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train_first_stage.py
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train_first_stage.py
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from config import cfg
import os
import time
from PIL import Image
import torch.backends.cudnn as cudnn
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as vutils
from model_train import G_NET, BACKGROUND_D, PARENT_D, CHILD_D, Encoder, Bi_Dis
from datasets import get_dataloader
import random
from utils import *
from itertools import chain
from copy import deepcopy
cudnn.benchmark = True
device = torch.device("cuda:" + cfg.GPU_ID)
################### Useful functions ###################
def define_optimizers( netG, netsD, BD, encoder ):
# define optimizer for D
optimizersD = []
for i in range(3):
if i == 0 or i==2:
optimizersD.append( optim.Adam(netsD[i].parameters(), lr=2e-4, betas=(0.5, 0.999)) )
else:
optimizersD.append(None)
optimizerBD = optim.Adam( BD.parameters(), lr=2e-4, betas=(0.5, 0.999))
params = chain( netG.parameters(), encoder.parameters(), netsD[1].parameters(), netsD[2].module.code_logits.parameters() )
optimizerGE = optim.Adam( params , lr=2e-4, betas=(0.5, 0.999) )
return optimizersD, optimizerBD, optimizerGE
def load_params(model, new_param):
for p, new_p in zip(model.parameters(), new_param):
p.data.copy_(new_p)
def copy_G_params(model):
flatten = deepcopy(list(p.data for p in model.parameters()))
return flatten
class CrossEntropy():
def __init__(self):
self.code_loss = nn.CrossEntropyLoss()
def __call__(self, prediction, label):
# check label if hard (onehot)
if label.max(dim=1)[0].min() == 1:
return self.code_loss(prediction, torch.nonzero( label.long() )[:, 1] )
else:
log_prediction = torch.log_softmax(prediction, dim=1)
return (- log_prediction*label).sum(dim=1).mean(dim=0)
def load_network():
gpus = [int(ix) for ix in cfg.GPU_ID.split(',')]
netG = G_NET()
netG.apply(weights_init)
netG = torch.nn.DataParallel(netG, device_ids=gpus)
netsD = [ BACKGROUND_D(), PARENT_D(), CHILD_D() ]
for i in range(len(netsD)):
netsD[i].apply(weights_init)
netsD[i] = torch.nn.DataParallel(netsD[i], device_ids=gpus)
BD = Bi_Dis()
BD = torch.nn.DataParallel(BD, device_ids=gpus)
encoder = Encoder()
encoder.apply(weights_init)
encoder = torch.nn.DataParallel(encoder, device_ids=gpus)
netG.to(device)
encoder.to(device)
BD.to(device)
for i in range(3):
netsD[i].to(device)
return netG, netsD, BD, encoder
def save_model( encoder, myG, D0, D1, D2, BD, epoch, model_dir):
torch.save(encoder.state_dict(), '%s/E_%d.pth' % (model_dir, epoch))
torch.save(myG.state_dict(), '%s/G_%d.pth' % (model_dir, epoch))
torch.save(D0.state_dict(), '%s/D0_%d.pth' % (model_dir, epoch))
torch.save(D1.state_dict(), '%s/D1_%d.pth' % (model_dir, epoch))
torch.save(D2.state_dict(), '%s/D2_%d.pth' % (model_dir, epoch))
torch.save(BD.state_dict(), '%s/BD_%d.pth' % (model_dir, epoch))
def save_opt( optimizerGE, optimizerD0, optimizerD2, optimizerBD, epoch, opt_dir):
torch.save(optimizerGE.state_dict(), '%s/GE_%d.pth' % (opt_dir, epoch))
torch.save(optimizerD0.state_dict(), '%s/D0_%d.pth' % (opt_dir, epoch))
torch.save(optimizerD2.state_dict(), '%s/D2_%d.pth' % (opt_dir, epoch))
torch.save(optimizerBD.state_dict(), '%s/BD_%d.pth' % (opt_dir, epoch))
############################### Trainer ############################
class Trainer(object):
def __init__(self, output_dir):
# make dir for all kinds of output
self.model_dir = os.path.join(output_dir , 'Model')
os.makedirs(self.model_dir)
self.image_dir = os.path.join(output_dir , 'Image')
os.makedirs(self.image_dir)
self.opt_dir = os.path.join(output_dir , 'Opt')
os.makedirs(self.opt_dir)
# make dataloader and code buffer
self.dataloader = get_dataloader()
# other variables
self.batch_size = cfg.TRAIN.BATCH_SIZE
self.patch_stride = 4.0
self.n_out = 24
self.recp_field = 34
# get fixed images used for comparison for each epoch
self.fixed_image = self.prepare_data( next(iter(self.dataloader)) )[1]
save_img_results( self.fixed_image.cpu(), None, -1, self.image_dir )
def prepare_code(self):
free_z = torch.FloatTensor( self.batch_size, cfg.GAN.Z_DIM ).normal_(0, 1).to(device)
free_c = torch.zeros( self.batch_size, cfg.FINE_GRAINED_CATEGORIES ).to(device)
idxs = torch.LongTensor( self.batch_size ).random_(0, cfg.FINE_GRAINED_CATEGORIES)
for i, idx in enumerate(idxs):
free_c[i,idx] = 1
free_p = torch.zeros( self.batch_size, cfg.SUPER_CATEGORIES ).to(device)
idxs = torch.LongTensor( self.batch_size ).random_(0, cfg.SUPER_CATEGORIES)
for i, idx in enumerate(idxs):
free_p[i,idx] = 1
free_b = torch.zeros( self.batch_size, cfg.FINE_GRAINED_CATEGORIES ).to(device)
idxs = torch.LongTensor( self.batch_size ).random_( 0, cfg.FINE_GRAINED_CATEGORIES )
for i, idx in enumerate(idxs):
free_b[i,idx] = 1
return free_z, free_b, free_p, free_c
def prepare_data(self, data):
real_img126, real_img, real_c, _, warped_bbox = data
real_img126 = real_img126.to(device)
real_img = real_img.to(device)
for i in range(len(warped_bbox)):
warped_bbox[i] = warped_bbox[i].float().to(device)
real_p = child_to_parent(real_c, cfg.FINE_GRAINED_CATEGORIES, cfg.SUPER_CATEGORIES ).to(device)
real_z = torch.FloatTensor( self.batch_size, cfg.GAN.Z_DIM ).normal_(0, 1).to(device)
real_c = real_c.to(device)
real_b = real_c
return real_img126, real_img, real_z, real_b, real_p, real_c, warped_bbox
def train_Dnet(self, idx):
assert(idx == 0 or idx ==2)
# choose net and opt
netD, optD = self.netsD[idx], self.optimizersD[idx]
netD.zero_grad()
# choose real and fake images
if idx == 0:
real_img = self.real_img126
fake_img = self.fake_imgs[0]
elif idx == 2:
real_img = self.real_img
fake_img = self.fake_imgs[2]
# # # # # # # #for background stage now # # # # # # #
if idx == 0:
# go throung D net to get prediction
class_prediction, real_prediction = netD(real_img)
_, fake_prediction = netD( fake_img.detach() )
real_label = torch.ones_like(real_prediction)
fake_label = torch.zeros_like(fake_prediction)
weights_real = torch.ones_like(real_prediction)
for i in range( self.batch_size ):
x1 = self.warped_bbox[0][i]
x2 = self.warped_bbox[2][i]
y1 = self.warped_bbox[1][i]
y2 = self.warped_bbox[3][i]
a1 = max(torch.tensor(0).float().to(device), torch.ceil((x1 - self.recp_field)/self.patch_stride))
a2 = min(torch.tensor(self.n_out - 1).float().to(device), torch.floor((self.n_out - 1) - ((126 - self.recp_field) - x2)/self.patch_stride)) + 1
b1 = max(torch.tensor(0).float().to(device), torch.ceil( (y1 - self.recp_field)/self.patch_stride))
b2 = min(torch.tensor(self.n_out - 1).float().to(device), torch.floor((self.n_out - 1) - ((126 - self.recp_field) - y2)/self.patch_stride)) + 1
if (x1 != x2 and y1 != y2):
weights_real[i, :, a1.type(torch.int): a2.type(torch.int), b1.type(torch.int): b2.type(torch.int)] = 0.0
norm_fact_real = weights_real.sum()
norm_fact_fake = weights_real.shape[0]*weights_real.shape[1]*weights_real.shape[2]*weights_real.shape[3]
# Real/Fake loss for 'real background' (on patch level)
real_prediction_loss = self.RF_loss_un( real_prediction, real_label )
# Masking output units which correspond to receptive fields which lie within the bounding box
real_prediction_loss = torch.mul(real_prediction_loss, weights_real).mean()
# Normalizing the real/fake loss for background after accounting the number of masked members in the output.
if (norm_fact_real > 0):
real_prediction_loss = real_prediction_loss * ((norm_fact_fake * 1.0) / (norm_fact_real * 1.0))
# Real/Fake loss for 'fake background' (on patch level)
fake_prediction_loss = self.RF_loss_un(fake_prediction, fake_label).mean()
# Background/foreground classification loss
class_prediction_loss = self.RF_loss_un( class_prediction, weights_real ).mean()
# add three losses together
D_loss = cfg.TRAIN.BG_LOSS_WT*(real_prediction_loss + fake_prediction_loss) + class_prediction_loss
# # # # # # # #for child stage now (only real/fake discriminator) # # # # # # #
if idx == 2:
# go through D net to get data
_, real_prediction = netD(real_img)
_, fake_prediction = netD( fake_img.detach() )
# get real/fake lables
real_label = torch.ones_like(real_prediction)
fake_label = torch.zeros_like(fake_prediction)
# get loss
real_prediction_loss = self.RF_loss(real_prediction, real_label)
fake_prediction_loss = self.RF_loss(fake_prediction, fake_label)
D_loss = real_prediction_loss+fake_prediction_loss
D_loss.backward()
optD.step()
def train_BD(self):
self.optimizerBD.zero_grad()
# make prediction on pairs
pred_enc_z, pred_enc_b, pred_enc_p, pred_enc_c = self.BD( self.real_img, self.fake_z.detach(), self.fake_b.detach(), self.fake_p.detach(), self.fake_c.detach() )
pred_gen_z, pred_gen_b, pred_gen_p, pred_gen_c = self.BD( self.fake_imgs[2].detach(), self.real_z, self.real_b, self.real_p, self.real_c )
real_data = [ self.real_img, self.fake_z.detach(), self.fake_b.detach(), self.fake_p.detach(), self.fake_c.detach() ]
fake_data = [ self.fake_imgs[2].detach(), self.real_z, self.real_b, self.real_p, self.real_c ]
penalty = cal_gradient_penalty( self.BD, real_data, fake_data, device, type='mixed', constant=1.0)
D_loss = -( pred_enc_z.mean()+pred_enc_b.mean()+pred_enc_p.mean()+pred_enc_c.mean() ) + ( pred_gen_z.mean()+pred_gen_b.mean()+pred_gen_p.mean()+pred_gen_c.mean() ) + penalty*10
D_loss.backward()
self.optimizerBD.step()
def train_EG(self):
self.optimizerGE.zero_grad()
# reconstruct code and calculate loss
self.rec_p, _ = self.netsD[1]( self.fg_mk[0])
self.rec_c, _ = self.netsD[2]( self.fg_mk[1])
p_code_loss = self.CE( self.rec_p , self.real_p )
c_code_loss = self.CE( self.rec_c, self.real_c )
# pred code and calculate loss (here no code constrain)
free_z, free_b, free_p, free_c = self.prepare_code()
with torch.no_grad():
free_fake_imgs, _, _, _ = self.netG( free_z, free_c, free_p, free_b, 'code' )
pred_z, pred_b, pred_p, pred_c = self.encoder( free_fake_imgs[2].detach(), 'logits' )
z_pred_loss = self.L1( pred_z , free_z )
b_pred_loss = self.CE( pred_b , free_b )
p_pred_loss = self.CE( pred_p , free_p )
c_pred_loss = self.CE( pred_c, free_c )
# aux and backgroud real/fake loss
self.bg_class_pred, self.bg_rf_pred = self.netsD[0]( self.fake_imgs[0] )
bg_rf_loss = self.RF_loss( self.bg_rf_pred, torch.ones_like( self.bg_rf_pred ) )*cfg.TRAIN.BG_LOSS_WT
bg_class_loss = self.RF_loss( self.bg_class_pred, torch.ones_like( self.bg_class_pred ) )
# child image real/fake loss
_, self.child_rf_pred = self.netsD[2]( self.fake_imgs[-1] )
child_rf_loss = self.RF_loss( self.child_rf_pred, torch.ones_like(self.child_rf_pred) )
# fool BD loss
pred_enc_z, pred_enc_b, pred_enc_p, pred_enc_c = self.BD( self.real_img, self.fake_z, self.fake_b, self.fake_p, self.fake_c )
pred_gen_z, pred_gen_b, pred_gen_p, pred_gen_c = self.BD( self.fake_imgs[2], self.real_z, self.real_b, self.real_p, self.real_c )
fool_BD_loss = ( pred_enc_z.mean()+pred_enc_b.mean()+pred_enc_p.mean()+pred_enc_c.mean() ) - ( pred_gen_z.mean()+pred_gen_b.mean()+pred_gen_p.mean()+pred_gen_c.mean() )
EG_loss = (p_code_loss+c_code_loss) + (bg_rf_loss+bg_class_loss) + child_rf_loss + fool_BD_loss + (5*z_pred_loss+5*b_pred_loss+10*p_pred_loss+10*c_pred_loss)
EG_loss.backward()
self.optimizerGE.step()
def train(self):
# prepare net, optimizer and loss
self.netG, self.netsD, self.BD, self.encoder = load_network()
self.optimizersD, self.optimizerBD, self.optimizerGE = define_optimizers( self.netG, self.netsD, self.BD, self.encoder )
self.RF_loss_un = nn.BCELoss(reduction='none')
self.RF_loss = nn.BCELoss()
self.CE = CrossEntropy()
self.L1 = nn.L1Loss()
# get init avg_G (the param in avg_G is what we want)
avg_param_G = copy_G_params(self.netG)
for epoch in range(cfg.TRAIN.FIRST_MAX_EPOCH):
for data in self.dataloader:
# prepare data
self.real_img126, self.real_img, self.real_z, self.real_b, self.real_p, self.real_c, self.warped_bbox = self.prepare_data(data)
# forward for both E and G
self.fake_z, self.fake_b, self.fake_p, self.fake_c = self.encoder( self.real_img, 'softmax' )
self.fake_imgs, self.fg_imgs, self.mk_imgs, self.fg_mk = self.netG( self.real_z, self.real_c, self.real_p, self.real_b, 'code' )
# Update Discriminator networks in FineGAN
self.train_Dnet(0)
self.train_Dnet(2)
# Update Bi Discriminator
self.train_BD()
# Update Encoder and G network
self.train_EG()
for avg_p, p in zip( avg_param_G, self.netG.parameters() ):
avg_p.mul_(0.999).add_(0.001, p.data)
# Save model&image for each epoch
backup_para = copy_G_params(self.netG)
load_params(self.netG, avg_param_G)
self.encoder.eval()
self.netG.eval()
with torch.no_grad():
self.code_z, self.code_b, self.code_p, self.code_c = self.encoder( self.fixed_image,'softmax')
self.fake_imgs, self.fg_imgs, self.mk_imgs, self.fg_mk = self.netG(self.code_z, self.code_c, self.code_p, self.code_b, 'code')
save_img_results(None, (self.fake_imgs+self.fg_imgs+self.mk_imgs+self.fg_mk), epoch, self.image_dir)
self.encoder.train()
self.netG.train()
backup_para = copy_G_params(self.netG)
load_params(self.netG, avg_param_G)
save_model( self.encoder, self.netG, self.netsD[0], self.netsD[1], self.netsD[2], self.BD, 0, self.model_dir )
save_opt( self.optimizerGE, self.optimizersD[0], self.optimizersD[2], self.optimizerBD, 0, self.opt_dir )
load_params(self.netG, backup_para)
print( str(epoch)+'th epoch finished' )
if __name__ == "__main__":
manualSeed = random.randint(1, 10000)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
torch.cuda.manual_seed_all(manualSeed)
# prepare output folder for this running and save all files
output_dir = make_output_dir()
shutil.copy2( sys.argv[0], output_dir)
shutil.copy2( 'model_train.py', output_dir)
shutil.copy2( 'config.py', output_dir)
shutil.copy2( 'utils.py', output_dir)
shutil.copy2( 'datasets.py', output_dir)
trainer = Trainer(output_dir)
print('start training now')
trainer.train()