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transformers/tests/models/got_ocr2/__init__.py Normal file
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transformers/tests/models/got_ocr2/test_image_processing_got_ocr2.py Normal file
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# Copyright 2022 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 unittest
from transformers.image_utils import SizeDict
from transformers.testing_utils import require_torch, require_vision
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 transformers import GotOcr2ImageProcessor
if is_torchvision_available():
from transformers import GotOcr2ImageProcessorFast
class GotOcr2ImageProcessingTester(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,
do_pad=False,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
do_convert_rgb=True,
):
super().__init__()
size = size if size is not None else {"height": 20, "width": 20}
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
self.image_mean = image_mean
self.image_std = image_std
self.do_pad = do_pad
self.do_convert_rgb = do_convert_rgb
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
"do_pad": self.do_pad,
}
def expected_output_image_shape(self, images):
return self.num_channels, 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 GotOcr2ProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = GotOcr2ImageProcessor if is_vision_available() else None
fast_image_processing_class = GotOcr2ImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
self.image_processor_tester = GotOcr2ImageProcessingTester(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_processor = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_slow_fast_equivalence_crop_to_patches(self):
dummy_image = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)[0]
image_processor_slow = self.image_processing_class(**self.image_processor_dict, crop_to_patches=True)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict, crop_to_patches=True)
encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
torch.testing.assert_close(encoding_slow.num_patches, encoding_fast.num_patches)
self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
def test_slow_fast_equivalence_batched_crop_to_patches(self):
# Prepare image inputs so that we have two groups of images with equal resolution with a group of images with
# different resolutions in between
dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
dummy_images += self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
dummy_images += self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
image_processor_slow = self.image_processing_class(**self.image_processor_dict, crop_to_patches=True)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict, crop_to_patches=True)
encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
torch.testing.assert_close(encoding_slow.num_patches, encoding_fast.num_patches)
self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
def test_crop_to_patches(self):
# test slow image processor
image_processor = self.image_processor_list[0](**self.image_processor_dict)
image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)[0]
processed_images = image_processor.crop_image_to_patches(
image,
min_patches=1,
max_patches=6,
use_thumbnail=True,
patch_size={"height": 20, "width": 20},
)
self.assertEqual(len(processed_images), 5)
self.assertEqual(processed_images[0].shape[:2], (20, 20))
# test fast image processor (process batch)
image_processor = self.image_processor_list[1](**self.image_processor_dict)
image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)[0]
processed_images = image_processor.crop_image_to_patches(
image.unsqueeze(0),
min_patches=1,
max_patches=6,
use_thumbnail=True,
patch_size=SizeDict(height=20, width=20),
)
self.assertEqual(len(processed_images[0]), 5)
self.assertEqual(processed_images.shape[-2:], (20, 20))
def test_get_num_patches_without_images(self):
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
num_patches = image_processing.get_number_of_image_patches(height=100, width=100, images_kwargs={})
self.assertEqual(num_patches, 1)
num_patches = image_processing.get_number_of_image_patches(
height=300, width=500, images_kwargs={"crop_to_patches": False}
)
self.assertEqual(num_patches, 1)
num_patches = image_processing.get_number_of_image_patches(
height=20, width=20, images_kwargs={"crop_to_patches": True}
)
self.assertEqual(num_patches, 1)
num_patches = image_processing.get_number_of_image_patches(
height=60, width=60, images_kwargs={"crop_to_patches": True}
)
self.assertEqual(num_patches, 10)
num_patches = image_processing.get_number_of_image_patches(
height=100, width=100, images_kwargs={"crop_to_patches": True}
)
self.assertEqual(num_patches, 10)
num_patches = image_processing.get_number_of_image_patches(
height=100, width=100, images_kwargs={"crop_to_patches": True, "max_patches": 200}
)
self.assertEqual(num_patches, 50)

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transformers/tests/models/got_ocr2/test_modeling_got_ocr2.py Normal file
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# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
#
# 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.
"""Testing suite for the PyTorch GotOcr2 model."""
import unittest
from transformers import (
AutoProcessor,
GotOcr2Config,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import cleanup, require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GotOcr2ForConditionalGeneration,
GotOcr2Model,
)
if is_vision_available():
from transformers.image_utils import load_image
class GotOcr2VisionText2TextModelTester:
def __init__(
self,
parent,
batch_size=3,
seq_length=7,
num_channels=3,
ignore_index=-100,
image_size=64,
image_token_index=1,
model_type="got_ocr2",
is_training=True,
text_config={
"model_type": "qwen2",
"vocab_size": 99,
"hidden_size": 128,
"intermediate_size": 37,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"num_key_value_heads": 2,
"output_channels": 64,
"hidden_act": "silu",
"max_position_embeddings": 512,
"rope_theta": 10000,
"mlp_ratio": 4,
"tie_word_embeddings": True,
"bos_token_id": 2,
"eos_token_id": 3,
"pad_token_id": 4,
},
vision_config={
"num_hidden_layers": 2,
"output_channels": 64,
"hidden_act": "quick_gelu",
"hidden_size": 32,
"mlp_dim": 128,
"num_attention_heads": 4,
"patch_size": 2,
"image_size": 64,
},
):
self.parent = parent
self.ignore_index = ignore_index
self.bos_token_id = text_config["bos_token_id"]
self.eos_token_id = text_config["eos_token_id"]
self.pad_token_id = text_config["pad_token_id"]
self.image_token_index = image_token_index
self.model_type = model_type
self.text_config = text_config
self.vision_config = vision_config
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.is_training = is_training
self.num_image_tokens = 64
self.seq_length = seq_length + self.num_image_tokens
self.num_hidden_layers = text_config["num_hidden_layers"]
self.vocab_size = text_config["vocab_size"]
self.hidden_size = text_config["hidden_size"]
self.num_attention_heads = text_config["num_attention_heads"]
def get_config(self):
return GotOcr2Config(
text_config=self.text_config,
vision_config=self.vision_config,
model_type=self.model_type,
image_token_index=self.image_token_index,
)
def prepare_config_and_inputs(self):
config = self.get_config()
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
return config, pixel_values
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
input_ids[input_ids == self.image_token_index] = self.pad_token_id
input_ids[:, : self.num_image_tokens] = self.image_token_index
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class GotOcr2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
GotOcr2Model,
GotOcr2ForConditionalGeneration,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"image-to-text": GotOcr2ForConditionalGeneration,
"image-text-to-text": GotOcr2ForConditionalGeneration,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def setUp(self):
self.model_tester = GotOcr2VisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=GotOcr2Config, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@require_torch
class GotOcr2IntegrationTest(unittest.TestCase):
def setUp(self):
self.processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
def tearDown(self):
cleanup(torch_device, gc_collect=True)
@slow
def test_small_model_integration_test_got_ocr_stop_strings(self):
model_id = "stepfun-ai/GOT-OCR-2.0-hf"
model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/iam_picture.jpeg"
)
inputs = self.processor(image, return_tensors="pt").to(torch_device)
generate_ids = model.generate(
**inputs,
do_sample=False,
num_beams=1,
tokenizer=self.processor.tokenizer,
stop_strings="<|im_end|>",
max_new_tokens=4096,
)
decoded_output = self.processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_output = "industre"
self.assertEqual(decoded_output, expected_output)
@slow
def test_small_model_integration_test_got_ocr_format(self):
model_id = "stepfun-ai/GOT-OCR-2.0-hf"
model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
)
inputs = self.processor(image, return_tensors="pt", format=True).to(torch_device)
generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4)
decoded_output = self.processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_output = "\\title{\nR"
self.assertEqual(decoded_output, expected_output)
@slow
def test_small_model_integration_test_got_ocr_fine_grained(self):
model_id = "stepfun-ai/GOT-OCR-2.0-hf"
model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
)
inputs = self.processor(image, return_tensors="pt", color="green").to(torch_device)
generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4)
decoded_output = self.processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_output = "You should keep in"
self.assertEqual(decoded_output, expected_output)
@slow
def test_small_model_integration_test_got_ocr_crop_to_patches(self):
model_id = "stepfun-ai/GOT-OCR-2.0-hf"
model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/one_column.png"
)
inputs = self.processor(image, return_tensors="pt", crop_to_patches=True).to(torch_device)
generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4)
decoded_output = self.processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_output = "on developing architectural improvements"
self.assertEqual(decoded_output, expected_output)
@slow
def test_small_model_integration_test_got_ocr_multi_pages(self):
model_id = "stepfun-ai/GOT-OCR-2.0-hf"
model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
image1 = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/one_column.png"
)
image2 = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
)
inputs = self.processor([image1, image2], return_tensors="pt", multi_page=True).to(torch_device)
generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4)
decoded_output = self.processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_output = "on developing architectural improvements"
self.assertEqual(decoded_output, expected_output)
@slow
def test_small_model_integration_test_got_ocr_batched(self):
model_id = "stepfun-ai/GOT-OCR-2.0-hf"
model = GotOcr2ForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
image1 = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
)
image2 = load_image(
"https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
)
inputs = self.processor([image1, image2], return_tensors="pt").to(torch_device)
generate_ids = model.generate(**inputs, do_sample=False, num_beams=1, max_new_tokens=4)
decoded_output = self.processor.batch_decode(
generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_output = ["Reducing the number", "R&D QUALITY"]
self.assertEqual(decoded_output, expected_output)

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transformers/tests/models/got_ocr2/test_processing_got_ocr2.py Normal file
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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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 shutil
import tempfile
import unittest
from transformers import AutoProcessor, GotOcr2Processor, PreTrainedTokenizerFast
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
from ...test_processing_common import ProcessorTesterMixin
if is_vision_available():
from transformers import GotOcr2ImageProcessor
@require_vision
class GotOcr2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = GotOcr2Processor
@classmethod
def setUpClass(cls):
cls.tmpdirname = tempfile.mkdtemp()
image_processor = GotOcr2ImageProcessor()
tokenizer = PreTrainedTokenizerFast.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
processor_kwargs = {}
processor = GotOcr2Processor(image_processor, tokenizer, **processor_kwargs)
processor.save_pretrained(cls.tmpdirname)
cls.image_token = processor.img_pad_token
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
def test_ocr_queries(self):
processor = self.get_processor()
image_input = self.prepare_image_inputs()
inputs = processor(image_input, return_tensors="pt")
self.assertEqual(inputs["input_ids"].shape, (1, 286))
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
inputs = processor(image_input, return_tensors="pt", format=True)
self.assertEqual(inputs["input_ids"].shape, (1, 288))
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
inputs = processor(image_input, return_tensors="pt", color="red")
self.assertEqual(inputs["input_ids"].shape, (1, 290))
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
inputs = processor(image_input, return_tensors="pt", box=[0, 0, 100, 100])
self.assertEqual(inputs["input_ids"].shape, (1, 303))
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
inputs = processor([image_input, image_input], return_tensors="pt", multi_page=True, format=True)
self.assertEqual(inputs["input_ids"].shape, (1, 547))
self.assertEqual(inputs["pixel_values"].shape, (2, 3, 384, 384))
inputs = processor(image_input, return_tensors="pt", crop_to_patches=True, max_patches=6)
self.assertEqual(inputs["input_ids"].shape, (1, 1826))
self.assertEqual(inputs["pixel_values"].shape, (7, 3, 384, 384))
def test_processor_text_has_no_visual(self):
# Overwritten: requires `multi_page` kwarg to process nested vision inputs
processor = self.get_processor()
text = self.prepare_text_inputs(batch_size=3, modalities="image")
image_inputs = self.prepare_image_inputs(batch_size=3)
processing_kwargs = {"return_tensors": "pt", "padding": True, "multi_page": True}
# Call with nested list of vision inputs
image_inputs_nested = [[image] if not isinstance(image, list) else image for image in image_inputs]
inputs_dict_nested = {"text": text, "images": image_inputs_nested}
inputs = processor(**inputs_dict_nested, **processing_kwargs)
self.assertTrue(self.text_input_name in inputs)
# Call with one of the samples with no associated vision input
plain_text = "lower newer"
image_inputs_nested[0] = []
text[0] = plain_text
inputs_dict_no_vision = {"text": text, "images": image_inputs_nested}
inputs_nested = processor(**inputs_dict_no_vision, **processing_kwargs)
self.assertListEqual(
inputs[self.text_input_name][1:].tolist(), inputs_nested[self.text_input_name][1:].tolist()
)