#!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: trainer_naiv512.py # Created Date: Sunday January 9th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Saturday, 29th January 2022 3:54:06 am # Modified By: Chen Xuanhong # Copyright (c) 2022 Shanghai Jiao Tong University ############################################################# import os import time import random import shutil from cv2 import sqrt import numpy as np import torch import torch.nn.functional as F from torchvision.utils import save_image from train_scripts.trainer_base import TrainerBase class Trainer(TrainerBase): def __init__(self, config, reporter): super(Trainer, self).__init__(config, reporter) import inspect print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) self.img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) self.img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) # TODO modify this function to build your models def init_framework(self): ''' This function is designed to define the framework, and print the framework information into the log file ''' #===============build models================# print("build models...") # TODO [import models here] model_config = self.config["model_configs"] if self.config["phase"] == "train": gscript_name = "components." + model_config["g_model"]["script"] file1 = os.path.join("components", model_config["g_model"]["script"]+".py") tgtfile1 = os.path.join(self.config["project_scripts"], model_config["g_model"]["script"]+".py") shutil.copyfile(file1,tgtfile1) elif self.config["phase"] == "finetune": gscript_name = self.config["com_base"] + model_config["g_model"]["script"] class_name = model_config["g_model"]["class_name"] package = __import__(gscript_name, fromlist=True) gen_class = getattr(package, class_name) self.gen = gen_class(**model_config["g_model"]["module_params"]) # print and recorde model structure self.reporter.writeInfo("Generator structure:") self.reporter.writeModel(self.gen.__str__()) # print and recorde model structure arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) self.arcface = arcface1['model'].module # train in GPU if self.config["cuda"] >=0: self.gen = self.gen.cuda() self.arcface= self.arcface.cuda() self.arcface.eval() self.arcface.requires_grad_(False) # if in finetune phase, load the pretrained checkpoint if self.config["phase"] == "finetune": model_path = os.path.join(self.config["project_checkpoints"], "step%d_%s.pth"%(self.config["checkpoint_step"], self.config["checkpoint_names"]["generator_name"])) self.gen.load_state_dict(torch.load(model_path)) print('loaded trained backbone model step {}...!'.format(self.config["project_checkpoints"])) # TODO modify this function to configurate the optimizer of your pipeline def __setup_optimizers__(self): g_train_opt = self.config['g_optim_config'] g_optim_params = [] for k, v in self.gen.named_parameters(): if v.requires_grad: g_optim_params.append(v) else: self.reporter.writeInfo(f'Params {k} will not be optimized.') print(f'Params {k} will not be optimized.') optim_type = self.config['optim_type'] if optim_type == 'Adam': self.g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) else: raise NotImplementedError( f'optimizer {optim_type} is not supperted yet.') # self.optimizers.append(self.optimizer_g) if self.config["phase"] == "finetune": opt_path = os.path.join(self.config["project_checkpoints"], "step%d_optim_%s.pth"%(self.config["checkpoint_step"], self.config["optimizer_names"]["generator_name"])) self.g_optimizer.load_state_dict(torch.load(opt_path)) print('loaded trained optimizer step {}...!'.format(self.config["project_checkpoints"])) # TODO modify this function to evaluate your model # Evaluate the checkpoint def __evaluation__(self, step = 0, **kwargs ): src_image1 = kwargs["src1"] self.gen.eval() with torch.no_grad(): id_vector_src1 = self.arcface(src_image1) img_fake = self.gen(id_vector_src1).cpu() img_fake = img_fake * self.img_std img_fake = img_fake + self.img_mean img_fake = img_fake.clamp_(0, 1) print("Save test data") save_image(img_fake, os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg'), nrow=8) def train(self): ckpt_dir = self.config["project_checkpoints"] log_freq = self.config["log_step"] model_freq = self.config["model_save_step"] sample_freq = self.config["sample_step"] total_step = self.config["total_step"] random_seed = self.config["dataset_params"]["random_seed"] self.batch_size = self.config["batch_size"] self.sample_dir = self.config["project_samples"] self.arcface_ckpt= self.config["arcface_ckpt"] super().train() #===============build losses===================# # TODO replace below lines to build your losses # MSE_loss = torch.nn.MSELoss() l1_loss = torch.nn.L1Loss() start_time = time.time() # Caculate the epoch number print("Total step = %d"%total_step) random.seed(random_seed) randindex = [i for i in range(self.batch_size)] random.shuffle(randindex) import datetime for step in range(self.start, total_step): self.gen.train() src_image1 = self.train_loader.next() latent_id = self.arcface(src_image1) img_fake = self.gen(latent_id.detach()) loss = l1_loss(img_fake, src_image1) self.g_optimizer.zero_grad() loss.backward() self.g_optimizer.step() # Print out log info if (step + 1) % log_freq == 0: elapsed = time.time() - start_time elapsed = str(datetime.timedelta(seconds=elapsed)) epochinformation="[{}], Elapsed [{}], Step [{}/{}], Reconstruction: {:.4f}". \ format(self.config["version"], elapsed, step, total_step, loss.item()) print(epochinformation) self.reporter.writeInfo(epochinformation) if self.config["logger"] == "tensorboard": self.logger.add_scalar('Rec_loss', loss.item(), step) elif self.config["logger"] == "wandb": self.logger.log({"Rec_loss": loss.item()}, step = step) if (step + 1) % sample_freq == 0: self.__evaluation__( step = step, **{ "src1": src_image1 }) #===============adjust learning rate============# # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: # print("Learning rate decay") # for p in self.optimizer.param_groups: # p['lr'] *= self.config["lr_decay"] # print("Current learning rate is %f"%p['lr']) #===============save checkpoints================# if (step+1) % model_freq==0: torch.save(self.gen.state_dict(), os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, self.config["checkpoint_names"]["generator_name"]))) torch.save(self.g_optimizer.state_dict(), os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, self.config["checkpoint_names"]["generator_name"]))) print("Save step %d model checkpoint!"%(step+1)) torch.cuda.empty_cache() self.__evaluation__( step = step, **{ "src1": src_image1 })