#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # #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 import numpy as np import time import math import tqdm import paddle.fluid as fluid import paddlex.utils.logging as logging from paddlex.utils import seconds_to_hms import paddlex from collections import OrderedDict from .base import BaseAPI class BaseClassifier(BaseAPI): """构建分类器，并实现其训练、评估、预测和模型导出。 Args: model_name (str): 分类器的模型名字，取值范围为['ResNet18', 'ResNet34', 'ResNet50', 'ResNet101', 'ResNet50_vd', 'ResNet101_vd', 'DarkNet53', 'MobileNetV1', 'MobileNetV2', 'Xception41', 'Xception65', 'Xception71']。默认为'ResNet50'。 num_classes (int): 类别数。默认为1000。 """ def __init__(self, model_name='ResNet50', num_classes=1000): self.init_params = locals() super(BaseClassifier, self).__init__('classifier') if not hasattr(paddlex.cv.nets, str.lower(model_name)): raise Exception( "ERROR: There's no model named {}.".format(model_name)) self.model_name = model_name self.labels = None self.num_classes = num_classes def build_net(self, mode='train'): image = fluid.data( dtype='float32', shape=[None, 3, None, None], name='image') if mode != 'test': label = fluid.data(dtype='int64', shape=[None, 1], name='label') model = getattr(paddlex.cv.nets, str.lower(self.model_name)) net_out = model(image, num_classes=self.num_classes) softmax_out = fluid.layers.softmax(net_out, use_cudnn=False) inputs = OrderedDict([('image', image)]) outputs = OrderedDict([('predict', softmax_out)]) if mode != 'test': cost = fluid.layers.cross_entropy(input=softmax_out, label=label) avg_cost = fluid.layers.mean(cost) acc1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1) k = min(5, self.num_classes) acck = fluid.layers.accuracy(input=softmax_out, label=label, k=k) if mode == 'train': self.optimizer.minimize(avg_cost) inputs = OrderedDict([('image', image), ('label', label)]) outputs = OrderedDict([('loss', avg_cost), ('acc1', acc1), ('acc{}'.format(k), acck)]) if mode == 'eval': del outputs['loss'] return inputs, outputs def default_optimizer(self, learning_rate, lr_decay_epochs, lr_decay_gamma, num_steps_each_epoch): boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs] values = [ learning_rate * (lr_decay_gamma**i) for i in range(len(lr_decay_epochs) + 1) ] lr_decay = fluid.layers.piecewise_decay( boundaries=boundaries, values=values) optimizer = fluid.optimizer.Momentum( lr_decay, momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-04)) return optimizer def train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.025, lr_decay_epochs=[30, 60, 90], lr_decay_gamma=0.1, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5): """训练。 Args: num_epochs (int): 训练迭代轮数。 train_dataset (paddlex.datasets): 训练数据读取器。 train_batch_size (int): 训练数据batch大小。同时作为验证数据batch大小。默认值为64。 eval_dataset (paddlex.datasets: 验证数据读取器。 save_interval_epochs (int): 模型保存间隔（单位：迭代轮数）。默认为1。 log_interval_steps (int): 训练日志输出间隔（单位：迭代步数）。默认为2。 save_dir (str): 模型保存路径。 pretrain_weights (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'IMAGENET'， 则自动下载在ImageNet图片数据上预训练的模型权重；若为None，则不使用预训练模型。默认为'IMAGENET'。 optimizer (paddle.fluid.optimizer): 优化器。当该参数为None时，使用默认优化器： fluid.layers.piecewise_decay衰减策略，fluid.optimizer.Momentum优化方法。 learning_rate (float): 默认优化器的初始学习率。默认为0.025。 lr_decay_epochs (list): 默认优化器的学习率衰减轮数。默认为[30, 60, 90]。 lr_decay_gamma (float): 默认优化器的学习率衰减率。默认为0.1。 use_vdl (bool): 是否使用VisualDL进行可视化。默认值为False。 sensitivities_file (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'， 则自动下载在ImageNet图片数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。 eval_metric_loss (float): 可容忍的精度损失。默认为0.05。 early_stop (bool): 是否使用提前终止训练策略。默认值为False。 early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内 连续下降或持平，则终止训练。默认值为5。 Raises: ValueError: 模型从inference model进行加载。 """ if not self.trainable: raise ValueError("Model is not trainable from load_model method.") self.labels = train_dataset.labels if optimizer is None: num_steps_each_epoch = train_dataset.num_samples // train_batch_size optimizer = self.default_optimizer( learning_rate=learning_rate, lr_decay_epochs=lr_decay_epochs, lr_decay_gamma=lr_decay_gamma, num_steps_each_epoch=num_steps_each_epoch) self.optimizer = optimizer # 构建训练、验证、预测网络 self.build_program() # 初始化网络权重 self.net_initialize( startup_prog=fluid.default_startup_program(), pretrain_weights=pretrain_weights, save_dir=save_dir, sensitivities_file=sensitivities_file, eval_metric_loss=eval_metric_loss) # 训练 self.train_loop( num_epochs=num_epochs, train_dataset=train_dataset, train_batch_size=train_batch_size, eval_dataset=eval_dataset, save_interval_epochs=save_interval_epochs, log_interval_steps=log_interval_steps, save_dir=save_dir, use_vdl=use_vdl, early_stop=early_stop, early_stop_patience=early_stop_patience) def evaluate(self, eval_dataset, batch_size=1, epoch_id=None, return_details=False): """评估。 Args: eval_dataset (paddlex.datasets): 验证数据读取器。 batch_size (int): 验证数据批大小。默认为1。 epoch_id (int): 当前评估模型所在的训练轮数。 return_details (bool): 是否返回详细信息。 Returns: dict: 当return_details为False时，返回dict, 包含关键字：'acc1'、'acc5'， 分别表示最大值的accuracy、前5个最大值的accuracy。 tuple (metrics, eval_details): 当return_details为True时，增加返回dict， 包含关键字：'true_labels'、'pred_scores'，分别代表真实类别id、每个类别的预测得分。 """ self.arrange_transforms( transforms=eval_dataset.transforms, mode='eval') data_generator = eval_dataset.generator( batch_size=batch_size, drop_last=False) k = min(5, self.num_classes) total_steps = math.ceil(eval_dataset.num_samples * 1.0 / batch_size) true_labels = list() pred_scores = list() if not hasattr(self, 'parallel_test_prog'): self.parallel_test_prog = fluid.CompiledProgram( self.test_prog).with_data_parallel( share_vars_from=self.parallel_train_prog) batch_size_each_gpu = self._get_single_card_bs(batch_size) logging.info( "Start to evaluating(total_samples={}, total_steps={})...".format( eval_dataset.num_samples, total_steps)) for step, data in tqdm.tqdm( enumerate(data_generator()), total=total_steps): images = np.array([d[0] for d in data]).astype('float32') labels = [d[1] for d in data] num_samples = images.shape[0] if num_samples < batch_size: num_pad_samples = batch_size - num_samples pad_images = np.tile(images[0:1], (num_pad_samples, 1, 1, 1)) images = np.concatenate([images, pad_images]) outputs = self.exe.run( self.parallel_test_prog, feed={'image': images}, fetch_list=list(self.test_outputs.values())) outputs = [outputs[0][:num_samples]] true_labels.extend(labels) pred_scores.extend(outputs[0].tolist()) logging.debug("[EVAL] Epoch={}, Step={}/{}".format( epoch_id, step + 1, total_steps)) pred_top1_label = np.argsort(pred_scores)[:, -1] pred_topk_label = np.argsort(pred_scores)[:, -k:] acc1 = sum(pred_top1_label == true_labels) / len(true_labels) acck = sum( [np.isin(x, y) for x, y in zip(true_labels, pred_topk_label)]) / len(true_labels) metrics = OrderedDict([('acc1', acc1), ('acc{}'.format(k), acck)]) if return_details: eval_details = { 'true_labels': true_labels, 'pred_scores': pred_scores } return metrics, eval_details return metrics def predict(self, img_file, transforms=None, topk=1): """预测。 Args: img_file (str): 预测图像路径。 transforms (paddlex.cls.transforms): 数据预处理操作。 topk (int): 预测时前k个最大值。 Returns: list: 其中元素均为字典。字典的关键字为'category_id'、'category'、'score'， 分别对应预测类别id、预测类别标签、预测得分。 """ if transforms is None and not hasattr(self, 'test_transforms'): raise Exception("transforms need to be defined, now is None.") true_topk = min(self.num_classes, topk) if transforms is not None: self.arrange_transforms(transforms=transforms, mode='test') im = transforms(img_file) else: self.arrange_transforms( transforms=self.test_transforms, mode='test') im = self.test_transforms(img_file) result = self.exe.run( self.test_prog, feed={'image': im}, fetch_list=list(self.test_outputs.values())) pred_label = np.argsort(result[0][0])[::-1][:true_topk] res = [{ 'category_id': l, 'category': self.labels[l], 'score': result[0][0][l] } for l in pred_label] return res class ResNet18(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet18, self).__init__( model_name='ResNet18', num_classes=num_classes) class ResNet34(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet34, self).__init__( model_name='ResNet34', num_classes=num_classes) class ResNet50(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet50, self).__init__( model_name='ResNet50', num_classes=num_classes) class ResNet101(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet101, self).__init__( model_name='ResNet101', num_classes=num_classes) class ResNet50_vd(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet50_vd, self).__init__( model_name='ResNet50_vd', num_classes=num_classes) class ResNet101_vd(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet101_vd, self).__init__( model_name='ResNet101_vd', num_classes=num_classes) class ResNet50_vd_ssld(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet50_vd_ssld, self).__init__( model_name='ResNet50_vd_ssld', num_classes=num_classes) class ResNet101_vd_ssld(BaseClassifier): def __init__(self, num_classes=1000): super(ResNet101_vd_ssld, self).__init__( model_name='ResNet101_vd_ssld', num_classes=num_classes) class DarkNet53(BaseClassifier): def __init__(self, num_classes=1000): super(DarkNet53, self).__init__( model_name='DarkNet53', num_classes=num_classes) class MobileNetV1(BaseClassifier): def __init__(self, num_classes=1000): super(MobileNetV1, self).__init__( model_name='MobileNetV1', num_classes=num_classes) class MobileNetV2(BaseClassifier): def __init__(self, num_classes=1000): super(MobileNetV2, self).__init__( model_name='MobileNetV2', num_classes=num_classes) class MobileNetV3_small(BaseClassifier): def __init__(self, num_classes=1000): super(MobileNetV3_small, self).__init__( model_name='MobileNetV3_small', num_classes=num_classes) class MobileNetV3_large(BaseClassifier): def __init__(self, num_classes=1000): super(MobileNetV3_large, self).__init__( model_name='MobileNetV3_large', num_classes=num_classes) class MobileNetV3_small_ssld(BaseClassifier): def __init__(self, num_classes=1000): super(MobileNetV3_small_ssld, self).__init__( model_name='MobileNetV3_small_ssld', num_classes=num_classes) class MobileNetV3_large_ssld(BaseClassifier): def __init__(self, num_classes=1000): super(MobileNetV3_large_ssld, self).__init__( model_name='MobileNetV3_large_ssld', num_classes=num_classes) class Xception65(BaseClassifier): def __init__(self, num_classes=1000): super(Xception65, self).__init__( model_name='Xception65', num_classes=num_classes) class Xception41(BaseClassifier): def __init__(self, num_classes=1000): super(Xception41, self).__init__( model_name='Xception41', num_classes=num_classes) class DenseNet121(BaseClassifier): def __init__(self, num_classes=1000): super(DenseNet121, self).__init__( model_name='DenseNet121', num_classes=num_classes) class DenseNet161(BaseClassifier): def __init__(self, num_classes=1000): super(DenseNet161, self).__init__( model_name='DenseNet161', num_classes=num_classes) class DenseNet201(BaseClassifier): def __init__(self, num_classes=1000): super(DenseNet201, self).__init__( model_name='DenseNet201', num_classes=num_classes) class ShuffleNetV2(BaseClassifier): def __init__(self, num_classes=1000): super(ShuffleNetV2, self).__init__( model_name='ShuffleNetV2', num_classes=num_classes)