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 tensorflow vit model from future import annotations import inspect import unittest from transformers import vitconfig from transformers testingutils import requiretf requirevision slow from transformers utils import cachedproperty istfavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingtfcommon import tfmodeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istfavailable import tensorflow as tf from transformers import tfvitforimageclassification tfvitmodel if isvisionavailable from pil import image from transformers import vitimageprocessor class tfvitmodeltester def init self parent batchsize13 imagesize30 patchsize2 numchannels3 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 typesequencelabelsize10 initializerrange0 02 numlabels3 scopenone 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 in vit 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 if self uselabels labels idstensorself batchsize self typesequencelabelsize config self getconfig return config pixelvalues labels def getconfigself return vitconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob isdecoderfalse initializerrangeself initializerrange def createandcheckmodelself config pixelvalues labels model tfvitmodelconfigconfig result modelpixelvalues trainingfalse self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize test with an image with different size than the one specified in config imagesize self imagesize 2 pixelvalues pixelvalues imagesize imagesize result modelpixelvalues interpolateposencodingtrue trainingfalse seqlength imagesize self patchsize 2 1 self parent assertequalresult lasthiddenstate shape self batchsize seqlength self hiddensize def createandcheckforimageclassificationself config pixelvalues labels config numlabels self typesequencelabelsize model tfvitforimageclassificationconfig result modelpixelvalues labelslabels trainingfalse self parent assertequalresult logits shape self batchsize self typesequencelabelsize test with an image with different size than the one specified in config imagesize self imagesize 2 pixelvalues pixelvalues imagesize imagesize result modelpixelvalues interpolateposencodingtrue trainingfalse self parent assertequalresult logits shape self batchsize self typesequencelabelsize test greyscale images config numchannels 1 model tfvitforimageclassificationconfig pixelvalues floatstensorself batchsize 1 self imagesize self imagesize result modelpixelvalues self parent assertequalresult logits shape self batchsize self typesequencelabelsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretf class tfvitmodeltesttfmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses tfvitmodel tfvitforimageclassification if istfavailable else pipelinemodelmapping featureextraction tfvitmodel imageclassification tfvitforimageclassification if istfavailable else testresizeembeddings false testheadmasking false testonnx false def setupself self modeltester tfvitmodeltesterself self configtester configtesterself configclassvitconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonvit does not use inputsembeds def testinputsembedsself pass unittest skipreasonvit does not use inputsembeds def testgraphmodewithinputsembedsself pass def testmodelcommonattributesself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig self assertisinstancemodel getinputembeddings tf keras layers layer x model getoutputembeddings self asserttruex is none or isinstancex tf keras layers layer def testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel call signature parameters is an ordereddict so argnames order is deterministic argnames signature parameters keys expectedargnames pixelvalues self assertlistequalargnames 1 expectedargnames def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs slow def testmodelfrompretrainedself model tfvitmodel frompretrainedgooglevitbasepatch16224 self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretf requirevision class tfvitmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return vitimageprocessor frompretrainedgooglevitbasepatch16224 if isvisionavailable else none slow def testinferenceimageclassificationheadself model tfvitforimageclassification frompretrainedgooglevitbasepatch16224 imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorstf forward pass outputs modelinputs verify the logits expectedshape tf tensorshape1 1000 self assertequaloutputs logits shape expectedshape expectedslice tf constant0 2744 0 8215 0 0836 tf debugging assertnearoutputs logits0 3 expectedslice atol1e4 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 tensorflow vit model in vit the seq length equals the number of patches 1 we add 1 for the cls token test with an image with different size than the one specified in config test with an image with different size than the one specified in config test greyscale images here we also overwrite some of the tests of test_modeling_tf_common py as vit does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic we will verify our results on an image of cute cats forward pass verify the logits
from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class TFViTModelTester:
    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,
    ):
        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

        
        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
        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 ViTConfig(
            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,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = TFViTModel(config=config)
        result = model(pixel_values, training=False)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

        
        image_size = self.image_size // 2
        pixel_values = pixel_values[:, :, :image_size, :image_size]
        result = model(pixel_values, interpolate_pos_encoding=True, training=False)
        seq_length = (image_size // self.patch_size) ** 2 + 1
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, seq_length, self.hidden_size))

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.type_sequence_label_size
        model = TFViTForImageClassification(config)
        result = model(pixel_values, labels=labels, training=False)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

        
        image_size = self.image_size // 2
        pixel_values = pixel_values[:, :, :image_size, :image_size]
        result = model(pixel_values, interpolate_pos_encoding=True, training=False)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

        
        config.num_channels = 1
        model = TFViTForImageClassification(config)
        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        result = model(pixel_values)
        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_tf
class TFViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    test_resize_embeddings = False
    test_head_masking = False
    test_onnx = False

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

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

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

    @unittest.skip(reason="ViT does not use inputs_embeds")
    def test_graph_mode_with_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(), (tf.keras.layers.Layer))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))

    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.call)
            
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], 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_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):
        model = TFViTModel.from_pretrained("google/vit-base-patch16-224")
        self.assertIsNotNone(model)



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


@require_tf
@require_vision
class TFViTModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224")

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="tf")

        
        outputs = model(**inputs)

        
        expected_shape = tf.TensorShape((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = tf.constant([-0.2744, 0.8215, -0.0836])

        tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)