def test_inference_classification_head(self):
# note that google/tapas-base-finetuned-tabfact should correspond to tapas_tabfact_inter_masklm_base_reset
model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact").to(torch_device)
tokenizer = self.default_tokenizer
table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
outputs = model(**inputs)
# test the classification logits
logits = outputs.logits
expected_shape = torch.Size((1, 2))
self.assertEqual(logits.shape, expected_shape)
expected_tensor = torch.tensor(
[[0.795137286, 9.5572]], device=torch_device
) # Note that the PyTorch model outputs [[0.8057, 9.5281]]
self.assertTrue(torch.allclose(outputs.logits, expected_tensor, atol=TOLERANCE))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
input_mask,
token_type_ids,
sequence_labels,
token_labels,
labels,
numeric_values,
numeric_values_scale,
float_answer,
aggregation_labels,
):
config.num_labels = self.num_labels
model = TapasForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def convert_tf_checkpoint_to_pytorch(task, reset_position_index_per_cell,
tf_checkpoint_path, tapas_config_file,
pytorch_dump_path):
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
config = TapasConfig.from_json_file(tapas_config_file)
# set absolute/relative position embeddings parameter
config.reset_position_index_per_cell = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
model = TapasForQuestionAnswering(config=config)
elif task == "WTQ":
# run_task_main.py hparams
config.num_aggregation_labels = 4
config.use_answer_as_supervision = True
# hparam_utils.py hparams
config.answer_loss_cutoff = 0.664694
config.cell_selection_preference = 0.207951
config.huber_loss_delta = 0.121194
config.init_cell_selection_weights_to_zero = True
config.select_one_column = True
config.allow_empty_column_selection = False
config.temperature = 0.0352513
model = TapasForQuestionAnswering(config=config)
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
config.num_aggregation_labels = 4
config.use_answer_as_supervision = False
# hparam_utils.py hparams
config.answer_loss_cutoff = 36.4519
config.cell_selection_preference = 0.903421
config.huber_loss_delta = 222.088
config.init_cell_selection_weights_to_zero = True
config.select_one_column = True
config.allow_empty_column_selection = True
config.temperature = 0.763141
model = TapasForQuestionAnswering(config=config)
elif task == "TABFACT":
model = TapasForSequenceClassification(config=config)
elif task == "MLM":
model = TapasForMaskedLM(config=config)
elif task == "INTERMEDIATE_PRETRAINING":
model = TapasModel(config=config)
else:
raise ValueError(f"Task {task} not supported.")
print(f"Building PyTorch model from configuration: {config}")
# Load weights from tf checkpoint
load_tf_weights_in_tapas(model, config, tf_checkpoint_path)
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}")
model.save_pretrained(pytorch_dump_path)
# Save tokenizer files
print(f"Save tokenizer files to {pytorch_dump_path}")
tokenizer = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] +
"vocab.txt",
model_max_length=512)
tokenizer.save_pretrained(pytorch_dump_path)
print("Used relative position embeddings:",
model.config.reset_position_index_per_cell)