198 lines
8.0 KiB
Python
198 lines
8.0 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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from typing import Any, Callable, Dict, Sequence
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import torch
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from torchvision.transforms import v2
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from torchvision.transforms.v2 import Transform
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from torchvision.transforms.v2 import functional as F # noqa: N812
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class RandomSubsetApply(Transform):
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"""Apply a random subset of N transformations from a list of transformations.
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Args:
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transforms: list of transformations.
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p: represents the multinomial probabilities (with no replacement) used for sampling the transform.
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If the sum of the weights is not 1, they will be normalized. If ``None`` (default), all transforms
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have the same probability.
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n_subset: number of transformations to apply. If ``None``, all transforms are applied.
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Must be in [1, len(transforms)].
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random_order: apply transformations in a random order.
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"""
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def __init__(
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self,
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transforms: Sequence[Callable],
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p: list[float] | None = None,
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n_subset: int | None = None,
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random_order: bool = False,
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) -> None:
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super().__init__()
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if not isinstance(transforms, Sequence):
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raise TypeError("Argument transforms should be a sequence of callables")
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if p is None:
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p = [1] * len(transforms)
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elif len(p) != len(transforms):
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raise ValueError(
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f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
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)
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if n_subset is None:
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n_subset = len(transforms)
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elif not isinstance(n_subset, int):
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raise TypeError("n_subset should be an int or None")
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elif not (1 <= n_subset <= len(transforms)):
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raise ValueError(f"n_subset should be in the interval [1, {len(transforms)}]")
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self.transforms = transforms
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total = sum(p)
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self.p = [prob / total for prob in p]
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self.n_subset = n_subset
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self.random_order = random_order
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def forward(self, *inputs: Any) -> Any:
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needs_unpacking = len(inputs) > 1
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selected_indices = torch.multinomial(torch.tensor(self.p), self.n_subset)
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if not self.random_order:
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selected_indices = selected_indices.sort().values
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selected_transforms = [self.transforms[i] for i in selected_indices]
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for transform in selected_transforms:
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outputs = transform(*inputs)
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inputs = outputs if needs_unpacking else (outputs,)
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return outputs
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def extra_repr(self) -> str:
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return (
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f"transforms={self.transforms}, "
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f"p={self.p}, "
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f"n_subset={self.n_subset}, "
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f"random_order={self.random_order}"
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)
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class SharpnessJitter(Transform):
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"""Randomly change the sharpness of an image or video.
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Similar to a v2.RandomAdjustSharpness with p=1 and a sharpness_factor sampled randomly.
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While v2.RandomAdjustSharpness applies — with a given probability — a fixed sharpness_factor to an image,
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SharpnessJitter applies a random sharpness_factor each time. This is to have a more diverse set of
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augmentations as a result.
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A sharpness_factor of 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness
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by a factor of 2.
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If the input is a :class:`torch.Tensor`,
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it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
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Args:
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sharpness: How much to jitter sharpness. sharpness_factor is chosen uniformly from
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[max(0, 1 - sharpness), 1 + sharpness] or the given
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[min, max]. Should be non negative numbers.
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"""
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def __init__(self, sharpness: float | Sequence[float]) -> None:
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super().__init__()
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self.sharpness = self._check_input(sharpness)
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def _check_input(self, sharpness):
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if isinstance(sharpness, (int, float)):
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if sharpness < 0:
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raise ValueError("If sharpness is a single number, it must be non negative.")
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sharpness = [1.0 - sharpness, 1.0 + sharpness]
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sharpness[0] = max(sharpness[0], 0.0)
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elif isinstance(sharpness, collections.abc.Sequence) and len(sharpness) == 2:
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sharpness = [float(v) for v in sharpness]
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else:
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raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.")
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if not 0.0 <= sharpness[0] <= sharpness[1]:
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raise ValueError(f"sharpnesss values should be between (0., inf), but got {sharpness}.")
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return float(sharpness[0]), float(sharpness[1])
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def _generate_value(self, left: float, right: float) -> float:
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return torch.empty(1).uniform_(left, right).item()
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def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
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sharpness_factor = self._generate_value(self.sharpness[0], self.sharpness[1])
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return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
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def get_image_transforms(
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brightness_weight: float = 1.0,
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brightness_min_max: tuple[float, float] | None = None,
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contrast_weight: float = 1.0,
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contrast_min_max: tuple[float, float] | None = None,
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saturation_weight: float = 1.0,
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saturation_min_max: tuple[float, float] | None = None,
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hue_weight: float = 1.0,
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hue_min_max: tuple[float, float] | None = None,
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sharpness_weight: float = 1.0,
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sharpness_min_max: tuple[float, float] | None = None,
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max_num_transforms: int | None = None,
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random_order: bool = False,
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):
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def check_value(name, weight, min_max):
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if min_max is not None:
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if len(min_max) != 2:
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raise ValueError(
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f"`{name}_min_max` is expected to be a tuple of 2 dimensions, but {min_max} provided."
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)
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if weight < 0.0:
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raise ValueError(
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f"`{name}_weight` is expected to be 0 or positive, but is negative ({weight})."
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)
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check_value("brightness", brightness_weight, brightness_min_max)
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check_value("contrast", contrast_weight, contrast_min_max)
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check_value("saturation", saturation_weight, saturation_min_max)
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check_value("hue", hue_weight, hue_min_max)
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check_value("sharpness", sharpness_weight, sharpness_min_max)
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weights = []
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transforms = []
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if brightness_min_max is not None and brightness_weight > 0.0:
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weights.append(brightness_weight)
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transforms.append(v2.ColorJitter(brightness=brightness_min_max))
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if contrast_min_max is not None and contrast_weight > 0.0:
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weights.append(contrast_weight)
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transforms.append(v2.ColorJitter(contrast=contrast_min_max))
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if saturation_min_max is not None and saturation_weight > 0.0:
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weights.append(saturation_weight)
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transforms.append(v2.ColorJitter(saturation=saturation_min_max))
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if hue_min_max is not None and hue_weight > 0.0:
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weights.append(hue_weight)
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transforms.append(v2.ColorJitter(hue=hue_min_max))
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if sharpness_min_max is not None and sharpness_weight > 0.0:
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weights.append(sharpness_weight)
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transforms.append(SharpnessJitter(sharpness=sharpness_min_max))
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n_subset = len(transforms)
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if max_num_transforms is not None:
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n_subset = min(n_subset, max_num_transforms)
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if n_subset == 0:
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return v2.Identity()
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else:
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# TODO(rcadene, aliberts): add v2.ToDtype float16?
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return RandomSubsetApply(transforms, p=weights, n_subset=n_subset, random_order=random_order)
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