# Copyright 2023 The HuggingFace Team. All rights reserved. # # 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 unittest import numpy as np from huggingface_hub.utils import insecure_hashlib from transformers import ( MODEL_FOR_MASK_GENERATION_MAPPING, is_torch_available, is_vision_available, pipeline, ) from transformers.pipelines import MaskGenerationPipeline from transformers.testing_utils import ( Expectations, is_pipeline_test, nested_simplify, require_torch, require_vision, slow, ) if is_torch_available(): from transformers import MODEL_FOR_MASK_GENERATION_MAPPING else: MODEL_FOR_MASK_GENERATION_MAPPING = None if is_vision_available(): from PIL import Image else: class Image: @staticmethod def open(*args, **kwargs): pass def hashimage(image: Image) -> str: m = insecure_hashlib.md5(image.tobytes()) return m.hexdigest()[:10] def mask_to_test_readable(mask: Image) -> dict: npimg = np.array(mask) shape = npimg.shape return {"hash": hashimage(mask), "shape": shape} @is_pipeline_test @require_vision @require_torch class MaskGenerationPipelineTests(unittest.TestCase): model_mapping = dict(list(MODEL_FOR_MASK_GENERATION_MAPPING.items()) if MODEL_FOR_MASK_GENERATION_MAPPING else []) def get_test_pipeline( self, model, tokenizer=None, image_processor=None, feature_extractor=None, processor=None, dtype="float32", ): image_segmenter = MaskGenerationPipeline( model=model, tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor, processor=processor, dtype=dtype, ) return image_segmenter, [ "./tests/fixtures/tests_samples/COCO/000000039769.png", "./tests/fixtures/tests_samples/COCO/000000039769.png", ] @unittest.skip(reason="TODO @Arthur: Implement me") def run_pipeline_test(self, mask_generator, examples): pass @slow @require_torch def test_small_model_pt(self): image_segmenter = pipeline("mask-generation", model="facebook/sam-vit-huge") outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", points_per_batch=256) # Shortening by hashing new_output = [] for i, o in enumerate(outputs["masks"]): new_output += [{"mask": mask_to_test_readable(o), "scores": outputs["scores"][i]}] # fmt: off last_output = Expectations({ ("xpu", None): {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8872}, ("cuda", None): {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}, ("rocm", (9, 5)): {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8872} }).get_expectation() self.assertEqual( nested_simplify(new_output, decimals=4), [ {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444}, {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021}, {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167}, {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132}, {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053}, {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967}, {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993}, {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909}, {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879}, {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834}, {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716}, {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612}, {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599}, {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552}, {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532}, {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516}, {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499}, {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483}, {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464}, {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943}, {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943}, {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408}, {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335}, {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326}, {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262}, {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999}, {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986}, {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984}, {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873}, last_output ], ) # fmt: on @require_torch @slow def test_threshold(self): model_id = "facebook/sam-vit-huge" image_segmenter = pipeline("mask-generation", model=model_id) outputs = image_segmenter( "http://images.cocodataset.org/val2017/000000039769.jpg", pred_iou_thresh=1, points_per_batch=256 ) # Shortening by hashing new_output = [] for i, o in enumerate(outputs["masks"]): new_output += [{"mask": mask_to_test_readable(o), "scores": outputs["scores"][i]}] self.assertEqual( nested_simplify(new_output, decimals=4), [ {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444}, {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0210}, {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167}, {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132}, {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053}, ], )