codingutf8 2022 the huggingface inc team 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 orglicenseslicense2 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 timesformer model import copy import unittest import numpy as np from huggingfacehub import hfhubdownload from transformers import timesformerconfig from transformers models auto import getvalues from transformers testingutils import requiretorch requirevision slow torchdevice from transformers utils import cachedproperty istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import modelforvideoclassificationmapping timesformerforvideoclassification timesformermodel from transformers models timesformer modelingtimesformer import timesformerpretrainedmodelarchivelist if isvisionavailable from transformers import videomaeimageprocessor class timesformermodeltester def init self parent batchsize13 imagesize10 numchannels3 patchsize2 numframes2 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 numlabels10 initializerrange0 02 attentiontypedividedspacetime scopenone self parent parent self batchsize batchsize self imagesize imagesize self numchannels numchannels self patchsize patchsize self numframes numframes self istraining istraining self uselabels uselabels self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self attentiontype attentiontype self initializerrange initializerrange self scope scope self numlabels numlabels in timesformer the number of spatial tokens equals numframes numpatches per frame 1 cls token self numpatchesperframe imagesize patchsize 2 self seqlength numframes self numpatchesperframe 1 def prepareconfigandinputsself pixelvalues floatstensor self batchsize self numframes self numchannels self imagesize self imagesize labels none if self uselabels labels idstensorself batchsize self numlabels config self getconfig return config pixelvalues labels def getconfigself config timesformerconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels numframesself numframes hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob initializerrangeself initializerrange attentiontypeself attentiontype config numlabels self numlabels return config def createandcheckmodelself config pixelvalues labels model timesformermodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckforvideoclassificationself config pixelvalues labels model timesformerforvideoclassificationconfig model totorchdevice model eval result modelpixelvalues verify the logits shape expectedshape torch sizeself batchsize self numlabels self parent assertequalresult logits shape expectedshape def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class timesformermodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses timesformermodel timesformerforvideoclassification if istorchavailable else pipelinemodelmapping featureextraction timesformermodel videoclassification timesformerforvideoclassification if istorchavailable else testpruning false testtorchscript false testresizeembeddings false testheadmasking false def setupself self modeltester timesformermodeltesterself self configtester configtester self configclasstimesformerconfig hastextmodalityfalse hiddensize37 def prepareforclassself inputsdict modelclass returnlabelsfalse inputsdict copy deepcopyinputsdict if returnlabels if modelclass in getvaluesmodelforvideoclassificationmapping inputsdictlabels torch zeros self modeltester batchsize dtypetorch long devicetorchdevice return inputsdict def testconfigself self configtester runcommontests unittest skipreasontimesformer does not use inputsembeds def testinputsembedsself pass def testmodelcommonattributesself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig self assertisinstancemodel getinputembeddings nn module x model getoutputembeddings self asserttruex is none or isinstancex nn linear def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testforvideoclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforvideoclassificationconfigandinputs slow def testmodelfrompretrainedself for modelname in timesformerpretrainedmodelarchivelist 1 model timesformermodel frompretrainedmodelname self assertisnotnonemodel def testattentionoutputsself if not self hasattentions pass else config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses seqlen self modeltester seqlength numframes self modeltester numframes inputsdictoutputattentions true inputsdictoutputhiddenstates false config returndict true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass attentions outputs attentions self assertequallenattentions self modeltester numhiddenlayers check that outputattentions also work using config del inputsdictoutputattentions config outputattentions true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass attentions outputs attentions self assertequallenattentions self modeltester numhiddenlayers attentions has shape batchsize x numframes x numheads x numpatches per frame 1 x numpatches per frame 1 self assertlistequal listattentions0 shape3 self modeltester numattentionheads seqlen numframes 1 seqlen numframes 1 outlen lenoutputs check attention is always last and order is fine inputsdictoutputattentions true inputsdictoutputhiddenstates true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass self assertequaloutlen 1 lenoutputs selfattentions outputs attentions self assertequallenselfattentions self modeltester numhiddenlayers attentions has shape batchsize x numframes x numheads x numpatches per frame 1 x numpatches per frame 1 self assertlistequal listselfattentions0 shape3 self modeltester numattentionheads seqlen numframes 1 seqlen numframes 1 def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumlayers self modeltester numhiddenlayers 1 self assertequallenhiddenstates expectednumlayers seqlength self modeltester seqlength self assertlistequal listhiddenstates0 shape2 seqlength self modeltester hiddensize config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses inputsdictoutputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass check that outputhiddenstates also work using config del inputsdictoutputhiddenstates config outputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass 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 preparevideo file hfhubdownload repoidhfinternaltestingspaghettivideo filenameeatingspaghetti npy repotypedataset video np loadfile return listvideo requiretorch requirevision class timesformermodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself logits were tested with a different mean and std so we use the same here return videomaeimageprocessorimagemean0 5 0 5 0 5 imagestd0 5 0 5 0 5 if isvisionavailable else none slow def testinferenceforvideoclassificationself model timesformerforvideoclassification frompretrainedfacebooktimesformerbasefinetunedk400 to torchdevice imageprocessor self defaultimageprocessor video preparevideo inputs imageprocessorvideo 8 returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 400 self assertequaloutputs logits shape expectedshape expectedslice torch tensor0 3016 0 7713 0 4205 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 coding utf 8 2022 the huggingface inc team 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 timesformer model in timesformer the number of spatial tokens equals num_frames num_patches per frame 1 cls token verify the logits shape here we also overwrite some of the tests of test_modeling_common py as timesformer does not use input_ids inputs_embeds attention_mask and seq_length check that output_attentions also work using config attentions has shape batch_size x num_frames x num_heads x num_patches per frame 1 x num_patches per frame 1 check attention is always last and order is fine attentions has shape batch_size x num_frames x num_heads x num_patches per frame 1 x num_patches per frame 1 check that output_hidden_states also work using config 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 logits were tested with a different mean and std so we use the same here forward pass verify the logits
import copy
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, 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,
        TimesformerForVideoClassification,
        TimesformerModel,
    )
    from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VideoMAEImageProcessor


class TimesformerModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=10,
        num_channels=3,
        patch_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,
        num_labels=10,
        initializer_range=0.02,
        attention_type="divided_space_time",
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.patch_size = patch_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.attention_type = attention_type
        self.initializer_range = initializer_range
        self.scope = scope
        self.num_labels = num_labels

        
        self.num_patches_per_frame = (image_size // patch_size) ** 2
        self.seq_length = (num_frames) * self.num_patches_per_frame + 1

    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.num_labels)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        config = TimesformerConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            num_frames=self.num_frames,
            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,
            initializer_range=self.initializer_range,
            attention_type=self.attention_type,
        )
        config.num_labels = self.num_labels
        return config

    def create_and_check_model(self, config, pixel_values, labels):
        model = TimesformerModel(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_video_classification(self, config, pixel_values, labels):
        model = TimesformerForVideoClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values)

        
        expected_shape = torch.Size((self.batch_size, self.num_labels))
        self.parent.assertEqual(result.logits.shape, expected_shape)

    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 TimesformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = TimesformerModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=TimesformerConfig, 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 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="TimeSformer does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_common_attributes(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_video_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_video_classification(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TimesformerModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_attention_outputs(self):
        if not self.has_attentions:
            pass

        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:
                seq_len = self.model_tester.seq_length
                num_frames = self.model_tester.num_frames

                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)

                
                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 // num_frames + 1, seq_len // num_frames + 1],
                )
                out_len = len(outputs)

                
                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 // num_frames + 1, seq_len // num_frames + 1],
                )

    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)

            seq_length = 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)

            
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)




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 TimesformerModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        
        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 = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400").to(
            torch_device
        )

        image_processor = self.default_image_processor
        video = prepare_video()
        inputs = image_processor(video[:8], return_tensors="pt").to(torch_device)

        
        with torch.no_grad():
            outputs = model(**inputs)

        
        expected_shape = torch.Size((1, 400))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([-0.3016, -0.7713, -0.4205]).to(torch_device)

        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))