161 lines
6.7 KiB
Python
161 lines
6.7 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from typing import Dict
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import numpy as np
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from huggingface_hub.utils import insecure_hashlib
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from transformers import (
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MODEL_FOR_MASK_GENERATION_MAPPING,
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TF_MODEL_FOR_MASK_GENERATION_MAPPING,
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is_vision_available,
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pipeline,
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)
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from transformers.pipelines import MaskGenerationPipeline
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from transformers.testing_utils import (
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is_pipeline_test,
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nested_simplify,
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require_tf,
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require_torch,
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require_vision,
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slow,
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)
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if is_vision_available():
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from PIL import Image
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else:
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class Image:
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@staticmethod
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def open(*args, **kwargs):
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pass
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def hashimage(image: Image) -> str:
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m = insecure_hashlib.md5(image.tobytes())
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return m.hexdigest()[:10]
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def mask_to_test_readable(mask: Image) -> Dict:
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npimg = np.array(mask)
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shape = npimg.shape
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return {"hash": hashimage(mask), "shape": shape}
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@is_pipeline_test
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@require_vision
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@require_torch
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class MaskGenerationPipelineTests(unittest.TestCase):
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model_mapping = dict(
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(list(MODEL_FOR_MASK_GENERATION_MAPPING.items()) if MODEL_FOR_MASK_GENERATION_MAPPING else [])
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)
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tf_model_mapping = dict(
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(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])
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)
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def get_test_pipeline(self, model, tokenizer, processor):
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image_segmenter = MaskGenerationPipeline(model=model, image_processor=processor)
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return image_segmenter, [
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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]
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# TODO: Implement me @Arthur
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def run_pipeline_test(self, mask_generator, examples):
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pass
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@require_tf
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@unittest.skip("Image segmentation not implemented in TF")
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def test_small_model_tf(self):
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pass
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@slow
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@require_torch
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def test_small_model_pt(self):
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image_segmenter = pipeline("mask-generation", model="facebook/sam-vit-huge")
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outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", points_per_batch=256)
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# Shortening by hashing
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new_outupt = []
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for i, o in enumerate(outputs["masks"]):
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new_outupt += [{"mask": mask_to_test_readable(o), "scores": outputs["scores"][i]}]
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# fmt: off
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self.assertEqual(
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nested_simplify(new_outupt, decimals=4),
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[
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{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
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{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
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{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
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{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
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{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
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{'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
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{'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
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{'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
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{'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
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{'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
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{'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
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{'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
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{'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
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{'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
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{'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
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{'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
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{'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
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{'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
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{'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
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{'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
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{'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
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{'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
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{'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
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{'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
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{'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
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{'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
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{'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
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{'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
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{'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
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{'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
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],
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)
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# fmt: on
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@require_torch
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@slow
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def test_threshold(self):
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model_id = "facebook/sam-vit-huge"
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image_segmenter = pipeline("mask-generation", model=model_id)
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outputs = image_segmenter(
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"http://images.cocodataset.org/val2017/000000039769.jpg", pred_iou_thresh=1, points_per_batch=256
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)
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# Shortening by hashing
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new_outupt = []
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for i, o in enumerate(outputs["masks"]):
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new_outupt += [{"mask": mask_to_test_readable(o), "scores": outputs["scores"][i]}]
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self.assertEqual(
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nested_simplify(new_outupt, decimals=4),
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[
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{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
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{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0210},
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{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
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{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
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{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
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],
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)
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