307 lines
12 KiB
Python
307 lines
12 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 shutil
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import tempfile
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import unittest
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import numpy as np
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from transformers.testing_utils import (
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is_pt_tf_cross_test,
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require_tf,
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require_torch,
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require_torchvision,
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require_vision,
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)
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from transformers.utils import is_tf_available, is_torch_available, is_vision_available
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if is_vision_available():
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from PIL import Image
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from transformers import AutoProcessor, SamImageProcessor, SamProcessor
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if is_torch_available():
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import torch
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if is_tf_available():
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import tensorflow as tf
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@require_vision
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@require_torchvision
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class SamProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = SamImageProcessor()
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processor = SamProcessor(image_processor)
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processor.save_pretrained(self.tmpdirname)
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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def prepare_mask_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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mask_inputs = [np.random.randint(255, size=(30, 400), dtype=np.uint8)]
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mask_inputs = [Image.fromarray(x) for x in mask_inputs]
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return mask_inputs
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def test_save_load_pretrained_additional_features(self):
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processor = SamProcessor(image_processor=self.get_image_processor())
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processor.save_pretrained(self.tmpdirname)
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image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
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processor = SamProcessor.from_pretrained(self.tmpdirname, do_normalize=False, padding_value=1.0)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, SamImageProcessor)
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def test_image_processor_no_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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input_feat_extract = image_processor(image_input, return_tensors="np")
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input_processor = processor(images=image_input, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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for image in input_feat_extract.pixel_values:
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self.assertEqual(image.shape, (3, 1024, 1024))
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for original_size in input_feat_extract.original_sizes:
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np.testing.assert_array_equal(original_size, np.array([30, 400]))
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for reshaped_input_size in input_feat_extract.reshaped_input_sizes:
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np.testing.assert_array_equal(
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reshaped_input_size, np.array([77, 1024])
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) # reshaped_input_size value is before padding
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def test_image_processor_with_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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mask_input = self.prepare_mask_inputs()
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input_feat_extract = image_processor(images=image_input, segmentation_maps=mask_input, return_tensors="np")
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input_processor = processor(images=image_input, segmentation_maps=mask_input, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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for label in input_feat_extract.labels:
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self.assertEqual(label.shape, (256, 256))
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@require_torch
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def test_post_process_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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dummy_masks = [torch.ones((1, 3, 5, 5))]
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original_sizes = [[1764, 2646]]
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reshaped_input_size = [[683, 1024]]
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masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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masks = processor.post_process_masks(
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dummy_masks, torch.tensor(original_sizes), torch.tensor(reshaped_input_size)
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)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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# should also work with np
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dummy_masks = [np.ones((1, 3, 5, 5))]
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masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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dummy_masks = [[1, 0], [0, 1]]
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with self.assertRaises(ValueError):
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masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
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@require_vision
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@require_tf
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class TFSamProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = SamImageProcessor()
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processor = SamProcessor(image_processor)
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processor.save_pretrained(self.tmpdirname)
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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def test_save_load_pretrained_additional_features(self):
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processor = SamProcessor(image_processor=self.get_image_processor())
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processor.save_pretrained(self.tmpdirname)
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image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
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processor = SamProcessor.from_pretrained(self.tmpdirname, do_normalize=False, padding_value=1.0)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, SamImageProcessor)
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def test_image_processor(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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input_feat_extract = image_processor(image_input, return_tensors="np")
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input_processor = processor(images=image_input, return_tensors="np")
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input_feat_extract.pop("original_sizes") # pop original_sizes as it is popped in the processor
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input_feat_extract.pop("reshaped_input_sizes") # pop reshaped_input_sizes as it is popped in the processor
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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@require_tf
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def test_post_process_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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dummy_masks = [tf.ones((1, 3, 5, 5))]
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original_sizes = [[1764, 2646]]
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reshaped_input_size = [[683, 1024]]
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masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size, return_tensors="tf")
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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masks = processor.post_process_masks(
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dummy_masks,
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tf.convert_to_tensor(original_sizes),
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tf.convert_to_tensor(reshaped_input_size),
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return_tensors="tf",
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)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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# should also work with np
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dummy_masks = [np.ones((1, 3, 5, 5))]
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masks = processor.post_process_masks(
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dummy_masks, np.array(original_sizes), np.array(reshaped_input_size), return_tensors="tf"
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)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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dummy_masks = [[1, 0], [0, 1]]
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with self.assertRaises(tf.errors.InvalidArgumentError):
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masks = processor.post_process_masks(
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dummy_masks, np.array(original_sizes), np.array(reshaped_input_size), return_tensors="tf"
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)
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@require_vision
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@require_torchvision
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class SamProcessorEquivalenceTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = SamImageProcessor()
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processor = SamProcessor(image_processor)
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processor.save_pretrained(self.tmpdirname)
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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@is_pt_tf_cross_test
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def test_post_process_masks_equivalence(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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dummy_masks = np.random.randint(0, 2, size=(1, 3, 5, 5)).astype(np.float32)
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tf_dummy_masks = [tf.convert_to_tensor(dummy_masks)]
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pt_dummy_masks = [torch.tensor(dummy_masks)]
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original_sizes = [[1764, 2646]]
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reshaped_input_size = [[683, 1024]]
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tf_masks = processor.post_process_masks(
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tf_dummy_masks, original_sizes, reshaped_input_size, return_tensors="tf"
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)
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pt_masks = processor.post_process_masks(
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pt_dummy_masks, original_sizes, reshaped_input_size, return_tensors="pt"
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)
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self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))
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@is_pt_tf_cross_test
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def test_image_processor_equivalence(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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pt_input_feat_extract = image_processor(image_input, return_tensors="pt")["pixel_values"].numpy()
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pt_input_processor = processor(images=image_input, return_tensors="pt")["pixel_values"].numpy()
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tf_input_feat_extract = image_processor(image_input, return_tensors="tf")["pixel_values"].numpy()
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tf_input_processor = processor(images=image_input, return_tensors="tf")["pixel_values"].numpy()
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self.assertTrue(np.allclose(pt_input_feat_extract, pt_input_processor))
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self.assertTrue(np.allclose(pt_input_feat_extract, tf_input_feat_extract))
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self.assertTrue(np.allclose(pt_input_feat_extract, tf_input_processor))
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