codingutf8 2023 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 vivit model import copy import inspect import unittest import numpy as np from huggingfacehub import hfhubdownload from transformers import vivitconfig 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 vivitforvideoclassification vivitmodel from transformers models vivit modelingvivit import vivitpretrainedmodelarchivelist if isvisionavailable from transformers import vivitimageprocessor class vivitmodeltester def init self parent batchsize2 istrainingtrue uselabelstrue numlabels10 imagesize10 numframes8 decreased because default 32 takes too much ram at inference tubeletsize2 4 4 numchannels3 hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelufast hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e06 qkvbiastrue scopenone self parent parent self batchsize batchsize self istraining istraining self uselabels uselabels self numlabels numlabels self imagesize imagesize self numframes numframes self tubeletsize tubeletsize self numchannels numchannels self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self layernormeps layernormeps self qkvbias qkvbias self scope scope self seqlength self imagesize self tubeletsize2 self imagesize self tubeletsize1 self numframes self tubeletsize0 1 cls token 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 vivitconfig numframesself numframes imagesizeself imagesize tubeletsizeself tubeletsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob initializerrangeself initializerrange layernormepsself layernormeps qkvbiasself qkvbias config numlabels self numlabels return config def createandcheckmodelself config pixelvalues labels model vivitmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckforvideoclassificationself config pixelvalues labels model vivitforvideoclassificationconfig 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 vivitmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses vivitmodel vivitforvideoclassification if istorchavailable else pipelinemodelmapping featureextraction vivitmodel videoclassification vivitforvideoclassification if istorchavailable else testpruning false testtorchscript false testresizeembeddings false testheadmasking false def setupself self modeltester vivitmodeltesterself self configtester configtesterself configclassvivitconfig 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 skipreasonvivit 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 testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel forward signature parameters is an ordereddict so argnames order is deterministic argnames signature parameters keys expectedargnames pixelvalues headmask self assertlistequalargnames 2 expectedargnames def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testforvideoclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforvideoclassificationconfigandinputs slow def testmodelfrompretrainedself for modelname in vivitpretrainedmodelarchivelist 1 model vivitmodel frompretrainedmodelname self assertisnotnonemodel def testattentionoutputsself config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses seqlen self modeltester seqlength 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 self assertlistequal listattentions0 shape3 self modeltester numattentionheads seqlen seqlen 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 self assertlistequal listselfattentions0 shape3 self modeltester numattentionheads seqlen seqlen 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 filenameeatingspaghetti32frames npy repotypedataset video np loadfile return listvideo requiretorch requirevision class vivitmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return vivitimageprocessor if isvisionavailable else none slow def testinferenceforvideoclassificationself model vivitforvideoclassification frompretrainedgooglevivitb16x2kinetics400 totorchdevice imageprocessor self defaultimageprocessor video preparevideo inputs imageprocessorvideo returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 400 self assertequaloutputs logits shape expectedshape taken from original model expectedslice torch tensor0 9498 2 7971 1 4049 0 1024 1 8353 totorchdevice self asserttruetorch allcloseoutputs logits0 5 expectedslice atol1e4 coding utf 8 2023 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 vivit model decreased because default 32 takes too much ram at inference cls token verify the logits shape here we also overwrite some of the tests of test_modeling_common py as vivit does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic check that output_attentions also work using config check attention is always last and order is fine 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 forward pass verify the logits taken from original model
import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
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, VivitForVideoClassification, VivitModel
    from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VivitImageProcessor


class VivitModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        is_training=True,
        use_labels=True,
        num_labels=10,
        image_size=10,
        num_frames=8,  
        tubelet_size=[2, 4, 4],
        num_channels=3,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu_fast",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-06,
        qkv_bias=True,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.is_training = is_training
        self.use_labels = use_labels
        self.num_labels = num_labels
        self.image_size = image_size
        self.num_frames = num_frames
        self.tubelet_size = tubelet_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.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.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.qkv_bias = qkv_bias
        self.scope = scope

        self.seq_length = (
            (self.image_size // self.tubelet_size[2])
            * (self.image_size // self.tubelet_size[1])
            * (self.num_frames // self.tubelet_size[0])
        ) + 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 = VivitConfig(
            num_frames=self.num_frames,
            image_size=self.image_size,
            tubelet_size=self.tubelet_size,
            num_channels=self.num_channels,
            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,
            layer_norm_eps=self.layer_norm_eps,
            qkv_bias=self.qkv_bias,
        )
        config.num_labels = self.num_labels
        return config

    def create_and_check_model(self, config, pixel_values, labels):
        model = VivitModel(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 = VivitForVideoClassification(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 VivitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (VivitModel, VivitForVideoClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": VivitModel, "video-classification": VivitForVideoClassification}
        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 = VivitModelTester(self)
        self.config_tester = ConfigTester(self, config_class=VivitConfig, 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="Vivit 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_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values", "head_mask"]
            self.assertListEqual(arg_names[:2], expected_arg_names)

    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 VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = VivitModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_attention_outputs(self):
        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

            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, seq_len],
            )
            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, 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)

            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_32_frames.npy", repo_type="dataset"
    )
    video = np.load(file)
    return list(video)


@require_torch
@require_vision
class VivitModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return VivitImageProcessor() if is_vision_available() else None

    @slow
    def test_inference_for_video_classification(self):
        model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400").to(torch_device)

        image_processor = self.default_image_processor
        video = prepare_video()
        inputs = image_processor(video, 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([-0.9498, 2.7971, -1.4049, 0.1024, -1.8353]).to(torch_device)

        self.assertTrue(torch.allclose(outputs.logits[0, :5], expected_slice, atol=1e-4))