616 lines
26 KiB
Python
616 lines
26 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 DETA model. """
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import inspect
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import math
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import unittest
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from transformers import DetaConfig, ResNetConfig, is_torch_available, is_torchvision_available, is_vision_available
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from transformers.file_utils import cached_property
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from transformers.testing_utils import require_torchvision, require_vision, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, 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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if is_torchvision_available():
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from transformers import DetaForObjectDetection, DetaModel
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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 DetaModelTester:
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def __init__(
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self,
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parent,
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batch_size=8,
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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=8,
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intermediate_size=4,
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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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num_queries=12,
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two_stage_num_proposals=12,
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num_channels=3,
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image_size=224,
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n_targets=8,
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num_labels=91,
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num_feature_levels=4,
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encoder_n_points=2,
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decoder_n_points=6,
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two_stage=True,
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assign_first_stage=True,
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assign_second_stage=True,
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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.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.num_queries = num_queries
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self.two_stage_num_proposals = two_stage_num_proposals
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self.num_channels = num_channels
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self.image_size = image_size
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self.n_targets = n_targets
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self.num_labels = num_labels
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self.num_feature_levels = num_feature_levels
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self.encoder_n_points = encoder_n_points
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self.decoder_n_points = decoder_n_points
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self.two_stage = two_stage
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self.assign_first_stage = assign_first_stage
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self.assign_second_stage = assign_second_stage
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# we also set the expected seq length for both encoder and decoder
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self.encoder_seq_length = (
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math.ceil(self.image_size / 8) ** 2
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+ math.ceil(self.image_size / 16) ** 2
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+ math.ceil(self.image_size / 32) ** 2
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+ math.ceil(self.image_size / 64) ** 2
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)
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self.decoder_seq_length = self.num_queries
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def prepare_config_and_inputs(self, model_class_name):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
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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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target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device)
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labels.append(target)
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config = self.get_config(model_class_name)
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return config, pixel_values, pixel_mask, labels
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def get_config(self, model_class_name):
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resnet_config = ResNetConfig(
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num_channels=3,
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embeddings_size=10,
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hidden_sizes=[10, 20, 30, 40],
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depths=[1, 1, 2, 1],
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hidden_act="relu",
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num_labels=3,
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out_features=["stage2", "stage3", "stage4"],
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out_indices=[2, 3, 4],
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)
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two_stage = model_class_name == "DetaForObjectDetection"
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assign_first_stage = model_class_name == "DetaForObjectDetection"
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assign_second_stage = model_class_name == "DetaForObjectDetection"
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return DetaConfig(
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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num_queries=self.num_queries,
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two_stage_num_proposals=self.two_stage_num_proposals,
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num_labels=self.num_labels,
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num_feature_levels=self.num_feature_levels,
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encoder_n_points=self.encoder_n_points,
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decoder_n_points=self.decoder_n_points,
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two_stage=two_stage,
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assign_first_stage=assign_first_stage,
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assign_second_stage=assign_second_stage,
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backbone_config=resnet_config,
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backbone=None,
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)
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def prepare_config_and_inputs_for_common(self, model_class_name="DetaModel"):
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config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs(model_class_name)
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inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
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return config, inputs_dict
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def create_and_check_deta_model(self, config, pixel_values, pixel_mask, labels):
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model = DetaModel(config=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, pixel_mask=pixel_mask)
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result = model(pixel_values)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size))
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def create_and_check_deta_freeze_backbone(self, config, pixel_values, pixel_mask, labels):
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model = DetaModel(config=config)
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model.to(torch_device)
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model.eval()
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model.freeze_backbone()
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for _, param in model.backbone.model.named_parameters():
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self.parent.assertEqual(False, param.requires_grad)
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def create_and_check_deta_unfreeze_backbone(self, config, pixel_values, pixel_mask, labels):
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model = DetaModel(config=config)
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model.to(torch_device)
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model.eval()
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model.unfreeze_backbone()
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for _, param in model.backbone.model.named_parameters():
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self.parent.assertEqual(True, param.requires_grad)
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def create_and_check_deta_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
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model = DetaForObjectDetection(config=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, pixel_mask=pixel_mask)
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result = model(pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.two_stage_num_proposals, self.num_labels))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.two_stage_num_proposals, 4))
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result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, 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.two_stage_num_proposals, self.num_labels))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.two_stage_num_proposals, 4))
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@require_torchvision
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class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (DetaModel, DetaForObjectDetection) if is_torchvision_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": DetaModel, "object-detection": DetaForObjectDetection}
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if is_torchvision_available()
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else {}
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)
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is_encoder_decoder = True
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test_torchscript = False
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test_pruning = False
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test_head_masking = False
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test_missing_keys = False
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "ObjectDetectionPipelineTests":
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return True
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return False
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@unittest.skip("Skip for now. PR #22437 causes some loading issue. See (not merged) #22656 for some discussions.")
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def test_can_use_safetensors(self):
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super().test_can_use_safetensors()
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# special case for head models
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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__ == "DetaForObjectDetection":
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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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target["masks"] = torch.ones(
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self.model_tester.n_targets,
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self.model_tester.image_size,
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self.model_tester.image_size,
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device=torch_device,
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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 = DetaModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DetaConfig, has_text_modality=False)
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def test_config(self):
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# we don't test common_properties and arguments_init as these don't apply for DETA
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self.config_tester.create_and_test_config_to_json_string()
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self.config_tester.create_and_test_config_to_json_file()
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self.config_tester.create_and_test_config_from_and_save_pretrained()
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self.config_tester.create_and_test_config_with_num_labels()
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self.config_tester.check_config_can_be_init_without_params()
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def test_deta_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaModel")
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self.model_tester.create_and_check_deta_model(*config_and_inputs)
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def test_deta_freeze_backbone(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaModel")
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self.model_tester.create_and_check_deta_freeze_backbone(*config_and_inputs)
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def test_deta_unfreeze_backbone(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaModel")
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self.model_tester.create_and_check_deta_unfreeze_backbone(*config_and_inputs)
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def test_deta_object_detection_head_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaForObjectDetection")
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self.model_tester.create_and_check_deta_object_detection_head_model(*config_and_inputs)
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@unittest.skip(reason="DETA does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="DETA does not have a get_input_embeddings method")
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def test_model_common_attributes(self):
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pass
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@unittest.skip(reason="DETA is not a generative model")
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def test_generate_without_input_ids(self):
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pass
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@unittest.skip(reason="DETA does not use token embeddings")
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def test_resize_tokens_embeddings(self):
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pass
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@unittest.skip(reason="Feed forward chunking is not implemented")
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def test_feed_forward_chunking(self):
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pass
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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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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.encoder_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.encoder_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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[
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self.model_tester.num_attention_heads,
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self.model_tester.num_feature_levels,
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self.model_tester.encoder_n_points,
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],
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)
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out_len = len(outputs)
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correct_outlen = 8
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# loss is at first position
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if "labels" in inputs_dict:
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correct_outlen += 1 # loss is added to beginning
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# Object Detection model returns pred_logits and pred_boxes
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if model_class.__name__ == "DetaForObjectDetection":
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correct_outlen += 2
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self.assertEqual(out_len, correct_outlen)
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# decoder attentions
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decoder_attentions = outputs.decoder_attentions
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries],
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)
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# cross attentions
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cross_attentions = outputs.cross_attentions
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self.assertIsInstance(cross_attentions, (list, tuple))
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self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(cross_attentions[0].shape[-3:]),
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[
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self.model_tester.num_attention_heads,
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self.model_tester.num_feature_levels,
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self.model_tester.decoder_n_points,
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],
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)
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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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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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elif self.is_encoder_decoder:
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added_hidden_states = 2
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else:
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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.encoder_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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[
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self.model_tester.num_attention_heads,
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self.model_tester.num_feature_levels,
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self.model_tester.encoder_n_points,
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],
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)
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# removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
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def test_retain_grad_hidden_states_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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config.output_attentions = True
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# no need to test all models as different heads yield the same functionality
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model_class = self.all_model_classes[0]
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model = model_class(config)
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model.to(torch_device)
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inputs = self._prepare_for_class(inputs_dict, model_class)
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outputs = model(**inputs)
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# we take the second output since last_hidden_state is the second item
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output = outputs[1]
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encoder_hidden_states = outputs.encoder_hidden_states[0]
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encoder_attentions = outputs.encoder_attentions[0]
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encoder_hidden_states.retain_grad()
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encoder_attentions.retain_grad()
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decoder_attentions = outputs.decoder_attentions[0]
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decoder_attentions.retain_grad()
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cross_attentions = outputs.cross_attentions[0]
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cross_attentions.retain_grad()
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output.flatten()[0].backward(retain_graph=True)
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self.assertIsNotNone(encoder_hidden_states.grad)
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self.assertIsNotNone(encoder_attentions.grad)
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self.assertIsNotNone(decoder_attentions.grad)
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self.assertIsNotNone(cross_attentions.grad)
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def test_forward_auxiliary_loss(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.auxiliary_loss = True
|
|
|
|
# only test for object detection and segmentation model
|
|
for model_class in self.all_model_classes[1:]:
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
outputs = model(**inputs)
|
|
|
|
self.assertIsNotNone(outputs.auxiliary_outputs)
|
|
self.assertEqual(len(outputs.auxiliary_outputs), self.model_tester.num_hidden_layers - 1)
|
|
|
|
def test_forward_signature(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
signature = inspect.signature(model.forward)
|
|
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
|
arg_names = [*signature.parameters.keys()]
|
|
|
|
if model.config.is_encoder_decoder:
|
|
expected_arg_names = ["pixel_values", "pixel_mask"]
|
|
expected_arg_names.extend(
|
|
["head_mask", "decoder_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" in arg_names
|
|
else []
|
|
)
|
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
|
else:
|
|
expected_arg_names = ["pixel_values", "pixel_mask"]
|
|
self.assertListEqual(arg_names[:1], expected_arg_names)
|
|
|
|
@unittest.skip(reason="Model doesn't use tied weights")
|
|
def test_tied_model_weights_key_ignore(self):
|
|
pass
|
|
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
# Skip the check for the backbone
|
|
for name, module in model.named_modules():
|
|
if module.__class__.__name__ == "DetaBackboneWithPositionalEncodings":
|
|
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 (
|
|
"level_embed" in name
|
|
or "sampling_offsets.bias" in name
|
|
or "value_proj" in name
|
|
or "output_proj" in name
|
|
or "reference_points" in name
|
|
or 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",
|
|
)
|
|
|
|
|
|
TOLERANCE = 1e-4
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
return image
|
|
|
|
|
|
@require_torchvision
|
|
@require_vision
|
|
@slow
|
|
class DetaModelIntegrationTests(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return AutoImageProcessor.from_pretrained("jozhang97/deta-resnet-50") if is_vision_available() else None
|
|
|
|
def test_inference_object_detection_head(self):
|
|
model = DetaForObjectDetection.from_pretrained("jozhang97/deta-resnet-50").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)
|
|
|
|
expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
|
|
self.assertEqual(outputs.logits.shape, expected_shape_logits)
|
|
|
|
expected_logits = torch.tensor(
|
|
[[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]
|
|
).to(torch_device)
|
|
expected_boxes = torch.tensor(
|
|
[[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
|
|
|
|
expected_shape_boxes = torch.Size((1, 300, 4))
|
|
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
|
|
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
|
|
|
|
# verify postprocessing
|
|
results = image_processor.post_process_object_detection(
|
|
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
|
|
)[0]
|
|
expected_scores = torch.tensor([0.6392, 0.6276, 0.5546, 0.5260, 0.4706], device=torch_device)
|
|
expected_labels = [75, 17, 17, 75, 63]
|
|
expected_slice_boxes = torch.tensor([40.5866, 73.2107, 176.1421, 117.1751], device=torch_device)
|
|
|
|
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))
|
|
|
|
def test_inference_object_detection_head_swin_backbone(self):
|
|
model = DetaForObjectDetection.from_pretrained("jozhang97/deta-swin-large").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)
|
|
|
|
expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
|
|
self.assertEqual(outputs.logits.shape, expected_shape_logits)
|
|
|
|
expected_logits = torch.tensor(
|
|
[[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]
|
|
).to(torch_device)
|
|
expected_boxes = torch.tensor(
|
|
[[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
|
|
|
|
expected_shape_boxes = torch.Size((1, 300, 4))
|
|
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
|
|
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
|
|
|
|
# verify postprocessing
|
|
results = image_processor.post_process_object_detection(
|
|
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
|
|
)[0]
|
|
expected_scores = torch.tensor([0.6831, 0.6826, 0.5684, 0.5464, 0.4392], device=torch_device)
|
|
expected_labels = [17, 17, 75, 75, 63]
|
|
expected_slice_boxes = torch.tensor([345.8478, 23.6754, 639.8562, 372.8265], device=torch_device)
|
|
|
|
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))
|