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 dpt model import unittest from transformers import dinov2config 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 transformers import modelmapping dptfordepthestimation from transformers models dpt modelingdpt import dptpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import dptimageprocessor class dptmodeltester def init self parent batchsize2 numchannels3 imagesize32 patchsize16 uselabelstrue numlabels3 istrainingtrue hiddensize4 numhiddenlayers2 numattentionheads2 intermediatesize8 outfeaturesstage1 stage2 applylayernormfalse reshapehiddenstatesfalse neckhiddensizes2 2 fusionhiddensize6 self parent parent self batchsize batchsize self numchannels numchannels self imagesize imagesize self patchsize patchsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self outfeatures outfeatures self applylayernorm applylayernorm self reshapehiddenstates reshapehiddenstates self uselabels uselabels self numlabels numlabels self istraining istraining self neckhiddensizes neckhiddensizes self fusionhiddensize fusionhiddensize dpt s sequence length self seqlength self imagesize self patchsize 2 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 backboneconfigself getbackboneconfig neckhiddensizesself neckhiddensizes fusionhiddensizeself fusionhiddensize def getbackboneconfigself return dinov2config imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize istrainingself istraining outfeaturesself outfeatures reshapehiddenstatesself reshapehiddenstates 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 prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class dptmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses dptfordepthestimation if istorchavailable else pipelinemodelmapping depthestimation dptfordepthestimation 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 with autobackbone does not have a base model and hence no inputembeddings def testinputsembedsself pass def testfordepthestimationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckfordepthestimationconfigandinputs 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 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 unittest skipreasondpt with autobackbone does not have a base model and hence no inputembeddings def testmodelcommonattributesself pass unittest skipreasondpt with autobackbone does not have a base model def testsaveloadfastinitfrombaseself pass unittest skipreasondpt with autobackbone does not have a base model def testsaveloadfastinittobaseself pass 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 slow def testmodelfrompretrainedself for modelname in dptpretrainedmodelarchivelist 1 model dptfordepthestimation 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 frompretrainedfacebookdptdinov2smallkitti model dptfordepthestimation frompretrainedfacebookdptdinov2smallkitti totorchdevice image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs predicteddepth outputs predicteddepth verify the predicted depth expectedshape torch size1 576 736 self assertequalpredicteddepth shape expectedshape expectedslice torch tensor 6 0433 7 1636 7 4268 6 9047 7 2471 7 2355 7 9261 8 0631 8 0244 totorchdevice self asserttruetorch allcloseoutputs predicteddepth0 3 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 dpt model dpt s sequence length 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
import unittest

from transformers import Dinov2Config, 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 transformers import MODEL_MAPPING, DPTForDepthEstimation
    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,
        num_channels=3,
        image_size=32,
        patch_size=16,
        use_labels=True,
        num_labels=3,
        is_training=True,
        hidden_size=4,
        num_hidden_layers=2,
        num_attention_heads=2,
        intermediate_size=8,
        out_features=["stage1", "stage2"],
        apply_layernorm=False,
        reshape_hidden_states=False,
        neck_hidden_sizes=[2, 2],
        fusion_hidden_size=6,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        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.out_features = out_features
        self.apply_layernorm = apply_layernorm
        self.reshape_hidden_states = reshape_hidden_states
        self.use_labels = use_labels
        self.num_labels = num_labels
        self.is_training = is_training
        self.neck_hidden_sizes = neck_hidden_sizes
        self.fusion_hidden_size = fusion_hidden_size
        
        self.seq_length = (self.image_size // self.patch_size) ** 2 + 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(
            backbone_config=self.get_backbone_config(),
            neck_hidden_sizes=self.neck_hidden_sizes,
            fusion_hidden_size=self.fusion_hidden_size,
        )

    def get_backbone_config(self):
        return Dinov2Config(
            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,
            is_training=self.is_training,
            out_features=self.out_features,
            reshape_hidden_states=self.reshape_hidden_states,
        )

    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 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 = (DPTForDepthEstimation,) if is_torch_available() else ()
    pipeline_model_mapping = {"depth-estimation": DPTForDepthEstimation} 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 with AutoBackbone does not have a base model and hence no input_embeddings")
    def test_inputs_embeds(self):
        pass

    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_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()

    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",
                    )

    @unittest.skip(reason="DPT with AutoBackbone does not have a base model and hence no input_embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="DPT with AutoBackbone does not have a base model")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="DPT with AutoBackbone does not have a base model")
    def test_save_load_fast_init_to_base(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(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

    @slow
    def test_model_from_pretrained(self):
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = DPTForDepthEstimation.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("facebook/dpt-dinov2-small-kitti")
        model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-small-kitti").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, 576, 736))
        self.assertEqual(predicted_depth.shape, expected_shape)

        expected_slice = torch.tensor(
            [[6.0433, 7.1636, 7.4268], [6.9047, 7.2471, 7.2355], [7.9261, 8.0631, 8.0244]]
        ).to(torch_device)

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