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