chenxuanhong
342 lines
15 KiB
Python
342 lines
15 KiB
Python
#!/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: Monday, 17th January 2022 5:31:43 pm
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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 numpy as np
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import torch
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import torch.nn.functional as F
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from utilities.plot import plot_batch
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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, config, reporter):
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super(Trainer, self).__init__(config, reporter)
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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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dscript_name = "components." + model_config["d_model"]["script"]
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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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dscript_name = self.config["com_base"] + model_config["d_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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class_name = model_config["d_model"]["class_name"]
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package = __import__(dscript_name, fromlist=True)
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dis_class = getattr(package, class_name)
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self.dis = dis_class(**model_config["d_model"]["module_params"])
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self.dis.feature_network.requires_grad_(False)
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# print and recorde model structure
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self.reporter.writeInfo("Discriminator structure:")
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self.reporter.writeModel(self.dis.__str__())
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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.dis = self.dis.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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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"]["discriminator_name"]))
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self.dis.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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d_train_opt = self.config['d_optim_config']
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g_optim_params = []
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d_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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for k, v in self.dis.named_parameters():
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if v.requires_grad:
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d_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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self.d_optimizer = torch.optim.Adam(d_optim_params,**d_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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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"]["discriminator_name"]))
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self.d_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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src_image2 = kwargs["src2"]
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batch_size = self.batch_size
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self.gen.eval()
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with torch.no_grad():
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imgs = []
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zero_img = (torch.zeros_like(src_image1[0,...]))
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imgs.append(zero_img.cpu().numpy())
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save_img = ((src_image1.cpu())* self.img_std + self.img_mean).numpy()
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for r in range(batch_size):
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imgs.append(save_img[r,...])
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arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic')
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id_vector_src1 = self.arcface(arcface_112)
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id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1)
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for i in range(batch_size):
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imgs.append(save_img[i,...])
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image_infer = src_image1[i, ...].repeat(batch_size, 1, 1, 1)
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img_fake = self.gen(image_infer, 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.numpy()
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for j in range(batch_size):
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imgs.append(img_fake[j,...])
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print("Save test data")
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imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1)
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plot_batch(imgs, os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg'))
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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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# prep_weights= self.config["layersWeight"]
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id_w = self.config["id_weight"]
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rec_w = self.config["reconstruct_weight"]
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feat_w = self.config["feature_match_weight"]
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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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cos_loss = torch.nn.CosineSimilarity()
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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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self.dis.train()
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for interval in range(2):
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random.shuffle(randindex)
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src_image1, src_image2 = self.train_loader.next()
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if step%2 == 0:
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img_id = src_image2
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else:
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img_id = src_image2[randindex]
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img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic')
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latent_id = self.arcface(img_id_112)
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latent_id = F.normalize(latent_id, p=2, dim=1)
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if interval:
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img_fake = self.gen(src_image1, latent_id)
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gen_logits,_ = self.dis(img_fake.detach(), None)
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loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean()
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real_logits,_ = self.dis(src_image2,None)
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loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean()
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loss_D = loss_Dgen + loss_Dreal
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self.d_optimizer.zero_grad()
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loss_D.backward()
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self.d_optimizer.step()
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else:
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# model.netD.requires_grad_(True)
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img_fake = self.gen(src_image1, latent_id)
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# G loss
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gen_logits,feat = self.dis(img_fake, None)
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loss_Gmain = (-gen_logits).mean()
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img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic')
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latent_fake = self.arcface(img_fake_down)
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latent_fake = F.normalize(latent_fake, p=2, dim=1)
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loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean()
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real_feat = self.dis.get_feature(src_image1)
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feat_match_loss = l1_loss(feat["3"],real_feat["3"])
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loss_G = loss_Gmain + loss_G_ID * id_w + \
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feat_match_loss * feat_w
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if step%2 == 0:
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#G_Rec
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loss_G_Rec = l1_loss(img_fake, src_image1)
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loss_G += loss_G_Rec * rec_w
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self.g_optimizer.zero_grad()
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loss_G.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 [{}/{}], \
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G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \
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D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \
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format(self.config["version"], elapsed, step, total_step, \
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loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \
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loss_D.item(), loss_Dgen.item(), loss_Dreal.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('G/G_loss', loss_G.item(), step)
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self.logger.add_scalar('G/Rec_loss', loss_G_Rec.item(), step)
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self.logger.add_scalar('G/Fm_loss', feat_match_loss.item(), step)
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self.logger.add_scalar('D/D_loss', loss_D.item(), step)
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self.logger.add_scalar('D/D_fake', loss_Dgen.item(), step)
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self.logger.add_scalar('D/D_real', loss_Dreal.item(), step)
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elif self.config["logger"] == "wandb":
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self.logger.log({"G_loss": loss_G.item()}, step = step)
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self.logger.log({"Rec_loss": loss_G_Rec.item()}, step = step)
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self.logger.log({"Fm_loss": feat_match_loss.item()}, step = step)
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self.logger.log({"D_loss": loss_D.item()}, step = step)
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self.logger.log({"D_fake": loss_Dgen.item()}, step = step)
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self.logger.log({"D_real": loss_Dreal.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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"src2": src_image2
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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.dis.state_dict(),
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os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1,
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self.config["checkpoint_names"]["discriminator_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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torch.save(self.d_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"]["discriminator_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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"src2": src_image2
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}) |