def robustness_exp(dataset, max_tree_depth, forest_depth, num_trees, num_experiments=1,
score='accuracy', weight=None, n_splits=10, use_agreement=False, delta=1):
kf_scores = []
kf = KFold(n_splits=n_splits)
x, y, X_test, y_test = datasets.prepare_data(dataset, return_test=True)
c = datasets.get_number_of_classes(dataset)
score_func, score_metric = metrics.get_socre_foncs(score)
trees = []
for train, test in kf.split(x):
X_train, _, y_train, _ = x[train], x[test], y[train], y[test]
X_train, X_val, y_train, y_val = datasets.prepare_val(X_train, y_train)
k_scores = []
for k in range(num_experiments):
rf, _, f_med, f_all, f_m = compress_tree(num_trees, max_tree_depth, forest_depth, X_train, y_train,
c, weight=weight, X_val=X_val, y_val=y_val,
score=score_metric, delta=delta)
k_scores.append(score_func(rf, None, f_med, f_all, f_m, X_train, y_train, X_test, y_test))
trees.append((rf, f_m, f_all, f_med))
kf_scores.append(metrics.average_scores(k_scores, num_experiments))
means = metrics.mean_and_std(kf_scores, mean_only=True)
output = metrics.agreement_score(trees, X_test) if use_agreement else None
return output, means
def evaluate(dataloader, cnn_model, rnn_model, batch_size):
cnn_model.eval()
rnn_model.eval()
s_total_loss = 0
w_total_loss = 0
for step, data in enumerate(dataloader, 0):
real_imgs, captions, cap_lens, class_ids, keys, \
wrong_caps, wrong_caps_len, wrong_cls_id = prepare_data(data)
words_features, sent_code = cnn_model(real_imgs[-1])
hidden = rnn_model.init_hidden(batch_size)
words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)
w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
cap_lens, class_ids, batch_size)
w_total_loss += (w_loss0 + w_loss1)
s_loss0, s_loss1 = \
sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
s_total_loss += (s_loss0 + s_loss1)
if step == 50:
break
s_cur_loss = s_total_loss / step
w_cur_loss = w_total_loss / step
return s_cur_loss, w_cur_loss
def generalization_exp(dataset, max_tree_depth, forest_depth, num_trees, num_experiments=1,
score='accuracy', weight=None, n_splits=10, delta=1):
kf_scores = []
kf = KFold(n_splits=n_splits)
x, y, _, _, = datasets.prepare_data(dataset, return_test=False)
c = datasets.get_number_of_classes(dataset)
score_func, score_metric = metrics.get_socre_foncs(score)
for k in range(num_experiments):
f_scores = []
for train, test in kf.split(x):
X_train, X_test, y_train, y_test = x[train], x[test], y[train], y[test]
X_train, X_val, y_train, y_val = datasets.prepare_val(X_train, y_train)
rf, _, f_med, f_all, f_m = compress_tree(num_trees, max_tree_depth, forest_depth, X_train, y_train,
c, weight=weight, X_val=X_val, y_val=y_val,
score=score_metric, delta=delta)
f_scores.append(score_func(rf, None, f_med, f_all, f_m, X_train, y_train, X_test, y_test))
mean_var_win = metrics.mean_and_std(f_scores, mean_only=False)
kf_scores.append(mean_var_win)
print('\nFinal results:')
print(f'Average RF mean {sum([score[0] for score in kf_scores]) / num_experiments}, var {sum([score[1] for score in kf_scores]) / num_experiments}')
idx = 2
for t in ('BM', 'VT', 'MED'):
t_mean = sum([score[idx] for score in kf_scores]) / num_experiments
t_wins = sum([score[idx + 2] for score in kf_scores]) / num_experiments
idx += 3
print(f'Average {t} mean {t_mean}, wins {t_wins}')
return
def robustness_exp(max_tree_depth, num_experiments, dataset, score='accuracy',
weight=None, n_splits=10, device='cpu', use_agreement=False, delta=1):
kf_scores = []
kf = KFold(n_splits=n_splits)
x, y, X_test, y_test = datasets.prepare_data(dataset, return_test=True)
score_func, score_metric = metrics.get_socre_foncs(score)
c = datasets.get_number_of_classes(dataset)
trees = []
for train, test in kf.split(x):
X_train, _, y_train, _ = x[train], x[test], y[train], y[test]
X_train, X_val, y_train, y_val = datasets.prepare_val(X_train, y_train)
X_nn = np.concatenate([X_train, X_val], axis=0)
y_nn = np.concatenate([y_train, y_val], axis=0)
k_scores = []
nn_score = []
for k in range(num_experiments):
p_ic, nn_test_score = train_oracle_and_predict(dataset, X_nn, y_nn, X_test, y_test, c, device)
f_med, f_voting, f_m = compress_tree(max_tree_depth, X_train, y_train, p_ic, weight=weight, X_val=X_val,
y_val=y_val, score=score_metric, delta=delta)
k_scores.append(score_func(None, None, f_med, f_voting, f_m, X_train, y_train, X_test, y_test))
nn_score.append(nn_test_score)
trees.append((f_m, f_voting, f_med))
kf_scores.append(metrics.average_scores(k_scores, num_experiments))
means = metrics.mean_and_std(kf_scores, mean_only=True, show_rf=False)
output = metrics.agreement_score(trees, X_test) if use_agreement else None
nn_av = np.mean(nn_score)
return output, means, nn_av
def evaluate(dataloader, cnn_model, rnn_model, batch_size):
cnn_model.eval()
rnn_model.eval()
s_total_loss = 0
w_total_loss = 0
for step, data in enumerate(dataloader, 0):
real_imgs, captions, cap_lens, \
class_ids, keys = prepare_data(data)
words_features, sent_code = cnn_model(real_imgs[-1])
# nef = words_features.size(1)
# words_features = words_features.view(batch_size, nef, -1)
hidden = rnn_model.init_hidden(batch_size)
words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)
w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
cap_lens, class_ids, batch_size)
w_total_loss += (w_loss0 + w_loss1).data
s_loss0, s_loss1 = \
sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
s_total_loss += (s_loss0 + s_loss1).data
if step == 50:
break
s_cur_loss = s_total_loss.item() / step
w_cur_loss = w_total_loss.item() / step
return s_cur_loss, w_cur_loss
def crembo_sklearn_example():
# set arguments
dataset = 'dermatology'
args = {
'dataset': 'iris',
'num_trees': 100,
'tree_depth': 4,
'forest_depth': 10,
'weight': 'balanced',
'sklearn': True
}
# Create train, test, val sets
x, y, X_test, y_test = datasets.prepare_data(dataset, return_test=True)
X_train, X_val, y_train, y_val = datasets.prepare_val(x, y)
train_loader = (X_train, y_train)
test_loader = (X_test, y_test)
val_loader = (X_val, y_val)
# train large model
M = RandomForestClassifier(n_estimators=args['num_trees'],
max_depth=args['forest_depth'],
class_weight=args['weight'])
M.fit(X_train, y_train)
# define create_model method
# Here we create a tree model. All sklearn models should be wrapped by a class that
# inherits from the MCSkLearnConsistensy class.
create_model_func = MCConsistentTree(depth=args['tree_depth'],
class_weight=args['weight']).get_clone
# define train_hypothesis method
train_hypothesis_func = train_sklearn
# define evel_model method
eval_model_func = eval_sklearn
# initiate CREMBO class
crembo = CREMBO(create_model_func,
train_hypothesis_func,
eval_model_func,
args,
delta=1)
# run crembo
f = crembo(M, train_loader, test_loader, val_loader, device=None)
# print scores
f_score = eval_model_func(f, test_loader, None)
M_score = eval_model_func(M, test_loader, None)
print(f'M score: {M_score}, CREMBO: {f_score}')
return f
def scatter(self, inputs, kwargs, device_ids):
# More like scatter and data prep at the same time. The point is we prep the data in such a way
# that no scatter is necessary, and there's no need to shuffle stuff around different GPUs.
devices = ['cuda:' + str(x) for x in device_ids] if args.cuda else None
splits = prepare_data(inputs[0],
devices,
allocation=args.batch_alloc,
batch_size=args.batch_size,
is_cuda=args.cuda,
train_mode=True)
return [[split[device_idx] for split in splits] for device_idx in range(len(devices))], \
[kwargs] * len(devices)
def evaluate(dataloader, cnn_model, rnn_model, batch_size, writer, count,
ixtoword, labels, image_dir):
cnn_model.eval()
rnn_model.eval()
s_total_loss = 0
w_total_loss = 0
for step, data in enumerate(dataloader, 0):
real_imgs, captions, cap_lens, \
class_ids, keys = prepare_data(data)
words_features, sent_code = cnn_model(real_imgs[-1])
# nef = words_features.size(1)
# words_features = words_features.view(batch_size, nef, -1)
nef, att_sze = words_features.size(1), words_features.size(2)
# hidden = rnn_model.init_hidden(batch_size)
words_emb, sent_emb = rnn_model(captions, cap_lens)
w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
cap_lens, class_ids, batch_size)
w_total_loss += (w_loss0 + w_loss1).data
s_loss0, s_loss1 = \
sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
s_total_loss += (s_loss0 + s_loss1).item()
if step == 50:
break
s_cur_loss = s_total_loss / step
w_cur_loss = w_total_loss / step
writer.add_scalars(main_tag="eval_loss",
tag_scalar_dict={
's_loss': s_cur_loss,
'w_loss': w_cur_loss
},
global_step=count)
# save a image
# attention Maps
img_set, _ = \
build_super_images(real_imgs[-1][:,:3].cpu(), captions,
ixtoword, attn, att_sze)
if img_set is not None:
im = Image.fromarray(img_set)
fullpath = '%s/attention_maps_eval_%d.png' % (image_dir, count)
im.save(fullpath)
writer.add_image(tag="image_DAMSM_eval",
img_tensor=transforms.ToTensor()(im),
global_step=count)
return s_cur_loss, w_cur_loss
def evaluate(dataloader, cnn_model, rnn_model, batch_size):
cnn_model.eval()
rnn_model.eval()
s_total_loss = 0
w_total_loss = 0
count = 0
img_lst = []
sent_lst = []
for step, data in enumerate(dataloader, 0):
real_imgs, captions, cap_lens, \
class_ids, keys = prepare_data(data)
count += batch_size
words_features, sent_code = cnn_model(real_imgs[-1])
# nef = words_features.size(1)
# words_features = words_features.view(batch_size, nef, -1)
hidden = rnn_model.init_hidden(batch_size)
words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)
img_lst.append(sent_code)
sent_lst.append(sent_emb)
w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
cap_lens, class_ids, batch_size)
w_total_loss += (w_loss0 + w_loss1).data
s_loss0, s_loss1 = \
sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
s_total_loss += (s_loss0 + s_loss1).data
if count >= 1000:
break
s_cur_loss = s_total_loss / step
w_cur_loss = w_total_loss / step
img_embs = torch.cat(img_lst)
sent_embs = torch.cat(sent_lst)
acc, pred = compute_topk(img_embs, sent_embs)
logger.info(
'| end epoch {:3d} | top-5 ({:4d}) {:5.2f} valid loss {:5.2f} {:5.2f} | lr {:.5f}|'
.format(epoch, count, acc, s_cur_loss, w_cur_loss, lr))
return s_cur_loss, w_cur_loss
def test(dataloader, cnn_model, rnn_model, batch_size, labels, ixtoword,
image_dir, input_channels):
cnn_model.eval()
rnn_model.eval()
# s_total_loss0 = 0
# s_total_loss1 = 0
# w_total_loss0 = 0
# w_total_loss1 = 0
# count = (epoch + 1) * len(dataloader)
# start_time = time.time()
for step, data in enumerate(dataloader, 0):
# print('step', step)
# rnn_model.zero_grad()
# cnn_model.zero_grad()
imgs, captions, cap_lens, \
class_ids, keys = prepare_data(data)
# words_features: batch_size x nef x 17 x 17
# sent_code: batch_size x nef
words_features, sent_code = cnn_model(imgs[-1])
# --> batch_size x nef x 17*17
nef, att_len, att_sze = words_features.size(1), words_features.size(
2), words_features.size(3)
# words_features = words_features.view(batch_size, nef, -1)
# hidden = rnn_model.init_hidden(batch_size)
hidden = rnn_model.init_hidden()
# words_emb: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)
w_loss0, w_loss1, attn_maps = words_loss(words_features, words_emb,
labels, cap_lens, class_ids,
batch_size)
img_set, _ = \
build_super_images(imgs[-1].cpu(), captions,
ixtoword, attn_maps, att_len, att_sze, input_channels)
if img_set is not None:
im = Image.fromarray(img_set)
fullpath = '%s/attention_maps%d.png' % (image_dir, step)
# fullpath = '%s/attention_maps%d.png' % (image_dir, count)
im.save(fullpath)
# return count
return step
def sampling(text_encoder, netG, dataloader, device):
model_dir = cfg.TRAIN.NET_G
split_dir = 'valid'
# Build and load the generator
netG.load_state_dict(torch.load('models/%s/netG.pth' % (cfg.CONFIG_NAME)))
netG.eval()
batch_size = cfg.TRAIN.BATCH_SIZE
s_tmp = model_dir
save_dir = '%s/%s' % (s_tmp, split_dir)
mkdir_p(save_dir)
cnt = 0
for i in range(1): # (cfg.TEXT.CAPTIONS_PER_IMAGE):
for step, data in enumerate(dataloader, 0):
imags, captions, cap_lens, class_ids, keys = prepare_data(data)
cnt += batch_size
if step % 100 == 0:
print('step: ', step)
# if step > 50:
# break
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
#######################################################
# (2) Generate fake images
######################################################
with torch.no_grad():
noise = torch.randn(batch_size, 100)
noise = noise.to(device)
fake_imgs = netG(noise, sent_emb)
for j in range(batch_size):
s_tmp = '%s/single/%s' % (save_dir, keys[j])
folder = s_tmp[:s_tmp.rfind('/')]
if not os.path.isdir(folder):
print('Make a new folder: ', folder)
mkdir_p(folder)
im = fake_imgs[j].data.cpu().numpy()
# [-1, 1] --> [0, 255]
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
fullpath = '%s_%3d.png' % (s_tmp, i)
im.save(fullpath)
def evaluate(dataloader, cnn_model, nlp_model, text_encoder_type, batch_size):
cnn_model.eval()
nlp_model.eval()
text_encoder_type = text_encoder_type.casefold()
assert text_encoder_type in (
'rnn',
'transformer',
)
s_total_loss = 0
w_total_loss = 0
for step, data in enumerate(dataloader, 0):
real_imgs, captions, cap_lens, \
class_ids, keys = prepare_data( data )
words_features, sent_code = cnn_model(real_imgs[-1])
# nef = words_features.size(1)
# words_features = words_features.view(batch_size, nef, -1)
if text_encoder_type == 'rnn':
hidden = nlp_model.init_hidden(batch_size)
words_emb, sent_emb = nlp_model(captions, cap_lens, hidden)
elif text_encoder_type == 'transformer':
words_emb = nlp_model(captions)[0].transpose(1, 2).contiguous()
sent_emb = words_emb[:, :, -1].contiguous()
# sent_emb = sent_emb.view(batch_size, -1)
w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
cap_lens, class_ids, batch_size)
w_total_loss += (w_loss0 + w_loss1).data
s_loss0, s_loss1 = \
sent_loss( sent_code, sent_emb, labels, class_ids, batch_size )
s_total_loss += (s_loss0 + s_loss1).data
if step == 50:
break
s_cur_loss = s_total_loss.item() / step
w_cur_loss = w_total_loss.item() / step
return s_cur_loss, w_cur_loss
def evaluate(dataloader, cnn_model, rnn_model, batch_size, labels):
cnn_model.eval()
rnn_model.eval()
s_total_loss = 0
w_total_loss = 0
for step, data in enumerate(dataloader, 0):
real_imgs, captions, cap_lens, class_ids = prepare_data(data)
words_features, sent_code = cnn_model(real_imgs[-1])
if step == len(dataloader)-1:
batch_size = len(subset_val)-(len(dataloader)-1)*batch_size
labels = Variable(torch.LongTensor(range(batch_size)))
labels = labels.cuda()
hidden = rnn_model.init_hidden(batch_size)
words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)
w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels, cap_lens, class_ids, batch_size)
w_total_loss += (w_loss0 + w_loss1).data
s_loss0, s_loss1 = sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
s_total_loss += (s_loss0 + s_loss1).data
s_cur_loss = s_total_loss.item() / len(dataloader)
w_cur_loss = w_total_loss.item() / len(dataloader)
return s_cur_loss, w_cur_loss
def generalization_exp(max_tree_depth, num_experiments=1, dataset='mnist',
score='accuracy', weight=None, device='cpu', delta=1, n_splits=10):
kf_scores = []
kf = KFold(n_splits=n_splits)
x, y, _, _, = datasets.prepare_data(dataset, return_test=False)
c = datasets.get_number_of_classes(dataset)
score_func, score_metric = metrics.get_socre_foncs(score)
nn_score = []
for k in range(num_experiments):
print(f'Experiment number {k+1}')
f_scores = []
for train, test in kf.split(x):
X_train, X_test, y_train, y_test = x[train], x[test], y[train], y[test]
X_train, X_val, y_train, y_val = datasets.prepare_val(X_train, y_train)
X_nn = np.concatenate([X_train, X_val], axis=0)
y_nn = np.concatenate([y_train, y_val], axis=0)
p_ic, nn_test_score = train_oracle_and_predict(dataset, X_nn, y_nn, X_test, y_test, c, device)
f_med, f_all, f_m = compress_tree(max_tree_depth, X_train, y_train, p_ic, weight=weight, X_val=X_val,
y_val=y_val, score=score_metric, delta=delta)
f_scores.append(score_func(None, None, f_med, f_all, f_m, X_train, y_train, X_test, y_test))
nn_score.append(nn_test_score)
mean_var_win = metrics.mean_and_std(f_scores, mean_only=False, show_rf=False, nn_score=nn_score)
kf_scores.append(mean_var_win)
print('\nFinal results:')
print(f'Average NN mean {sum([score[0] for score in kf_scores]) / num_experiments}, std {sum([score[1] for score in kf_scores]) / num_experiments}')
idx = 2
for t in ('BM', 'VT', 'MED'):
t_mean = sum([score[idx] for score in kf_scores]) / num_experiments
t_wins = sum([score[idx + 2] for score in kf_scores]) / num_experiments
idx += 3
print(f'Average {t} mean {t_mean}, wins {t_wins}')
return
def train(self):
torch.autograd.set_detect_anomaly(True)
text_encoder, image_encoder, netG, netsD, start_epoch = self.build_models()
avg_param_G = copy_G_params(netG)
optimizerG, optimizersD = self.define_optimizers(netG, netsD)
real_labels, fake_labels, match_labels = self.prepare_labels()
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
batch_sizes = self.batch_size
noise, local_noise, fixed_noise = [], [], []
for batch_size in batch_sizes:
noise.append(Variable(torch.FloatTensor(batch_size, cfg.GAN.GLOBAL_Z_DIM)).to(cfg.DEVICE))
local_noise.append(Variable(torch.FloatTensor(batch_size, cfg.GAN.LOCAL_Z_DIM)).to(cfg.DEVICE))
fixed_noise.append(Variable(torch.FloatTensor(batch_size, cfg.GAN.GLOBAL_Z_DIM).normal_(0, 1)).to(cfg.DEVICE))
else:
batch_size = self.batch_size[0]
noise = Variable(torch.FloatTensor(batch_size, cfg.GAN.GLOBAL_Z_DIM)).to(cfg.DEVICE)
local_noise = Variable(torch.FloatTensor(batch_size, cfg.GAN.LOCAL_Z_DIM)).to(cfg.DEVICE)
fixed_noise = Variable(torch.FloatTensor(batch_size, cfg.GAN.GLOBAL_Z_DIM).normal_(0, 1)).to(cfg.DEVICE)
for epoch in range(start_epoch, self.max_epoch):
logger.info("Epoch nb: %s" % epoch)
gen_iterations = 0
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
data_iter = []
for _idx in range(len(self.data_loader)):
data_iter.append(iter(self.data_loader[_idx]))
total_batches_left = sum([len(self.data_loader[i]) for i in range(len(self.data_loader))])
current_probability = [len(self.data_loader[i]) for i in range(len(self.data_loader))]
current_probability_percent = [current_probability[i] / float(total_batches_left) for i in
range(len(current_probability))]
else:
data_iter = iter(self.data_loader)
_dataset = tqdm(range(self.num_batches))
for step in _dataset:
######################################################
# (1) Prepare training data and Compute text embeddings
######################################################
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
subset_idx = np.random.choice(range(len(self.data_loader)), size=None,
p=current_probability_percent)
total_batches_left -= 1
if total_batches_left > 0:
current_probability[subset_idx] -= 1
current_probability_percent = [current_probability[i] / float(total_batches_left) for i in
range(len(current_probability))]
max_objects = subset_idx
data = data_iter[subset_idx].next()
else:
data = data_iter.next()
max_objects = 3
_dataset.set_description('Obj-{}'.format(max_objects))
imgs, captions, cap_lens, class_ids, keys, transformation_matrices, label_one_hot = prepare_data(data)
transf_matrices = transformation_matrices[0]
transf_matrices_inv = transformation_matrices[1]
with torch.no_grad():
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
hidden = text_encoder.init_hidden(batch_sizes[subset_idx])
else:
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
mask = (captions == 0).bool()
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
noise[subset_idx].data.normal_(0, 1)
local_noise[subset_idx].data.normal_(0, 1)
inputs = (noise[subset_idx], local_noise[subset_idx], sent_emb, words_embs, mask, transf_matrices,
transf_matrices_inv, label_one_hot, max_objects)
else:
noise.data.normal_(0, 1)
local_noise.data.normal_(0, 1)
inputs = (noise, local_noise, sent_emb, words_embs, mask, transf_matrices, transf_matrices_inv,
label_one_hot, max_objects)
inputs = tuple((inp.to(cfg.DEVICE) if isinstance(inp, torch.Tensor) else inp) for inp in inputs)
fake_imgs, _, mu, logvar = netG(*inputs)
#######################################################
# (3) Update D network
######################################################
# errD_total = 0
D_logs = ''
for i in range(len(netsD)):
netsD[i].zero_grad()
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
errD = discriminator_loss(netsD[i], imgs[i], fake_imgs[i],
sent_emb, real_labels[subset_idx], fake_labels[subset_idx],
local_labels=label_one_hot, transf_matrices=transf_matrices,
transf_matrices_inv=transf_matrices_inv, cfg=cfg,
max_objects=max_objects)
else:
errD = discriminator_loss(netsD[i], imgs[i], fake_imgs[i],
sent_emb, real_labels, fake_labels,
local_labels=label_one_hot, transf_matrices=transf_matrices,
transf_matrices_inv=transf_matrices_inv, cfg=cfg,
max_objects=max_objects)
# backward and update parameters
errD.backward()
optimizersD[i].step()
D_logs += 'errD%d: %.2f ' % (i, errD.item())
#######################################################
# (4) Update G network: maximize log(D(G(z)))
######################################################
# compute total loss for training G
# step += 1
gen_iterations += 1
# do not need to compute gradient for Ds
netG.zero_grad()
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
errG_total = \
generator_loss(netsD, image_encoder, fake_imgs, real_labels[subset_idx],
words_embs, sent_emb, match_labels[subset_idx], cap_lens, class_ids,
local_labels=label_one_hot, transf_matrices=transf_matrices,
transf_matrices_inv=transf_matrices_inv, max_objects=max_objects)
else:
errG_total = \
generator_loss(netsD, image_encoder, fake_imgs, real_labels,
words_embs, sent_emb, match_labels, cap_lens, class_ids,
local_labels=label_one_hot, transf_matrices=transf_matrices,
transf_matrices_inv=transf_matrices_inv, max_objects=max_objects)
kl_loss = KL_loss(mu, logvar)
errG_total += kl_loss
# backward and update parameters
errG_total.backward()
optimizerG.step()
for p, avg_p in zip(netG.parameters(), avg_param_G):
avg_p.mul_(0.999).add_(p.data, alpha=0.001)
if cfg.TRAIN.EMPTY_CACHE:
torch.cuda.empty_cache()
# save images
if (
2 * gen_iterations == self.num_batches
or 2 * gen_iterations + 1 == self.num_batches
or gen_iterations + 1 == self.num_batches
):
logger.info('Saving images...')
backup_para = copy_G_params(netG)
load_params(netG, avg_param_G)
if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
self.save_img_results(netG, fixed_noise[subset_idx], sent_emb,
words_embs, mask, image_encoder,
captions, cap_lens, epoch, transf_matrices_inv,
label_one_hot, local_noise[subset_idx], transf_matrices,
max_objects, subset_idx, name='average')
else:
self.save_img_results(netG, fixed_noise, sent_emb,
words_embs, mask, image_encoder,
captions, cap_lens, epoch, transf_matrices_inv,
label_one_hot, local_noise, transf_matrices,
max_objects, None, name='average')
load_params(netG, backup_para)
self.save_model(netG, avg_param_G, netsD, optimizerG, optimizersD, epoch)
self.save_model(netG, avg_param_G, netsD, optimizerG, optimizersD, epoch)
def train(dataloader, cnn_model, rnn_model, batch_size, labels, optimizer,
epoch, ixtoword, image_dir):
cnn_model.train()
rnn_model.train()
s_total_loss0 = 0
s_total_loss1 = 0
w_total_loss0 = 0
w_total_loss1 = 0
count = (epoch + 1) * len(dataloader)
start_time = time.time()
for step, data in enumerate(dataloader, 0):
# print('step', step)
rnn_model.zero_grad()
cnn_model.zero_grad()
imgs, captions, cap_lens, \
class_ids, keys = prepare_data(data)
# words_features: batch_size x nef x 17 x 17
# sent_code: batch_size x nef
words_features, sent_code = cnn_model(imgs[-1])
# --> batch_size x nef x 17*17
nef, att_sze = words_features.size(1), words_features.size(2)
# words_features = words_features.view(batch_size, nef, -1)
hidden = rnn_model.init_hidden(batch_size)
# words_emb: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)
w_loss0, w_loss1, attn_maps = words_loss(words_features, words_emb,
labels, cap_lens, class_ids,
batch_size)
w_total_loss0 += w_loss0.data
w_total_loss1 += w_loss1.data
loss = w_loss0 + w_loss1
s_loss0, s_loss1 = \
sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
loss += s_loss0 + s_loss1
s_total_loss0 += s_loss0.data
s_total_loss1 += s_loss1.data
#
loss.backward()
#
# `clip_grad_norm` helps prevent
# the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm(rnn_model.parameters(),
cfg.TRAIN.RNN_GRAD_CLIP)
optimizer.step()
if step % UPDATE_INTERVAL == 0:
count = epoch * len(dataloader) + step
s_cur_loss0 = s_total_loss0 / UPDATE_INTERVAL
s_cur_loss1 = s_total_loss1 / UPDATE_INTERVAL
w_cur_loss0 = w_total_loss0 / UPDATE_INTERVAL
w_cur_loss1 = w_total_loss1 / UPDATE_INTERVAL
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches | ms/batch {:5.2f} | '
's_loss {:5.2f} {:5.2f} | '
'w_loss {:5.2f} {:5.2f}'.format(
epoch, step, len(dataloader),
elapsed * 1000. / UPDATE_INTERVAL, s_cur_loss0,
s_cur_loss1, w_cur_loss0, w_cur_loss1))
s_total_loss0 = 0
s_total_loss1 = 0
w_total_loss0 = 0
w_total_loss1 = 0
start_time = time.time()
# attention Maps
img_set, _ = \
build_super_images(imgs[-1].cpu(), captions,
ixtoword, attn_maps, att_sze)
if img_set is not None:
im = Image.fromarray(img_set)
fullpath = '%s/attention_maps%d.png' % (image_dir, step)
im.save(fullpath)
return count
def compute_ppl(evaluator,
space='smart',
num_samples=100000,
eps=1e-4,
net='vgg'):
"""Perceptual Path Length: PyTorch implementation of the `PPL` class in
https://github.com/NVlabs/stylegan/blob/master/metrics/perceptual_path_length.py
"""
assert space in ['z', 'w', 'smart']
assert net in ['vgg', 'alex']
ppl_loss_fn = lpips.LPIPS(net=net, lpips=True)
ppl_loss_fn.cuda()
text_encoder, netG = evaluator.build_models_eval(init_func=weights_init)
if space == 'smart':
space = 'w' if cfg.GAN.B_STYLEGEN else 'z'
if space == 'w':
init_res = (
netG.h_net1.init_layer.shape[-2],
netG.h_net1.init_layer.shape[-1],
)
upscale_fctr = netG.h_net1.upsampler.scale_factor
res_init_layers = [
int(np.rint(r * upscale_fctr**((n - n % 2) // 2)))
for n, r in enumerate(init_res * 5)
]
res_2G = int(np.rint(netG.h_net2.res))
res_3G = int(np.rint(netG.h_net3.res))
batch_size = evaluator.batch_size
nz = cfg.GAN.Z_DIM
with torch.no_grad():
z_code01 = Variable(torch.FloatTensor(batch_size * 2, nz))
z_code01 = z_code01.cuda()
t = Variable(torch.FloatTensor(batch_size, 1))
t = t.cuda()
ppls = []
dl_itr = iter(evaluator.data_loader)
# for step, data in enumerate( evaluator.data_loader, 0 ):
pbar = tqdm(range(num_samples // batch_size), dynamic_ncols=True)
for step in pbar:
try:
data = next(dl_itr)
except StopIteration:
dl_itr = iter(evaluator.data_loader)
data = next(dl_itr)
if step % 100 == 0:
pbar.set_description('step: {}'.format(step))
imgs, captions, cap_lens, class_ids, keys = prepare_data(data)
#######################################################
# (1) Extract text embeddings
######################################################
if evaluator.text_encoder_type == 'rnn':
hidden = text_encoder.init_hidden(batch_size)
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
elif evaluator.text_encoder_type == 'transformer':
words_embs = text_encoder(captions)[0].transpose(1, 2).contiguous()
sent_emb = words_embs[:, :, -1].contiguous()
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
t.data.uniform_(0, 1)
z_code01.data.normal_(0, 1)
c_code, _, _ = netG.ca_net(sent_emb)
sent_emb = torch.cat((
sent_emb,
sent_emb,
), 0).detach()
words_embs = torch.cat((
words_embs,
words_embs,
), 0).detach()
mask = torch.cat((
mask,
mask,
), 0).detach()
if space == 'w':
# Control out the StyleGAN noise, as we are trying to measure feature interpolability only in PPL
netG.noise_net1 = [
torch.randn(batch_size,
1,
res,
res,
dtype=torch.float32,
device=z_code01.device) for res in res_init_layers
]
netG.noise_net1 = [
torch.cat((
noise,
noise,
), 0).detach() for noise in netG.noise_net1
]
netG.noise_net2 = [
torch.randn(batch_size,
1,
res_2G,
res_2G,
dtype=torch.float32,
device=z_code01.device) for _ in range(2)
]
netG.noise_net2 = [
torch.cat((
noise,
noise,
), 0).detach() for noise in netG.noise_net2
]
netG.noise_net3 = [
torch.randn(batch_size,
1,
res_3G,
res_3G,
dtype=torch.float32,
device=z_code01.device) for _ in range(2)
]
netG.noise_net3 = [
torch.cat((
noise,
noise,
), 0).detach() for noise in netG.noise_net3
]
w_code01 = netG.map_net(z_code01, torch.cat((
c_code,
c_code,
), 0))
w_code0, w_code1 = w_code01[0::2], w_code01[1::2]
w_code0_lerp = lerp(w_code0, w_code1, t)
w_code1_lerp = lerp(w_code0, w_code1, t + eps)
w_code_lerp = torch.cat((
w_code0_lerp,
w_code1_lerp,
), 0).detach()
fake_imgs01, _, _, _ = netG(w_code_lerp,
sent_emb,
words_embs,
mask,
is_dlatent=True)
else:
z_code0, z_code1 = z_code01[0::2], z_code01[1::2]
z_code0_slerp = slerp(z_code0, z_code1, t)
z_code1_slerp = slerp(z_code0, z_code1, t + eps)
z_code_slerp = torch.cat((
z_code0_slerp,
z_code1_slerp,
), 0).detach()
fake_imgs01, _, _, _ = netG(z_code_slerp, sent_emb, words_embs,
mask)
fake_imgs01 = fake_imgs01[-1]
# Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
if fake_imgs01.shape[2] > 256:
factor = fake_imgs01.shape[2] // 256
fake_imgs01 = torch.reshape(fake_imgs01, [
-1, fake_imgs01.shape[1], fake_imgs01.shape[2] // factor,
factor, fake_imgs01.shape[3] // factor, factor
])
fake_imgs01 = torch.mean(fake_imgs01, dim=(
3,
5,
), keepdim=False)
# # Scale dynamic range to [-1,1] for the lpips VGG.
# fake_imgs01 = (fake_imgs01 + 1) * (255 / 2)
# fake_imgs01.clamp_( 0, 255 )
fake_imgs01.clamp_(-1., 1.)
fake_imgs0, fake_imgs1 = fake_imgs01[:batch_size], fake_imgs01[
batch_size:]
# fake_imgs0, fake_imgs1 = fake_imgs01[0::2], fake_imgs01[1::2]
# Evaluate perceptual distances.
ppls.append(
ppl_loss_fn.forward(fake_imgs0,
fake_imgs1).squeeze().detach().cpu().numpy() *
(1 / 1e-4**2))
ppls = np.concatenate(ppls, axis=0)
# Reject outliers.
lo = np.percentile(ppls, 1, interpolation='lower')
hi = np.percentile(ppls, 99, interpolation='higher')
ppls = np.extract(np.logical_and(lo <= ppls, ppls <= hi), ppls)
return np.mean(ppls)
def train(self):
text_encoder, image_encoder, netG, netsD, start_epoch = self.build_models()
avg_param_G = copy_G_params(netG)
optimizerG, optimizersD = self.define_optimizers(netG, netsD)
real_labels, fake_labels, match_labels = self.prepare_labels()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz))
fixed_noise = Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
gen_iterations = 0
# gen_iterations = start_epoch * self.num_batches
for epoch in range(start_epoch, self.max_epoch):
start_t = time.time()
data_iter = iter(self.data_loader)
step = 0
while step < self.num_batches:
batch_t_begin = time.time()
# reset requires_grad to be trainable for all Ds
# self.set_requires_grad_value(netsD, True)
######################################################
# (1) Prepare training data and Compute text embeddings
######################################################
data = data_iter.next()
imgs, captions, cap_lens, class_ids, color_ids,sleeve_ids,gender_ids, keys = prepare_data(data)
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
fake_imgs, _, mu, logvar = netG(noise, sent_emb, words_embs, mask)
#######################################################
# (3) Update D network
######################################################
errD_total = 0
D_logs = ''
D_logs_cls = ''
for i in range(len(netsD)):
netsD[i].zero_grad()
imgs[i] = gaussian_to_input(imgs[i]) ## INSTANCE NOISE
errD, cls_D= discriminator_loss(netsD[i], imgs[i], fake_imgs[i],
sent_emb, real_labels, fake_labels, class_ids, color_ids, sleeve_ids, gender_ids)
# backward and update parameters
errD_both = errD + cls_D/3.
# backward and update parameters
errD_both.backward()
optimizersD[i].step()
errD_total += errD
errD_total += cls_D/3.0
D_logs += 'errD%d: %.2f ' % (i, errD.data)
D_logs_cls += 'clsD%d: %.2f ' % (i, cls_D.data)
#######################################################
# (4) Update G network: maximize log(D(G(z)))
######################################################
# compute total loss for training G
step += 1
gen_iterations += 1
# do not need to compute gradient for Ds
# self.set_requires_grad_value(netsD, False)
netG.zero_grad()
errG_total, G_logs = \
generator_loss(netsD, image_encoder, fake_imgs, real_labels,
words_embs, sent_emb, match_labels, cap_lens, class_ids, color_ids,sleeve_ids, gender_ids,imgs)
kl_loss = KL_loss(mu, logvar)
errG_total += kl_loss
G_logs += 'kl_loss: %.2f ' % kl_loss.data
# backward and update parameters
errG_total.backward()
optimizerG.step()
for p, avg_p in zip(netG.parameters(), avg_param_G):
avg_p.mul_(0.999).add_(0.001, p.data)
if gen_iterations % 100 == 0:
batch_t_end = time.time()
print('| epoch {:3d} | {:5d}/{:5d} batches | batch_timer: {:5.2f} | '
.format(epoch, step, self.num_batches,
batch_t_end - batch_t_begin,))
print(D_logs + '\n' + D_logs_cls + '\n' + G_logs)
# save images
if gen_iterations % 1000 == 0:
backup_para = copy_G_params(netG)
load_params(netG, avg_param_G)
self.save_img_results(netG, fixed_noise, sent_emb,
words_embs, mask, image_encoder,
captions, cap_lens, epoch, name='average')
load_params(netG, backup_para)
#
# self.save_img_results(netG, fixed_noise, sent_emb,
# words_embs, mask, image_encoder,
# captions, cap_lens,
# epoch, name='current')
end_t = time.time()
print('''[%d/%d][%d]
Loss_D: %.2f Loss_G: %.2f Time: %.2fs'''
% (epoch, self.max_epoch, self.num_batches,
errD_total.data, errG_total.data,
end_t - start_t))
if epoch % cfg.TRAIN.SNAPSHOT_INTERVAL == 0: # and epoch != 0:
self.save_model(netG, avg_param_G, netsD, epoch)
self.save_model(netG, avg_param_G, netsD, self.max_epoch)
def sampling(self, split_dir):
if cfg.TRAIN.NET_G == '':
print('Error: the path for models is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
netG.apply(weights_init)
netG.cuda()
netG.eval()
#
text_encoder = RNN_ENCODER(self.n_words,
nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = \
torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder = text_encoder.cuda()
text_encoder.eval()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz), volatile=True)
noise = noise.cuda()
model_dir = cfg.TRAIN.NET_G
state_dict = \
torch.load(model_dir, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', model_dir)
# the path to save generated images
s_tmp = model_dir[:model_dir.rfind('.pth')]
save_dir = '%s/%s' % (s_tmp, split_dir)
mkdir_p(save_dir)
cnt = 0
idx = 0 ###
avg_ddva = 0
for _ in range(1):
for step, data in enumerate(self.data_loader, 0):
cnt += batch_size
if step % 100 == 0:
print('step: ', step)
captions, cap_lens, imperfect_captions, imperfect_cap_lens, misc = data
# Generate images for human-text ----------------------------------------------------------------
data_human = [captions, cap_lens, misc]
imgs, captions, cap_lens, class_ids, keys, wrong_caps,\
wrong_caps_len, wrong_cls_id= prepare_data(data_human)
hidden = text_encoder.init_hidden(batch_size)
words_embs, sent_emb = text_encoder(
captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(
), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
noise.data.normal_(0, 1)
fake_imgs, _, _, _ = netG(noise, sent_emb, words_embs,
mask)
# Generate images for imperfect caption-text-------------------------------------------------------
data_imperfect = [
imperfect_captions, imperfect_cap_lens, misc
]
imgs, imperfect_captions, imperfect_cap_lens, class_ids, imperfect_keys, wrong_caps,\
wrong_caps_len, wrong_cls_id = prepare_data(data_imperfect)
hidden = text_encoder.init_hidden(batch_size)
words_embs, sent_emb = text_encoder(
imperfect_captions, imperfect_cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(
), sent_emb.detach()
mask = (imperfect_captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
noise.data.normal_(0, 1)
imperfect_fake_imgs, _, _, _ = netG(
noise, sent_emb, words_embs, mask)
# Sort the results by keys to align ----------------------------------------------------------------
keys, captions, cap_lens, fake_imgs, _, _ = sort_by_keys(
keys, captions, cap_lens, fake_imgs, None, None)
imperfect_keys, imperfect_captions, imperfect_cap_lens, imperfect_fake_imgs, true_imgs, _ = \
sort_by_keys(imperfect_keys, imperfect_captions, imperfect_cap_lens, imperfect_fake_imgs,\
imgs, None)
# Shift device for the imgs, target_imgs and imperfect_imgs------------------------------------------------
for i in range(len(imgs)):
imgs[i] = imgs[i].to(secondary_device)
imperfect_fake_imgs[i] = imperfect_fake_imgs[i].to(
secondary_device)
fake_imgs[i] = fake_imgs[i].to(secondary_device)
for j in range(batch_size):
s_tmp = '%s/single' % (save_dir)
folder = s_tmp[:s_tmp.rfind('/')]
if not os.path.isdir(folder):
print('Make a new folder: ', folder)
mkdir_p(folder)
k = -1
im = fake_imgs[k][j].data.cpu().numpy()
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
cap_im = imperfect_fake_imgs[k][j].data.cpu().numpy()
cap_im = (cap_im + 1.0) * 127.5
cap_im = cap_im.astype(np.uint8)
cap_im = np.transpose(cap_im, (1, 2, 0))
# Uncomment to scale true image
true_im = true_imgs[k][j].data.cpu().numpy()
true_im = (true_im + 1.0) * 127.5
true_im = true_im.astype(np.uint8)
true_im = np.transpose(true_im, (1, 2, 0))
# Uncomment to save images.
#true_im = Image.fromarray(true_im)
#fullpath = '%s_true_s%d.png' % (s_tmp, idx)
#true_im.save(fullpath)
im = Image.fromarray(im)
fullpath = '%s_s%d.png' % (s_tmp, idx)
im.save(fullpath)
#cap_im = Image.fromarray(cap_im)
#fullpath = '%s_imperfect_s%d.png' % (s_tmp, idx)
idx = idx + 1
#cap_im.save(fullpath)
neg_ddva = negative_ddva(
imperfect_fake_imgs,
imgs,
fake_imgs,
reduce='mean',
final_only=True).data.cpu().numpy()
avg_ddva += neg_ddva * (-1)
#text_caps = [[self.ixtoword[word] for word in sent if word!=0] for sent in captions.tolist()]
#imperfect_text_caps = [[self.ixtoword[word] for word in sent if word!=0] for sent in
# imperfect_captions.tolist()]
print(step)
avg_ddva = avg_ddva / (step + 1)
print('\n\nAvg_DDVA: ', avg_ddva)
def train(self):
text_encoder, image_encoder, netG, target_netG, netsD, start_epoch, style_loss = self.build_models(
)
avg_param_G = copy_G_params(netG)
optimizerG, optimizersD = self.define_optimizers(netG, netsD)
real_labels, fake_labels, match_labels = self.prepare_labels()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz))
fixed_noise = Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
gen_iterations = 0
for epoch in range(start_epoch, self.max_epoch):
start_t = time.time()
data_iter = iter(self.data_loader)
step = 0
while step < self.num_batches:
data = data_iter.next()
captions, cap_lens, imperfect_captions, imperfect_cap_lens, misc = data
# Generate images for human-text ----------------------------------------------------------------
data_human = [captions, cap_lens, misc]
imgs, captions, cap_lens, class_ids, keys, wrong_caps, \
wrong_caps_len, wrong_cls_id = prepare_data(data_human)
hidden = text_encoder.init_hidden(batch_size)
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
# wrong word and sentence embeddings
w_words_embs, w_sent_emb = text_encoder(
wrong_caps, wrong_caps_len, hidden)
w_words_embs, w_sent_emb = w_words_embs.detach(
), w_sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
noise.data.normal_(0, 1)
fake_imgs, _, mu, logvar = netG(noise, sent_emb, words_embs,
mask)
# Generate images for imperfect caption-text-------------------------------------------------------
data_imperfect = [imperfect_captions, imperfect_cap_lens, misc]
imgs, imperfect_captions, imperfect_cap_lens, i_class_ids, imperfect_keys, i_wrong_caps,\
i_wrong_caps_len, i_wrong_cls_id = prepare_data(data_imperfect)
i_hidden = text_encoder.init_hidden(batch_size)
i_words_embs, i_sent_emb = text_encoder(
imperfect_captions, imperfect_cap_lens, i_hidden)
i_words_embs, i_sent_emb = i_words_embs.detach(
), i_sent_emb.detach()
i_mask = (imperfect_captions == 0)
i_num_words = i_words_embs.size(2)
if i_mask.size(1) > i_num_words:
i_mask = i_mask[:, :i_num_words]
# Move tensors to the secondary device.
noise = noise.to(secondary_device
) # IMPORTANT! We are reusing the same noise.
i_sent_emb = i_sent_emb.to(secondary_device)
i_words_embs = i_words_embs.to(secondary_device)
i_mask = i_mask.to(secondary_device)
# Generate images.
imperfect_fake_imgs, _, _, _ = target_netG(
noise, i_sent_emb, i_words_embs, i_mask)
# Sort the results by keys to align ------------------------------------------------------------------------
bag = [
sent_emb, real_labels, fake_labels, words_embs, class_ids,
w_words_embs, wrong_caps_len, wrong_cls_id
]
keys, captions, cap_lens, fake_imgs, _, sorted_bag = sort_by_keys(keys, captions, cap_lens, fake_imgs,\
None, bag)
sent_emb, real_labels, fake_labels, words_embs, class_ids, w_words_embs, wrong_caps_len, wrong_cls_id = \
sorted_bag
imperfect_keys, imperfect_captions, imperfect_cap_lens, imperfect_fake_imgs, imgs, _ = \
sort_by_keys(imperfect_keys, imperfect_captions, imperfect_cap_lens, imperfect_fake_imgs, imgs,None)
#-----------------------------------------------------------------------------------------------------------
errD_total = 0
D_logs = ''
for i in range(len(netsD)):
netsD[i].zero_grad()
errD = discriminator_loss(netsD[i], imgs[i], fake_imgs[i],
sent_emb, real_labels,
fake_labels, words_embs,
cap_lens, image_encoder,
class_ids, w_words_embs,
wrong_caps_len, wrong_cls_id)
# backward and update parameters
errD.backward(retain_graph=True)
optimizersD[i].step()
errD_total += errD
D_logs += 'errD%d: %.2f ' % (i, errD)
step += 1
gen_iterations += 1
netG.zero_grad()
errG_total, G_logs = \
generator_loss(netsD, image_encoder, fake_imgs, real_labels,
words_embs, sent_emb, match_labels, cap_lens, class_ids, style_loss, imgs)
kl_loss = KL_loss(mu, logvar)
errG_total += kl_loss
G_logs += 'kl_loss: %.2f ' % kl_loss
# Shift device for the imgs and target_imgs.-----------------------------------------------------
for i in range(len(imgs)):
imgs[i] = imgs[i].to(secondary_device)
fake_imgs[i] = fake_imgs[i].to(secondary_device)
# Compute and add ddva loss ---------------------------------------------------------------------
neg_ddva = negative_ddva(imperfect_fake_imgs, imgs, fake_imgs)
neg_ddva *= 10. # Scale so that the ddva score is not overwhelmed by other losses.
errG_total += neg_ddva.to(cfg.GPU_ID)
G_logs += 'negative_ddva_loss: %.2f ' % neg_ddva
#------------------------------------------------------------------------------------------------
errG_total.backward()
optimizerG.step()
for p, avg_p in zip(netG.parameters(), avg_param_G):
avg_p.mul_(0.999).add_(0.001, p.data)
if gen_iterations % 100 == 0:
print(D_logs + '\n' + G_logs)
# Copy parameters to the target network.
if gen_iterations % 20 == 0:
load_params(target_netG, copy_G_params(netG))
end_t = time.time()
print('''[%d/%d][%d]
Loss_D: %.2f Loss_G: %.2f neg_ddva: %.2f Time: %.2fs''' %
(epoch, self.max_epoch, self.num_batches, errD_total,
errG_total, neg_ddva, end_t - start_t))
if epoch % cfg.TRAIN.SNAPSHOT_INTERVAL == 0:
self.save_model(netG, avg_param_G, netsD, epoch)
self.save_model(netG, avg_param_G, netsD, self.max_epoch)
def embedding(self, split_dir, model):
if cfg.TRAIN.NET_G == '':
print('Error: the path for morels is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
# Build and load the generator
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
netG.apply(weights_init)
if cfg.GPU_ID != -1:
netG.cuda()
netG.eval()
#
model_dir = cfg.TRAIN.NET_G
state_dict = \
torch.load(model_dir, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', model_dir)
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
img_encoder_path = cfg.TRAIN.NET_E.replace('text_encoder',
'image_encoder')
print(img_encoder_path)
print('Load image encoder from:', img_encoder_path)
state_dict = \
torch.load(img_encoder_path, map_location=lambda storage, loc: storage)
image_encoder.load_state_dict(state_dict)
if cfg.GPU_ID != -1:
image_encoder = image_encoder.cuda()
image_encoder.eval()
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder = RNN_ENCODER(self.n_words,
nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = \
torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
if cfg.GPU_ID != -1:
text_encoder = text_encoder.cuda()
text_encoder.eval()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
with torch.no_grad():
noise = Variable(torch.FloatTensor(batch_size, nz))
if cfg.GPU_ID != -1:
noise = noise.cuda()
# the path to save generated images
save_dir = model_dir[:model_dir.rfind('.pth')]
cnt = 0
# new
if cfg.TRAIN.CLIP_SENTENCODER:
print("Use CLIP SentEncoder for sampling")
img_features = dict()
txt_features = dict()
with torch.no_grad():
for _ in range(1): # (cfg.TEXT.CAPTIONS_PER_IMAGE):
for step, data in enumerate(self.data_loader, 0):
cnt += batch_size
if step % 100 == 0:
print('step: ', step)
imgs, captions, cap_lens, class_ids, keys, texts = prepare_data(
data)
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(
captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(
), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
if cfg.TRAIN.CLIP_SENTENCODER:
# random select one paragraph for each training example
sents = []
for idx in range(len(texts)):
sents_per_image = texts[idx].split(
'\n') # new 3/11
if len(sents_per_image) > 1:
sent_ix = np.random.randint(
0,
len(sents_per_image) - 1)
else:
sent_ix = 0
sents.append(sents_per_image[0])
# print('sents: ', sents)
sent = clip.tokenize(sents) # .to(device)
# load clip
#model = torch.jit.load("model.pt").cuda().eval()
sent_input = sent
if cfg.GPU_ID != -1:
sent_input = sent.cuda()
# print("text input", sent_input)
sent_emb_clip = model.encode_text(
sent_input).float()
if CLIP:
sent_emb = sent_emb_clip
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
fake_imgs, _, _, _ = netG(noise, sent_emb, words_embs,
mask)
if CLIP:
images = []
for j in range(fake_imgs[-1].shape[0]):
image = fake_imgs[-1][j].cpu().clone()
image = image.squeeze(0)
unloader = transforms.ToPILImage()
image = unloader(image)
image = preprocess(
image.convert("RGB")) # 256*256 -> 224*224
images.append(image)
image_mean = torch.tensor(
[0.48145466, 0.4578275, 0.40821073]).cuda()
image_std = torch.tensor(
[0.26862954, 0.26130258, 0.27577711]).cuda()
image_input = torch.tensor(np.stack(images)).cuda()
image_input -= image_mean[:, None, None]
image_input /= image_std[:, None, None]
cnn_codes = model.encode_image(image_input).float()
else:
region_features, cnn_codes = image_encoder(
fake_imgs[-1])
for j in range(batch_size):
cnn_code = cnn_codes[j]
temp = keys[j].replace('b', '').replace("'", '')
img_features[temp] = cnn_code.cpu().numpy()
txt_features[temp] = sent_emb[j].cpu().numpy()
with open(save_dir + ".pkl", 'wb') as f:
pickle.dump(img_features, f)
with open(save_dir + "_text.pkl", 'wb') as f:
pickle.dump(txt_features, f)
def sampling(self, split_dir, model):
if cfg.TRAIN.NET_G == '':
print('Error: the path for morels is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
# Build and load the generator
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
netG.apply(weights_init)
if cfg.GPU_ID != -1:
netG.cuda()
netG.eval()
#
text_encoder = RNN_ENCODER(self.n_words,
nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = \
torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load text encoder from:', cfg.TRAIN.NET_E)
if cfg.GPU_ID != -1:
text_encoder = text_encoder.cuda()
text_encoder.eval()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
with torch.no_grad():
noise = Variable(torch.FloatTensor(batch_size, nz))
if cfg.GPU_ID != -1:
noise = noise.cuda()
model_dir = cfg.TRAIN.NET_G
state_dict = \
torch.load(model_dir, map_location=lambda storage, loc: storage)
# state_dict = torch.load(cfg.TRAIN.NET_G)
netG.load_state_dict(state_dict)
print('Load G from: ', model_dir)
# the path to save generated images
s_tmp = model_dir[:model_dir.rfind('.pth')]
save_dir = '%s/%s' % (s_tmp, split_dir)
mkdir_p(save_dir)
cnt = 0
#new
if cfg.TRAIN.CLIP_SENTENCODER:
print("Use CLIP SentEncoder for sampling")
for _ in range(1): # (cfg.TEXT.CAPTIONS_PER_IMAGE):
for step, data in enumerate(self.data_loader, 0):
cnt += batch_size
if step % 100 == 0:
print('step: ', step)
# if step > 50:
# break
#imgs, captions, cap_lens, class_ids, keys = prepare_data(data)
#new
imgs, captions, cap_lens, class_ids, keys, texts = prepare_data(
data)
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(
captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(
), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
# new
if cfg.TRAIN.CLIP_SENTENCODER:
# random select one paragraph for each training example
sents = []
for idx in range(len(texts)):
sents_per_image = texts[idx].split(
'\n') # new 3/11
if len(sents_per_image) > 1:
sent_ix = np.random.randint(
0,
len(sents_per_image) - 1)
else:
sent_ix = 0
sents.append(sents_per_image[sent_ix])
with open('%s/%s' % (save_dir, 'eval_sents.txt'),
'a+') as f:
f.write(sents_per_image[sent_ix] + '\n')
# print('sents: ', sents)
sent = clip.tokenize(sents) # .to(device)
# load clip
#model = torch.jit.load("model.pt").cuda().eval()
sent_input = sent
if cfg.GPU_ID != -1:
sent_input = sent.cuda()
# print("text input", sent_input)
with torch.no_grad():
sent_emb = model.encode_text(sent_input).float()
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
fake_imgs, _, _, _ = netG(noise, sent_emb, words_embs,
mask)
for j in range(batch_size):
s_tmp = '%s/fake/%s' % (save_dir, keys[j])
folder = s_tmp[:s_tmp.rfind('/')]
if not os.path.isdir(folder):
print('Make a new folder: ', folder)
mkdir_p(folder)
print('Make a new folder: ', f'{save_dir}/real')
mkdir_p(f'{save_dir}/real')
print('Make a new folder: ', f'{save_dir}/text')
mkdir_p(f'{save_dir}/text')
k = -1
# for k in range(len(fake_imgs)):
im = fake_imgs[k][j].data.cpu().numpy()
# [-1, 1] --> [0, 255]
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
fullpath = '%s_s%d.png' % (s_tmp, k)
im.save(fullpath)
temp = keys[j].replace('b', '').replace("'", '')
shutil.copy(f"../data/Face/images/{temp}.jpg",
f"{save_dir}/real/")
shutil.copy(f"../data/Face/text/{temp}.txt",
f"{save_dir}/text/")
def sampling(self, split_dir, num_samples=30000):
if cfg.TRAIN.NET_G == '':
print('Error: the path for morels is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
# Build and load the generator
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
netG.apply(weights_init)
netG.cuda()
netG.eval()
#
text_encoder = RNN_ENCODER(self.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = \
torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder = text_encoder.cuda()
text_encoder.eval()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz))
noise = noise.cuda()
model_dir = cfg.TRAIN.NET_G
state_dict = \
torch.load(model_dir, map_location=lambda storage, loc: storage)
# state_dict = torch.load(cfg.TRAIN.NET_G)
netG.load_state_dict(state_dict["netG"])
print('Load G from: ', model_dir)
# the path to save generated images
s_tmp = model_dir[:model_dir.rfind('.pth')]
save_dir = '%s/%s' % (s_tmp, split_dir)
mkdir_p(save_dir)
cnt = 0
for _ in range(1): # (cfg.TEXT.CAPTIONS_PER_IMAGE):
for step, data in enumerate(self.data_loader, 0):
cnt += batch_size
if step % 10000 == 0:
print('step: ', step)
if step >= num_samples:
break
imgs, captions, cap_lens, class_ids, keys, transformation_matrices, label_one_hot = prepare_data(data)
transf_matrices_inv = transformation_matrices[1]
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
inputs = (noise, sent_emb, words_embs, mask, transf_matrices_inv, label_one_hot)
with torch.no_grad():
fake_imgs, _, mu, logvar = nn.parallel.data_parallel(netG, inputs, self.gpus)
for j in range(batch_size):
s_tmp = '%s/single/%s' % (save_dir, keys[j])
folder = s_tmp[:s_tmp.rfind('/')]
if not os.path.isdir(folder):
print('Make a new folder: ', folder)
mkdir_p(folder)
k = -1
# for k in range(len(fake_imgs)):
im = fake_imgs[k][j].data.cpu().numpy()
# [-1, 1] --> [0, 255]
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
fullpath = '%s_s%d.png' % (s_tmp, k)
im.save(fullpath)
def sampling(self, split_dir, num_samples=30000):
if cfg.TRAIN.NET_G == '':
logger.error('Error: the path for morels is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
# Build and load the generator
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
netG.apply(weights_init)
netG.to(cfg.DEVICE)
netG.eval()
#
text_encoder = RNN_ENCODER(self.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
text_encoder = text_encoder.to(cfg.DEVICE)
text_encoder.eval()
logger.info('Loaded text encoder from: %s', cfg.TRAIN.NET_E)
batch_size = self.batch_size[0]
nz = cfg.GAN.GLOBAL_Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz)).to(cfg.DEVICE)
local_noise = Variable(torch.FloatTensor(batch_size, cfg.GAN.LOCAL_Z_DIM)).to(cfg.DEVICE)
model_dir = cfg.TRAIN.NET_G
state_dict = torch.load(model_dir, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict["netG"])
max_objects = 10
logger.info('Load G from: %s', model_dir)
# the path to save generated images
s_tmp = model_dir[:model_dir.rfind('.pth')].split("/")[-1]
save_dir = '%s/%s/%s' % ("../output", s_tmp, split_dir)
mkdir_p(save_dir)
logger.info("Saving images to: {}".format(save_dir))
number_batches = num_samples // batch_size
if number_batches < 1:
number_batches = 1
data_iter = iter(self.data_loader)
for step in tqdm(range(number_batches)):
data = data_iter.next()
imgs, captions, cap_lens, class_ids, keys, transformation_matrices, label_one_hot, _ = prepare_data(
data, eval=True)
transf_matrices = transformation_matrices[0]
transf_matrices_inv = transformation_matrices[1]
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
local_noise.data.normal_(0, 1)
inputs = (noise, local_noise, sent_emb, words_embs, mask, transf_matrices, transf_matrices_inv, label_one_hot, max_objects)
inputs = tuple((inp.to(cfg.DEVICE) if isinstance(inp, torch.Tensor) else inp) for inp in inputs)
with torch.no_grad():
fake_imgs, _, mu, logvar = netG(*inputs)
for batch_idx, j in enumerate(range(batch_size)):
s_tmp = '%s/%s' % (save_dir, keys[j])
folder = s_tmp[:s_tmp.rfind('/')]
if not os.path.isdir(folder):
logger.info('Make a new folder: %s', folder)
mkdir_p(folder)
k = -1
# for k in range(len(fake_imgs)):
im = fake_imgs[k][j].data.cpu().numpy()
# [-1, 1] --> [0, 255]
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
fullpath = '%s_s%d.png' % (s_tmp, step*batch_size+batch_idx)
im.save(fullpath)
def train(dataloader, netG, netD, text_encoder, optimizerG, optimizerD_enc,
optimizerD_proj, state_epoch, batch_size, device, output_dir,
logger):
#i = -1
for epoch in range(state_epoch + 1, cfg.TRAIN.MAX_EPOCH + 1):
for step, data in enumerate(dataloader, 0):
#i+=1
imags, captions, cap_lens, class_ids, keys = prepare_data(data)
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
imgs = imags[0].to(device)
real_features = netD(imgs)
output = netD.COND_DNET(real_features, sent_emb)
errD_real = torch.nn.ReLU()(1.0 - output).mean()
output = netD.COND_DNET(real_features[:(batch_size - 1)],
sent_emb[1:batch_size])
errD_mismatch = torch.nn.ReLU()(1.0 + output).mean()
# synthesize fake images
noise = torch.randn(batch_size, 100)
noise = noise.to(device)
fake = netG(noise, sent_emb)
#if not cfg.TRAIN.ONLY_REAL:
# G does not need update with D
#if cfg.TRAIN.ONLY_REAL:
# for p in netD.COND_DNET.parameters():
# p.requires_grad_(False)
fake_features = netD(fake.detach())
errD_fake = netD.COND_DNET(fake_features, sent_emb)
errD_fake = torch.nn.ReLU()(1.0 + errD_fake).mean()
#if cfg.TRAIN.ONLY_REAL:
# for p in netD.COND_DNET.parameters():
# p.requires_grad_(True)
errD_enc = errD_real + (errD_fake + errD_mismatch) / 2.0
optimizerD_enc.zero_grad()
optimizerD_proj.zero_grad()
optimizerG.zero_grad()
errD_enc.backward(retain_graph=True)
optimizerD_enc.step()
errD_proj = errD_real + errD_mismatch
optimizerD_enc.zero_grad()
optimizerD_proj.zero_grad()
optimizerG.zero_grad()
errD_proj.backward()
optimizerD_proj.step()
#MA-GP
interpolated = (imgs.data).requires_grad_()
sent_inter = (sent_emb.data).requires_grad_()
features = netD(interpolated)
out = netD.COND_DNET(features, sent_inter)
grads = torch.autograd.grad(outputs=out,
inputs=(interpolated, sent_inter),
grad_outputs=torch.ones(
out.size()).cuda(),
retain_graph=True,
create_graph=True,
only_inputs=True)
grad0 = grads[0].view(grads[0].size(0), -1)
grad1 = grads[1].view(grads[1].size(0), -1)
grad = torch.cat((grad0, grad1), dim=1)
grad_l2norm = torch.sqrt(torch.sum(grad**2, dim=1))
d_loss_gp = torch.mean((grad_l2norm)**6)
d_loss = 2.0 * d_loss_gp
optimizerD_enc.zero_grad()
optimizerD_proj.zero_grad()
optimizerG.zero_grad()
d_loss.backward(retain_graph=True)
optimizerD_enc.step()
optimizerD_enc.zero_grad()
optimizerD_proj.zero_grad()
optimizerG.zero_grad()
d_loss.backward()
optimizerD_proj.step()
# update G
#if (i+1) % cfg.TRAIN.N_CRITIC == 0:
#i = -1
features = netD(fake)
output = netD.COND_DNET(features, sent_emb)
errG = -output.mean()
optimizerG.zero_grad()
optimizerD.zero_grad()
errG.backward()
optimizerG.step()
logger.info(
'[%d/%d][%d/%d] Loss_encD: %.3f Loss_projD: %.3f Loss_G %.3f errD_real %.3f errD_mis %.3f errD_fake %.3f magp %.3f'
% (epoch, cfg.TRAIN.MAX_EPOCH, step, len(dataloader),
errD_enc.item(), errD_proj.item(), errG.item(),
errD_real.item(), errD_mismatch.item(), errD_fake.item(),
d_loss_gp.item()))
vutils.save_image(fake.data,
'%s/imgs/fake_samples_epoch_%03d.png' %
(output_dir, epoch),
normalize=True)
if epoch % 1 == 0:
torch.save(netG.state_dict(),
'%s/models/netG_%03d.pth' % (output_dir, epoch))
torch.save(netD.state_dict(),
'%s/models/netD_%03d.pth' % (output_dir, epoch))
torch.save(optimizerG.state_dict(),
'%s/models/optimizerG.pth' % (output_dir))
torch.save(optimizerD.state_dict(),
'%s/models/optimizerD.pth' % (output_dir))
return
def sampling(self, split_dir):
if cfg.TRAIN.NET_G == '':
print('Error: the path for morels is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
# Build and load the generator
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
netG.apply(weights_init)
netG.cuda()
netG.eval()
# load text encoder
text_encoder = RNN_ENCODER(self.n_words,
nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = torch.load(cfg.TRAIN.NET_E,
map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder = text_encoder.cuda()
text_encoder.eval()
#load image encoder
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
img_encoder_path = cfg.TRAIN.NET_E.replace('text_encoder',
'image_encoder')
state_dict = torch.load(img_encoder_path,
map_location=lambda storage, loc: storage)
image_encoder.load_state_dict(state_dict)
print('Load image encoder from:', img_encoder_path)
image_encoder = image_encoder.cuda()
image_encoder.eval()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz), volatile=True)
noise = noise.cuda()
model_dir = cfg.TRAIN.NET_G
state_dict = torch.load(model_dir,
map_location=lambda storage, loc: storage)
# state_dict = torch.load(cfg.TRAIN.NET_G)
netG.load_state_dict(state_dict)
print('Load G from: ', model_dir)
# the path to save generated images
s_tmp = model_dir[:model_dir.rfind('.pth')]
save_dir = '%s/%s' % (s_tmp, split_dir)
mkdir_p(save_dir)
cnt = 0
R_count = 0
R = np.zeros(30000)
cont = True
for ii in range(11): # (cfg.TEXT.CAPTIONS_PER_IMAGE):
if (cont == False):
break
for step, data in enumerate(self.data_loader, 0):
cnt += batch_size
if (cont == False):
break
if step % 100 == 0:
print('cnt: ', cnt)
# if step > 50:
# break
imgs, captions, cap_lens, class_ids, keys = prepare_data(
data)
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(
captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(
), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
fake_imgs, _, _, _ = netG(noise, sent_emb, words_embs,
mask, cap_lens)
for j in range(batch_size):
s_tmp = '%s/single/%s' % (save_dir, keys[j])
folder = s_tmp[:s_tmp.rfind('/')]
if not os.path.isdir(folder):
#print('Make a new folder: ', folder)
mkdir_p(folder)
k = -1
# for k in range(len(fake_imgs)):
im = fake_imgs[k][j].data.cpu().numpy()
# [-1, 1] --> [0, 255]
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
fullpath = '%s_s%d_%d.png' % (s_tmp, k, ii)
im.save(fullpath)
_, cnn_code = image_encoder(fake_imgs[-1])
for i in range(batch_size):
mis_captions, mis_captions_len = self.dataset.get_mis_caption(
class_ids[i])
hidden = text_encoder.init_hidden(99)
_, sent_emb_t = text_encoder(mis_captions,
mis_captions_len, hidden)
rnn_code = torch.cat(
(sent_emb[i, :].unsqueeze(0), sent_emb_t), 0)
### cnn_code = 1 * nef
### rnn_code = 100 * nef
scores = torch.mm(cnn_code[i].unsqueeze(0),
rnn_code.transpose(0, 1)) # 1* 100
cnn_code_norm = torch.norm(cnn_code[i].unsqueeze(0),
2,
dim=1,
keepdim=True)
rnn_code_norm = torch.norm(rnn_code,
2,
dim=1,
keepdim=True)
norm = torch.mm(cnn_code_norm,
rnn_code_norm.transpose(0, 1))
scores0 = scores / norm.clamp(min=1e-8)
if torch.argmax(scores0) == 0:
R[R_count] = 1
R_count += 1
if R_count >= 30000:
sum = np.zeros(10)
np.random.shuffle(R)
for i in range(10):
sum[i] = np.average(R[i * 3000:(i + 1) * 3000 - 1])
R_mean = np.average(sum)
R_std = np.std(sum)
print("R mean:{:.4f} std:{:.4f}".format(R_mean, R_std))
cont = False
def train(self):
text_encoder, image_encoder, netG, netsD, start_epoch = self.build_models()
avg_param_G = copy_G_params(netG)
optimizerG, optimizersD = self.define_optimizers(netG, netsD)
real_labels, fake_labels, match_labels = self.prepare_labels()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz))
fixed_noise = Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
gen_iterations = 0
# gen_iterations = start_epoch * self.num_batches
for epoch in range(start_epoch, self.max_epoch):
start_t = time.time()
data_iter = iter(self.data_loader)
step = 0
while step < self.num_batches:
# reset requires_grad to be trainable for all Ds
# self.set_requires_grad_value(netsD, True)
######################################################
# (1) Prepare training data and Compute text embeddings
######################################################
data = data_iter.next()
imgs, captions, cap_lens, class_ids, keys, transformation_matrices, label_one_hot = prepare_data(data)
transf_matrices = transformation_matrices[0]
transf_matrices_inv = transformation_matrices[1]
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
inputs = (noise, sent_emb, words_embs, mask, transf_matrices_inv, label_one_hot)
fake_imgs, _, mu, logvar = nn.parallel.data_parallel(netG, inputs, self.gpus)
#######################################################
# (3) Update D network
######################################################
errD_total = 0
D_logs = ''
for i in range(len(netsD)):
netsD[i].zero_grad()
if i == 0:
errD = discriminator_loss(netsD[i], imgs[i], fake_imgs[i],
sent_emb, real_labels, fake_labels, self.gpus,
local_labels=label_one_hot, transf_matrices=transf_matrices,
transf_matrices_inv=transf_matrices_inv)
else:
errD = discriminator_loss(netsD[i], imgs[i], fake_imgs[i],
sent_emb, real_labels, fake_labels, self.gpus)
# backward and update parameters
errD.backward()
optimizersD[i].step()
errD_total += errD
D_logs += 'errD%d: %.2f ' % (i, errD.item())
#######################################################
# (4) Update G network: maximize log(D(G(z)))
######################################################
# compute total loss for training G
step += 1
gen_iterations += 1
# do not need to compute gradient for Ds
# self.set_requires_grad_value(netsD, False)
netG.zero_grad()
errG_total, G_logs = \
generator_loss(netsD, image_encoder, fake_imgs, real_labels,
words_embs, sent_emb, match_labels, cap_lens, class_ids, self.gpus,
local_labels=label_one_hot, transf_matrices=transf_matrices,
transf_matrices_inv=transf_matrices_inv)
kl_loss = KL_loss(mu, logvar)
errG_total += kl_loss
G_logs += 'kl_loss: %.2f ' % kl_loss.item()
# backward and update parameters
errG_total.backward()
optimizerG.step()
for p, avg_p in zip(netG.parameters(), avg_param_G):
avg_p.mul_(0.999).add_(0.001, p.data)
# save images
if gen_iterations % 1000 == 0:
print(D_logs + '\n' + G_logs)
backup_para = copy_G_params(netG)
load_params(netG, avg_param_G)
self.save_img_results(netG, fixed_noise, sent_emb,
words_embs, mask, image_encoder,
captions, cap_lens, epoch, transf_matrices_inv,
label_one_hot, name='average')
load_params(netG, backup_para)
end_t = time.time()
print('''[%d/%d][%d]
Loss_D: %.2f Loss_G: %.2f Time: %.2fs'''
% (epoch, self.max_epoch, self.num_batches,
errD_total.item(), errG_total.item(),
end_t - start_t))
if epoch % cfg.TRAIN.SNAPSHOT_INTERVAL == 0: # and epoch != 0:
self.save_model(netG, avg_param_G, netsD, optimizerG, optimizersD, epoch)
self.save_model(netG, avg_param_G, netsD, optimizerG, optimizersD, epoch)
def genDiscOutputs(self, split_dir, num_samples=57140):
if cfg.TRAIN.NET_G == '':
logger.error('Error: the path for morels is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
# Build and load the generator
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
netG.apply(weights_init)
netG.to(cfg.DEVICE)
netG.eval()
#
text_encoder = RNN_ENCODER(self.n_words,
nhidden=cfg.TEXT.EMBEDDING_DIM) ###HACK
state_dict = torch.load(cfg.TRAIN.NET_E,
map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
text_encoder = text_encoder.to(cfg.DEVICE)
text_encoder.eval()
logger.info('Loaded text encoder from: %s', cfg.TRAIN.NET_E)
batch_size = self.batch_size[0]
nz = cfg.GAN.GLOBAL_Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz)).to(cfg.DEVICE)
local_noise = Variable(
torch.FloatTensor(batch_size,
cfg.GAN.LOCAL_Z_DIM)).to(cfg.DEVICE)
model_dir = cfg.TRAIN.NET_G
state_dict = torch.load(model_dir,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict["netG"])
for keys in state_dict.keys():
print(keys)
logger.info('Load G from: %s', model_dir)
max_objects = 3
from model import D_NET256
netD = D_NET256()
netD.load_state_dict(state_dict["netD"][2])
netD.eval()
# the path to save generated images
s_tmp = model_dir[:model_dir.rfind('.pth')].split("/")[-1]
save_dir = '%s/%s/%s' % ("../output", s_tmp, split_dir)
mkdir_p(save_dir)
logger.info("Saving images to: {}".format(save_dir))
number_batches = num_samples // batch_size
if number_batches < 1:
number_batches = 1
data_iter = iter(self.data_loader)
real_labels, fake_labels, match_labels = self.prepare_labels()
for step in tqdm(range(number_batches)):
data = data_iter.next()
imgs, captions, cap_lens, class_ids, keys, transformation_matrices, label_one_hot, _ = prepare_data(
data, eval=True)
transf_matrices = transformation_matrices[0]
transf_matrices_inv = transformation_matrices[1]
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs.detach(), sent_emb.detach()
mask = (captions == 0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
local_noise.data.normal_(0, 1)
inputs = (noise, local_noise, sent_emb, words_embs, mask,
transf_matrices, transf_matrices_inv, label_one_hot,
max_objects)
inputs = tuple(
(inp.to(cfg.DEVICE) if isinstance(inp, torch.Tensor
) else inp)
for inp in inputs)
with torch.no_grad():
fake_imgs, _, mu, logvar = netG(*inputs)
inputs = (fake_imgs, fake_labels, transf_matrices,
transf_matrices_inv, max_objects)
codes = netsD[-1].partial_forward(*inputs)
def sample(self, split_dir, num_samples=25, draw_bbox=False):
from PIL import Image, ImageDraw, ImageFont
import cPickle as pickle
import torchvision
import torchvision.utils as vutils
if cfg.TRAIN.NET_G == '':
print('Error: the path for model NET_G is not found!')
else:
if split_dir == 'test':
split_dir = 'valid'
# Build and load the generator
text_encoder = RNN_ENCODER(self.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = \
torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder = text_encoder.cuda()
text_encoder.eval()
batch_size = cfg.TRAIN.BATCH_SIZE
nz = cfg.GAN.Z_DIM
model_dir = cfg.TRAIN.NET_G
state_dict = torch.load(model_dir, map_location=lambda storage, loc: storage)
# state_dict = torch.load(cfg.TRAIN.NET_G)
netG = G_NET()
print('Load G from: ', model_dir)
netG.apply(weights_init)
netG.load_state_dict(state_dict["netG"])
netG.cuda()
netG.eval()
# the path to save generated images
s_tmp = model_dir[:model_dir.rfind('.pth')]
save_dir = '%s_%s' % (s_tmp, split_dir)
mkdir_p(save_dir)
#######################################
noise = Variable(torch.FloatTensor(9, nz))
imsize = 256
for step, data in enumerate(self.data_loader, 0):
if step >= num_samples:
break
imgs, captions, cap_lens, class_ids, keys, transformation_matrices, label_one_hot, bbox = \
prepare_data(data, eval=True)
transf_matrices_inv = transformation_matrices[1][0].unsqueeze(0)
label_one_hot = label_one_hot[0].unsqueeze(0)
img = imgs[-1][0]
val_image = img.view(1, 3, imsize, imsize)
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
words_embs, sent_emb = words_embs[0].unsqueeze(0).detach(), sent_emb[0].unsqueeze(0).detach()
words_embs = words_embs.repeat(9, 1, 1)
sent_emb = sent_emb.repeat(9, 1)
mask = (captions == 0)
mask = mask[0].unsqueeze(0)
num_words = words_embs.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
mask = mask.repeat(9, 1)
transf_matrices_inv = transf_matrices_inv.repeat(9, 1, 1, 1)
label_one_hot = label_one_hot.repeat(9, 1, 1)
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
inputs = (noise, sent_emb, words_embs, mask, transf_matrices_inv, label_one_hot)
with torch.no_grad():
fake_imgs, _, mu, logvar = nn.parallel.data_parallel(netG, inputs, self.gpus)
data_img = torch.FloatTensor(10, 3, imsize, imsize).fill_(0)
data_img[0] = val_image
data_img[1:10] = fake_imgs[-1]
if draw_bbox:
for idx in range(3):
x, y, w, h = tuple([int(imsize*x) for x in bbox[0, idx]])
w = imsize-1 if w > imsize-1 else w
h = imsize-1 if h > imsize-1 else h
if x <= -1:
break
data_img[:10, :, y, x:x + w] = 1
data_img[:10, :, y:y + h, x] = 1
data_img[:10, :, y+h, x:x + w] = 1
data_img[:10, :, y:y + h, x + w] = 1
# get caption
cap = captions[0].data.cpu().numpy()
sentence = ""
for j in range(len(cap)):
if cap[j] == 0:
break
word = self.ixtoword[cap[j]].encode('ascii', 'ignore').decode('ascii')
sentence += word + " "
sentence = sentence[:-1]
vutils.save_image(data_img, '{}/{}_{}.png'.format(save_dir, sentence, step), normalize=True, nrow=10)
print("Saved {} files to {}".format(step, save_dir))
def train(self, model):
text_encoder, image_encoder, netG, netsD, start_epoch = self.build_models(
) #load encoder
avg_param_G = copy_G_params(netG)
optimizerG, optimizersD = self.define_optimizers(netG, netsD)
real_labels, fake_labels, match_labels = self.prepare_labels()
batch_size = self.batch_size
nz = cfg.GAN.Z_DIM
noise = Variable(torch.FloatTensor(batch_size, nz))
fixed_noise = Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
gen_iterations = 0
# gen_iterations = start_epoch * self.num_batches
if cfg.TRAIN.CLIP_SENTENCODER:
print("CLIP Sentence Encoder: True")
if cfg.TRAIN.CLIP_LOSS:
print("CLIP Loss: True")
if cfg.TRAIN.EXTRA_LOSS:
print("Extra DAMSM Loss in G: True")
print("DAMSM Weight: ", cfg.TRAIN.WEIGHT_DAMSM_LOSS)
for epoch in range(start_epoch, self.max_epoch):
start_t = time.time()
data_iter = iter(self.data_loader)
step = 0
while step < self.num_batches:
# reset requires_grad to be trainable for all Ds
# self.set_requires_grad_value(netsD, True)
######################################################
# (1) Prepare training data and Compute text embeddings
######################################################
data = data_iter.next()
# imgs, captions, cap_lens, class_ids, keys = prepare_data(data) #new sents:, sents
# new: return raw texts
imgs, captions, cap_lens, class_ids, keys, texts = prepare_data(
data)
hidden = text_encoder.init_hidden(batch_size)
# words_embs: batch_size x nef x seq_len
# sent_emb: batch_size x nef
# new: rename
words_embs_damsm, sent_emb_damsm = text_encoder(
captions, cap_lens, hidden)
#print('captions shape from trainer: ', captions.shape) torch.Size([12, 18])
#print('sentence emb size: ', sent_emb.shape) torch.Size([12, 256])
words_embs_damsm, sent_emb_damsm = words_embs_damsm.detach(
), sent_emb_damsm.detach()
#print('sentence emb size after detach: ', sent_emb[0]) torch.Size([12, 256])
mask = (captions == 0)
num_words = words_embs_damsm.size(2)
if mask.size(1) > num_words:
mask = mask[:, :num_words]
# new: use clip sentence encoder
if cfg.TRAIN.CLIP_SENTENCODER or cfg.TRAIN.CLIP_LOSS:
sents = []
# randomly select one paragraph for each training example
for idx in range(len(texts)):
sents_per_image = texts[idx].split(
'\n') #new: '\n' rather than '.'
if len(sents_per_image) > 1:
sent_ix = np.random.randint(
0,
len(sents_per_image) - 1)
else:
sent_ix = 0
sents.append(sents_per_image[sent_ix])
#print('sents: ', sents)
sent = clip.tokenize(sents) #.to(device)
# load clip
#model = torch.jit.load("model.pt").cuda().eval() # ViT-B/32
sent_input = sent.cuda()
with torch.no_grad():
sent_emb_clip = model.encode_text(sent_input).float()
if cfg.TRAIN.CLIP_SENTENCODER:
sent_emb = sent_emb_clip
else:
sent_emb = sent_emb_damsm
else:
sent_emb_clip = 0
sent_emb = sent_emb_damsm
words_embs = words_embs_damsm
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
fake_imgs, _, mu, logvar = netG(noise, sent_emb, words_embs,
mask)
#######################################################
# (3) Update D network
######################################################
errD_total = 0
D_logs = ''
for i in range(len(netsD)):
netsD[i].zero_grad()
errD = discriminator_loss(netsD[i], imgs[i], fake_imgs[i],
sent_emb, real_labels,
fake_labels)
# backward and update parameters
errD.backward()
optimizersD[i].step()
errD_total += errD
D_logs += 'errD%d: %.2f ' % (i, errD.item())
#######################################################
# (4) Update G network: maximize log(D(G(z)))
######################################################
# compute total loss for training G
step += 1
gen_iterations += 1
# do not need to compute gradient for Ds
# self.set_requires_grad_value(netsD, False)
netG.zero_grad()
# new: pass clip model and sent_emb_damsm for CLIP_LOSS = True
errG_total, G_logs = \
generator_loss(netsD, image_encoder, fake_imgs, real_labels,
words_embs, sent_emb, match_labels, cap_lens, class_ids, model, sent_emb_damsm, sent_emb_clip)
kl_loss = KL_loss(mu, logvar)
errG_total += kl_loss
G_logs += 'kl_loss: %.2f ' % kl_loss.item()
# backward and update parameters
errG_total.backward()
optimizerG.step()
for p, avg_p in zip(netG.parameters(), avg_param_G):
avg_p.mul_(0.999).add_(0.001, p.data)
if gen_iterations % 100 == 0:
print(D_logs + '\n' + G_logs)
# save images
if gen_iterations % 1000 == 0:
backup_para = copy_G_params(netG)
load_params(netG, avg_param_G)
self.save_img_results(netG,
fixed_noise,
sent_emb,
words_embs,
mask,
image_encoder,
captions,
cap_lens,
epoch,
name='average')
load_params(netG, backup_para)
#
# self.save_img_results(netG, fixed_noise, sent_emb,
# words_embs, mask, image_encoder,
# captions, cap_lens,
# epoch, name='current')
end_t = time.time()
print('''[%d/%d][%d]
Loss_D: %.2f Loss_G: %.2f Time: %.2fs''' %
(epoch, self.max_epoch, self.num_batches, errD_total.item(),
errG_total.item(), end_t - start_t))
if epoch % cfg.TRAIN.SNAPSHOT_INTERVAL == 0 or epoch % 10 == 0: # and epoch != 0:
self.save_model(netG, avg_param_G, netsD, epoch)
self.save_model(netG, avg_param_G, netsD, self.max_epoch)
