# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Image Classification Trainer."""
from typing import Optional, List, Union
import numpy as np
from PIL.Image import Image
from mindspore.train import Callback
from mindspore import Tensor
from mindspore.nn import Cell
from mindspore.dataset import GeneratorDataset
from mindformers.dataset import BaseDataset
from mindformers.models import BaseModel, BaseTokenizer, BaseImageProcessor
from mindformers.tools.logger import logger
from mindformers.tools.register import MindFormerRegister, \
MindFormerModuleType, MindFormerConfig
from ...dataset.dataloader import build_dataset_loader
from ..config_args import ConfigArguments
from ..training_args import TrainingArguments
from ..base_trainer import BaseTrainer
__all__ = ['ZeroShotImageClassificationTrainer']
[文档]@MindFormerRegister.register(MindFormerModuleType.TRAINER)
class ZeroShotImageClassificationTrainer(BaseTrainer):
r"""ZeroShotImageClassification Task For Trainer.
Args:
model_name (str): The model name of Task-Trainer. Default: None
Raises:
NotImplementedError: If train method or evaluate method or predict method not implemented.
Examples:
>>> from mindformers import ZeroShotImageClassificationTrainer
>>> trainer = ZeroShotImageClassificationTrainer(model_name="clip_vit_b_32")
>>> trainer.evaluate()
>>> trainer.predict()
"""
def __init__(self, model_name: str = None):
super(ZeroShotImageClassificationTrainer, self).__init__("zero_shot_image_classification", model_name)
def train(self, *args, **kwargs):
raise NotImplementedError(
"The ZeroShotImageClassification task does not support train.")
[文档] def evaluate(self,
config: Optional[Union[dict, MindFormerConfig, ConfigArguments, TrainingArguments]] = None,
network: Optional[Union[Cell, BaseModel]] = None,
dataset: Optional[Union[BaseDataset, GeneratorDataset]] = None,
callbacks: Optional[Union[Callback, List[Callback]]] = None,
compute_metrics: Optional[Union[dict, set]] = None,
**kwargs):
r"""Evaluate task for ZeroShotImageClassification Trainer.
This function is used to evaluate the network.
The trainer interface is used to quickly start training for general task.
It also allows users to customize the network, dataset, callbacks, compute_metrics.
Args:
config (Optional[Union[dict, MindFormerConfig, ConfigArguments, TrainingArguments]]):
The task config which is used to configure the dataset, the hyper-parameter, optimizer, etc.
It supports config dict or MindFormerConfig or TrainingArguments or ConfigArguments class.
Default: None.
network (Optional[Union[Cell, BaseModel]]): The network for trainer.
It supports model name or BaseModel or MindSpore Cell class.
Default: None.
dataset (Optional[Union[BaseDataset, GeneratorDataset]]): The evaluate dataset.
It supports real dataset path or BaseDateset class or MindSpore Dataset class.
Default: None.
callbacks (Optional[Union[Callback, List[Callback]]]): The training callback function.
It supports CallBack or CallBack List of MindSpore.
Default: None.
compute_metrics (Optional[Union[dict, set]]): The metric of evaluating.
It supports dict or set in MindSpore's Metric class.
Default: None.
"""
metric_name = "Top1 Accuracy"
kwargs.setdefault("metric_name", metric_name)
self.evaluate_process(
config=config,
network=network,
dataset=dataset,
compute_metrics=compute_metrics,
callbacks=callbacks,
**kwargs
)
[文档] def predict(self,
config: Optional[Union[dict, MindFormerConfig, ConfigArguments, TrainingArguments]] = None,
input_data: Optional[Union[GeneratorDataset,
Tensor, np.ndarray, Image, str, list]] = None,
network: Optional[Union[Cell, BaseModel]] = None,
tokenizer: Optional[BaseTokenizer] = None,
image_processor: Optional[BaseImageProcessor] = None, **kwargs):
r"""Predict task for ZeroShotImageClassification Trainer.
This function is used to predict the network.
Args:
config (Optional[Union[dict, MindFormerConfig, ConfigArguments, TrainingArguments]]):
The task config which is used to configure the dataset, the hyper-parameter, optimizer, etc.
It supports config dict or MindFormerConfig or TrainingArguments or ConfigArguments class.
Default: None.
network (Optional[Union[Cell, BaseModel]]): The network for trainer.
It supports model name or BaseModel or MindSpore Cell class.
Default: None.
input_data (Optional[Union[GeneratorDataset, Tensor, np.ndarray, Image, str, list]]):
The dataset. It supports real dataset path or
BaseDateset class or MindSpore Dataset class.
Default: None.
tokenizer (Optional[BaseTokenizer]): Used for text process.
image_processor (Optional[BaseImageProcessor]): Used for image process.
"""
config = self.set_config(config)
logger.info(".........Build Input Data For Predict..........")
if input_data is None and config.input_data is not None:
input_data = config.input_data
if input_data is None:
input_data = build_dataset_loader(config.eval_dataset.data_loader)
candidate_labels = kwargs.pop("candidate_labels", None)
if candidate_labels is None:
if hasattr(input_data, "label_names"):
candidate_labels = input_data.label_names
else:
candidate_labels = ["sunflower", "tree", "dog", "cat", "toy"]
hypothesis_template = kwargs.pop("hypothesis_template", None)
if hypothesis_template is None:
if config.eval_dataset.data_loader.hypothesis_template is not None:
hypothesis_template = config.eval_dataset.data_loader.hypothesis_template
else:
hypothesis_template = "{}"
return self.predict_process(config=config,
input_data=input_data,
task='zero_shot_image_classification',
network=network,
tokenizer=tokenizer,
image_processor=image_processor,
candidate_labels=candidate_labels,
hypothesis_template=hypothesis_template,
**kwargs)