forked from tangger/lerobot
Add data augmentation in LeRobotDataset (#234)
Co-authored-by: Simon Alibert <alibert.sim@gmail.com> Co-authored-by: Remi Cadene <re.cadene@gmail.com>
This commit is contained in:
Marina Barannikov
committed by
GitHub
GitHub
parent
1cf050d412
commit
ff8f6aa6cd
197
lerobot/common/datasets/transforms.py
Normal file
197
lerobot/common/datasets/transforms.py
Normal file
@@ -0,0 +1,197 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import collections
|
||||
from typing import Any, Callable, Dict, Sequence
|
||||
|
||||
import torch
|
||||
from torchvision.transforms import v2
|
||||
from torchvision.transforms.v2 import Transform
|
||||
from torchvision.transforms.v2 import functional as F # noqa: N812
|
||||
|
||||
|
||||
class RandomSubsetApply(Transform):
|
||||
"""Apply a random subset of N transformations from a list of transformations.
|
||||
|
||||
Args:
|
||||
transforms: list of transformations.
|
||||
p: represents the multinomial probabilities (with no replacement) used for sampling the transform.
|
||||
If the sum of the weights is not 1, they will be normalized. If ``None`` (default), all transforms
|
||||
have the same probability.
|
||||
n_subset: number of transformations to apply. If ``None``, all transforms are applied.
|
||||
Must be in [1, len(transforms)].
|
||||
random_order: apply transformations in a random order.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
transforms: Sequence[Callable],
|
||||
p: list[float] | None = None,
|
||||
n_subset: int | None = None,
|
||||
random_order: bool = False,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
if not isinstance(transforms, Sequence):
|
||||
raise TypeError("Argument transforms should be a sequence of callables")
|
||||
if p is None:
|
||||
p = [1] * len(transforms)
|
||||
elif len(p) != len(transforms):
|
||||
raise ValueError(
|
||||
f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
|
||||
)
|
||||
|
||||
if n_subset is None:
|
||||
n_subset = len(transforms)
|
||||
elif not isinstance(n_subset, int):
|
||||
raise TypeError("n_subset should be an int or None")
|
||||
elif not (1 <= n_subset <= len(transforms)):
|
||||
raise ValueError(f"n_subset should be in the interval [1, {len(transforms)}]")
|
||||
|
||||
self.transforms = transforms
|
||||
total = sum(p)
|
||||
self.p = [prob / total for prob in p]
|
||||
self.n_subset = n_subset
|
||||
self.random_order = random_order
|
||||
|
||||
def forward(self, *inputs: Any) -> Any:
|
||||
needs_unpacking = len(inputs) > 1
|
||||
|
||||
selected_indices = torch.multinomial(torch.tensor(self.p), self.n_subset)
|
||||
if not self.random_order:
|
||||
selected_indices = selected_indices.sort().values
|
||||
|
||||
selected_transforms = [self.transforms[i] for i in selected_indices]
|
||||
|
||||
for transform in selected_transforms:
|
||||
outputs = transform(*inputs)
|
||||
inputs = outputs if needs_unpacking else (outputs,)
|
||||
|
||||
return outputs
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return (
|
||||
f"transforms={self.transforms}, "
|
||||
f"p={self.p}, "
|
||||
f"n_subset={self.n_subset}, "
|
||||
f"random_order={self.random_order}"
|
||||
)
|
||||
|
||||
|
||||
class SharpnessJitter(Transform):
|
||||
"""Randomly change the sharpness of an image or video.
|
||||
|
||||
Similar to a v2.RandomAdjustSharpness with p=1 and a sharpness_factor sampled randomly.
|
||||
While v2.RandomAdjustSharpness applies — with a given probability — a fixed sharpness_factor to an image,
|
||||
SharpnessJitter applies a random sharpness_factor each time. This is to have a more diverse set of
|
||||
augmentations as a result.
|
||||
|
||||
A sharpness_factor of 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness
|
||||
by a factor of 2.
|
||||
|
||||
If the input is a :class:`torch.Tensor`,
|
||||
it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
|
||||
|
||||
Args:
|
||||
sharpness: How much to jitter sharpness. sharpness_factor is chosen uniformly from
|
||||
[max(0, 1 - sharpness), 1 + sharpness] or the given
|
||||
[min, max]. Should be non negative numbers.
|
||||
"""
|
||||
|
||||
def __init__(self, sharpness: float | Sequence[float]) -> None:
|
||||
super().__init__()
|
||||
self.sharpness = self._check_input(sharpness)
|
||||
|
||||
def _check_input(self, sharpness):
|
||||
if isinstance(sharpness, (int, float)):
|
||||
if sharpness < 0:
|
||||
raise ValueError("If sharpness is a single number, it must be non negative.")
|
||||
sharpness = [1.0 - sharpness, 1.0 + sharpness]
|
||||
sharpness[0] = max(sharpness[0], 0.0)
|
||||
elif isinstance(sharpness, collections.abc.Sequence) and len(sharpness) == 2:
|
||||
sharpness = [float(v) for v in sharpness]
|
||||
else:
|
||||
raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.")
|
||||
|
||||
if not 0.0 <= sharpness[0] <= sharpness[1]:
|
||||
raise ValueError(f"sharpnesss values should be between (0., inf), but got {sharpness}.")
|
||||
|
||||
return float(sharpness[0]), float(sharpness[1])
|
||||
|
||||
def _generate_value(self, left: float, right: float) -> float:
|
||||
return torch.empty(1).uniform_(left, right).item()
|
||||
|
||||
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
|
||||
sharpness_factor = self._generate_value(self.sharpness[0], self.sharpness[1])
|
||||
return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
|
||||
|
||||
|
||||
def get_image_transforms(
|
||||
brightness_weight: float = 1.0,
|
||||
brightness_min_max: tuple[float, float] | None = None,
|
||||
contrast_weight: float = 1.0,
|
||||
contrast_min_max: tuple[float, float] | None = None,
|
||||
saturation_weight: float = 1.0,
|
||||
saturation_min_max: tuple[float, float] | None = None,
|
||||
hue_weight: float = 1.0,
|
||||
hue_min_max: tuple[float, float] | None = None,
|
||||
sharpness_weight: float = 1.0,
|
||||
sharpness_min_max: tuple[float, float] | None = None,
|
||||
max_num_transforms: int | None = None,
|
||||
random_order: bool = False,
|
||||
):
|
||||
def check_value(name, weight, min_max):
|
||||
if min_max is not None:
|
||||
if len(min_max) != 2:
|
||||
raise ValueError(
|
||||
f"`{name}_min_max` is expected to be a tuple of 2 dimensions, but {min_max} provided."
|
||||
)
|
||||
if weight < 0.0:
|
||||
raise ValueError(
|
||||
f"`{name}_weight` is expected to be 0 or positive, but is negative ({weight})."
|
||||
)
|
||||
|
||||
check_value("brightness", brightness_weight, brightness_min_max)
|
||||
check_value("contrast", contrast_weight, contrast_min_max)
|
||||
check_value("saturation", saturation_weight, saturation_min_max)
|
||||
check_value("hue", hue_weight, hue_min_max)
|
||||
check_value("sharpness", sharpness_weight, sharpness_min_max)
|
||||
|
||||
weights = []
|
||||
transforms = []
|
||||
if brightness_min_max is not None and brightness_weight > 0.0:
|
||||
weights.append(brightness_weight)
|
||||
transforms.append(v2.ColorJitter(brightness=brightness_min_max))
|
||||
if contrast_min_max is not None and contrast_weight > 0.0:
|
||||
weights.append(contrast_weight)
|
||||
transforms.append(v2.ColorJitter(contrast=contrast_min_max))
|
||||
if saturation_min_max is not None and saturation_weight > 0.0:
|
||||
weights.append(saturation_weight)
|
||||
transforms.append(v2.ColorJitter(saturation=saturation_min_max))
|
||||
if hue_min_max is not None and hue_weight > 0.0:
|
||||
weights.append(hue_weight)
|
||||
transforms.append(v2.ColorJitter(hue=hue_min_max))
|
||||
if sharpness_min_max is not None and sharpness_weight > 0.0:
|
||||
weights.append(sharpness_weight)
|
||||
transforms.append(SharpnessJitter(sharpness=sharpness_min_max))
|
||||
|
||||
n_subset = len(transforms)
|
||||
if max_num_transforms is not None:
|
||||
n_subset = min(n_subset, max_num_transforms)
|
||||
|
||||
if n_subset == 0:
|
||||
return v2.Identity()
|
||||
else:
|
||||
# TODO(rcadene, aliberts): add v2.ToDtype float16?
|
||||
return RandomSubsetApply(transforms, p=weights, n_subset=n_subset, random_order=random_order)
|
||||
Reference in New Issue
Block a user