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chenxuanhong
2022-01-24 19:01:00 +08:00
parent 0f8c2f929e
commit 94534e2e30
10 changed files with 765 additions and 79 deletions
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train_scripts/trainer_FM.py
+15 -10
View File
@@ -5,7 +5,7 @@
# Created Date: Sunday January 9th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Saturday, 22nd January 2022 12:45:09 pm
# Last Modified: Monday, 24th January 2022 6:56:17 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -28,6 +28,9 @@ class Trainer(TrainerBase):
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)
@@ -276,25 +279,27 @@ class Trainer(TrainerBase):
elapsed = str(datetime.timedelta(seconds=elapsed))
epochinformation="[{}], Elapsed [{}], Step [{}/{}], \
G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \
D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \
format(self.config["version"], elapsed, step, total_step, \
loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \
loss_D.item(), loss_Dgen.item(), loss_Dreal.item())
G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \
D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \
format(self.config["version"], elapsed, step, total_step, \
loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \
loss_D.item(), loss_Dgen.item(), loss_Dreal.item())
print(epochinformation)
self.reporter.writeInfo(epochinformation)
if self.config["logger"] == "tensorboard":
self.logger.add_scalar('G/G_loss', loss_G.item(), step)
self.logger.add_scalar('G/Rec_loss', loss_G_Rec.item(), step)
self.logger.add_scalar('G/Fm_loss', feat_match_loss.item(), step)
self.logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step)
self.logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step)
self.logger.add_scalar('G/G_ID', loss_G_ID.item(), step)
self.logger.add_scalar('D/D_loss', loss_D.item(), step)
self.logger.add_scalar('D/D_fake', loss_Dgen.item(), step)
self.logger.add_scalar('D/D_real', loss_Dreal.item(), step)
elif self.config["logger"] == "wandb":
self.logger.log({"G_loss": loss_G.item()}, step = step)
self.logger.log({"Rec_loss": loss_G_Rec.item()}, step = step)
self.logger.log({"Fm_loss": feat_match_loss.item()}, step = step)
self.logger.log({"G_Rec": loss_G_Rec.item()}, step = step)
self.logger.log({"G_feat_match": feat_match_loss.item()}, step = step)
self.logger.log({"G_ID": loss_G_ID.item()}, step = step)
self.logger.log({"D_loss": loss_D.item()}, step = step)
self.logger.log({"D_fake": loss_Dgen.item()}, step = step)
self.logger.log({"D_real": loss_Dreal.item()}, step = step)
train_scripts/trainer_GramFM.py
+351
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@@ -0,0 +1,351 @@
#!/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: Monday, 24th January 2022 6:23:16 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
import os
import time
import random
import numpy as np
import torch
import torch.nn.functional as F
from utilities.plot import plot_batch
from train_scripts.trainer_base import TrainerBase
from utilities.utilities import Gram
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"]
dscript_name = "components." + model_config["d_model"]["script"]
elif self.config["phase"] == "finetune":
gscript_name = self.config["com_base"] + model_config["g_model"]["script"]
dscript_name = self.config["com_base"] + model_config["d_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__())
class_name = model_config["d_model"]["class_name"]
package = __import__(dscript_name, fromlist=True)
dis_class = getattr(package, class_name)
self.dis = dis_class(**model_config["d_model"]["module_params"])
self.dis.feature_network.requires_grad_(False)
# print and recorde model structure
self.reporter.writeInfo("Discriminator structure:")
self.reporter.writeModel(self.dis.__str__())
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.dis = self.dis.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))
model_path = os.path.join(self.config["project_checkpoints"],
"step%d_%s.pth"%(self.config["checkpoint_step"],
self.config["checkpoint_names"]["discriminator_name"]))
self.dis.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']
d_train_opt = self.config['d_optim_config']
g_optim_params = []
d_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.')
for k, v in self.dis.named_parameters():
if v.requires_grad:
d_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)
self.d_optimizer = torch.optim.Adam(d_optim_params,**d_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))
opt_path = os.path.join(self.config["project_checkpoints"],
"step%d_optim_%s.pth"%(self.config["checkpoint_step"],
self.config["optimizer_names"]["discriminator_name"]))
self.d_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"]
src_image2 = kwargs["src2"]
batch_size = self.batch_size
self.gen.eval()
with torch.no_grad():
imgs = []
zero_img = (torch.zeros_like(src_image1[0,...]))
imgs.append(zero_img.cpu().numpy())
save_img = ((src_image1.cpu())* self.img_std + self.img_mean).numpy()
for r in range(batch_size):
imgs.append(save_img[r,...])
arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic')
id_vector_src1 = self.arcface(arcface_112)
id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1)
for i in range(batch_size):
imgs.append(save_img[i,...])
image_infer = src_image1[i, ...].repeat(batch_size, 1, 1, 1)
img_fake = self.gen(image_infer, id_vector_src1).cpu()
img_fake = img_fake * self.img_std
img_fake = img_fake + self.img_mean
img_fake = img_fake.numpy()
for j in range(batch_size):
imgs.append(img_fake[j,...])
print("Save test data")
imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1)
plot_batch(imgs, os.path.join(self.sample_dir, 'step_'+str(step+1)+'.jpg'))
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"]
# prep_weights= self.config["layersWeight"]
id_w = self.config["id_weight"]
rec_w = self.config["reconstruct_weight"]
feat_w = self.config["feature_match_weight"]
super().train()
#===============build losses===================#
# TODO replace below lines to build your losses
MSE_loss = torch.nn.MSELoss()
l1_loss = torch.nn.L1Loss()
cos_loss = torch.nn.CosineSimilarity()
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()
self.dis.train()
for interval in range(2):
random.shuffle(randindex)
src_image1, src_image2 = self.train_loader.next()
if step%2 == 0:
img_id = src_image2
else:
img_id = src_image2[randindex]
img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic')
latent_id = self.arcface(img_id_112)
latent_id = F.normalize(latent_id, p=2, dim=1)
if interval:
img_fake = self.gen(src_image1, latent_id)
gen_logits,_ = self.dis(img_fake.detach(), None)
loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean()
real_logits,_ = self.dis(src_image2,None)
loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean()
loss_D = loss_Dgen + loss_Dreal
self.d_optimizer.zero_grad()
loss_D.backward()
self.d_optimizer.step()
else:
# model.netD.requires_grad_(True)
img_fake = self.gen(src_image1, latent_id)
# G loss
gen_logits,feat = self.dis(img_fake, None)
loss_Gmain = (-gen_logits).mean()
img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic')
latent_fake = self.arcface(img_fake_down)
latent_fake = F.normalize(latent_fake, p=2, dim=1)
loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean()
real_feat = self.dis.get_feature(src_image1)
feat_match_loss = l1_loss(Gram(feat["3"]), Gram(real_feat["3"])) + \
l1_loss(Gram(feat["2"]), Gram(real_feat["2"]))
loss_G = loss_Gmain + loss_G_ID * id_w + \
feat_match_loss * feat_w
if step%2 == 0:
#G_Rec
loss_G_Rec = l1_loss(img_fake, src_image1)
loss_G += loss_G_Rec * rec_w
self.g_optimizer.zero_grad()
loss_G.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 [{}/{}], \
G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \
D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \
format(self.config["version"], elapsed, step, total_step, \
loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \
loss_D.item(), loss_Dgen.item(), loss_Dreal.item())
print(epochinformation)
self.reporter.writeInfo(epochinformation)
if self.config["logger"] == "tensorboard":
self.logger.add_scalar('G/G_loss', loss_G.item(), step)
self.logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step)
self.logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step)
self.logger.add_scalar('G/G_ID', loss_G_ID.item(), step)
self.logger.add_scalar('D/D_loss', loss_D.item(), step)
self.logger.add_scalar('D/D_fake', loss_Dgen.item(), step)
self.logger.add_scalar('D/D_real', loss_Dreal.item(), step)
elif self.config["logger"] == "wandb":
self.logger.log({"G_loss": loss_G.item()}, step = step)
self.logger.log({"G_Rec": loss_G_Rec.item()}, step = step)
self.logger.log({"G_feat_match": feat_match_loss.item()}, step = step)
self.logger.log({"G_ID": loss_G_ID.item()}, step = step)
self.logger.log({"D_loss": loss_D.item()}, step = step)
self.logger.log({"D_fake": loss_Dgen.item()}, step = step)
self.logger.log({"D_real": loss_Dreal.item()}, step = step)
if (step + 1) % sample_freq == 0:
self.__evaluation__(
step = step,
**{
"src1": src_image1,
"src2": src_image2
})
#===============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.dis.state_dict(),
os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1,
self.config["checkpoint_names"]["discriminator_name"])))
torch.save(self.g_optimizer.state_dict(),
os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1,
self.config["checkpoint_names"]["generator_name"])))
torch.save(self.d_optimizer.state_dict(),
os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1,
self.config["checkpoint_names"]["discriminator_name"])))
print("Save step %d model checkpoint!"%(step+1))
torch.cuda.empty_cache()
self.__evaluation__(
step = step,
**{
"src1": src_image1,
"src2": src_image2
})