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 tensorflow convnext model from future import annotations import inspect import unittest from typing import list tuple from transformers import convnextconfig 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 tfconvnextforimageclassification tfconvnextmodel if isvisionavailable from pil import image from transformers import convnextimageprocessor class tfconvnextmodeltester def init self parent batchsize13 imagesize32 numchannels3 numstages4 hiddensizes10 20 30 40 depths2 2 3 2 istrainingtrue uselabelstrue intermediatesize37 hiddenactgelu typesequencelabelsize10 initializerrange0 02 numlabels3 scopenone self parent parent self batchsize batchsize self imagesize imagesize self numchannels numchannels self numstages numstages self hiddensizes hiddensizes self depths depths self istraining istraining self uselabels uselabels self intermediatesize intermediatesize self hiddenact hiddenact self typesequencelabelsize typesequencelabelsize self initializerrange initializerrange self scope scope 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 convnextconfig numchannelsself numchannels hiddensizesself hiddensizes depthsself depths numstagesself numstages hiddenactself hiddenact isdecoderfalse initializerrangeself initializerrange def createandcheckmodelself config pixelvalues labels model tfconvnextmodelconfigconfig result modelpixelvalues trainingfalse expected last hidden states b c h 32 w 32 self parent assertequal result lasthiddenstate shape self batchsize self hiddensizes1 self imagesize 32 self imagesize 32 def createandcheckforimageclassificationself config pixelvalues labels config numlabels self typesequencelabelsize model tfconvnextforimageclassificationconfig result modelpixelvalues labelslabels trainingfalse 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 tfconvnextmodeltesttfmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses tfconvnextmodel tfconvnextforimageclassification if istfavailable else pipelinemodelmapping featureextraction tfconvnextmodel imageclassification tfconvnextforimageclassification if istfavailable else testpruning false testonnx false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester tfconvnextmodeltesterself self configtester configtester self configclassconvnextconfig hastextmodalityfalse hiddensize37 unittest skipreasonconvnext does not use inputsembeds def testinputsembedsself pass 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 skipreasonconvnext does not support input and output embeddings def testmodelcommonattributesself pass 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 unittest skipif not istfavailable or lentf config listphysicaldevicesgpu 0 reasontf does not support backprop for grouped convolutions on cpu def testdatasetconversionself super testdatasetconversion def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs encoderhiddenstates if config isencoderdecoder else outputs hiddenstates expectednumstages self modeltester numstages self assertequallenhiddenstates expectednumstages 1 convnext s feature maps are of shape batchsize numchannels height width self assertlistequal listhiddenstates0 shape2 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 since convnext does not have any attention we need to rewrite this test def testmodeloutputsequivalenceself config inputsdict self modeltester prepareconfigandinputsforcommon def checkequivalencemodel tupleinputs dictinputs additionalkwargs tupleoutput modeltupleinputs returndictfalse additionalkwargs dictoutput modeldictinputs returndicttrue additionalkwargs totuple def recursivechecktupleobject dictobject if isinstancetupleobject list tuple for tupleiterablevalue dictiterablevalue in ziptupleobject dictobject recursivechecktupleiterablevalue dictiterablevalue elif tupleobject is none return else self asserttrue alltf equaltupleobject dictobject msg tuple and dict output are not equal difference f tf math reducemaxtf abstupleobject dictobject recursivechecktupleoutput dictoutput for modelclass in self allmodelclasses model modelclassconfig tupleinputs self prepareforclassinputsdict modelclass dictinputs self prepareforclassinputsdict modelclass checkequivalencemodel tupleinputs dictinputs tupleinputs self prepareforclassinputsdict modelclass returnlabelstrue dictinputs self prepareforclassinputsdict modelclass returnlabelstrue checkequivalencemodel tupleinputs dictinputs tupleinputs self prepareforclassinputsdict modelclass dictinputs self prepareforclassinputsdict modelclass checkequivalencemodel tupleinputs dictinputs outputhiddenstates true tupleinputs self prepareforclassinputsdict modelclass returnlabelstrue dictinputs self prepareforclassinputsdict modelclass returnlabelstrue checkequivalencemodel tupleinputs dictinputs outputhiddenstates true def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs slow def testmodelfrompretrainedself model tfconvnextmodel frompretrainedfacebookconvnexttiny224 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 tfconvnextmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return convnextimageprocessor frompretrainedfacebookconvnexttiny224 if isvisionavailable else none slow def testinferenceimageclassificationheadself model tfconvnextforimageclassification frompretrainedfacebookconvnexttiny224 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 0260 0 4739 0 1911 tf debugging assertnearoutputs logits0 3 expectedslice atol1e4 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 tensorflow convnext model expected last hidden states b c h 32 w 32 here we also overwrite some of the tests of test_modeling_common py as convnext does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic convnext s feature maps are of shape batch_size num_channels height width check that output_hidden_states also work using config since convnext does not have any attention we need to rewrite this test 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 typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor


class TFConvNextModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        num_channels=3,
        num_stages=4,
        hidden_sizes=[10, 20, 30, 40],
        depths=[2, 2, 3, 2],
        is_training=True,
        use_labels=True,
        intermediate_size=37,
        hidden_act="gelu",
        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.num_channels = num_channels
        self.num_stages = num_stages
        self.hidden_sizes = hidden_sizes
        self.depths = depths
        self.is_training = is_training
        self.use_labels = use_labels
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.type_sequence_label_size = type_sequence_label_size
        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
        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 ConvNextConfig(
            num_channels=self.num_channels,
            hidden_sizes=self.hidden_sizes,
            depths=self.depths,
            num_stages=self.num_stages,
            hidden_act=self.hidden_act,
            is_decoder=False,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = TFConvNextModel(config=config)
        result = model(pixel_values, training=False)
        
        self.parent.assertEqual(
            result.last_hidden_state.shape,
            (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
        )

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.type_sequence_label_size
        model = TFConvNextForImageClassification(config)
        result = model(pixel_values, labels=labels, training=False)
        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 TFConvNextModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

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

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

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

    @unittest.skip(reason="ConvNext does not use inputs_embeds")
    def test_inputs_embeds(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.",
    )
    @slow
    def test_keras_fit(self):
        super().test_keras_fit()

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

    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)

    @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()

    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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            expected_num_stages = self.model_tester.num_stages
            self.assertEqual(len(hidden_states), expected_num_stages + 1)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [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_outputs_equivalence(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
            dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

            def recursive_check(tuple_object, dict_object):
                if isinstance(tuple_object, (List, Tuple)):
                    for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                        recursive_check(tuple_iterable_value, dict_iterable_value)
                elif tuple_object is None:
                    return
                else:
                    self.assertTrue(
                        all(tf.equal(tuple_object, dict_object)),
                        msg=(
                            "Tuple and dict output are not equal. Difference:"
                            f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
                        ),
                    )

                recursive_check(tuple_output, dict_output)

        for model_class in self.all_model_classes:
            model = model_class(config)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

    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 = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
        self.assertIsNotNone(model)



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


@require_tf
@require_vision
class TFConvNextModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-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([-0.0260, -0.4739, 0.1911])

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