def main():
"""Forward sequences."""
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', '-bs', type=int, help='batch size', default=10)
parser.add_argument('--save_path', '-sp', type=str, help='path to save the model',
default='models')
parser.add_argument('--load_path', '-mp', type=str, help='path to load the model',
default=None)
parser.add_argument('--lr', '-lr', type=float, help='initial learning rate',
default=0.2)
parser.add_argument('--model_type', '-t', type=str, help='type of the model: inception,'
'vgg or squeezenet', default='inception')
parser.add_argument('--cuda', dest='cuda', help='use cuda', action='store_true')
parser.add_argument('--no-cuda', dest='cuda', help="don't use cuda", action='store_false')
parser.add_argument('--freeze', '-fr', dest='freeze', help='freeze cnn layers',
action='store_true')
parser.add_argument('--batch_first', dest='batch_first', action='store_true')
parser.add_argument('--no-batch_first', dest='batch_first', action='store_false')
parser.add_argument('--multigpu', nargs='*', default=[], help='list of gpus to use')
parser.set_defaults(cuda=True)
parser.set_defaults(freeze=False)
parser.set_defaults(batch_first=True)
args = parser.parse_args()
filenames = {'train': 'train_no_dup.json',
'test': 'test_no_dup.json',
'val': 'valid_no_dup.json'}
tic = time.time()
print("Reading all texts and creating the vocabulary")
# Create the vocabulary with all the texts.
vocab = create_vocab([TXT_TEST_VAL_TF(t['name']) for d in json.load(
open(os.path.join('data/label', filenames['train'])))
for t in d['items']])
print("Vocabulary creation took %.2f secs - %d words" % (time.time() - tic, len(vocab)))
import epdb; epdb.set_trace()
data_params = {'img_dir': 'data/images',
'json_dir': 'data/label',
'json_files': filenames,
'batch_size': args.batch_size,
'batch_first': args.batch_first}
opt_params = {'learning_rate': args.lr,
'weight_decay': 1e-4}
model, dataloaders, optimizer, criterion, contrastive_criterion = config(
net_params=[args.model_type, 512, 512, 0.2, len(vocab), args.load_path, args.freeze],
data_params=data_params,
opt_params=opt_params,
cuda_params={'cuda': args.cuda,
'multigpu': args.multigpu})
print("before training: lr = %.4f" % optimizer.param_groups[0]['lr'])
scheduler = StepLR(optimizer, 2, 0.5)
train([model, criterion, contrastive_criterion, optimizer, scheduler, vocab, args.freeze],
dataloaders, args.cuda, args.batch_first,
[100, 500, args.save_path])
def __getitem__(self, index):
im = cv2.imread(os.path.join(self.bpath, self.im_filenames[index]), 1)
if im is None:
im = cv2.imread(
os.path.join(self.bpath, self.im_filenames[index]) + '.jpg', 1)
if im is None:
fs = os.path.join(self.bpath, self.im_filenames[index])
os.system('echo %s >> failed_images' % fs)
im = np.zeros([300, 300, 3], dtype=np.uint8)
if not self.validation:
im = self.seq.augment_image(im)
rows, cols = im.shape[:2]
if rows < self.input_size\
or cols < self.input_size:
im = cv2.resize(im, (self.input_size, self.input_size))
else:
# random crop
try:
off_x = np.random.randint(
0, max(1, (cols - self.input_size) // 2))
off_y = np.random.randint(
0, max(1, (rows - self.input_size) // 2))
random_w = np.random.randint(
self.input_size, max(self.input_size + 1, cols - off_x))
random_h = np.random.randint(
self.input_size, max(self.input_size + 1, rows - off_y))
except:
import epdb
epdb.set_trace()
im = im[off_y:off_y + random_h, off_x:off_x + random_w, :]
im = cv2.resize(im, (self.input_size, self.input_size))
# BGR 2 RGB
im = im[:, :, ::-1].copy()
# print im.shape
# im = preprocessing.contrast_enhanced(im)
# mean = [0.3940, 0.2713, 0.1869], RGB
# std = [0.2777, 0.1981, 0.1574]
# mean and std, of imaterialist, are
# 0.5883, 0.5338, 0.5273 and
# 0.3363, 0.3329, 0.3268
im = ToTensor()(im)
for t, m, s in zip(im, [0.5883, 0.5338, 0.5273],
[0.3363, 0.3329, 0.3268]):
t.sub_(m).div_(s)
label = np.zeros([228], dtype=np.float32)
label[self.im_labels[index]] = 1
return im, label
def visualize_batch_fn(images, labels, label_lengths):
N = images.shape[0]
image_mean = np.array([0.485, 0.456, 0.406]).reshape(1, 1, 3)
image_std = np.array([0.229, 0.224, 0.225]).reshape(1, 1, 3)
for i in range(N):
image = images[i].data.cpu().numpy()
image = image.transpose(1, 2, 0)
image *= image_std
image += image_mean
image = (255.0 * image).astype(np.uint8)
indexes = labels[i].data.cpu().numpy().tolist(
)[1:label_lengths[i].item() - 1]
indexes = [x for x in indexes]
labels_batch = [categories[x] for x in indexes]
cv2.imwrite("batches/%d.jpg" % i, image[:, :, ::-1])
print '%d %s' % (i, ','.join(labels_batch))
import epdb
epdb.set_trace()
def train(train_params, dataloaders, cuda, batch_first, epoch_params):
"""Train the model.
"""
model, criterion, contrastive_criterion, optimizer, scheduler, vocab, freeze = train_params
numepochs, nsave, save_path = epoch_params
log_name = ('runs/L2/lr%.3f' % optimizer.param_groups[0]['initial_lr'])
if freeze:
log_name += '_frozen'
writer = SummaryWriter(log_name)
n_iter = 0
tic_e = time.time()
for epoch in range(numepochs):
print("Epoch %d - lr = %.4f" % (epoch, optimizer.param_groups[0]['lr']))
scheduler.step()
for batch in dataloaders['train']:
tic = time.time()
# Clear gradients, reset hidden state.
model.zero_grad()
hidden = model.init_hidden(len(batch))
# Get a list of images and texts from sequences:
images, texts, seq_lens, im_lookup_table, txt_lookup_table = seqs2batch(batch, vocab)
images = autograd.Variable(images)
texts = autograd.Variable(texts)
if cuda:
hidden = (hidden[0].cuda(), hidden[1].cuda())
images = images.cuda()
texts = texts.cuda()
packed_batch, (im_feats, txt_feats), (out, hidden) = model.forward(images,
seq_lens,
im_lookup_table,
txt_lookup_table,
hidden,
texts)
out, _ = pad_packed_sequence(out, batch_first=batch_first)
fw_loss, bw_loss = criterion(packed_batch, out)
cont_loss = contrastive_criterion(im_feats, txt_feats)
lstm_loss = fw_loss + bw_loss
loss = lstm_loss + cont_loss
if np.isnan(loss.cpu().data.numpy()) or lstm_loss.cpu().data[0] < 0:
import epdb
epdb.set_trace()
im_feats.register_hook(save_grad('im_feats'))
loss.backward()
# Gradient clipping
torch.nn.utils.clip_grad_norm(model.parameters(), 5.0)
optimizer.step()
print("iteration %d took %.2f secs" % (n_iter, time.time() - tic))
print("\033[4;32miter %d\033[0m" % n_iter)
# Removing the [0] to avoid error from all
print("\033[1;34mTotal loss: %.3f ||| LSTM loss: %.3f ||| Contr. loss: %.3f\033[0m" %
(loss.data, lstm_loss.data, cont_loss.data))
print("Seq lens:", [len(b['texts']) for b in batch])
dists = torch.sum(1 - F.cosine_similarity(im_feats, txt_feats))/im_feats.size()[0]
# Removing the [0] to avoid error from all
print("\033[0;31mmdists: %.3f\033[0m" % dists.data)
# Removing the [0] to avoid error from all
write_data = {'data/loss': loss.data,
'data/lstm_loss': lstm_loss.data,
'data/cont_loss': cont_loss.data,
'data/pos_dists': dists.data,}
#'grads/im_feats': torch.mean(torch.cat([torch.norm(t)
# for t in GRADS['im_feats']]))}
write_tensorboard(writer, write_data, n_iter)
n_iter += 1
if not n_iter % nsave:
if not os.path.exists(save_path):
os.makedirs(save_path)
print("Epoch %d (%d iters) -- Saving model in %s" % (epoch, n_iter, save_path))
if not os.path.exists(save_path):
os.makedirs(save_path)
torch.save(model.state_dict(), "%s_%d.pth" % (
os.path.join(save_path, 'model'), n_iter))
print("\033[1;30mEpoch %i/%i: %f seconds\033[0m" % (epoch, numepochs, time.time() - tic_e))
writer.close()
# i + epoch * len(train_dataloader))
# tflogger.visualize_histogram(summary_writer, model,
# i + epoch * len(train_dataloader))
summary_writer.add_scalar('loss/train',
loss.data.cpu().numpy(),
i + epoch * len(train_dataloader))
torch.save({'epoch': epoch,
'state_dict': model.state_dict()},
'%s_epoch_%d.pth' % (args.out, epoch))
model.eval()
val_loss = 0
targets = np.zeros([0, 228])
predictions = np.zeros([0, 228])
with torch.no_grad():
for i, (X, y) in enumerate(tqdm.tqdm(val_dataloader)):
X = torch.autograd.Variable(X.cuda(),
requires_grad=False)
y = torch.autograd.Variable(y.cuda())
y_pred = model(X)
prob = torch.nn.functional.sigmoid(y_pred)
predictions = np.vstack((predictions, prob.cpu().numpy()))
targets = np.vstack((targets, y.cpu().numpy()))
import epdb; epdb.set_trace()
mean_f1 = []
for cls in range(228):
f1, thr = find_best_f1_score(predictions[:, cls],
targets[:, cls])
mean_f1.append(f1)
np.mean(mean_f1)
def import_item(self, item, wordpress_namespace, out_folder=None):
"""Takes an item from the feed and creates a post file."""
if out_folder is None:
out_folder = 'posts'
title = get_text_tag(item, 'title', 'NO TITLE')
# link is something like http://foo.com/2012/09/01/hello-world/
# So, take the path, utils.slugify it, and that's our slug
link = get_text_tag(item, 'link', None)
path = unquote(urlparse(link).path)
# In python 2, path is a str. slug requires a unicode
# object. According to wikipedia, unquoted strings will
# usually be UTF8
if isinstance(path, utils.bytes_str):
path = path.decode('utf8')
# Remove date form the slug
slug = utils.slugify(path)[8:]
if not slug: # it happens if the post has no "nice" URL
slug = get_text_tag(
item, '{{{0}}}post_name'.format(wordpress_namespace), None)
if not slug: # it *may* happen
slug = get_text_tag(
item, '{{{0}}}post_id'.format(wordpress_namespace), None)
if not slug: # should never happen
print("Error converting post:", title)
return
description = get_text_tag(item, 'description', '')
post_date = get_text_tag(
item, '{{{0}}}post_date'.format(wordpress_namespace), None)
try:
dt = utils.to_datetime(post_date)
except:
import epdb;epdb.set_trace()
if dt.tzinfo and self.timezone is None:
self.timezone = utils.get_tzname(dt)
status = get_text_tag(
item, '{{{0}}}status'.format(wordpress_namespace), 'publish')
content = get_text_tag(
item, '{http://purl.org/rss/1.0/modules/content/}encoded', '')
tags = []
if status == 'trash':
print('Trashed post "{0}" will not be imported.'.format(title))
return
elif status != 'publish':
tags.append('draft')
is_draft = True
else:
is_draft = False
for tag in item.findall('category'):
text = tag.text
if text == 'Uncategorized':
continue
tags.append(text)
if is_draft and self.exclude_drafts:
print('Draft "{0}" will not be imported.'.format(title))
elif content.strip():
# If no content is found, no files are written.
self.url_map[link] = self.context['SITE_URL'] + '/' + \
out_folder + '/' + slug + '.html'
content = self.transform_content(content)
self.write_metadata(os.path.join(self.output_folder, out_folder,
slug + '.meta'),
title, slug, post_date, description, tags)
self.write_content(
os.path.join(self.output_folder, out_folder, slug + '.wp'),
content)
else:
print('Not going to import "{0}" because it seems to contain'
' no content.'.format(title))
