377 lines
16 KiB
Python
377 lines
16 KiB
Python
# Copyright 2021 HuggingFace Inc.
|
|
#
|
|
# 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 json
|
|
import os
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import requests
|
|
from datasets import load_dataset
|
|
from packaging import version
|
|
|
|
from transformers import AutoImageProcessor
|
|
from transformers.testing_utils import (
|
|
check_json_file_has_correct_format,
|
|
require_torch,
|
|
require_torch_accelerator,
|
|
require_vision,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
|
|
|
|
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
from transformers import ImageGPTImageProcessor
|
|
|
|
if is_torchvision_available():
|
|
from transformers import ImageGPTImageProcessorFast
|
|
|
|
|
|
class ImageGPTImageProcessingTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=7,
|
|
num_channels=3,
|
|
image_size=18,
|
|
min_resolution=30,
|
|
max_resolution=400,
|
|
do_resize=True,
|
|
size=None,
|
|
do_normalize=True,
|
|
):
|
|
size = size if size is not None else {"height": 18, "width": 18}
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.num_channels = num_channels
|
|
self.image_size = image_size
|
|
self.min_resolution = min_resolution
|
|
self.max_resolution = max_resolution
|
|
self.do_resize = do_resize
|
|
self.size = size
|
|
self.do_normalize = do_normalize
|
|
|
|
def prepare_image_processor_dict(self):
|
|
return {
|
|
# here we create 2 clusters for the sake of simplicity
|
|
"clusters": np.asarray(
|
|
[
|
|
[0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
|
|
[-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
|
|
]
|
|
),
|
|
"do_resize": self.do_resize,
|
|
"size": self.size,
|
|
"do_normalize": self.do_normalize,
|
|
}
|
|
|
|
def expected_output_image_shape(self, images):
|
|
return (self.size["height"] * self.size["width"],)
|
|
|
|
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
|
|
return prepare_image_inputs(
|
|
batch_size=self.batch_size,
|
|
num_channels=self.num_channels,
|
|
min_resolution=self.min_resolution,
|
|
max_resolution=self.max_resolution,
|
|
equal_resolution=equal_resolution,
|
|
numpify=numpify,
|
|
torchify=torchify,
|
|
)
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class ImageGPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
|
image_processing_class = ImageGPTImageProcessor if is_vision_available() else None
|
|
fast_image_processing_class = ImageGPTImageProcessorFast if is_torchvision_available() else None
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.image_processor_tester = ImageGPTImageProcessingTester(self)
|
|
|
|
@property
|
|
def image_processor_dict(self):
|
|
return self.image_processor_tester.prepare_image_processor_dict()
|
|
|
|
def test_image_processor_properties(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
self.assertTrue(hasattr(image_processing, "clusters"))
|
|
self.assertTrue(hasattr(image_processing, "do_resize"))
|
|
self.assertTrue(hasattr(image_processing, "size"))
|
|
self.assertTrue(hasattr(image_processing, "do_normalize"))
|
|
|
|
def test_image_processor_from_dict_with_kwargs(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor = image_processing_class.from_dict(self.image_processor_dict)
|
|
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
|
|
|
|
image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
|
|
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
|
|
|
|
def test_image_processor_to_json_string(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor = image_processing_class(**self.image_processor_dict)
|
|
obj = json.loads(image_processor.to_json_string())
|
|
for key, value in self.image_processor_dict.items():
|
|
if key == "clusters":
|
|
self.assertTrue(np.array_equal(value, obj[key]))
|
|
else:
|
|
self.assertEqual(obj[key], value)
|
|
|
|
def test_image_processor_to_json_file(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor_first = image_processing_class(**self.image_processor_dict)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
json_file_path = os.path.join(tmpdirname, "image_processor.json")
|
|
image_processor_first.to_json_file(json_file_path)
|
|
image_processor_second = image_processing_class.from_json_file(json_file_path).to_dict()
|
|
|
|
image_processor_first = image_processor_first.to_dict()
|
|
for key, value in image_processor_first.items():
|
|
if key == "clusters":
|
|
self.assertTrue(np.array_equal(value, image_processor_second[key]))
|
|
else:
|
|
self.assertEqual(image_processor_first[key], value)
|
|
|
|
def test_image_processor_from_and_save_pretrained(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor_first = image_processing_class(**self.image_processor_dict)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
image_processor_first.save_pretrained(tmpdirname)
|
|
image_processor_second = image_processing_class.from_pretrained(tmpdirname).to_dict()
|
|
|
|
image_processor_first = image_processor_first.to_dict()
|
|
for key, value in image_processor_first.items():
|
|
if key == "clusters":
|
|
self.assertTrue(np.array_equal(value, image_processor_second[key]))
|
|
else:
|
|
self.assertEqual(value, value)
|
|
|
|
def test_image_processor_save_load_with_autoimageprocessor(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor_first = image_processing_class(**self.image_processor_dict)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
|
|
check_json_file_has_correct_format(saved_file)
|
|
|
|
image_processor_second = AutoImageProcessor.from_pretrained(tmpdirname)
|
|
|
|
image_processor_first = image_processor_first.to_dict()
|
|
image_processor_second = image_processor_second.to_dict()
|
|
|
|
for key, value in image_processor_first.items():
|
|
if key == "clusters":
|
|
self.assertTrue(np.array_equal(value, image_processor_second[key]))
|
|
else:
|
|
self.assertEqual(value, value)
|
|
|
|
@unittest.skip(reason="ImageGPT requires clusters at initialization")
|
|
def test_init_without_params(self):
|
|
pass
|
|
|
|
# Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
|
|
def test_call_pil(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
# Initialize image_processing
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
# create random PIL images
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, Image.Image)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images)
|
|
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
|
|
|
# Test batched
|
|
encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
|
|
self.assertEqual(
|
|
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
|
|
)
|
|
|
|
# Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
|
|
def test_call_numpy(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
# Initialize image_processing
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
# create random numpy tensors
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, np.ndarray)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images)
|
|
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
|
|
|
# Test batched
|
|
encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
|
|
self.assertEqual(
|
|
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
|
|
)
|
|
|
|
@unittest.skip(reason="ImageGPT assumes clusters for 3 channels")
|
|
def test_call_numpy_4_channels(self):
|
|
pass
|
|
|
|
# Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
|
|
def test_call_pytorch(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
# Initialize image_processing
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
# create random PyTorch tensors
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
|
|
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, torch.Tensor)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
|
|
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
|
|
|
# Test batched
|
|
encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
|
|
self.assertEqual(
|
|
tuple(encoded_images.shape),
|
|
(self.image_processor_tester.batch_size, *expected_output_image_shape),
|
|
)
|
|
|
|
# For quantization-based processors, use absolute tolerance only to avoid infinity issues
|
|
@require_vision
|
|
@require_torch
|
|
def test_slow_fast_equivalence(self):
|
|
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test")
|
|
|
|
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
|
|
|
|
dummy_image = Image.open(
|
|
requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
|
|
)
|
|
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
|
|
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
|
|
|
|
encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
|
|
encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
|
|
self._assert_slow_fast_tensors_equivalence(
|
|
encoding_slow.input_ids.float(), encoding_fast.input_ids.float(), atol=1.0, rtol=0
|
|
)
|
|
|
|
@require_vision
|
|
@require_torch
|
|
def test_slow_fast_equivalence_batched(self):
|
|
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test")
|
|
|
|
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
|
|
|
|
if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
|
|
self.skipTest(
|
|
reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
|
|
)
|
|
|
|
dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
|
|
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
|
|
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
|
|
|
|
encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
|
|
encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
|
|
|
|
self._assert_slow_fast_tensors_equivalence(
|
|
encoding_slow.input_ids.float(), encoding_fast.input_ids.float(), atol=1.0, rtol=0
|
|
)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
@require_vision
|
|
@pytest.mark.torch_compile_test
|
|
def test_can_compile_fast_image_processor(self):
|
|
if self.fast_image_processing_class is None:
|
|
self.skipTest("Skipping compilation test as fast image processor is not defined")
|
|
if version.parse(torch.__version__) < version.parse("2.3"):
|
|
self.skipTest(reason="This test requires torch >= 2.3 to run.")
|
|
|
|
torch.compiler.reset()
|
|
input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
|
|
image_processor = self.fast_image_processing_class(**self.image_processor_dict)
|
|
output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")
|
|
|
|
image_processor = torch.compile(image_processor, mode="reduce-overhead")
|
|
output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")
|
|
self._assert_slow_fast_tensors_equivalence(
|
|
output_eager.input_ids.float(), output_compiled.input_ids.float(), atol=1.0, rtol=0
|
|
)
|
|
|
|
|
|
def prepare_images():
|
|
# we use revision="refs/pr/1" until the PR is merged
|
|
# https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
|
|
dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1")
|
|
|
|
image1 = dataset[4]["image"]
|
|
image2 = dataset[5]["image"]
|
|
|
|
images = [image1, image2]
|
|
|
|
return images
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
class ImageGPTImageProcessorIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_image(self):
|
|
image_processing = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small")
|
|
|
|
images = prepare_images()
|
|
|
|
# test non-batched
|
|
encoding = image_processing(images[0], return_tensors="pt")
|
|
|
|
self.assertIsInstance(encoding.input_ids, torch.LongTensor)
|
|
self.assertEqual(encoding.input_ids.shape, (1, 1024))
|
|
|
|
expected_slice = [306, 191, 191]
|
|
self.assertEqual(encoding.input_ids[0, :3].tolist(), expected_slice)
|
|
|
|
# test batched
|
|
encoding = image_processing(images, return_tensors="pt")
|
|
|
|
self.assertIsInstance(encoding.input_ids, torch.LongTensor)
|
|
self.assertEqual(encoding.input_ids.shape, (2, 1024))
|
|
|
|
expected_slice = [303, 13, 13]
|
|
self.assertEqual(encoding.input_ids[1, -3:].tolist(), expected_slice)
|