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 data2vecvision model import unittest from transformers import data2vecvisionconfig from transformers models auto import getvalues from transformers testingutils import requiretorch requiretorchmultigpu requirevision slow torchdevice from transformers utils import cachedproperty istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin configzeroinit floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import modelmapping data2vecvisionforimageclassification data2vecvisionforsemanticsegmentation data2vecvisionmodel from transformers models data2vec modelingdata2vecvision import data2vecvisionpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import beitimageprocessor class data2vecvisionmodeltester def init self parent vocabsize100 batchsize13 imagesize30 patchsize2 numchannels3 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 typesequencelabelsize10 initializerrange0 02 numlabels3 scopenone outindices0 1 2 3 self parent parent self vocabsize 100 self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels 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 typesequencelabelsize typesequencelabelsize self initializerrange initializerrange self scope scope self outindices outindices self numlabels numlabels in beit the seq length equals the number of patches 1 we add 1 for the cls token numpatches imagesize patchsize 2 self seqlength numpatches 1 def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none pixellabels none if self uselabels labels idstensorself batchsize self typesequencelabelsize pixellabels idstensorself batchsize self imagesize self imagesize self numlabels config self getconfig return config pixelvalues labels pixellabels def getconfigself return data2vecvisionconfig vocabsizeself vocabsize imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob isdecoderfalse initializerrangeself initializerrange outindicesself outindices def createandcheckmodelself config pixelvalues labels pixellabels model data2vecvisionmodelconfigconfig model totorchdevice model eval result modelpixelvalues expected sequence length numpatches 1 we add 1 for the cls token numpatches self imagesize self patchsize 2 self parent assertequalresult lasthiddenstate shape self batchsize numpatches 1 self hiddensize def createandcheckforimageclassificationself config pixelvalues labels pixellabels config numlabels self typesequencelabelsize model data2vecvisionforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize def createandcheckforimagesegmentationself config pixelvalues labels pixellabels config numlabels self numlabels model data2vecvisionforsemanticsegmentationconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result logits shape self batchsize self numlabels self imagesize 2 self imagesize 2 result modelpixelvalues labelspixellabels self parent assertequal result logits shape self batchsize self numlabels self imagesize 2 self imagesize 2 def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels pixellabels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class data2vecvisionmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses data2vecvisionmodel data2vecvisionforimageclassification data2vecvisionforsemanticsegmentation if istorchavailable else pipelinemodelmapping featureextraction data2vecvisionmodel imageclassification data2vecvisionforimageclassification imagesegmentation data2vecvisionforsemanticsegmentation if istorchavailable else testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester data2vecvisionmodeltesterself self configtester configtester self configclassdata2vecvisionconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests def testinputsembedsself data2vecvision does not use inputsembeds pass requiretorchmultigpu unittest skip reasondata2vecvision has some layers using addmodule which doesn t work well with nn dataparallel def testmultigpudataparallelforwardself 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 testforimagesegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimagesegmentationconfigandinputs def testtrainingself if not self modeltester istraining return config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses if modelclass in getvaluesmodelmapping continue model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward def testtraininggradientcheckpointingself config inputsdict self modeltester prepareconfigandinputsforcommon if not self modeltester istraining return config usecache false config returndict true for modelclass in self allmodelclasses if modelclass in getvaluesmodelmapping or not modelclass supportsgradientcheckpointing continue todo remove the following 3 lines once we have a modelforsemanticsegmentationmapping this can then be incorporated into prepareforclass in testmodelingcommon py elif modelclass name data2vecvisionforsemanticsegmentation batchsize numchannels height width inputsdictpixelvalues shape inputsdictlabels torch zeros self modeltester batchsize height width devicetorchdevice long model modelclassconfig model gradientcheckpointingenable model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters we skip lambda parameters as these require special initial values determined by config layerscaleinitvalue if lambda in name continue if param requiresgrad self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized def checkpttfoutputsself tfoutputs ptoutputs modelclass tol2e4 nameoutputs attributesnone we override with a slightly higher tol value as semseg models tend to diverge a bit more super checkpttfoutputstfoutputs ptoutputs modelclass tol name attributes def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs slow def testmodelfrompretrainedself for modelname in data2vecvisionpretrainedmodelarchivelist 1 model data2vecvisionmodel frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretorch requirevision class data2vecvisionmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return beitimageprocessor frompretrainedfacebookdata2vecvisionbaseft1k if isvisionavailable else none slow def testinferenceimageclassificationheadimagenet1kself model data2vecvisionforimageclassification frompretrainedfacebookdata2vecvisionbaseft1k to torchdevice imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs logits outputs logits verify the logits expectedshape torch size1 1000 self assertequallogits shape expectedshape expectedslice torch tensor0 3277 0 1395 0 0911 totorchdevice self asserttruetorch allcloselogits0 3 expectedslice atol1e4 expectedtop2 model config label2idi for i in remote control remote tabby tabby cat self assertequallogits0 topk2 indices cpu tolist expectedtop2 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 data2vecvision model in beit the seq length equals the number of patches 1 we add 1 for the cls token expected sequence length num_patches 1 we add 1 for the cls token here we also overwrite some of the tests of test_modeling_common py as data2vecvision does not use input_ids inputs_embeds attention_mask and seq_length data2vecvision does not use inputs_embeds todo remove the following 3 lines once we have a model_for_semantic_segmentation_mapping this can then be incorporated into _prepare_for_class in test_modeling_common py we skip lambda parameters as these require special initial values determined by config layer_scale_init_value we override with a slightly higher tol value as semseg models tend to diverge a bit more we will verify our results on an image of cute cats forward pass verify the logits
import unittest

from transformers import Data2VecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_MAPPING,
        Data2VecVisionForImageClassification,
        Data2VecVisionForSemanticSegmentation,
        Data2VecVisionModel,
    )
    from transformers.models.data2vec.modeling_data2vec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import BeitImageProcessor


class Data2VecVisionModelTester:
    def __init__(
        self,
        parent,
        vocab_size=100,
        batch_size=13,
        image_size=30,
        patch_size=2,
        num_channels=3,
        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,
        num_labels=3,
        scope=None,
        out_indices=[0, 1, 2, 3],
    ):
        self.parent = parent
        self.vocab_size = 100
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        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.scope = scope
        self.out_indices = out_indices
        self.num_labels = num_labels

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

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        labels = None
        pixel_labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)

        config = self.get_config()

        return config, pixel_values, labels, pixel_labels

    def get_config(self):
        return Data2VecVisionConfig(
            vocab_size=self.vocab_size,
            image_size=self.image_size,
            patch_size=self.patch_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,
            is_decoder=False,
            initializer_range=self.initializer_range,
            out_indices=self.out_indices,
        )

    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
        model = Data2VecVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        
        num_patches = (self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))

    def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.type_sequence_label_size
        model = Data2VecVisionForImageClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.num_labels
        model = Data2VecVisionForSemanticSegmentation(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
        )
        result = model(pixel_values, labels=pixel_labels)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, labels, pixel_labels = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": Data2VecVisionModel,
            "image-classification": Data2VecVisionForImageClassification,
            "image-segmentation": Data2VecVisionForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = Data2VecVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_inputs_embeds(self):
        
        pass

    @require_torch_multi_gpu
    @unittest.skip(
        reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"
    )
    def test_multi_gpu_data_parallel_forward(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_image_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs)

    def test_training(self):
        if not self.model_tester.is_training:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            if model_class in [*get_values(MODEL_MAPPING)]:
                continue

            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    def test_training_gradient_checkpointing(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
            return

        config.use_cache = False
        config.return_dict = True

        for model_class in self.all_model_classes:
            if model_class in [*get_values(MODEL_MAPPING)] or not model_class.supports_gradient_checkpointing:
                continue
            
            
            elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
                inputs_dict["labels"] = torch.zeros(
                    [self.model_tester.batch_size, height, width], device=torch_device
                ).long()
            model = model_class(config)
            model.gradient_checkpointing_enable()
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                
                
                if "lambda" in name:
                    continue
                if param.requires_grad:
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )

    def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None):
        
        super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)

    def test_for_image_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = Data2VecVisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)



def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_torch
@require_vision
class Data2VecVisionModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None
        )

    @slow
    def test_inference_image_classification_head_imagenet_1k(self):
        model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to(
            torch_device
        )

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        
        expected_shape = torch.Size((1, 1000))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device)

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

        expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]]
        self.assertEqual(logits[0].topk(2).indices.cpu().tolist(), expected_top2)