test_feature_extraction_common.py

# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# 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 json
import os
import sys
import tempfile
import unittest
from pathlib import Path

from huggingface_hub import Repository, delete_repo, login
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import PASS, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


if is_torch_available():
    import numpy as np
    import torch

if is_vision_available():
    from PIL import Image


SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")


def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
    """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
    or a list of PyTorch tensors if one specifies torchify=True.
    """

    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    if equal_resolution:
        image_inputs = []
        for i in range(feature_extract_tester.batch_size):
            image_inputs.append(
                np.random.randint(
                    255,
                    size=(
                        feature_extract_tester.num_channels,
                        feature_extract_tester.max_resolution,
                        feature_extract_tester.max_resolution,
                    ),
                    dtype=np.uint8,
                )
            )
    else:
        image_inputs = []
        for i in range(feature_extract_tester.batch_size):
            width, height = np.random.choice(
                np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
            )
            image_inputs.append(
                np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8)
            )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

    if torchify:
        image_inputs = [torch.from_numpy(x) for x in image_inputs]

    return image_inputs


class FeatureExtractionSavingTestMixin:
    def test_feat_extract_to_json_string(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        obj = json.loads(feat_extract.to_json_string())
        for key, value in self.feat_extract_dict.items():
            self.assertEqual(obj[key], value)

    def test_feat_extract_to_json_file(self):
        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            json_file_path = os.path.join(tmpdirname, "feat_extract.json")
            feat_extract_first.to_json_file(json_file_path)
            feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)

        self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())

    def test_feat_extract_from_and_save_pretrained(self):
        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            feat_extract_first.save_pretrained(tmpdirname)
            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)

        self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())

    def test_init_without_params(self):
        feat_extract = self.feature_extraction_class()
        self.assertIsNotNone(feat_extract)


@is_staging_test
class ConfigPushToHubTester(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls._token = login(username=USER, password=PASS)

    @classmethod
    def tearDownClass(cls):
        try:
            delete_repo(token=cls._token, name="test-dynamic-feature-extractor")
        except HTTPError:
            pass

    def test_push_to_hub_dynamic_feature_extractor(self):
        CustomFeatureExtractor.register_for_auto_class()
        feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)

        with tempfile.TemporaryDirectory() as tmp_dir:
            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-feature-extractor", use_auth_token=self._token)
            feature_extractor.save_pretrained(tmp_dir)

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map,
                {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
            )
            # The code has been copied from fixtures
            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_feature_extraction.py")))

            repo.push_to_hub()

        new_feature_extractor = AutoFeatureExtractor.from_pretrained(
            f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
        )
        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
        self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")