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| import os | |
| import pytest | |
| import torch | |
| import open_clip | |
| import util_test | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '' | |
| models_to_test = set(open_clip.list_models()) | |
| # testing excemptions | |
| models_to_test = models_to_test.difference({ | |
| # not available with timm yet | |
| # see https://github.com/mlfoundations/open_clip/issues/219 | |
| 'convnext_xlarge', | |
| 'convnext_xxlarge', | |
| 'convnext_xxlarge_320', | |
| 'vit_medium_patch16_gap_256', | |
| # exceeds GH runner memory limit | |
| 'ViT-bigG-14', | |
| 'ViT-e-14', | |
| 'mt5-xl-ViT-H-14', | |
| }) | |
| if 'OPEN_CLIP_TEST_REG_MODELS' in os.environ: | |
| external_model_list = os.environ['OPEN_CLIP_TEST_REG_MODELS'] | |
| with open(external_model_list, 'r') as f: | |
| models_to_test = set(f.read().splitlines()).intersection(models_to_test) | |
| print(f"Selected models from {external_model_list}: {models_to_test}") | |
| models_to_test = list(models_to_test) | |
| models_to_test.sort() | |
| def test_inference_with_data( | |
| model_name, | |
| pretrained = None, | |
| pretrained_hf = False, | |
| precision = 'fp32', | |
| jit = False, | |
| force_quick_gelu = False, | |
| ): | |
| util_test.seed_all() | |
| model, _, preprocess_val = open_clip.create_model_and_transforms( | |
| model_name, | |
| pretrained = pretrained, | |
| precision = precision, | |
| jit = jit, | |
| force_quick_gelu = force_quick_gelu, | |
| pretrained_hf = pretrained_hf | |
| ) | |
| model_id = f'{model_name}_{pretrained or pretrained_hf}_{precision}' | |
| input_dir, output_dir = util_test.get_data_dirs() | |
| # text | |
| input_text_path = os.path.join(input_dir, 'random_text.pt') | |
| gt_text_path = os.path.join(output_dir, f'{model_id}_random_text.pt') | |
| if not os.path.isfile(input_text_path): | |
| pytest.skip(reason = f"missing test data, expected at {input_text_path}") | |
| if not os.path.isfile(gt_text_path): | |
| pytest.skip(reason = f"missing test data, expected at {gt_text_path}") | |
| input_text = torch.load(input_text_path) | |
| gt_text = torch.load(gt_text_path) | |
| y_text = util_test.inference_text(model, model_name, input_text) | |
| assert (y_text == gt_text).all(), f"text output differs @ {input_text_path}" | |
| # image | |
| image_size = model.visual.image_size | |
| if not isinstance(image_size, tuple): | |
| image_size = (image_size, image_size) | |
| input_image_path = os.path.join(input_dir, f'random_image_{image_size[0]}_{image_size[1]}.pt') | |
| gt_image_path = os.path.join(output_dir, f'{model_id}_random_image.pt') | |
| if not os.path.isfile(input_image_path): | |
| pytest.skip(reason = f"missing test data, expected at {input_image_path}") | |
| if not os.path.isfile(gt_image_path): | |
| pytest.skip(reason = f"missing test data, expected at {gt_image_path}") | |
| input_image = torch.load(input_image_path) | |
| gt_image = torch.load(gt_image_path) | |
| y_image = util_test.inference_image(model, preprocess_val, input_image) | |
| assert (y_image == gt_image).all(), f"image output differs @ {input_image_path}" | |