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JsonIndexDatasetProviderV2

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Summary: A new version of json index dataset provider supporting CO3Dv2

Reviewed By: shapovalov

Differential Revision: D37690918

fbshipit-source-id: bf2d5fc9d0f1220259e08661dafc69cdbe6b7f94
This commit is contained in:
David Novotny
2022-07-09 17:16:24 -07:00
committed by Facebook GitHub Bot
parent 4300030d7a
commit e8390d3500
5 changed files with 584 additions and 0 deletions
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42
pytorch3d/implicitron/dataset/json_index_dataset.py
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@@ -4,6 +4,7 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import copy
import functools
import gzip
import hashlib
@@ -255,6 +256,47 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
return dataset_idx
def subset_from_frame_index(
self,
frame_index: List[Union[Tuple[str, int], Tuple[str, int, str]]],
allow_missing_indices: bool = True,
) -> "JsonIndexDataset":
# Get the indices into the frame annots.
dataset_indices = self.seq_frame_index_to_dataset_index(
[frame_index],
allow_missing_indices=self.is_filtered() and allow_missing_indices,
)[0]
valid_dataset_indices = [i for i in dataset_indices if i is not None]
# Deep copy the whole dataset except frame_annots, which are large so we
# deep copy only the requested subset of frame_annots.
memo = {id(self.frame_annots): None} # pyre-ignore[16]
dataset_new = copy.deepcopy(self, memo)
dataset_new.frame_annots = copy.deepcopy(
[self.frame_annots[i] for i in valid_dataset_indices]
)
# This will kill all unneeded sequence annotations.
dataset_new._invalidate_indexes(filter_seq_annots=True)
# Finally annotate the frame annotations with the name of the subset
# stored in meta.
for frame_annot in dataset_new.frame_annots:
frame_annotation = frame_annot["frame_annotation"]
if frame_annotation.meta is not None:
frame_annot["subset"] = frame_annotation.meta.get("frame_type", None)
# A sanity check - this will crash in case some entries from frame_index are missing
# in dataset_new.
valid_frame_index = [
fi for fi, di in zip(frame_index, dataset_indices) if di is not None
]
dataset_new.seq_frame_index_to_dataset_index(
[valid_frame_index], allow_missing_indices=False
)
return dataset_new
def __str__(self) -> str:
# pyre-ignore[16]
return f"JsonIndexDataset #frames={len(self.frame_annots)}"

3
pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py
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@@ -92,6 +92,9 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase): # pyre-ignore [13]
to use for the dataset.
dataset_X_args (e.g. dataset_JsonIndexDataset_args): arguments passed
to all the dataset constructors.
path_manager_factory: (Optional) An object that generates an instance of
PathManager that can translate provided file paths.
path_manager_factory_class_type: The class type of `path_manager_factory`.
"""
category: str

343
pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py Normal file
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@@ -0,0 +1,343 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
import os
import warnings
from typing import Dict, List, Type
from pytorch3d.implicitron.dataset.dataset_map_provider import (
DatasetMap,
DatasetMapProviderBase,
PathManagerFactory,
)
from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
from pytorch3d.implicitron.tools.config import (
expand_args_fields,
registry,
run_auto_creation,
)
_CO3DV2_DATASET_ROOT: str = os.getenv("CO3DV2_DATASET_ROOT", "")
logger = logging.getLogger(__name__)
@registry.register
class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase): # pyre-ignore [13]
"""
Generates the training, validation, and testing dataset objects for
a dataset laid out on disk like CO3Dv2, with annotations in gzipped json files.
The dataset is organized in the filesystem as follows:
```
self.dataset_root
├── <category_0>
│ ├── <sequence_name_0>
│ │ ├── depth_masks
│ │ ├── depths
│ │ ├── images
│ │ ├── masks
│ │ └── pointcloud.ply
│ ├── <sequence_name_1>
│ │ ├── depth_masks
│ │ ├── depths
│ │ ├── images
│ │ ├── masks
│ │ └── pointcloud.ply
│ ├── ...
│ ├── <sequence_name_N>
│ ├── set_lists
│ ├── set_lists_<subset_name_0>.json
│ ├── set_lists_<subset_name_1>.json
│ ├── ...
│ ├── set_lists_<subset_name_M>.json
│ ├── eval_batches
│ │ ├── eval_batches_<subset_name_0>.json
│ │ ├── eval_batches_<subset_name_1>.json
│ │ ├── ...
│ │ ├── eval_batches_<subset_name_M>.json
│ ├── frame_annotations.jgz
│ ├── sequence_annotations.jgz
├── <category_1>
├── ...
├── <category_K>
```
The dataset contains sequences named `<sequence_name_i>` from `K` categories with
names `<category_j>`. Each category comprises sequence folders
`<category_k>/<sequence_name_i>` containing the list of sequence images, depth maps,
foreground masks, and valid-depth masks `images`, `depths`, `masks`, and `depth_masks`
respectively. Furthermore, `<category_k>/<sequence_name_i>/set_lists/` stores `M`
json files `set_lists_<subset_name_l>.json`, each describing a certain sequence subset.
Users specify the loaded dataset subset by setting `self.subset_name` to one of the
available subset names `<subset_name_l>`.
`frame_annotations.jgz` and `sequence_annotations.jgz` are gzipped json files containing
the list of all frames and sequences of the given category stored as lists of
`FrameAnnotation` and `SequenceAnnotation` objects respectivelly.
Each `set_lists_<subset_name_l>.json` file contains the following dictionary:
```
{
"train": [
(sequence_name: str, frame_number: int, image_path: str),
...
],
"val": [
(sequence_name: str, frame_number: int, image_path: str),
...
],
"test": [
(sequence_name: str, frame_number: int, image_path: str),
...
],
]
```
defining the list of frames (identified with their `sequence_name` and `frame_number`)
in the "train", "val", and "test" subsets of the dataset.
Note that `frame_number` can be obtained only from `frame_annotations.jgz` and
does not necesarrily correspond to the numeric suffix of the corresponding image
file name (e.g. a file `<category_0>/<sequence_name_0>/images/frame00005.jpg` can
have its frame number set to `20`, not 5).
Each `eval_batches_<subset_name_l>.json` file contains a list of evaluation examples
in the following form:
```
[
[ # batch 1
(sequence_name: str, frame_number: int, image_path: str),
...
],
[ # batch 1
(sequence_name: str, frame_number: int, image_path: str),
...
],
]
```
Note that the evaluation examples always come from the `"test"` subset of the dataset.
(test frames can repeat across batches).
Args:
category: The object category of the dataset.
subset_name: The name of the dataset subset. For CO3Dv2, these include
e.g. "manyview_dev_0", "fewview_test", ...
dataset_root: The root folder of the dataset.
test_on_train: Construct validation and test datasets from
the training subset.
only_test_set: Load only the test set. Incompatible with `test_on_train`.
load_eval_batches: Load the file containing eval batches pointing to the
test dataset.
dataset_args: Specifies additional arguments to the
JsonIndexDataset constructor call.
path_manager_factory: (Optional) An object that generates an instance of
PathManager that can translate provided file paths.
path_manager_factory_class_type: The class type of `path_manager_factory`.
"""
category: str
subset_name: str
dataset_root: str = _CO3DV2_DATASET_ROOT
test_on_train: bool = False
only_test_set: bool = False
load_eval_batches: bool = True
dataset_class_type: str = "JsonIndexDataset"
dataset: JsonIndexDataset
path_manager_factory: PathManagerFactory
path_manager_factory_class_type: str = "PathManagerFactory"
def __post_init__(self):
super().__init__()
run_auto_creation(self)
if self.only_test_set and self.test_on_train:
raise ValueError("Cannot have only_test_set and test_on_train")
frame_file = os.path.join(
self.dataset_root, self.category, "frame_annotations.jgz"
)
sequence_file = os.path.join(
self.dataset_root, self.category, "sequence_annotations.jgz"
)
path_manager = self.path_manager_factory.get()
# setup the common dataset arguments
common_dataset_kwargs = getattr(self, f"dataset_{self.dataset_class_type}_args")
common_dataset_kwargs = {
**common_dataset_kwargs,
"dataset_root": self.dataset_root,
"frame_annotations_file": frame_file,
"sequence_annotations_file": sequence_file,
"subsets": None,
"subset_lists_file": "",
"path_manager": path_manager,
}
# get the used dataset type
dataset_type: Type[JsonIndexDataset] = registry.get(
JsonIndexDataset, self.dataset_class_type
)
expand_args_fields(dataset_type)
dataset = dataset_type(**common_dataset_kwargs)
available_subset_names = self._get_available_subset_names()
logger.debug(f"Available subset names: {str(available_subset_names)}.")
if self.subset_name not in available_subset_names:
raise ValueError(
f"Unknown subset name {self.subset_name}."
+ f" Choose one of available subsets: {str(available_subset_names)}."
)
# load the list of train/val/test frames
subset_mapping = self._load_annotation_json(
os.path.join(
self.category, "set_lists", f"set_lists_{self.subset_name}.json"
)
)
# load the evaluation batches
if self.load_eval_batches:
eval_batch_index = self._load_annotation_json(
os.path.join(
self.category,
"eval_batches",
f"eval_batches_{self.subset_name}.json",
)
)
train_dataset = None
if not self.only_test_set:
# load the training set
logger.debug("Extracting train dataset.")
train_dataset = dataset.subset_from_frame_index(subset_mapping["train"])
logger.info(f"Train dataset: {str(train_dataset)}")
if self.test_on_train:
assert train_dataset is not None
val_dataset = test_dataset = train_dataset
else:
# load the val and test sets
logger.debug("Extracting val dataset.")
val_dataset = dataset.subset_from_frame_index(subset_mapping["val"])
logger.info(f"Val dataset: {str(val_dataset)}")
logger.debug("Extracting test dataset.")
test_dataset = dataset.subset_from_frame_index(subset_mapping["test"])
logger.info(f"Test dataset: {str(test_dataset)}")
if self.load_eval_batches:
# load the eval batches
logger.debug("Extracting eval batches.")
try:
test_dataset.eval_batches = (
test_dataset.seq_frame_index_to_dataset_index(
eval_batch_index,
)
)
except IndexError:
warnings.warn(
"@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n"
+ "Some eval batches are missing from the test dataset.\n"
+ "The evaluation results will be incomparable to the\n"
+ "evaluation results calculated on the original dataset.\n"
+ "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@"
)
test_dataset.eval_batches = (
test_dataset.seq_frame_index_to_dataset_index(
eval_batch_index,
allow_missing_indices=True,
remove_missing_indices=True,
)
)
logger.info(f"# eval batches: {len(test_dataset.eval_batches)}")
self.dataset_map = DatasetMap(
train=train_dataset, val=val_dataset, test=test_dataset
)
def create_dataset(self):
# The dataset object is created inside `self.get_dataset_map`
pass
def get_dataset_map(self) -> DatasetMap:
return self.dataset_map # pyre-ignore [16]
def get_category_to_subset_name_list(self) -> Dict[str, List[str]]:
"""
Returns a global dataset index containing the available subset names per category
as a dictionary.
Returns:
category_to_subset_name_list: A dictionary containing subset names available
per category of the following form:
```
{
category_0: [category_0_subset_name_0, category_0_subset_name_1, ...],
category_1: [category_1_subset_name_0, category_1_subset_name_1, ...],
...
}
```
"""
category_to_subset_name_list_json = "category_to_subset_name_list.json"
category_to_subset_name_list = self._load_annotation_json(
category_to_subset_name_list_json
)
return category_to_subset_name_list
def _load_annotation_json(self, json_filename: str):
full_path = os.path.join(
self.dataset_root,
json_filename,
)
logger.info(f"Loading frame index json from {full_path}.")
path_manager = self.path_manager_factory.get()
if path_manager is not None:
full_path = path_manager.get_local_path(full_path)
if not os.path.isfile(full_path):
# The batch indices file does not exist.
# Most probably the user has not specified the root folder.
raise ValueError(
f"Looking for dataset json file in {full_path}. "
+ "Please specify a correct dataset_root folder."
)
with open(full_path, "r") as f:
data = json.load(f)
return data
def _get_available_subset_names(self):
path_manager = self.path_manager_factory.get()
if path_manager is not None:
dataset_root = path_manager.get_local_path(self.dataset_root)
else:
dataset_root = self.dataset_root
return get_available_subset_names(dataset_root, self.category)
def get_available_subset_names(dataset_root: str, category: str) -> List[str]:
"""
Get the available subset names for a given category folder inside a root dataset
folder `dataset_root`.
"""
category_dir = os.path.join(dataset_root, category)
if not os.path.isdir(category_dir):
raise ValueError(
f"Looking for dataset files in {category_dir}. "
+ "Please specify a correct dataset_root folder."
)
set_list_jsons = os.listdir(os.path.join(category_dir, "set_lists"))
return [
json_file.replace("set_lists_", "").replace(".json", "")
for json_file in set_list_jsons
]