404 lines
16 KiB
Python
404 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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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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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ..test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
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if is_torch_available():
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import torch
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if is_vision_available():
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from transformers import MaskFormerFeatureExtractor
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from transformers.models.maskformer.modeling_maskformer import MaskFormerForInstanceSegmentationOutput
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if is_vision_available():
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from PIL import Image
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class MaskFormerFeatureExtractionTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=32,
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max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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num_labels=10,
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reduce_labels=True,
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ignore_index=255,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.max_size = max_size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.size_divisibility = 0
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# for the post_process_functions
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self.batch_size = 2
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self.num_queries = 3
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self.num_classes = 2
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self.height = 3
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self.width = 4
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self.num_labels = num_labels
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self.reduce_labels = reduce_labels
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self.ignore_index = ignore_index
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def prepare_feat_extract_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"max_size": self.max_size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"size_divisibility": self.size_divisibility,
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"num_labels": self.num_labels,
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"reduce_labels": self.reduce_labels,
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"ignore_index": self.ignore_index,
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}
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def get_expected_values(self, image_inputs, batched=False):
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"""
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This function computes the expected height and width when providing images to MaskFormerFeatureExtractor,
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assuming do_resize is set to True with a scalar size.
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"""
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if not batched:
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image = image_inputs[0]
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if isinstance(image, Image.Image):
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w, h = image.size
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else:
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h, w = image.shape[1], image.shape[2]
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if w < h:
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expected_height = int(self.size * h / w)
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expected_width = self.size
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elif w > h:
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expected_height = self.size
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expected_width = int(self.size * w / h)
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else:
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expected_height = self.size
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expected_width = self.size
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else:
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expected_values = []
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for image in image_inputs:
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expected_height, expected_width = self.get_expected_values([image])
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expected_values.append((expected_height, expected_width))
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expected_height = max(expected_values, key=lambda item: item[0])[0]
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expected_width = max(expected_values, key=lambda item: item[1])[1]
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return expected_height, expected_width
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def get_fake_maskformer_outputs(self):
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return MaskFormerForInstanceSegmentationOutput(
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# +1 for null class
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class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)),
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masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)),
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)
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@require_torch
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@require_vision
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class MaskFormerFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
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feature_extraction_class = MaskFormerFeatureExtractor if (is_vision_available() and is_torch_available()) else None
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def setUp(self):
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self.feature_extract_tester = MaskFormerFeatureExtractionTester(self)
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@property
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def feat_extract_dict(self):
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return self.feature_extract_tester.prepare_feat_extract_dict()
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def test_feat_extract_properties(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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self.assertTrue(hasattr(feature_extractor, "image_mean"))
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self.assertTrue(hasattr(feature_extractor, "image_std"))
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self.assertTrue(hasattr(feature_extractor, "do_normalize"))
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self.assertTrue(hasattr(feature_extractor, "do_resize"))
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self.assertTrue(hasattr(feature_extractor, "size"))
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self.assertTrue(hasattr(feature_extractor, "max_size"))
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self.assertTrue(hasattr(feature_extractor, "ignore_index"))
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self.assertTrue(hasattr(feature_extractor, "num_labels"))
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def test_batch_feature(self):
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pass
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def test_call_pil(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PIL images
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
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self.assertEqual(
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encoded_images.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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# Test batched
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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def test_call_numpy(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random numpy tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
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self.assertEqual(
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encoded_images.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
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self.assertEqual(
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encoded_images.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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def test_call_pytorch(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PyTorch tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
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self.assertEqual(
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encoded_images.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
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self.assertEqual(
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encoded_images.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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def test_equivalence_pad_and_create_pixel_mask(self):
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# Initialize feature_extractors
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feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
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feature_extractor_2 = self.feature_extraction_class(
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do_resize=False, do_normalize=False, num_labels=self.feature_extract_tester.num_classes
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)
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# create random PyTorch tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors
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encoded_images_with_method = feature_extractor_1.encode_inputs(image_inputs, return_tensors="pt")
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encoded_images = feature_extractor_2(image_inputs, return_tensors="pt")
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self.assertTrue(
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torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
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)
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self.assertTrue(
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torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
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)
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def comm_get_feature_extractor_inputs(
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self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"
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):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# prepare image and target
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batch_size = self.feature_extract_tester.batch_size
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num_labels = self.feature_extract_tester.num_labels
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annotations = None
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instance_id_to_semantic_id = None
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if with_segmentation_maps:
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high = num_labels
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if is_instance_map:
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high * 2
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labels_expanded = list(range(num_labels)) * 2
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instance_id_to_semantic_id = {
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instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
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}
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annotations = [np.random.randint(0, high, (384, 384)).astype(np.uint8) for _ in range(batch_size)]
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if segmentation_type == "pil":
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annotations = [Image.fromarray(annotation) for annotation in annotations]
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
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inputs = feature_extractor(
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image_inputs,
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annotations,
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return_tensors="pt",
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instance_id_to_semantic_id=instance_id_to_semantic_id,
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pad_and_return_pixel_mask=True,
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)
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return inputs
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def test_init_without_params(self):
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pass
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def test_with_size_divisibility(self):
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size_divisibilities = [8, 16, 32]
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weird_input_sizes = [(407, 802), (582, 1094)]
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for size_divisibility in size_divisibilities:
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feat_extract_dict = {**self.feat_extract_dict, **{"size_divisibility": size_divisibility}}
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feature_extractor = self.feature_extraction_class(**feat_extract_dict)
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for weird_input_size in weird_input_sizes:
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inputs = feature_extractor([np.ones((3, *weird_input_size))], return_tensors="pt")
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pixel_values = inputs["pixel_values"]
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# check if divisible
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self.assertTrue((pixel_values.shape[-1] % size_divisibility) == 0)
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self.assertTrue((pixel_values.shape[-2] % size_divisibility) == 0)
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def test_call_with_segmentation_maps(self):
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def common(is_instance_map=False, segmentation_type=None):
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inputs = self.comm_get_feature_extractor_inputs(
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with_segmentation_maps=True, is_instance_map=is_instance_map, segmentation_type=segmentation_type
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)
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mask_labels = inputs["mask_labels"]
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class_labels = inputs["class_labels"]
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pixel_values = inputs["pixel_values"]
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# check the batch_size
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for mask_label, class_label in zip(mask_labels, class_labels):
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self.assertEqual(mask_label.shape[0], class_label.shape[0])
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# this ensure padding has happened
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self.assertEqual(mask_label.shape[1:], pixel_values.shape[2:])
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common()
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common(is_instance_map=True)
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common(is_instance_map=False, segmentation_type="pil")
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common(is_instance_map=True, segmentation_type="pil")
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def test_post_process_segmentation(self):
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fature_extractor = self.feature_extraction_class(num_labels=self.feature_extract_tester.num_classes)
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outputs = self.feature_extract_tester.get_fake_maskformer_outputs()
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segmentation = fature_extractor.post_process_segmentation(outputs)
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self.assertEqual(
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segmentation.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_classes,
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self.feature_extract_tester.height,
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self.feature_extract_tester.width,
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),
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)
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target_size = (1, 4)
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segmentation = fature_extractor.post_process_segmentation(outputs, target_size=target_size)
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self.assertEqual(
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segmentation.shape,
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(self.feature_extract_tester.batch_size, self.feature_extract_tester.num_classes, *target_size),
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)
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def test_post_process_semantic_segmentation(self):
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fature_extractor = self.feature_extraction_class(num_labels=self.feature_extract_tester.num_classes)
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outputs = self.feature_extract_tester.get_fake_maskformer_outputs()
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segmentation = fature_extractor.post_process_semantic_segmentation(outputs)
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self.assertEqual(
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segmentation.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.height,
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self.feature_extract_tester.width,
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),
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)
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target_size = (1, 4)
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segmentation = fature_extractor.post_process_semantic_segmentation(outputs, target_size=target_size)
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self.assertEqual(segmentation.shape, (self.feature_extract_tester.batch_size, *target_size))
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def test_post_process_panoptic_segmentation(self):
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fature_extractor = self.feature_extraction_class(num_labels=self.feature_extract_tester.num_classes)
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outputs = self.feature_extract_tester.get_fake_maskformer_outputs()
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segmentation = fature_extractor.post_process_panoptic_segmentation(outputs, object_mask_threshold=0)
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self.assertTrue(len(segmentation) == self.feature_extract_tester.batch_size)
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for el in segmentation:
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self.assertTrue("segmentation" in el)
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self.assertTrue("segments" in el)
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self.assertEqual(type(el["segments"]), list)
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self.assertEqual(
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el["segmentation"].shape, (self.feature_extract_tester.height, self.feature_extract_tester.width)
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)
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