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train.py
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train.py
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"""Main"""
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from datetime import datetime
from matplotlib import pyplot as plt
import os
from dataset.dataset_class import VidDataSet
from dataset.video_extraction_conversion import *
from loss.loss_discriminator import *
from loss.loss_generator import *
from network.blocks import *
from network.model import *
"""Create dataset and net"""
device = torch.device("cuda:0")
cpu = torch.device("cpu")
path_to_chkpt = 'model_weights.tar'
path_to_backup = 'backup_model_weights.tar'
dataset = VidDataSet(K=6, path_to_mp4 = 'mp4', device=device)
dataLoader = DataLoader(dataset, batch_size=2, shuffle=True)
G = Generator(224).to(device)
E = Embedder(224).to(device)
D = Discriminator(dataset.__len__()).to(device)
G.train()
E.train()
D.train()
optimizerG = optim.Adam(params = list(E.parameters()) + list(G.parameters()), lr=5e-5)
optimizerD = optim.Adam(params = D.parameters(), lr=2e-4)
"""Criterion"""
criterionG = LossG(VGGFace_body_path='Pytorch_VGGFACE_IR.py',
VGGFace_weight_path='Pytorch_VGGFACE.pth', device=device)
criterionDreal = LossDSCreal()
criterionDfake = LossDSCfake()
"""Training init"""
epochCurrent = epoch = i_batch = 0
lossesG = []
lossesD = []
i_batch_current = 0
num_epochs = 750
#initiate checkpoint if inexistant
if not os.path.isfile(path_to_chkpt):
print('Initiating new checkpoint...')
torch.save({
'epoch': epoch,
'lossesG': lossesG,
'lossesD': lossesD,
'E_state_dict': E.state_dict(),
'G_state_dict': G.state_dict(),
'D_state_dict': D.state_dict(),
'optimizerG_state_dict': optimizerG.state_dict(),
'optimizerD_state_dict': optimizerD.state_dict(),
'num_vid': dataset.__len__(),
'i_batch': i_batch
}, path_to_chkpt)
print('...Done')
"""Loading from past checkpoint"""
checkpoint = torch.load(path_to_chkpt, map_location=cpu)
E.load_state_dict(checkpoint['E_state_dict'])
G.load_state_dict(checkpoint['G_state_dict'])
D.load_state_dict(checkpoint['D_state_dict'])
optimizerG.load_state_dict(checkpoint['optimizerG_state_dict'])
optimizerD.load_state_dict(checkpoint['optimizerD_state_dict'])
epochCurrent = checkpoint['epoch']
lossesG = checkpoint['lossesG']
lossesD = checkpoint['lossesD']
num_vid = checkpoint['num_vid']
i_batch_current = checkpoint['i_batch'] +1
G.train()
E.train()
D.train()
"""Training"""
batch_start = datetime.now()
for epoch in range(epochCurrent, num_epochs):
for i_batch, (f_lm, x, g_y, i) in enumerate(dataLoader, start=i_batch_current):
if i_batch > len(dataLoader):
i_batch_current = 0
break
with torch.autograd.enable_grad():
#zero the parameter gradients
optimizerG.zero_grad()
optimizerD.zero_grad()
#forward
# Calculate average encoding vector for video
f_lm_compact = f_lm.view(-1, f_lm.shape[-4], f_lm.shape[-3], f_lm.shape[-2], f_lm.shape[-1]) #BxK,2,3,224,224
e_vectors = E(f_lm_compact[:,0,:,:,:], f_lm_compact[:,1,:,:,:]) #BxK,512,1
e_vectors = e_vectors.view(-1, f_lm.shape[1], 512, 1) #B,K,512,1
e_hat = e_vectors.mean(dim=1)
#train G and D
x_hat = G(g_y, e_hat)
r_hat, D_hat_res_list = D(x_hat, g_y, i)
r, D_res_list = D(x, g_y, i)
lossG = criterionG(x, x_hat, r_hat, D_res_list, D_hat_res_list, e_vectors, D.W_i, i)
lossDfake = criterionDfake(r_hat)
lossDreal = criterionDreal(r)
loss = lossDreal + lossDfake + lossG
loss.backward(retain_graph=False)
optimizerG.step()
optimizerD.step()
#train D again
optimizerG.zero_grad()
optimizerD.zero_grad()
x_hat.detach_()
r_hat, D_hat_res_list = D(x_hat, g_y, i)
r, D_res_list = D(x, g_y, i)
lossDfake = criterionDfake(r_hat)
lossDreal = criterionDreal(r)
lossD = lossDreal + lossDfake
lossD.backward(retain_graph=False)
optimizerD.step()
# Output training stats
if i_batch % 10 == 0:
batch_end = datetime.now()
avg_time = (batch_end - batch_start) / 10
print('\n\navg batch time for batch size of', x.shape[0],':',avg_time)
batch_start = datetime.now()
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(y)): %.4f'
% (epoch, num_epochs, i_batch, len(dataLoader),
lossD.item(), lossG.item(), r.mean(), r_hat.mean()))
plt.clf()
out = x_hat.transpose(1, 3)[0]
for img_no in range(1, x_hat.shape[0]):
out = torch.cat((out, x_hat.transpose(1,3)[img_no]), dim=1)
out = out.type(torch.int32).to(cpu).numpy()
plt.imshow(out)
plt.savefig('samples/{}_x_hat.png'.format(i_batch))
plt.clf()
out = x.transpose(1, 3)[0]
for img_no in range(1, x.shape[0]):
out = torch.cat((out, x.transpose(1, 3)[img_no]), dim=1)
out = out.type(torch.int32).to(cpu).numpy()
plt.imshow(out)
plt.savefig('samples/{}_x.png'.format(i_batch))
plt.clf()
out = g_y.transpose(1,3)[0]
for img_no in range(1,g_y.shape[0]):
out = torch.cat((out, g_y.transpose(1,3)[img_no]), dim = 1)
out = out.type(torch.int32).to(cpu).numpy()
plt.imshow(out)
plt.savefig('samples/{}_g_y.png'.format(i_batch))
if i_batch % 100 == 99:
lossesD.append(lossD.item())
lossesG.append(lossG.item())
plt.clf()
plt.plot(lossesG) #blue
plt.plot(lossesD) #orange
plt.show()
try:
plt.savefig('samples/losses.png')
except:
print('saving loss plot failes')
pass
print('Saving latest...')
torch.save({
'epoch': epoch,
'lossesG': lossesG,
'lossesD': lossesD,
'E_state_dict': E.state_dict(),
'G_state_dict': G.state_dict(),
'D_state_dict': D.state_dict(),
'optimizerG_state_dict': optimizerG.state_dict(),
'optimizerD_state_dict': optimizerD.state_dict(),
'num_vid': dataset.__len__(),
'i_batch': i_batch
}, path_to_chkpt)
print('...Done saving latest')
if i_batch % 500 == 499:
print('Saving latest...')
torch.save({
'epoch': epoch,
'lossesG': lossesG,
'lossesD': lossesD,
'E_state_dict': E.state_dict(),
'G_state_dict': G.state_dict(),
'D_state_dict': D.state_dict(),
'optimizerG_state_dict': optimizerG.state_dict(),
'optimizerD_state_dict': optimizerD.state_dict(),
'num_vid': dataset.__len__(),
'i_batch': i_batch
}, path_to_backup)
print('...Done saving latest')