codingutf8 2023 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 convnextv2 model import unittest from transformers import convnextv2config from transformers models auto import getvalues from transformers models auto modelingauto import modelforbackbonemappingnames modelmappingnames 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 convnextv2backbone convnextv2forimageclassification convnextv2model from transformers models convnextv2 modelingconvnextv2 import convnextv2pretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class convnextv2modeltester def init self parent batchsize13 imagesize32 numchannels3 numstages4 hiddensizes10 20 30 40 depths2 2 3 2 istrainingtrue uselabelstrue intermediatesize37 hiddenactgelu numlabels10 initializerrange0 02 outfeaturesstage2 stage3 stage4 outindices2 3 4 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 numlabels numlabels self initializerrange initializerrange self outfeatures outfeatures self outindices outindices self scope scope def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels labels idstensorself batchsize self numlabels config self getconfig return config pixelvalues labels def getconfigself return convnextv2config numchannelsself numchannels hiddensizesself hiddensizes depthsself depths numstagesself numstages hiddenactself hiddenact isdecoderfalse initializerrangeself initializerrange outfeaturesself outfeatures outindicesself outindices numlabelsself numlabels def createandcheckmodelself config pixelvalues labels model convnextv2modelconfigconfig model totorchdevice model eval result modelpixelvalues 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 model convnextv2forimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self numlabels def createandcheckbackboneself config pixelvalues labels model convnextv2backboneconfigconfig model totorchdevice model eval result modelpixelvalues verify hidden states self parent assertequallenresult featuremaps lenconfig outfeatures self parent assertlistequallistresult featuremaps0 shape self batchsize self hiddensizes1 4 4 verify channels self parent assertequallenmodel channels lenconfig outfeatures self parent assertlistequalmodel channels config hiddensizes1 verify backbone works with outfeaturesnone config outfeatures none model convnextv2backboneconfigconfig model totorchdevice model eval result modelpixelvalues verify feature maps self parent assertequallenresult featuremaps 1 self parent assertlistequallistresult featuremaps0 shape self batchsize self hiddensizes1 1 1 verify channels self parent assertequallenmodel channels 1 self parent assertlistequalmodel channels config hiddensizes1 def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict def prepareconfigandinputswithlabelsself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues labels labels return config inputsdict requiretorch class convnextv2modeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses convnextv2model convnextv2forimageclassification convnextv2backbone if istorchavailable else pipelinemodelmapping featureextraction convnextv2model imageclassification convnextv2forimageclassification if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester convnextv2modeltesterself self configtester configtesterself configclassconvnextv2config hastextmodalityfalse hiddensize37 def testconfigself self createandtestconfigcommonproperties self configtester createandtestconfigtojsonstring self configtester createandtestconfigtojsonfile self configtester createandtestconfigfromandsavepretrained self configtester createandtestconfigwithnumlabels self configtester checkconfigcanbeinitwithoutparams self configtester checkconfigargumentsinit def createandtestconfigcommonpropertiesself return unittest skipreasonconvnextv2 does not use inputsembeds def testinputsembedsself pass unittest skipreasonconvnextv2 does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonconvnextv2 does not use feedforward chunking def testfeedforwardchunkingself pass def testtrainingself if not self modeltester istraining return for modelclass in self allmodelclasses config inputsdict self modeltester prepareconfigandinputswithlabels config returndict true if modelclass name in getvaluesmodelmappingnames getvaluesmodelforbackbonemappingnames continue model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward def testtraininggradientcheckpointingself if not self modeltester istraining return for modelclass in self allmodelclasses config inputsdict self modeltester prepareconfigandinputswithlabels config usecache false config returndict true if modelclass name in getvaluesmodelmappingnames getvaluesmodelforbackbonemappingnames or not modelclass supportsgradientcheckpointing continue model modelclassconfig model totorchdevice model gradientcheckpointingenable model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward 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 encoderhiddenstates if config isencoderdecoder else outputs hiddenstates expectednumstages self modeltester numstages self assertequallenhiddenstates expectednumstages 1 convnextv2 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 def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs slow def testmodelfrompretrainedself for modelname in convnextv2pretrainedmodelarchivelist 1 model convnextv2model 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 convnextv2modelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return autoimageprocessor frompretrainedfacebookconvnextv2tiny1k224 if isvisionavailable else none slow def testinferenceimageclassificationheadself model convnextv2forimageclassification frompretrainedfacebookconvnextv2tiny1k224 totorchdevice preprocessor self defaultimageprocessor image prepareimg inputs preprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 1000 self assertequaloutputs logits shape expectedshape expectedslice torch tensor0 9996 0 1966 0 4386 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 coding utf 8 2023 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 convnextv2 model expected last hidden states b c h 32 w 32 verify hidden states verify channels verify backbone works with out_features none verify feature maps verify channels here we also overwrite some of the tests of test_modeling_common py as convnextv2 does not use input_ids inputs_embeds attention_mask and seq_length convnextv2 s feature maps are of shape batch_size num_channels height width 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
import unittest

from transformers import ConvNextV2Config
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 ConvNextV2Backbone, ConvNextV2ForImageClassification, ConvNextV2Model
    from transformers.models.convnextv2.modeling_convnextv2 import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class ConvNextV2ModelTester:
    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",
        num_labels=10,
        initializer_range=0.02,
        out_features=["stage2", "stage3", "stage4"],
        out_indices=[2, 3, 4],
        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.num_labels = num_labels
        self.initializer_range = initializer_range
        self.out_features = out_features
        self.out_indices = out_indices
        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.num_labels)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return ConvNextV2Config(
            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,
            out_features=self.out_features,
            out_indices=self.out_indices,
            num_labels=self.num_labels,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = ConvNextV2Model(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        
        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):
        model = ConvNextV2ForImageClassification(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_backbone(self, config, pixel_values, labels):
        model = ConvNextV2Backbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        
        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4])

        
        self.parent.assertEqual(len(model.channels), len(config.out_features))
        self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])

        
        config.out_features = None
        model = ConvNextV2Backbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        
        self.parent.assertEqual(len(result.feature_maps), 1)
        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1])

        
        self.parent.assertEqual(len(model.channels), 1)
        self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])

    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

    def prepare_config_and_inputs_with_labels(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, labels = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values, "labels": labels}
        return config, inputs_dict


@require_torch
class ConvNextV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (
            ConvNextV2Model,
            ConvNextV2ForImageClassification,
            ConvNextV2Backbone,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {"feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification}
        if is_torch_available()
        else {}
    )

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

    def setUp(self):
        self.model_tester = ConvNextV2ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ConvNextV2Config, has_text_modality=False, hidden_size=37)

    def test_config(self):
        self.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()

    def create_and_test_config_common_properties(self):
        return

    @unittest.skip(reason="ConvNextV2 does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="ConvNextV2 does not support input and output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="ConvNextV2 does not use feedforward chunking")
    def test_feed_forward_chunking(self):
        pass

    def test_training(self):
        if not self.model_tester.is_training:
            return

        for model_class in self.all_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels()
            config.return_dict = True

            if model_class.__name__ in [
                *get_values(MODEL_MAPPING_NAMES),
                *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES),
            ]:
                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):
        if not self.model_tester.is_training:
            return

        for model_class in self.all_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels()
            config.use_cache = False
            config.return_dict = True

            if (
                model_class.__name__
                in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)]
                or not model_class.supports_gradient_checkpointing
            ):
                continue

            model = model_class(config)
            model.to(torch_device)
            model.gradient_checkpointing_enable()
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    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.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_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 CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ConvNextV2Model.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 ConvNextV2ModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224").to(torch_device)

        preprocessor = self.default_image_processor
        image = prepare_img()
        inputs = preprocessor(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([0.9996, 0.1966, -0.4386]).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))