init

transformers/tests/models/videomae/test_modeling_videomae.py

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+# Copyright 2022 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 VideoMAE model."""
+
+import copy
+import tempfile
+import unittest
+from functools import cached_property
+
+import numpy as np
+from huggingface_hub import hf_hub_download
+from pytest import mark
+
+from transformers import VideoMAEConfig
+from transformers.models.auto import get_values
+from transformers.testing_utils import (
+    Expectations,
+    is_flaky,
+    require_flash_attn,
+    require_torch,
+    require_torch_gpu,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import check_torch_load_is_safe, is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import (
+        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
+        VideoMAEForPreTraining,
+        VideoMAEForVideoClassification,
+        VideoMAEModel,
+    )
+
+
+if is_vision_available():
+    from transformers import VideoMAEImageProcessor
+
+
+class VideoMAEModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=10,
+        num_channels=3,
+        patch_size=2,
+        tubelet_size=2,
+        num_frames=2,
+        is_training=True,
+        use_labels=True,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        type_sequence_label_size=10,
+        initializer_range=0.02,
+        mask_ratio=0.9,
+        scope=None,
+        attn_implementation="eager",
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.tubelet_size = tubelet_size
+        self.num_frames = num_frames
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.mask_ratio = mask_ratio
+        self.scope = scope
+        self.attn_implementation = attn_implementation
+
+        # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
+        self.num_patches_per_frame = (image_size // patch_size) ** 2
+        self.seq_length = (num_frames // tubelet_size) * self.num_patches_per_frame
+
+        # use this variable to define bool_masked_pos
+        self.num_masks = int(mask_ratio * self.seq_length)
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor(
+            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]
+        )
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return VideoMAEConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            num_frames=self.num_frames,
+            tubelet_size=self.tubelet_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            decoder_hidden_size=self.hidden_size,
+            decoder_intermediate_size=self.intermediate_size,
+            decoder_num_attention_heads=self.num_attention_heads,
+            decoder_num_hidden_layers=self.num_hidden_layers,
+            attn_implementation=self.attn_implementation,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = VideoMAEModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_pretraining(self, config, pixel_values, labels):
+        model = VideoMAEForPreTraining(config)
+        model.to(torch_device)
+        model.eval()
+        # important: each video needs to have the same number of masked patches
+        # hence we define a single mask, which we then repeat for each example in the batch
+        mask = torch.ones((self.num_masks,))
+        mask = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
+        bool_masked_pos = mask.expand(self.batch_size, -1).bool()
+
+        result = model(pixel_values, bool_masked_pos)
+        # model only returns predictions for masked patches
+        num_masked_patches = mask.sum().item()
+        decoder_num_labels = 3 * self.tubelet_size * self.patch_size**2
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_masked_patches, decoder_num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class VideoMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as VideoMAE does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (
+        (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
+    )
+    pipeline_model_mapping = (
+        {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
+        if is_torch_available()
+        else {}
+    )
+    # Addition keys that are required for forward, used in tests where we manipulate and create new input dict from scratch
+    additional_model_inputs = ["bool_masked_pos"]
+    test_pruning = False
+    test_torchscript = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    test_torch_exportable = True
+
+    def setUp(self):
+        self.model_tester = VideoMAEModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=VideoMAEConfig, has_text_modality=False, hidden_size=37)
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = copy.deepcopy(inputs_dict)
+
+        if model_class == VideoMAEForPreTraining:
+            # important: each video needs to have the same number of masked patches
+            # hence we define a single mask, which we then repeat for each example in the batch
+            mask = torch.ones((self.model_tester.num_masks,))
+            mask = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
+            batch_size = inputs_dict["pixel_values"].shape[0]
+            bool_masked_pos = mask.expand(batch_size, -1).bool()
+            inputs_dict["bool_masked_pos"] = bool_masked_pos.to(torch_device)
+
+        if return_labels:
+            if model_class in [
+                *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING),
+            ]:
+                inputs_dict["labels"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+
+        return inputs_dict
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="VideoMAE does not use inputs_embeds")
+    def test_inputs_embeds(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)
+
+    def test_for_pretraining(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "MCG-NJU/videomae-base"
+        model = VideoMAEModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    def test_attention_outputs(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model does not have attentions")
+
+        else:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            config.return_dict = True
+
+            for model_class in self.all_model_classes:
+                num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks
+                seq_len = (
+                    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
+                )
+
+                inputs_dict["output_attentions"] = True
+                inputs_dict["output_hidden_states"] = False
+                config.return_dict = True
+                model = model_class(config)
+                model.to(torch_device)
+                model.eval()
+                with torch.no_grad():
+                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+                attentions = outputs.attentions
+                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+                # check that output_attentions also work using config
+                del inputs_dict["output_attentions"]
+                config.output_attentions = True
+                model = model_class(config)
+                model.to(torch_device)
+                model.eval()
+                with torch.no_grad():
+                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+                attentions = outputs.attentions
+                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+                self.assertListEqual(
+                    list(attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+                out_len = len(outputs)
+
+                # Check attention is always last and order is fine
+                inputs_dict["output_attentions"] = True
+                inputs_dict["output_hidden_states"] = True
+                model = model_class(config)
+                model.to(torch_device)
+                model.eval()
+                with torch.no_grad():
+                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+                self.assertEqual(out_len + 1, len(outputs))
+
+                self_attentions = outputs.attentions
+
+                self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
+                self.assertListEqual(
+                    list(self_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.hidden_states
+            expected_num_layers = self.model_tester.num_hidden_layers + 1
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks
+            seq_length = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    @require_flash_attn
+    @require_torch_gpu
+    @mark.flash_attn_test
+    @slow
+    @is_flaky()
+    def test_flash_attn_2_inference_equivalence(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        for model_class in self.all_model_classes:
+            if not model_class._supports_flash_attn:
+                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
+
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            inputs_dict["pixel_values"] = inputs_dict["pixel_values"].to(torch.bfloat16)
+
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model_fa = model_class.from_pretrained(
+                    tmpdirname, dtype=torch.bfloat16, attn_implementation="flash_attention_2"
+                )
+                model_fa.to(torch_device)
+
+                model = model_class.from_pretrained(tmpdirname, dtype=torch.bfloat16)
+                model.to(torch_device)
+
+                outputs = model(**inputs_dict, output_hidden_states=True)
+                outputs_fa = model_fa(**inputs_dict, output_hidden_states=True)
+
+                logits = (
+                    outputs.hidden_states[-1]
+                    if not model.config.is_encoder_decoder
+                    else outputs.decoder_hidden_states[-1]
+                )
+                logits_fa = (
+                    outputs_fa.hidden_states[-1]
+                    if not model.config.is_encoder_decoder
+                    else outputs_fa.decoder_hidden_states[-1]
+                )
+
+                assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)
+
+                # check with inference + dropout
+                model.train()
+                _ = model_fa(**inputs_dict)
+
+    @unittest.skip("Not applicable for VideoMAE")
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        pass
+
+
+# We will verify our results on a video of eating spaghetti
+# Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227]
+def prepare_video():
+    file = hf_hub_download(
+        repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset"
+    )
+    video = np.load(file)
+    return list(video)
+
+
+@require_torch
+@require_vision
+class VideoMAEModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        # logits were tested with a different mean and std, so we use the same here
+        return (
+            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
+            if is_vision_available()
+            else None
+        )
+
+    @slow
+    def test_inference_for_video_classification(self):
+        model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics").to(
+            torch_device
+        )
+
+        image_processor = self.default_image_processor
+        video = prepare_video()
+        inputs = image_processor(video, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 400))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expectations = Expectations(
+            {
+                (None, None): [0.3669, -0.0688, -0.2421],
+                ("cuda", 8): [0.3668, -0.0690, -0.2421],
+            }
+        )
+        expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
+        torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=2e-4, atol=2e-4)
+
+    @slow
+    def test_inference_for_pretraining(self):
+        model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short").to(torch_device)
+
+        image_processor = self.default_image_processor
+        video = prepare_video()
+        inputs = image_processor(video, return_tensors="pt").to(torch_device)
+
+        # add boolean mask, indicating which patches to mask
+        local_path = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos", filename="bool_masked_pos.pt")
+        check_torch_load_is_safe()
+        inputs["bool_masked_pos"] = torch.load(local_path, weights_only=True)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size([1, 1408, 1536])
+        expected_slice = torch.tensor(
+            [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]], device=torch_device
+        )
+        self.assertEqual(outputs.logits.shape, expected_shape)
+        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+        # verify the loss (`config.norm_pix_loss` = `True`)
+        expected_loss = torch.tensor([0.5142], device=torch_device)
+        torch.testing.assert_close(outputs.loss, expected_loss, rtol=1e-4, atol=1e-4)
+
+        # verify the loss (`config.norm_pix_loss` = `False`)
+        model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short", norm_pix_loss=False).to(
+            torch_device
+        )
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        expected_loss = torch.tensor(torch.tensor([0.6469]), device=torch_device)
+        torch.testing.assert_close(outputs.loss, expected_loss, rtol=1e-4, atol=1e-4)