,
|
|
||||||
pady1 + (fh - downy) // 2,
|
|
||||||
]
|
|
||||||
return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
|
|
||||||
|
|
||||||
|
|
||||||
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
|
|
||||||
|
|
||||||
|
|
||||||
# ----------------------------------------------------------------------------
|
|
||||||
|
|
||||||
|
|
||||||
def normalize_2nd_moment(x, dim=1, eps=1e-8):
|
|
||||||
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
|
|
||||||
|
|
||||||
|
|
||||||
class FullyConnectedLayer(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())
|
|
||||||
out = x + b.reshape([-1 if i == x.ndim - 1 else 1 for i in range(x.ndim)])
|
|
||||||
else:
|
|
||||||
x = x.matmul(w.t())
|
|
||||||
out = bias_act(x, b, act=self.activation, dim=x.ndim - 1)
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
class Conv2dLayer(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.
|
|
||||||
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
|
|
||||||
|
|
||||||
weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size])
|
|
||||||
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
|
|
||||||
|
|
||||||
|
|
||||||
class ModulatedConv2d(nn.Module):
|
class ModulatedConv2d(nn.Module):
|
||||||
def __init__(self,
|
def __init__(self,
|
||||||
in_channels, # Number of input channels.
|
in_channels, # Number of input channels.
|
||||||
@ -983,31 +388,6 @@ class DisBlock(nn.Module):
|
|||||||
return out
|
return out
|
||||||
|
|
||||||
|
|
||||||
class MinibatchStdLayer(torch.nn.Module):
|
|
||||||
def __init__(self, group_size, num_channels=1):
|
|
||||||
super().__init__()
|
|
||||||
self.group_size = group_size
|
|
||||||
self.num_channels = num_channels
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
N, C, H, W = x.shape
|
|
||||||
G = torch.min(torch.as_tensor(self.group_size),
|
|
||||||
torch.as_tensor(N)) if self.group_size is not None else N
|
|
||||||
F = self.num_channels
|
|
||||||
c = C // F
|
|
||||||
|
|
||||||
y = x.reshape(G, -1, F, c, H,
|
|
||||||
W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
|
|
||||||
y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group.
|
|
||||||
y = y.square().mean(dim=0) # [nFcHW] Calc variance over group.
|
|
||||||
y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
|
|
||||||
y = y.mean(dim=[2, 3, 4]) # [nF] Take average over channels and pixels.
|
|
||||||
y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
|
|
||||||
y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels.
|
|
||||||
x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels.
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
class Discriminator(torch.nn.Module):
|
class Discriminator(torch.nn.Module):
|
||||||
def __init__(self,
|
def __init__(self,
|
||||||
c_dim, # Conditioning label (C) dimensionality.
|
c_dim, # Conditioning label (C) dimensionality.
|
||||||
|
|||||||
@ -1,7 +1,12 @@
|
|||||||
import math
|
import math
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
import collections
|
||||||
|
from itertools import repeat
|
||||||
|
|
||||||
|
from torch import conv2d, conv_transpose2d
|
||||||
|
|
||||||
|
|
||||||
def make_beta_schedule(device, schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
def make_beta_schedule(device, schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
||||||
@ -84,3 +89,621 @@ def timestep_embedding(device, timesteps, dim, max_period=10000, repeat_only=Fal
|
|||||||
if dim % 2:
|
if dim % 2:
|
||||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
||||||
return embedding
|
return embedding
|
||||||
|
|
||||||
|
|
||||||
|
###### MAT and FcF #######
|
||||||
|
|
||||||
|
|
||||||
|
def normalize_2nd_moment(x, dim=1, eps=1e-8):
|
||||||
|
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
|
||||||
|
|
||||||
|
|
||||||
|
class EasyDict(dict):
|
||||||
|
"""Convenience class that behaves like a dict but allows access with the attribute syntax."""
|
||||||
|
|
||||||
|
def __getattr__(self, name: str) -> Any:
|
||||||
|
try:
|
||||||
|
return self[name]
|
||||||
|
except KeyError:
|
||||||
|
raise AttributeError(name)
|
||||||
|
|
||||||
|
def __setattr__(self, name: str, value: Any) -> None:
|
||||||
|
self[name] = value
|
||||||
|
|
||||||
|
def __delattr__(self, name: str) -> None:
|
||||||
|
del self[name]
|
||||||
|
|
||||||
|
|
||||||
|
def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
|
||||||
|
"""Slow reference implementation of `bias_act()` using standard TensorFlow ops.
|
||||||
|
"""
|
||||||
|
assert isinstance(x, torch.Tensor)
|
||||||
|
assert clamp is None or clamp >= 0
|
||||||
|
spec = activation_funcs[act]
|
||||||
|
alpha = float(alpha if alpha is not None else spec.def_alpha)
|
||||||
|
gain = float(gain if gain is not None else spec.def_gain)
|
||||||
|
clamp = float(clamp if clamp is not None else -1)
|
||||||
|
|
||||||
|
# Add bias.
|
||||||
|
if b is not None:
|
||||||
|
assert isinstance(b, torch.Tensor) and b.ndim == 1
|
||||||
|
assert 0 <= dim < x.ndim
|
||||||
|
assert b.shape[0] == x.shape[dim]
|
||||||
|
x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
|
||||||
|
|
||||||
|
# Evaluate activation function.
|
||||||
|
alpha = float(alpha)
|
||||||
|
x = spec.func(x, alpha=alpha)
|
||||||
|
|
||||||
|
# Scale by gain.
|
||||||
|
gain = float(gain)
|
||||||
|
if gain != 1:
|
||||||
|
x = x * gain
|
||||||
|
|
||||||
|
# Clamp.
|
||||||
|
if clamp >= 0:
|
||||||
|
x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='ref'):
|
||||||
|
r"""Fused bias and activation function.
|
||||||
|
|
||||||
|
Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
|
||||||
|
and scales the result by `gain`. Each of the steps is optional. In most cases,
|
||||||
|
the fused op is considerably more efficient than performing the same calculation
|
||||||
|
using standard PyTorch ops. It supports first and second order gradients,
|
||||||
|
but not third order gradients.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
x: Input activation tensor. Can be of any shape.
|
||||||
|
b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
|
||||||
|
as `x`. The shape must be known, and it must match the dimension of `x`
|
||||||
|
corresponding to `dim`.
|
||||||
|
dim: The dimension in `x` corresponding to the elements of `b`.
|
||||||
|
The value of `dim` is ignored if `b` is not specified.
|
||||||
|
act: Name of the activation function to evaluate, or `"linear"` to disable.
|
||||||
|
Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
|
||||||
|
See `activation_funcs` for a full list. `None` is not allowed.
|
||||||
|
alpha: Shape parameter for the activation function, or `None` to use the default.
|
||||||
|
gain: Scaling factor for the output tensor, or `None` to use default.
|
||||||
|
See `activation_funcs` for the default scaling of each activation function.
|
||||||
|
If unsure, consider specifying 1.
|
||||||
|
clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
|
||||||
|
the clamping (default).
|
||||||
|
impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tensor of the same shape and datatype as `x`.
|
||||||
|
"""
|
||||||
|
assert isinstance(x, torch.Tensor)
|
||||||
|
assert impl in ['ref', 'cuda']
|
||||||
|
return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
|
||||||
|
|
||||||
|
|
||||||
|
def _get_filter_size(f):
|
||||||
|
if f is None:
|
||||||
|
return 1, 1
|
||||||
|
|
||||||
|
assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
|
||||||
|
fw = f.shape[-1]
|
||||||
|
fh = f.shape[0]
|
||||||
|
|
||||||
|
fw = int(fw)
|
||||||
|
fh = int(fh)
|
||||||
|
assert fw >= 1 and fh >= 1
|
||||||
|
return fw, fh
|
||||||
|
|
||||||
|
|
||||||
|
def _get_weight_shape(w):
|
||||||
|
shape = [int(sz) for sz in w.shape]
|
||||||
|
return shape
|
||||||
|
|
||||||
|
|
||||||
|
def _parse_scaling(scaling):
|
||||||
|
if isinstance(scaling, int):
|
||||||
|
scaling = [scaling, scaling]
|
||||||
|
assert isinstance(scaling, (list, tuple))
|
||||||
|
assert all(isinstance(x, int) for x in scaling)
|
||||||
|
sx, sy = scaling
|
||||||
|
assert sx >= 1 and sy >= 1
|
||||||
|
return sx, sy
|
||||||
|
|
||||||
|
|
||||||
|
def _parse_padding(padding):
|
||||||
|
if isinstance(padding, int):
|
||||||
|
padding = [padding, padding]
|
||||||
|
assert isinstance(padding, (list, tuple))
|
||||||
|
assert all(isinstance(x, int) for x in padding)
|
||||||
|
if len(padding) == 2:
|
||||||
|
padx, pady = padding
|
||||||
|
padding = [padx, padx, pady, pady]
|
||||||
|
padx0, padx1, pady0, pady1 = padding
|
||||||
|
return padx0, padx1, pady0, pady1
|
||||||
|
|
||||||
|
|
||||||
|
def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
|
||||||
|
r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
f: Torch tensor, numpy array, or python list of the shape
|
||||||
|
`[filter_height, filter_width]` (non-separable),
|
||||||
|
`[filter_taps]` (separable),
|
||||||
|
`[]` (impulse), or
|
||||||
|
`None` (identity).
|
||||||
|
device: Result device (default: cpu).
|
||||||
|
normalize: Normalize the filter so that it retains the magnitude
|
||||||
|
for constant input signal (DC)? (default: True).
|
||||||
|
flip_filter: Flip the filter? (default: False).
|
||||||
|
gain: Overall scaling factor for signal magnitude (default: 1).
|
||||||
|
separable: Return a separable filter? (default: select automatically).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Float32 tensor of the shape
|
||||||
|
`[filter_height, filter_width]` (non-separable) or
|
||||||
|
`[filter_taps]` (separable).
|
||||||
|
"""
|
||||||
|
# Validate.
|
||||||
|
if f is None:
|
||||||
|
f = 1
|
||||||
|
f = torch.as_tensor(f, dtype=torch.float32)
|
||||||
|
assert f.ndim in [0, 1, 2]
|
||||||
|
assert f.numel() > 0
|
||||||
|
if f.ndim == 0:
|
||||||
|
f = f[np.newaxis]
|
||||||
|
|
||||||
|
# Separable?
|
||||||
|
if separable is None:
|
||||||
|
separable = (f.ndim == 1 and f.numel() >= 8)
|
||||||
|
if f.ndim == 1 and not separable:
|
||||||
|
f = f.ger(f)
|
||||||
|
assert f.ndim == (1 if separable else 2)
|
||||||
|
|
||||||
|
# Apply normalize, flip, gain, and device.
|
||||||
|
if normalize:
|
||||||
|
f /= f.sum()
|
||||||
|
if flip_filter:
|
||||||
|
f = f.flip(list(range(f.ndim)))
|
||||||
|
f = f * (gain ** (f.ndim / 2))
|
||||||
|
f = f.to(device=device)
|
||||||
|
return f
|
||||||
|
|
||||||
|
|
||||||
|
def _ntuple(n):
|
||||||
|
def parse(x):
|
||||||
|
if isinstance(x, collections.abc.Iterable):
|
||||||
|
return x
|
||||||
|
return tuple(repeat(x, n))
|
||||||
|
|
||||||
|
return parse
|
||||||
|
|
||||||
|
|
||||||
|
to_2tuple = _ntuple(2)
|
||||||
|
|
||||||
|
activation_funcs = {
|
||||||
|
'linear': EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False),
|
||||||
|
'relu': EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2,
|
||||||
|
ref='y', has_2nd_grad=False),
|
||||||
|
'lrelu': EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2,
|
||||||
|
def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False),
|
||||||
|
'tanh': EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y',
|
||||||
|
has_2nd_grad=True),
|
||||||
|
'sigmoid': EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y',
|
||||||
|
has_2nd_grad=True),
|
||||||
|
'elu': EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y',
|
||||||
|
has_2nd_grad=True),
|
||||||
|
'selu': EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y',
|
||||||
|
has_2nd_grad=True),
|
||||||
|
'softplus': EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8,
|
||||||
|
ref='y', has_2nd_grad=True),
|
||||||
|
'swish': EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x',
|
||||||
|
has_2nd_grad=True),
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
|
||||||
|
r"""Pad, upsample, filter, and downsample a batch of 2D images.
|
||||||
|
|
||||||
|
Performs the following sequence of operations for each channel:
|
||||||
|
|
||||||
|
1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
|
||||||
|
|
||||||
|
2. Pad the image with the specified number of zeros on each side (`padding`).
|
||||||
|
Negative padding corresponds to cropping the image.
|
||||||
|
|
||||||
|
3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
|
||||||
|
so that the footprint of all output pixels lies within the input image.
|
||||||
|
|
||||||
|
4. Downsample the image by keeping every Nth pixel (`down`).
|
||||||
|
|
||||||
|
This sequence of operations bears close resemblance to scipy.signal.upfirdn().
|
||||||
|
The fused op is considerably more efficient than performing the same calculation
|
||||||
|
using standard PyTorch ops. It supports gradients of arbitrary order.
|
||||||
|
|
||||||
|
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).
|
||||||
|
down: Integer downsampling factor. Can be a single int or a list/tuple
|
||||||
|
`[x, y]` (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).
|
||||||
|
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]`.
|
||||||
|
"""
|
||||||
|
# assert isinstance(x, torch.Tensor)
|
||||||
|
# assert impl in ['ref', 'cuda']
|
||||||
|
return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
|
||||||
|
|
||||||
|
|
||||||
|
def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
|
||||||
|
"""Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
|
||||||
|
"""
|
||||||
|
# Validate arguments.
|
||||||
|
assert isinstance(x, torch.Tensor) and x.ndim == 4
|
||||||
|
if f is None:
|
||||||
|
f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
|
||||||
|
assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
|
||||||
|
assert f.dtype == torch.float32 and not f.requires_grad
|
||||||
|
batch_size, num_channels, in_height, in_width = x.shape
|
||||||
|
# upx, upy = _parse_scaling(up)
|
||||||
|
# downx, downy = _parse_scaling(down)
|
||||||
|
|
||||||
|
upx, upy = up, up
|
||||||
|
downx, downy = down, down
|
||||||
|
|
||||||
|
# padx0, padx1, pady0, pady1 = _parse_padding(padding)
|
||||||
|
padx0, padx1, pady0, pady1 = padding[0], padding[1], padding[2], padding[3]
|
||||||
|
|
||||||
|
# Upsample by inserting zeros.
|
||||||
|
x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
|
||||||
|
x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
|
||||||
|
x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
|
||||||
|
|
||||||
|
# Pad or crop.
|
||||||
|
x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
|
||||||
|
x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0), max(-padx0, 0): x.shape[3] - max(-padx1, 0)]
|
||||||
|
|
||||||
|
# Setup filter.
|
||||||
|
f = f * (gain ** (f.ndim / 2))
|
||||||
|
f = f.to(x.dtype)
|
||||||
|
if not flip_filter:
|
||||||
|
f = f.flip(list(range(f.ndim)))
|
||||||
|
|
||||||
|
# Convolve with the filter.
|
||||||
|
f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
|
||||||
|
if f.ndim == 4:
|
||||||
|
x = conv2d(input=x, weight=f, groups=num_channels)
|
||||||
|
else:
|
||||||
|
x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
|
||||||
|
x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
|
||||||
|
|
||||||
|
# Downsample by throwing away pixels.
|
||||||
|
x = x[:, :, ::downy, ::downx]
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
|
||||||
|
r"""Downsample a batch of 2D images using the given 2D FIR filter.
|
||||||
|
|
||||||
|
By default, the result is padded so that its shape is a fraction of the input.
|
||||||
|
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).
|
||||||
|
down: Integer downsampling factor. Can be a single int or a list/tuple
|
||||||
|
`[x, y]` (default: 1).
|
||||||
|
padding: Padding with respect to the input. 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]`.
|
||||||
|
"""
|
||||||
|
downx, downy = _parse_scaling(down)
|
||||||
|
# padx0, padx1, pady0, pady1 = _parse_padding(padding)
|
||||||
|
padx0, padx1, pady0, pady1 = padding, padding, padding, padding
|
||||||
|
|
||||||
|
fw, fh = _get_filter_size(f)
|
||||||
|
p = [
|
||||||
|
padx0 + (fw - downx + 1) // 2,
|
||||||
|
padx1 + (fw - downx) // 2,
|
||||||
|
pady0 + (fh - downy + 1) // 2,
|
||||||
|
pady1 + (fh - downy) // 2,
|
||||||
|
]
|
||||||
|
return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
|
||||||
|
|
||||||
|
|
||||||
|
def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
|
||||||
|
r"""Upsample a batch of 2D images using the given 2D FIR filter.
|
||||||
|
|
||||||
|
By default, the result is padded so that its shape is a multiple of the input.
|
||||||
|
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 MinibatchStdLayer(torch.nn.Module):
|
||||||
|
def __init__(self, group_size, num_channels=1):
|
||||||
|
super().__init__()
|
||||||
|
self.group_size = group_size
|
||||||
|
self.num_channels = num_channels
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
N, C, H, W = x.shape
|
||||||
|
G = torch.min(torch.as_tensor(self.group_size),
|
||||||
|
torch.as_tensor(N)) if self.group_size is not None else N
|
||||||
|
F = self.num_channels
|
||||||
|
c = C // F
|
||||||
|
|
||||||
|
y = x.reshape(G, -1, F, c, H,
|
||||||
|
W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
|
||||||
|
y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group.
|
||||||
|
y = y.square().mean(dim=0) # [nFcHW] Calc variance over group.
|
||||||
|
y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
|
||||||
|
y = y.mean(dim=[2, 3, 4]) # [nF] Take average over channels and pixels.
|
||||||
|
y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
|
||||||
|
y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels.
|
||||||
|
x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels.
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
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())
|
||||||
|
out = x + b.reshape([-1 if i == x.ndim - 1 else 1 for i in range(x.ndim)])
|
||||||
|
else:
|
||||||
|
x = x.matmul(w.t())
|
||||||
|
out = bias_act(x, b, act=self.activation, dim=x.ndim - 1)
|
||||||
|
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)
|
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|
return out
|
||||||
|
|||||||
@ -1,3 +1,4 @@
|
|||||||
|
from lama_cleaner.model.fcf import FcF
|
||||||
from lama_cleaner.model.lama import LaMa
|
from lama_cleaner.model.lama import LaMa
|
||||||
from lama_cleaner.model.ldm import LDM
|
from lama_cleaner.model.ldm import LDM
|
||||||
from lama_cleaner.model.mat import MAT
|
from lama_cleaner.model.mat import MAT
|
||||||
@ -8,7 +9,8 @@ models = {
|
|||||||
'lama': LaMa,
|
'lama': LaMa,
|
||||||
'ldm': LDM,
|
'ldm': LDM,
|
||||||
'zits': ZITS,
|
'zits': ZITS,
|
||||||
'mat': MAT
|
'mat': MAT,
|
||||||
|
'fcf': FcF
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -1,8 +1,6 @@
|
|||||||
import os
|
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
import cv2
|
import cv2
|
||||||
import numpy as np
|
|
||||||
import pytest
|
import pytest
|
||||||
|
|
||||||
from lama_cleaner.model_manager import ModelManager
|
from lama_cleaner.model_manager import ModelManager
|
||||||
@ -16,14 +14,9 @@ def get_data(fx=1, fy=1.0):
|
|||||||
img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
|
img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
|
||||||
mask = cv2.imread(str(current_dir / "mask.png"), cv2.IMREAD_GRAYSCALE)
|
mask = cv2.imread(str(current_dir / "mask.png"), cv2.IMREAD_GRAYSCALE)
|
||||||
|
|
||||||
# img = cv2.imread("/Users/qing/code/github/MAT/test_sets/Places/images/test1.jpg")
|
|
||||||
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
||||||
# mask = cv2.imread("/Users/qing/code/github/MAT/test_sets/Places/masks/mask1.png", cv2.IMREAD_GRAYSCALE)
|
|
||||||
# mask = 255 - mask
|
|
||||||
|
|
||||||
if fx != 1:
|
if fx != 1:
|
||||||
img = cv2.resize(img, None, fx=fx, fy=fy)
|
img = cv2.resize(img, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
|
||||||
mask = cv2.resize(mask, None, fx=fx, fy=fy)
|
mask = cv2.resize(mask, None, fx=fx, fy=fy, interpolation=cv2.INTER_NEAREST)
|
||||||
return img, mask
|
return img, mask
|
||||||
|
|
||||||
|
|
||||||
@ -134,3 +127,19 @@ def test_mat(strategy):
|
|||||||
cfg,
|
cfg,
|
||||||
f"mat_{strategy.capitalize()}_result.png",
|
f"mat_{strategy.capitalize()}_result.png",
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize(
|
||||||
|
"strategy", [HDStrategy.ORIGINAL]
|
||||||
|
)
|
||||||
|
def test_fcf(strategy):
|
||||||
|
model = ModelManager(name="fcf", device="cpu")
|
||||||
|
cfg = get_config(strategy)
|
||||||
|
|
||||||
|
assert_equal(
|
||||||
|
model,
|
||||||
|
cfg,
|
||||||
|
f"fcf_{strategy.capitalize()}_result.png",
|
||||||
|
fx=2,
|
||||||
|
fy=2
|
||||||
|
)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user