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# coding=utf-8
# 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
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class ImageGPTImageProcessingTester(unittest.TestCase):
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,
}
@require_torch
@require_vision
class ImageGPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = ImageGPTImageProcessor if is_vision_available() else None
def setUp(self):
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):
image_processing = self.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):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
image_processor = self.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):
image_processor = self.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):
image_processor_first = self.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 = self.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):
image_processor_first = self.image_processing_class(**self.image_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(tmpdirname)
image_processor_second = self.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(image_processor_first[key], value)
@unittest.skip("ImageGPT requires clusters at initialization")
def test_init_without_params(self):
pass
def prepare_images():
dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test")
image1 = Image.open(dataset[4]["file"])
image2 = Image.open(dataset[5]["file"])
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)
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