def forward(self, imgs, objs, boxes, masks, obj_to_img, z_rand, attribute, masks_shift, boxes_shift, attribute_est):
crops_input = crop_bbox_batch(imgs, boxes, obj_to_img, self.obj_size)
z_rec, mu, logvar = self.crop_encoder(crops_input, objs)
objs_att = self.attribute_encoder(objs, attribute)
objs_att_est = self.attribute_encoder(objs, attribute_est)
# (n, clstm_dim*2, 8, 8)
h_rec = self.layout_encoder(objs_att_est, masks, obj_to_img, z_rec, objs)
h_rand = self.layout_encoder(objs_att, masks, obj_to_img, z_rand, objs)
h_shift = self.layout_encoder(objs_att, masks_shift, obj_to_img, z_rand, objs)
# global context encoder
h_rec_global = self.global_encoder(h_rec)
h_rand_global = self.global_encoder(h_rand)
h_shift_global = self.global_encoder(h_shift)
img_rec = self.decoder(h_rec, h_rec_global)
img_rand = self.decoder(h_rand, h_rand_global)
img_shift = self.decoder(h_shift, h_shift_global)
crops_rand = crop_bbox_batch(img_rand, boxes, obj_to_img, self.obj_size)
_, z_rand_rec, _ = self.crop_encoder(crops_rand, objs)
crops_input_rec = crop_bbox_batch(img_rec, boxes, obj_to_img, self.obj_size)
crops_shift = crop_bbox_batch(img_shift, boxes_shift, obj_to_img, self.obj_size)
_, z_rand_shift, _ = self.crop_encoder(crops_shift, objs)
return crops_input, crops_input_rec, crops_rand, crops_shift, img_rec, img_rand, img_shift, mu, logvar, z_rand_rec, z_rand_shift
def forward(self, imgs, objs, boxes, masks, obj_to_img, z_rand):
crops_input = crop_bbox_batch(imgs, boxes, obj_to_img, self.obj_size)
z_rec, mu, logvar = self.crop_encoder(crops_input, objs)
# (n, clstm_dim*2, 8, 8)
h_rec = self.layout_encoder(objs, masks, obj_to_img, z_rec)
h_rand = self.layout_encoder(objs, masks, obj_to_img, z_rand)
img_rec = self.decoder(h_rec)
img_rand = self.decoder(h_rand)
crops_rand = crop_bbox_batch(img_rand, boxes, obj_to_img,
self.obj_size)
_, z_rand_rec, _ = self.crop_encoder(crops_rand, objs)
crops_input_rec = crop_bbox_batch(img_rec, boxes, obj_to_img,
self.obj_size)
return crops_input, crops_input_rec, crops_rand, img_rec, img_rand, mu, logvar, z_rand_rec
def test_model(model, dataloaders, criterion, input_size=224):
since = time.time()
val_acc_history = []
phase = 'val'
model.eval()
running_loss = 0.0
running_corrects = 0
running_count = 0
# Iterate over data.
for i, batch in enumerate(dataloaders[phase]):
imgs, objs, boxes, masks, obj_to_img, attributes = batch
inputs = crop_bbox_batch(imgs, boxes, obj_to_img, input_size)
inputs = inputs.to(device)
labels = objs.to(device)
# forward
# track history if only in train
with torch.no_grad():
outputs = model(inputs)
loss = criterion(outputs, labels)
_, preds = torch.max(outputs, 1)
# statistics
running_loss += loss.item() * labels.size(0)
running_corrects += torch.sum(preds == labels.data)
running_count += labels.size(0)
if (i + 1) % 20 == 0:
print('loss: {:.4f} accu: {:.4f}'.format(loss.item(), torch.mean((preds == labels.data).float())))
epoch_loss = running_loss / running_count
epoch_acc = running_corrects.double() / running_count
print('================================================================')
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
def main(config):
matrix = torch.load("matrix_obj_vs_att.pt")
cudnn.benchmark = True
device = torch.device('cuda:1')
log_save_dir, model_save_dir, sample_save_dir, result_save_dir = prepare_dir(
config.exp_name)
attribute_nums = 106
data_loader, _ = get_dataloader_vg(batch_size=config.batch_size,
attribute_embedding=attribute_nums,
image_size=config.image_size)
vocab_num = data_loader.dataset.num_objects
if config.clstm_layers == 0:
netG = Generator_nolstm(num_embeddings=vocab_num,
embedding_dim=config.embedding_dim,
z_dim=config.z_dim).to(device)
else:
netG = Generator(num_embeddings=vocab_num,
obj_att_dim=config.embedding_dim,
z_dim=config.z_dim,
clstm_layers=config.clstm_layers,
obj_size=config.object_size,
attribute_dim=attribute_nums).to(device)
netD_image = ImageDiscriminator(conv_dim=config.embedding_dim).to(device)
netD_object = ObjectDiscriminator(n_class=vocab_num).to(device)
netD_att = AttributeDiscriminator(n_attribute=attribute_nums).to(device)
netD_image = add_sn(netD_image)
netD_object = add_sn(netD_object)
netD_att = add_sn(netD_att)
netG_optimizer = torch.optim.Adam(netG.parameters(), config.learning_rate,
[0.5, 0.999])
netD_image_optimizer = torch.optim.Adam(netD_image.parameters(),
config.learning_rate, [0.5, 0.999])
netD_object_optimizer = torch.optim.Adam(netD_object.parameters(),
config.learning_rate,
[0.5, 0.999])
netD_att_optimizer = torch.optim.Adam(netD_att.parameters(),
config.learning_rate, [0.5, 0.999])
start_iter_ = load_model(netD_object,
model_dir=model_save_dir,
appendix='netD_object',
iter=config.resume_iter)
start_iter_ = load_model(netD_att,
model_dir=model_save_dir,
appendix='netD_attribute',
iter=config.resume_iter)
start_iter_ = load_model(netD_image,
model_dir=model_save_dir,
appendix='netD_image',
iter=config.resume_iter)
start_iter = load_model(netG,
model_dir=model_save_dir,
appendix='netG',
iter=config.resume_iter)
data_iter = iter(data_loader)
if start_iter < config.niter:
if config.use_tensorboard: writer = SummaryWriter(log_save_dir)
for i in range(start_iter, config.niter):
# =================================================================================== #
# 1. Preprocess input data #
# =================================================================================== #
try:
batch = next(data_iter)
except:
data_iter = iter(data_loader)
batch = next(data_iter)
imgs, objs, boxes, masks, obj_to_img, attribute, masks_shift, boxes_shift = batch
z = torch.randn(objs.size(0), config.z_dim)
att_idx = attribute.sum(dim=1).nonzero().squeeze()
# print("Train D")
# =================================================================================== #
# 2. Train the discriminator #
# =================================================================================== #
imgs, objs, boxes, masks, obj_to_img, z, attribute, masks_shift, boxes_shift \
= imgs.to(device), objs.to(device), boxes.to(device), masks.to(device), obj_to_img, z.to(
device), attribute.to(device), masks_shift.to(device), boxes_shift.to(device)
attribute_GT = attribute.clone()
# estimate attributes
attribute_est = attribute.clone()
att_mask = torch.zeros(attribute.shape[0])
att_mask = att_mask.scatter(0, att_idx, 1).to(device)
crops_input = crop_bbox_batch(imgs, boxes, obj_to_img,
config.object_size)
estimated_att = netD_att(crops_input)
max_idx = estimated_att.argmax(1)
max_idx = max_idx.float() * (~att_mask.byte()).float().to(device)
for row in range(attribute.shape[0]):
if row not in att_idx:
attribute_est[row, int(max_idx[row])] = 1
# change GT attribute:
num_img_to_change = math.floor(imgs.shape[0] / 3)
for img_idx in range(num_img_to_change):
obj_indices = torch.nonzero(obj_to_img == img_idx).view(-1)
num_objs_to_change = math.floor(len(obj_indices) / 2)
for changed, obj_idx in enumerate(obj_indices):
if changed >= num_objs_to_change:
break
obj = objs[obj_idx]
# change GT attribute
old_attributes = torch.nonzero(
attribute_GT[obj_idx]).view(-1)
new_attribute = random.choices(range(106),
matrix[obj].scatter(
0, old_attributes.cpu(),
0),
k=random.randrange(1, 3))
attribute[obj_idx] = 0 # remove all attributes for obj
attribute[obj_idx] = attribute[obj_idx].scatter(
0,
torch.LongTensor(new_attribute).to(device),
1) # assign new attribute
# change estimated attributes
attribute_est[obj_idx] = 0 # remove all attributes for obj
attribute_est[obj_idx] = attribute[obj_idx].scatter(
0,
torch.LongTensor(new_attribute).to(device), 1)
# Generate fake image
output = netG(imgs, objs, boxes, masks, obj_to_img, z, attribute,
masks_shift, boxes_shift, attribute_est)
crops_input, crops_input_rec, crops_rand, crops_shift, img_rec, img_rand, img_shift, mu, logvar, z_rand_rec, z_rand_shift = output
# Compute image adv loss with fake images.
out_logits = netD_image(img_rec.detach())
d_image_adv_loss_fake_rec = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 0))
out_logits = netD_image(img_rand.detach())
d_image_adv_loss_fake_rand = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 0))
# shift image adv loss
out_logits = netD_image(img_shift.detach())
d_image_adv_loss_fake_shift = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 0))
d_image_adv_loss_fake = 0.4 * d_image_adv_loss_fake_rec + 0.4 * d_image_adv_loss_fake_rand + 0.2 * d_image_adv_loss_fake_shift
# Compute image src loss with real images rec.
out_logits = netD_image(imgs)
d_image_adv_loss_real = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 1))
# Compute object sn adv loss with fake rec crops
out_logits, _ = netD_object(crops_input_rec.detach(), objs)
g_object_adv_loss_rec = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 0))
# Compute object sn adv loss with fake rand crops
out_logits, _ = netD_object(crops_rand.detach(), objs)
d_object_adv_loss_fake_rand = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 0))
# shift obj adv loss
out_logits, _ = netD_object(crops_shift.detach(), objs)
d_object_adv_loss_fake_shift = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 0))
d_object_adv_loss_fake = 0.4 * g_object_adv_loss_rec + 0.4 * d_object_adv_loss_fake_rand + 0.2 * d_object_adv_loss_fake_shift
# Compute object sn adv loss with real crops.
out_logits_src, out_logits_cls = netD_object(
crops_input.detach(), objs)
d_object_adv_loss_real = F.binary_cross_entropy_with_logits(
out_logits_src, torch.full_like(out_logits_src, 1))
# cls
d_object_cls_loss_real = F.cross_entropy(out_logits_cls, objs)
# attribute
att_cls = netD_att(crops_input.detach())
att_idx = attribute_GT.sum(dim=1).nonzero().squeeze()
att_cls_annotated = torch.index_select(att_cls, 0, att_idx)
attribute_annotated = torch.index_select(attribute_GT, 0, att_idx)
d_object_att_cls_loss_real = F.binary_cross_entropy_with_logits(
att_cls_annotated,
attribute_annotated,
pos_weight=pos_weight.to(device))
# Backward and optimize.
d_loss = 0
d_loss += config.lambda_img_adv * (d_image_adv_loss_fake +
d_image_adv_loss_real)
d_loss += config.lambda_obj_adv * (d_object_adv_loss_fake +
d_object_adv_loss_real)
d_loss += config.lambda_obj_cls * d_object_cls_loss_real
d_loss += config.lambda_att_cls * d_object_att_cls_loss_real
netD_image.zero_grad()
netD_object.zero_grad()
netD_att.zero_grad()
d_loss.backward()
netD_image_optimizer.step()
netD_object_optimizer.step()
netD_att_optimizer.step()
# Logging.
loss = {}
loss['D/loss'] = d_loss.item()
loss['D/image_adv_loss_real'] = d_image_adv_loss_real.item()
loss['D/image_adv_loss_fake'] = d_image_adv_loss_fake.item()
loss['D/object_adv_loss_real'] = d_object_adv_loss_real.item()
loss['D/object_adv_loss_fake'] = d_object_adv_loss_fake.item()
loss['D/object_cls_loss_real'] = d_object_cls_loss_real.item()
loss['D/object_att_cls_loss'] = d_object_att_cls_loss_real.item()
# print("train G")
# =================================================================================== #
# 3. Train the generator #
# =================================================================================== #
# Generate fake image
output = netG(imgs, objs, boxes, masks, obj_to_img, z, attribute,
masks_shift, boxes_shift, attribute_est)
crops_input, crops_input_rec, crops_rand, crops_shift, img_rec, img_rand, img_shift, mu, logvar, z_rand_rec, z_rand_shift = output
# reconstruction loss of ae and img
rec_img_mask = torch.ones(imgs.shape[0]).scatter(
0, torch.LongTensor(range(num_img_to_change)), 0).to(device)
g_img_rec_loss = rec_img_mask * torch.abs(img_rec - imgs).view(
imgs.shape[0], -1).mean(1)
g_img_rec_loss = g_img_rec_loss.sum() / (imgs.shape[0] -
num_img_to_change)
g_z_rec_loss_rand = torch.abs(z_rand_rec - z).mean()
g_z_rec_loss_shift = torch.abs(z_rand_shift - z).mean()
g_z_rec_loss = 0.5 * g_z_rec_loss_rand + 0.5 * g_z_rec_loss_shift
# kl loss
kl_element = mu.pow(2).add_(
logvar.exp()).mul_(-1).add_(1).add_(logvar)
g_kl_loss = torch.sum(kl_element).mul_(-0.5)
# Compute image adv loss with fake images.
out_logits = netD_image(img_rec)
g_image_adv_loss_fake_rec = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 1))
out_logits = netD_image(img_rand)
g_image_adv_loss_fake_rand = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 1))
# shift image adv loss
out_logits = netD_image(img_shift)
g_image_adv_loss_fake_shift = F.binary_cross_entropy_with_logits(
out_logits, torch.full_like(out_logits, 1))
g_image_adv_loss_fake = 0.4 * g_image_adv_loss_fake_rec + 0.4 * g_image_adv_loss_fake_rand + 0.2 * g_image_adv_loss_fake_shift
# Compute object adv loss with fake images.
out_logits_src, out_logits_cls = netD_object(crops_input_rec, objs)
g_object_adv_loss_rec = F.binary_cross_entropy_with_logits(
out_logits_src, torch.full_like(out_logits_src, 1))
g_object_cls_loss_rec = F.cross_entropy(out_logits_cls, objs)
# attribute
att_cls = netD_att(crops_input_rec)
att_idx = attribute.sum(dim=1).nonzero().squeeze()
attribute_annotated = torch.index_select(attribute, 0, att_idx)
att_cls_annotated = torch.index_select(att_cls, 0, att_idx)
g_object_att_cls_loss_rec = F.binary_cross_entropy_with_logits(
att_cls_annotated,
attribute_annotated,
pos_weight=pos_weight.to(device))
out_logits_src, out_logits_cls = netD_object(crops_rand, objs)
g_object_adv_loss_rand = F.binary_cross_entropy_with_logits(
out_logits_src, torch.full_like(out_logits_src, 1))
g_object_cls_loss_rand = F.cross_entropy(out_logits_cls, objs)
# attribute
att_cls = netD_att(crops_rand)
att_cls_annotated = torch.index_select(att_cls, 0, att_idx)
g_object_att_cls_loss_rand = F.binary_cross_entropy_with_logits(
att_cls_annotated,
attribute_annotated,
pos_weight=pos_weight.to(device))
# shift adv obj loss
out_logits_src, out_logits_cls = netD_object(crops_shift, objs)
g_object_adv_loss_shift = F.binary_cross_entropy_with_logits(
out_logits_src, torch.full_like(out_logits_src, 1))
g_object_cls_loss_shift = F.cross_entropy(out_logits_cls, objs)
# attribute
att_cls = netD_att(crops_shift)
att_cls_annotated = torch.index_select(att_cls, 0, att_idx)
g_object_att_cls_loss_shift = F.binary_cross_entropy_with_logits(
att_cls_annotated,
attribute_annotated,
pos_weight=pos_weight.to(device))
g_object_att_cls_loss = 0.4 * g_object_att_cls_loss_rec + 0.4 * g_object_att_cls_loss_rand + 0.2 * g_object_att_cls_loss_shift
g_object_adv_loss = 0.4 * g_object_adv_loss_rec + 0.4 * g_object_adv_loss_rand + 0.2 * g_object_adv_loss_shift
g_object_cls_loss = 0.4 * g_object_cls_loss_rec + 0.4 * g_object_cls_loss_rand + 0.2 * g_object_cls_loss_shift
# Backward and optimize.
g_loss = 0
g_loss += config.lambda_img_rec * g_img_rec_loss
g_loss += config.lambda_z_rec * g_z_rec_loss
g_loss += config.lambda_img_adv * g_image_adv_loss_fake
g_loss += config.lambda_obj_adv * g_object_adv_loss
g_loss += config.lambda_obj_cls * g_object_cls_loss
g_loss += config.lambda_att_cls * g_object_att_cls_loss
g_loss += config.lambda_kl * g_kl_loss
netG.zero_grad()
g_loss.backward()
netG_optimizer.step()
loss['G/loss'] = g_loss.item()
loss['G/image_adv_loss'] = g_image_adv_loss_fake.item()
loss['G/object_adv_loss'] = g_object_adv_loss.item()
loss['G/object_cls_loss'] = g_object_cls_loss.item()
loss['G/rec_img'] = g_img_rec_loss.item()
loss['G/rec_z'] = g_z_rec_loss.item()
loss['G/kl'] = g_kl_loss.item()
loss['G/object_att_cls_loss'] = g_object_att_cls_loss.item()
# =================================================================================== #
# 4. Log #
# =================================================================================== #
if (i + 1) % config.log_step == 0:
log = 'iter [{:06d}/{:06d}]'.format(i + 1, config.niter)
for tag, roi_value in loss.items():
log += ", {}: {:.4f}".format(tag, roi_value)
print(log)
if (i + 1
) % config.tensorboard_step == 0 and config.use_tensorboard:
for tag, roi_value in loss.items():
writer.add_scalar(tag, roi_value, i + 1)
writer.add_images(
'Result/crop_real',
imagenet_deprocess_batch(crops_input).float() / 255, i + 1)
writer.add_images(
'Result/crop_real_rec',
imagenet_deprocess_batch(crops_input_rec).float() / 255,
i + 1)
writer.add_images(
'Result/crop_rand',
imagenet_deprocess_batch(crops_rand).float() / 255, i + 1)
writer.add_images('Result/img_real',
imagenet_deprocess_batch(imgs).float() / 255,
i + 1)
writer.add_images(
'Result/img_real_rec',
imagenet_deprocess_batch(img_rec).float() / 255, i + 1)
writer.add_images(
'Result/img_fake_rand',
imagenet_deprocess_batch(img_rand).float() / 255, i + 1)
if (i + 1) % config.save_step == 0:
# netG_noDP.load_state_dict(new_state_dict)
save_model(netG,
model_dir=model_save_dir,
appendix='netG',
iter=i + 1,
save_num=2,
save_step=config.save_step)
save_model(netD_image,
model_dir=model_save_dir,
appendix='netD_image',
iter=i + 1,
save_num=2,
save_step=config.save_step)
save_model(netD_object,
model_dir=model_save_dir,
appendix='netD_object',
iter=i + 1,
save_num=2,
save_step=config.save_step)
save_model(netD_att,
model_dir=model_save_dir,
appendix='netD_attribute',
iter=i + 1,
save_num=2,
save_step=config.save_step)
if config.use_tensorboard: writer.close()
def test_model(model, pickle_files, criterion, input_size=224):
since = time.time()
val_acc_history = []
phase = 'val'
model.eval()
running_loss_real = 0.0
running_corrects_real = 0
running_count_real = 0
running_loss_fake = 0.0
running_corrects_fake = 0
running_count_fake = 0
running_loss_fake_shift = 0.0
running_corrects_fake_shift = 0
running_count_fake_shift = 0
# Iterate over data.
for i, pickle_file in enumerate(pickle_files):
batch = pickle.load(open(pickle_file, 'rb'))
imgs, imgs_rand, imgs_shift, objs, boxes, boxes_shift, obj_to_img = batch[
'imgs'], batch['imgs_rand'], batch['imgs_shift'], batch[
'objs'], batch['boxes'], batch['boxes_shift'], batch[
'obj_to_img']
inputs_real = crop_bbox_batch(imgs, boxes, obj_to_img, input_size)
inputs_real = inputs_real.to(device)
inputs_fake = crop_bbox_batch(imgs_rand, boxes, obj_to_img, input_size)
inputs_fake = inputs_fake.to(device)
inputs_fake_shift = crop_bbox_batch(imgs_shift, boxes_shift,
obj_to_img, input_size)
inputs_fake_shift = inputs_fake_shift.to(device)
labels = objs.to(device)
# forward
# track history if only in train
with torch.no_grad():
outputs_real = model(inputs_real)
loss_real = criterion(outputs_real, labels)
_, preds_real = torch.max(outputs_real, 1)
outputs_fake = model(inputs_fake)
loss_fake = criterion(outputs_fake, labels)
_, preds_fake = torch.max(outputs_fake, 1)
outputs_fake_shift = model(inputs_fake_shift)
loss_fake_shift = criterion(outputs_fake_shift, labels)
_, preds_fake_shift = torch.max(outputs_fake_shift, 1)
# statistics
running_loss_real += loss_real.item() * labels.size(0)
running_corrects_real += torch.sum(preds_real == labels.data)
running_count_real += labels.size(0)
running_loss_fake += loss_fake.item() * labels.size(0)
running_corrects_fake += torch.sum(preds_fake == labels.data)
running_count_fake += labels.size(0)
running_loss_fake_shift += loss_fake_shift.item() * labels.size(0)
running_corrects_fake_shift += torch.sum(
preds_fake_shift == labels.data)
running_count_fake_shift += labels.size(0)
if (i + 1) % 20 == 0:
print(
'real loss: {:.4f} accu: {:.4f} fake loss: {:.4f} accu: {:.4f} shift loss: {:.4f} accu: {:.4f}'
.format(loss_real.item(),
torch.mean((preds_real == labels.data).float()),
loss_fake.item(),
torch.mean((preds_fake == labels.data).float()),
loss_fake_shift.item(),
torch.mean((preds_fake_shift == labels.data).float())))
epoch_loss_real = running_loss_real / running_count_real
epoch_acc_real = running_corrects_real.double() / running_count_real
epoch_loss_fake = running_loss_fake / running_count_fake
epoch_acc_fake = running_corrects_fake.double() / running_count_fake
epoch_loss_fake_shift = running_loss_fake_shift / running_count_fake_shift
epoch_acc_fake_shift = running_corrects_fake_shift.double(
) / running_count_fake_shift
print('================================================================')
print(
'{} Real Loss: {:.4f} Acc: {:.4f} Fake Loss: {:.4f} Acc: {:.4f} shift loss: {:.4f} accu: {:.4f}'
.format(phase, epoch_loss_real, epoch_acc_real, epoch_loss_fake,
epoch_acc_fake, epoch_loss_fake_shift, epoch_acc_fake_shift))
def main(config):
cudnn.benchmark = True
device = torch.device('cuda:1')
log_save_dir, model_save_dir, sample_save_dir, result_save_dir = prepare_dir(
config.exp_name)
with open("data/vocab.json", 'r') as f:
vocab = json.load(f)
att_idx_to_name = np.array(vocab['attribute_idx_to_name'])
print(att_idx_to_name)
object_idx_to_name = np.array(vocab['object_idx_to_name'])
attribute_nums = 106
train_data_loader, val_data_loader = get_dataloader_vg(
batch_size=config.batch_size, attribute_embedding=attribute_nums)
vocab_num = train_data_loader.dataset.num_objects
netG = Generator(num_embeddings=vocab_num,
obj_att_dim=config.embedding_dim,
z_dim=config.z_dim,
clstm_layers=config.clstm_layers,
obj_size=config.object_size,
attribute_dim=attribute_nums).to(device)
netD_att = AttributeDiscriminator(n_attribute=attribute_nums).to(device)
netD_att = add_sn(netD_att)
start_iter_ = load_model(netD_att,
model_dir="~/models/trained_models",
appendix='netD_attribute',
iter=config.resume_iter)
_ = load_model(netG,
model_dir=model_save_dir,
appendix='netG',
iter=config.resume_iter)
data_loader = val_data_loader
data_iter = iter(data_loader)
cur_obj_start_idx = 0
attributes_pred = np.zeros((253468, attribute_nums))
attributes_gt = None
with torch.no_grad():
netG.eval()
for i, batch in enumerate(data_iter):
print('batch {}'.format(i))
imgs, objs, boxes, masks, obj_to_img, attribute, masks_shift, boxes_shift = batch
z = torch.randn(objs.size(0), config.z_dim)
att_idx = attribute.sum(dim=1).nonzero().squeeze()
imgs, objs, boxes, masks, obj_to_img, z, attribute, masks_shift, boxes_shift = \
imgs.to(device), objs.to(device), boxes.to(device), masks.to(device), \
obj_to_img, z.to(device), attribute.to(device), masks_shift.to(device), boxes_shift.to(device)
# estimate attributes
attribute_est = attribute.clone()
att_mask = torch.zeros(attribute.shape[0])
att_mask = att_mask.scatter(0, att_idx, 1).to(device)
crops_input = crop_bbox_batch(imgs, boxes, obj_to_img,
config.object_size)
estimated_att = netD_att(crops_input)
max_idx = estimated_att.argmax(1)
max_idx = max_idx.float() * (~att_mask.byte()).float().to(device)
for row in range(attribute.shape[0]):
if row not in att_idx:
attribute_est[row, int(max_idx[row])] = 1
# Generate fake image
output = netG(imgs, objs, boxes, masks, obj_to_img, z, attribute,
masks_shift, boxes_shift, attribute_est)
crops_input, crops_input_rec, crops_rand, crops_shift, img_rec, img_rand, img_shift, mu, logvar, z_rand_rec, z_rand_shift = output
# predict attribute on generated
crops_rand_yes = torch.index_select(crops_rand, 0,
att_idx.to(device))
attribute_yes = torch.index_select(attribute, 0,
att_idx.to(device))
estimated_att_rand = netD_att(crops_rand_yes)
att_cls = torch.sigmoid(estimated_att_rand)
for k in att_idx:
ll = att_idx.cpu().numpy().tolist().index(k)
non0_idx_cls = (att_cls[ll] > 0.9).nonzero()
pred_idx = non0_idx_cls.squeeze().cpu().numpy()
attributes_pred[cur_obj_start_idx, pred_idx] = 1
cur_obj_start_idx += 1
# construct GT array
attributes_gt = attribute_yes.clone().cpu(
) if attributes_gt is None else np.vstack(
[attributes_gt, attribute_yes.clone().cpu()])
img_rand = imagenet_deprocess_batch(img_rand)
img_shift = imagenet_deprocess_batch(img_shift)
img_rec = imagenet_deprocess_batch(img_rec)
# attribute modification
changed_list = []
src = 2 # 94 blue, 95 black
tgt = 95 # 8 red, 2 white
for idx, o in enumerate(objs):
attribute[idx, [2, 8, 0, 94, 90, 95, 96, 34, 25, 70, 58, 104
]] = 0 # remove other color
attribute[idx, tgt] = 1
attribute_est[idx,
[2, 8, 0, 94, 90, 95, 96, 34, 25, 70, 58, 104
]] = 0 # remove other color
attribute_est[idx, tgt] = 1
changed_list.append(idx)
# Generate red image
z = torch.randn(objs.size(0), config.z_dim).to(device)
output = netG(imgs, objs, boxes, masks, obj_to_img, z, attribute,
masks_shift, boxes_shift, attribute_est)
crops_input, crops_input_rec, crops_rand_y, crops_shift_y, img_rec_y, img_rand_y, img_shift_y, mu, logvar, z_rand_rec, z_rand_shift = output
img_rand_y = imagenet_deprocess_batch(img_rand_y)
img_shift_y = imagenet_deprocess_batch(img_shift_y)
img_rec_y = imagenet_deprocess_batch(img_rec_y)
imgs = imagenet_deprocess_batch(imgs)
# 2 top k
estimated_att = netD_att(crops_rand)
max_idx = estimated_att.topk(5)[1]
changed_list = [i for i in changed_list if tgt not in max_idx[i]]
estimated_att_y = netD_att(crops_rand_y)
max_idx_y = estimated_att_y.topk(3)[1]
changed_list_success = [
i for i in changed_list if tgt in max_idx_y[i]
]
for j in range(imgs.shape[0]):
img_np = img_shift[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir,
'img{:06d}_shift.png'.format(i * config.batch_size + j))
imwrite(img_path, img_np)
img_np = img_rand[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir,
'img{:06d}_rand.png'.format(i * config.batch_size + j))
imwrite(img_path, img_np)
img_rec_np = img_rec[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir,
'img{:06d}_rec.png'.format(i * config.batch_size + j))
imwrite(img_path, img_rec_np)
img_real_np = imgs[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir,
'img{:06d}_real.png'.format(i * config.batch_size + j))
imwrite(img_path, img_real_np)
cur_obj_success = [
int(objs[c]) for c in changed_list_success
if obj_to_img[c] == j
]
# save successfully modified images
if len(cur_obj_success) > 0:
img_shift_y = img_shift_y[j].numpy().transpose(1, 2, 0)
img_path_red = os.path.join(
result_save_dir,
'img{:06d}_shift_{}_modified.png'.format(
i * config.batch_size + j,
object_idx_to_name[cur_obj_success]))
imwrite(img_path_red, img_shift_y)
img_rec_np = img_rec_y[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir, 'img{:06d}_rec_modified.png'.format(
i * config.batch_size + j,
object_idx_to_name[cur_obj_success]))
imwrite(img_path, img_rec_np)
img_rand_np = img_rand_y[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir, 'img{:06d}_rand_modified.png'.format(
i * config.batch_size + j,
object_idx_to_name[cur_obj_success]))
imwrite(img_path, img_rand_np)
# calculate recall precision
num_data = attributes_gt.shape[0]
count = np.zeros((num_data, 4))
recall, precision = np.zeros((num_data)), np.zeros((num_data))
for i in range(num_data):
# tn, fp, fn, tp = confusion_matrix(attributes_pred, attributes_gt).ravel()
count[i] = confusion_matrix(attributes_gt[i],
attributes_pred[i]).ravel()
recall[i] = count[i][3] / (count[i][3] + count[i][2])
precision[i] = safe_division(count[i][3],
count[i][3] + count[i][1])
print("average precision = {}".format(precision.mean()))
print("average recall = {}".format(recall.mean()))
print("average pred # per obj")
print((count[:, 1].sum() + count[:, 3].sum()) / count.shape[0])
print("average GT # per obj")
print((count[:, 2].sum() + count[:, 3].sum()) / count.shape[0])
print("% of data that predict something")
print(((count[:, 1] + count[:, 3]) > 0).sum() / count.shape[0])
print("% of data at least predicted correct once")
print((count[:, 3] > 0).sum() / count.shape[0])
def main(config):
cudnn.benchmark = True
device = torch.device('cuda:0')
log_save_dir, model_save_dir, sample_save_dir, result_save_dir = prepare_dir(
config.exp_name)
resinet_dir = "~/pickle/128_vg_pkl_resinet50_247k"
if not os.path.exists(resinet_dir): os.mkdir(resinet_dir)
attribute_nums = 106
if config.dataset == 'vg':
train_data_loader, val_data_loader = get_dataloader_vg(
batch_size=config.batch_size, attribute_embedding=attribute_nums)
elif config.dataset == 'coco':
train_data_loader, val_data_loader = get_dataloader_coco(
batch_size=config.batch_size)
vocab_num = train_data_loader.dataset.num_objects
if config.clstm_layers == 0:
netG = Generator_nolstm(num_embeddings=vocab_num,
embedding_dim=config.embedding_dim,
z_dim=config.z_dim).to(device)
else:
netG = Generator(num_embeddings=vocab_num,
obj_att_dim=config.embedding_dim,
z_dim=config.z_dim,
clstm_layers=config.clstm_layers,
obj_size=config.object_size,
attribute_dim=attribute_nums).to(device)
_ = load_model(netG,
model_dir=model_save_dir,
appendix='netG',
iter=config.resume_iter)
netD_att = train_att_change.AttributeDiscriminator(
n_attribute=attribute_nums).to(device)
netD_att = train_att_change.add_sn(netD_att)
start_iter_ = load_model(netD_att,
model_dir="~/models/trained_models",
appendix='netD_attribute',
iter=config.resume_iter)
_ = load_model(netG,
model_dir=model_save_dir,
appendix='netG',
iter=config.resume_iter)
data_loader = val_data_loader
data_iter = iter(data_loader)
with torch.no_grad():
netG.eval()
for i, batch in enumerate(data_iter):
print('batch {}'.format(i))
imgs, objs, boxes, masks, obj_to_img, attribute, masks_shift, boxes_shift = batch
att_idx = attribute.sum(dim=1).nonzero().squeeze()
z = torch.randn(objs.size(0), config.z_dim)
imgs, objs, boxes, masks, obj_to_img, z, attribute, masks_shift, boxes_shift = \
imgs.to(device), objs.to(device), boxes.to(device), masks.to(device), \
obj_to_img, z.to(device), attribute.to(device), masks_shift.to(device), boxes_shift.to(device)
# estimate attributes
attribute_est = attribute.clone()
att_mask = torch.zeros(attribute.shape[0])
att_mask = att_mask.scatter(0, att_idx, 1).to(device)
crops_input = crop_bbox_batch(imgs, boxes, obj_to_img,
config.object_size)
estimated_att = netD_att(crops_input)
max_idx = estimated_att.argmax(1)
max_idx = max_idx.float() * (~att_mask.byte()).float().to(device)
for row in range(attribute.shape[0]):
if row not in att_idx:
attribute_est[row, int(max_idx[row])] = 1
# Generate fake image
output = netG(imgs, objs, boxes, masks, obj_to_img, z, attribute,
masks_shift, boxes_shift, attribute_est)
crops_input, crops_input_rec, crops_rand, crops_shift, img_rec, img_rand, img_shift, mu, logvar, z_rand_rec, z_rand_shift = output
dict_to_save = {
'imgs': imgs,
'imgs_rand': img_rand,
'imgs_shift': img_shift,
'objs': objs,
'boxes': boxes,
'boxes_shift': boxes_shift,
'obj_to_img': obj_to_img
}
out_name = os.path.join(resinet_dir, 'batch_{}.pkl'.format(i))
pickle.dump(dict_to_save, open(out_name, 'wb'))
img_rand = imagenet_deprocess_batch(img_rand)
img_shift = imagenet_deprocess_batch(img_shift)
# Save the generated images
for j in range(img_rand.shape[0]):
# layout = draw_layout(boxes[obj_to_img==j], objs[obj_to_img==j], True, dset_mode='vg')
# img_path = os.path.join(result_save_dir, 'layout/img{:06d}_layout.png'.format(i*config.batch_size+j))
# imwrite(img_path, layout)
img_np = img_rand[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir,
'img{:06d}.png'.format(i * config.batch_size + j))
imwrite(img_path, img_np)
# layout = draw_layout(boxes_shift[obj_to_img==j], objs[obj_to_img==j], True, dset_mode='vg')
# img_path = os.path.join(result_save_dir, 'layout/img{:06d}_layouts.png'.format(i*config.batch_size+j))
# imwrite(img_path, layout)
img_np = img_shift[j].numpy().transpose(1, 2, 0)
img_path = os.path.join(
result_save_dir,
'img{:06d}s.png'.format(i * config.batch_size + j))
imwrite(img_path, img_np)
writer = SummaryWriter(log_save_dir)
for i in range(start_iter, niter):
try:
batch = next(data_iter)
except:
data_iter = iter(data_loader)
batch = next(data_iter)
imgs, objs, boxes, masks, obj_to_img, attribute, masks_shift, boxes_shift = batch
imgs, objs, boxes, masks, obj_to_img, attribute, masks_shift, boxes_shift = \
imgs.to(device), objs.to(device), boxes.to(device), masks.to(device), \
obj_to_img, attribute.to(device), masks_shift.to(device), boxes_shift.to(device)
crops_input = crop_bbox_batch(imgs, boxes, obj_to_img, 64)
att_cls = netD_att(crops_input.detach())
att_idx = attribute.sum(dim=1).nonzero().squeeze()
att_cls_annotated = torch.index_select(att_cls, 0, att_idx)
attribute_annotated = torch.index_select(attribute, 0, att_idx)
d_object_att_cls_loss_real = F.binary_cross_entropy_with_logits(
att_cls_annotated,
attribute_annotated,
pos_weight=pos_weight.to(device))
d_loss = 0
d_loss += d_object_att_cls_loss_real
netD_att.zero_grad()
def main(config):
cudnn.benchmark = True
device = torch.device('cuda:0')
log_save_dir, model_save_dir, sample_save_dir, result_save_dir1, result_save_dir2 = prepare_dir(
config.exp_name)
resinet_dir = "~/pickle/vg_pkl_resinet50"
if not os.path.exists(resinet_dir): os.mkdir(resinet_dir)
attribute_nums = 106
if config.dataset == 'vg':
train_data_loader, val_data_loader = get_dataloader_vg(
batch_size=config.batch_size, attribute_embedding=attribute_nums)
elif config.dataset == 'coco':
train_data_loader, val_data_loader = get_dataloader_coco(
batch_size=config.batch_size)
vocab_num = train_data_loader.dataset.num_objects
if config.clstm_layers == 0:
netG = Generator_nolstm(num_embeddings=vocab_num,
embedding_dim=config.embedding_dim,
z_dim=config.z_dim).to(device)
else:
netG = Generator(num_embeddings=vocab_num,
obj_att_dim=config.embedding_dim,
z_dim=config.z_dim,
clstm_layers=config.clstm_layers,
obj_size=config.object_size,
attribute_dim=attribute_nums).to(device)
_ = load_model(netG,
model_dir=model_save_dir,
appendix='netG',
iter=config.resume_iter)
netD_att = train_att_change.AttributeDiscriminator(
n_attribute=attribute_nums).to(device)
netD_att = train_att_change.add_sn(netD_att)
start_iter_ = load_model(netD_att,
model_dir="~/models/trained_models",
appendix='netD_attribute',
iter=config.resume_iter)
_ = load_model(netG,
model_dir=model_save_dir,
appendix='netG',
iter=config.resume_iter)
data_loader = val_data_loader
data_iter = iter(data_loader)
L1_dist_b = 0
L1_dist_f = 0
L1_rand_dist = 0
with torch.no_grad():
netG.eval()
for i, batch in enumerate(data_iter):
print('batch {}'.format(i))
imgs, objs, boxes, masks, obj_to_img, attribute, masks_shift, boxes_shift = batch
att_idx = attribute.sum(dim=1).nonzero().squeeze()
z = torch.randn(objs.size(0), config.z_dim)
imgs, objs, boxes, masks, obj_to_img, z, attribute, masks_shift, boxes_shift = \
imgs.to(device), objs.to(device), boxes.to(device), masks.to(device), \
obj_to_img, z.to(device), attribute.to(device), masks_shift.to(device), boxes_shift.to(device)
# estimate attributes
attribute_est = attribute.clone()
att_mask = torch.zeros(attribute.shape[0])
att_mask = att_mask.scatter(0, att_idx, 1).to(device)
crops_input = crop_bbox_batch(imgs, boxes, obj_to_img,
config.object_size)
estimated_att = netD_att(crops_input)
max_idx = estimated_att.argmax(1)
max_idx = max_idx.float() * (~att_mask.byte()).float().to(device)
for row in range(attribute.shape[0]):
if row not in att_idx:
attribute_est[row, int(max_idx[row])] = 1
# Generate fake image
output = netG(imgs, objs, boxes, masks, obj_to_img, z, attribute,
masks_shift, boxes_shift, attribute_est)
crops_input, crops_input_rec, crops_rand, crops_shift, img_rec, img_rand, img_shift, mu, logvar, z_rand_rec, z_rand_shift = output
foreground = list(
map(lambda x: (x.data[2] - x.data[0]) < 0.5, boxes))
dict_to_save = {
'imgs': imgs,
'imgs_rand': img_rand,
'imgs_shift': img_shift,
'objs': objs,
'boxes': boxes,
'boxes_shift': boxes_shift,
'obj_to_img': obj_to_img,
'foreground': foreground
}
out_name = os.path.join(resinet_dir, 'batch_{}.pkl'.format(i))
pickle.dump(dict_to_save, open(out_name, 'wb'))
img_rand = imagenet_deprocess_batch(img_rand).type(torch.int32)
imgs = imagenet_deprocess_batch(imgs).type(torch.int32)
img_shift = imagenet_deprocess_batch(img_shift).type(torch.int32)
# Save the generated images
fore_count = 0
background_dist = 0
foreground_dist = 0
rand_diff = 0
for j in range(img_rand.shape[0]):
obj_indices = torch.nonzero(obj_to_img == j).view(-1)
foreground_obj_indices = [
int(i) for i in obj_indices if foreground[i] == 1
]
if foreground_obj_indices != []:
foreground_mask = torch.max(
torch.max(masks[foreground_obj_indices], 0)[0],
torch.max(masks_shift[foreground_obj_indices],
0)[0]).byte()
else:
print("no foreground")
foreground_mask = torch.zeros(1, 64, 64).byte().to(device)
background_dist += safe_division(
torch.abs((img_rand[j] - img_shift[j]) *
(~foreground_mask).cpu().int()).sum(),
(3 * (~foreground_mask).cpu().float().sum()))
img_np = (img_rand[j] *
(~foreground_mask).int().cpu()).numpy().transpose(
1, 2, 0)
img_path = os.path.join(
result_save_dir1,
'img{:06d}.png'.format(i * config.batch_size + j))
imwrite(img_path, img_np)
img_np = (img_shift[j] *
(~foreground_mask).int().cpu()).numpy().transpose(
1, 2, 0)
img_path = os.path.join(
result_save_dir2,
'img{:06d}.png'.format(i * config.batch_size + j))
imwrite(img_path, img_np)
for foreground_i in foreground_obj_indices:
try:
foreground_dist += torch.abs(
torch.masked_select(
img_rand[j], masks[foreground_i].cpu().byte())
- torch.masked_select(
img_shift[j], masks_shift[foreground_i].cpu().
byte())).sum() / (3 *
(masks[foreground_i]).sum())
fore_count += 1
except:
continue
rand_diff = torch.abs(img_rand[j] -
img_shift[j - 1]).sum() / (3 * 64 * 64)
L1_dist_b += background_dist / config.batch_size
L1_dist_f += safe_division(foreground_dist, fore_count)
L1_rand_dist += rand_diff
try:
print("runing b_dist = %d, f_dist = %d, rand_diff = %d" %
(background_dist / config.batch_size,
safe_division(foreground_dist, fore_count), rand_diff))
except:
print(math.isnan(background_dist / config.batch_size),
math.isnan(safe_division(foreground_dist, fore_count)),
math.isnan(rand_diff))
print("Background L1 dist = %f" % (L1_dist_b / i))
print("Foreground L1 dist = %f" % (L1_dist_f / i))
print("Rand L1 dist = %f" % (L1_rand_dist / i))
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False, input_size=224, start_epoch=0):
since = time.time()
val_acc_history = []
writer = {"train": SummaryWriter(log_save_dir + exp_name + "_train/"),
"val": SummaryWriter(log_save_dir + exp_name + "_val/")}
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(start_epoch, num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
running_count = 0
# Iterate over data.
for i, batch in enumerate(dataloaders[phase]):
imgs, objs, boxes, masks, obj_to_img, attribute, masks_shift, boxes_shift = batch
inputs = crop_bbox_batch(imgs, boxes, obj_to_img, input_size)
inputs = inputs.to(device)
labels = objs.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
# Get model outputs and calculate loss
# Special case for inception because in training it has an auxiliary output. In train
# mode we calculate the loss by summing the final output and the auxiliary output
# but in testing we only consider the final output.
if is_inception and phase == 'train':
# From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
outputs, aux_outputs = model(inputs)
loss1 = criterion(outputs, labels)
loss2 = criterion(aux_outputs, labels)
loss = loss1 + 0.4 * loss2
else:
outputs = model(inputs)
loss = criterion(outputs, labels)
_, preds = torch.max(outputs, 1)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * labels.size(0)
running_corrects += torch.sum(preds == labels.data)
running_count += labels.size(0)
if (i+1) % 20 == 0:
print('epoch: {:04d} iter: {:08d} loss: {:.4f} accu: {:.4f}'.format(epoch+1, i+1, loss.item(), torch.mean((preds == labels.data).float())))
# Logging.
loss_logging = {}
loss_logging['avg loss'] = loss.item()
loss_logging['accu'] = torch.mean((preds == labels.data).float())
if (i + 1) % 100 == 0:
for tag, roi_value in loss_logging.items():
writer[phase].add_scalar(tag, roi_value, epoch * iters_per_epoch[phase] + i + 1)
epoch_loss = running_loss / running_count
epoch_acc = running_corrects.double() / running_count
print('================================================================')
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val': # and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
save_model(model, model_save_dir, '128_resinet50_vg_best', epoch + 1, save_step=1)
if phase == 'val':
val_acc_history.append(epoch_acc)
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
writer['train'].close()
writer['test'].close()
# load best model weights
model.load_state_dict(best_model_wts)
return model, val_acc_history