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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 Table Transformer model."""
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import inspect
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import math
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import unittest
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from huggingface_hub import hf_hub_download
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from transformers import ResNetConfig, TableTransformerConfig, is_torch_available, is_vision_available
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from transformers.testing_utils import Expectations, require_timm, require_torch, require_vision, slow, torch_device
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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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from transformers import TableTransformerForObjectDetection, TableTransformerModel
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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 TableTransformerModelTester:
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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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num_channels=3,
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min_size=200,
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max_size=200,
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n_targets=8,
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num_labels=3,
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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.num_channels = num_channels
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self.min_size = min_size
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self.max_size = max_size
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self.n_targets = n_targets
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self.num_labels = num_labels
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# we also set the expected seq length for both encoder and decoder
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self.encoder_seq_length = math.ceil(self.min_size / 32) * math.ceil(self.max_size / 32)
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self.decoder_seq_length = self.num_queries
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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.min_size, self.max_size])
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pixel_mask = torch.ones([self.batch_size, self.min_size, self.max_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.min_size, self.max_size, 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, pixel_mask, labels
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def get_config(self):
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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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return TableTransformerConfig(
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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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num_labels=self.num_labels,
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use_timm_backbone=False,
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backbone_config=resnet_config,
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backbone=None,
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use_pretrained_backbone=False,
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)
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def prepare_config_and_inputs_for_common(self):
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config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
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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_table_transformer_model(self, config, pixel_values, pixel_mask, labels):
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model = TableTransformerModel(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(
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result.last_hidden_state.shape, (self.batch_size, self.decoder_seq_length, self.hidden_size)
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)
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def create_and_check_table_transformer_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
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model = TableTransformerForObjectDetection(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.num_queries, self.num_labels + 1))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 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.num_queries, self.num_labels + 1))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
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def create_and_check_table_transformer_no_timm_backbone(self, config, pixel_values, pixel_mask, labels):
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config.use_timm_backbone = False
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config.backbone_config = ResNetConfig()
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model = TableTransformerForObjectDetection(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.num_queries, self.num_labels + 1))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 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.num_queries, self.num_labels + 1))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
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@require_torch
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class TableTransformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TableTransformerModel,
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TableTransformerForObjectDetection,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{"image-feature-extraction": TableTransformerModel, "object-detection": TableTransformerForObjectDetection}
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if is_torch_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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zero_init_hidden_state = True
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test_torch_exportable = True
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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__ in ["TableTransformerForObjectDetection"]:
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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.min_size,
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self.model_tester.max_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 = TableTransformerModelTester(self)
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self.config_tester = ConfigTester(self, config_class=TableTransformerConfig, has_text_modality=False)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_table_transformer_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_table_transformer_model(*config_and_inputs)
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def test_table_transformer_object_detection_head_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_table_transformer_object_detection_head_model(*config_and_inputs)
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def test_table_transformer_no_timm_backbone(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_table_transformer_no_timm_backbone(*config_and_inputs)
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@unittest.skip(reason="Table Transformer 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="Table Transformer does not use inputs_embeds")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip(reason="Table Transformer does not have a get_input_embeddings method")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="Table Transformer 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="Table Transformer does not use token embeddings")
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def test_resize_tokens_embeddings(self):
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pass
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@slow
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@unittest.skip(reason="TODO Niels: fix me!")
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def test_model_outputs_equivalence(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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decoder_seq_length = self.model_tester.decoder_seq_length
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encoder_seq_length = self.model_tester.encoder_seq_length
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decoder_key_length = self.model_tester.decoder_seq_length
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encoder_key_length = self.model_tester.encoder_seq_length
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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._from_config(config, attn_implementation="eager")
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config = model.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 if config.is_encoder_decoder else 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.encoder_attentions if config.is_encoder_decoder else 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, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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if self.is_encoder_decoder:
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correct_outlen = 5
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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__ == "TableTransformerForObjectDetection":
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correct_outlen += 2
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if "past_key_values" in outputs:
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correct_outlen += 1 # past_key_values have been returned
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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, decoder_seq_length, decoder_key_length],
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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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decoder_seq_length,
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encoder_key_length,
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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 if config.is_encoder_decoder else 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, encoder_seq_length, encoder_key_length],
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)
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def test_retain_grad_hidden_states_attentions(self):
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# removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
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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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output = outputs[0]
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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()
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config.auxiliary_loss = True
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# only test for object detection and segmentation model
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for model_class in self.all_model_classes[1:]:
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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, return_labels=True)
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outputs = model(**inputs)
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self.assertIsNotNone(outputs.auxiliary_outputs)
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self.assertEqual(len(outputs.auxiliary_outputs), self.model_tester.num_hidden_layers - 1)
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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)
|
||||
|
||||
def test_different_timm_backbone(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# let's pick a random timm backbone
|
||||
config.backbone = "tf_mobilenetv3_small_075"
|
||||
config.backbone_config = None
|
||||
config.use_timm_backbone = True
|
||||
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
if model_class.__name__ == "TableTransformerForObjectDetection":
|
||||
expected_shape = (
|
||||
self.model_tester.batch_size,
|
||||
self.model_tester.num_queries,
|
||||
self.model_tester.num_labels + 1,
|
||||
)
|
||||
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||
# Confirm out_indices was propagated to backbone
|
||||
self.assertEqual(len(model.model.backbone.conv_encoder.intermediate_channel_sizes), 3)
|
||||
else:
|
||||
# Confirm out_indices was propagated to backbone
|
||||
self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 3)
|
||||
|
||||
self.assertTrue(outputs)
|
||||
|
||||
def test_hf_backbone(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# Load a pretrained HF checkpoint as backbone
|
||||
config.backbone = "microsoft/resnet-18"
|
||||
config.backbone_config = None
|
||||
config.use_timm_backbone = False
|
||||
config.use_pretrained_backbone = True
|
||||
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
if model_class.__name__ == "TableTransformerForObjectDetection":
|
||||
expected_shape = (
|
||||
self.model_tester.batch_size,
|
||||
self.model_tester.num_queries,
|
||||
self.model_tester.num_labels + 1,
|
||||
)
|
||||
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||
# Confirm out_indices was propagated to backbone
|
||||
self.assertEqual(len(model.model.backbone.conv_encoder.intermediate_channel_sizes), 3)
|
||||
else:
|
||||
# Confirm out_indices was propagated to backbone
|
||||
self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 3)
|
||||
|
||||
self.assertTrue(outputs)
|
||||
|
||||
def test_greyscale_images(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# use greyscale pixel values
|
||||
inputs_dict["pixel_values"] = floats_tensor(
|
||||
[self.model_tester.batch_size, 1, self.model_tester.min_size, self.model_tester.max_size]
|
||||
)
|
||||
|
||||
# let's set num_channels to 1
|
||||
config.num_channels = 1
|
||||
config.backbone_config.num_channels = 1
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
self.assertTrue(outputs)
|
||||
|
||||
def test_initialization(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
configs_no_init = _config_zero_init(config)
|
||||
configs_no_init.init_xavier_std = 1e9
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config=configs_no_init)
|
||||
for name, param in model.named_parameters():
|
||||
if param.requires_grad:
|
||||
if "bbox_attention" in name and "bias" not in name:
|
||||
self.assertLess(
|
||||
100000,
|
||||
abs(param.data.max().item()),
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
else:
|
||||
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_timm
|
||||
@require_vision
|
||||
@slow
|
||||
class TableTransformerModelIntegrationTests(unittest.TestCase):
|
||||
def test_table_detection(self):
|
||||
image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection")
|
||||
model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection")
|
||||
model.to(torch_device)
|
||||
|
||||
file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png")
|
||||
image = Image.open(file_path).convert("RGB")
|
||||
inputs = image_processor(image, return_tensors="pt").to(torch_device)
|
||||
|
||||
# forward pass
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
|
||||
expected_shape = (1, 15, 3)
|
||||
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||
|
||||
expected_logits_data = Expectations({
|
||||
("cuda", None): [[-6.7329, -16.9590, 6.7447], [-8.0038, -22.3071, 6.9288], [-7.2445, -20.9855, 7.3465]],
|
||||
("rocm", (9, 4)): [[-6.7668, -16.9917, 6.7738], [-8.0046, -22.2668, 6.9491], [-7.2834, -21.0321, 7.3785]],
|
||||
}).get_expectation() # fmt: skip
|
||||
|
||||
expected_logits = torch.tensor(expected_logits_data, device=torch_device)
|
||||
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-4, atol=1e-4)
|
||||
|
||||
expected_boxes_data = Expectations({
|
||||
("cuda", None): [[0.4868, 0.1764, 0.6729], [0.6674, 0.4621, 0.3864], [0.4720, 0.1757, 0.6362]],
|
||||
("rocm", (9, 4)): [[0.4868, 0.1766, 0.6732], [0.6686, 0.4526, 0.3859], [0.4717, 0.1760, 0.6362]],
|
||||
}).get_expectation() # fmt: skip
|
||||
|
||||
expected_boxes = torch.tensor(expected_boxes_data, device=torch_device)
|
||||
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-3, atol=1e-3)
|
||||
Reference in New Issue
Block a user