testing suite for the tensorflow cvt model from future import annotations import inspect import unittest from math import floor import numpy as np from transformers import cvtconfig 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 tfcvtforimageclassification tfcvtmodel from transformers models cvt modelingtfcvt import tfcvtpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class tfcvtconfigtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig embeddim self parent asserttruehasattrconfig numheads class tfcvtmodeltester def init self parent batchsize13 imagesize64 numchannels3 embeddim16 32 48 numheads1 2 3 depth1 2 10 patchsizes7 3 3 patchstride4 2 2 patchpadding2 1 1 stridekv2 2 2 clstokenfalse false true attentiondroprate0 0 0 0 0 0 initializerrange0 02 layernormeps1e12 istrainingtrue uselabelstrue numlabels2 self parent parent self batchsize batchsize self imagesize imagesize self patchsizes patchsizes self patchstride patchstride self patchpadding patchpadding self istraining istraining self uselabels uselabels self numlabels numlabels self numchannels numchannels self embeddim embeddim self numheads numheads self stridekv stridekv self depth depth self clstoken clstoken self attentiondroprate attentiondroprate self initializerrange initializerrange self layernormeps layernormeps def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels create a random int32 tensor of given shape labels idstensorself batchsize self numlabels config self getconfig return config pixelvalues labels def getconfigself return cvtconfig imagesizeself imagesize numlabelsself numlabels numchannelsself numchannels embeddimself embeddim numheadsself numheads patchsizesself patchsizes patchpaddingself patchpadding patchstrideself patchstride stridekvself stridekv depthself depth clstokenself clstoken attentiondroprateself attentiondroprate initializerrangeself initializerrange def createandcheckmodelself config pixelvalues labels model tfcvtmodelconfigconfig result modelpixelvalues trainingfalse imagesize self imagesize self imagesize height width imagesize0 imagesize1 for i in rangelenself depth height floorheight 2 self patchpaddingi self patchsizesi self patchstridei 1 width floorwidth 2 self patchpaddingi self patchsizesi self patchstridei 1 self parent assertequalresult lasthiddenstate shape self batchsize self embeddim1 height width def createandcheckforimageclassificationself config pixelvalues labels config numlabels self numlabels model tfcvtforimageclassificationconfig result modelpixelvalues labelslabels trainingfalse self parent assertequalresult logits shape self batchsize self numlabels def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretf class tfcvtmodeltesttfmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses tfcvtmodel tfcvtforimageclassification if istfavailable else pipelinemodelmapping featureextraction tfcvtmodel imageclassification tfcvtforimageclassification if istfavailable else testpruning false testresizeembeddings false testheadmasking false hasattentions false testonnx false def setupself self modeltester tfcvtmodeltesterself self configtester tfcvtconfigtesterself configclasscvtconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester createandtestconfigcommonproperties self configtester createandtestconfigtojsonstring self configtester createandtestconfigtojsonfile self configtester createandtestconfigfromandsavepretrained self configtester createandtestconfigwithnumlabels self configtester checkconfigcanbeinitwithoutparams self configtester checkconfigargumentsinit unittest skipreasoncvt does not output attentions def testattentionoutputsself pass unittest skipreasoncvt does not use inputsembeds def testinputsembedsself pass unittest skipreasoncvt does not support input and output embeddings def testmodelcommonattributesself pass unittest skipif not istfavailable or lentf config listphysicaldevicesgpu 0 reasontf does not support backprop for grouped convolutions on cpu def testdatasetconversionself super testdatasetconversion unittest skipif not istfavailable or lentf config listphysicaldevicesgpu 0 reasontf does not support backprop for grouped convolutions on cpu slow def testkerasfitself super testkerasfit unittest skipreasonget failed to determine best cudnn convolution algo error after using tf 2 12cuda 11 8 def testkerasfitmixedprecisionself policy tf keras mixedprecision policymixedfloat16 tf keras mixedprecision setglobalpolicypolicy super testkerasfit tf keras mixedprecision setglobalpolicyfloat32 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 testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumlayers lenself modeltester depth self assertequallenhiddenstates expectednumlayers verify the first hidden states first block self assertlistequal listhiddenstates0 shape3 self modeltester embeddim0 self modeltester imagesize 4 self modeltester imagesize 4 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 testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs slow def testmodelfrompretrainedself for modelname in tfcvtpretrainedmodelarchivelist 1 model tfcvtmodel frompretrainedmodelname 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 tfcvtmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return autoimageprocessor frompretrainedtfcvtpretrainedmodelarchivelist0 slow def testinferenceimageclassificationheadself model tfcvtforimageclassification frompretrainedtfcvtpretrainedmodelarchivelist0 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 9285 0 9015 0 3150 self asserttruenp allcloseoutputs logits0 3 numpy expectedslice atol1e4 create a random int32 tensor of given shape here we also overwrite some of the tests of test_modeling_common py as cvt does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic verify the first hidden states first block 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
from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
    from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class TFCvtConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "embed_dim"))
        self.parent.assertTrue(hasattr(config, "num_heads"))


class TFCvtModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        num_channels=3,
        embed_dim=[16, 32, 48],
        num_heads=[1, 2, 3],
        depth=[1, 2, 10],
        patch_sizes=[7, 3, 3],
        patch_stride=[4, 2, 2],
        patch_padding=[2, 1, 1],
        stride_kv=[2, 2, 2],
        cls_token=[False, False, True],
        attention_drop_rate=[0.0, 0.0, 0.0],
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        is_training=True,
        use_labels=True,
        num_labels=2,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_sizes = patch_sizes
        self.patch_stride = patch_stride
        self.patch_padding = patch_padding
        self.is_training = is_training
        self.use_labels = use_labels
        self.num_labels = num_labels
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.stride_kv = stride_kv
        self.depth = depth
        self.cls_token = cls_token
        self.attention_drop_rate = attention_drop_rate
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps

    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.num_labels)

        config = self.get_config()
        return config, pixel_values, labels

    def get_config(self):
        return CvtConfig(
            image_size=self.image_size,
            num_labels=self.num_labels,
            num_channels=self.num_channels,
            embed_dim=self.embed_dim,
            num_heads=self.num_heads,
            patch_sizes=self.patch_sizes,
            patch_padding=self.patch_padding,
            patch_stride=self.patch_stride,
            stride_kv=self.stride_kv,
            depth=self.depth,
            cls_token=self.cls_token,
            attention_drop_rate=self.attention_drop_rate,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = TFCvtModel(config=config)
        result = model(pixel_values, training=False)
        image_size = (self.image_size, self.image_size)
        height, width = image_size[0], image_size[1]
        for i in range(len(self.depth)):
            height = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
            width = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dim[-1], height, width))

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

    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 TFCvtModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
        if is_tf_available()
        else {}
    )
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False
    test_onnx = False

    def setUp(self):
        self.model_tester = TFCvtModelTester(self)
        self.config_tester = TFCvtConfigTester(self, config_class=CvtConfig, has_text_modality=False, hidden_size=37)

    def test_config(self):
        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

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

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

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

    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
        reason="TF does not support backprop for grouped convolutions on CPU.",
    )
    def test_dataset_conversion(self):
        super().test_dataset_conversion()

    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
        reason="TF does not support backprop for grouped convolutions on CPU.",
    )
    @slow
    def test_keras_fit(self):
        super().test_keras_fit()

    @unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8")
    def test_keras_fit_mixed_precision(self):
        policy = tf.keras.mixed_precision.Policy("mixed_float16")
        tf.keras.mixed_precision.set_global_policy(policy)
        super().test_keras_fit()
        tf.keras.mixed_precision.set_global_policy("float32")

    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_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)

            outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            hidden_states = outputs.hidden_states

            expected_num_layers = len(self.model_tester.depth)
            self.assertEqual(len(hidden_states), expected_num_layers)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]),
                [
                    self.model_tester.embed_dim[0],
                    self.model_tester.image_size // 4,
                    self.model_tester.image_size // 4,
                ],
            )

        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_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):
        for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFCvtModel.from_pretrained(model_name)
            self.assertIsNotNone(model)



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


@require_tf
@require_vision
class TFCvtModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])

    @slow
    def test_inference_image_classification_head(self):
        model = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])

        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([0.9285, 0.9015, -0.3150])
        self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), expected_slice, atol=1e-4))