codingutf8 2021 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 deit model import unittest import warnings from transformers import deitconfig from transformers models auto import getvalues from transformers testingutils import requireaccelerate requiretorch requiretorchaccelerator requiretorchfp16 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 modelforimageclassificationmapping modelforsequenceclassificationmapping modelmapping deitforimageclassification deitforimageclassificationwithteacher deitformaskedimagemodeling deitmodel from transformers models deit modelingdeit import deitpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import deitimageprocessor class deitmodeltester def init self parent batchsize13 imagesize30 patchsize2 numchannels3 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 typesequencelabelsize10 initializerrange0 02 numlabels3 scopenone encoderstride2 self parent parent 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 encoderstride encoderstride in deit the seq length equals the number of patches 2 we add 2 for the cls and distilation tokens numpatches imagesize patchsize 2 self seqlength numpatches 2 def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels labels idstensorself batchsize self typesequencelabelsize config self getconfig return config pixelvalues labels def getconfigself return deitconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob isdecoderfalse initializerrangeself initializerrange encoderstrideself encoderstride def createandcheckmodelself config pixelvalues labels model deitmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckformaskedimagemodelingself config pixelvalues labels model deitformaskedimagemodelingconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result reconstruction shape self batchsize self numchannels self imagesize self imagesize test greyscale images config numchannels 1 model deitformaskedimagemodelingconfig model totorchdevice model eval pixelvalues floatstensorself batchsize 1 self imagesize self imagesize result modelpixelvalues self parent assertequalresult reconstruction shape self batchsize 1 self imagesize self imagesize def createandcheckforimageclassificationself config pixelvalues labels config numlabels self typesequencelabelsize model deitforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize test greyscale images config numchannels 1 model deitforimageclassificationconfig model totorchdevice model eval pixelvalues floatstensorself batchsize 1 self imagesize self imagesize result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class deitmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses deitmodel deitforimageclassification deitforimageclassificationwithteacher deitformaskedimagemodeling if istorchavailable else pipelinemodelmapping featureextraction deitmodel imageclassification deitforimageclassification deitforimageclassificationwithteacher if istorchavailable else testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester deitmodeltesterself self configtester configtesterself configclassdeitconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasondeit 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 testformaskedimagemodelingself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckformaskedimagemodelingconfigandinputs def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs special case for deitforimageclassificationwithteacher model def prepareforclassself inputsdict modelclass returnlabelsfalse inputsdict super prepareforclassinputsdict modelclass returnlabelsreturnlabels if returnlabels if modelclass name deitforimageclassificationwithteacher del inputsdictlabels return inputsdict def testtrainingself if not self modeltester istraining return config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses deitforimageclassificationwithteacher supports inferenceonly if modelclass in getvaluesmodelmapping or modelclass name deitforimageclassificationwithteacher 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 deitforimageclassificationwithteacher supports inferenceonly if modelclass name deitforimageclassificationwithteacher continue model modelclassconfig model gradientcheckpointingenable model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass def testproblemtypesself config inputsdict self modeltester prepareconfigandinputsforcommon problemtypes title multilabelclassification numlabels 2 dtype torch float title singlelabelclassification numlabels 1 dtype torch long title regression numlabels 1 dtype torch float for modelclass in self allmodelclasses if modelclass not in getvaluesmodelforsequenceclassificationmapping getvaluesmodelforimageclassificationmapping or modelclass name deitforimageclassificationwithteacher continue for problemtype in problemtypes with self subtestmsgftesting modelclass with problemtype title config problemtype problemtypetitle config numlabels problemtypenumlabels model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue if problemtypenumlabels 1 inputslabels inputslabels unsqueeze1 repeat1 problemtypenumlabels inputslabels inputslabels toproblemtypedtype this tests that we do not trigger the warning form pytorch using a target size that is different to the input size this will likely lead to incorrect results due to broadcasting please ensure they have the same size which is a symptom something in wrong for the regression problem see https github comhuggingfacetransformersissues11780 with warnings catchwarningsrecordtrue as warninglist loss modelinputs loss for w in warninglist if using a target size that is different to the input size in strw message raise valueerror fsomething is going wrong in the regression problem intercepted w message loss backward slow def testmodelfrompretrainedself for modelname in deitpretrainedmodelarchivelist 1 model deitmodel 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 deitmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return deitimageprocessor frompretrainedfacebookdeitbasedistilledpatch16224 if isvisionavailable else none slow def testinferenceimageclassificationheadself model deitforimageclassificationwithteacher frompretrainedfacebookdeitbasedistilledpatch16224 to torchdevice imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 1000 self assertequaloutputs logits shape expectedshape expectedslice torch tensor1 0266 0 1912 1 2861 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 slow requireaccelerate requiretorchaccelerator requiretorchfp16 def testinferencefp16self r a small test to make sure that inference work in half precision without any problem forward pass to make sure inference works in fp16 coding utf 8 2021 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 deit model in deit the seq length equals the number of patches 2 we add 2 for the cls and distilation tokens test greyscale images test greyscale images here we also overwrite some of the tests of test_modeling_common py as deit does not use input_ids inputs_embeds attention_mask and seq_length special case for deitforimageclassificationwithteacher model deitforimageclassificationwithteacher supports inference only deitforimageclassificationwithteacher supports inference only this tests that we do not trigger the warning form pytorch using a target size that is different to the input size this will likely lead to incorrect results due to broadcasting please ensure they have the same size which is a symptom something in wrong for the regression problem see https github com huggingface transformers issues 11780 we will verify our results on an image of cute cats forward pass verify the logits a small test to make sure that inference work in half precision without any problem forward pass to make sure inference works in fp16
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_accelerator,
    require_torch_fp16,
    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_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        DeiTForImageClassification,
        DeiTForImageClassificationWithTeacher,
        DeiTForMaskedImageModeling,
        DeiTModel,
    )
    from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor


class DeiTModelTester:
    def __init__(
        self,
        parent,
        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,
        encoder_stride=2,
    ):
        self.parent = parent
        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.encoder_stride = encoder_stride

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

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, 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 DeiTConfig(
            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,
            encoder_stride=self.encoder_stride,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = DeiTModel(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_masked_image_modeling(self, config, pixel_values, labels):
        model = DeiTForMaskedImageModeling(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size)
        )

        
        config.num_channels = 1
        model = DeiTForMaskedImageModeling(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        result = model(pixel_values)
        self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size))

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.type_sequence_label_size
        model = DeiTForImageClassification(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))

        
        config.num_channels = 1
        model = DeiTForImageClassification(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    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 DeiTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (
            DeiTModel,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": DeiTModel,
            "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

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

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

    @unittest.skip(reason="DeiT 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_masked_image_modeling(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs)

    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)

    
    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                del inputs_dict["labels"]

        return inputs_dict

    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)
                or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
            ):
                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
            
            if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                continue
            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()

    @unittest.skip(
        reason="This architecure 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 architecure 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_problem_types(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        problem_types = [
            {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
            {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
            {"title": "regression", "num_labels": 1, "dtype": torch.float},
        ]

        for model_class in self.all_model_classes:
            if (
                model_class
                not in [
                    *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
                    *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
                ]
                or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
            ):
                continue

            for problem_type in problem_types:
                with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
                    config.problem_type = problem_type["title"]
                    config.num_labels = problem_type["num_labels"]

                    model = model_class(config)
                    model.to(torch_device)
                    model.train()

                    inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)

                    if problem_type["num_labels"] > 1:
                        inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])

                    inputs["labels"] = inputs["labels"].to(problem_type["dtype"])

                    
                    
                    
                    
                    with warnings.catch_warnings(record=True) as warning_list:
                        loss = model(**inputs).loss
                    for w in warning_list:
                        if "Using a target size that is different to the input size" in str(w.message):
                            raise ValueError(
                                f"Something is going wrong in the regression problem: intercepted {w.message}"
                            )

                    loss.backward()

    @slow
    def test_model_from_pretrained(self):
        for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = DeiTModel.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 DeiTModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224").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)

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

        expected_slice = torch.tensor([-1.0266, 0.1912, -1.2861]).to(torch_device)

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

    @slow
    @require_accelerate
    @require_torch_accelerator
    @require_torch_fp16
    def test_inference_fp16(self):
        r
        model = DeiTModel.from_pretrained(
            "facebook/deit-base-distilled-patch16-224", torch_dtype=torch.float16, device_map="auto"
        )
        image_processor = self.default_image_processor

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

        
        with torch.no_grad():
            _ = model(pixel_values)