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 yolos model import unittest from transformers import yolosconfig from transformers testingutils import requiretorch requirevision slow torchdevice from transformers utils import cachedproperty istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import yolosforobjectdetection yolosmodel from transformers models yolos modelingyolos import yolospretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class yolosmodeltester def init self parent batchsize13 imagesize30 30 patchsize2 numchannels3 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 typesequencelabelsize10 initializerrange0 02 numlabels3 scopenone ntargets8 numdetectiontokens10 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 numlabels numlabels self scope scope self ntargets ntargets self numdetectiontokens numdetectiontokens we set the expected sequence length which is used in several tests expected sequence length numpatches 1 we add 1 for the cls token numdetectiontokens numpatches imagesize1 patchsize imagesize0 patchsize self expectedseqlen numpatches 1 self numdetectiontokens def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize0 self imagesize1 labels none if self uselabels labels is a list of dict each dict being the labels for a given example in the batch labels for i in rangeself batchsize target targetclasslabels torch randint highself numlabels sizeself ntargets devicetorchdevice targetboxes torch randself ntargets 4 devicetorchdevice labels appendtarget config self getconfig return config pixelvalues labels def getconfigself return yolosconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob isdecoderfalse initializerrangeself initializerrange numdetectiontokensself numdetectiontokens numlabelsself numlabels def createandcheckmodelself config pixelvalues labels model yolosmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result lasthiddenstate shape self batchsize self expectedseqlen self hiddensize def createandcheckforobjectdetectionself config pixelvalues labels model yolosforobjectdetectionconfig model totorchdevice model eval result modelpixelvaluespixelvalues result modelpixelvalues self parent assertequalresult logits shape self batchsize self numdetectiontokens self numlabels 1 self parent assertequalresult predboxes shape self batchsize self numdetectiontokens 4 result modelpixelvaluespixelvalues labelslabels self parent assertequalresult loss shape self parent assertequalresult logits shape self batchsize self numdetectiontokens self numlabels 1 self parent assertequalresult predboxes shape self batchsize self numdetectiontokens 4 def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class yolosmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses yolosmodel yolosforobjectdetection if istorchavailable else pipelinemodelmapping featureextraction yolosmodel objectdetection yolosforobjectdetection if istorchavailable else testpruning false testresizeembeddings false testheadmasking false testtorchscript false special case for head model def prepareforclassself inputsdict modelclass returnlabelsfalse inputsdict super prepareforclassinputsdict modelclass returnlabelsreturnlabels if returnlabels if modelclass name yolosforobjectdetection labels for i in rangeself modeltester batchsize target targetclasslabels torch ones sizeself modeltester ntargets devicetorchdevice dtypetorch long targetboxes torch ones self modeltester ntargets 4 devicetorchdevice dtypetorch float labels appendtarget inputsdictlabels labels return inputsdict def setupself self modeltester yolosmodeltesterself self configtester configtesterself configclassyolosconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests def testinputsembedsself yolos does not use inputsembeds 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 testattentionoutputsself config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true in yolos the seqlen is different seqlen self modeltester expectedseqlen 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 self assertequallenattentions self modeltester numhiddenlayers 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 self modeltester numhiddenlayers self assertlistequal listattentions0 shape3 self modeltester numattentionheads seqlen seqlen 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 addedhiddenstates 1 self assertequaloutlen addedhiddenstates lenoutputs selfattentions outputs attentions self assertequallenselfattentions self modeltester numhiddenlayers self assertlistequal listselfattentions0 shape3 self modeltester numattentionheads seqlen seqlen def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumlayers getattr self modeltester expectednumhiddenlayers self modeltester numhiddenlayers 1 self assertequallenhiddenstates expectednumlayers yolos has a different seqlength seqlength self modeltester expectedseqlen self assertlistequal listhiddenstates0 shape2 seqlength self modeltester hiddensize 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 testforobjectdetectionself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforobjectdetectionconfigandinputs slow def testmodelfrompretrainedself for modelname in yolospretrainedmodelarchivelist 1 model yolosmodel 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 yolosmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return autoimageprocessor frompretrainedhustvlyolossmall if isvisionavailable else none slow def testinferenceobjectdetectionheadself model yolosforobjectdetection frompretrainedhustvlyolossmall totorchdevice imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs pixelvalues verify outputs expectedshape torch size1 100 92 self assertequaloutputs logits shape expectedshape expectedslicelogits torch tensor 24 0248 10 3024 14 8290 42 0392 16 8200 27 4334 27 2743 11 8154 18 7148 devicetorchdevice expectedsliceboxes torch tensor 0 2559 0 5455 0 4706 0 2989 0 7279 0 1875 0 7732 0 4017 0 4462 devicetorchdevice self asserttruetorch allcloseoutputs logits0 3 3 expectedslicelogits atol1e4 self asserttruetorch allcloseoutputs predboxes0 3 3 expectedsliceboxes atol1e4 verify postprocessing results imageprocessor postprocessobjectdetection outputs threshold0 3 targetsizesimage size 1 0 expectedscores torch tensor0 9994 0 9790 0 9964 0 9972 0 9861 totorchdevice expectedlabels 75 75 17 63 17 expectedsliceboxes torch tensor335 0609 79 3848 375 4216 187 2495 totorchdevice self assertequallenresultsscores 5 self asserttruetorch allcloseresultsscores expectedscores atol1e4 self assertsequenceequalresultslabels tolist expectedlabels self asserttruetorch allcloseresultsboxes0 expectedsliceboxes 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 yolos model we set the expected sequence length which is used in several tests expected sequence length num_patches 1 we add 1 for the cls token num_detection_tokens labels is a list of dict each dict being the labels for a given example in the batch here we also overwrite some of the tests of test_modeling_common py as yolos does not use input_ids inputs_embeds attention_mask and seq_length special case for head model yolos does not use inputs_embeds in yolos the seq_len is different check that output_attentions also work using config check attention is always last and order is fine yolos has a different seq_length check that output_hidden_states also work using config we will verify our results on an image of cute cats forward pass verify outputs verify postprocessing
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import YolosForObjectDetection, YolosModel
    from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class YolosModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=[30, 30],
        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,
        num_labels=3,
        scope=None,
        n_targets=8,
        num_detection_tokens=10,
    ):
        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.num_labels = num_labels
        self.scope = scope
        self.n_targets = n_targets
        self.num_detection_tokens = num_detection_tokens
        
        
        num_patches = (image_size[1] // patch_size) * (image_size[0] // patch_size)
        self.expected_seq_len = num_patches + 1 + self.num_detection_tokens

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

        labels = None
        if self.use_labels:
            
            labels = []
            for i in range(self.batch_size):
                target = {}
                target["class_labels"] = torch.randint(
                    high=self.num_labels, size=(self.n_targets,), device=torch_device
                )
                target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
                labels.append(target)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return YolosConfig(
            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,
            num_detection_tokens=self.num_detection_tokens,
            num_labels=self.num_labels,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = YolosModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
        )

    def create_and_check_for_object_detection(self, config, pixel_values, labels):
        model = YolosForObjectDetection(config)
        model.to(torch_device)
        model.eval()

        result = model(pixel_values=pixel_values)
        result = model(pixel_values)

        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))

        result = model(pixel_values=pixel_values, labels=labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))

    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 YolosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    test_torchscript = False

    
    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "YolosForObjectDetection":
                labels = []
                for i in range(self.model_tester.batch_size):
                    target = {}
                    target["class_labels"] = torch.ones(
                        size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
                    )
                    target["boxes"] = torch.ones(
                        self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
                    )
                    labels.append(target)
                inputs_dict["labels"] = labels

        return inputs_dict

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

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

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

        
        seq_len = self.model_tester.expected_seq_len
        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
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

            
            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), self.model_tester.num_hidden_layers)

            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, seq_len, 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))

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

            self_attentions = outputs.attentions

            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, seq_len, 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 = getattr(
                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            
            seq_length = self.model_tester.expected_seq_len

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [seq_length, self.model_tester.hidden_size],
            )

        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_object_detection(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = YolosModel.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 YolosModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained("hustvl/yolos-small") if is_vision_available() else None

    @slow
    def test_inference_object_detection_head(self):
        model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small").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.pixel_values)

        
        expected_shape = torch.Size((1, 100, 92))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice_logits = torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]],
            device=torch_device,
        )
        expected_slice_boxes = torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]], device=torch_device
        )
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))

        
        results = image_processor.post_process_object_detection(
            outputs, threshold=0.3, target_sizes=[image.size[::-1]]
        )[0]
        expected_scores = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(torch_device)
        expected_labels = [75, 75, 17, 63, 17]
        expected_slice_boxes = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(torch_device)

        self.assertEqual(len(results["scores"]), 5)
        self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
        self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
        self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))