chenxuanhong
237 lines
9.1 KiB
Python
237 lines
9.1 KiB
Python
#!/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
|
|
}) |