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Package folder structure (#1417)
* Move files * Replace imports & paths * Update relative paths * Update doc symlinks * Update instructions paths * Fix imports * Update grpc files * Update more instructions * Downgrade grpc-tools * Update manifest * Update more paths * Update config paths * Update CI paths * Update bandit exclusions * Remove walkthrough section
This commit is contained in:
Simon Alibert
249
src/lerobot/datasets/transforms.py
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249
src/lerobot/datasets/transforms.py
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#!/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 dataclasses import dataclass, field
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from typing import Any, Callable, 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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self.selected_transforms = None
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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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self.selected_transforms = [self.transforms[i] for i in selected_indices]
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for transform in self.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"sharpness 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 make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
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sharpness_factor = torch.empty(1).uniform_(self.sharpness[0], self.sharpness[1]).item()
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return {"sharpness_factor": sharpness_factor}
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def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
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sharpness_factor = params["sharpness_factor"]
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return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
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@dataclass
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class ImageTransformConfig:
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"""
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For each transform, the following parameters are available:
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weight: This represents the multinomial probability (with no replacement)
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used for sampling the transform. If the sum of the weights is not 1,
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they will be normalized.
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type: The name of the class used. This is either a class available under torchvision.transforms.v2 or a
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custom transform defined here.
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kwargs: Lower & upper bound respectively used for sampling the transform's parameter
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(following uniform distribution) when it's applied.
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"""
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weight: float = 1.0
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type: str = "Identity"
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kwargs: dict[str, Any] = field(default_factory=dict)
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@dataclass
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class ImageTransformsConfig:
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"""
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These transforms are all using standard torchvision.transforms.v2
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You can find out how these transformations affect images here:
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https://pytorch.org/vision/0.18/auto_examples/transforms/plot_transforms_illustrations.html
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We use a custom RandomSubsetApply container to sample them.
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"""
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# Set this flag to `true` to enable transforms during training
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enable: bool = False
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# This is the maximum number of transforms (sampled from these below) that will be applied to each frame.
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# It's an integer in the interval [1, number_of_available_transforms].
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max_num_transforms: int = 3
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# By default, transforms are applied in Torchvision's suggested order (shown below).
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# Set this to True to apply them in a random order.
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random_order: bool = False
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tfs: dict[str, ImageTransformConfig] = field(
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default_factory=lambda: {
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"brightness": ImageTransformConfig(
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weight=1.0,
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type="ColorJitter",
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kwargs={"brightness": (0.8, 1.2)},
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),
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"contrast": ImageTransformConfig(
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weight=1.0,
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type="ColorJitter",
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kwargs={"contrast": (0.8, 1.2)},
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),
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"saturation": ImageTransformConfig(
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weight=1.0,
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type="ColorJitter",
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kwargs={"saturation": (0.5, 1.5)},
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),
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"hue": ImageTransformConfig(
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weight=1.0,
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type="ColorJitter",
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kwargs={"hue": (-0.05, 0.05)},
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),
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"sharpness": ImageTransformConfig(
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weight=1.0,
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type="SharpnessJitter",
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kwargs={"sharpness": (0.5, 1.5)},
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),
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}
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)
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def make_transform_from_config(cfg: ImageTransformConfig):
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if cfg.type == "Identity":
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return v2.Identity(**cfg.kwargs)
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elif cfg.type == "ColorJitter":
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return v2.ColorJitter(**cfg.kwargs)
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elif cfg.type == "SharpnessJitter":
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return SharpnessJitter(**cfg.kwargs)
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else:
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raise ValueError(f"Transform '{cfg.type}' is not valid.")
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class ImageTransforms(Transform):
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"""A class to compose image transforms based on configuration."""
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def __init__(self, cfg: ImageTransformsConfig) -> None:
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super().__init__()
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self._cfg = cfg
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self.weights = []
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self.transforms = {}
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for tf_name, tf_cfg in cfg.tfs.items():
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if tf_cfg.weight <= 0.0:
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continue
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self.transforms[tf_name] = make_transform_from_config(tf_cfg)
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self.weights.append(tf_cfg.weight)
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n_subset = min(len(self.transforms), cfg.max_num_transforms)
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if n_subset == 0 or not cfg.enable:
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self.tf = v2.Identity()
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else:
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self.tf = RandomSubsetApply(
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transforms=list(self.transforms.values()),
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p=self.weights,
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n_subset=n_subset,
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random_order=cfg.random_order,
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)
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def forward(self, *inputs: Any) -> Any:
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return self.tf(*inputs)
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