fix_image_processing_fast_for_glm4v (#40483)

lambertwjh · Your Name · yonigozlan · web-flow · commit dae1ccfb9806 · 2025-09-10T21:05:27.000Z
* fix_image_processing_fast_for_glm4v

* fix(format): auto-ruff format

* add test image processing glm4v

* fix quality

---------

Co-authored-by: Your Name &lt;you@example.com&gt;
Co-authored-by: yonigozlan &lt;yoni.gozlan@huggingface.co&gt;
diff --git a/src/transformers/models/glm4v/image_processing_glm4v_fast.py b/src/transformers/models/glm4v/image_processing_glm4v_fast.py
@@ -22,6 +22,8 @@
 from ...image_processing_utils_fast import (
     BaseImageProcessorFast,
     DefaultFastImageProcessorKwargs,
+    group_images_by_shape,
+    reorder_images,
 )
 from ...image_utils import (
     OPENAI_CLIP_MEAN,
@@ -128,46 +130,54 @@ def _preprocess(
         Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
         """
 
-        processed_images = []
-        processed_grids = []
-
-        all_target_sizes = []
-        for image in images:
-            height, width = image.shape[-2:]
-            resized_height, resized_width = smart_resize(
-                num_frames=temporal_patch_size,
-                height=height,
-                width=width,
-                temporal_factor=temporal_patch_size,
-                factor=patch_size * merge_size,
-                min_pixels=size.shortest_edge,
-                max_pixels=size.longest_edge,
-            )
-            all_target_sizes.append((resized_height, resized_width))
-
-        target_height = max([s[0] for s in all_target_sizes])
-        target_width = max([s[1] for s in all_target_sizes])
-
-        for image in images:
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            height, width = stacked_images.shape[-2:]
             if do_resize:
-                image = self.resize(
-                    image,
-                    size=SizeDict(height=target_height, width=target_width),
+                resized_height, resized_width = smart_resize(
+                    num_frames=temporal_patch_size,
+                    height=height,
+                    width=width,
+                    temporal_factor=temporal_patch_size,
+                    factor=patch_size * merge_size,
+                    min_pixels=size.shortest_edge,
+                    max_pixels=size.longest_edge,
+                )
+                stacked_images = self.resize(
+                    stacked_images,
+                    size=SizeDict(height=resized_height, width=resized_width),
                     interpolation=interpolation,
                 )
+            resized_images_grouped[shape] = stacked_images
+
+        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+        processed_images_grouped = {}
+        processed_grids = {}
+
+        for shape, stacked_images in grouped_images.items():
+            resized_height, resized_width = stacked_images.shape[-2:]
+
+            patches = self.rescale_and_normalize(
+                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            if patches.ndim == 4:  # (B, C, H, W)
+                patches = patches.unsqueeze(1)  # (B, T=1, C, H, W)
+
+            if patches.shape[1] % temporal_patch_size != 0:
+                repeats = patches[:, -1:].repeat(
+                    1, temporal_patch_size - (patches.shape[1] % temporal_patch_size), 1, 1, 1
+                )
+                patches = torch.cat([patches, repeats], dim=1)
+
+            batch_size, t_len, channel = patches.shape[:3]
+            grid_t = t_len // temporal_patch_size
+            grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
 
-            image = self.rescale_and_normalize(
-                image.unsqueeze(0), do_rescale, rescale_factor, do_normalize, image_mean, image_std
-            ).squeeze(0)
-
-            patches = image.unsqueeze(0)
-            if patches.shape[0] % temporal_patch_size != 0:
-                repeats = patches[-1:].repeat(temporal_patch_size - (patches.shape[0] % temporal_patch_size), 1, 1, 1)
-                patches = torch.cat([patches, repeats], dim=0)
-            channel = patches.shape[1]
-            grid_t = patches.shape[0] // temporal_patch_size
-            grid_h, grid_w = target_height // patch_size, target_width // patch_size
             patches = patches.view(
+                batch_size,
                 grid_t,
                 temporal_patch_size,
                 channel,
@@ -178,15 +188,22 @@ def _preprocess(
                 merge_size,
                 patch_size,
             )
-            patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
+            # (B, grid_t, gh, gw, mh, mw, C, tp, ph, pw)
+            patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
+
             flatten_patches = patches.reshape(
+                batch_size,
                 grid_t * grid_h * grid_w,
                 channel * temporal_patch_size * patch_size * patch_size,
             )
-            processed_images.append(flatten_patches)
-            processed_grids.append([grid_t, grid_h, grid_w])
 
-        pixel_values = torch.stack(processed_images, dim=0)
+            processed_images_grouped[shape] = flatten_patches
+            processed_grids[shape] = [[grid_t, grid_h, grid_w]] * batch_size
+
+        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+        processed_grids = reorder_images(processed_grids, grouped_images_index)
+
+        pixel_values = torch.cat(processed_images, dim=0)
         image_grid_thw = torch.tensor(processed_grids)
 
         return BatchFeature(
diff --git a/tests/models/glm4v/test_image_processing_glm4v.py b/tests/models/glm4v/test_image_processing_glm4v.py
@@ -0,0 +1,254 @@
+# 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 unittest
+
+import numpy as np
+
+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 Glm4vImageProcessor
+    from transformers.models.glm4v.image_processing_glm4v import smart_resize
+
+    if is_torchvision_available():
+        from transformers import Glm4vImageProcessorFast
+
+
+class Glm4vImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        min_resolution=30,
+        max_resolution=80,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+        temporal_patch_size=2,
+        patch_size=14,
+        merge_size=2,
+    ):
+        size = size if size is not None else {"longest_edge": 20, "shortest_edge": 10}
+        self.parent = parent
+        self.batch_size = batch_size
+        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.temporal_patch_size = temporal_patch_size
+        self.patch_size = patch_size
+        self.merge_size = merge_size
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "temporal_patch_size": self.temporal_patch_size,
+            "patch_size": self.patch_size,
+            "merge_size": self.merge_size,
+        }
+
+    def expected_output_image_shape(self, images):
+        grid_t = 1
+        hidden_dim = self.num_channels * self.temporal_patch_size * self.patch_size * self.patch_size
+        seq_len = 0
+        for image in images:
+            if isinstance(image, list) and isinstance(image[0], Image.Image):
+                image = np.stack([np.array(frame) for frame in image])
+            elif hasattr(image, "shape"):
+                pass
+            else:
+                image = np.array(image)
+            if hasattr(image, "shape") and len(image.shape) >= 3:
+                if isinstance(image, np.ndarray):
+                    if len(image.shape) == 4:
+                        height, width = image.shape[1:3]
+                    elif len(image.shape) == 3:
+                        height, width = image.shape[:2]
+                    else:
+                        height, width = self.min_resolution, self.min_resolution
+                else:
+                    height, width = image.shape[-2:]
+            else:
+                height, width = self.min_resolution, self.min_resolution
+
+            resized_height, resized_width = smart_resize(
+                self.temporal_patch_size,
+                height,
+                width,
+                factor=self.patch_size * self.merge_size,
+                min_pixels=self.size["shortest_edge"],
+                max_pixels=self.size["longest_edge"],
+            )
+            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_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 ViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = Glm4vImageProcessor if is_vision_available() else None
+    fast_image_processing_class = Glm4vImageProcessorFast if is_torchvision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Glm4vImageProcessingTester(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, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"shortest_edge": 10, "longest_edge": 20})
+
+            image_processor = image_processing_class.from_dict(
+                self.image_processor_dict, size={"shortest_edge": 42, "longest_edge": 42}
+            )
+            self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 42})
+
+    # batch size is flattened
+    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 = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(tuple(encoded_images.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 = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(tuple(encoded_images.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 = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_numpy_4_channels(self):
+        for image_processing_class in self.image_processor_list:
+            # Test that can process images which have an arbitrary number of channels
+            # Initialize image_processing
+            image_processor = image_processing_class(**self.image_processor_dict)
+
+            # create random numpy tensors
+            self.image_processor_tester.num_channels = 4
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+
+            # Test not batched input
+            encoded_images = image_processor(
+                image_inputs[0],
+                return_tensors="pt",
+                input_data_format="channels_last",
+                image_mean=0,
+                image_std=1,
+            ).pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processor(
+                image_inputs,
+                return_tensors="pt",
+                input_data_format="channels_last",
+                image_mean=0,
+                image_std=1,
+            ).pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
