################################################################################
# Copyright (c) 2021 ContinualAI. #
# Copyrights licensed under the MIT License. #
# See the accompanying LICENSE file for terms. #
# #
# Date: 28-06-2021 #
# Author: Timm Hess #
# E-mail: hess@ccc.cs.uni-frankfurt.de #
# Website: www.continualai.org #
################################################################################
"""
This module contains the high-level EndlessCLSim scenario
generator. It returns an iterable scenario object
``GenericCLScenario`` given a number of configuration parameters.
"""
from avalanche.benchmarks.datasets.endless_cl_sim.endless_cl_sim import (
EndlessCLSimDataset,
)
from pathlib import Path
from typing import List, Union, Optional, Any
from torchvision.transforms import ToTensor
from torchvision.transforms.transforms import Compose
from avalanche.benchmarks.datasets import default_dataset_location
from avalanche.benchmarks.scenarios.deprecated.generators import dataset_benchmark
from avalanche.benchmarks.utils import _make_taskaware_classification_dataset
_default_transform = Compose([ToTensor()])
_scenario_names = ["Classes", "Illumination", "Weather"]
[docs]def EndlessCLSim(
*,
scenario: str = _scenario_names[0],
patch_size: int = 64,
sequence_order: Optional[List[int]] = None,
task_order: Optional[List[int]] = None,
train_transform: Optional[Any] = _default_transform,
eval_transform: Optional[Any] = _default_transform,
dataset_root: Optional[Union[str, Path]] = None,
semseg=False
):
"""Creates a CL scenario for the Endless-Continual-Learning Simulator's
derived `datasets <https://zenodo.org/record/4899267>`__, or custom
datasets created from
the Endless-Continual-Learning-Simulator's `standalone application <
https://zenodo.org/record/4899294>`__.
Both are part of the publication of `A Procedural World Generation
Framework for Systematic Evaluation of Continual Learning
<https://arxiv.org/abs/2106.02585>`__.
If the dataset is not present in the computer, this method will
automatically download and store it.
All generated scenarios make use of 'task labels'. We regard a full dataset
as one learning 'sequence', aligned to the terminology in the above paper,
with 'subsequences' being the iterative learning tasks. Each subsequence
is realized as one `AvalancheDataset` with ordering inforaced by task
labels.
:param scenario: Available, predefined, learning scenarios are:
'Classes': An learning scenario based on incremental availability of
object class examples,
'Illumination': A learning scenario based on iteratively decreasing
scene illumination.
'Weather': A learning scenario based on iteratively shifting weather
conditions.
:param patch_size: The dimension of the image-patches. Int in the case of
image-patch classification, because the image-patches need to be
quadratic. Tuple of integers for image segmentation tasks.
:param sequence_order: List of intergers indexing the subsequences,
enables reordering of the subsequences, especially subsequences can
be omitted. Defaults to None, loading subsequences in their
original order.
:param task_order: List of intergers, assigning task labels to each
respective subsequence.
:param train_transform: The transformation to apply to the training data.
Defaults to `_default_transform`, i.e. conversion ToTensor of
torchvision.
:param eval_transform: The transformation to apply to the eval data.
Defaults to `_default_transform`, i.e. conversion ToTensor of
torchvision.
:param dataset_root: Absolute path indicating where to store the dataset.
Defaults to None, which means the default location for
'endless-cl-sim' will be used.
:param semseg: boolean to indicate the use of targets for a semantic
segmentation task. Defaults to False.
:returns: A properly initialized :class:`EndlessCLSim` instance.
"""
# Check scenario name is valid
assert scenario in _scenario_names, (
"The selected scenario is not "
"recognized: it should be "
"'Classes', 'Illumination', "
"or 'Weather'."
)
# Assign default dataset root if None provided
if dataset_root is None:
dataset_root = default_dataset_location("endless-cl-sim")
# Download and prepare the dataset
endless_cl_sim_dataset = EndlessCLSimDataset(
root=dataset_root,
scenario=scenario,
transform=None,
download=True,
semseg=semseg,
)
# Default sequence_order if None
if sequence_order is None:
sequence_order = list(range(len(endless_cl_sim_dataset)))
# Default sequence_order if None
if task_order is None:
task_order = list(range(len(endless_cl_sim_dataset)))
train_datasets = []
eval_datasets = []
for i in range(len(sequence_order)):
train_data, eval_data = endless_cl_sim_dataset[sequence_order[i]]
train_data.transform = train_transform
eval_data.transform = eval_transform
train_datasets.append(
_make_taskaware_classification_dataset(
dataset=train_data, task_labels=task_order[i]
)
)
eval_datasets.append(
_make_taskaware_classification_dataset(
dataset=eval_data, task_labels=task_order[i]
)
)
scenario_obj = dataset_benchmark(train_datasets, eval_datasets)
return scenario_obj
__all__ = ["EndlessCLSim"]
if __name__ == "__main__":
from torch.utils.data.dataloader import DataLoader
from torchvision.transforms import ToPILImage, ToTensor
import matplotlib.pyplot as plt
scenario_obj = EndlessCLSim(
scenario="Classes",
sequence_order=[0, 1, 2, 3],
task_order=[0, 1, 2, 3],
semseg=True,
dataset_root="/data/avalanche",
)
# FIXME: check_vision_benchmark function is crashing -> this is not..
# check_vision_benchmark(scenario_obj)
print(
"The benchmark instance contains",
len(scenario_obj.train_stream),
"training experiences.",
)
for i, exp in enumerate(scenario_obj.train_stream):
dataset, t = exp.dataset, exp.task_label
print(dataset, t)
print(len(dataset))
dataloader = DataLoader(dataset, batch_size=300)
print("Train experience", exp.current_experience)
for batch in dataloader:
x, y, *other = batch
print("X tensor:", x.shape)
print("Y tensor:", y.shape)
if len(other) > 0:
print("T tensor:", other[0].shape)
img = ToPILImage()(x[0])
plt.title("Experience: " + str(exp.current_experience))
plt.imshow(img)
# plt.show()
break
print("Done..")