def set_network(depth, ctx, lr, beta1, ndf, ngf, append=True, solver='adam'):
# Pixel2pixel networks
if append:
netD = models.Discriminator(in_channels=6, n_layers =1 , ndf=ndf)##netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
else:
netD = models.Discriminator(in_channels=3, n_layers =1 , ndf=ndf)
#netG = models.UnetGenerator(in_channels=3, num_downs =depth, ngf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
#netD = models.Discriminator(in_channels=6, n_layers =depth-1, ndf=ngf/4)
netEn = models.Encoder(in_channels=3, n_layers =depth, ndf=ngf)
netDe = models.Decoder(in_channels=3, n_layers =depth, ndf=ngf)
# Initialize parameters
models.network_init(netEn, ctx=ctx)
models.network_init(netDe, ctx=ctx)
models.network_init(netD, ctx=ctx)
if solver=='adam':
# trainer for the generator and the discriminato
trainerEn = gluon.Trainer(netEn.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerDe = gluon.Trainer(netDe.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
elif solver == 'sgd':
print('sgd')
trainerG = gluon.Trainer(netG.collect_params(), 'sgd', {'learning_rate': lr, 'momentum': 0.9} )
trainerD = gluon.Trainer(netD.collect_params(), 'sgd', {'learning_rate': lr, 'momentum': 0.9})
return netEn, netDe, netD, trainerEn, trainerDe, trainerD
def set_network(depth, ctx, lr, beta1, ndf, ngf,latent, append=True, solver='adam'):
# Pixel2pixel networks
if append:
netD = models.Discriminator(in_channels=6, n_layers =2 , ndf=ndf)##netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD2 = models.LatentDiscriminator(in_channels=6, n_layers =2 , ndf=ndf)##netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
else:
netD = models.Discriminator(in_channels=3, n_layers =2 , ndf=ndf)
netD2 = models.LatentDiscriminator(in_channels=3, n_layers =2 , ndf=ndf)
#netG = models.UnetGenerator(in_channels=3, num_downs =depth, ngf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
#netD = models.Discriminator(in_channels=6, n_layers =depth-1, ndf=ngf/4)
netEn = models.Encoder(in_channels=3, n_layers =depth,latent=latent, ndf=ngf)
netDe = models.Decoder(in_channels=3, n_layers =depth, latent=latent, ndf=ngf)
# Initialize parameters
models.network_init(netEn, ctx=ctx)
models.network_init(netDe, ctx=ctx)
models.network_init(netD, ctx=ctx)
models.network_init(netD2, ctx=ctx)
trainerEn = gluon.Trainer(netEn.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerDe = gluon.Trainer(netDe.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerD2 = gluon.Trainer(netD2.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
return netEn, netDe, netD, netD2, trainerEn, trainerDe, trainerD, trainerD2
def main():
latent_dim = 16
datasource = allocate_datasource(args.env)
num_actions = datasource.binary_input_channels
num_rewards = datasource.scalar_output_channels
input_channels = datasource.conv_input_channels
output_channels = datasource.conv_output_channels
encoder = (models.Encoder(latent_dim, input_channels))
decoder = (models.Decoder(latent_dim, output_channels))
reward_predictor = (models.RewardPredictor(latent_dim, num_rewards))
discriminator = (models.Discriminator())
transition = (models.Transition(latent_dim, num_actions))
if args.load_from is None:
print('No --load-from directory specified: initializing new networks')
elif 'model-encoder.pth' not in os.listdir(args.load_from):
print('Error: Failed to load saved models from directory {}'.format(
args.load_from))
raise ValueError('Failed to load weights from *.pth')
else:
print('Loading models from directory {}'.format(args.load_from))
encoder.load_state_dict(
torch.load(os.path.join(args.load_from, 'model-encoder.pth')))
decoder.load_state_dict(
torch.load(os.path.join(args.load_from, 'model-decoder.pth')))
transition.load_state_dict(
torch.load(os.path.join(args.load_from, 'model-transition.pth')))
discriminator.load_state_dict(
torch.load(os.path.join(args.load_from,
'model-discriminator.pth')))
reward_predictor.load_state_dict(
torch.load(
os.path.join(args.load_from, 'model-reward_predictor.pth')))
if args.evaluate:
print('Finished {} playthroughs'.format(args.evaluations))
for _ in range(args.evaluations):
with torch.no_grad():
play(latent_dim, datasource, num_actions, num_rewards, encoder,
decoder, reward_predictor, discriminator, transition)
print('Finished {} playthroughs'.format(args.evaluations))
evaluate(datasource,
encoder,
decoder,
transition,
discriminator,
reward_predictor,
latent_dim,
use_training_set=True)
else:
train(latent_dim, datasource, num_actions, num_rewards, encoder,
decoder, reward_predictor, discriminator, transition)
print('Finished execution, terminating')
def test_size(self):
"""Test that the Decoder produced the correct size output."""
batch_size = 32
image_size = 64
latent_size = 128
D = models.Decoder(latent_size, image_size)
tensor_in = Variable(2.0 * (torch.rand(
(batch_size, latent_size, 1, 1)) - 0.5))
tensor_out = D.forward(tensor_in)
self.assertEqual(tensor_out.size(),
torch.Size([batch_size, 3, image_size, image_size]))
def create_models(image_size=64, latent_size=128):
"""Creates encoder and decoder and moves them to the GPU if requested."""
E = models.Encoder(latent_size, image_size)
D = models.Decoder(latent_size, image_size)
if RUN_ON_GPU:
print('Moving models to GPU.')
E.cuda()
D.cuda()
else:
print('Keeping models on CPU.')
return E, D
def set_network(depth, ctx, ngf):
# Pixel2pixel networks
#netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netEn = models.Encoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netDe = models.Decoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD = models.Discriminator(in_channels=3, n_layers =depth, ndf=ngf, isthreeway=True)
# Initialize parameters
models.network_init(netDe, ctx=ctx)
models.network_init(netEn, ctx=ctx)
models.network_init(netD, ctx=ctx)
return netEn, netDe, netD
def set_network(depth, ctx, ndf):
#netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netEn = models.Encoder(
in_channels=3, n_layers=depth, ndf=ndf,
usetanh=True) # UnetGenerator(in_channels=3, num_downs=8) #
netDe = models.Decoder(
in_channels=3, n_layers=depth,
ndf=ndf) # UnetGenerator(in_channels=3, num_downs=8) #
netD = models.Discriminator(in_channels=3,
n_layers=depth,
ndf=ndf,
isthreeway=False)
netD2 = models.Discriminator(in_channels=3,
n_layers=depth,
ndf=ndf,
isthreeway=False)
return netEn, netDe, netD, netD2
def set_network(depth, ctx, lr, beta1, ngf):
# Pixel2pixel networks
#netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netEn = models.Encoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netDe = models.Decoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD = models.Discriminator(in_channels=3, n_layers =depth, ndf=ngf, isthreeway=True)
# Initialize parameters
models.network_init(netDe, ctx=ctx)
models.network_init(netEn, ctx=ctx)
models.network_init(netD, ctx=ctx)
# trainer for the generator and the discriminator
trainerEn = gluon.Trainer(netEn.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerDe = gluon.Trainer(netDe.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
return netEn, netDe, netD, trainerEn, trainerDe, trainerD
def test_layers(self):
"""Test that the Decoder produced the correct size output for each layer."""
batch_size = 32
image_size = 64
latent_size = 128
D = models.Decoder(latent_size, image_size)
tensor = Variable(2.0 * (torch.rand(
(batch_size, latent_size, 1, 1)) - 0.5))
tensor = D.layer1.forward(tensor)
self.assertEqual(tensor.size(), torch.Size([batch_size, 128, 7, 7]))
tensor = D.layer2.forward(tensor)
self.assertEqual(tensor.size(), torch.Size([batch_size, 64, 15, 15]))
tensor = D.layer3.forward(tensor)
self.assertEqual(tensor.size(), torch.Size([batch_size, 64, 31, 31]))
tensor = D.layer4.forward(tensor)
self.assertEqual(tensor.size(), torch.Size([batch_size, 32, 63, 63]))
tensor = D.layer5.forward(tensor)
self.assertEqual(tensor.size(),
torch.Size([batch_size, 3, image_size, image_size]))
def set_network(depth, ctx, lr, beta1, ndf, ngf, append=True):
if append:
netD = models.Discriminator(in_channels=6,
n_layers=depth - 1,
istest=True,
ndf=ndf)
else:
netD = models.Discriminator(in_channels=3,
n_layers=depth - 1,
istest=True,
ndf=ndf)
netEn = models.Encoder(
in_channels=3, n_layers=depth, istest=True, latent=4096,
ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netDe = models.Decoder(
in_channels=3, n_layers=depth, istest=True, latent=4096,
ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD2 = None # models.LatentDiscriminator(in_channels=6, n_layers =2 , ndf=ndf)
return netEn, netDe, netD, netD2
def main():
batch_size = 16
latent_dim = 12
true_latent_dim = 4
num_actions = 4
encoder = models.Encoder(latent_dim)
decoder = models.Decoder(latent_dim)
transition = models.Transition(latent_dim, num_actions)
blur = models.GaussianSmoothing(channels=3, kernel_size=11, sigma=4.)
higgins_scores = []
#load_from_dir = '/mnt/nfs/experiments/demo_2018_12_12/scm-gan_81bd12cd'
load_from_dir = '.'
print('Loading models from directory {}'.format(load_from_dir))
encoder.load_state_dict(
torch.load(os.path.join(load_from_dir, 'model-encoder.pth')))
decoder.load_state_dict(
torch.load(os.path.join(load_from_dir, 'model-decoder.pth')))
transition.load_state_dict(
torch.load(os.path.join(load_from_dir, 'model-transition.pth')))
encoder.eval()
decoder.eval()
transition.eval()
for model in (encoder, decoder, transition):
for child in model.children():
if type(child) == nn.BatchNorm2d or type(child) == nn.BatchNorm1d:
child.momentum = 0
states, rewards, dones, actions = datasource.get_trajectories(batch_size,
timesteps=1)
states = torch.Tensor(states).cuda()
# Reconstruct the first timestep
reconstructed = decoder(encoder(states[:, 0]))
imutil.show(reconstructed)
def pick_model(args, dicts):
"""
Use args to initialize the appropriate model
"""
Y = get_num_labels(args.Y)
if args.model == "rnn":
model = models.VanillaRNN(Y, args.embed_file, dicts, args.rnn_dim,
args.cell_type, args.rnn_layers, args.gpu,
args.embed_size, args.bidirectional)
elif args.model == "cnn_vanilla":
filter_size = int(args.filter_size)
model = models.VanillaConv(Y, args.embed_file, filter_size,
args.num_filter_maps, args.gpu, dicts,
args.embed_size, args.dropout)
elif args.model == "conv_attn":
filter_size = int(args.filter_size)
model = models.ConvAttnPool(Y,
args.embed_file,
filter_size,
args.num_filter_maps,
args.lmbda,
args.gpu,
dicts,
embed_size=args.embed_size,
dropout=args.dropout)
elif args.model == "rnn_attn":
encoder = models.Encoder(Y,
args.embed_file,
dicts,
embed_size=args.embed_size)
decoder = models.Decoder(Y, args.embed_file, dicts)
model = models.Seq2Seq(encoder, decoder, Y, args.embed_file, dicts)
elif args.model == "saved":
model = torch.load(args.test_model)
if args.gpu:
model.cuda()
return model
import metrics
import utils
if __name__ == '__main__':
cudnn.benchmark = True
#load data
train_data = datasets.Flickr8k
train_data_loader = data.DataLoader(dataset=train_data,
batch_size=config.BATCH_SIZE,
shuffle=True,
collate_fn=datasets.collate_fn)
#load model
encoder = models.Encoder()
decoder = models.Decoder(train_data.num_word)
if config.LOAD == True:
utils.load_model(encoder,
os.path.join('./Model', str(config.EPOCH_START)),
'encoder.pth')
utils.load_model(decoder,
os.path.join('./Model', str(config.EPOCH_START)),
'decoder.pth')
#set optimizer
encoder_optim = optim.Adam(encoder.parameters(), lr=config.LR)
decoder_optim = optim.Adam(decoder.parameters(), lr=config.LR)
#set loss and meter
criterion = losses.MaskLoss()
ARTICLE, TITLE, train,test = read_data()
INPUT_DIM = len(ARTICLE.vocab)
OUTPUT_DIM = len(TITLE.vocab)
ENC_EMB_DIM = 512
DEC_EMB_DIM = 512
ENC_HID_DIM = 256
DEC_HID_DIM = 256
ENC_DROPOUT = 0.5
DEC_DROPOUT = 0.5
PAD_IDX = ARTICLE.vocab.stoi['<pad>']
SOS_IDX = TITLE.vocab.stoi['<sos>']
EOS_IDX = TITLE.vocab.stoi['<eos>']
attn = Attention(ENC_HID_DIM, DEC_HID_DIM)
encoder = models.Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, ENC_DROPOUT)
decoder = models.Decoder(OUTPUT_DIM, DEC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, DEC_DROPOUT, attn)
model = models.Seq2Seq(encoder, decoder, device,PAD_IDX,SOS_IDX,EOS_IDX).to(device)
weigth = th.load("save_model/title_generate.pth")
model.load_state_dict(weigth)
article = "7月2日,プライベート写真が流出したことでAKB48としての活動を辞退した米沢瑠美が、新しい事務所が決まったことを自身のツイッターで明かした。米沢は7月1日、「みんなに早く伝えたかった事を、話せる準備が整ってきましたっ☆ まず、所属事務所のご報告。エムズエンタープライズさんに所属することになりました☆」と報告。今年3月いっぱいで所属事務所との契約が満了したため、約2年間続いたブログを閉鎖することとなった米沢だが、今回事務所が決まったことで、新たなオフィシャルブログを製作中。今月中旬頃にはスタートする予定だという。また、「これからは演技のお仕事を中心に頑張っていきたいと思っております(^^)」と今後の方針を示唆。どんどん活動の場を広げると思われる米沢から、今後も目が離せそうにない。"
idx_ = 90 # article に対応するtitleのindex
#article = "".join(vars(train.examples[idx_])["article"])
true = "".join(vars(train.examples[idx_])["title"])
pred_title, attention = generate(model,article)
print("".join(article))
print("[predict]","".join(pred_title))
print("[true]",true)
display_attention(article,pred_title,attention)
def main():
print("init data folders")
encoder = {}
decoder = {}
encoder_optim = {}
decoder_optim = {}
encoder_scheduler = {}
decoder_scheduler = {}
for s in ['s1', 's2']:
encoder[s] = {}
decoder[s] = {}
encoder_optim[s] = {}
decoder_optim[s] = {}
encoder_scheduler[s] = {}
decoder_scheduler[s] = {}
for lv in ['lv1', 'lv2', 'lv3']:
encoder[s][lv] = models.Encoder()
decoder[s][lv] = models.Decoder()
encoder[s][lv].apply(weight_init).cuda(GPU)
decoder[s][lv].apply(weight_init).cuda(GPU)
encoder_optim[s][lv] = torch.optim.Adam(encoder[s][lv].parameters(),lr=LEARNING_RATE)
encoder_scheduler[s][lv] = StepLR(encoder_optim[s][lv],step_size=1000,gamma=0.1)
decoder_optim[s][lv] = torch.optim.Adam(decoder[s][lv].parameters(),lr=LEARNING_RATE)
decoder_scheduler[s][lv] = StepLR(decoder_optim[s][lv],step_size=1000,gamma=0.1)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_" + s + "_" + lv + ".pkl")):
encoder[s][lv].load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_" + s + "_" + lv + ".pkl")))
print("load encoder_" + s + "_" + lv + " successfully!")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_" + s + "_" + lv + ".pkl")):
decoder[s][lv].load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_" + s + "_" + lv + ".pkl")))
print("load decoder_" + s + "_" + lv + " successfully!")
if os.path.exists('./test_results/' + EXPDIR) == False:
os.system('mkdir ./test_results/' + EXPDIR)
iteration = 0.0
test_time = 0.0
for images_name in os.listdir(SAMPLE_DIR):
with torch.no_grad():
images = {}
feature = {}
residual = {}
for s in ['s1', 's2']:
feature[s] = {}
residual[s] = {}
images['lv1'] = transforms.ToTensor()(Image.open(SAMPLE_DIR + '/' + images_name).convert('RGB'))
images['lv1'] = Variable(images['lv1'] - 0.5).unsqueeze(0).cuda(GPU)
start = time.time()
H = images['lv1'].size(2)
W = images['lv1'].size(3)
images['lv2_1'] = images['lv1'][:,:,0:int(H/2),:]
images['lv2_2'] = images['lv1'][:,:,int(H/2):H,:]
images['lv3_1'] = images['lv2_1'][:,:,:,0:int(W/2)]
images['lv3_2'] = images['lv2_1'][:,:,:,int(W/2):W]
images['lv3_3'] = images['lv2_2'][:,:,:,0:int(W/2)]
images['lv3_4'] = images['lv2_2'][:,:,:,int(W/2):W]
s = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](images['lv3_1'])
feature[s]['lv3_2'] = encoder[s]['lv3'](images['lv3_2'])
feature[s]['lv3_3'] = encoder[s]['lv3'](images['lv3_3'])
feature[s]['lv3_4'] = encoder[s]['lv3'](images['lv3_4'])
feature[s]['lv3_top'] = torch.cat((feature[s]['lv3_1'], feature[s]['lv3_2']), 3)
feature[s]['lv3_bot'] = torch.cat((feature[s]['lv3_3'], feature[s]['lv3_4']), 3)
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](images['lv2_1'] + residual[s]['lv3_top']) + feature[s]['lv3_top']
feature[s]['lv2_2'] = encoder[s]['lv2'](images['lv2_2'] + residual[s]['lv3_bot']) + feature[s]['lv3_bot']
feature[s]['lv2'] = torch.cat((feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](images['lv1'] + residual[s]['lv2']) + feature[s]['lv2']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's2'
ps = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,0:int(H/2),0:int(W/2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,0:int(H/2),int(W/2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,int(H/2):H,0:int(W/2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,int(H/2):H,int(W/2):W])
feature[s]['lv3_top'] = torch.cat((feature[s]['lv3_1'], feature[s]['lv3_2']), 3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat((feature[s]['lv3_3'], feature[s]['lv3_4']), 3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](residual[ps]['lv1'][:,:,0:int(H/2),:] + residual[s]['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](residual[ps]['lv1'][:,:,int(H/2):H,:] + residual[s]['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat((feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](residual[ps]['lv1'] + residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
deblurred_image = residual[s]['lv1']
stop = time.time()
test_time += stop-start
print('RunTime:%.4f'%(stop-start), ' Average Runtime:%.4f'%(test_time/(iteration+1)))
save_images(deblurred_image.data + 0.5, images_name)
iteration += 1
if train:
optim_encoder_reg.zero_grad()
G_loss.backward()
optim_encoder_reg.step()
if i % 100 == 0:
print(
'\n Step [%d], recon_loss: %.4f, discriminator_loss :%.4f , generator_loss:%.4f'
% (i, ae_loss.item(), D_loss.item(), G_loss.item()))
M = len(dataloader.dataset)
return total_rec_loss / M, total_disc_loss / M, total_gen_loss / M
encoder = models.Encoder().to(device)
decoder = models.Decoder().to(device)
Disc = models.Discriminator().to(device)
#encode/decode optimizers
optim_encoder = torch.optim.Adam(encoder.parameters(), lr=args.lr)
optim_decoder = torch.optim.Adam(decoder.parameters(), lr=args.lr)
optim_D = torch.optim.Adam(Disc.parameters(), lr=args.lr)
optim_encoder_reg = torch.optim.Adam(encoder.parameters(), lr=0.0001)
schedulerDisc = torch.optim.lr_scheduler.ExponentialLR(optim_D, gamma=0.99)
schedulerD = torch.optim.lr_scheduler.ExponentialLR(optim_decoder, gamma=0.99)
schedulerE = torch.optim.lr_scheduler.ExponentialLR(optim_encoder, gamma=0.99)
train_loss = []
val_loss = []
for epoch in range(args.epochs):
def main():
print("init data folders")
encoder_lv1 = models.Encoder()
encoder_lv2 = models.Encoder()
encoder_lv3 = models.Encoder()
decoder_lv1 = models.Decoder()
decoder_lv2 = models.Decoder()
decoder_lv3 = models.Decoder()
encoder_lv1.apply(weight_init).cuda(GPU)
encoder_lv2.apply(weight_init).cuda(GPU)
encoder_lv3.apply(weight_init).cuda(GPU)
decoder_lv1.apply(weight_init).cuda(GPU)
decoder_lv2.apply(weight_init).cuda(GPU)
decoder_lv3.apply(weight_init).cuda(GPU)
encoder_lv1_optim = torch.optim.Adam(encoder_lv1.parameters(),lr=LEARNING_RATE)
# encoder_lv1_scheduler = StepLR(encoder_lv1_optim,step_size=10,gamma=0.1)
encoder_lv2_optim = torch.optim.Adam(encoder_lv2.parameters(),lr=LEARNING_RATE)
# encoder_lv2_scheduler = StepLR(encoder_lv2_optim,step_size=10,gamma=0.1)
encoder_lv3_optim = torch.optim.Adam(encoder_lv3.parameters(),lr=LEARNING_RATE)
# encoder_lv3_scheduler = StepLR(encoder_lv3_optim,step_size=10,gamma=0.1)
decoder_lv1_optim = torch.optim.Adam(decoder_lv1.parameters(),lr=LEARNING_RATE)
# decoder_lv1_scheduler = StepLR(decoder_lv1_optim,step_size=10,gamma=0.1)
decoder_lv2_optim = torch.optim.Adam(decoder_lv2.parameters(),lr=LEARNING_RATE)
# decoder_lv2_scheduler = StepLR(decoder_lv2_optim,step_size=10,gamma=0.1)
decoder_lv3_optim = torch.optim.Adam(decoder_lv3.parameters(),lr=LEARNING_RATE)
# decoder_lv3_scheduler = StepLR(decoder_lv3_optim,step_size=10,gamma=0.1)
# if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
# encoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
# print("load encoder_lv1 success")
# if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
# encoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
# print("load encoder_lv2 success")
# if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
# encoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
# print("load encoder_lv3 success")
# if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
# decoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
# print("load encoder_lv1 success")
# if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
# decoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
# print("load decoder_lv2 success")
# if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
# decoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
# print("load decoder_lv3 success")
# LOAD models here
saved_epoch = 99
encoder_lv1.load_state_dict(torch.load(str('./checkpoints2/' + METHOD + "/ep" + str(saved_epoch)+ "_encoder_lv1.pkl")))
encoder_lv2.load_state_dict(torch.load(str('./checkpoints2/' + METHOD + "/ep" + str(saved_epoch)+ "_encoder_lv2.pkl")))
encoder_lv3.load_state_dict(torch.load(str('./checkpoints2/' + METHOD + "/ep" + str(saved_epoch)+ "_encoder_lv3.pkl")))
decoder_lv1.load_state_dict(torch.load(str('./checkpoints2/' + METHOD + "/ep" + str(saved_epoch)+ "_decoder_lv1.pkl")))
decoder_lv2.load_state_dict(torch.load(str('./checkpoints2/' + METHOD + "/ep" + str(saved_epoch)+ "_decoder_lv2.pkl")))
decoder_lv3.load_state_dict(torch.load(str('./checkpoints2/' + METHOD + "/ep" + str(saved_epoch)+ "_decoder_lv3.pkl")))
if os.path.exists('./checkpoints2/' + METHOD) == False:
os.system('mkdir ./checkpoints2/' + METHOD)
for epoch in range(start_epoch, EPOCHS):
# encoder_lv1_scheduler.step(epoch)
# encoder_lv2_scheduler.step(epoch)
# encoder_lv3_scheduler.step(epoch)
# decoder_lv1_scheduler.step(epoch)
# decoder_lv2_scheduler.step(epoch)
# decoder_lv3_scheduler.step(epoch)
print("Training...")
train_dataset = NH_HazeDataset(
hazed_image_files = 'new_dataset/train_patch_hazy.txt', # make changes here !
dehazed_image_files = 'new_dataset/train_patch_gt.txt',
root_dir = 'new_dataset/',
crop = False,
rotation = False,
crop_size = IMAGE_SIZE,
transform = transforms.Compose([transforms.Resize((128,160)),
transforms.ToTensor()
]))
train_dataloader = DataLoader(train_dataset, batch_size = BATCH_SIZE, shuffle=True)
start = 0
iLoss = 0
print('Epoch: ',epoch)
# torch.save(encoder_lv1.state_dict(),str('./checkpoints2/' + METHOD + "/encoder_lv1.pkl"))
for iteration, images in enumerate(train_dataloader):
# mse = nn.MSELoss().cuda(GPU)
# mae = nn.L1Loss().cuda(GPU)
custom_loss_fn = CustomLoss_function().cuda(GPU)
gt = Variable(images['dehazed_image'] - 0.5).cuda(GPU)
H = gt.size(2)
W = gt.size(3)
images_lv1 = Variable(images['hazed_image'] - 0.5).cuda(GPU)
images_lv2 = F.interpolate(images_lv1, scale_factor = 0.5, mode = 'bilinear')
images_lv3 = F.interpolate(images_lv2, scale_factor = 0.5, mode = 'bilinear')
feature_lv3 = encoder_lv3(images_lv3)
residual_lv3 = decoder_lv3(feature_lv3)
residual_lv3 = F.interpolate(residual_lv3, scale_factor=2, mode= 'bilinear')
feature_lv3 = F.interpolate(feature_lv3, scale_factor=2, mode= 'bilinear')
feature_lv2 = encoder_lv2(images_lv2 + residual_lv3)
residual_lv2 = decoder_lv2(feature_lv2 + feature_lv3)
residual_lv2 = F.interpolate(residual_lv2, scale_factor=2, mode= 'bilinear')
feature_lv2 = F.interpolate(feature_lv2, scale_factor=2, mode= 'bilinear')
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2)
dehazed_image = decoder_lv1(feature_lv1 + feature_lv2)
loss_lv1, loss_recn, loss_perc, loss_tv = custom_loss_fn(dehazed_image,gt)
loss = loss_lv1
iLoss += loss.item()
encoder_lv1.zero_grad()
encoder_lv2.zero_grad()
encoder_lv3.zero_grad()
decoder_lv1.zero_grad()
decoder_lv2.zero_grad()
decoder_lv3.zero_grad()
loss.backward()
encoder_lv1_optim.step()
encoder_lv2_optim.step()
encoder_lv3_optim.step()
decoder_lv1_optim.step()
decoder_lv2_optim.step()
decoder_lv3_optim.step()
writer.add_scalar('Loss',loss.item(),epoch*len(train_dataloader)+iteration)
writer.add_scalar('Loss_recn',loss_recn.item(),epoch*len(train_dataloader)+iteration)
writer.add_scalar('Loss_perc',loss_perc.item(),epoch*len(train_dataloader)+iteration)
writer.add_scalar('Loss_tv',loss_tv.item(),epoch*len(train_dataloader)+iteration)
if (iteration+1)%10 == 0:
stop = time.time()
print("epoch:", epoch, "iteration:", iteration+1, "loss:%.4f"%loss.item(), 'time:%.4f'%(stop-start))
start = time.time()
torch.save(encoder_lv1.state_dict(),str('./checkpoints2/' + METHOD + "/ep" + str(epoch)+ "_encoder_lv1.pkl"))
torch.save(encoder_lv2.state_dict(),str('./checkpoints2/' + METHOD + "/ep" + str(epoch)+"_encoder_lv2.pkl"))
torch.save(encoder_lv3.state_dict(),str('./checkpoints2/' + METHOD + "/ep" + str(epoch)+"_encoder_lv3.pkl"))
torch.save(decoder_lv1.state_dict(),str('./checkpoints2/' + METHOD + "/ep" + str(epoch)+"_decoder_lv1.pkl"))
torch.save(decoder_lv2.state_dict(),str('./checkpoints2/' + METHOD + "/ep" + str(epoch)+"_decoder_lv2.pkl"))
torch.save(decoder_lv3.state_dict(),str('./checkpoints2/' + METHOD + "/ep" + str(epoch)+"_decoder_lv3.pkl"))
print ('Training Loss:', iLoss/len(train_dataloader))
def main():
print("init data folders")
encoder_lv1 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv2 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv3 = models.Encoder().apply(weight_init).cuda(GPU)
decoder_lv1 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv2 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv3 = models.Decoder().apply(weight_init).cuda(GPU)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
encoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
encoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
print("load encoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
encoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
print("load encoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
decoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
decoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
print("load decoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
decoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
print("load decoder_lv3 success")
if os.path.exists('./test_results/' + EXPDIR) == False:
os.system('mkdir ./test_results/' + EXPDIR)
iteration = 0.0
test_time = 0.0
for images_name in os.listdir(SAMPLE_DIR):
with torch.no_grad():
images_lv1 = transforms.ToTensor()(Image.open(SAMPLE_DIR + '/' + images_name).convert('RGB'))
images_lv1 = Variable(images_lv1 - 0.5).unsqueeze(0).cuda(GPU)
start = time.time()
H = images_lv1.size(2)
W = images_lv1.size(3)
images_lv2_1 = images_lv1[:,:,0:int(H/2),:]
images_lv2_2 = images_lv1[:,:,int(H/2):H,:]
images_lv3_1 = images_lv2_1[:,:,:,0:int(W/2)]
images_lv3_2 = images_lv2_1[:,:,:,int(W/2):W]
images_lv3_3 = images_lv2_2[:,:,:,0:int(W/2)]
images_lv3_4 = images_lv2_2[:,:,:,int(W/2):W]
feature_lv3_1 = encoder_lv3(images_lv3_1)
feature_lv3_2 = encoder_lv3(images_lv3_2)
feature_lv3_3 = encoder_lv3(images_lv3_3)
feature_lv3_4 = encoder_lv3(images_lv3_4)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3)
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3)
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat((feature_lv2_1, feature_lv2_2), 2) + feature_lv3
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
stop = time.time()
test_time += stop-start
print('RunTime:%.4f'%(stop-start), ' Average Runtime:%.4f'%(test_time/(iteration+1)))
save_images(deblur_image.data + 0.5, images_name)
iteration += 1
hidden_size=hidden_size,
vocab_size=vocab_size,
embedding_dict=embeddings,
num_layers=1,
dropout=0,
rnn_type='gru',
)
print("Encoder online")
# Create decoder
decoder = models.Decoder(
input_size=input_size,
hidden_size=hidden_size,
vocab_size=vocab_size,
embedding_dict=embeddings,
num_layers=1,
dropout=0,
rnn_type='gru',
)
print("Decoder online")
if constants.USE_CUDA:
encoder = encoder.cuda()
decoder = decoder.cuda()
print("Synchronized with graphics unit")
# Create Adam optimizers. Decoder has 5* the learning rate of the encoder.
encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=learning_rate)
def build_model(args, vocab):
"""
Build the model, optimizer, and loss according to experiment args
Parameters
----------
args : argparse.Namespace
Experiment arguments
vocab : dict
vocab for language model
Returns
-------
model : torch.nn.Module
The model to be trained
optimizer_func : () -> torch.optim.Optimizer
A function that creates an optimizer (done because we may need to re-initialize the optimizer)
loss : torch.nn.Module
The loss function
"""
params_to_optimize = []
# Build the model according to the given arguments
if args.backbone == "mlp" and args.dataset == "coco":
backbone_input_dim = 512
else:
backbone_input_dim = None
encoder = models.ImageEncoder(
backbone=args.backbone,
pretrained=args.pretrained_backbone,
grayscale="mnist" in args.dataset,
backbone_input_dim=backbone_input_dim,
)
encoder.fine_tune(not args.freeze_encoder)
if not args.freeze_encoder:
params_to_optimize.append({
"params": [p for p in encoder.parameters() if p.requires_grad],
"lr":
args.encoder_lr,
})
if args.attention:
decoder = models.DecoderWithAttention(
embed_dim=args.emb_dim,
decoder_dim=args.decoder_dim,
vocab_size=vocab,
encoder_dim=encoder.output_dim,
dropout=args.dropout,
attention_dim=args.attention_dim,
)
else:
decoder = models.Decoder(
embed_dim=args.emb_dim,
decoder_dim=args.decoder_dim,
vocab=vocab,
encoder_dim=encoder.output_dim,
dropout=args.dropout,
)
params_to_optimize.append({
"params": [p for p in decoder.parameters() if p.requires_grad],
"lr":
args.decoder_lr,
})
loss = nn.CrossEntropyLoss()
model = models.Captioner(encoder, decoder, encoder_dropout=args.dropout)
optimizer_func = lambda: optim.Adam(params_to_optimize)
if args.cuda:
model = model.cuda()
loss = loss.cuda()
return model, optimizer_func, loss
import metrics
import utils
if __name__ == '__main__':
cudnn.benchmark = True
#load data
train_data = datasets.Cornell
train_data_loader = data.DataLoader(dataset=train_data,
batch_size=config.BATCH_SIZE,
shuffle=True,
collate_fn=datasets.collate_fn)
#load model
encoder = models.Encoder(train_data.num_word, 512, 2, dropout=0.1)
decoder = models.Decoder(train_data.num_word, 512, 2, 'dot', dropout=0.1)
if config.LOAD == True:
utils.load_model(encoder,
os.path.join('./Model', str(config.EPOCH_START)),
'encoder.pth')
utils.load_model(decoder,
os.path.join('./Model', str(config.EPOCH_START)),
'decoder.pth')
#set optimizer
encoder_optim = optim.Adam(encoder.parameters(), lr=config.LR)
decoder_optim = optim.Adam(decoder.parameters(), lr=config.LR * 5)
#set loss and meter
criterion = losses.MaskLoss()
loss_meter = metrics.LossMeter()
def main():
train = True
input_size = 768
# input_size = 256 # set to none for default cropping
print("Training with Places365 Dataset")
max_its = 300000
max_eps = 20000
optimizer = 'adam' # separate optimizers for discriminator and autoencoder
lr = 0.0002
batch_size = 1
step_lr_gamma = 0.1
step_lr_step = 200000
discr_success_rate = 0.8
win_rate = 0.8
log_interval = int(max_its // 100)
# log_interval = 100
if log_interval < 10:
print("\n WARNING: VERY SMALL LOG INTERVAL\n")
lam = 0.001
disc_wt = 1.
trans_wt = 100.
style_wt = 100.
alpha = 0.05
tblock_kernel = 10
# Models
encoder = models.Encoder()
decoder = models.Decoder()
tblock = models.TransformerBlock(kernel_size=tblock_kernel)
discrim = models.Discriminator()
# init weights
models.init_weights(encoder)
models.init_weights(decoder)
models.init_weights(tblock)
models.init_weights(discrim)
if torch.cuda.is_available():
encoder = encoder.cuda()
decoder = decoder.cuda()
tblock = tblock.cuda()
discrim = discrim.cuda()
if train:
# load tmp weights
if os.path.exists('tmp'):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
encoder = torch.load("tmp/encoder.pt", map_location=device)
decoder = torch.load("tmp/decoder.pt", map_location=device)
tblock = torch.load("tmp/tblock.pt", map_location=device)
discrim = torch.load("tmp/discriminator.pt", map_location=device)
# Losses
gen_loss = losses.SoftmaxLoss()
disc_loss = losses.SoftmaxLoss()
transf_loss = losses.TransformedLoss()
style_aware_loss = losses.StyleAwareContentLoss()
# # optimizer for encoder/decoder (and tblock? - think it has no parameters though)
# params_to_update = []
# for m in [encoder, decoder, tblock, discrim]:
# for param in m.parameters():
# param.requires_grad = True
# params_to_update.append(param)
# # optimizer = torch.optim.Adam(params_to_update, lr=lr)
data_dir = '../Datasets/WikiArt-Sorted/data/vincent-van-gogh_road-with-cypresses-1890'
# data_dir = '../Datasets/WikiArt-Sorted/data/edvard-munch/'
style_data = datasets.StyleDataset(data_dir)
num_workers = 8
# if mpii:
# dataloaders = {'train': DataLoader(datasets.MpiiDataset(train=True, input_size=input_size,
# style_dataset=style_data, crop_size=crop_size),
# batch_size=batch_size, shuffle=True, num_workers=num_workers),
# 'test': DataLoader(datasets.MpiiDataset(train=False, style_dataset=style_data, input_size=input_size),
# batch_size=1, shuffle=False, num_workers=num_workers)}
# else:
dataloaders = {
'train':
DataLoader(datasets.PlacesDataset(train=True,
input_size=input_size,
style_dataset=style_data),
batch_size=batch_size,
shuffle=True,
num_workers=num_workers),
'test':
DataLoader(datasets.TestDataset(),
batch_size=1,
shuffle=False,
num_workers=num_workers)
}
# optimizer for encoder/decoder (and tblock? - think it has no parameters though)
gen_params = []
for m in [encoder, decoder]:
for param in m.parameters():
param.requires_grad = True
gen_params.append(param)
g_optimizer = torch.optim.Adam(gen_params, lr=lr)
# optimizer for disciminator
disc_params = []
for param in discrim.parameters():
param.requires_grad = True
disc_params.append(param)
d_optimizer = torch.optim.Adam(disc_params, lr=lr)
scheduler_g = torch.optim.lr_scheduler.StepLR(g_optimizer,
step_lr_step,
gamma=step_lr_gamma,
last_epoch=-1)
scheduler_d = torch.optim.lr_scheduler.StepLR(d_optimizer,
step_lr_step,
gamma=step_lr_gamma,
last_epoch=-1)
its = 0
print('Begin Training:')
g_steps = 0
d_steps = 0
image_id = 0
time_per_it = []
if max_its is None:
max_its = len(dataloaders['train'])
# set models to train()
encoder.train()
decoder.train()
tblock.train()
discrim.train()
d_loss = 0
g_loss = 0
gen_acc = 0
d_acc = 0
for epoch in range(max_eps):
if its > max_its:
break
for images, style_images in dataloaders['train']:
t0 = process_time()
# utils.export_image(images[0, :, :, :], style_images[0, :, :, :], 'input_images.jpg')
# zero gradients
g_optimizer.zero_grad()
d_optimizer.zero_grad()
if its > max_its:
break
if torch.cuda.is_available():
images = images.cuda()
if style_images is not None:
style_images = style_images.cuda()
# autoencoder
emb = encoder(images)
stylized_im = decoder(emb)
# if training do losses etc
stylized_emb = encoder(stylized_im)
# add losses
# tblock
transformed_inputs, transformed_outputs = tblock(
images, stylized_im)
# add loss
# # # GENERATOR TRAIN # # # #
g_optimizer.zero_grad()
d_out_fake = discrim(
stylized_im
) # keep attached to generator because grads needed
# accuracy given the fake output, generator images
gen_acc = utils.accuracy(
d_out_fake,
target_label=1) # accuracy given only the output image
del g_loss
g_loss = disc_wt * gen_loss(d_out_fake, target_label=1)
g_loss += trans_wt * transf_loss(transformed_inputs,
transformed_outputs)
g_loss += style_wt * style_aware_loss(emb, stylized_emb)
g_loss.backward()
d_optimizer.step()
discr_success_rate = discr_success_rate * (
1. - alpha) + alpha * (1. - gen_acc)
g_steps += 1
# # # DISCRIMINATOR TRAIN # # # #
d_optimizer.zero_grad()
# detach from generator, so not propagating unnecessary gradients
d_out_fake = discrim(stylized_im.clone().detach())
d_out_real_ph = discrim(images)
d_out_real_style = discrim(style_images)
# accuracy given all the images
d_acc_real_ph = utils.accuracy(d_out_real_ph, target_label=0)
d_acc_fake_style = utils.accuracy(d_out_fake, target_label=0)
d_acc_real_style = utils.accuracy(d_out_real_style,
target_label=1)
gen_acc = 1 - d_acc_fake_style
d_acc = (d_acc_real_ph + d_acc_fake_style +
d_acc_real_style) / 3
# Loss calculation
d_loss = disc_loss(d_out_fake, target_label=0)
d_loss += disc_loss(d_out_real_style, target_label=1)
d_loss += disc_loss(d_out_real_ph, target_label=0)
d_loss.backward()
d_optimizer.step()
discr_success_rate = discr_success_rate * (
1. - alpha) + alpha * d_acc
d_steps += 1
# print(g_loss.item(), g_steps, d_loss.item(), d_steps)
t1 = process_time()
time_per_it.append((t1 - t0) / 3600)
if len(time_per_it) > 100:
time_per_it.pop(0)
if not its % log_interval:
running_mean_it_time = sum(time_per_it) / len(time_per_it)
time_rem = (max_its - its + 1) * running_mean_it_time
print(
"{}/{} -- {} G Steps -- G Loss {:.2f} -- G Acc {:.2f} -"
"- {} D Steps -- D Loss {:.2f} -- D Acc {:.2f} -"
"- {:.2f} D Success -- {:.1f} Hours remaing...".format(
its, max_its, g_steps, g_loss, gen_acc, d_steps,
d_loss, d_acc, discr_success_rate, time_rem))
for idx in range(images.size(0)):
output_path = 'outputs/training/'.format(epoch)
if not os.path.exists(output_path):
os.makedirs(output_path)
output_path += 'iteration_{:06d}_example_{}.jpg'.format(
its, idx)
utils.export_image([
images[idx, :, :, :], style_images[idx, :, :, :],
stylized_im[idx, :, :, :]
], output_path)
its += 1
scheduler_g.step()
scheduler_d.step()
if not its % 10000:
if not os.path.exists('tmp'):
os.mkdir('tmp')
torch.save(encoder, "tmp/encoder.pt")
torch.save(decoder, "tmp/decoder.pt")
torch.save(tblock, "tmp/tblock.pt")
torch.save(discrim, "tmp/discriminator.pt")
# only save if running on gpu (otherwise I'm just fixing bugs)
torch.save(encoder, "encoder.pt")
torch.save(decoder, "decoder.pt")
torch.save(tblock, "tblock.pt")
torch.save(discrim, "discriminator.pt")
evaluate(encoder, decoder, dataloaders['test'])
else:
encoder = torch.load('encoder.pt', map_location='cpu')
decoder = torch.load('decoder.pt', map_location='cpu')
# encoder.load_state_dict(encoder_dict)
# decoder.load_state_dict(decoder_dict)
dataloader = DataLoader(datasets.TestDataset(),
batch_size=1,
shuffle=False,
num_workers=8)
evaluate(encoder, decoder, dataloader)
raise NotImplementedError('Not implemented standalone ')
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--loader_workers', type=int, default=64)
parser.add_argument('--epochs', type=int, default=1200)
parser.add_argument('--batch_size', type=int, default=512)
parser.add_argument('--save_interval', type=int, default=10)
parser.add_argument('--save_dir', type=str, default='logs/')
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--table', type=str, default='data/Cangjie5.txt')
parser.add_argument('--codemap', type=str, default='data/codemap_cangjie5.txt')
parser.add_argument('--fonts', nargs='+', default=['data/hanazono/HanaMinA.ttf', 'data/hanazono/HanaMinB.ttf'])
parser.add_argument('--encoder_lr', type=float, default=1e-3)
parser.add_argument('--decoder_lr', type=float, default=1e-3)
parser.add_argument('--alpha_c', type=float, default=1.)
parser.add_argument('--grad_clip', type=float, default=5.)
args = parser.parse_args()
args.save_dir = os.path.join(args.save_dir, datetime.datetime.now().strftime("%m-%d-%Y-%H:%M:%S"))
os.makedirs(args.save_dir)
glyph = dset.Glyph(args.fonts)
dataset = dset.CodeTableDataset(glyph, table=args.table, codemap=args.codemap)
train_length = int(len(dataset) * 0.7)
train_set, val_set = torch.utils.data.random_split(dataset, [train_length, len(dataset) - train_length])
train_loader = torch.utils.data.DataLoader(train_set, args.batch_size, True,
collate_fn=dset.collate_batch,
num_workers=args.loader_workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_set, args.batch_size, False,
collate_fn=dset.collate_batch,
num_workers=args.loader_workers,
pin_memory=True)
encoder = models.Encoder(encode_channels=256).to(device)
encoder_optim = torch.optim.Adam(encoder.parameters(), lr=args.encoder_lr)
decoder = models.Decoder(128, 256, 256, 26 + 2, encoder_dim=256, dropout=0.5).to(device)
decoder_optim = torch.optim.Adam(decoder.parameters(), lr=args.decoder_lr)
epoch_start = 0
if args.resume != None:
print('loading checkpoint: %s'%args.resume)
checkpoint = torch.load(args.resume, map_location=device)
decoder.load_state_dict(checkpoint['decoder'])
decoder_optim.load_state_dict(checkpoint['decoder_optimizer'])
encoder.load_state_dict(checkpoint['encoder'])
encoder_optim.load_state_dict(checkpoint['encoder_optimizer'])
epoch_start = checkpoint['epoch']
criterion = nn.CrossEntropyLoss().to(device)
logger = PredLogger(dataset, args.save_dir, args.codemap)
writer = SummaryWriter(args.save_dir)
best_acc = 0
for epoch in tqdm(range(epoch_start, args.epochs), position=0):
train(train_loader=train_loader,
encoder=encoder,
decoder=decoder,
criterion=criterion,
encoder_optimizer=encoder_optim,
decoder_optimizer=decoder_optim,
epoch=epoch,
logger=logger,
writer=writer,
args=args)
acc, imgs, scores, alphas = validate(val_loader=val_loader,
encoder=encoder,
decoder=decoder,
criterion=criterion,
epoch=epoch,
logger=logger,
writer=writer,
args=args)
is_best = best_acc < acc # and epoch > 0
best_acc = max(acc, best_acc)
if epoch % args.save_interval == args.save_interval - 1 or is_best:
save_checkpoint(epoch, encoder, decoder, encoder_optim, decoder_optim, acc, is_best, args.save_dir)
vis = visualize_att(T.ToPILImage()(imgs[0].cpu()), scores[0].topk(1, dim=-1).indices.flatten().tolist(), alphas[0].view(-1, 13, 13).cpu(), logger.map_rev)
vis.savefig(os.path.join(args.save_dir, 'val_visualize_%d.png'%epoch))
def main():
print("init data folders")
encoder_lv1 = models.Encoder()
encoder_lv2 = models.Encoder()
encoder_lv3 = models.Encoder()
encoder_lv4 = models.Encoder()
decoder_lv1 = models.Decoder()
decoder_lv2 = models.Decoder()
decoder_lv3 = models.Decoder()
decoder_lv4 = models.Decoder()
encoder_lv1.apply(weight_init).cuda(GPU)
encoder_lv2.apply(weight_init).cuda(GPU)
encoder_lv3.apply(weight_init).cuda(GPU)
encoder_lv4.apply(weight_init).cuda(GPU)
decoder_lv1.apply(weight_init).cuda(GPU)
decoder_lv2.apply(weight_init).cuda(GPU)
decoder_lv3.apply(weight_init).cuda(GPU)
decoder_lv4.apply(weight_init).cuda(GPU)
encoder_lv1_optim = torch.optim.Adam(encoder_lv1.parameters(),lr=LEARNING_RATE)
encoder_lv1_scheduler = StepLR(encoder_lv1_optim,step_size=1000,gamma=0.1)
encoder_lv2_optim = torch.optim.Adam(encoder_lv2.parameters(),lr=LEARNING_RATE)
encoder_lv2_scheduler = StepLR(encoder_lv2_optim,step_size=1000,gamma=0.1)
encoder_lv3_optim = torch.optim.Adam(encoder_lv3.parameters(),lr=LEARNING_RATE)
encoder_lv3_scheduler = StepLR(encoder_lv3_optim,step_size=1000,gamma=0.1)
encoder_lv4_optim = torch.optim.Adam(encoder_lv4.parameters(),lr=LEARNING_RATE)
encoder_lv4_scheduler = StepLR(encoder_lv4_optim,step_size=1000,gamma=0.1)
decoder_lv1_optim = torch.optim.Adam(decoder_lv1.parameters(),lr=LEARNING_RATE)
decoder_lv1_scheduler = StepLR(decoder_lv1_optim,step_size=1000,gamma=0.1)
decoder_lv2_optim = torch.optim.Adam(decoder_lv2.parameters(),lr=LEARNING_RATE)
decoder_lv2_scheduler = StepLR(decoder_lv2_optim,step_size=1000,gamma=0.1)
decoder_lv3_optim = torch.optim.Adam(decoder_lv3.parameters(),lr=LEARNING_RATE)
decoder_lv3_scheduler = StepLR(decoder_lv3_optim,step_size=1000,gamma=0.1)
decoder_lv4_optim = torch.optim.Adam(decoder_lv4.parameters(),lr=LEARNING_RATE)
decoder_lv4_scheduler = StepLR(decoder_lv4_optim,step_size=1000,gamma=0.1)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
encoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
encoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
print("load encoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
encoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
print("load encoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")):
encoder_lv4.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")))
print("load encoder_lv4 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
decoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
decoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
print("load decoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
decoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
print("load decoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")):
decoder_lv4.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")))
print("load decoder_lv4 success")
if os.path.exists('./checkpoints/' + METHOD) == False:
os.system('mkdir ./checkpoints/' + METHOD)
for epoch in range(args.start_epoch, EPOCHS):
encoder_lv1_scheduler.step(epoch)
encoder_lv2_scheduler.step(epoch)
encoder_lv3_scheduler.step(epoch)
encoder_lv4_scheduler.step(epoch)
decoder_lv1_scheduler.step(epoch)
decoder_lv2_scheduler.step(epoch)
decoder_lv3_scheduler.step(epoch)
decoder_lv4_scheduler.step(epoch)
print("Training...")
train_dataset = GoProDataset(
blur_image_files = './datas/GoPro/train_blur_file.txt',
sharp_image_files = './datas/GoPro/train_sharp_file.txt',
root_dir = './datas/GoPro',
crop = True,
crop_size = IMAGE_SIZE,
transform = transforms.Compose([
transforms.ToTensor()
]))
train_dataloader = DataLoader(train_dataset, batch_size = BATCH_SIZE, shuffle=True)
start = 0
for iteration, images in enumerate(train_dataloader): # images：{(3,256,256),(3,256,256)}
mse = nn.MSELoss().cuda(GPU)
gt = Variable(images['sharp_image'] - 0.5).cuda(GPU) #(2,3,256,256)
H = gt.size(2) #256
W = gt.size(3) #256
# 将图片划分为1，2，4，8个小块
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU) #(2,3,256,256)
images_lv2_1 = images_lv1[:,:,0:int(H/2),:]
images_lv2_2 = images_lv1[:,:,int(H/2):H,:]
images_lv3_1 = images_lv2_1[:,:,:,0:int(W/2)]
images_lv3_2 = images_lv2_1[:,:,:,int(W/2):W]
images_lv3_3 = images_lv2_2[:,:,:,0:int(W/2)]
images_lv3_4 = images_lv2_2[:,:,:,int(W/2):W]
images_lv4_1 = images_lv3_1[:,:,0:int(H/4),:]
images_lv4_2 = images_lv3_1[:,:,int(H/4):int(H/2),:]
images_lv4_3 = images_lv3_2[:,:,0:int(H/4),:]
images_lv4_4 = images_lv3_2[:,:,int(H/4):int(H/2),:]
images_lv4_5 = images_lv3_3[:,:,0:int(H/4),:]
images_lv4_6 = images_lv3_3[:,:,int(H/4):int(H/2),:]
images_lv4_7 = images_lv3_4[:,:,0:int(H/4),:]
images_lv4_8 = images_lv3_4[:,:,int(H/4):int(H/2),:]
feature_lv4_1 = encoder_lv4(images_lv4_1)
feature_lv4_2 = encoder_lv4(images_lv4_2)
feature_lv4_3 = encoder_lv4(images_lv4_3)
feature_lv4_4 = encoder_lv4(images_lv4_4)
feature_lv4_5 = encoder_lv4(images_lv4_5)
feature_lv4_6 = encoder_lv4(images_lv4_6)
feature_lv4_7 = encoder_lv4(images_lv4_7)
feature_lv4_8 = encoder_lv4(images_lv4_8)
feature_lv4_top_left = torch.cat((feature_lv4_1, feature_lv4_2), 2)
feature_lv4_top_right = torch.cat((feature_lv4_3, feature_lv4_4), 2)
feature_lv4_bot_left = torch.cat((feature_lv4_5, feature_lv4_6), 2)
feature_lv4_bot_right = torch.cat((feature_lv4_7, feature_lv4_8), 2)
feature_lv4_top = torch.cat((feature_lv4_top_left, feature_lv4_top_right), 3)
feature_lv4_bot = torch.cat((feature_lv4_bot_left, feature_lv4_bot_right), 3)
feature_lv4 = torch.cat((feature_lv4_top, feature_lv4_bot), 2)
residual_lv4_top_left = decoder_lv4(feature_lv4_top_left)
residual_lv4_top_right = decoder_lv4(feature_lv4_top_right)
residual_lv4_bot_left = decoder_lv4(feature_lv4_bot_left)
residual_lv4_bot_right = decoder_lv4(feature_lv4_bot_right)
feature_lv3_1 = encoder_lv3(images_lv3_1 + residual_lv4_top_left)
feature_lv3_2 = encoder_lv3(images_lv3_2 + residual_lv4_top_right)
feature_lv3_3 = encoder_lv3(images_lv3_3 + residual_lv4_bot_left)
feature_lv3_4 = encoder_lv3(images_lv3_4 + residual_lv4_bot_right)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3) + feature_lv4_top
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3) + feature_lv4_bot
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat((feature_lv2_1, feature_lv2_2), 2) + feature_lv3
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
loss = mse(deblur_image, gt)
encoder_lv1.zero_grad()
encoder_lv2.zero_grad()
encoder_lv3.zero_grad()
encoder_lv4.zero_grad()
decoder_lv1.zero_grad()
decoder_lv2.zero_grad()
decoder_lv3.zero_grad()
decoder_lv4.zero_grad()
loss.backward()
encoder_lv1_optim.step()
encoder_lv2_optim.step()
encoder_lv3_optim.step()
encoder_lv4_optim.step()
decoder_lv1_optim.step()
decoder_lv2_optim.step()
decoder_lv3_optim.step()
decoder_lv4_optim.step()
if (iteration+1)%10 == 0:
stop = time.time()
print("epoch:", epoch, "iteration:", iteration+1, "loss:%.4f"%loss.item(), 'time:%.4f'%(stop-start))
start = time.time()
if (epoch)%100==0:
if os.path.exists('./checkpoints/' + METHOD + '/epoch' + str(epoch)) == False:
os.system('mkdir ./checkpoints/' + METHOD + '/epoch' + str(epoch))
print("Testing...")
test_dataset = GoProDataset(
blur_image_files = './datas/GoPro/test_blur_file.txt',
sharp_image_files = './datas/GoPro/test_sharp_file.txt',
root_dir = './datas/GoPro',
transform = transforms.Compose([
transforms.ToTensor()
]))
test_dataloader = DataLoader(test_dataset, batch_size = 1, shuffle=False)
test_time = 0
for iteration, images in enumerate(test_dataloader):
with torch.no_grad():
start = time.time()
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
H = images_lv1.size(2)
W = images_lv1.size(3)
images_lv2_1 = images_lv1[:,:,0:int(H/2),:]
images_lv2_2 = images_lv1[:,:,int(H/2):H,:]
images_lv3_1 = images_lv2_1[:,:,:,0:int(W/2)]
images_lv3_2 = images_lv2_1[:,:,:,int(W/2):W]
images_lv3_3 = images_lv2_2[:,:,:,0:int(W/2)]
images_lv3_4 = images_lv2_2[:,:,:,int(W/2):W]
images_lv4_1 = images_lv3_1[:,:,0:int(H/4),:]
images_lv4_2 = images_lv3_1[:,:,int(H/4):int(H/2),:]
images_lv4_3 = images_lv3_2[:,:,0:int(H/4),:]
images_lv4_4 = images_lv3_2[:,:,int(H/4):int(H/2),:]
images_lv4_5 = images_lv3_3[:,:,0:int(H/4),:]
images_lv4_6 = images_lv3_3[:,:,int(H/4):int(H/2),:]
images_lv4_7 = images_lv3_4[:,:,0:int(H/4),:]
images_lv4_8 = images_lv3_4[:,:,int(H/4):int(H/2),:]
feature_lv4_1 = encoder_lv4(images_lv4_1)
feature_lv4_2 = encoder_lv4(images_lv4_2)
feature_lv4_3 = encoder_lv4(images_lv4_3)
feature_lv4_4 = encoder_lv4(images_lv4_4)
feature_lv4_5 = encoder_lv4(images_lv4_5)
feature_lv4_6 = encoder_lv4(images_lv4_6)
feature_lv4_7 = encoder_lv4(images_lv4_7)
feature_lv4_8 = encoder_lv4(images_lv4_8)
feature_lv4_top_left = torch.cat((feature_lv4_1, feature_lv4_2), 2)
feature_lv4_top_right = torch.cat((feature_lv4_3, feature_lv4_4), 2)
feature_lv4_bot_left = torch.cat((feature_lv4_5, feature_lv4_6), 2)
feature_lv4_bot_right = torch.cat((feature_lv4_7, feature_lv4_8), 2)
feature_lv4_top = torch.cat((feature_lv4_top_left, feature_lv4_top_right), 3)
feature_lv4_bot = torch.cat((feature_lv4_bot_left, feature_lv4_bot_right), 3)
residual_lv4_top_left = decoder_lv4(feature_lv4_top_left)
residual_lv4_top_right = decoder_lv4(feature_lv4_top_right)
residual_lv4_bot_left = decoder_lv4(feature_lv4_bot_left)
residual_lv4_bot_right = decoder_lv4(feature_lv4_bot_right)
feature_lv3_1 = encoder_lv3(images_lv3_1 + residual_lv4_top_left)
feature_lv3_2 = encoder_lv3(images_lv3_2 + residual_lv4_top_right)
feature_lv3_3 = encoder_lv3(images_lv3_3 + residual_lv4_bot_left)
feature_lv3_4 = encoder_lv3(images_lv3_4 + residual_lv4_bot_right)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3) + feature_lv4_top
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3) + feature_lv4_bot
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat((feature_lv2_1, feature_lv2_2), 2) + torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
stop = time.time()
test_time += stop - start
print('RunTime:%.4f'%(stop-start), ' Average Runtime:%.4f'%(test_time/(iteration+1)))
save_deblur_images(deblur_image.data + 0.5, iteration, epoch)
torch.save(encoder_lv1.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv1.pkl"))
torch.save(encoder_lv2.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv2.pkl"))
torch.save(encoder_lv3.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv3.pkl"))
torch.save(encoder_lv4.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv4.pkl"))
torch.save(decoder_lv1.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv1.pkl"))
torch.save(decoder_lv2.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv2.pkl"))
torch.save(decoder_lv3.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv3.pkl"))
torch.save(decoder_lv4.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv4.pkl"))
images = [
np.asarray(Image.open(image_path)) for image_path in image_paths
][:1]
for i in range(len(images)):
image = images[i]
dimensions = image.shape[:2]
ratio = min([min(d, 480) / d for d in dimensions])
scaled_dimensions = [int(ratio * d) for d in dimensions]
image = resize(image, (*scaled_dimensions, 3), anti_aliasing=True)
image = image[:, :, [2, 1, 0]]
image = np.transpose(image, (2, 0, 1))
images[i] = image
encoder = models.Encoder(embedding_size=img_embedding_size).to(device)
decoder = models.Decoder(img_embedding_size=img_embedding_size,
pos_embedding_size=pos_embedding_size,
hidden_size=decoder_hidden_size).to(device)
encoder_opt = torch.optim.Adam(encoder.parameters(), lr=0.004)
decoder_opt = torch.optim.Adam(decoder.parameters(), lr=0.004)
encoder_scheduler = torch.optim.lr_scheduler.ExponentialLR(encoder_opt,
gamma=0.99999)
decoder_scheduler = torch.optim.lr_scheduler.ExponentialLR(decoder_opt,
gamma=0.99999)
for i in range(100000000):
image_idxs = np.random.randint(low=0,
high=len(images),
size=batch_size)
image = [images[idx] for idx in image_idxs]
shape_idx = np.random.randint(low=0, high=batch_size)
def main():
global device
parser = argparse.ArgumentParser(description='Neuro Cangjie')
parser.add_argument('--model',
'-m',
help='path to model',
default='logs/cangjie5.pth.tar')
parser.add_argument(
'--fonts',
'-f',
nargs='+',
default=['data/hanazono/HanaMinA.ttf', 'data/hanazono/HanaMinB.ttf'])
parser.add_argument('--codemap',
'-cm',
help='path to code map',
default='data/codemap_cangjie5.txt')
parser.add_argument('--beam_size',
'-b',
default=5,
type=int,
help='beam size for beam search')
parser.add_argument('--dont_smooth',
dest='smooth',
action='store_false',
help='do not smooth alpha overlay')
parser.add_argument('--use_cpu',
action='store_true',
help='use cpu for model inference')
args = parser.parse_args()
if args.use_cpu:
device = torch.device('cpu')
# Load model
checkpoint = torch.load(args.model, map_location=device)
encoder = models.Encoder(encode_channels=256).to(device)
decoder = models.Decoder(128,
256,
256,
26 + 2,
encoder_dim=256,
dropout=0.5).to(device)
decoder.load_state_dict(checkpoint['decoder'])
encoder.load_state_dict(checkpoint['encoder'])
decoder.eval()
encoder.eval()
word_map, rev_word_map = utils.load_map(args.codemap)
glyph = dset.Glyph(args.fonts)
while True:
ch = input('>> ')[0]
img = glyph.draw(ch)
# img.save('exp.png')
# Encode, decode with attention and beam search
seq, alphas, all_seq = beam_search(encoder, decoder, img, word_map,
args.beam_size)
# seq, alphas, all_seq = first_out(encoder, decoder, img, word_map)
print(''.join([rev_word_map[ind] for ind in seq[1:-1]]))
alphas = torch.FloatTensor(alphas)
# Visualize caption and attention of best sequence
plt = utils.visualize_att(img, seq, alphas, rev_word_map, args.smooth)
plt.savefig('result.png')
def main():
print("init data folders")
encoder = {}
decoder = {}
encoder_optim = {}
decoder_optim = {}
encoder_scheduler = {}
decoder_scheduler = {}
for s in ['s1', 's2', 's3', 's4']:
encoder[s] = {}
decoder[s] = {}
encoder_optim[s] = {}
decoder_optim[s] = {}
encoder_scheduler[s] = {}
decoder_scheduler[s] = {}
for lv in ['lv1', 'lv2', 'lv3']:
encoder[s][lv] = models.Encoder()
decoder[s][lv] = models.Decoder()
encoder[s][lv].apply(weight_init).cuda(GPU)
decoder[s][lv].apply(weight_init).cuda(GPU)
encoder_optim[s][lv] = torch.optim.Adam(
encoder[s][lv].parameters(), lr=LEARNING_RATE)
encoder_scheduler[s][lv] = StepLR(encoder_optim[s][lv],
step_size=1000,
gamma=0.1)
decoder_optim[s][lv] = torch.optim.Adam(
decoder[s][lv].parameters(), lr=LEARNING_RATE)
decoder_scheduler[s][lv] = StepLR(decoder_optim[s][lv],
step_size=1000,
gamma=0.1)
if os.path.exists(
str('./checkpoints/' + METHOD + "/encoder_" + s + "_" +
lv + ".pkl")):
encoder[s][lv].load_state_dict(
torch.load(
str('./checkpoints/' + METHOD + "/encoder_" + s + "_" +
lv + ".pkl")))
print("load encoder_" + s + "_" + lv + " successfully!")
if os.path.exists(
str('./checkpoints/' + METHOD + "/decoder_" + s + "_" +
lv + ".pkl")):
decoder[s][lv].load_state_dict(
torch.load(
str('./checkpoints/' + METHOD + "/decoder_" + s + "_" +
lv + ".pkl")))
print("load decoder_" + s + "_" + lv + " successfully!")
if os.path.exists('./checkpoints/' + METHOD) == False:
os.system('mkdir ./checkpoints/' + METHOD)
for epoch in range(args.start_epoch, EPOCHS):
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
encoder_scheduler[s][lv].step(epoch)
decoder_scheduler[s][lv].step(epoch)
print("Training...")
train_dataset = GoProDataset(
blur_image_files='./datas/GoPro/train_blur_file.txt',
sharp_image_files='./datas/GoPro/train_sharp_file.txt',
root_dir='./datas/GoPro/',
crop=True,
crop_size=CROP_SIZE,
rotation=True,
color_augment=True,
transform=transforms.Compose([transforms.ToTensor()]))
train_dataloader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
for iteration, inputs in enumerate(train_dataloader):
mse = nn.MSELoss().cuda(GPU)
images = {}
feature = {}
residual = {}
for s in ['s1', 's2', 's3', 's4']:
feature[s] = {}
residual[s] = {}
images['gt'] = Variable(inputs['sharp_image'] - 0.5).cuda(GPU)
images['lv1'] = Variable(inputs['blur_image'] - 0.5).cuda(GPU)
H = images['lv1'].size(2)
W = images['lv1'].size(3)
images['lv2_1'] = images['lv1'][:, :, 0:int(H / 2), :]
images['lv2_2'] = images['lv1'][:, :, int(H / 2):H, :]
images['lv3_1'] = images['lv2_1'][:, :, :, 0:int(W / 2)]
images['lv3_2'] = images['lv2_1'][:, :, :, int(W / 2):W]
images['lv3_3'] = images['lv2_2'][:, :, :, 0:int(W / 2)]
images['lv3_4'] = images['lv2_2'][:, :, :, int(W / 2):W]
s = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](images['lv3_1'])
feature[s]['lv3_2'] = encoder[s]['lv3'](images['lv3_2'])
feature[s]['lv3_3'] = encoder[s]['lv3'](images['lv3_3'])
feature[s]['lv3_4'] = encoder[s]['lv3'](images['lv3_4'])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']), 3)
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']), 3)
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
images['lv2_1'] +
residual[s]['lv3_top']) + feature[s]['lv3_top']
feature[s]['lv2_2'] = encoder[s]['lv2'](
images['lv2_2'] +
residual[s]['lv3_bot']) + feature[s]['lv3_bot']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
images['lv1'] + residual[s]['lv2']) + feature[s]['lv2']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's2'
ps = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] + residual[s]
['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] + residual[s]
['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](
feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] +
residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's3'
ps = 's2'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] + residual[s]
['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] + residual[s]
['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](
feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] +
residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's4'
ps = 's3'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] + residual[s]
['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] + residual[s]
['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](
feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] +
residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
loss = mse(residual['s4']['lv1'], images['gt']) + mse(
residual['s3']['lv1'], images['gt']) + mse(
residual['s2']['lv1'], images['gt']) + mse(
residual['s1']['lv1'], images['gt'])
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
encoder[s][lv].zero_grad()
decoder[s][lv].zero_grad()
loss.backward()
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
encoder_optim[s][lv].step()
decoder_optim[s][lv].step()
if (iteration + 1) % 10 == 0:
print(METHOD + " epoch:", epoch, "iteration:", iteration + 1,
"loss:", loss.data[0])
if (epoch) % 200 == 0:
if os.path.exists('./checkpoints/' + METHOD + '/epoch' +
str(epoch)) == False:
os.system('mkdir ./checkpoints/' + METHOD + '/epoch' +
str(epoch))
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
torch.save(
encoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/encoder_" + s + "_" + lv + ".pkl"))
torch.save(
decoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/decoder_" + s + "_" + lv + ".pkl"))
print("Testing...")
test_dataset = GoProDataset(
blur_image_files='./datas/GoPro/test_blur_file.txt',
sharp_image_files='./datas/GoPro/test_sharp_file.txt',
root_dir='./datas/GoPro/',
transform=transforms.Compose([transforms.ToTensor()]))
test_dataloader = DataLoader(test_dataset,
batch_size=1,
shuffle=False)
for iteration, inputs in enumerate(test_dataloader):
with torch.no_grad():
images['lv1'] = Variable(inputs['blur_image'] -
0.5).cuda(GPU)
H = images['lv1'].size(2)
W = images['lv1'].size(3)
images['lv2_1'] = images['lv1'][:, :, 0:int(H / 2), :]
images['lv2_2'] = images['lv1'][:, :, int(H / 2):H, :]
images['lv3_1'] = images['lv2_1'][:, :, :, 0:int(W / 2)]
images['lv3_2'] = images['lv2_1'][:, :, :, int(W / 2):W]
images['lv3_3'] = images['lv2_2'][:, :, :, 0:int(W / 2)]
images['lv3_4'] = images['lv2_2'][:, :, :, int(W / 2):W]
s = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](images['lv3_1'])
feature[s]['lv3_2'] = encoder[s]['lv3'](images['lv3_2'])
feature[s]['lv3_3'] = encoder[s]['lv3'](images['lv3_3'])
feature[s]['lv3_4'] = encoder[s]['lv3'](images['lv3_4'])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']), 3)
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']), 3)
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
images['lv2_1'] +
residual[s]['lv3_top']) + feature[s]['lv3_top']
feature[s]['lv2_2'] = encoder[s]['lv2'](
images['lv2_2'] +
residual[s]['lv3_bot']) + feature[s]['lv3_bot']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
images['lv1'] + residual[s]['lv2']) + feature[s]['lv2']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's2'
ps = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] +
residual[s]['lv3_top']
) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] +
residual[s]['lv3_bot']
) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] + residual[s]['lv2']
) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's3'
ps = 's2'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] +
residual[s]['lv3_top']
) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] +
residual[s]['lv3_bot']
) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] + residual[s]['lv2']
) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's4'
ps = 's3'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] +
residual[s]['lv3_top']
) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] +
residual[s]['lv3_bot']
) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] + residual[s]['lv2']
) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
deblurred_image = residual[s]['lv1']
save_deblur_images(deblurred_image.data + 0.5, 4,
iteration, epoch)
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
torch.save(
encoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + "/encoder_" + s + "_" +
lv + ".pkl"))
torch.save(
decoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + "/decoder_" + s + "_" +
lv + ".pkl"))
num_epochs1 = 110
num_epochs2 = 300
num_epochs3 = 150
use_cuda=True if torch.cuda.is_available() else False
device=torch.device('cuda:0') if use_cuda else torch.device('cpu')
#encoder = models.Encoder()
encoder = tmodels.resnet18(pretrained=True)
#encoder = tmodels.inception_v3(pretrained=True)
#encoder.aux_logits=False
encoder.fc = nn.Sequential()
#discriminator = models.DCDPro(input_features=128)
decoder = models.Decoder()
encoder.to(device)
decoder = decoder.to(device)
loss_fn=torch.nn.MSELoss()
# -----------------------------------------------------------------------------
## etapa 1: entrenar g y h
print("||||| Stage 1 |||||")
optimizer=torch.optim.Adam(list(decoder.parameters()), lr = 0.0001)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[100], gamma=0.1)
data_transforms = {
'train': transforms.Compose([
transforms.CenterCrop((img_size, img_size)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
#################################
# initialize tensors
imInputBatch = Variable(
torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize))
imInputMaskBatch = Variable(
torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize))
# need a variable size placeholder to handle variable number of views per fyuse...
# initialize models
encoderInit = nn.DataParallel(models.Encoder(), device_ids=opt.deviceIds)
decoderInit = nn.DataParallel(
models.Decoder(numVertices=642 + 1),
device_ids=opt.deviceIds) # Center to be predicted too
colorInit = nn.DataParallel(models.Color(numVertices=642),
device_ids=opt.deviceIds)
############## ######################
# Send things into GPU
if opt.cuda:
imInputBatch = imInputBatch.cuda(opt.gpuId)
imInputMaskBatch = imInputMaskBatch.cuda(opt.gpuId)
encoderInit = encoderInit.cuda(opt.gpuId)
decoderInit = decoderInit.cuda(opt.gpuId)
colorInit = colorInit.cuda(opt.gpuId)
####################################
def main():
train_chairs = [
'chair_0001', 'chair_0005', 'chair_0101', 'chair_0084', 'chair_0497',
'chair_0724', 'chair_0878'
]
test_chairs = ['chair_0957']
features = []
np.random.seed(0)
torch.manual_seed(0)
logger.info('Loading data...')
train_loader, val_loader, classes = custom_dataset.load_data(args)
# override autodetect if n_classes is given
if args.n_classes > 0:
classes = np.arange(args.n_classes)
model = load_model(classes)
logger.info('Loaded model; params={}'.format(util.count_parameters(model)))
if not args.cpu:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = "cpu"
model.to(device)
cudnn.benchmark = True
logger.info('Running on ' + str(device))
summary_writer = Logger(args.logdir)
# Loss and Optimizer
n_epochs = args.epochs
if args.label_smoothing > 0:
criterion = nn.BCEWithLogitsLoss()
else:
criterion = nn.CrossEntropyLoss()
train_state = init_train_state()
# freeze layers
for l in args.freeze_layers:
for p in getattr(model, l).parameters():
p.requires_grad = False
if args.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=train_state['lr'],
weight_decay=args.weight_decay)
elif args.optimizer == 'nesterov':
optimizer = torch.optim.SGD(model.parameters(),
lr=train_state['lr'],
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
# this is used to warm-start
if args.warm_start_from:
logger.info('Warm-starting from {}'.format(args.warm_start_from))
assert os.path.isfile(args.warm_start_from)
train_state = load_checkpoint(args.warm_start_from, model, optimizer)
logger.info('Params loaded.')
# do not override train_state these when warm staring
train_state = init_train_state()
ckptfile = str(Path(args.logdir) / args.latest_fname)
if os.path.isfile(ckptfile):
logger.info('Loading checkpoint: {}'.format(ckptfile))
train_state = load_checkpoint(ckptfile, model, optimizer)
logger.info('Params loaded.')
else:
logger.info('Checkpoint {} not found; ignoring.'.format(ckptfile))
# Training / Eval loop
epoch_time = [] # store time per epoch
# we save epoch+1 to checkpoints; but for eval we should repeat prev. epoch
if args.skip_train:
train_state['start_epoch'] -= 1
for epoch in range(0, n_epochs):
logger.info('Epoch: [%d/%d]' % (epoch + 1, n_epochs))
start = time.time()
if not args.skip_train:
model.train()
if epoch == n_epochs - 1:
features = train(train_loader,
device,
model,
criterion,
optimizer,
summary_writer,
train_state,
1,
train_chairs,
n_classes=len(classes))
PIK = "descriptors.dat"
with open(PIK, "wb") as f:
pickle.dump(train_desc, f)
else:
train(train_loader,
device,
model,
criterion,
optimizer,
summary_writer,
train_state,
0,
train_chairs,
n_classes=len(classes))
logger.info('Time taken: %.2f sec...' % (time.time() - start))
if epoch == 0:
train_state['steps_epoch'] = train_state['step']
# always eval on last epoch
if not args.skip_eval or epoch == n_epochs + 1:
#print("-------------SAVING MODEL----------------");
#torch.save(model,"saved.pth")
logger.info('\n Starting evaluation...')
model.eval()
eval_shrec = True if epoch == n_epochs - 1 and args.retrieval_dir else False
metrics, inputs = eval(val_loader, device, model, criterion,
eval_shrec, 0, test_chairs, features)
logger.info('\tcombined: %.2f, Acc: %.2f, mAP: %.2f, Loss: %.4f' %
(metrics['combined'], metrics['acc_inst'],
metrics.get('mAP_inst', 0.), metrics['loss']))
# Log epoch to tensorboard
# See log using: tensorboard --logdir='logs' --port=6006
ims = get_summary_ims(inputs)
if not args.nolog:
util.logEpoch(summary_writer, model, epoch + 1, metrics, ims)
else:
metrics = None
# Decaying Learning Rate
if args.lr_decay_mode == 'step':
if (epoch + 1) % args.lr_decay_freq == 0:
train_state['lr'] *= args.lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] = train_state['lr']
# Save model
if not args.skip_train:
logger.info('\tSaving latest model')
util.save_checkpoint(
{
'epoch': epoch + 1,
'step': train_state['step'],
'steps_epoch': train_state['steps_epoch'],
'state_dict': model.state_dict(),
'metrics': metrics,
'optimizer': optimizer.state_dict(),
'lr': train_state['lr'],
}, str(Path(args.logdir) / args.latest_fname))
total_epoch_time = time.time() - start
epoch_time.append(total_epoch_time)
logger.info('Total time for this epoch: {} s'.format(total_epoch_time))
if args.skip_train:
# if evaluating, run it once
break
if time.perf_counter() + np.max(
epoch_time) > start_time + args.exit_after:
logger.info(
'Next epoch will likely exceed alotted time; exiting...')
break
print("Encoder training done")
print("Now training the Decoder")
###############################Decoder ###########################################
decoder = models.Decoder()
print(decoder)
decoder.to(device)
train_state = init_train_state()
crit = nn.MSELoss()
optim = torch.optim.SGD(decoder.parameters(),
lr=train_state['lr'],
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
path = str("/home/smjadhav/Research/emvn/decoder_model/latest.pth.tar")
if os.path.isfile(path):
logger.info('Loading decoder checkpoint: {}'.format(path))
train_state = load_checkpoint(path, decoder, optimizer)
logger.info('Params loaded.')
else:
print("Decoder model not found")
train_size = len(train_loader)
metrics = {}
for epoch in range(0, 50):
print("Epoch ", epoch + 1)
decoder.train()
PIK = "D1.dat"
with open(PIK, "rb") as f:
try:
i = 0
while (True):
data = pickle.load(f)
inputs = torch.from_numpy(data[1]).to(device)
target_img = torch.from_numpy(data[0]).to(device)
outputs = decoder(inputs)
optim.zero_grad()
loss = crit(outputs, target_img)
loss.backward()
optim.step()
if args.lr_decay_mode == 'cos':
# estimate steps_epoch from first epoch (we may have dropped entries)
steps_epoch = (train_state['steps_epoch']
if train_state['steps_epoch'] > 0 else
len(train_loader))
# TODO: there will be a jump here if many entries are dropped
# and we only figure out # of steps after first epoch
if train_state['step'] < steps_epoch:
train_state['lr'] = args.lr * train_state[
'step'] / steps_epoch
else:
nsteps = steps_epoch * args.epochs
train_state['lr'] = (0.5 * args.lr * (1 + np.cos(
train_state['step'] * np.pi / nsteps)))
for param_group in optim.param_groups:
param_group['lr'] = train_state['lr']
if (i + 1) % args.print_freq == 0:
print("\tIter [%d/%d] Loss: %.4f" %
(i + 1, train_size, loss.item()))
if args.max_steps > 0 and i > args.max_steps:
break
i = i + 1
except:
exit
if ((epoch + 1) % 5 == 0):
print("Saving Decoder model")
util.save_checkpoint(
{
'epoch': epoch + 1,
'step': train_state['step'],
'steps_epoch': train_state['steps_epoch'],
'state_dict': decoder.state_dict(),
'metrics': metrics,
'optimizer': optimizer.state_dict(),
'lr': train_state['lr'],
}, path)
PIK = "images.dat"
with open(PIK, "wb") as f:
pickle.dump(outputs, f)