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334
transformers/tests/models/glm4v/test_video_processing_glm4v.py
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334
transformers/tests/models/glm4v/test_video_processing_glm4v.py
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# coding=utf-8
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
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from ...test_video_processing_common import VideoProcessingTestMixin, prepare_video_inputs
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if is_torch_available():
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from PIL import Image
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if is_vision_available():
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if is_torchvision_available():
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from transformers import Glm4vVideoProcessor
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from transformers.models.glm4v.video_processing_glm4v import smart_resize
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class Glm4vVideoProcessingTester:
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def __init__(
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self,
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parent,
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batch_size=5,
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num_frames=8,
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num_channels=3,
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min_resolution=30,
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max_resolution=80,
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temporal_patch_size=2,
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patch_size=14,
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merge_size=2,
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do_resize=True,
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size=None,
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do_normalize=True,
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image_mean=IMAGENET_STANDARD_MEAN,
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image_std=IMAGENET_STANDARD_STD,
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do_convert_rgb=True,
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):
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size = size if size is not None else {"longest_edge": 20, "shortest_edge": 10}
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self.parent = parent
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self.batch_size = batch_size
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self.num_frames = num_frames
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self.num_channels = num_channels
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_convert_rgb = do_convert_rgb
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self.temporal_patch_size = temporal_patch_size
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self.patch_size = patch_size
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self.merge_size = merge_size
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def prepare_video_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_convert_rgb": self.do_convert_rgb,
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"do_sample_frames": True,
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}
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def prepare_video_metadata(self, videos):
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video_metadata = []
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for video in videos:
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if isinstance(video, list):
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num_frames = len(video)
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elif hasattr(video, "shape"):
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if len(video.shape) == 4: # (T, H, W, C)
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num_frames = video.shape[0]
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else:
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num_frames = 1
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else:
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num_frames = self.num_frames
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metadata = {
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"fps": 2,
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"duration": num_frames / 2,
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"total_num_frames": num_frames,
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}
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video_metadata.append(metadata)
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return video_metadata
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def expected_output_video_shape(self, videos):
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grid_t = self.num_frames // self.temporal_patch_size
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hidden_dim = self.num_channels * self.temporal_patch_size * self.patch_size * self.patch_size
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seq_len = 0
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for video in videos:
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if isinstance(video, list) and isinstance(video[0], Image.Image):
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video = np.stack([np.array(frame) for frame in video])
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elif hasattr(video, "shape"):
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pass
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else:
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video = np.array(video)
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if hasattr(video, "shape") and len(video.shape) >= 3:
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if len(video.shape) == 4:
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t, height, width = video.shape[:3]
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elif len(video.shape) == 3:
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height, width = video.shape[:2]
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t = 1
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else:
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t, height, width = self.num_frames, self.min_resolution, self.min_resolution
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else:
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t, height, width = self.num_frames, self.min_resolution, self.min_resolution
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resized_height, resized_width = smart_resize(
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t,
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height,
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width,
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factor=self.patch_size * self.merge_size,
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min_pixels=self.size["shortest_edge"],
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max_pixels=self.size["longest_edge"],
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)
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grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
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seq_len += grid_t * grid_h * grid_w
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return [seq_len, hidden_dim]
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def prepare_video_inputs(self, equal_resolution=False, return_tensors="pil"):
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videos = prepare_video_inputs(
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batch_size=self.batch_size,
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num_frames=self.num_frames,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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return_tensors=return_tensors,
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)
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return videos
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@require_torch
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@require_vision
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class Glm4vVideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
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fast_video_processing_class = Glm4vVideoProcessor if is_torchvision_available() else None
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input_name = "pixel_values_videos"
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def setUp(self):
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super().setUp()
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self.video_processor_tester = Glm4vVideoProcessingTester(self)
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@property
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def video_processor_dict(self):
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return self.video_processor_tester.prepare_video_processor_dict()
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def test_video_processor_from_dict_with_kwargs(self):
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video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict)
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self.assertEqual(video_processor.size, {"longest_edge": 20, "shortest_edge": 10})
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video_processor = self.fast_video_processing_class.from_dict(
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self.video_processor_dict, size={"longest_edge": 42, "shortest_edge": 42}
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)
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self.assertEqual(video_processor.size, {"longest_edge": 42, "shortest_edge": 42})
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def test_call_pil(self):
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for video_processing_class in self.video_processor_list:
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video_processing = video_processing_class(**self.video_processor_dict)
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video_inputs = self.video_processor_tester.prepare_video_inputs(
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equal_resolution=False, return_tensors="pil"
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)
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for video in video_inputs:
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self.assertIsInstance(video[0], Image.Image)
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video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
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encoded_videos = video_processing(
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video_inputs[0], video_metadata=[video_metadata[0]], return_tensors="pt"
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)[self.input_name]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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encoded_videos = video_processing(video_inputs, video_metadata=video_metadata, return_tensors="pt")[
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self.input_name
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]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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def test_call_numpy(self):
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for video_processing_class in self.video_processor_list:
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video_processing = video_processing_class(**self.video_processor_dict)
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video_inputs = self.video_processor_tester.prepare_video_inputs(
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equal_resolution=False, return_tensors="np"
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)
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video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
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encoded_videos = video_processing(
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video_inputs[0], video_metadata=[video_metadata[0]], return_tensors="pt"
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)[self.input_name]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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encoded_videos = video_processing(video_inputs, video_metadata=video_metadata, return_tensors="pt")[
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self.input_name
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]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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def test_call_pytorch(self):
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for video_processing_class in self.video_processor_list:
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video_processing = video_processing_class(**self.video_processor_dict)
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video_inputs = self.video_processor_tester.prepare_video_inputs(
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equal_resolution=False, return_tensors="pt"
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)
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video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
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encoded_videos = video_processing(
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video_inputs[0], video_metadata=[video_metadata[0]], return_tensors="pt"
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)[self.input_name]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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encoded_videos = video_processing(video_inputs, video_metadata=video_metadata, return_tensors="pt")[
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self.input_name
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]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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@unittest.skip("Skip for now, the test needs adjustment for GLM-4.1V")
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def test_call_numpy_4_channels(self):
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for video_processing_class in self.video_processor_list:
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# Test that can process videos which have an arbitrary number of channels
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# Initialize video_processing
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video_processor = video_processing_class(**self.video_processor_dict)
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# create random numpy tensors
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self.video_processor_tester.num_channels = 4
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video_inputs = self.video_processor_tester.prepare_video_inputs(
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equal_resolution=False, return_tensors="np"
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)
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# Test not batched input
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encoded_videos = video_processor(
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video_inputs[0],
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return_tensors="pt",
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input_data_format="channels_last",
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image_mean=0,
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image_std=1,
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)[self.input_name]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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# Test batched
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encoded_videos = video_processor(
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video_inputs,
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return_tensors="pt",
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input_data_format="channels_last",
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image_mean=0,
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image_std=1,
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)[self.input_name]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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def test_nested_input(self):
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"""Tests that the processor can work with nested list where each video is a list of arrays"""
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for video_processing_class in self.video_processor_list:
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video_processing = video_processing_class(**self.video_processor_dict)
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video_inputs = self.video_processor_tester.prepare_video_inputs(
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equal_resolution=False, return_tensors="np"
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)
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video_inputs_nested = [list(video) for video in video_inputs]
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video_metadata = self.video_processor_tester.prepare_video_metadata(video_inputs)
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# Test not batched input
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encoded_videos = video_processing(
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video_inputs_nested[0], video_metadata=[video_metadata[0]], return_tensors="pt"
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)[self.input_name]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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# Test batched
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encoded_videos = video_processing(video_inputs_nested, video_metadata=video_metadata, return_tensors="pt")[
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self.input_name
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]
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expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
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self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
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def test_call_sample_frames(self):
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for video_processing_class in self.video_processor_list:
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video_processor_dict = self.video_processor_dict.copy()
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video_processing = video_processing_class(**video_processor_dict)
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prev_num_frames = self.video_processor_tester.num_frames
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self.video_processor_tester.num_frames = 8
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prev_min_resolution = getattr(self.video_processor_tester, "min_resolution", None)
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prev_max_resolution = getattr(self.video_processor_tester, "max_resolution", None)
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self.video_processor_tester.min_resolution = 56
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self.video_processor_tester.max_resolution = 112
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video_inputs = self.video_processor_tester.prepare_video_inputs(
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equal_resolution=False,
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return_tensors="torch",
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)
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metadata = [[{"total_num_frames": 8, "fps": 4}]]
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batched_metadata = metadata * len(video_inputs)
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encoded_videos = video_processing(video_inputs[0], return_tensors="pt", video_metadata=metadata)[
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self.input_name
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]
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encoded_videos_batched = video_processing(
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video_inputs, return_tensors="pt", video_metadata=batched_metadata
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)[self.input_name]
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self.assertIsNotNone(encoded_videos)
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self.assertIsNotNone(encoded_videos_batched)
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self.assertEqual(len(encoded_videos.shape), 2)
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self.assertEqual(len(encoded_videos_batched.shape), 2)
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with self.assertRaises(ValueError):
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video_processing(video_inputs[0], return_tensors="pt")[self.input_name]
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self.video_processor_tester.num_frames = prev_num_frames
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if prev_min_resolution is not None:
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self.video_processor_tester.min_resolution = prev_min_resolution
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if prev_max_resolution is not None:
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self.video_processor_tester.max_resolution = prev_max_resolution
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