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 glpn model import unittest from transformers 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 floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import modelmapping glpnconfig glpnfordepthestimation glpnmodel from transformers models glpn modelingglpn import glpnpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import glpnimageprocessor class glpnconfigtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig hiddensizes self parent asserttruehasattrconfig numattentionheads self parent asserttruehasattrconfig numencoderblocks class glpnmodeltester def init self parent batchsize13 imagesize64 numchannels3 numencoderblocks4 depths2 2 2 2 srratios8 4 2 1 hiddensizes16 32 64 128 downsamplingrates1 4 8 16 numattentionheads1 2 4 8 istrainingtrue uselabelstrue hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 initializerrange0 02 decoderhiddensize16 numlabels3 scopenone self parent parent self batchsize batchsize self imagesize imagesize self numchannels numchannels self numencoderblocks numencoderblocks self srratios srratios self depths depths self hiddensizes hiddensizes self downsamplingrates downsamplingrates self numattentionheads numattentionheads self istraining istraining self uselabels uselabels self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self decoderhiddensize decoderhiddensize self numlabels numlabels self scope scope 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 glpnconfig imagesizeself imagesize numchannelsself numchannels numencoderblocksself numencoderblocks depthsself depths hiddensizesself hiddensizes numattentionheadsself numattentionheads hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob initializerrangeself initializerrange decoderhiddensizeself decoderhiddensize def createandcheckmodelself config pixelvalues labels model glpnmodelconfigconfig model totorchdevice model eval result modelpixelvalues expectedheight expectedwidth self imagesize self downsamplingrates1 2 self parent assertequal result lasthiddenstate shape self batchsize self hiddensizes1 expectedheight expectedwidth def createandcheckfordepthestimationself config pixelvalues labels config numlabels self numlabels model glpnfordepthestimationconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult predicteddepth shape self batchsize self imagesize self imagesize result modelpixelvalues labelslabels 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 glpnmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses glpnmodel glpnfordepthestimation if istorchavailable else pipelinemodelmapping depthestimation glpnfordepthestimation featureextraction glpnmodel if istorchavailable else testheadmasking false testpruning false testresizeembeddings false def setupself self modeltester glpnmodeltesterself self configtester glpnconfigtesterself configclassglpnconfig def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testfordepthestimationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckfordepthestimationconfigandinputs unittest skipglpn does not use inputsembeds def testinputsembedsself pass unittest skipglpn does not have getinputembeddings method and getoutputembeddings methods def testmodelcommonattributesself pass def testattentionoutputsself config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses inputsdictoutputattentions true inputsdictoutputhiddenstates false config returndict true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass attentions outputs attentions expectednumattentions sumself modeltester depths self assertequallenattentions expectednumattentions check that outputattentions also work using config del inputsdictoutputattentions config outputattentions true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass attentions outputs attentions self assertequallenattentions expectednumattentions verify the first attentions first block first layer expectedseqlen self modeltester imagesize 4 2 expectedreducedseqlen self modeltester imagesize 4 self modeltester srratios0 2 self assertlistequal listattentions0 shape3 self modeltester numattentionheads0 expectedseqlen expectedreducedseqlen verify the last attentions last block last layer expectedseqlen self modeltester imagesize 32 2 expectedreducedseqlen self modeltester imagesize 32 self modeltester srratios1 2 self assertlistequal listattentions1 shape3 self modeltester numattentionheads1 expectedseqlen expectedreducedseqlen outlen lenoutputs check attention is always last and order is fine inputsdictoutputattentions true inputsdictoutputhiddenstates true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass self assertequaloutlen 1 lenoutputs selfattentions outputs attentions self assertequallenselfattentions expectednumattentions verify the first attentions first block first layer expectedseqlen self modeltester imagesize 4 2 expectedreducedseqlen self modeltester imagesize 4 self modeltester srratios0 2 self assertlistequal listselfattentions0 shape3 self modeltester numattentionheads0 expectedseqlen expectedreducedseqlen def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumlayers self modeltester numencoderblocks self assertequallenhiddenstates expectednumlayers verify the first hidden states first block self assertlistequal listhiddenstates0 shape3 self modeltester hiddensizes0 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 testtrainingself if not self modeltester istraining return config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses if modelclass in getvaluesmodelmapping continue todo remove the following 3 lines once we have a modelfordepthestimationmapping this can then be incorporated into prepareforclass in testmodelingcommon py if modelclass name glpnfordepthestimation batchsize numchannels height width inputsdictpixelvalues shape inputsdictlabels torch zeros self modeltester batchsize height width devicetorchdevice long model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward slow def testmodelfrompretrainedself for modelname in glpnpretrainedmodelarchivelist 1 model glpnmodel 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 glpnmodelintegrationtestunittest testcase slow def testinferencedepthestimationself imageprocessor glpnimageprocessor frompretrainedglpnpretrainedmodelarchivelist0 model glpnfordepthestimation frompretrainedglpnpretrainedmodelarchivelist0 totorchdevice image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the predicted depth expectedshape torch size1 480 640 self assertequaloutputs predicteddepth shape expectedshape expectedslice torch tensor 3 4291 2 7865 2 5151 3 2841 2 7021 2 3502 3 1147 2 4625 2 2481 totorchdevice self asserttruetorch allcloseoutputs predicteddepth0 3 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 pytorch glpn model check that output_attentions also work using config verify the first attentions first block first layer verify the last attentions last block last layer check attention is always last and order is fine verify the first attentions first block first layer verify the first hidden states first block check that output_hidden_states also work using config todo remove the following 3 lines once we have a model_for_depth_estimation_mapping this can then be incorporated into _prepare_for_class in test_modeling_common py we will verify our results on an image of cute cats forward pass verify the predicted depth
import unittest

from transformers 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
    from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import GLPNImageProcessor


class GLPNConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "num_attention_heads"))
        self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))


class GLPNModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        num_channels=3,
        num_encoder_blocks=4,
        depths=[2, 2, 2, 2],
        sr_ratios=[8, 4, 2, 1],
        hidden_sizes=[16, 32, 64, 128],
        downsampling_rates=[1, 4, 8, 16],
        num_attention_heads=[1, 2, 4, 8],
        is_training=True,
        use_labels=True,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        initializer_range=0.02,
        decoder_hidden_size=16,
        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_encoder_blocks = num_encoder_blocks
        self.sr_ratios = sr_ratios
        self.depths = depths
        self.hidden_sizes = hidden_sizes
        self.downsampling_rates = downsampling_rates
        self.num_attention_heads = num_attention_heads
        self.is_training = is_training
        self.use_labels = use_labels
        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.decoder_hidden_size = decoder_hidden_size
        self.num_labels = num_labels
        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.image_size, self.image_size], self.num_labels)

        config = self.get_config()
        return config, pixel_values, labels

    def get_config(self):
        return GLPNConfig(
            image_size=self.image_size,
            num_channels=self.num_channels,
            num_encoder_blocks=self.num_encoder_blocks,
            depths=self.depths,
            hidden_sizes=self.hidden_sizes,
            num_attention_heads=self.num_attention_heads,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            initializer_range=self.initializer_range,
            decoder_hidden_size=self.decoder_hidden_size,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = GLPNModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
        )

    def create_and_check_for_depth_estimation(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = GLPNForDepthEstimation(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))
        result = model(pixel_values, labels=labels)
        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 GLPNModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
    )

    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = GLPNModelTester(self)
        self.config_tester = GLPNConfigTester(self, config_class=GLPNConfig)

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

    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)

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

    @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods")
    def test_model_common_attributes(self):
        pass

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

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions

            expected_num_attentions = sum(self.model_tester.depths)
            self.assertEqual(len(attentions), expected_num_attentions)

            
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions

            self.assertEqual(len(attentions), expected_num_attentions)

            
            expected_seq_len = (self.model_tester.image_size // 4) ** 2
            expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
            )

            
            expected_seq_len = (self.model_tester.image_size // 32) ** 2
            expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
            self.assertListEqual(
                list(attentions[-1].shape[-3:]),
                [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],
            )
            out_len = len(outputs)

            
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            self.assertEqual(out_len + 1, len(outputs))

            self_attentions = outputs.attentions

            self.assertEqual(len(self_attentions), expected_num_attentions)
            
            expected_seq_len = (self.model_tester.image_size // 4) ** 2
            expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
            )

    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.hidden_states

            expected_num_layers = self.model_tester.num_encoder_blocks
            self.assertEqual(len(hidden_states), expected_num_layers)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]),
                [
                    self.model_tester.hidden_sizes[0],
                    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_training(self):
        if not self.model_tester.is_training:
            return

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

        for model_class in self.all_model_classes:
            if model_class in get_values(MODEL_MAPPING):
                continue
            
            
            if model_class.__name__ == "GLPNForDepthEstimation":
                batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
                inputs_dict["labels"] = torch.zeros(
                    [self.model_tester.batch_size, height, width], device=torch_device
                ).long()
            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()

    @slow
    def test_model_from_pretrained(self):
        for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = GLPNModel.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 GLPNModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_depth_estimation(self):
        image_processor = GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
        model = GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).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, 480, 640])
        self.assertEqual(outputs.predicted_depth.shape, expected_shape)

        expected_slice = torch.tensor(
            [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]
        ).to(torch_device)

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