From 7eb5b344fe36e64185af9ac0aac92040d9b5dd94 Mon Sep 17 00:00:00 2001 From: Vladimir Iglovikov Date: Thu, 15 Feb 2024 17:51:11 -0800 Subject: [PATCH] Fix serialization tensor v2 tests (#1519) * Updated json outputs for totensor serialization * Updated serializtion to work with file buffers as well as with file paths * Cleanup * Fix in CI --- .github/workflows/ci.yml | 4 +- .gitignore | 4 + .pre-commit-config.yaml | 1 - .../augmentations/geometric/functional.py | 4 +- albumentations/core/serialization.py | 105 ++++++++++------ black.toml | 25 ---- requirements-dev.txt | 1 + tests/conftest.py | 4 +- tests/files/output_v1.1.0_with_totensor.json | 1 - .../files/output_v1.1.0_without_totensor.json | 1 - tests/test_serialization.py | 113 +++++++++--------- 11 files changed, 137 insertions(+), 126 deletions(-) delete mode 100644 black.toml delete mode 100644 tests/files/output_v1.1.0_with_totensor.json delete mode 100644 tests/files/output_v1.1.0_without_totensor.json diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 15b6c9269..bee9d11bb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -35,6 +35,8 @@ jobs: - name: Install PyTorch on MacOS if: matrix.operating-system == 'macos-latest' run: pip install torch==2.2.0 torchvision==0.17.0 + - name: Install dev requirements + run: pip install -r requirements-dev.txt - name: Install dependencies run: | pip install .[tests] @@ -61,7 +63,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: Update pip run: python -m pip install --upgrade pip - - name: Install linters + - name: Install dev requirements run: pip install -r requirements-dev.txt - name: Run checks run: pre-commit run --files $(find albumentations -type f) diff --git a/.gitignore b/.gitignore index a2c3382b1..4958f46a6 100644 --- a/.gitignore +++ b/.gitignore @@ -161,3 +161,7 @@ conda_build/ .vscode/ conda.recipe/ + +.gitingore + +*.ipynb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f44439095..8f4ec5b20 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -30,7 +30,6 @@ repos: - id: mixed-line-ending - id: destroyed-symlinks - id: fix-byte-order-marker - - id: pretty-format-json - id: check-json - id: check-yaml args: [ --unsafe ] diff --git a/albumentations/augmentations/geometric/functional.py b/albumentations/augmentations/geometric/functional.py index f02af7e17..6a32d43b5 100644 --- a/albumentations/augmentations/geometric/functional.py +++ b/albumentations/augmentations/geometric/functional.py @@ -968,9 +968,7 @@ def bbox_flip(bbox: BoxInternalType, d: int, rows: int, cols: int) -> BoxInterna return bbox -def bbox_transpose( - bbox: KeypointInternalType, axis: int, rows: int, cols: int -) -> KeypointInternalType: # skipcq: PYL-W0613 +def bbox_transpose(bbox: KeypointInternalType, axis: int, rows: int, cols: int) -> KeypointInternalType: """Transposes a bounding box along given axis. Args: diff --git a/albumentations/core/serialization.py b/albumentations/core/serialization.py index 8cdcb2da9..974b424c8 100644 --- a/albumentations/core/serialization.py +++ b/albumentations/core/serialization.py @@ -1,8 +1,8 @@ import json -import typing import warnings from abc import ABC, ABCMeta, abstractmethod -from typing import Any, Dict, Optional, Tuple, Type, Union +from pathlib import Path +from typing import Any, Dict, Optional, TextIO, Tuple, Type, Union, cast try: import yaml @@ -146,7 +146,7 @@ def from_dict( ) -> Optional[Serializable]: """ Args: - transform_dict (dict): A dictionary with serialized transform pipeline. + transform_dict: A dictionary with serialized transform pipeline. nonserializable (dict): A dictionary that contains non-serializable transforms. This dictionary is required when you are restoring a pipeline that contains non-serializable transforms. Keys in that dictionary should be named same as `name` arguments in respective transforms from @@ -155,7 +155,7 @@ def from_dict( """ if lambda_transforms != "deprecated": warnings.warn("lambda_transforms argument is deprecated, please use 'nonserializable'", DeprecationWarning) - nonserializable = typing.cast(Optional[Dict[str, Any]], lambda_transforms) + nonserializable = cast(Optional[Dict[str, Any]], lambda_transforms) register_additional_transforms() transform = transform_dict["transform"] @@ -176,57 +176,94 @@ def check_data_format(data_format: str) -> None: def save( - transform: Serializable, filepath: str, data_format: str = "json", on_not_implemented_error: str = "raise" + transform: "Serializable", + filepath_or_buffer: Union[str, Path, TextIO], + data_format: str = "json", + on_not_implemented_error: str = "raise", ) -> None: """ - Take a transform pipeline, serialize it and save a serialized version to a file - using either json or yaml format. + Serialize a transform pipeline and save it to either a file specified by a path or a file-like object + in either JSON or YAML format. Args: - transform (obj): Transform to serialize. - filepath (str): Filepath to write to. - data_format (str): Serialization format. Should be either `json` or 'yaml'. - on_not_implemented_error (str): Parameter that describes what to do if a transform doesn't implement - the `to_dict` method. If 'raise' then `NotImplementedError` is raised, if `warn` then the exception will be - ignored and no transform arguments will be saved. + transform (Serializable): The transform pipeline to serialize. + filepath_or_buffer (Union[str, Path, TextIO]): The file path or file-like object to write the serialized + data to. + If a string is provided, it is interpreted as a path to a file. If a file-like object is provided, + the serialized data will be written to it directly. + data_format (str): The format to serialize the data in. Valid options are 'json' and 'yaml'. + Defaults to 'json'. + on_not_implemented_error (str): Determines the behavior if a transform does not implement the `to_dict` method. + If set to 'raise', a `NotImplementedError` is raised. If set to 'warn', the exception is ignored, and + no transform arguments are saved. Defaults to 'raise'. + + Raises: + ValueError: If `data_format` is 'yaml' but PyYAML is not installed. """ check_data_format(data_format) transform_dict = transform.to_dict(on_not_implemented_error=on_not_implemented_error) - with open(filepath, "w") as f: + + # Determine whether to write to a file or a file-like object + if isinstance(filepath_or_buffer, (str, Path)): # It's a filepath + with open(filepath_or_buffer, "w") as f: + if data_format == "yaml": + if not yaml_available: + raise ValueError("You need to install PyYAML to save a pipeline in YAML format") + yaml.safe_dump(transform_dict, f, default_flow_style=False) + elif data_format == "json": + json.dump(transform_dict, f) + else: # Assume it's a file-like object if data_format == "yaml": if not yaml_available: - raise ValueError("You need to install PyYAML to save a pipeline in yaml format") - yaml.safe_dump(transform_dict, f, default_flow_style=False) + raise ValueError("You need to install PyYAML to save a pipeline in YAML format") + yaml.safe_dump(transform_dict, filepath_or_buffer, default_flow_style=False) elif data_format == "json": - json.dump(transform_dict, f) + json.dump(transform_dict, filepath_or_buffer) def load( - filepath: str, + filepath_or_buffer: Union[str, Path, TextIO], data_format: str = "json", nonserializable: Optional[Dict[str, Any]] = None, - lambda_transforms: Union[Optional[Dict[str, Any]], str] = "deprecated", ) -> object: """ - Load a serialized pipeline from a json or yaml file and construct a transform pipeline. + Load a serialized pipeline from a file or file-like object and construct a transform pipeline. Args: - filepath (str): Filepath to read from. - data_format (str): Serialization format. Should be either `json` or 'yaml'. - nonserializable (dict): A dictionary that contains non-serializable transforms. - This dictionary is required when you are restoring a pipeline that contains non-serializable transforms. - Keys in that dictionary should be named same as `name` arguments in respective transforms from - a serialized pipeline. - lambda_transforms (dict): Deprecated. Use 'nonserizalizable' instead. + filepath_or_buffer (Union[str, Path, TextIO]): The file path or file-like object to read the serialized + data from. + If a string is provided, it is interpreted as a path to a file. If a file-like object is provided, + the serialized data will be read from it directly. + data_format (str): The format of the serialized data. Valid options are 'json' and 'yaml'. + Defaults to 'json'. + nonserializable (Optional[Dict[str, Any]]): A dictionary that contains non-serializable transforms. + This dictionary is required when restoring a pipeline that contains non-serializable transforms. + Keys in the dictionary should be named the same as the `name` arguments in respective transforms + from the serialized pipeline. Defaults to None. + + Returns: + object: The deserialized transform pipeline. + + Raises: + ValueError: If `data_format` is 'yaml' but PyYAML is not installed. """ - if lambda_transforms != "deprecated": - warnings.warn("lambda_transforms argument is deprecated, please use 'nonserializable'", DeprecationWarning) - nonserializable = typing.cast(Optional[Dict[str, Any]], lambda_transforms) - check_data_format(data_format) - load_fn = json.load if data_format == "json" else yaml.safe_load - with open(filepath) as f: - transform_dict = load_fn(f) # type: ignore + + if isinstance(filepath_or_buffer, (str, Path)): # Assume it's a filepath + with open(filepath_or_buffer) as f: + if data_format == "json": + transform_dict = json.load(f) + else: + if not yaml_available: + raise ValueError("You need to install PyYAML to load a pipeline in yaml format") + transform_dict = yaml.safe_load(f) + else: # Assume it's a file-like object + if data_format == "json": + transform_dict = json.load(filepath_or_buffer) + else: + if not yaml_available: + raise ValueError("You need to install PyYAML to load a pipeline in yaml format") + transform_dict = yaml.safe_load(filepath_or_buffer) return from_dict(transform_dict, nonserializable=nonserializable) diff --git a/black.toml b/black.toml deleted file mode 100644 index f21062ee3..000000000 --- a/black.toml +++ /dev/null @@ -1,25 +0,0 @@ -# Example configuration for Black. - -# NOTE: you have to use single-quoted strings in TOML for regular expressions. -# It's the equivalent of r-strings in Python. Multiline strings are treated as -# verbose regular expressions by Black. Use [ ] to denote a significant space -# character. - -[tool.black] -line-length = 120 -target-version = ['py35', 'py36', 'py37', 'py38', 'py39'] -include = '\.pyi?$' -exclude = ''' -/( - \.eggs - | \.git - | \.hg - | \.mypy_cache - | \.tox - | \.venv - | _build - | buck-out - | build - | dist -)/ -''' diff --git a/requirements-dev.txt b/requirements-dev.txt index a49c26806..ce2f0b1a0 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,4 +1,5 @@ black==24.2.0 +deepdiff==6.7.1 flake8==7.0.0 isort==5.13.2 mypy==1.8.0 diff --git a/tests/conftest.py b/tests/conftest.py index 77ee62d8f..85623a2b1 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,8 +6,8 @@ import pytest try: - import torch # skipcq: PYL-W0611 - import torchvision # skipcq: PYL-W0611 + import torch + import torchvision torch_available = True except ImportError: diff --git a/tests/files/output_v1.1.0_with_totensor.json b/tests/files/output_v1.1.0_with_totensor.json deleted file mode 100644 index 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import Path from unittest.mock import patch import cv2 import numpy as np import pytest +from deepdiff import DeepDiff import albumentations as A -import albumentations.augmentations.functional as F import albumentations.augmentations.geometric.functional as FGeometric from albumentations.core.serialization import SERIALIZABLE_REGISTRY, shorten_class_name from albumentations.core.transforms_interface import ImageOnlyTransform @@ -808,61 +808,58 @@ def vflip_keypoint(keypoint, **kwargs): assert np.array_equal(aug_data["keypoints"], deserialized_aug_data["keypoints"]) -# def test_serialization_v2_conversion_without_totensor(): -# current_directory = os.path.dirname(os.path.abspath(__file__)) -# files_directory = os.path.join(current_directory, "files") -# transform_1_1_0 = A.load(os.path.join(files_directory, "transform_v1.1.0_without_totensor.json")) -# with open(os.path.join(files_directory, "output_v1.1.0_without_totensor.json")) as f: -# output_1_1_0 = json.load(f) -# np.random.seed(42) -# image = np.random.randint(low=0, high=255, size=(256, 256, 3), dtype=np.uint8) -# random.seed(42) -# transformed_image = transform_1_1_0(image=image)["image"] -# assert transformed_image.tolist() == output_1_1_0 - - -# @skipif_no_torch -# def test_serialization_v2_conversion_with_totensor(): -# current_directory = os.path.dirname(os.path.abspath(__file__)) -# files_directory = os.path.join(current_directory, "files") -# transform_1_1_0 = A.load(os.path.join(files_directory, "transform_v1.1.0_with_totensor.json")) -# with open(os.path.join(files_directory, "output_v1.1.0_with_totensor.json")) as f: -# output_1_1_0 = json.load(f) -# np.random.seed(42) -# random.seed(42) -# image = np.random.randint(low=0, high=255, size=(256, 256, 3), dtype=np.uint8) -# transformed_image = transform_1_1_0(image=image)["image"] -# assert transformed_image.numpy().tolist() == output_1_1_0 - - -# def test_serialization_v2_without_totensor(): -# current_directory = os.path.dirname(os.path.abspath(__file__)) -# files_directory = os.path.join(current_directory, "files") -# transform = A.load(os.path.join(files_directory, "transform_serialization_v2_without_totensor.json")) -# with open(os.path.join(files_directory, "output_v1.1.0_without_totensor.json")) as f: -# output_1_1_0 = json.load(f) -# np.random.seed(42) -# random.seed(42) -# image = np.random.randint(low=0, high=255, size=(256, 256, 3), dtype=np.uint8) -# transformed_image = transform(image=image)["image"] - -# with open("1.json", "w") as f: -# json.dump(transformed_image.tolist(), f) -# assert transformed_image.tolist() == output_1_1_0 - - -# @skipif_no_torch -# def test_serialization_v2_with_totensor(): -# current_directory = os.path.dirname(os.path.abspath(__file__)) -# files_directory = os.path.join(current_directory, "files") -# transform = A.load(os.path.join(files_directory, "transform_serialization_v2_with_totensor.json")) -# with open(os.path.join(files_directory, "output_v1.1.0_with_totensor.json")) as f: -# output_1_1_0 = json.load(f) -# np.random.seed(42) -# image = np.random.randint(low=0, high=255, size=(256, 256, 3), dtype=np.uint8) -# random.seed(42) -# transformed_image = transform(image=image)["image"] -# assert transformed_image.numpy().tolist() == output_1_1_0 +@pytest.mark.parametrize( + "transform_file_name", + ["transform_v1.1.0_without_totensor.json", "transform_serialization_v2_without_totensor.json"], +) +def test_serialization_conversion_without_totensor(transform_file_name): + # Step 1: Load transform from file + current_directory = Path(__file__).resolve().parent + files_directory = current_directory / "files" + transform_file_path = files_directory / transform_file_name + transform = A.load(transform_file_path, data_format="json") + + # Step 2: Serialize it to buffer in memory + buffer = io.StringIO() + A.save(transform, buffer, data_format="json") + buffer.seek(0) # Reset buffer position to the beginning + + # Step 3: Load transform from this memory buffer + transform_from_buffer = A.load(buffer, data_format="json") + + # Ensure the buffer is closed after use + buffer.close() + + assert ( + DeepDiff(transform.to_dict(), transform_from_buffer.to_dict()) == {} + ), "The loaded transform is not equal to the original one" + + +@pytest.mark.parametrize( + "transform_file_name", + ["transform_v1.1.0_with_totensor.json", "transform_serialization_v2_with_totensor.json"], +) +@skipif_no_torch +def test_serialization_conversion_with_totensor(transform_file_name): + # Load transform from file + current_directory = Path(__file__).resolve().parent + files_directory = current_directory / "files" + transform_file_path = files_directory / transform_file_name + + transform = A.load(transform_file_path, data_format="json") + + # Serialize it to buffer in memory + buffer = io.StringIO() + A.save(transform, buffer, data_format="json") + buffer.seek(0) # Reset buffer position to the beginning + + # Load transform from this memory buffer + transform_from_buffer = A.load(buffer, data_format="json") + buffer.close() # Ensure the buffer is closed after use + + assert ( + DeepDiff(transform.to_dict(), transform_from_buffer.to_dict()) == {} + ), "The loaded transform is not equal to the original one" def test_custom_transform_with_overlapping_name():