def __init__(self, caption_file, saveable, cuda=False, profile=False):
# flags
self.cuda = cuda
self.profile = profile
if self.profile:
print('Initializing Generator...')
print('cuda={}\nprofile={}'.format(self.cuda, self.profile))
# load caption indices
x = pickle.load(open(caption_file, 'rb'))
self.ixtoword = x[2]
self.wordtoix = x[3]
del x
# load text encoder
self.text_encoder = RNN_ENCODER(len(self.wordtoix), nhidden=cfg.TEXT.EMBEDDING_DIM)
state_dict = torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
self.text_encoder.load_state_dict(state_dict)
if self.cuda:
self.text_encoder.cuda()
self.text_encoder.eval()
# load generative model
self.netG = G_NET()
state_dict = torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
self.netG.load_state_dict(state_dict)
if self.cuda:
self.netG.cuda()
self.netG.eval()
# saveable items -> push to storage
self.saveable = saveable
def build_models():
# build model ############################################################
text_encoder = RNN_ENCODER(dataset.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
labels = Variable(torch.LongTensor(range(batch_size)))
start_epoch = 0
if cfg.TRAIN.NET_E != '':
state_dict = torch.load(cfg.TRAIN.NET_E)
text_encoder.load_state_dict(state_dict)
print('Load ', cfg.TRAIN.NET_E)
#
name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
state_dict = torch.load(name)
image_encoder.load_state_dict(state_dict)
print('Load ', name)
istart = cfg.TRAIN.NET_E.rfind('_') + 8
iend = cfg.TRAIN.NET_E.rfind('.')
start_epoch = cfg.TRAIN.NET_E[istart:iend]
start_epoch = int(start_epoch) + 1
print('start_epoch', start_epoch)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
labels = labels.cuda()
return text_encoder, image_encoder, labels, start_epoch
def build_models():
text_encoder = RNN_ENCODER(dataset.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
labels = Variable(torch.LongTensor(range(batch_size)))
start_epoch = 0
lr = cfg.TRAIN.ENCODER_LR
if cfg.TRAIN.NET_E != '':
state_dict = torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load {}'.format(cfg.TRAIN.NET_E))
name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
state_dict = torch.load(name, map_location=lambda storage, loc: storage)
image_encoder.load_state_dict(state_dict)
print('Load {}'.format(name))
istart = cfg.TRAIN.NET_E.rfind('_') + 8
iend = cfg.TRAIN.NET_E.rfind('.')
start_epoch = cfg.TRAIN.NET_E[istart:iend]
start_epoch = int(start_epoch) + 1
print('start_epoch', start_epoch)
# initial lr with the right value
# note that the turning point is always epoch 114
if start_epoch < 114:
lr = cfg.TRAIN.ENCODER_LR * (0.98 ** start_epoch)
else:
lr = cfg.TRAIN.ENCODER_LR / 10
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
labels = labels.cuda()
return text_encoder, image_encoder, labels, start_epoch, lr
def build_text_encoder(ntokens):
text_encoder = RNN_ENCODER(ntokens, nhidden=cfg.TEXT.EMBEDDING_DIM)
if cfg.TRAIN.NET_E != '':
state_dict = torch.load(cfg.TRAIN.NET_E,
map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load ', cfg.TRAIN.NET_E)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
return text_encoder
def models(modelname, cfg, word_len):
#print(word_len)
text_encoder = cache.get(modelname + '_text_encoder', None)
if text_encoder is None:
#print("text_encoder not cached")
text_encoder = RNN_ENCODER(word_len, 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.CUDA:
text_encoder.cuda()
text_encoder.eval()
cache[modelname + '_text_encoder'] = text_encoder
netG = cache.get(modelname + '_netG', None)
if netG is None:
#print("netG not cached")
netG = G_NET()
state_dict = torch.load(cfg.TRAIN.NET_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
if cfg.CUDA:
netG.cuda()
netG.eval()
cache[modelname + '_netG'] = netG
return text_encoder, netG
def models(word_len):
print('Loading Model', word_len)
text_encoder = cache.get('text_encoder')
print('Text enconder', text_encoder)
if text_encoder is None:
print("text_encoder not cached")
text_encoder = RNN_ENCODER(word_len, nhidden=256)
state_dict = torch.load('../DAMSMencoders/coco/text_encoder100.pth', map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('loaded text encoder')
text_encoder.cuda()
print('text encoder cuda')
text_encoder.eval()
print('text encoder eval')
#cache.set('text_encoder', text_encoder, timeout=60 * 60 * 24)
print('Got Text Encoder, moving to netG')
netG = cache.get('netG')
if netG is None:
print("netG not cached")
netG = G_NET()
state_dict = torch.load('../models/coco_AttnGAN2.pth', map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
if cfg.CUDA:
netG.cuda()
netG.eval()
#cache.set('netG', netG, timeout=60 * 60 * 24)
print('Got NetG')
return text_encoder, netG
def models(word_len):
#print(word_len)
text_encoder = cache.get('text_encoder')
if text_encoder is None:
#print("text_encoder not cached")
text_encoder = RNN_ENCODER(word_len, 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.CUDA:
text_encoder.cuda()
text_encoder.eval()
cache.set('text_encoder', text_encoder, timeout=60 * 60 * 24)
netG = cache.get('netG')
if netG is None:
#print("netG not cached")
netG = G_NET()
state_dict = torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
if cfg.CUDA:
netG.cuda()
netG.eval()
cache.set('netG', netG, timeout=60 * 60 * 24)
return text_encoder, netG
def build_models(text_encoder_type):
# build model ############################################################
text_encoder_type = text_encoder_type.casefold()
if text_encoder_type not in ('rnn', 'transformer'):
raise ValueError('Unsupported text_encoder_type')
if text_encoder_type == 'rnn':
text_encoder = RNN_ENCODER(dataset.n_words,
nhidden=cfg.TEXT.EMBEDDING_DIM)
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
labels = Variable(torch.LongTensor(range(batch_size)))
start_epoch = 0
if cfg.TRAIN.NET_E:
if text_encoder_type == 'rnn':
state_dict = torch.load(cfg.TRAIN.NET_E)
text_encoder.load_state_dict(state_dict)
elif text_encoder_type == 'transformer':
text_encoder = GPT2Model.from_pretrained(cfg.TRAIN.NET_E)
# output_hidden_states = True )
print('Load ', cfg.TRAIN.NET_E)
#
name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
state_dict = torch.load(name)
image_encoder.load_state_dict(state_dict)
print('Load ', name)
istart = cfg.TRAIN.NET_E.rfind('_') + 8
iend = cfg.TRAIN.NET_E.rfind('.')
start_epoch = cfg.TRAIN.NET_E[istart:iend]
start_epoch = int(start_epoch) + 1
else:
if text_encoder_type == 'rnn':
print('Training RNN from scratch')
elif text_encoder_type == 'transformer':
# don't initialize the weights of these huge models from scratch...
print('Training Transformer starting from pretrained model')
text_encoder = GPT2Model.from_pretrained(TRANSFORMER_ENCODER)
# output_hidden_states = True )
print('Training CNN starting from ImageNet pretrained Inception-v3')
print('start_epoch', start_epoch)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
labels = labels.cuda()
return text_encoder, image_encoder, labels, start_epoch
def generate(captions, copies):
x = pickle.load(open('data/captions.pickle', 'rb'))
# print(x)
ixtoword = x[2]
wordtoix = x[3]
del x
word_len = len(wordtoix)
text_encoder = RNN_ENCODER(word_len, 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.eval()
netG = G_NET()
state_dict = torch.load(cfg.TRAIN.NET_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
# netG.eval()
# seed = 100
# random.seed(seed)
# np.random.seed(seed)
# torch.manual_seed(seed)
# load word vector
captions, cap_lens = vectorize_caption(wordtoix, captions, copies)
n_words = len(wordtoix)
# only one to generate
batch_size = captions.shape[0]
nz = cfg.GAN.Z_DIM
captions = Variable(torch.from_numpy(captions), volatile=True)
cap_lens = Variable(torch.from_numpy(cap_lens), volatile=True)
noise = Variable(torch.FloatTensor(batch_size, nz), volatile=True)
#######################################################
# (1) Extract text embeddings
#######################################################
hidden = text_encoder.init_hidden(batch_size)
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
mask = (captions == 0)
#######################################################
# (2) Generate fake images
#######################################################
noise.data.normal_(0, 1)
fake_imgs, attention_maps, _, _ = netG(noise, sent_emb, words_embs, mask)
# G attention
cap_lens_np = cap_lens.cpu().data.numpy()
# # storing to blob storage
# container_name = "images"
# full_path = "https://attgan.blob.core.windows.net/images/%s"
# prefix = datetime.now().strftime('%Y/%B/%d/%H_%M_%S_%f')
imgs = []
# only look at first one
#j = 0
return imglist(fake_imgs, batch_size)
def build_models():
# build model ############################################################
print('build_model(), model_type: ', model_type)
if (model_type == 'bert'):
#cfg.LOCAL_PRETRAINED = False
if (cfg.LOCAL_PRETRAINED):
tokenizer = tokenization.FullTokenizer(
vocab_file=cfg.BERT_ENCODER.VOCAB, do_lower_case=True)
vocab_size = len(tokenizer.vocab)
#vocab_size = 3770
#vocab_size = 4000
else:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
vocab_size = len(tokenizer.vocab)
#vocab_size = 30522
text_encoder = BERT_RNN_ENCODER(vocab_size,
nhidden=cfg.TEXT.EMBEDDING_DIM)
image_encoder = BERT_CNN_ENCODER_RNN_DECODER(cfg.TEXT.EMBEDDING_DIM,
cfg.CNN_RNN.HIDDEN_DIM,
vocab_size,
rec_unit=cfg.RNN_TYPE)
else:
vocab_size = dataset_val.n_words
text_encoder = RNN_ENCODER(vocab_size, nhidden=cfg.TEXT.EMBEDDING_DIM)
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
labels = Variable(torch.LongTensor(range(batch_size)))
start_epoch = 0
if cfg.TRAIN.NET_E != '':
state_dict = torch.load(cfg.TRAIN.NET_E)
text_encoder.load_state_dict(state_dict)
print('Load ', cfg.TRAIN.NET_E)
#
name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
state_dict = torch.load(name)
image_encoder.load_state_dict(state_dict)
print('Load ', name)
istart = cfg.TRAIN.NET_E.rfind('_') + 8
iend = cfg.TRAIN.NET_E.rfind('.')
start_epoch = cfg.TRAIN.NET_E[istart:iend]
start_epoch = int(start_epoch) + 1
print('start_epoch', start_epoch)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
labels = labels.cuda()
return text_encoder, image_encoder, labels, start_epoch
def build_text_encoder(self):
# Load trained text encoder model
text_encoder = RNN_ENCODER(self.n_words, nhidden=self.embedding_dim)
state_dict = torch.load(self.net_E,
map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
text_encoder.eval()
self.text_encoder = text_encoder
def models(word_len):
text_encoder = cache.get('text_encoder')
if text_encoder is None:
text_encoder = RNN_ENCODER(word_len, nhidden=256)
state_dict = torch.load('../DAMSMencoders/coco/text_encoder100.pth', map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
text_encoder.cuda()
text_encoder.eval()
#cache.set('text_encoder', text_encoder, timeout=60 * 60 * 24)
netG = cache.get('netG')
if netG is None:
netG = G_NET()
state_dict = torch.load('../models/coco_AttnGAN2.pth', map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
if cfg.CUDA:
netG.cuda()
netG.eval()
#cache.set('netG', netG, timeout=60 * 60 * 24)
return text_encoder, netG
def models(word_len):
print( word_len )
text_encoder = cache.get('text_encoder')
if text_encoder is None:
print( "text_encoder not cached" )
if sys.argv[1].casefold() == 'rnn':
text_encoder = RNN_ENCODER(word_len, nhidden=cfg.TEXT.EMBEDDING_DIM)
elif sys.argv[1].casefold() == 'transformer':
text_encoder = GPT2Model.from_pretrained( TRANSFORMER_ENCODER )
state_dict = torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
if cfg.CUDA:
text_encoder.cuda()
text_encoder.eval()
cache.set('text_encoder', text_encoder, timeout=60 * 60 * 24)
netG = cache.get('netG')
if netG is None:
print( "netG not cached" )
if cfg.GAN.B_STYLEGEN:
netG = G_NET_STYLED()
else:
netG = G_NET()
checkpoint = torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
if cfg.GAN.B_STYLEGEN:
netG.w_ewma = checkpoint[ 'w_ewma' ]
if cfg.CUDA:
netG.w_ewma = netG.w_ewma.to( 'cuda:' + str( cfg.GPU_ID ) )
netG.load_state_dict( checkpoint[ 'netG_state_dict' ] )
else:
netG.load_state_dict( checkpoint )
if cfg.CUDA:
netG.cuda()
netG.eval()
cache.set('netG', netG, timeout=60 * 60 * 24)
return text_encoder, netG
def build_models(dataset, batch_size, audio_flag=False):
# build model ############################################################
if audio_flag:
text_encoder = CNNRNN_Attn(40,
nhidden=cfg.TEXT.EMBEDDING_DIM,
nsent=cfg.TEXT.SENT_EMBEDDING_DIM)
else:
text_encoder = RNN_ENCODER(dataset.n_words,
nhidden=cfg.TEXT.EMBEDDING_DIM,
nsent=cfg.TEXT.SENT_EMBEDDING_DIM)
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM,
condition=cfg.TRAIN.MASK_COND,
condition_channel=0)
labels = torch.LongTensor(range(batch_size))
start_epoch = 0
if cfg.TRAIN.NET_E != '':
state_dict = torch.load(cfg.TRAIN.NET_E,
map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
print('Load ', cfg.TRAIN.NET_E)
#
name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
state_dict = torch.load(name,
map_location=lambda storage, loc: storage)
image_encoder.load_state_dict(state_dict)
print('Load ', name)
# istart = cfg.TRAIN.NET_E.rfind('encoder')
# iend = cfg.TRAIN.NET_E.rfind('.')
start_epoch = re.match(r'.*_encoder(\d+).*', cfg.TRAIN.NET_E).group(1)
start_epoch = int(start_epoch) + 1
print('start_epoch', start_epoch)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
labels = labels.cuda()
return text_encoder, image_encoder, labels, start_epoch
def build_models():
# build model ############################################################
text_encoder = RNN_ENCODER(dataset.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
labels = Variable(torch.LongTensor(range(batch_size)))
start_epoch = 0
# MODIFIED
if cfg.PRETRAINED_RNN:
text_encoder_params = torch.load(
cfg.PRETRAINED_RNN, map_location=lambda storage, loc: storage)
text_encoder.rnn.load_state_dict(text_encoder_params['encoder'])
pad_idx = text_encoder_params['vocab']['word2id']['<pad>']
n_words, embed_size = text_encoder.encoder.weight.size()
text_encoder.encoder = nn.Embedding(n_words, embed_size, pad_idx)
text_encoder.encoder.load_state_dict(text_encoder_params['embedding'])
if cfg.PRETRAINED_CNN:
image_encoder_params = torch.load(
cfg.PRETRAINED_CNN, map_location=lambda storage, loc: storage)
image_encoder.load_state_dict(image_encoder_params)
if cfg.TRAIN.NET_E != '':
state_dict = torch.load(cfg.TRAIN.NET_E)
text_encoder.load_state_dict(state_dict)
print('Load ', cfg.TRAIN.NET_E)
#
name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
state_dict = torch.load(name)
image_encoder.load_state_dict(state_dict)
print('Load ', name)
istart = cfg.TRAIN.NET_E.rfind('_') + 8
iend = cfg.TRAIN.NET_E.rfind('.')
start_epoch = cfg.TRAIN.NET_E[istart:iend]
start_epoch = int(start_epoch) + 1
print('start_epoch', start_epoch)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
labels = labels.cuda()
return text_encoder, image_encoder, labels, start_epoch
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 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 gen_example(n_words, wordtoix, ixtoword, model_dir):
'''generate images from example sentences'''
# filepath = 'example_captions.txt'
filepath = 'caption.txt'
data_dic = {}
with open(filepath, "r") as f:
filenames = f.read().split('\n')
captions = []
cap_lens = []
for sent in filenames:
if len(sent) == 0:
continue
sent = sent.replace("\ufffd\ufffd", " ")
tokenizer = RegexpTokenizer(r'\w+')
tokens = tokenizer.tokenize(sent.lower())
if len(tokens) == 0:
print('sentence token == 0 !')
continue
rev = []
for t in tokens:
t = t.encode('ascii', 'ignore').decode('ascii')
if len(t) > 0 and t in wordtoix:
rev.append(wordtoix[t])
captions.append(rev)
cap_lens.append(len(rev))
max_len = np.max(cap_lens)
sorted_indices = np.argsort(cap_lens)[::-1]
cap_lens = np.asarray(cap_lens)
cap_lens = cap_lens[sorted_indices]
cap_array = np.zeros((len(captions), max_len), dtype='int64')
for i in range(len(captions)):
idx = sorted_indices[i]
cap = captions[idx]
c_len = len(cap)
cap_array[i, :c_len] = cap
# key = name[(name.rfind('/') + 1):]
key = 0
data_dic[key] = [cap_array, cap_lens, sorted_indices]
# algo.gen_example(data_dic)
text_encoder = RNN_ENCODER(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.eval()
netG = G_NET()
netG.apply(weights_init)
# netG.cuda()
netG.eval()
state_dict = torch.load(model_dir,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', model_dir)
save_dir = 'results'
mkdir_p(save_dir)
for key in data_dic:
captions, cap_lens, sorted_indices = data_dic[key]
batch_size = captions.shape[0]
nz = cfg.GAN.Z_DIM
with torch.no_grad():
captions = Variable(torch.from_numpy(captions))
cap_lens = Variable(torch.from_numpy(cap_lens))
# captions = captions.cuda()
# cap_lens = cap_lens.cuda()
for i in range(image_per_caption): # 16
with torch.no_grad():
noise = Variable(torch.FloatTensor(batch_size, nz))
# noise = noise.cuda()
# (1) Extract text embeddings
hidden = text_encoder.init_hidden(batch_size)
words_embs, sent_emb = text_encoder(captions, cap_lens, hidden)
mask = (captions == 0)
# (2) Generate fake images
noise.data.normal_(0, 1)
fake_imgs, attention_maps, _, _ = netG(noise, sent_emb, words_embs,
mask)
cap_lens_np = cap_lens.data.numpy()
for j in range(batch_size):
save_name = '%s/%d_%d' % (save_dir, i, sorted_indices[j])
for k in range(len(fake_imgs)):
im = fake_imgs[k][j].data.cpu().numpy()
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
# print('im', im.shape)
im = np.transpose(im, (1, 2, 0))
# print('im', im.shape)
im = Image.fromarray(im)
fullpath = '%s_g%d.png' % (save_name, k)
im.save(fullpath)
for k in range(len(attention_maps)):
if len(fake_imgs) > 1:
im = fake_imgs[k + 1]
else:
im = fake_imgs[0]
attn_maps = attention_maps[k]
att_sze = attn_maps.size(2)
img_set, sentences = \
build_super_images2(im[j].unsqueeze(0),
captions[j].unsqueeze(0),
[cap_lens_np[j]], ixtoword,
[attn_maps[j]], att_sze)
if img_set is not None:
im = Image.fromarray(img_set)
fullpath = '%s_a%d_attention.png' % (save_name, 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 build_models(self):
if cfg.TRAIN.NET_E == '':
print('Error: no pretrained text-image encoders')
return
# vgg16 network
style_loss = VGGNet()
for p in style_loss.parameters():
p.requires_grad = False
print("Load the style loss model")
style_loss.eval()
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)
for p in image_encoder.parameters():
p.requires_grad = False
print('Load image encoder from:', img_encoder_path)
image_encoder.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)
for p in text_encoder.parameters():
p.requires_grad = False
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder.eval()
netsD = []
if cfg.GAN.B_DCGAN:
if cfg.TREE.BRANCH_NUM == 1:
from model import D_NET64 as D_NET
elif cfg.TREE.BRANCH_NUM == 2:
from model import D_NET128 as D_NET
else: # cfg.TREE.BRANCH_NUM == 3:
from model import D_NET256 as D_NET
netG = G_DCGAN()
netsD = [D_NET(b_jcu=False)]
else:
from model import D_NET64, D_NET128, D_NET256
netG = G_NET()
if cfg.TREE.BRANCH_NUM > 0:
netsD.append(D_NET64())
if cfg.TREE.BRANCH_NUM > 1:
netsD.append(D_NET128())
if cfg.TREE.BRANCH_NUM > 2:
netsD.append(D_NET256())
netG.apply(weights_init)
for i in range(len(netsD)):
netsD[i].apply(weights_init)
print('# of netsD', len(netsD))
#
epoch = 0
if cfg.TRAIN.NET_G != '':
state_dict = \
torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', cfg.TRAIN.NET_G)
istart = cfg.TRAIN.NET_G.rfind('_') + 1
iend = cfg.TRAIN.NET_G.rfind('.')
epoch = cfg.TRAIN.NET_G[istart:iend]
epoch = int(epoch) + 1
if cfg.TRAIN.B_NET_D:
Gname = cfg.TRAIN.NET_G
for i in range(len(netsD)):
s_tmp = Gname[:Gname.rfind('/')]
Dname = '%s/netD%d.pth' % (s_tmp, i)
print('Load D from: ', Dname)
state_dict = \
torch.load(Dname, map_location=lambda storage, loc: storage)
netsD[i].load_state_dict(state_dict)
# Create a target network.
target_netG = deepcopy(netG)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
style_loss = style_loss.cuda()
# The target network is stored on the scondary GPU.---------------------------------
target_netG.cuda(secondary_device)
target_netG.ca_net.device = secondary_device
#-----------------------------------------------------------------------------------
netG.cuda()
for i in range(len(netsD)):
netsD[i] = netsD[i].cuda()
# Disable training in the target network:
for p in target_netG.parameters():
p.requires_grad = False
return [
text_encoder, image_encoder, netG, target_netG, netsD, epoch,
style_loss
]
def build_models(self):
# ###################encoders######################################## #
if cfg.TRAIN.NET_E == '':
print('Error: no pretrained text-image encoders')
return
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)
for p in image_encoder.parameters():
p.requires_grad = False
print('Load image encoder from:', img_encoder_path)
image_encoder.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)
for p in text_encoder.parameters():
p.requires_grad = False
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder.eval()
# #######################generator and discriminators############## #
netsD = []
if cfg.GAN.B_DCGAN:
if cfg.TREE.BRANCH_NUM == 1:
from model import D_NET64 as D_NET
elif cfg.TREE.BRANCH_NUM == 2:
from model import D_NET128 as D_NET
else: # cfg.TREE.BRANCH_NUM == 3:
from model import D_NET256 as D_NET
# TODO: elif cfg.TREE.BRANCH_NUM > 3:
netG = G_DCGAN()
netsD = [D_NET(b_jcu=False)]
else:
from model import D_NET64, D_NET128, D_NET256
netG = G_NET()
if cfg.TREE.BRANCH_NUM > 0:
netsD.append(D_NET64())
if cfg.TREE.BRANCH_NUM > 1:
netsD.append(D_NET128())
if cfg.TREE.BRANCH_NUM > 2:
netsD.append(D_NET256())
# TODO: if cfg.TREE.BRANCH_NUM > 3:
netG.apply(weights_init)
# print(netG)
for i in range(len(netsD)):
netsD[i].apply(weights_init)
# print(netsD[i])
print('# of netsD', len(netsD))
#
epoch = 0
# MODIFIED
if cfg.PRETRAINED_G != '':
state_dict = torch.load(cfg.PRETRAINED_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', cfg.PRETRAINED_G)
if cfg.TRAIN.B_NET_D:
Gname = cfg.PRETRAINED_G
s_tmp = Gname[:Gname.rfind('/')]
for i in range(len(netsD)):
Dname = '%s/netD%d.pth' % (
s_tmp, i
) # the name of Ds should be consistent and differ from each other in i
print('Load D from: ', Dname)
state_dict = torch.load(
Dname, map_location=lambda storage, loc: storage)
netsD[i].load_state_dict(state_dict)
if cfg.TRAIN.NET_G != '':
state_dict = \
torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', cfg.TRAIN.NET_G)
istart = cfg.TRAIN.NET_G.rfind('_') + 1
iend = cfg.TRAIN.NET_G.rfind('.')
epoch = cfg.TRAIN.NET_G[istart:iend]
epoch = int(epoch) + 1
if cfg.TRAIN.B_NET_D:
Gname = cfg.TRAIN.NET_G
for i in range(len(netsD)):
s_tmp = Gname[:Gname.rfind('/')]
Dname = '%s/netD%d.pth' % (s_tmp, i)
print('Load D from: ', Dname)
state_dict = \
torch.load(Dname, map_location=lambda storage, loc: storage)
netsD[i].load_state_dict(state_dict)
# ########################################################### #
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
netG.cuda()
for i in range(len(netsD)):
netsD[i].cuda()
return [text_encoder, image_encoder, netG, netsD, 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):
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 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 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 build_models(self):
def count_parameters(model):
total_param = 0
for name, param in model.named_parameters():
if param.requires_grad:
num_param = np.prod(param.size())
if param.dim() > 1:
print(name, ':',
'x'.join(str(x) for x in list(param.size())),
'=', num_param)
else:
print(name, ':', num_param)
total_param += num_param
return total_param
# ###################encoders######################################## #
if cfg.TRAIN.NET_E == '':
print('Error: no pretrained text-image encoders')
return
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)
for p in image_encoder.parameters():
p.requires_grad = False
print('Load image encoder from:', img_encoder_path)
image_encoder.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)
for p in text_encoder.parameters():
p.requires_grad = False
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder.eval()
# #######################generator and discriminators############## #
netsD = []
if cfg.GAN.B_DCGAN:
if cfg.TREE.BRANCH_NUM == 1:
from model import D_NET64 as D_NET
elif cfg.TREE.BRANCH_NUM == 2:
from model import D_NET128 as D_NET
else: # cfg.TREE.BRANCH_NUM == 3:
from model import D_NET256 as D_NET
# TODO: elif cfg.TREE.BRANCH_NUM > 3:
netG = G_DCGAN()
netsD = [D_NET(b_jcu=False)]
else:
from model import D_NET64, D_NET128, D_NET256
netG = G_NET()
if cfg.TREE.BRANCH_NUM > 0:
netsD.append(D_NET64())
if cfg.TREE.BRANCH_NUM > 1:
netsD.append(D_NET128())
if cfg.TREE.BRANCH_NUM > 2:
netsD.append(D_NET256())
# TODO: if cfg.TREE.BRANCH_NUM > 3:
print('number of trainable parameters =', count_parameters(netG))
print('number of trainable parameters =', count_parameters(netsD[-1]))
netG.apply(weights_init)
# print(netG)
for i in range(len(netsD)):
netsD[i].apply(weights_init)
# print(netsD[i])
print('# of netsD', len(netsD))
#
epoch = 0
if cfg.TRAIN.NET_G != '':
state_dict = \
torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', cfg.TRAIN.NET_G)
istart = cfg.TRAIN.NET_G.rfind('_') + 1
iend = cfg.TRAIN.NET_G.rfind('.')
epoch = cfg.TRAIN.NET_G[istart:iend]
epoch = int(epoch) + 1
if cfg.TRAIN.B_NET_D:
Gname = cfg.TRAIN.NET_G
for i in range(len(netsD)):
s_tmp = Gname[:Gname.rfind('/')]
Dname = '%s/netD%d.pth' % (s_tmp, i)
print('Load D from: ', Dname)
state_dict = \
torch.load(Dname, map_location=lambda storage, loc: storage)
netsD[i].load_state_dict(state_dict)
# ########################################################### #
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
netG.cuda()
for i in range(len(netsD)):
netsD[i].cuda()
return [text_encoder, image_encoder, netG, netsD, epoch]
def build_models(self):
# text encoders
if cfg.TRAIN.NET_E == '':
print('Error: no pretrained text-image encoders')
return
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)
for p in image_encoder.parameters():
p.requires_grad = False
print('Load image encoder from:', img_encoder_path)
image_encoder.eval()
# self.n_words = 156
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)
for p in text_encoder.parameters():
p.requires_grad = False
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder.eval()
# Caption models - cnn_encoder and rnn_decoder
caption_cnn = CAPTION_CNN(cfg.CAP.embed_size)
caption_cnn.load_state_dict(torch.load(cfg.CAP.caption_cnn_path, map_location=lambda storage, loc: storage))
for p in caption_cnn.parameters():
p.requires_grad = False
print('Load caption model from:', cfg.CAP.caption_cnn_path)
caption_cnn.eval()
# self.n_words = 9
caption_rnn = CAPTION_RNN(cfg.CAP.embed_size, cfg.CAP.hidden_size * 2, self.n_words, cfg.CAP.num_layers)
# caption_rnn = CAPTION_RNN(cfg.CAP.embed_size, cfg.CAP.hidden_size * 2, self.n_words, cfg.CAP.num_layers)
caption_rnn.load_state_dict(torch.load(cfg.CAP.caption_rnn_path, map_location=lambda storage, loc: storage))
for p in caption_rnn.parameters():
p.requires_grad = False
print('Load caption model from:', cfg.CAP.caption_rnn_path)
# Generator and Discriminator:
netsD = []
if cfg.GAN.B_DCGAN:
if cfg.TREE.BRANCH_NUM == 1:
from model import D_NET64 as D_NET
elif cfg.TREE.BRANCH_NUM == 2:
from model import D_NET128 as D_NET
else: # cfg.TREE.BRANCH_NUM == 3:
from model import D_NET256 as D_NET
netG = G_DCGAN()
netsD = [D_NET(b_jcu=False)]
else:
from model import D_NET64, D_NET128, D_NET256
netG = G_NET()
if cfg.TREE.BRANCH_NUM > 0:
netsD.append(D_NET64())
if cfg.TREE.BRANCH_NUM > 1:
netsD.append(D_NET128())
if cfg.TREE.BRANCH_NUM > 2:
netsD.append(D_NET256())
netG.apply(weights_init)
# print(netG)
for i in range(len(netsD)):
netsD[i].apply(weights_init)
# print(netsD[i])
print('# of netsD', len(netsD))
epoch = 0
if cfg.TRAIN.NET_G != '':
state_dict = \
torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', cfg.TRAIN.NET_G)
istart = cfg.TRAIN.NET_G.rfind('_') + 1
iend = cfg.TRAIN.NET_G.rfind('.')
epoch = cfg.TRAIN.NET_G[istart:iend]
# print(epoch)
# print(state_dict.keys())
# print(netG.keys())
# epoch = state_dict['epoch']
epoch = int(epoch) + 1
# epoch = 187
if cfg.TRAIN.B_NET_D:
Gname = cfg.TRAIN.NET_G
for i in range(len(netsD)):
s_tmp = Gname[:Gname.rfind('/')]
Dname = '%s/netD%d.pth' % (s_tmp, i)
print('Load D from: ', Dname)
state_dict = \
torch.load(Dname, map_location=lambda storage, loc: storage)
netsD[i].load_state_dict(state_dict)
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
caption_cnn = caption_cnn.cuda()
caption_rnn = caption_rnn.cuda()
netG.cuda()
for i in range(len(netsD)):
netsD[i].cuda()
return [text_encoder, image_encoder, caption_cnn, caption_rnn, netG, netsD, epoch]
def build_models(self):
# ###################encoders######################################## #
if cfg.TRAIN.NET_E == '':
print('Error: no pretrained text-image encoders')
return
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)
for p in image_encoder.parameters():
p.requires_grad = False
print('Load image encoder from:', img_encoder_path)
image_encoder.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)
for p in text_encoder.parameters():
p.requires_grad = False
print('Load text encoder from:', cfg.TRAIN.NET_E)
text_encoder.eval()
# #######################generator and discriminators############## #
netsD = []
from model import D_NET64, D_NET128, D_NET256
netG = G_NET()
if cfg.TREE.BRANCH_NUM > 0:
netsD.append(D_NET64())
if cfg.TREE.BRANCH_NUM > 1:
netsD.append(D_NET128())
if cfg.TREE.BRANCH_NUM > 2:
netsD.append(D_NET256())
netG.apply(weights_init)
# print(netG)
for i in range(len(netsD)):
netsD[i].apply(weights_init)
# print(netsD[i])
print('# of netsD', len(netsD))
epoch = 0
if self.resume:
checkpoint_list = sorted([ckpt for ckpt in glob.glob(self.model_dir + "/" + '*.pth')])
latest_checkpoint = checkpoint_list[-1]
state_dict = torch.load(latest_checkpoint, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict["netG"])
for i in range(len(netsD)):
netsD[i].load_state_dict(state_dict["netD"][i])
epoch = int(latest_checkpoint[-8:-4]) + 1
print("Resuming training from checkpoint {} at epoch {}.".format(latest_checkpoint, epoch))
#
if cfg.TRAIN.NET_G != '':
state_dict = \
torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load G from: ', cfg.TRAIN.NET_G)
istart = cfg.TRAIN.NET_G.rfind('_') + 1
iend = cfg.TRAIN.NET_G.rfind('.')
epoch = cfg.TRAIN.NET_G[istart:iend]
epoch = int(epoch) + 1
if cfg.TRAIN.B_NET_D:
Gname = cfg.TRAIN.NET_G
for i in range(len(netsD)):
s_tmp = Gname[:Gname.rfind('/')]
Dname = '%s/netD%d.pth' % (s_tmp, i)
print('Load D from: ', Dname)
state_dict = \
torch.load(Dname, map_location=lambda storage, loc: storage)
netsD[i].load_state_dict(state_dict)
# ########################################################### #
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
netG.cuda()
for i in range(len(netsD)):
netsD[i].cuda()
return [text_encoder, image_encoder, netG, netsD, epoch]
def gen_example(self, data_dic):
if cfg.TRAIN.NET_G == '':
print('Error: the path for morels is not found!')
else:
# 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()
# the path to save generated images
if cfg.GAN.B_DCGAN:
netG = G_DCGAN()
else:
netG = G_NET()
s_tmp = cfg.TRAIN.NET_G[:cfg.TRAIN.NET_G.rfind('.pth')]
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)
netG.cuda()
netG.eval()
for key in data_dic:
save_dir = '%s/%s' % (s_tmp, key)
mkdir_p(save_dir)
captions, cap_lens, sorted_indices = data_dic[key]
batch_size = captions.shape[0]
nz = cfg.GAN.Z_DIM
captions = Variable(torch.from_numpy(captions), volatile=True)
cap_lens = Variable(torch.from_numpy(cap_lens), volatile=True)
captions = captions.cuda()
cap_lens = cap_lens.cuda()
for i in range(1): # 16
noise = Variable(torch.FloatTensor(batch_size, nz), volatile=True)
noise = noise.cuda()
#######################################################
# (1) Extract text embeddings
######################################################
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)
mask = (captions == 0)
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
with torch.no_grad():
fake_imgs, attention_maps, _, _ = netG(noise, sent_emb, words_embs, mask)
# G attention
cap_lens_np = cap_lens.cpu().data.numpy()
for j in range(batch_size):
save_name = '%s/%d_s_%d' % (save_dir, i, sorted_indices[j])
for k in range(len(fake_imgs)):
im = fake_imgs[k][j].data.cpu().numpy()
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
# print('im', im.shape)
im = np.transpose(im, (1, 2, 0))
# print('im', im.shape)
im = Image.fromarray(im)
fullpath = '%s_g%d.png' % (save_name, k)
im.save(fullpath)
for k in range(len(attention_maps)):
if len(fake_imgs) > 1:
im = fake_imgs[k + 1].detach().cpu()
else:
im = fake_imgs[0].detach().cpu()
attn_maps = attention_maps[k]
att_sze = attn_maps.size(2)
img_set, sentences = \
build_super_images2(im[j].unsqueeze(0),
captions[j].unsqueeze(0),
[cap_lens_np[j]], self.ixtoword,
[attn_maps[j]], att_sze)
if img_set is not None:
im = Image.fromarray(img_set)
fullpath = '%s_a%d.png' % (save_name, k)
im.save(fullpath)
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
