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 mobilevit model import unittest from transformers import mobilevitconfig 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 transformers import mobilevitforimageclassification mobilevitforsemanticsegmentation mobilevitmodel from transformers models mobilevit modelingmobilevit import mobilevitpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import mobilevitimageprocessor class mobilevitconfigtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig hiddensizes self parent asserttruehasattrconfig neckhiddensizes self parent asserttruehasattrconfig numattentionheads class mobilevitmodeltester def init self parent batchsize13 imagesize32 patchsize2 numchannels3 lasthiddensize32 numattentionheads4 hiddenactsilu convkernelsize3 outputstride32 hiddendropoutprob0 1 attentionprobsdropoutprob0 1 classifierdropoutprob0 1 initializerrange0 02 istrainingtrue uselabelstrue numlabels10 scopenone self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self lasthiddensize lasthiddensize self numattentionheads numattentionheads self hiddenact hiddenact self convkernelsize convkernelsize self outputstride outputstride self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self classifierdropoutprob classifierdropoutprob self uselabels uselabels self istraining istraining self numlabels numlabels self initializerrange initializerrange self scope scope def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none pixellabels none if self uselabels labels idstensorself batchsize self numlabels pixellabels idstensorself batchsize self imagesize self imagesize self numlabels config self getconfig return config pixelvalues labels pixellabels def getconfigself return mobilevitconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels numattentionheadsself numattentionheads hiddenactself hiddenact convkernelsizeself convkernelsize outputstrideself outputstride hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob classifierdropoutprobself classifierdropoutprob initializerrangeself initializerrange hiddensizes12 16 20 neckhiddensizes8 8 16 16 32 32 32 def createandcheckmodelself config pixelvalues labels pixellabels model mobilevitmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result lasthiddenstate shape self batchsize self lasthiddensize self imagesize self outputstride self imagesize self outputstride def createandcheckforimageclassificationself config pixelvalues labels pixellabels config numlabels self numlabels model mobilevitforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self numlabels def createandcheckforsemanticsegmentationself config pixelvalues labels pixellabels config numlabels self numlabels model mobilevitforsemanticsegmentationconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result logits shape self batchsize self numlabels self imagesize self outputstride self imagesize self outputstride result modelpixelvalues labelspixellabels self parent assertequal result logits shape self batchsize self numlabels self imagesize self outputstride self imagesize self outputstride def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels pixellabels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class mobilevitmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses mobilevitmodel mobilevitforimageclassification mobilevitforsemanticsegmentation if istorchavailable else pipelinemodelmapping featureextraction mobilevitmodel imageclassification mobilevitforimageclassification imagesegmentation mobilevitforsemanticsegmentation if istorchavailable else testpruning false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester mobilevitmodeltesterself self configtester mobilevitconfigtesterself configclassmobilevitconfig hastextmodalityfalse def testconfigself self configtester runcommontests unittest skipreasonmobilevit does not use inputsembeds def testinputsembedsself pass unittest skipreasonmobilevit does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonmobilevit does not output attentions def testattentionoutputsself pass def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumstages 5 self assertequallenhiddenstates expectednumstages mobilevit s feature maps are of shape batchsize numchannels height width with the width and height being successively divided by 2 divisor 2 for i in rangelenhiddenstates self assertlistequal listhiddenstatesi shape2 self modeltester imagesize divisor self modeltester imagesize divisor divisor 2 self assertequalself modeltester outputstride divisor 2 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 def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs def testforsemanticsegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforsemanticsegmentationconfigandinputs slow def testmodelfrompretrainedself for modelname in mobilevitpretrainedmodelarchivelist 1 model mobilevitmodel 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 mobilevitmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return mobilevitimageprocessor frompretrainedapplemobilevitxxsmall if isvisionavailable else none slow def testinferenceimageclassificationheadself model mobilevitforimageclassification frompretrainedapplemobilevitxxsmall totorchdevice 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 9364 1 2327 0 4653 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 slow def testinferencesemanticsegmentationself model mobilevitforsemanticsegmentation frompretrainedappledeeplabv3mobilevitxxsmall model model totorchdevice imageprocessor mobilevitimageprocessor frompretrainedappledeeplabv3mobilevitxxsmall image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs logits outputs logits verify the logits expectedshape torch size1 21 32 32 self assertequallogits shape expectedshape expectedslice torch tensor 6 9713 6 9786 7 2422 7 2893 7 2825 7 4446 7 6580 7 8797 7 9420 10 6869 10 3250 10 3471 10 4228 9 9868 9 7132 11 0405 11 0221 10 7318 3 3089 2 8539 2 6740 3 2706 2 5621 2 5108 3 2534 2 6615 2 6651 devicetorchdevice self asserttruetorch allcloselogits0 3 3 3 expectedslice atol1e4 slow def testpostprocessingsemanticsegmentationself model mobilevitforsemanticsegmentation frompretrainedappledeeplabv3mobilevitxxsmall model model totorchdevice imageprocessor mobilevitimageprocessor frompretrainedappledeeplabv3mobilevitxxsmall image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs outputs logits outputs logits detach cpu segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs targetsizes50 60 expectedshape torch size50 60 self assertequalsegmentation0 shape expectedshape segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs expectedshape torch size32 32 self assertequalsegmentation0 shape expectedshape 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 mobilevit model here we also overwrite some of the tests of test_modeling_common py as mobilevit does not use input_ids inputs_embeds attention_mask and seq_length mobilevit s feature maps are of shape batch_size num_channels height width with the width and height being successively divided by 2 check that output_hidden_states also work using config we will verify our results on an image of cute cats forward pass verify the logits forward pass verify the logits forward pass
import unittest

from transformers import MobileViTConfig
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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
    from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor


class MobileViTConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "num_attention_heads"))


class MobileViTModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        patch_size=2,
        num_channels=3,
        last_hidden_size=32,
        num_attention_heads=4,
        hidden_act="silu",
        conv_kernel_size=3,
        output_stride=32,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        classifier_dropout_prob=0.1,
        initializer_range=0.02,
        is_training=True,
        use_labels=True,
        num_labels=10,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.last_hidden_size = last_hidden_size
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.conv_kernel_size = conv_kernel_size
        self.output_stride = output_stride
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.classifier_dropout_prob = classifier_dropout_prob
        self.use_labels = use_labels
        self.is_training = is_training
        self.num_labels = num_labels
        self.initializer_range = initializer_range
        self.scope = scope

    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.num_labels)
            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 MobileViTConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            num_attention_heads=self.num_attention_heads,
            hidden_act=self.hidden_act,
            conv_kernel_size=self.conv_kernel_size,
            output_stride=self.output_stride,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            classifier_dropout_prob=self.classifier_dropout_prob,
            initializer_range=self.initializer_range,
            hidden_sizes=[12, 16, 20],
            neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32],
        )

    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
        model = MobileViTModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.last_hidden_state.shape,
            (
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            ),
        )

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

    def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.num_labels
        model = MobileViTForSemanticSegmentation(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 // self.output_stride,
                self.image_size // self.output_stride,
            ),
        )
        result = model(pixel_values, labels=pixel_labels)
        self.parent.assertEqual(
            result.logits.shape,
            (
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            ),
        )

    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 MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": MobileViTModel,
            "image-classification": MobileViTForImageClassification,
            "image-segmentation": MobileViTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False

    def setUp(self):
        self.model_tester = MobileViTModelTester(self)
        self.config_tester = MobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False)

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

    @unittest.skip(reason="MobileViT does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="MobileViT does not support input and output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="MobileViT does not output attentions")
    def test_attention_outputs(self):
        pass

    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_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_stages = 5
            self.assertEqual(len(hidden_states), expected_num_stages)

            
            
            divisor = 2
            for i in range(len(hidden_states)):
                self.assertListEqual(
                    list(hidden_states[i].shape[-2:]),
                    [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
                )
                divisor *= 2

            self.assertEqual(self.model_tester.output_stride, divisor // 2)

        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 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 test_for_semantic_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = MobileViTModel.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 MobileViTModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small").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([-1.9364, -1.2327, -0.4653]).to(torch_device)

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

    @slow
    def test_inference_semantic_segmentation(self):
        model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
        model = model.to(torch_device)

        image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

        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, 21, 32, 32))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [
                [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
            ],
            device=torch_device,
        )

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

    @slow
    def test_post_processing_semantic_segmentation(self):
        model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
        model = model.to(torch_device)

        image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

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

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

        outputs.logits = outputs.logits.detach().cpu()

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(50, 60)])
        expected_shape = torch.Size((50, 60))
        self.assertEqual(segmentation[0].shape, expected_shape)

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
        expected_shape = torch.Size((32, 32))
        self.assertEqual(segmentation[0].shape, expected_shape)