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 dpt model import unittest from transformers import dptconfig from transformers fileutils import istorchavailable isvisionavailable from transformers models auto import getvalues from transformers testingutils import requiretorch requirevision slow torchdevice 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 modelmapping dptfordepthestimation dptforsemanticsegmentation dptmodel from transformers models dpt modelingdpt import dptpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import dptimageprocessor class dptmodeltester def init self parent batchsize2 imagesize32 patchsize16 numchannels3 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers2 backboneoutindices0 1 2 3 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 initializerrange0 02 numlabels3 neckhiddensizes16 32 ishybridfalse 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 backboneoutindices backboneoutindices self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self numlabels numlabels self scope scope self ishybrid ishybrid self neckhiddensizes neckhiddensizes sequence length of dpt numpatches 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 imagesize self imagesize self numlabels config self getconfig return config pixelvalues labels def getconfigself return dptconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize fusionhiddensizeself hiddensize numhiddenlayersself numhiddenlayers backboneoutindicesself backboneoutindices numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob isdecoderfalse initializerrangeself initializerrange ishybridself ishybrid neckhiddensizesself neckhiddensizes def createandcheckmodelself config pixelvalues labels model dptmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckfordepthestimationself config pixelvalues labels config numlabels self numlabels model dptfordepthestimationconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult predicteddepth shape self batchsize self imagesize self imagesize def createandcheckforsemanticsegmentationself config pixelvalues labels config numlabels self numlabels model dptforsemanticsegmentationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequal result logits shape self batchsize self numlabels self imagesize self imagesize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class dptmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses dptmodel dptfordepthestimation dptforsemanticsegmentation if istorchavailable else pipelinemodelmapping depthestimation dptfordepthestimation featureextraction dptmodel imagesegmentation dptforsemanticsegmentation if istorchavailable else testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester dptmodeltesterself self configtester configtesterself configclassdptconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasondpt 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 testfordepthestimationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckfordepthestimationconfigandinputs def testforsemanticsegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforsemanticsegmentationconfigandinputs def testtrainingself for modelclass in self allmodelclasses if modelclass name dptfordepthestimation continue config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true if modelclass in getvaluesmodelmapping continue model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward def testtraininggradientcheckpointingself for modelclass in self allmodelclasses if modelclass name dptfordepthestimation continue config inputsdict self modeltester prepareconfigandinputsforcommon config usecache false config returndict true if modelclass in getvaluesmodelmapping or not modelclass supportsgradientcheckpointing continue model modelclassconfig model totorchdevice model gradientcheckpointingenable model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit skip the check for the backbone backboneparams for name module in model namedmodules if module class name dptvithybridembeddings 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 dptpretrainedmodelarchivelist 1 model dptmodel 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 slow class dptmodelintegrationtestunittest testcase def testinferencedepthestimationself imageprocessor dptimageprocessor frompretrainedinteldptlarge model dptfordepthestimation frompretrainedinteldptlarge totorchdevice image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs predicteddepth outputs predicteddepth verify the predicted depth expectedshape torch size1 384 384 self assertequalpredicteddepth shape expectedshape expectedslice torch tensor 6 3199 6 3629 6 4148 6 3850 6 3615 6 4166 6 3519 6 3176 6 3575 totorchdevice self asserttruetorch allcloseoutputs predicteddepth0 3 3 expectedslice atol1e4 def testinferencesemanticsegmentationself imageprocessor dptimageprocessor frompretrainedinteldptlargeade model dptforsemanticsegmentation frompretrainedinteldptlargeade totorchdevice image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 150 480 480 self assertequaloutputs logits shape expectedshape expectedslice torch tensor 4 0480 4 2420 4 4360 4 3124 4 5693 4 8261 4 5768 4 8965 5 2163 totorchdevice self asserttruetorch allcloseoutputs logits0 0 3 3 expectedslice atol1e4 def testpostprocessingsemanticsegmentationself imageprocessor dptimageprocessor frompretrainedinteldptlargeade model dptforsemanticsegmentation frompretrainedinteldptlargeade totorchdevice image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs outputs logits outputs logits detach cpu segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs targetsizes500 300 expectedshape torch size500 300 self assertequalsegmentation0 shape expectedshape segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs expectedshape torch size480 480 self assertequalsegmentation0 shape expectedshape 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 dpt model sequence length of dpt num_patches 1 we add 1 for the cls token here we also overwrite some of the tests of test_modeling_common py as dpt 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 predicted depth forward pass verify the logits forward pass
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
    from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor


class DPTModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        image_size=32,
        patch_size=16,
        num_channels=3,
        is_training=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=2,
        backbone_out_indices=[0, 1, 2, 3],
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        initializer_range=0.02,
        num_labels=3,
        neck_hidden_sizes=[16, 32],
        is_hybrid=False,
        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.backbone_out_indices = backbone_out_indices
        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.initializer_range = initializer_range
        self.num_labels = num_labels
        self.scope = scope
        self.is_hybrid = is_hybrid
        self.neck_hidden_sizes = neck_hidden_sizes
        
        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.image_size, self.image_size], self.num_labels)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return DPTConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            fusion_hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            backbone_out_indices=self.backbone_out_indices,
            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,
            is_hybrid=self.is_hybrid,
            neck_hidden_sizes=self.neck_hidden_sizes,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = DPTModel(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_depth_estimation(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = DPTForDepthEstimation(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size))

    def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = DPTForSemanticSegmentation(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, self.image_size, self.image_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 DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "depth-estimation": DPTForDepthEstimation,
            "feature-extraction": DPTModel,
            "image-segmentation": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

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

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

    @unittest.skip(reason="DPT 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_depth_estimation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*config_and_inputs)

    def test_for_semantic_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)

    def test_training(self):
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            if model_class in get_values(MODEL_MAPPING):
                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):
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.use_cache = False
            config.return_dict = True

            if model_class in get_values(MODEL_MAPPING) 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()

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    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)
            
            backbone_params = []
            for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                    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 DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = DPTModel.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
@slow
class DPTModelIntegrationTest(unittest.TestCase):
    def test_inference_depth_estimation(self):
        image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
        model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large").to(torch_device)

        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        
        with torch.no_grad():
            outputs = model(**inputs)
            predicted_depth = outputs.predicted_depth

        
        expected_shape = torch.Size((1, 384, 384))
        self.assertEqual(predicted_depth.shape, expected_shape)

        expected_slice = torch.tensor(
            [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]
        ).to(torch_device)

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

    def test_inference_semantic_segmentation(self):
        image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
        model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device)

        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, 150, 480, 480))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]
        ).to(torch_device)

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

    def test_post_processing_semantic_segmentation(self):
        image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
        model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device)

        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        
        with torch.no_grad():
            outputs = model(**inputs)

        outputs.logits = outputs.logits.detach().cpu()

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
        expected_shape = torch.Size((500, 300))
        self.assertEqual(segmentation[0].shape, expected_shape)

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
        expected_shape = torch.Size((480, 480))
        self.assertEqual(segmentation[0].shape, expected_shape)