adding templates
This commit is contained in:
thomwolf
256
templates/adding_a_new_model/tests/modeling_tf_xxx_test.py
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256
templates/adding_a_new_model/tests/modeling_tf_xxx_test.py
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# coding=utf-8
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# Copyright 2018 XXX Authors.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import shutil
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import pytest
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import sys
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from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
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from .configuration_common_test import ConfigTester
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from transformers import XxxConfig, is_tf_available
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if is_tf_available():
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import tensorflow as tf
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from transformers.modeling_tf_xxx import (TFXxxModel, TFXxxForMaskedLM,
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TFXxxForSequenceClassification,
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TFXxxForTokenClassification,
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TFXxxForQuestionAnswering,
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TF_XXX_PRETRAINED_MODEL_ARCHIVE_MAP)
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else:
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pytestmark = pytest.mark.skip("Require TensorFlow")
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class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester):
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all_model_classes = (TFXxxModel, TFXxxForMaskedLM, TFXxxForQuestionAnswering,
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TFXxxForSequenceClassification,
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TFXxxForTokenClassification) if is_tf_available() else ()
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class TFXxxModelTester(object):
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def __init__(self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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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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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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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.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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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.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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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.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = XxxConfig(
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vocab_size_or_config_json_file=self.vocab_size,
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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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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def create_and_check_xxx_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = TFXxxModel(config=config)
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inputs = {'input_ids': input_ids,
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'attention_mask': input_mask,
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'token_type_ids': token_type_ids}
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sequence_output, pooled_output = model(inputs)
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inputs = [input_ids, input_mask]
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sequence_output, pooled_output = model(inputs)
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sequence_output, pooled_output = model(input_ids)
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result = {
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"sequence_output": sequence_output.numpy(),
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"pooled_output": pooled_output.numpy(),
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].shape),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertListEqual(list(result["pooled_output"].shape), [self.batch_size, self.hidden_size])
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def create_and_check_xxx_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = TFXxxForMaskedLM(config=config)
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inputs = {'input_ids': input_ids,
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'attention_mask': input_mask,
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'token_type_ids': token_type_ids}
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prediction_scores, = model(inputs)
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result = {
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"prediction_scores": prediction_scores.numpy(),
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].shape),
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[self.batch_size, self.seq_length, self.vocab_size])
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def create_and_check_xxx_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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config.num_labels = self.num_labels
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model = TFXxxForSequenceClassification(config=config)
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inputs = {'input_ids': input_ids,
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'attention_mask': input_mask,
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'token_type_ids': token_type_ids}
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logits, = model(inputs)
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result = {
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"logits": logits.numpy(),
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}
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self.parent.assertListEqual(
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list(result["logits"].shape),
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[self.batch_size, self.num_labels])
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def create_and_check_xxx_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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config.num_labels = self.num_labels
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model = TFXxxForTokenClassification(config=config)
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inputs = {'input_ids': input_ids,
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'attention_mask': input_mask,
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'token_type_ids': token_type_ids}
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logits, = model(inputs)
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result = {
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"logits": logits.numpy(),
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}
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self.parent.assertListEqual(
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list(result["logits"].shape),
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[self.batch_size, self.seq_length, self.num_labels])
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def create_and_check_xxx_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = TFXxxForQuestionAnswering(config=config)
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inputs = {'input_ids': input_ids,
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'attention_mask': input_mask,
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'token_type_ids': token_type_ids}
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start_logits, end_logits = model(inputs)
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result = {
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"start_logits": start_logits.numpy(),
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"end_logits": end_logits.numpy(),
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}
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self.parent.assertListEqual(
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list(result["start_logits"].shape),
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[self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(result["end_logits"].shape),
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[self.batch_size, self.seq_length])
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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, input_ids, token_type_ids, input_mask,
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sequence_labels, token_labels, choice_labels) = config_and_inputs
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inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
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return config, inputs_dict
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def setUp(self):
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self.model_tester = TFXxxModelTest.TFXxxModelTester(self)
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self.config_tester = ConfigTester(self, config_class=XxxConfig, 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_xxx_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_xxx_model(*config_and_inputs)
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def test_for_masked_lm(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_xxx_for_masked_lm(*config_and_inputs)
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def test_for_question_answering(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_xxx_for_question_answering(*config_and_inputs)
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def test_for_sequence_classification(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_xxx_for_sequence_classification(*config_and_inputs)
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def test_for_token_classification(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_xxx_for_token_classification(*config_and_inputs)
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@pytest.mark.slow
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def test_model_from_pretrained(self):
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cache_dir = "/tmp/transformers_test/"
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for model_name in ['xxx-base-uncased']:
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model = TFXxxModel.from_pretrained(model_name, cache_dir=cache_dir)
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shutil.rmtree(cache_dir)
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self.assertIsNotNone(model)
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if __name__ == "__main__":
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unittest.main()
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255
templates/adding_a_new_model/tests/modeling_xxx_test.py
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255
templates/adding_a_new_model/tests/modeling_xxx_test.py
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# coding=utf-8
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# Copyright 2018 XXX Authors.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import shutil
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import pytest
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from transformers import is_torch_available
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from .modeling_common_test import (CommonTestCases, ids_tensor)
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from .configuration_common_test import ConfigTester
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if is_torch_available():
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from transformers import (XxxConfig, XxxModel, XxxForMaskedLM,
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XxxForNextSentencePrediction, XxxForPreTraining,
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XxxForQuestionAnswering, XxxForSequenceClassification,
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XxxForTokenClassification, XxxForMultipleChoice)
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from transformers.modeling_xxx import XXX_PRETRAINED_MODEL_ARCHIVE_MAP
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else:
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pytestmark = pytest.mark.skip("Require Torch")
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class XxxModelTest(CommonTestCases.CommonModelTester):
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all_model_classes = (XxxModel, XxxForMaskedLM, XxxForQuestionAnswering,
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XxxForSequenceClassification,
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XxxForTokenClassification) if is_torch_available() else ()
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class XxxModelTester(object):
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def __init__(self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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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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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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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.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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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.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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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.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = XxxConfig(
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vocab_size_or_config_json_file=self.vocab_size,
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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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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def check_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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||||
[])
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def create_and_check_xxx_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = XxxModel(config=config)
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model.eval()
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sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids)
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result = {
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"sequence_output": sequence_output,
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"pooled_output": pooled_output,
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}
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||||
self.parent.assertListEqual(
|
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list(result["sequence_output"].size()),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
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def create_and_check_xxx_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = XxxForMaskedLM(config=config)
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model.eval()
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loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
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||||
result = {
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"loss": loss,
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||||
"prediction_scores": prediction_scores,
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||||
}
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||||
self.parent.assertListEqual(
|
||||
list(result["prediction_scores"].size()),
|
||||
[self.batch_size, self.seq_length, self.vocab_size])
|
||||
self.check_loss_output(result)
|
||||
|
||||
|
||||
def create_and_check_xxx_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
model = XxxForQuestionAnswering(config=config)
|
||||
model.eval()
|
||||
loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
|
||||
start_positions=sequence_labels, end_positions=sequence_labels)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"start_logits": start_logits,
|
||||
"end_logits": end_logits,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["start_logits"].size()),
|
||||
[self.batch_size, self.seq_length])
|
||||
self.parent.assertListEqual(
|
||||
list(result["end_logits"].size()),
|
||||
[self.batch_size, self.seq_length])
|
||||
self.check_loss_output(result)
|
||||
|
||||
|
||||
def create_and_check_xxx_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
config.num_labels = self.num_labels
|
||||
model = XxxForSequenceClassification(config)
|
||||
model.eval()
|
||||
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"logits": logits,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["logits"].size()),
|
||||
[self.batch_size, self.num_labels])
|
||||
self.check_loss_output(result)
|
||||
|
||||
|
||||
def create_and_check_xxx_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
config.num_labels = self.num_labels
|
||||
model = XxxForTokenClassification(config=config)
|
||||
model.eval()
|
||||
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"logits": logits,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["logits"].size()),
|
||||
[self.batch_size, self.seq_length, self.num_labels])
|
||||
self.check_loss_output(result)
|
||||
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(config, input_ids, token_type_ids, input_mask,
|
||||
sequence_labels, token_labels, choice_labels) = config_and_inputs
|
||||
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = XxxModelTest.XxxModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=XxxConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_xxx_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_xxx_model(*config_and_inputs)
|
||||
|
||||
def test_for_masked_lm(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_xxx_for_masked_lm(*config_and_inputs)
|
||||
|
||||
def test_for_question_answering(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_xxx_for_question_answering(*config_and_inputs)
|
||||
|
||||
def test_for_sequence_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_xxx_for_sequence_classification(*config_and_inputs)
|
||||
|
||||
def test_for_token_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_xxx_for_token_classification(*config_and_inputs)
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_model_from_pretrained(self):
|
||||
cache_dir = "/tmp/transformers_test/"
|
||||
for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
model = XxxModel.from_pretrained(model_name, cache_dir=cache_dir)
|
||||
shutil.rmtree(cache_dir)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
57
templates/adding_a_new_model/tests/tokenization_xxx_test.py
Normal file
57
templates/adding_a_new_model/tests/tokenization_xxx_test.py
Normal file
@@ -0,0 +1,57 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 XXX Authors.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import absolute_import, division, print_function, unicode_literals
|
||||
|
||||
import os
|
||||
import unittest
|
||||
from io import open
|
||||
|
||||
from transformers.tokenization_bert import (XxxTokenizer, VOCAB_FILES_NAMES)
|
||||
|
||||
from .tokenization_tests_commons import CommonTestCases
|
||||
|
||||
class XxxTokenizationTest(CommonTestCases.CommonTokenizerTester):
|
||||
|
||||
tokenizer_class = XxxTokenizer
|
||||
|
||||
def setUp(self):
|
||||
super(XxxTokenizationTest, self).setUp()
|
||||
|
||||
vocab_tokens = [
|
||||
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
|
||||
"##ing", ",", "low", "lowest",
|
||||
]
|
||||
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
|
||||
with open(self.vocab_file, "w", encoding='utf-8') as vocab_writer:
|
||||
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return XxxTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_input_output_texts(self):
|
||||
input_text = u"UNwant\u00E9d,running"
|
||||
output_text = u"unwanted, running"
|
||||
return input_text, output_text
|
||||
|
||||
def test_full_tokenizer(self):
|
||||
tokenizer = self.tokenizer_class(self.vocab_file)
|
||||
|
||||
tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
|
||||
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
|
||||
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user