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 vit hybrid model import unittest from transformers import vithybridconfig from transformers testingutils import requireaccelerate requiretorch requirevision slow torchdevice from transformers utils import cachedproperty istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin configzeroinit floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import vithybridforimageclassification vithybridimageprocessor vithybridmodel from transformers models vithybrid modelingvithybrid import vithybridpretrainedmodelarchivelist if isvisionavailable from pil import image class vithybridmodeltester def init self parent batchsize13 imagesize64 patchsize2 numchannels3 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 typesequencelabelsize10 initializerrange0 02 backbonefeatmapshape1 16 4 4 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 self backbonefeatmapshape backbonefeatmapshape in vit hybrid the seq length equals the number of patches 1 we add 1 for the cls token the number of patches is based on the feature map of the backbone which by default uses an output stride of 32 which means that the feature map has a spatial resolution of 132 of the input image size numpatches self imagesize 32 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 backboneconfig globalpadding same layertype bottleneck depths 3 4 9 outfeatures stage1 stage2 stage3 embeddingdynamicpadding true hiddensizes 4 8 16 32 numgroups 2 return vithybridconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob isdecoderfalse initializerrangeself initializerrange backbonefeatmapshapeself backbonefeatmapshape backboneconfigbackboneconfig def createandcheckmodelself config pixelvalues labels model vithybridmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckforimageclassificationself config pixelvalues labels config numlabels self typesequencelabelsize model vithybridforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class vithybridmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses vithybridmodel vithybridforimageclassification if istorchavailable else pipelinemodelmapping featureextraction vithybridmodel imageclassification vithybridforimageclassification if istorchavailable else testpruning false testresizeembeddings false testheadmasking false modelsplitpercents 0 5 0 9 def setupself self modeltester vithybridmodeltesterself self configtester configtesterself configclassvithybridconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonvit does not use inputsembeds def testinputsembedsself pass def testmodelcommonattributesself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig self assertisinstancemodel getinputembeddings nn module x model getoutputembeddings self asserttruex is none or isinstancex nn linear def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit skip the check for the backbone for name module in model namedmodules if module class name vithybridpatchembeddings backboneparams fname key for key in module statedict keys break for name param in model namedparameters if param requiresgrad if name in backboneparams continue self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized slow def testmodelfrompretrainedself for modelname in vithybridpretrainedmodelarchivelist 1 model vithybridmodel 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 vitmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return vithybridimageprocessor frompretrainedvithybridpretrainedmodelarchivelist0 if isvisionavailable else none slow def testinferenceimageclassificationheadself model vithybridforimageclassification frompretrainedvithybridpretrainedmodelarchivelist0 to torchdevice 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 9090 0 4993 0 2389 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 slow requireaccelerate def testaccelerateinferenceself imageprocessor vithybridimageprocessor frompretrainedgooglevithybridbasebit384 model vithybridforimageclassification frompretrainedgooglevithybridbasebit384 devicemapauto image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice outputs modelinputs logits outputs logits model predicts one of the 1000 imagenet classes predictedclassidx logits argmax1 item self asserttruemodel config id2labelpredictedclassidx tabby tabby cat 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 vit hybrid model in vit hybrid the seq length equals the number of patches 1 we add 1 for the cls token the number of patches is based on the feature map of the backbone which by default uses an output stride of 32 which means that the feature map has a spatial resolution of 1 32 of the input image size here we also overwrite some of the tests of test_modeling_common py as vit does not use input_ids inputs_embeds attention_mask and seq_length skip the check for the backbone we will verify our results on an image of cute cats forward pass verify the logits model predicts one of the 1000 imagenet classes
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
    from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image


class ViTHybridModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        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,
        backbone_featmap_shape=[1, 16, 4, 4],
        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
        self.backbone_featmap_shape = backbone_featmap_shape

        
        
        
        num_patches = (self.image_size // 32) ** 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):
        backbone_config = {
            "global_padding": "same",
            "layer_type": "bottleneck",
            "depths": [3, 4, 9],
            "out_features": ["stage1", "stage2", "stage3"],
            "embedding_dynamic_padding": True,
            "hidden_sizes": [4, 8, 16, 32],
            "num_groups": 2,
        }

        return ViTHybridConfig(
            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,
            backbone_featmap_shape=self.backbone_featmap_shape,
            backbone_config=backbone_config,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = ViTHybridModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.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 = ViTHybridForImageClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, labels=labels)
        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_torch
class ViTHybridModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
        if is_torch_available()
        else {}
    )
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    model_split_percents = [0.5, 0.9]

    def setUp(self):
        self.model_tester = ViTHybridModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ViTHybridConfig, 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

    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(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    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)

    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            
            for name, module in model.named_modules():
                if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                    backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
                    break

            for name, param in model.named_parameters():
                if param.requires_grad:
                    if name in backbone_params:
                        continue
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )

    @slow
    def test_model_from_pretrained(self):
        for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ViTHybridModel.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 ViTModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).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([-1.9090, -0.4993, -0.2389]).to(torch_device)

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

    @slow
    @require_accelerate
    def test_accelerate_inference(self):
        image_processor = ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384")
        model = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384", device_map="auto")

        image = prepare_img()

        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
        outputs = model(**inputs)
        logits = outputs.logits
        
        predicted_class_idx = logits.argmax(-1).item()

        self.assertTrue(model.config.id2label[predicted_class_idx], "tabby, tabby cat")