# -*- coding: utf-8 -*- # Copyright (c) 2019 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 print_function import os import json from paddle import fluid import time import numpy as np import paddlepalm.utils.basic_helper as helper from paddlepalm.utils import reader_helper, saver from paddlepalm.distribute import gpu_dev_count, data_feeder, decode_fake # from paddlepalm.default_settings import * DEBUG=False class Trainer(object): """ The core unit to start a training/predicting session for single task. A trainer is to build computation graph, manage training and evaluation process, achieve model/checkpoint saving and pretrain_model/checkpoint loading. """ def __init__(self, name, mix_ratio=1.0, reuse_head_with=None): """Create a new trainer. Args: name: string. The name of the trainer(training task). mix_ratio: sampling weight of this trainer in multi-task learning mode. Default is 1.0. reuse_head_with: reuse parameters of task head with another trainer. Default is None, not reuse with others. """ self._name = name self._pred_reader = None self._task_head = None self._pred_head = None self._train_reader = None self._predict_reader = None self._train_iterator = None self._predict_iterator = None self._train_init = False self._predict_init = False self._train_init_prog = None self._pred_init_prog = None self._check_save = lambda: False # if save_predict_model: # self._save_predict_model = True # assert pred_head is not None, "pred_head is required to save predict model." # self._pred_reader = reader.clone(phase='predict') # else: # assert pred_head is None, "You should set save_predict_model as True, or the pred_head is invalid." # self._save_predict_model = False # self._pred_reader = None # self._save_steps = save_steps self._task_reuse_scope = name if reuse_head_with is None else reuse_head_with self._feeded_var_names = None self._target_vars = None self._num_examples = 0 self._multi_task = False self._as_auxilary = False self._task_id = None # training process management self._mix_ratio = mix_ratio self._expected_train_steps = None self._expected_train_epochs = None self._steps_pur_epoch = None self._pred_steps_pur_epoch = None self._cur_train_epoch = 0 self._cur_train_step = 0 self._train_finish = False self._inputname_to_varname = {} self._pred_input_name_list = [] self._pred_input_varname_list = [] self._pred_fetch_name_list = [] self._pred_fetch_var_list = [] # exe is built when random_init_params is called. # self._exe = helper.build_executor(gpu_dev_count>0) self._exe = None self._save_protocol = { 'input_names': 'self._pred_input_name_list', 'input_varnames': 'self._pred_input_varname_list', 'fetch_list': 'self._pred_fetch_name_list'} self._lock = False self._build_forward = False def build_forward(self, backbone, task_head): """ Build forward computation graph for training, which usually built from input layer to loss node. Args: backbone: a Backbone object with phase == 'train', which is used to extract multi-level text features, e.g., contextual word embedding and sentence embedding. head: a Head object with phase == 'train', which is used to build task specific output layers. Return: loss_var: a Variable object. The computational graph variable(node) of loss. """ # assert not self._multi_task, "you cannot build_forward in trainer when a train is wrapper by MultiHeadTrainer." self._task_head = task_head self._backbone = backbone # assert self._backbone is not None, "backbone is required for Trainer to build net forward to run with single task mode" self._build_forward = True # create reader, task # then check i/o across reader, backbone and task_layer task_attrs = [] pred_task_attrs = [] task_attr_from_reader = helper.encode_inputs(self._task_head.inputs_attrs['reader'], self.name) # task_attr_from_reader = self._task_head.inputs_attrs['reader'] # _check_io(backbone.inputs_attr, inst._reader['train'].outputs_attr, in_name=bb_name+'_backbone', out_name='reader.train') # _check_io(inst.taskblock['train'].inputs_attrs['reader'], inst._reader['train'].outputs_attr, in_name='task_paradigm.train.reader', out_name='reader.train') # _check_io(inst._taskblock['train'].inputs_attrs['backbone'], train_backbone.outputs_attr, in_name='task_paradigm.train.backbone', out_name=bb_name+'_backbone') # merge reader input attrs from backbone and task_instances input_names, shape_and_dtypes, name_to_position = reader_helper.merge_input_attrs(backbone.inputs_attr, task_attr_from_reader, insert_taskid=False) # shapes: [task_id, shapes_of_backbone, shapes_of_inst1, ..., shapes_of_instN] self._shape_and_dtypes = shape_and_dtypes self._name_to_position = name_to_position self._input_names = input_names if DEBUG: print('----- for debug -----') print('joint input names:') print(joint_input_names) print('joint input shape and dtypes:') print(joint_shape_and_dtypes) input_attrs = [[i, j, k] for i, (j,k) in zip(input_names, shape_and_dtypes)] train_prog = fluid.Program() train_init_prog = fluid.Program() self._train_prog = train_prog self._train_init_prog = train_init_prog if not self._multi_task: with fluid.program_guard(train_prog, train_init_prog): net_inputs = reader_helper.create_net_inputs(input_attrs, async=False) bb_output_vars = backbone.build(net_inputs) else: net_inputs = reader_helper.create_net_inputs(input_attrs, async=False) bb_output_vars = backbone.build(net_inputs) self._net_inputs = net_inputs assert sorted(bb_output_vars.keys()) == sorted(backbone.outputs_attr.keys()) # self._bb_output_vars.keys # fluid.framework.switch_main_program(train_prog) # fluid.framework.switch_startup_program(train_init_prog) task_output_vars = {} task_inputs = {'backbone': bb_output_vars} task_inputs_from_reader = helper.decode_inputs(net_inputs, self.name) task_inputs['reader'] = task_inputs_from_reader scope = self.name+'.' if not self._multi_task: with fluid.program_guard(train_prog, train_init_prog): with fluid.unique_name.guard(scope): output_vars = self._build_head(task_inputs, phase='train', scope=scope) else: with fluid.unique_name.guard(scope): output_vars = self._build_head(task_inputs, phase='train', scope=scope) output_vars = {self.name+'.'+key: val for key, val in output_vars.items()} old = len(task_output_vars) # for debug task_output_vars.update(output_vars) assert len(task_output_vars) - old == len(output_vars) # for debug bb_fetches = {k: v.name for k,v in bb_output_vars.items()} task_fetches = {k: v.name for k,v in task_output_vars.items()} self._fetches = task_fetches self._fetch_names, self._fetch_list = zip(*self._fetches.items()) # fetches = task_fetches # fetches['__task_id'] = net_inputs['__task_id'].name # compute loss # task_id_var = net_inputs['__task_id'] # task_id_vec = layers.one_hot(task_id_var, num_instances) # losses = fluid.layers.concat([task_output_vars[inst.name+'/loss'] for inst in instances], axis=0) # loss = layers.reduce_sum(task_id_vec * losses) if not self._multi_task: with fluid.program_guard(train_prog, train_init_prog): loss_var = fluid.layers.reduce_sum(task_output_vars[self.name+'.loss']) else: loss_var = fluid.layers.reduce_sum(task_output_vars[self.name+'.loss']) # for _id, block in enumerate(self._train_prog.blocks): # for var in block.vars: # print("[debug] : %d, %s" % (_id, var)) self._loss_var = loss_var if not self._multi_task: self._init_exe_prog(for_train=True) return loss_var def build_predict_forward(self, pred_backbone, pred_head): """ Build computation graph for evaluation and prediction. Arguments: - pred_backbone: a Backbone object with phase == 'predict'. For evaluating model during training, the predict backbone should keep the same with train backbone. - pred_head: a Head object with phase == 'predict'. For evaluating model during training, the predict head should keep the same with train head. Return: - output_vars: dict type. Each value is a computational graph variable(node) argumented by pred_head outputs_attr. """ self._pred_head = pred_head self._pred_backbone = pred_backbone # self._pred_reader = self._reader.clone(phase='pred') pred_task_attr_from_reader = helper.encode_inputs(self._pred_head.inputs_attrs['reader'], self.name) # pred_task_attr_from_reader = self._pred_head.inputs_attrs['reader'] # _check_io(pred_backbone.inputs_attr, pred_reader.outputs_attr, in_name=bb_name+'_backbone', out_name='reader.pred') # _check_io(pred_backbone.inputs_attr, pred_reader.outputs_attr, in_name=bb_name+'_backbone', out_name='reader.pred') # _check_io(pred_parad.inputs_attrs['reader'], pred_reader.outputs_attr, in_name='task_paradigm.pred.reader', out_name='reader.pred') # _check_io(pred_parad.inputs_attrs['backbone'], pred_backbone.outputs_attr, in_name='task_paradigm.pred.backbone', out_name=bb_name+'_backbone') pred_input_names, pred_shape_and_dtypes, pred_name_to_position = reader_helper.merge_input_attrs(pred_backbone.inputs_attr, pred_task_attr_from_reader, insert_taskid=False) pred_input_attrs = [[i, j, k] for i, (j,k) in zip(pred_input_names, pred_shape_and_dtypes)] self._pred_shape_and_dtypes = pred_shape_and_dtypes self._pred_name_to_position = pred_name_to_position pred_prog = fluid.Program() self._pred_prog = pred_prog pred_init_prog = fluid.Program() self._pred_init_prog = pred_init_prog with fluid.program_guard(pred_prog, pred_init_prog): pred_net_inputs = reader_helper.create_net_inputs(pred_input_attrs) # pred_bb_output_vars = pred_backbone.build(pred_net_inputs, scope_name='__paddlepalm_') pred_bb_output_vars = pred_backbone.build(pred_net_inputs) self._pred_net_inputs = pred_net_inputs # prepare predict vars for saving inference model with fluid.program_guard(pred_prog, pred_init_prog): cur_inputs = helper.decode_inputs(pred_net_inputs, self.name) # self.pred_input = cur_inputs self._pred_input_name_list, self._pred_input_varname_list = \ zip(*[[k, v.name] for k,v in cur_inputs.items()]) pred_task_inputs = {'backbone': pred_bb_output_vars, 'reader': cur_inputs} scope = self.name + '.' with fluid.unique_name.guard(scope): output_vars = self._build_head(pred_task_inputs, phase='predict', scope=scope) if output_vars is not None: self._pred_fetch_name_list, self._pred_fetch_list = zip(*output_vars.items()) else: self._pred_fetch_name_list = [] self._pred_fetch_var_list = [] if not self._multi_task: self._init_exe_prog(for_train=False) self._exe.run(self._pred_init_prog) return output_vars def build_backward(self, optimizer, weight_decay=None, use_ema=False, ema_decay=None): """ Build backward computation graph and training strategy. Arguments: - optimizer: - weight_decay: optional, default is None (disable weight decay). - use_ema: optional, default is False. The flag to control whether to apply Exponential Moving Average strategy on parameter updates. - ema_decay: optional, default is None. Only works with use_ema == True. Control decay rate of EMA strategy. """ # assert not self._multi_task, "you cannot build_backward in trainer when a train is wrapper by MultiHeadTrainer." # build optimizer assert self._loss_var is not None and self._train_init_prog is not None, "train graph not foung! You should build_forward first." optimizer._set_prog(self._train_prog, self._train_init_prog) with fluid.program_guard(self._train_prog, self._train_init_prog): param_grads = optimizer._build() if weight_decay is not None: param_list = dict() for param in self._train_prog.global_block().all_parameters(): param_list[param.name] = param * 1.0 param_list[param.name].stop_gradient = True def exclude_from_weight_decay(name): if name.find("layer_norm") > -1: return True bias_suffix = ["_bias", "_b", ".b_0"] for suffix in bias_suffix: if name.endswith(suffix): return True return False for param, grad in param_grads: if exclude_from_weight_decay(param.name): continue with param.block.program._optimized_guard( [param, grad]), fluid.framework.name_scope("weight_decay"): updated_param = param - param_list[ param.name] * weight_decay * optimizer.get_cur_learning_rate() fluid.layers.assign(output=param, input=updated_param) # loss.persistable = True if use_ema: ema = fluid.optimizer.ExponentialMovingAverage(ema_decay) ema.update() # for bid, block in enumerate(self._train_prog.blocks): # print('block id: '+str(bid)) # for var in block.vars: # print("%d : %s" % (bid, var)) # print(self._train_prog) self._exe.run(self._train_init_prog) def set_as_aux(self): """Set the task in this trainer as auxilary task. \nCAUSIOUS: This API only works on multi-task learning mode. Each task is set as target task by default. """ self._as_auxilary = True def fit_reader(self, reader, phase='train'): """ Bind a reader and loaded train/predict data to trainer. Args: reader: a Reader object. The running phase of the reader should be consistent with `phase` argument of this method. phase: running phase. Currently support: train, predict. """ # assert not self._multi_task, "you cannot fit_reader in trainer when a train is wrapper by MultiHeadTrainer." # load data self._check_phase(phase) if phase=='train': assert self._shape_and_dtypes is not None, "You need to build_forward or build_predict_head first to prepare input features." else: assert self._pred_shape_and_dtypes is not None, "You need to build_forward or build_predict_head first to prepare input features." # 这里不确定是否要向上取整，需确认 # tail = self._num_examples % batch_size > 0 # self._steps_pur_epoch = self._num_examples // batch_size + 1 if tail else 0 batch_size = reader._batch_size self._num_epochs = reader.num_epochs if phase == 'train': self._train_reader = reader self._steps_pur_epoch = reader.num_examples // batch_size shape_and_dtypes = self._shape_and_dtypes name_to_position = self._name_to_position if self._task_id is not None: self._net_inputs['__task_id'] = self._task_id net_inputs = self._net_inputs self._train_batch_size = batch_size self._num_examples = reader.num_examples reader_helper.check_io(self._backbone.inputs_attr, reader.outputs_attr, in_name='backbone', out_name='reader(train)') reader_helper.check_io(self._task_head.inputs_attrs['reader'], reader.outputs_attr, in_name='task_head(reader)', out_name='reader(train)') reader_helper.check_io(self._task_head.inputs_attrs['backbone'], self._backbone.outputs_attr, in_name='task_head(backbone, train)', out_name='backbone') elif phase == 'predict': self._predict_reader = reader # tail = self._num_examples % batch_size > 0 # self._pred_steps_pur_epoch = reader.num_examples // batch_size + 1 if tail else 0 self._pred_steps_pur_epoch = reader.num_examples // batch_size shape_and_dtypes = self._pred_shape_and_dtypes name_to_position = self._pred_name_to_position net_inputs = self._pred_net_inputs self._predict_batch_size = batch_size self._pred_num_examples = reader.num_examples reader_helper.check_io(self._pred_backbone.inputs_attr, reader.outputs_attr, in_name='backbone', out_name='reader(predict)') reader_helper.check_io(self._pred_head.inputs_attrs['reader'], reader.outputs_attr, in_name='task_head(reader)', out_name='reader(predict)') reader_helper.check_io(self._pred_head.inputs_attrs['backbone'], self._pred_backbone.outputs_attr, in_name='task_head(backbone, predict)', out_name='backbone') else: raise NotImplementedError() print('ok!') # merge dataset iterators and create net input vars iterator = reader._iterator() prefix = self.name # merge dataset iterators and create net input vars iterator = reader._iterator() prefix = self.name # 对yield出的数据进行runtime检查和适配 iterator_fn = reader_helper.create_iterator_fn(iterator, prefix, shape_and_dtypes, name_to_position, return_type='dict') self._raw_iterator_fn = iterator_fn feed_batch_process_fn = reader_helper.create_feed_batch_process_fn(net_inputs) if gpu_dev_count > 1: distribute_feeder_fn = data_feeder(iterator_fn, feed_batch_process_fn, phase=phase) else: distribute_feeder_fn = iterator_fn() if phase == 'train': self._train_iterator = distribute_feeder_fn self._feed_batch_process_fn = feed_batch_process_fn elif phase == 'predict': self._predict_iterator = distribute_feeder_fn self._pred_feed_batch_process_fn = feed_batch_process_fn # return distribute_feeder_fn() def load_ckpt(self, model_path): """ load training checkpoint for further training or predicting. Args: model_path: the path of saved checkpoint/parameters. """ # load pretrain model (or ckpt) # assert self._exe is not None, "You need to random_init_params before load checkpoints." # if phase == 'train' and not self._train_init: # self._init_exe_prog(for_train=True) # self._exe.run(self._train_init_prog) # if phase == 'predict' and not self._predict_init: # self._init_exe_prog(for_train=False) # self._exe.run(self._pred_init_prog) assert self._train_init_prog is not None or self._pred_init_prog is not None, "model graph not built. You should at least build_forward or build_predict_forward to load its checkpoint." # if phase == 'train': # assert self._train_init_prog is not None, "train graph not found! You should build_forward first before load checkpoint." if self._train_init_prog is not None: saver.init_pretraining_params( self._exe, model_path, convert=False, main_program=self._train_init_prog, strict=True) # elif phase == 'predict': elif self._pred_init_prog is not None: # assert self._pred_init_prog is not None, "predict graph not found! You should build_predict_head first before load checkpoint." saver.init_pretraining_params( self._exe, model_path, convert=False, main_program=self._pred_init_prog, strict=True) else: raise Exception("model not found. You should at least build_forward or build_predict_forward to load its checkpoint.") def load_predict_model(self, model_path, convert=False): """ load pretrain models(backbone) for training. Args: model_path: the path of saved pretrained parameters. """ assert self._pred_prog is not None, "training graph not found. You should at least build_forward to load its pretrained parameters." saver.init_pretraining_params( self._exe, model_path, convert=convert, main_program=self._pred_prog) # raise NotImplementedError() def load_pretrain(self, model_path, convert=False): """ load pretrain models(backbone) for training. Args: model_path: the path of saved pretrained parameters. """ # load pretrain model (or ckpt) # assert self._exe is not None, "You need to random_init_params before load pretrain models." assert self._train_init_prog is not None, "training graph not found. You should at least build_forward to load its pretrained parameters." saver.init_pretraining_params( self._exe, model_path, convert=convert, main_program=self._train_init_prog) def set_saver(self, save_path, save_steps, save_type='ckpt', is_multi=False): """ create a build-in saver into trainer. A saver will automatically save checkpoint or predict model every `save_steps` training steps. Args: save_path: a string. the path to save checkpoints or predict models. save_steps: an integer. the frequency to save models. save_type: a string. The type of saved model. Currently support checkpoint(ckpt) and predict model(predict), default is ckpt. If both two types are needed to save, you can set as "ckpt,predict". """ save_type = save_type.split(',') if 'predict' in save_type: assert self._pred_head is not None, "Predict head not found! You should build_predict_head first if you want to save predict model." assert save_path is not None and save_steps is not None, 'save_path and save_steps is required to save model.' self._save_predict = True if not os.path.exists(save_path): os.makedirs(save_path) else: self._save_predict = False if 'ckpt' in save_type: if save_path is not None and save_steps is not None: self._save_ckpt = True if not os.path.exists(save_path): os.makedirs(save_path) else: "WARNING: save_path or save_steps is not set, model will not be saved during training." self._save_ckpt = False else: self._save_ckpt = False def temp_func(): if (self._save_predict or self._save_ckpt) and self._cur_train_step % save_steps == 0: if self._save_predict: if is_multi: self._save(save_path, suffix='-pred.step'+str(self._cur_train_step)) print('predict model has been saved at '+os.path.join(save_path, 'pred.step'+str(self._cur_train_step))) else: self._save(save_path, suffix='pred.step'+str(self._cur_train_step)) print('predict model has been saved at '+os.path.join(save_path, 'pred.step'+str(self._cur_train_step))) if self._save_ckpt: if is_multi: fluid.io.save_persistables(self._exe, os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)), self._train_prog) print('checkpoint has been saved at '+os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step))) else: fluid.io.save_persistables(self._exe, os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)), self._train_prog) print('checkpoint has been saved at '+os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step))) return True else: return False self._check_save = temp_func def train(self, print_steps=5): """ start training. Args: print_steps: int. Logging frequency of training message, e.g., current step, loss and speed. """ iterator = self._train_iterator self._distribute_train_prog = fluid.CompiledProgram(self._train_prog).with_data_parallel(loss_name=self._loss_var.name) # if save_path is not None or save_steps is not None: # assert self._save_predict_model, "If you want to save model, you need set save_predict_model=True when this trainer is built." # if self._save_predict_model: # if save_path is None and save_steps is None: # print('Warning: model will not be saved for this run. If you want to save model, set save_path and save_steps.') # else: # assert save_path is not None, "argument save_path is required to save models." # assert save_steps == -1 or save_steps > 0, "argument save_steps should be -1 (only save the last step of this task) or larger than 0" # if save_path is not None and not os.path.exists(save_path): # os.makedirs(save_path) # else: # assert save_path is None, "You should set save_predict_model as True, or the argument save_path is invalid." # assert save_steps is None, "You should set save_predict_model as True, or the argument save_steps is invalid." time_begin = time.time() for feed in iterator: rt_outputs = self.train_one_step(feed) # if gpu_dev_count > 1: # feed, mask = feed # rt_outputs = self._exe.run(self._train_prog, feed=feed, fetch_list=self._fetch_list) # print(rt_outputs) # print(len(rt_outputs)) # if gpu_dev_count > 1: # while mask.pop() == False: # rt_outputs.pop() # rt_outputs = {k:v for k,v in zip(self._fetch_names, rt_outputs)} task_rt_outputs = {k[len(self.name+'.'):]: v for k,v in rt_outputs.items() if k.startswith(self.name+'.')} self._task_head.batch_postprocess(task_rt_outputs) if print_steps > 0 and self._cur_train_step % print_steps == 0: loss = rt_outputs[self.name+'.loss'] loss = np.mean(np.squeeze(loss)).tolist() time_end = time.time() time_cost = time_end - time_begin print("step {}/{} (epoch {}), loss: {:.3f}, speed: {:.2f} steps/s".format( (self._cur_train_step-1) % self._steps_pur_epoch + 1 , self._steps_pur_epoch, self._cur_train_epoch, loss, print_steps / time_cost)) time_begin = time.time() # self._check_save() # if cur_task.train_finish and cur_task.cur_train_step + cur_task.cur_train_epoch * cur_task.steps_pur_epoch == cur_task.expected_train_steps: # print(cur_task.name+': train finished!') # cur_task.save() if self._num_epochs is None and not self._multi_task and self._cur_train_step == self._steps_pur_epoch: break # save_path = os.path.join(main_conf['save_path'], 'ckpt', # "step_" + str(global_step)) # fluid.io.save_persistables(self.exe, save_path, saver_program) # print("ALL tasks train finished, exiting...") def predict(self, output_dir=None, print_steps=1000): """ start predicting. Args: output_dir: str. The path to save prediction results, default is None. If set as None, the results would output to screen directly. print_steps: int. Logging frequency of predicting message, e.g., current progress and speed. """ iterator = self._predict_iterator self._distribute_pred_prog = fluid.CompiledProgram(self._pred_prog).with_data_parallel() if output_dir is not None and not os.path.exists(output_dir): os.makedirs(output_dir) time_begin = time.time() cur_predict_step = 0 for feed in iterator: rt_outputs = self.predict_one_batch(feed) # rt_outputs = {k[len(self.name+'.'):]: v for k,v in rt_outputs.items() if k.startswith(self.name+'.')} self._pred_head.batch_postprocess(rt_outputs) cur_predict_step += 1 if print_steps > 0 and cur_predict_step % print_steps == 0: time_end = time.time() time_cost = time_end - time_begin print("batch {}/{}, speed: {:.2f} steps/s".format( cur_predict_step, self._pred_steps_pur_epoch, print_steps / time_cost)) time_begin = time.time() if self._pred_head.epoch_inputs_attrs: reader_outputs = self._predict_reader.get_epoch_outputs() else: reader_outputs = None results = self._pred_head.epoch_postprocess({'reader':reader_outputs}, output_dir=output_dir) return results def _check_phase(self, phase): assert phase in ['train', 'predict'], "Supported phase: train, predict," def _set_multitask(self): self._multi_task = True def _set_task_id(self, task_id): self._task_id = task_id def _init_exe_prog(self, for_train=True): if not self._train_init and not self._predict_init: on_gpu = gpu_dev_count > 0 self._exe = helper.build_executor(on_gpu) if for_train: assert self._train_prog is not None, "train graph not found! You should build_forward first before you random init parameters." self._train_init = True else: assert self._pred_prog is not None, "predict graph not found! You should build_predict_head first before you random init parameters." self._predict_init = True # def random_init_params(self): # """ # randomly initialize model parameters. # """ # # if not self._train_init: # self._init_exe_prog() # # print('random init params...') # self._exe.run(self._train_init_prog) def get_one_batch(self, phase='train'): self._check_phase(phase) if phase == 'train': return next(self._train_reader) elif phase == 'predict': return next(self._predict_reader) else: raise NotImplementedError() def _set_exe(self, exe): self._exe = exe def _set_dist_train(self, prog): self._distribute_train_prog = prog def _set_fetch_list(self, fetch_list): self._fetch_list = fetch_list # def train_one_step(self, batch, executor=None, distribute_train_prog=None, fetch_list=None): def train_one_step(self, batch): # exe = self._exe if executor is None else executor # distribute_train_prog = self._distribute_train_prog if distribute_train_prog is None else distribute_train_prog # fetch_list = self._fetch_list if fetch_list is None else fetch_list exe = self._exe distribute_train_prog = self._distribute_train_prog fetch_list = self._fetch_list if gpu_dev_count > 1: feed, mask = batch rt_outputs = exe.run(distribute_train_prog, feed=feed, fetch_list=fetch_list) num_fakes = decode_fake(len(rt_outputs[0]), mask, self._train_batch_size) if num_fakes: rt_outputs = [i[:-num_fakes] for i in rt_outputs] else: feed = self._feed_batch_process_fn(batch) rt_outputs = exe.run(distribute_train_prog, feed=feed, fetch_list=fetch_list) rt_outputs = {k:v for k,v in zip(self._fetch_names, rt_outputs)} self._cur_train_step += 1 self._check_save() self._cur_train_epoch = (self._cur_train_step-1) // self._steps_pur_epoch return rt_outputs def predict_one_batch(self, batch): if gpu_dev_count > 1: feed, mask = batch rt_outputs = self._exe.run(self._distribute_pred_prog, feed=feed, fetch_list=self._pred_fetch_list) num_fakes = decode_fake(len(rt_outputs[0]), mask, self._predict_batch_size) if num_fakes: rt_outputs = [i[:-num_fakes] for i in rt_outputs] else: feed = self._pred_feed_batch_process_fn(batch) rt_outputs = self._exe.run(self._distribute_pred_prog, feed=feed, fetch_list=self._pred_fetch_list) rt_outputs = {k:v for k,v in zip(self._pred_fetch_name_list, rt_outputs)} return rt_outputs @property def name(self): return self._name @property def num_examples(self): return self._num_examples @property def mix_ratio(self): return self._mix_ratio @mix_ratio.setter def mix_ratio(self, value): self._mix_ratio = value @property def num_epochs(self): return self._num_epochs @property def cur_train_step(self): return self._cur_train_step @property def cur_train_epoch(self): return self._cur_train_epoch @property def steps_pur_epoch(self): return self._steps_pur_epoch def _build_head(self, net_inputs, phase, scope=""): self._check_phase(phase) if phase == 'train': output_vars = self._task_head.build(net_inputs, scope_name=scope) if phase == 'predict': output_vars = self._pred_head.build(net_inputs, scope_name=scope) return output_vars def _save(self, save_path, suffix=None): # dirpath = save_path.rstrip('/').rstrip('\\') + suffix if suffix is not None: dirpath = os.path.join(save_path, suffix) else: dirpath = save_path self._pred_input_varname_list = [str(i) for i in self._pred_input_varname_list] prog = self._pred_prog.clone() fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, prog) conf = {} for k, strv in self._save_protocol.items(): d = None v = locals() exec('d={}'.format(strv), globals(), v) conf[k] = v['d'] with open(os.path.join(dirpath, '__conf__'), 'w') as writer: writer.write(json.dumps(conf, indent=1)) print(self._name + ': predict model saved at ' + dirpath) def _load(self, infer_model_path=None): if infer_model_path is None: infer_model_path = self._save_infermodel_path for k,v in json.load(open(os.path.join(infer_model_path, '__conf__'))).items(): strv = self._save_protocol[k] exec('{}=v'.format(strv)) pred_prog, self._pred_input_varname_list, self._pred_fetch_var_list = \ fluid.io.load_inference_model(infer_model_path, self._exe) print(self._name+': inference model loaded from ' + infer_model_path) return pred_prog