init

transformers/tests/models/vjepa2/test_modeling_vjepa2.py

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+# coding=utf-8
+# Copyright 2025 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 V-JEPA2 model."""
+
+import unittest
+from functools import cached_property
+
+import numpy as np
+
+from transformers import VJEPA2Config
+from transformers.testing_utils import (
+    is_flaky,
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+from ...test_video_processing_common import (
+    prepare_video_inputs,
+)
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import VJEPA2ForVideoClassification, VJEPA2Model
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoVideoProcessor
+
+VJEPA_HF_MODEL = "facebook/vjepa2-vitl-fpc64-256"
+
+
+class VJEPA2ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        image_size=16,
+        patch_size=16,
+        num_channels=3,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        num_frames=2,
+        mlp_ratio=1,
+        pred_hidden_size=32,
+        pred_num_attention_heads=2,
+        pred_num_hidden_layers=2,
+        pred_num_mask_tokens=10,
+        is_training=False,
+        attn_implementation="sdpa",
+        mask_ratio=0.5,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_frames = num_frames
+        self.mlp_ratio = mlp_ratio
+        self.pred_hidden_size = pred_hidden_size
+        self.pred_num_attention_heads = pred_num_attention_heads
+        self.pred_num_hidden_layers = pred_num_hidden_layers
+        self.pred_num_mask_tokens = pred_num_mask_tokens
+        self.attn_implementation = attn_implementation
+        self.is_training = is_training
+        self.mask_ratio = mask_ratio
+
+        num_patches = ((image_size // patch_size) ** 2) * (num_frames // 2)
+        self.seq_length = num_patches
+        self.num_masks = int(self.mask_ratio * self.seq_length)
+        self.mask_length = num_patches
+
+    def prepare_config_and_inputs(self):
+        pixel_values_videos = floats_tensor(
+            [
+                self.batch_size,
+                self.num_frames,
+                self.num_channels,
+                self.image_size,
+                self.image_size,
+            ]
+        )
+
+        config = self.get_config()
+
+        return config, pixel_values_videos
+
+    def get_config(self):
+        return VJEPA2Config(
+            crop_size=self.image_size,
+            frames_per_clip=self.num_frames,
+            hidden_size=self.hidden_size,
+            num_attention_heads=self.num_attention_heads,
+            num_hidden_layers=self.num_hidden_layers,
+            mlp_ratio=self.mlp_ratio,
+            pred_hidden_size=self.pred_hidden_size,
+            pred_num_attention_heads=self.pred_num_attention_heads,
+            pred_num_hidden_layers=self.pred_num_hidden_layers,
+            pred_num_mask_tokens=self.pred_num_mask_tokens,
+        )
+
+    def create_and_check_model(self, config, pixel_values_videos):
+        model = VJEPA2Model(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values_videos)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape,
+            (self.batch_size, self.seq_length, self.hidden_size),
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            pixel_values_videos,
+        ) = config_and_inputs
+        inputs_dict = {"pixel_values_videos": pixel_values_videos}
+        return config, inputs_dict
+
+
+@require_torch
+class VJEPA2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as VJEPA2 does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    test_torch_exportable = True
+
+    all_model_classes = (VJEPA2Model, VJEPA2ForVideoClassification) if is_torch_available() else ()
+
+    fx_compatible = False
+
+    pipeline_model_mapping = {}
+
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = VJEPA2ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=VJEPA2Config, has_text_modality=False, hidden_size=37)
+
+    @is_flaky(max_attempts=3, description="`torch.nn.init.trunc_normal_` is flaky.")
+    def test_initialization(self):
+        super().test_initialization()
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="VJEPA2 does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="VJEPA2 does not support feedforward chunking yet")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model = VJEPA2Model.from_pretrained(VJEPA_HF_MODEL)
+        self.assertIsNotNone(model)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+def prepare_random_video(image_size=256):
+    videos = prepare_video_inputs(
+        batch_size=1,
+        num_frames=16,
+        num_channels=3,
+        min_resolution=image_size,
+        max_resolution=image_size,
+        equal_resolution=True,
+        return_tensors="torch",
+    )
+    return videos
+
+
+@require_torch
+@require_vision
+class VJEPA2ModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_video_processor(self):
+        return AutoVideoProcessor.from_pretrained(VJEPA_HF_MODEL) if is_vision_available() else None
+
+    @slow
+    def test_inference_image(self):
+        model = VJEPA2Model.from_pretrained(VJEPA_HF_MODEL).to(torch_device)
+
+        video_processor = self.default_video_processor
+        image = prepare_img()
+        inputs = video_processor(torch.Tensor(np.array(image)), return_tensors="pt").to(torch_device)
+        pixel_values_videos = inputs.pixel_values_videos
+        pixel_values_videos = pixel_values_videos.repeat(1, model.config.frames_per_clip, 1, 1, 1)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(pixel_values_videos)
+
+        # verify the last hidden states
+        expected_shape = torch.Size((1, 8192, 1024))
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[-0.0061, -1.8365, 2.7343], [-2.5938, -2.7181, -0.1663], [-1.7993, -2.2430, -1.1388]],
+            device=torch_device,
+        )
+        torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=8e-2, atol=8e-2)
+
+    @slow
+    def test_inference_video(self):
+        model = VJEPA2Model.from_pretrained(VJEPA_HF_MODEL).to(torch_device)
+
+        video_processor = self.default_video_processor
+        video = prepare_random_video()
+        inputs = video_processor(video, return_tensors="pt").to(torch_device)
+        pixel_values_videos = inputs.pixel_values_videos
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(pixel_values_videos)
+
+        # verify the last hidden states
+        expected_shape = torch.Size((1, 2048, 1024))
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
+
+    @slow
+    def test_predictor_outputs(self):
+        model = VJEPA2Model.from_pretrained(VJEPA_HF_MODEL).to(torch_device)
+
+        video_processor = self.default_video_processor
+        video = prepare_random_video()
+        inputs = video_processor(video, return_tensors="pt").to(torch_device)
+        pixel_values_videos = inputs.pixel_values_videos
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(pixel_values_videos)
+
+        # verify the last hidden states
+        expected_shape = torch.Size((1, 2048, 1024))
+        self.assertEqual(outputs.predictor_output.last_hidden_state.shape, expected_shape)
+
+    @slow
+    def test_predictor_full_mask(self):
+        model = VJEPA2Model.from_pretrained(VJEPA_HF_MODEL).to(torch_device)
+
+        video_processor = self.default_video_processor
+        video = prepare_random_video()
+        inputs = video_processor(video, return_tensors="pt").to(torch_device)
+        pixel_values_videos = inputs.pixel_values_videos
+
+        # forward pass
+        with torch.no_grad():
+            context_mask = [torch.arange(2048, device=pixel_values_videos.device).unsqueeze(0)]
+            predictor_mask = context_mask
+            outputs = model(pixel_values_videos, context_mask=context_mask, target_mask=predictor_mask)
+
+        # verify the last hidden states
+        expected_shape = torch.Size((1, 2048, 1024))
+        self.assertEqual(outputs.predictor_output.last_hidden_state.shape, expected_shape)
+
+    @slow
+    def test_predictor_partial_mask(self):
+        model = VJEPA2Model.from_pretrained(VJEPA_HF_MODEL).to(torch_device)
+
+        video_processor = self.default_video_processor
+        video = prepare_random_video()
+        inputs = video_processor(video, return_tensors="pt").to(torch_device)
+        pixel_values_videos = inputs.pixel_values_videos
+
+        num_patches = 2048
+        num_masks = 100
+        # forward pass
+        with torch.no_grad():
+            pos_ids = torch.arange(num_patches, device=pixel_values_videos.device)
+            context_mask = [pos_ids[0 : num_patches - num_masks].unsqueeze(0)]
+            predictor_mask = [pos_ids[num_patches - num_masks :].unsqueeze(0)]
+            outputs = model(pixel_values_videos, context_mask=context_mask, target_mask=predictor_mask)
+
+        # verify the last hidden states
+        expected_shape = torch.Size((1, num_masks, 1024))
+        self.assertEqual(outputs.predictor_output.last_hidden_state.shape, expected_shape)
+
+    @slow
+    def test_video_classification(self):
+        checkpoint = "facebook/vjepa2-vitl-fpc16-256-ssv2"
+
+        model = VJEPA2ForVideoClassification.from_pretrained(checkpoint).to(torch_device)
+        video_processor = AutoVideoProcessor.from_pretrained(checkpoint)
+
+        sample_video = np.ones((16, 3, 256, 256))
+        inputs = video_processor(sample_video, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        self.assertEqual(outputs.logits.shape, (1, 174))
+
+        expected_logits = torch.tensor([0.8814, -0.1195, -0.6389], device=torch_device)
+        resulted_logits = outputs.logits[0, 100:103]
+        torch.testing.assert_close(resulted_logits, expected_logits, rtol=1e-2, atol=1e-2)