mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 22:30:09 +06:00
9d6f0ddcec
* Add optimized PixtralImageProcessorFast * make style * Add dummy_vision_object * Review comments * Format * Fix dummy * Format * np.ceil for math.ceil
279 lines
12 KiB
Python
279 lines
12 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 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 random
|
|
import time
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import requests
|
|
|
|
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 PIL import Image
|
|
|
|
from transformers import PixtralImageProcessor
|
|
|
|
if is_torchvision_available():
|
|
from transformers import PixtralImageProcessorFast
|
|
|
|
|
|
class PixtralImageProcessingTester(unittest.TestCase):
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=7,
|
|
num_channels=3,
|
|
image_size=18,
|
|
max_num_images_per_sample=3,
|
|
min_resolution=30,
|
|
max_resolution=400,
|
|
do_resize=True,
|
|
size=None,
|
|
patch_size=None,
|
|
do_normalize=True,
|
|
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 {"longest_edge": 24}
|
|
patch_size = patch_size if patch_size is not None else {"height": 8, "width": 8}
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.num_channels = num_channels
|
|
self.image_size = image_size
|
|
self.max_num_images_per_sample = max_num_images_per_sample
|
|
self.min_resolution = min_resolution
|
|
self.max_resolution = max_resolution
|
|
self.do_resize = do_resize
|
|
self.size = size
|
|
self.patch_size = patch_size
|
|
self.do_normalize = do_normalize
|
|
self.image_mean = image_mean
|
|
self.image_std = image_std
|
|
self.do_convert_rgb = do_convert_rgb
|
|
|
|
def prepare_image_processor_dict(self):
|
|
return {
|
|
"do_resize": self.do_resize,
|
|
"size": self.size,
|
|
"patch_size": self.patch_size,
|
|
"do_normalize": self.do_normalize,
|
|
"image_mean": self.image_mean,
|
|
"image_std": self.image_std,
|
|
"do_convert_rgb": self.do_convert_rgb,
|
|
}
|
|
|
|
def expected_output_image_shape(self, image):
|
|
if isinstance(image, Image.Image):
|
|
width, height = image.size
|
|
elif isinstance(image, np.ndarray):
|
|
height, width = image.shape[:2]
|
|
elif isinstance(image, torch.Tensor):
|
|
height, width = image.shape[-2:]
|
|
|
|
max_height = max_width = self.size.get("longest_edge")
|
|
|
|
ratio = max(height / max_height, width / max_width)
|
|
if ratio > 1:
|
|
height = int(np.ceil(height / ratio))
|
|
width = int(np.ceil(width / ratio))
|
|
|
|
patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
|
|
num_height_tokens = (height - 1) // patch_height + 1
|
|
num_width_tokens = (width - 1) // patch_width + 1
|
|
|
|
height = num_height_tokens * patch_height
|
|
width = num_width_tokens * patch_width
|
|
|
|
return self.num_channels, height, width
|
|
|
|
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
|
|
# Use prepare_image_inputs to make a list of list of single images
|
|
|
|
images_list = []
|
|
for _ in range(self.batch_size):
|
|
images = []
|
|
for _ in range(random.randint(1, self.max_num_images_per_sample)):
|
|
img = prepare_image_inputs(
|
|
batch_size=1,
|
|
num_channels=self.num_channels,
|
|
min_resolution=self.min_resolution,
|
|
max_resolution=self.max_resolution,
|
|
equal_resolution=equal_resolution,
|
|
numpify=numpify,
|
|
torchify=torchify,
|
|
)[0]
|
|
images.append(img)
|
|
images_list.append(images)
|
|
return images_list
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class PixtralImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
|
image_processing_class = PixtralImageProcessor if is_vision_available() else None
|
|
fast_image_processing_class = PixtralImageProcessorFast if is_torchvision_available() else None
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.image_processor_tester = PixtralImageProcessingTester(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, "do_resize"))
|
|
self.assertTrue(hasattr(image_processing, "size"))
|
|
self.assertTrue(hasattr(image_processing, "patch_size"))
|
|
self.assertTrue(hasattr(image_processing, "do_rescale"))
|
|
self.assertTrue(hasattr(image_processing, "rescale_factor"))
|
|
self.assertTrue(hasattr(image_processing, "do_normalize"))
|
|
self.assertTrue(hasattr(image_processing, "image_mean"))
|
|
self.assertTrue(hasattr(image_processing, "image_std"))
|
|
self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
|
|
|
|
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_list = self.image_processor_tester.prepare_image_inputs()
|
|
for image_inputs in image_inputs_list:
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, Image.Image)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(
|
|
image_inputs_list[0][0]
|
|
)
|
|
self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)
|
|
|
|
# Test batched
|
|
batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
|
|
for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
|
|
for encoded_image, image in zip(encoded_images, images):
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
|
|
self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)
|
|
|
|
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_list = self.image_processor_tester.prepare_image_inputs(numpify=True)
|
|
for image_inputs in image_inputs_list:
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, np.ndarray)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(
|
|
image_inputs_list[0][0]
|
|
)
|
|
self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)
|
|
|
|
# Test batched
|
|
batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
|
|
for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
|
|
for encoded_image, image in zip(encoded_images, images):
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
|
|
self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)
|
|
|
|
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_list = self.image_processor_tester.prepare_image_inputs(torchify=True)
|
|
for image_inputs in image_inputs_list:
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, torch.Tensor)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(
|
|
image_inputs_list[0][0]
|
|
)
|
|
self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)
|
|
|
|
# Test batched
|
|
batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
|
|
for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
|
|
for encoded_image, image in zip(encoded_images, images):
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
|
|
self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)
|
|
|
|
@require_vision
|
|
@require_torch
|
|
def test_fast_is_faster_than_slow(self):
|
|
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
|
self.skipTest(reason="Skipping speed test")
|
|
|
|
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
|
self.skipTest(reason="Skipping speed test as one of the image processors is not defined")
|
|
|
|
def measure_time(image_processor, image):
|
|
start = time.time()
|
|
_ = image_processor(image, return_tensors="pt")
|
|
return time.time() - start
|
|
|
|
image_inputs_list = self.image_processor_tester.prepare_image_inputs(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)
|
|
|
|
fast_time = measure_time(image_processor_fast, image_inputs_list)
|
|
slow_time = measure_time(image_processor_slow, image_inputs_list)
|
|
|
|
self.assertLessEqual(fast_time, slow_time)
|
|
|
|
@require_vision
|
|
@require_torch
|
|
def test_slow_fast_equivalence(self):
|
|
dummy_image = Image.open(
|
|
requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
|
|
)
|
|
|
|
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")
|
|
|
|
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.assertTrue(torch.allclose(encoding_slow.pixel_values[0][0], encoding_fast.pixel_values[0][0], atol=1e-2))
|
|
|
|
@unittest.skip(reason="PixtralImageProcessor doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy
|
|
def test_call_numpy_4_channels(self):
|
|
pass
|