mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-25 07:18:58 +06:00
af9870265e
* 20250508 Model Architecture * Update modeling_glm4v.py * Update modeling_glm4v.py * Update modeling_glm4v.py * update 1447 * 0526 * update * format * problem * update * update with only image embed diff * Final * upload * update * 1 * upload with ruff * update * update * work * 1 * 1 * update with new note * 2 * Update convert_glm4v_mgt_weights_to_hf.py * Update tokenization_auto.py * update with new format * remove rmsnrom * draft with videos * draft * update * update * fix for review problem * try to remove min_pixel * update * for test * remove timestamps * remove item * update with remove * change * update 2200 * update * Delete app.py * format * update * Update test_video_processing_glm4v.py * 1 * 2 * use new name * Update test_video_processing_glm4v.py * remove docs * change * update for image processors update * 2108 * 2128 * Update modular_glm4v.py * 1 * update some * update * rename * 1 * remove tests output * 2 * add configuration * update * Update test_video_processing_glm4v.py * fix simple forward tests * update with modular * 1 * fix more tests * fix generation test * fix beam search and init * modular changed * fix beam search in case of single-image/video. Fails if multiple visuals per text * update processor * update test * pass * fix beam search * update * param correct * Update convert_glm4v_mgt_weights_to_hf.py * 1 * Update test_modeling_glm4v.py * 4 * 2 * 2123 video process * 2 * revert * 1 * 2 * revert processing * update preprocesor * changed * 1 * update * update * 6 * update * update * update * Delete tmp.txt * config * Update video_processing_glm4v.py * apply modular correctly * move functions * fix order * update the longest_edge * style * simplify a lot * fix random order of classes * skip integration tests * correctly fix the tests * fix TP plan --------- Co-authored-by: raushan <raushan@huggingface.co> Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co> Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
331 lines
14 KiB
Python
331 lines
14 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 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
|
|
|
|
import numpy as np
|
|
|
|
from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
|
|
from transformers.testing_utils import require_torch, require_vision
|
|
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
|
|
|
|
from ...test_video_processing_common import VideoProcessingTestMixin, prepare_video_inputs
|
|
|
|
|
|
if is_torch_available():
|
|
from PIL import Image
|
|
|
|
if is_vision_available():
|
|
if is_torchvision_available():
|
|
from transformers import Glm4vVideoProcessor
|
|
from transformers.models.glm4v.video_processing_glm4v import smart_resize
|
|
|
|
|
|
class Glm4vVideoProcessingTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=5,
|
|
num_frames=8,
|
|
num_channels=3,
|
|
min_resolution=30,
|
|
max_resolution=80,
|
|
temporal_patch_size=2,
|
|
patch_size=14,
|
|
merge_size=2,
|
|
do_resize=True,
|
|
size=None,
|
|
do_normalize=True,
|
|
image_mean=IMAGENET_STANDARD_MEAN,
|
|
image_std=IMAGENET_STANDARD_STD,
|
|
do_convert_rgb=True,
|
|
):
|
|
size = size if size is not None else {"longest_edge": 20}
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.num_frames = num_frames
|
|
self.num_channels = num_channels
|
|
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_convert_rgb = do_convert_rgb
|
|
self.temporal_patch_size = temporal_patch_size
|
|
self.patch_size = patch_size
|
|
self.merge_size = merge_size
|
|
|
|
def prepare_video_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_sample_frames": True,
|
|
}
|
|
|
|
def prepare_video_metadata(self, videos):
|
|
video_metadata = []
|
|
for video in videos:
|
|
if isinstance(video, list):
|
|
num_frames = len(video)
|
|
elif hasattr(video, "shape"):
|
|
if len(video.shape) == 4: # (T, H, W, C)
|
|
num_frames = video.shape[0]
|
|
else:
|
|
num_frames = 1
|
|
else:
|
|
num_frames = self.num_frames
|
|
|
|
metadata = {
|
|
"fps": 2,
|
|
"duration": num_frames / 2,
|
|
"total_frames": num_frames,
|
|
}
|
|
video_metadata.append(metadata)
|
|
return video_metadata
|
|
|
|
def expected_output_video_shape(self, videos):
|
|
grid_t = self.num_frames // self.temporal_patch_size
|
|
hidden_dim = self.num_channels * self.temporal_patch_size * self.patch_size * self.patch_size
|
|
seq_len = 0
|
|
for video in videos:
|
|
if isinstance(video, list) and isinstance(video[0], Image.Image):
|
|
video = np.stack([np.array(frame) for frame in video])
|
|
elif hasattr(video, "shape"):
|
|
pass
|
|
else:
|
|
video = np.array(video)
|
|
|
|
if hasattr(video, "shape") and len(video.shape) >= 3:
|
|
if len(video.shape) == 4:
|
|
t, height, width = video.shape[:3]
|
|
elif len(video.shape) == 3:
|
|
height, width = video.shape[:2]
|
|
t = 1
|
|
else:
|
|
t, height, width = self.num_frames, self.min_resolution, self.min_resolution
|
|
else:
|
|
t, height, width = self.num_frames, self.min_resolution, self.min_resolution
|
|
|
|
resized_height, resized_width = smart_resize(
|
|
t,
|
|
height,
|
|
width,
|
|
factor=self.patch_size * self.merge_size,
|
|
)
|
|
grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
|
|
seq_len += grid_t * grid_h * grid_w
|
|
return [seq_len, hidden_dim]
|
|
|
|
def prepare_video_inputs(self, equal_resolution=False, return_tensors="pil"):
|
|
videos = prepare_video_inputs(
|
|
batch_size=self.batch_size,
|
|
num_frames=self.num_frames,
|
|
num_channels=self.num_channels,
|
|
min_resolution=self.min_resolution,
|
|
max_resolution=self.max_resolution,
|
|
equal_resolution=equal_resolution,
|
|
return_tensors=return_tensors,
|
|
)
|
|
return videos
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class Glm4vVideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
|
|
fast_video_processing_class = Glm4vVideoProcessor if is_torchvision_available() else None
|
|
input_name = "pixel_values_videos"
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.video_processor_tester = Glm4vVideoProcessingTester(self)
|
|
|
|
@property
|
|
def video_processor_dict(self):
|
|
return self.video_processor_tester.prepare_video_processor_dict()
|
|
|
|
def test_video_processor_from_dict_with_kwargs(self):
|
|
video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict)
|
|
self.assertEqual(video_processor.size, {"longest_edge": 20})
|
|
|
|
video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict, size=42)
|
|
self.assertEqual(video_processor.size, {"height": 42, "width": 42})
|
|
|
|
def test_call_pil(self):
|
|
for video_processing_class in self.video_processor_list:
|
|
video_processing = video_processing_class(**self.video_processor_dict)
|
|
video_inputs = self.video_processor_tester.prepare_video_inputs(
|
|
equal_resolution=False, return_tensors="pil"
|
|
)
|
|
|
|
for video in video_inputs:
|
|
self.assertIsInstance(video[0], Image.Image)
|
|
|
|
video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
|
|
encoded_videos = video_processing(
|
|
video_inputs[0], video_metadata=[video_metadata[0]], return_tensors="pt"
|
|
)[self.input_name]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
encoded_videos = video_processing(video_inputs, video_metadata=video_metadata, return_tensors="pt")[
|
|
self.input_name
|
|
]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
def test_call_numpy(self):
|
|
for video_processing_class in self.video_processor_list:
|
|
video_processing = video_processing_class(**self.video_processor_dict)
|
|
video_inputs = self.video_processor_tester.prepare_video_inputs(
|
|
equal_resolution=False, return_tensors="np"
|
|
)
|
|
|
|
video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
|
|
encoded_videos = video_processing(
|
|
video_inputs[0], video_metadata=[video_metadata[0]], return_tensors="pt"
|
|
)[self.input_name]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
encoded_videos = video_processing(video_inputs, video_metadata=video_metadata, return_tensors="pt")[
|
|
self.input_name
|
|
]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
def test_call_pytorch(self):
|
|
for video_processing_class in self.video_processor_list:
|
|
video_processing = video_processing_class(**self.video_processor_dict)
|
|
video_inputs = self.video_processor_tester.prepare_video_inputs(
|
|
equal_resolution=False, return_tensors="pt"
|
|
)
|
|
video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
|
|
encoded_videos = video_processing(
|
|
video_inputs[0], video_metadata=[video_metadata[0]], return_tensors="pt"
|
|
)[self.input_name]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
encoded_videos = video_processing(video_inputs, video_metadata=video_metadata, return_tensors="pt")[
|
|
self.input_name
|
|
]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
@unittest.skip("Skip for now, the test needs adjustment fo GLM-4.1V")
|
|
def test_call_numpy_4_channels(self):
|
|
for video_processing_class in self.video_processor_list:
|
|
# Test that can process videos which have an arbitrary number of channels
|
|
# Initialize video_processing
|
|
video_processor = video_processing_class(**self.video_processor_dict)
|
|
|
|
# create random numpy tensors
|
|
self.video_processor_tester.num_channels = 4
|
|
video_inputs = self.video_processor_tester.prepare_video_inputs(
|
|
equal_resolution=False, return_tensors="np"
|
|
)
|
|
|
|
# Test not batched input
|
|
encoded_videos = video_processor(
|
|
video_inputs[0],
|
|
return_tensors="pt",
|
|
input_data_format="channels_last",
|
|
image_mean=0,
|
|
image_std=1,
|
|
)[self.input_name]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
# Test batched
|
|
encoded_videos = video_processor(
|
|
video_inputs,
|
|
return_tensors="pt",
|
|
input_data_format="channels_last",
|
|
image_mean=0,
|
|
image_std=1,
|
|
)[self.input_name]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
def test_nested_input(self):
|
|
"""Tests that the processor can work with nested list where each video is a list of arrays"""
|
|
for video_processing_class in self.video_processor_list:
|
|
video_processing = video_processing_class(**self.video_processor_dict)
|
|
video_inputs = self.video_processor_tester.prepare_video_inputs(
|
|
equal_resolution=False, return_tensors="np"
|
|
)
|
|
|
|
video_inputs_nested = [list(video) for video in video_inputs]
|
|
video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
|
|
|
|
# Test not batched input
|
|
encoded_videos = video_processing(
|
|
video_inputs_nested[0], video_metadata=[video_metadata[0]], return_tensors="pt"
|
|
)[self.input_name]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
# Test batched
|
|
encoded_videos = video_processing(video_inputs_nested, video_metadata=video_metadata, return_tensors="pt")[
|
|
self.input_name
|
|
]
|
|
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
|
|
self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
|
|
|
|
def test_call_sample_frames(self):
|
|
for video_processing_class in self.video_processor_list:
|
|
video_processor_dict = self.video_processor_dict.copy()
|
|
video_processing = video_processing_class(**video_processor_dict)
|
|
|
|
prev_num_frames = self.video_processor_tester.num_frames
|
|
self.video_processor_tester.num_frames = 8
|
|
prev_min_resolution = getattr(self.video_processor_tester, "min_resolution", None)
|
|
prev_max_resolution = getattr(self.video_processor_tester, "max_resolution", None)
|
|
self.video_processor_tester.min_resolution = 56
|
|
self.video_processor_tester.max_resolution = 112
|
|
|
|
video_inputs = self.video_processor_tester.prepare_video_inputs(
|
|
equal_resolution=False,
|
|
return_tensors="torch",
|
|
)
|
|
|
|
metadata = [[{"total_num_frames": 8, "fps": 4}]]
|
|
batched_metadata = metadata * len(video_inputs)
|
|
|
|
encoded_videos = video_processing(video_inputs[0], return_tensors="pt", video_metadata=metadata)[
|
|
self.input_name
|
|
]
|
|
encoded_videos_batched = video_processing(
|
|
video_inputs, return_tensors="pt", video_metadata=batched_metadata
|
|
)[self.input_name]
|
|
|
|
self.assertIsNotNone(encoded_videos)
|
|
self.assertIsNotNone(encoded_videos_batched)
|
|
self.assertEqual(len(encoded_videos.shape), 2)
|
|
self.assertEqual(len(encoded_videos_batched.shape), 2)
|
|
|
|
with self.assertRaises(ValueError):
|
|
video_processing(video_inputs[0], return_tensors="pt")[self.input_name]
|
|
|
|
self.video_processor_tester.num_frames = prev_num_frames
|
|
if prev_min_resolution is not None:
|
|
self.video_processor_tester.min_resolution = prev_min_resolution
|
|
if prev_max_resolution is not None:
|
|
self.video_processor_tester.max_resolution = prev_max_resolution
|