379 lines
15 KiB
Python
379 lines
15 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch YOLOS model."""
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import unittest
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from transformers import YolosConfig
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import YolosForObjectDetection, YolosModel
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if is_vision_available():
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from PIL import Image
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from transformers import AutoImageProcessor
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class YolosModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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image_size=[30, 30],
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patch_size=2,
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num_channels=3,
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is_training=True,
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use_labels=True,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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type_sequence_label_size=10,
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initializer_range=0.02,
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num_labels=3,
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scope=None,
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n_targets=8,
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num_detection_tokens=10,
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attn_implementation="eager",
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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.use_labels = use_labels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.scope = scope
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self.n_targets = n_targets
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self.num_detection_tokens = num_detection_tokens
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self.attn_implementation = attn_implementation
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# we set the expected sequence length (which is used in several tests)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
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num_patches = (image_size[1] // patch_size) * (image_size[0] // patch_size)
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self.expected_seq_len = num_patches + 1 + self.num_detection_tokens
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
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labels = None
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if self.use_labels:
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# labels is a list of Dict (each Dict being the labels for a given example in the batch)
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labels = []
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for i in range(self.batch_size):
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target = {}
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target["class_labels"] = torch.randint(
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high=self.num_labels, size=(self.n_targets,), device=torch_device
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)
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target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
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labels.append(target)
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config = self.get_config()
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return config, pixel_values, labels
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def get_config(self):
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return YolosConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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is_decoder=False,
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initializer_range=self.initializer_range,
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num_detection_tokens=self.num_detection_tokens,
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num_labels=self.num_labels,
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attn_implementation=self.attn_implementation,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = YolosModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
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)
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def create_and_check_for_object_detection(self, config, pixel_values, labels):
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model = YolosForObjectDetection(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values=pixel_values)
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result = model(pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))
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result = model(pixel_values=pixel_values, labels=labels)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, labels = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class YolosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as YOLOS does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": YolosModel, "object-detection": YolosForObjectDetection}
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if is_torch_available()
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else {}
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)
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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test_torchscript = False
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# special case for head model
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "YolosForObjectDetection":
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labels = []
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for i in range(self.model_tester.batch_size):
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target = {}
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target["class_labels"] = torch.ones(
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size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
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)
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target["boxes"] = torch.ones(
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self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
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)
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labels.append(target)
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inputs_dict["labels"] = labels
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return inputs_dict
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def setUp(self):
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self.model_tester = YolosModelTester(self)
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self.config_tester = ConfigTester(self, config_class=YolosConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_inputs_embeds(self):
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# YOLOS does not use inputs_embeds
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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# in YOLOS, the seq_len is different
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seq_len = self.model_tester.expected_seq_len
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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# YOLOS has a different seq_length
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seq_length = self.model_tester.expected_seq_len
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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def test_for_object_detection(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_object_detection(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "hustvl/yolos-small"
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model = YolosModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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@require_vision
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class YolosModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return AutoImageProcessor.from_pretrained("hustvl/yolos-small") if is_vision_available() else None
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@slow
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def test_inference_object_detection_head(self):
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model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small").to(torch_device)
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image_processor = self.default_image_processor
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(inputs.pixel_values)
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# verify outputs
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expected_shape = torch.Size((1, 100, 92))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice_logits = torch.tensor(
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[[-23.7219, -10.3165, -14.9083], [-41.5429, -15.2403, -24.1478], [-29.3909, -12.7173, -19.4650]],
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device=torch_device,
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)
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expected_slice_boxes = torch.tensor(
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[[0.2536, 0.5449, 0.4643], [0.2037, 0.7735, 0.3672], [0.7692, 0.4056, 0.4549]], device=torch_device
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)
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self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
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self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
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# verify postprocessing
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results = image_processor.post_process_object_detection(
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outputs, threshold=0.3, target_sizes=[image.size[::-1]]
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)[0]
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expected_scores = torch.tensor([0.9991, 0.9801, 0.9978, 0.9875, 0.9848]).to(torch_device)
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expected_labels = [75, 75, 17, 63, 17]
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expected_slice_boxes = torch.tensor([331.8438, 80.5440, 369.9546, 188.0579]).to(torch_device)
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self.assertEqual(len(results["scores"]), 5)
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self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
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self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
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self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))
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