# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # 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 paddle.fluid as fluid import os from os import path as osp import numpy as np from collections import OrderedDict import copy import math import time import tqdm import cv2 import yaml import paddleslim as slim import utils import utils.logging as logging from utils import seconds_to_hms from utils import ConfusionMatrix from utils import get_environ_info from nets import DeepLabv3p, ShuffleSeg, HRNet import transforms as T def dict2str(dict_input): out = '' for k, v in dict_input.items(): try: v = round(float(v), 6) except: pass out = out + '{}={}, '.format(k, v) return out.strip(', ') class SegModel(object): # DeepLab mobilenet def __init__(self, num_classes=2, use_bce_loss=False, use_dice_loss=False, class_weight=None, ignore_index=255, sync_bn=True): self.init_params = locals() if num_classes > 2 and (use_bce_loss or use_dice_loss): raise ValueError( "dice loss and bce loss is only applicable to binary classfication" ) if class_weight is not None: if isinstance(class_weight, list): if len(class_weight) != num_classes: raise ValueError( "Length of class_weight should be equal to number of classes" ) elif isinstance(class_weight, str): if class_weight.lower() != 'dynamic': raise ValueError( "if class_weight is string, must be dynamic!") else: raise TypeError( 'Expect class_weight is a list or string but receive {}'. format(type(class_weight))) self.num_classes = num_classes self.use_bce_loss = use_bce_loss self.use_dice_loss = use_dice_loss self.class_weight = class_weight self.ignore_index = ignore_index self.sync_bn = sync_bn self.labels = None self.env_info = get_environ_info() if self.env_info['place'] == 'cpu': self.places = fluid.cpu_places() else: self.places = fluid.cuda_places() self.exe = fluid.Executor(self.places[0]) self.train_prog = None self.test_prog = None self.parallel_train_prog = None self.train_inputs = None self.test_inputs = None self.train_outputs = None self.test_outputs = None self.train_data_loader = None self.eval_metrics = None # 当前模型状态 self.status = 'Normal' def _get_single_car_bs(self, batch_size): if batch_size % len(self.places) == 0: return int(batch_size // len(self.places)) else: raise Exception("Please support correct batch_size, \ which can be divided by available cards({}) in {}". format(self.env_info['num'], self.env_info['place'])) def build_net(self, mode='train'): """应根据不同的情况进行构建""" pass def build_program(self): # build training network self.train_inputs, self.train_outputs = self.build_net(mode='train') self.train_prog = fluid.default_main_program() startup_prog = fluid.default_startup_program() # build prediction network self.test_prog = fluid.Program() with fluid.program_guard(self.test_prog, startup_prog): with fluid.unique_name.guard(): self.test_inputs, self.test_outputs = self.build_net( mode='test') self.test_prog = self.test_prog.clone(for_test=True) def arrange_transform(self, transforms, mode='train'): arrange_transform = T.ArrangeSegmenter if type(transforms.transforms[-1]).__name__.startswith('Arrange'): transforms.transforms[-1] = arrange_transform(mode=mode) else: transforms.transforms.append(arrange_transform(mode=mode)) def build_train_data_loader(self, dataset, batch_size): # init data_loader if self.train_data_loader is None: self.train_data_loader = fluid.io.DataLoader.from_generator( feed_list=list(self.train_inputs.values()), capacity=64, use_double_buffer=True, iterable=True) batch_size_each_gpu = self._get_single_car_bs(batch_size) self.train_data_loader.set_sample_list_generator( dataset.generator(batch_size=batch_size_each_gpu), places=self.places) def net_initialize(self, startup_prog=None, pretrained_weights=None, resume_weights=None): if startup_prog is None: startup_prog = fluid.default_startup_program() self.exe.run(startup_prog) if resume_weights is not None: logging.info("Resume weights from {}".format(resume_weights)) if not osp.exists(resume_weights): raise Exception("Path {} not exists.".format(resume_weights)) fluid.load(self.train_prog, osp.join(resume_weights, 'model'), self.exe) # Check is path ended by path spearator if resume_weights[-1] == os.sep: resume_weights = resume_weights[0:-1] epoch_name = osp.basename(resume_weights) # If resume weights is end of digit, restore epoch status epoch = epoch_name.split('_')[-1] if epoch.isdigit(): self.begin_epoch = int(epoch) else: raise ValueError("Resume model path is not valid!") logging.info("Model checkpoint loaded successfully!") elif pretrained_weights is not None: logging.info( "Load pretrain weights from {}.".format(pretrained_weights)) utils.load_pretrained_weights(self.exe, self.train_prog, pretrained_weights) def get_model_info(self): # 存储相应的信息到yml文件 info = dict() info['Model'] = self.__class__.__name__ if 'self' in self.init_params: del self.init_params['self'] if '__class__' in self.init_params: del self.init_params['__class__'] info['_init_params'] = self.init_params info['_Attributes'] = dict() info['_Attributes']['num_classes'] = self.num_classes info['_Attributes']['labels'] = self.labels try: info['_Attributes']['eval_metric'] = dict() for k, v in self.eval_metrics.items(): if isinstance(v, np.ndarray): if v.size > 1: v = [float(i) for i in v] else: v = float(v) info['_Attributes']['eval_metric'][k] = v except: pass if hasattr(self, 'test_transforms'): if self.test_transforms is not None: info['test_transforms'] = list() for op in self.test_transforms.transforms: name = op.__class__.__name__ attr = op.__dict__ info['test_transforms'].append({name: attr}) if hasattr(self, 'train_transforms'): if self.train_transforms is not None: info['train_transforms'] = list() for op in self.train_transforms.transforms: name = op.__class__.__name__ attr = op.__dict__ info['train_transforms'].append({name: attr}) if hasattr(self, 'train_init'): if 'self' in self.train_init: del self.train_init['self'] if 'train_dataset' in self.train_init: del self.train_init['train_dataset'] if 'eval_dataset' in self.train_init: del self.train_init['eval_dataset'] if 'optimizer' in self.train_init: del self.train_init['optimizer'] info['train_init'] = self.train_init return info def save_model(self, save_dir): if not osp.isdir(save_dir): if osp.exists(save_dir): os.remove(save_dir) os.makedirs(save_dir) model_info = self.get_model_info() if self.status == 'Normal': fluid.save(self.train_prog, osp.join(save_dir, 'model')) elif self.status == 'Quant': float_prog, _ = slim.quant.convert( self.test_prog, self.exe.place, save_int8=True) test_input_names = [ var.name for var in list(self.test_inputs.values()) ] test_outputs = list(self.test_outputs.values()) fluid.io.save_inference_model( dirname=save_dir, executor=self.exe, params_filename='__params__', feeded_var_names=test_input_names, target_vars=test_outputs, main_program=float_prog) model_info['_ModelInputsOutputs'] = dict() model_info['_ModelInputsOutputs']['test_inputs'] = [ [k, v.name] for k, v in self.test_inputs.items() ] model_info['_ModelInputsOutputs']['test_outputs'] = [ [k, v.name] for k, v in self.test_outputs.items() ] model_info['status'] = self.status with open( osp.join(save_dir, 'model.yml'), encoding='utf-8', mode='w') as f: yaml.dump(model_info, f) # The flag of model for saving successfully open(osp.join(save_dir, '.success'), 'w').close() logging.info("Model saved in {}.".format(save_dir)) def export_inference_model(self, save_dir): test_input_names = [var.name for var in list(self.test_inputs.values())] test_outputs = list(self.test_outputs.values()) fluid.io.save_inference_model( dirname=save_dir, executor=self.exe, params_filename='__params__', feeded_var_names=test_input_names, target_vars=test_outputs, main_program=self.test_prog) model_info = self.get_model_info() model_info['status'] = 'Infer' # Save input and output descrition of model model_info['_ModelInputsOutputs'] = dict() model_info['_ModelInputsOutputs']['test_inputs'] = [ [k, v.name] for k, v in self.test_inputs.items() ] model_info['_ModelInputsOutputs']['test_outputs'] = [ [k, v.name] for k, v in self.test_outputs.items() ] with open( osp.join(save_dir, 'model.yml'), encoding='utf-8', mode='w') as f: yaml.dump(model_info, f) # The flag of model for saving successfully open(osp.join(save_dir, '.success'), 'w').close() logging.info("Model for inference deploy saved in {}.".format(save_dir)) def export_quant_model(self, dataset, save_dir, batch_size=1, batch_nums=10, cache_dir="./.temp"): self.arrange_transform(transforms=dataset.transforms, mode='quant') dataset.num_samples = batch_size * batch_nums try: from utils import HumanSegPostTrainingQuantization except: raise Exception( "Model Quantization is not available, try to upgrade your paddlepaddle>=1.7.0" ) is_use_cache_file = True if cache_dir is None: is_use_cache_file = False post_training_quantization = HumanSegPostTrainingQuantization( executor=self.exe, dataset=dataset, program=self.test_prog, inputs=self.test_inputs, outputs=self.test_outputs, batch_size=batch_size, batch_nums=batch_nums, scope=None, algo='KL', quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"], is_full_quantize=False, is_use_cache_file=is_use_cache_file, cache_dir=cache_dir) post_training_quantization.quantize() post_training_quantization.save_quantized_model(save_dir) if cache_dir is not None: os.system('rm -r' + cache_dir) model_info = self.get_model_info() model_info['status'] = 'Quant' # Save input and output descrition of model model_info['_ModelInputsOutputs'] = dict() model_info['_ModelInputsOutputs']['test_inputs'] = [ [k, v.name] for k, v in self.test_inputs.items() ] model_info['_ModelInputsOutputs']['test_outputs'] = [ [k, v.name] for k, v in self.test_outputs.items() ] with open( osp.join(save_dir, 'model.yml'), encoding='utf-8', mode='w') as f: yaml.dump(model_info, f) # The flag of model for saving successfully open(osp.join(save_dir, '.success'), 'w').close() logging.info("Model for quant saved in {}.".format(save_dir)) def default_optimizer(self, learning_rate, num_epochs, num_steps_each_epoch, lr_decay_power=0.9, regularization_coeff=4e-5): decay_step = num_epochs * num_steps_each_epoch lr_decay = fluid.layers.polynomial_decay( learning_rate, decay_step, end_learning_rate=0, power=lr_decay_power) optimizer = fluid.optimizer.Momentum( lr_decay, momentum=0.9, regularization=fluid.regularizer.L2Decay( regularization_coeff=regularization_coeff)) return optimizer def train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrained_weights=None, resume_weights=None, optimizer=None, learning_rate=0.01, lr_decay_power=0.9, regularization_coeff=4e-5, use_vdl=False, quant=False): self.labels = train_dataset.labels self.train_transforms = train_dataset.transforms self.train_init = locals() self.begin_epoch = 0 if optimizer is None: num_steps_each_epoch = train_dataset.num_samples // train_batch_size optimizer = self.default_optimizer( learning_rate=learning_rate, num_epochs=num_epochs, num_steps_each_epoch=num_steps_each_epoch, lr_decay_power=lr_decay_power, regularization_coeff=regularization_coeff) self.optimizer = optimizer self.build_program() self.net_initialize( startup_prog=fluid.default_startup_program(), pretrained_weights=pretrained_weights, resume_weights=resume_weights) # 进行量化 if quant: # 当 for_test=False ，返回类型为 fluid.CompiledProgram # 当 for_test=True ，返回类型为 fluid.Program self.train_prog = slim.quant.quant_aware( self.train_prog, self.exe.place, for_test=False) self.test_prog = slim.quant.quant_aware( self.test_prog, self.exe.place, for_test=True) # self.parallel_train_prog = self.train_prog.with_data_parallel( # loss_name=self.train_outputs['loss'].name) self.status = 'Quant' if self.begin_epoch >= num_epochs: raise ValueError( ("begin epoch[{}] is larger than num_epochs[{}]").format( self.begin_epoch, num_epochs)) if not osp.isdir(save_dir): if osp.exists(save_dir): os.remove(save_dir) os.makedirs(save_dir) # add arrange op tor transforms self.arrange_transform( transforms=train_dataset.transforms, mode='train') self.build_train_data_loader( dataset=train_dataset, batch_size=train_batch_size) if eval_dataset is not None: self.eval_transforms = eval_dataset.transforms self.test_transforms = copy.deepcopy(eval_dataset.transforms) lr = self.optimizer._learning_rate lr.persistable = True if isinstance(lr, fluid.framework.Variable): self.train_outputs['lr'] = lr # 多卡训练 if self.parallel_train_prog is None: build_strategy = fluid.compiler.BuildStrategy() if self.env_info['place'] != 'cpu' and len(self.places) > 1: build_strategy.sync_batch_norm = self.sync_bn exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_iteration_per_drop_scope = 1 if quant: build_strategy.fuse_all_reduce_ops = False build_strategy.sync_batch_norm = False self.parallel_train_prog = self.train_prog.with_data_parallel( loss_name=self.train_outputs['loss'].name, build_strategy=build_strategy, exec_strategy=exec_strategy) else: self.parallel_train_prog = fluid.CompiledProgram( self.train_prog).with_data_parallel( loss_name=self.train_outputs['loss'].name, build_strategy=build_strategy, exec_strategy=exec_strategy) total_num_steps = math.floor( train_dataset.num_samples / train_batch_size) num_steps = 0 time_stat = list() time_train_one_epoch = None time_eval_one_epoch = None total_num_steps_eval = 0 # eval times total_eval_times = math.ceil(num_epochs / save_interval_epochs) eval_batch_size = train_batch_size if eval_dataset is not None: total_num_steps_eval = math.ceil( eval_dataset.num_samples / eval_batch_size) if use_vdl: from visualdl import LogWriter vdl_logdir = osp.join(save_dir, 'vdl_log') log_writer = LogWriter(vdl_logdir) best_miou = -1.0 best_model_epoch = 1 for i in range(self.begin_epoch, num_epochs): records = list() step_start_time = time.time() epoch_start_time = time.time() for step, data in enumerate(self.train_data_loader()): outputs = self.exe.run( self.parallel_train_prog, feed=data, fetch_list=list(self.train_outputs.values())) outputs_avg = np.mean(np.array(outputs), axis=1) records.append(outputs_avg) # time estimated to complete the training currend_time = time.time() step_cost_time = currend_time - step_start_time step_start_time = currend_time if len(time_stat) < 20: time_stat.append(step_cost_time) else: time_stat[num_steps % 20] = step_cost_time num_steps += 1 if num_steps % log_interval_steps == 0: step_metrics = OrderedDict( zip(list(self.train_outputs.keys()), outputs_avg)) if use_vdl: for k, v in step_metrics.items(): log_writer.add_scalar( step=num_steps, tag='train/{}'.format(k), value=v) # 计算剩余时间 avg_step_time = np.mean(time_stat) if time_train_one_epoch is not None: eta = (num_epochs - i - 1) * time_train_one_epoch + ( total_num_steps - step - 1) * avg_step_time else: eta = ((num_epochs - i) * total_num_steps - step - 1) * avg_step_time if time_eval_one_epoch is not None: eval_eta = (total_eval_times - i // save_interval_epochs ) * time_eval_one_epoch else: eval_eta = (total_eval_times - i // save_interval_epochs ) * total_num_steps_eval * avg_step_time eta_str = seconds_to_hms(eta + eval_eta) logging.info( "[TRAIN] Epoch={}/{}, Step={}/{}, {}, time_each_step={}s, eta={}" .format(i + 1, num_epochs, step + 1, total_num_steps, dict2str(step_metrics), round(avg_step_time, 2), eta_str)) train_metrics = OrderedDict( zip(list(self.train_outputs.keys()), np.mean(records, axis=0))) logging.info('[TRAIN] Epoch {} finished, {} .'.format( i + 1, dict2str(train_metrics))) time_train_one_epoch = time.time() - epoch_start_time eval_epoch_start_time = time.time() if (i + 1) % save_interval_epochs == 0 or i == num_epochs - 1: current_save_dir = osp.join(save_dir, "epoch_{}".format(i + 1)) if not osp.isdir(current_save_dir): os.makedirs(current_save_dir) if eval_dataset is not None: self.eval_metrics = self.evaluate( eval_dataset=eval_dataset, batch_size=eval_batch_size, epoch_id=i + 1) # 保存最优模型 current_miou = self.eval_metrics['miou'] if current_miou > best_miou: best_miou = current_miou best_model_epoch = i + 1 best_model_dir = osp.join(save_dir, "best_model") self.save_model(save_dir=best_model_dir) if use_vdl: for k, v in self.eval_metrics.items(): if isinstance(v, list): continue if isinstance(v, np.ndarray): if v.size > 1: continue log_writer.add_scalar( step=num_steps, tag='evaluate/{}'.format(k), value=v) self.save_model(save_dir=current_save_dir) time_eval_one_epoch = time.time() - eval_epoch_start_time if eval_dataset is not None: logging.info( 'Current evaluated best model in eval_dataset is epoch_{}, miou={}' .format(best_model_epoch, best_miou)) def evaluate(self, eval_dataset, batch_size=1, epoch_id=None): """评估。 Args: eval_dataset (paddlex.datasets): 评估数据读取器。 batch_size (int): 评估时的batch大小。默认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。默认False。 Returns: dict: 当return_details为False时，返回dict。包含关键字：'miou'、'category_iou'、'macc'、 'category_acc'和'kappa'，分别表示平均iou、各类别iou、平均准确率、各类别准确率和kappa系数。 tuple (metrics, eval_details)：当return_details为True时，增加返回dict (eval_details)， 包含关键字：'confusion_matrix'，表示评估的混淆矩阵。 """ self.arrange_transform(transforms=eval_dataset.transforms, mode='train') total_steps = math.ceil(eval_dataset.num_samples * 1.0 / batch_size) conf_mat = ConfusionMatrix(self.num_classes, streaming=True) data_generator = eval_dataset.generator( batch_size=batch_size, drop_last=False) 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) 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]) labels = np.array([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]) feed_data = {'image': images} outputs = self.exe.run( self.parallel_test_prog, feed=feed_data, fetch_list=list(self.test_outputs.values()), return_numpy=True) pred = outputs[0] if num_samples < batch_size: pred = pred[0:num_samples] mask = labels != self.ignore_index conf_mat.calculate(pred=pred, label=labels, ignore=mask) _, iou = conf_mat.mean_iou() logging.debug("[EVAL] Epoch={}, Step={}/{}, iou={}".format( epoch_id, step + 1, total_steps, iou)) category_iou, miou = conf_mat.mean_iou() category_acc, macc = conf_mat.accuracy() metrics = OrderedDict( zip(['miou', 'category_iou', 'macc', 'category_acc', 'kappa'], [miou, category_iou, macc, category_acc, conf_mat.kappa()])) logging.info('[EVAL] Finished, Epoch={}, {} .'.format( epoch_id, dict2str(metrics))) return metrics def predict(self, im_file, transforms=None): """预测。 Args: img_file(str|np.ndarray): 预测图像。 transforms(paddlex.cv.transforms): 数据预处理操作。 Returns: dict: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图， 像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes) """ if isinstance(im_file, str): if not osp.exists(im_file): raise ValueError( 'The Image file does not exist: {}'.format(im_file)) if transforms is None and not hasattr(self, 'test_transforms'): raise Exception("transforms need to be defined, now is None.") if transforms is not None: self.arrange_transform(transforms=transforms, mode='test') im, im_info = transforms(im_file) else: self.arrange_transform(transforms=self.test_transforms, mode='test') im, im_info = self.test_transforms(im_file) im = np.expand_dims(im, axis=0) result = self.exe.run( self.test_prog, feed={'image': im}, fetch_list=list(self.test_outputs.values())) pred = result[0] logit = result[1] logit = np.squeeze(logit) logit = np.transpose(logit, (1, 2, 0)) pred = np.squeeze(pred).astype('uint8') keys = list(im_info.keys()) for k in keys[::-1]: if k == 'shape_before_resize': h, w = im_info[k][0], im_info[k][1] pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST) logit = cv2.resize(logit, (w, h), cv2.INTER_LINEAR) elif k == 'shape_before_padding': h, w = im_info[k][0], im_info[k][1] pred = pred[0:h, 0:w] logit = logit[0:h, 0:w, :] return {'label_map': pred, 'score_map': logit} class HumanSegLite(SegModel): # DeepLab ShuffleNet def build_net(self, mode='train'): """应根据不同的情况进行构建""" model = ShuffleSeg( self.num_classes, mode=mode, use_bce_loss=self.use_bce_loss, use_dice_loss=self.use_dice_loss, class_weight=self.class_weight, ignore_index=self.ignore_index) inputs = model.generate_inputs() model_out = model.build_net(inputs) outputs = OrderedDict() if mode == 'train': self.optimizer.minimize(model_out) outputs['loss'] = model_out else: outputs['pred'] = model_out[0] outputs['logit'] = model_out[1] return inputs, outputs class HumanSegServer(SegModel): # DeepLab Xception def __init__(self, num_classes=2, backbone='Xception65', output_stride=16, aspp_with_sep_conv=True, decoder_use_sep_conv=True, encoder_with_aspp=True, enable_decoder=True, use_bce_loss=False, use_dice_loss=False, class_weight=None, ignore_index=255, sync_bn=True): super().__init__( num_classes=num_classes, use_bce_loss=use_bce_loss, use_dice_loss=use_dice_loss, class_weight=class_weight, ignore_index=ignore_index, sync_bn=sync_bn) self.init_params = locals() self.output_stride = output_stride if backbone not in ['Xception65', 'Xception41']: raise ValueError("backbone: {} is set wrong. it should be one of " "('Xception65', 'Xception41')".format(backbone)) self.backbone = backbone self.aspp_with_sep_conv = aspp_with_sep_conv self.decoder_use_sep_conv = decoder_use_sep_conv self.encoder_with_aspp = encoder_with_aspp self.enable_decoder = enable_decoder self.sync_bn = sync_bn def build_net(self, mode='train'): model = DeepLabv3p( self.num_classes, mode=mode, backbone=self.backbone, output_stride=self.output_stride, aspp_with_sep_conv=self.aspp_with_sep_conv, decoder_use_sep_conv=self.decoder_use_sep_conv, encoder_with_aspp=self.encoder_with_aspp, enable_decoder=self.enable_decoder, use_bce_loss=self.use_bce_loss, use_dice_loss=self.use_dice_loss, class_weight=self.class_weight, ignore_index=self.ignore_index) inputs = model.generate_inputs() model_out = model.build_net(inputs) outputs = OrderedDict() if mode == 'train': self.optimizer.minimize(model_out) outputs['loss'] = model_out else: outputs['pred'] = model_out[0] outputs['logit'] = model_out[1] return inputs, outputs class HumanSegMobile(SegModel): def __init__(self, num_classes=2, stage1_num_modules=1, stage1_num_blocks=[1], stage1_num_channels=[32], stage2_num_modules=1, stage2_num_blocks=[2, 2], stage2_num_channels=[16, 32], stage3_num_modules=1, stage3_num_blocks=[2, 2, 2], stage3_num_channels=[16, 32, 64], stage4_num_modules=1, stage4_num_blocks=[2, 2, 2, 2], stage4_num_channels=[16, 32, 64, 128], use_bce_loss=False, use_dice_loss=False, class_weight=None, ignore_index=255, sync_bn=True): super().__init__( num_classes=num_classes, use_bce_loss=use_bce_loss, use_dice_loss=use_dice_loss, class_weight=class_weight, ignore_index=ignore_index, sync_bn=sync_bn) self.init_params = locals() self.stage1_num_modules = stage1_num_modules self.stage1_num_blocks = stage1_num_blocks self.stage1_num_channels = stage1_num_channels self.stage2_num_modules = stage2_num_modules self.stage2_num_blocks = stage2_num_blocks self.stage2_num_channels = stage2_num_channels self.stage3_num_modules = stage3_num_modules self.stage3_num_blocks = stage3_num_blocks self.stage3_num_channels = stage3_num_channels self.stage4_num_modules = stage4_num_modules self.stage4_num_blocks = stage4_num_blocks self.stage4_num_channels = stage4_num_channels def build_net(self, mode='train'): """应根据不同的情况进行构建""" model = HRNet( self.num_classes, mode=mode, stage1_num_modules=self.stage1_num_modules, stage1_num_blocks=self.stage1_num_blocks, stage1_num_channels=self.stage1_num_channels, stage2_num_modules=self.stage2_num_modules, stage2_num_blocks=self.stage2_num_blocks, stage2_num_channels=self.stage2_num_channels, stage3_num_modules=self.stage3_num_modules, stage3_num_blocks=self.stage3_num_blocks, stage3_num_channels=self.stage3_num_channels, stage4_num_modules=self.stage4_num_modules, stage4_num_blocks=self.stage4_num_blocks, stage4_num_channels=self.stage4_num_channels, use_bce_loss=self.use_bce_loss, use_dice_loss=self.use_dice_loss, class_weight=self.class_weight, ignore_index=self.ignore_index) inputs = model.generate_inputs() model_out = model.build_net(inputs) outputs = OrderedDict() if mode == 'train': self.optimizer.minimize(model_out) outputs['loss'] = model_out else: outputs['pred'] = model_out[0] outputs['logit'] = model_out[1] return inputs, outputs def train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrained_weights=None, resume_weights=None, optimizer=None, learning_rate=0.01, lr_decay_power=0.9, regularization_coeff=5e-4, use_vdl=False, quant=False): super().train( 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, pretrained_weights=pretrained_weights, resume_weights=resume_weights, optimizer=optimizer, learning_rate=learning_rate, lr_decay_power=lr_decay_power, regularization_coeff=regularization_coeff, use_vdl=use_vdl, quant=quant)