def extract(FLAG):
video_path = FLAG.video_path
video_list = getVideoList(FLAG.video_list)
df = pd.DataFrame.from_dict(video_list)
vgg16 = Extractor(shape=(240, 320, 3))
vgg16.build(vgg16_npy_path=FLAG.init)
load_extract_video(video_path=video_path,
df=df,
model=vgg16,
filename=os.path.join(FLAG.save_dir, FLAG.filename))
def filter():
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
dataset = ImageFolder(root=args.icdar_patches, transform=transform)
loader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True)
model = Extractor()
for params in model.parameters():
params.requires_grad = False
acc = clustering(loader=loader, model=model)
def _build_models(self):
gpu_flag = torch.cuda.is_available()
extractor = Extractor()
classifier = Classifier()
discriminator = Discrimimator()
if gpu_flag:
self.device = torch.device('cuda:0')
else:
self.device = torch.device('cpu')
self.extractor = extractor.to(self.device)
self.classifier = classifier.to(self.device)
self.discriminator = discriminator.to(self.device)
def __init__(self):
super(Generator, self).__init__()
weights = get_pretrained_weights(args.pretrained_wordembedding)
self._embedding = WordEmbedding(args.embedding_size, weights, trainable=args.trainable_wordembed)
self._senenc = ConvSentEncoder(args.embedding_size,
args.sentembed_size,
args.max_filter_length,
args.dropout)
self._docenc = DocEncoder(args.sentembed_size,
args.size,
args.num_layers,
args.rnn_cell,
args.dropout,
args.bidirectional)
self._docext = AttnExtractor(args.sentembed_size,
args.size,
args.num_layers,
args.rnn_cell,
coverage=args.coverage) if args.attn else Extractor(args.sentembed_size,
args.size,
args.num_layers,
args.rnn_cell)
if args.aux_embedding:
weights = get_pretrained_weights(args.pretrained_aux_wordembedding)
self._aux_embedding = WordEmbedding(args.embedding_size, weights, trainable=args.trainable_wordembed)
self._aux_senenc = ConvSentEncoder(args.embedding_size,
args.sentembed_size,
args.max_filter_length,
args.dropout)
self._linear = nn.Linear(args.sentembed_size*2, args.sentembed_size)
def main(unused_argv):
if FLAGS.checkpoint_dir == '' or not os.path.exists(FLAGS.checkpoint_dir):
raise ValueError('invalid checkpoint directory {}'.format(
FLAGS.checkpoint_dir))
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, '')
if FLAGS.output_dir == '':
raise ValueError('invalid output directory {}'.format(
FLAGS.output_dir))
elif not os.path.exists(FLAGS.output_dir):
assert FLAGS.output_dir != FLAGS.checkpoint_dir
os.makedirs(FLAGS.output_dir)
print('reconstructing models and inputs.')
image = Image('/data/yuming/watermark-data/image_paths.mat',
FLAGS.image_seq)()
wm = Watermark('/data/yuming/watermark-data/watermark.mat')()
dim = [1, FLAGS.img_height, FLAGS.img_width, FLAGS.num_chans]
image_upsampler = Upsampler(dim)
wm_upsampler = Upsampler([1] + dim[1:])
downsampler = Downsampler(dim)
blender = Blender(dim)
extrator = Extractor(dim)
image_upsampled = image_upsampler(image)
wm_upsampled = wm_upsampler(wm)
image_blended = blender(image_upsampled, wm_upsampled)
image_downsampled = downsampler(image_blended)
mask = Mask(FLAGS.img_height, FLAGS.img_width, 80)()
mask = tf.cast(mask, tf.complex64)
freqimage = FreqImage(mask)
image_freqfiltered = freqimage(image_downsampled)
wm_extracted = extrator(image_freqfiltered)
enhance = Enhance(sharpen=True)
wm_extracted = enhance(wm_extracted)
saver = tf.train.Saver()
writer = tf.summary.FileWriter(FLAGS.output_dir, tf.get_default_graph())
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
assert (FLAGS.gpus != ''), 'invalid GPU specification'
config.gpu_options.visible_device_list = FLAGS.gpus
with tf.Session(config=config) as sess:
sess.run(tf.local_variables_initializer())
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
else:
print('No checkpoint file found')
return
wm_val, image_downsampled_val, image_freqfiltered_val, wm_extracted_val = \
sess.run([wm, image_downsampled, image_freqfiltered, wm_extracted])
images = [{
'data':
np.squeeze(image_downsampled_val[0, :, :, :].astype(np.uint8)),
'title':
"watermarked image"
}, {
'data':
np.squeeze(image_freqfiltered_val[0, :, :, :].astype(np.uint8)),
'title':
"filtered image"
}, {
'data': np.squeeze(wm_val[0, :, :, :].astype(np.uint8)),
'title': "original watermark"
}, {
'data':
np.squeeze(wm_extracted_val[0, :, :, :].astype(np.uint8)),
'title':
"extracted watermark"
}]
image_str = draw_image(images)
writer.add_summary(image_str, global_step=0)
np.set_printoptions(threshold=sys.maxsize)
print(np.squeeze(wm_extracted_val))
writer.close()
s3_loader_raw = torch.utils.data.DataLoader(s3_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
drop_last=True)
t_loader_raw = torch.utils.data.DataLoader(t_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
drop_last=True)
t_loader_test = torch.utils.data.DataLoader(t_set_test,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
extractor = Extractor()
s1_classifier = Classifier(num_classes=num_classes)
s2_classifier = Classifier(num_classes=num_classes)
s3_classifier = Classifier(num_classes=num_classes)
s1_t_discriminator = Discriminator()
s2_t_discriminator = Discriminator()
s3_t_discriminator = Discriminator()
extractor.load_state_dict(
torch.load(
osp.join(
MAIN_DIR,
"MSDA/A_W_2_D_Open/bvlc_A_W_2_D/pretrain/bvlc_extractor.pth")))
extractor = nn.DataParallel(extractor)
extractor = extractor.cuda()
pbar.update(1)
im, ann, im_fns = data[0], data[1], data[2]
im = im.cuda()
feat = model(im)
x = feat.cpu().numpy()
y = ann.numpy()
precision = precision + clf.score(x, y)
pbar.close()
print("\nAverage Precision is {}".format(precision / len(loader)))
if __name__ == '__main__':
icdar_patches = "./data/icdar2015/patches"
dataset = ImageFolder(root=icdar_patches, transform=transforms)
loader = torch.utils.data.DataLoader(dataset, batch_size=20, shuffle=True)
# extractor for deep features
model = Extractor()
model = model.cuda()
for params in model.parameters():
params.requires_grad = False
# vanilla svm
clf = svm.SVC(kernel="rbf", gamma=10)
train(loader=loader, model=model, clf=clf)
shuffle=shuffle,
num_workers=num_workers)
s2_loader_raw = torch.utils.data.DataLoader(s2_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
t_loader_raw = torch.utils.data.DataLoader(t_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
t_loader_test = torch.utils.data.DataLoader(t_set_test,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
extractor = Extractor().cpu() #.cuda(gpu_id)
#extractor.load_state_dict(torch.load("/home/xuruijia/ZJY/ADW/bvlc_A_W_2_D/pretrain/bvlc_extractor.pth"))
s1_classifier = Classifier(num_classes=num_classes).cpu() #.cuda(gpu_id)
s2_classifier = Classifier(num_classes=num_classes).cpu() #.cuda(gpu_id)
#s1_classifier.load_state_dict(torch.load("/home/xuruijia/ZJY/ADW/bvlc_A_W_2_D/pretrain/bvlc_s1_cls.pth"))
#s2_classifier.load_state_dict(torch.load("/home/xuruijia/ZJY/ADW/bvlc_A_W_2_D/pretrain/bvlc_s2_cls.pth"))
s1_t_discriminator = Discriminator().cpu() #.cuda(gpu_id)
s2_t_discriminator = Discriminator().cpu() #.cuda(gpu_id)
def print_log(step, epoch, epoches, lr, l1, l2, l3, l4, l5, l6, l7, l8, flag,
ploter, count):
print("Step [%d/%d] Epoch [%d/%d] lr: %f, s1_cls_loss: %.4f, s2_cls_loss: %.4f, s1_t_dis_loss: %.4f, " \
"s2_t_dis_loss: %.4f, s1_t_confusion_loss_s1: %.4f, s1_t_confusion_loss_t: %.4f, " \
"s2_t_confusion_loss_s2: %.4f, s2_t_confusion_loss_t: %.4f, selected_source: %s" \
% (step, steps, epoch, epoches, lr, l1, l2, l3, l4, l5, l6, l7, l8, flag))
def main(unused_argv):
summ = Summaries()
if FLAGS.data_dir == '' or not os.path.exists(FLAGS.data_dir):
raise ValueError('invalid data directory {}'.format(FLAGS.data_dir))
if FLAGS.output_dir == '':
raise ValueError('invalid output directory {}'.format(FLAGS.output_dir))
elif not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
event_log_dir = os.path.join(FLAGS.output_dir, '')
checkpoint_path = os.path.join(FLAGS.output_dir, 'model.ckpt')
print('Constructing models.')
dim = [FLAGS.batch_size, FLAGS.img_height, FLAGS.img_width, FLAGS.num_chans]
image_upsampler = Upsampler(dim)
wm_upsampler = Upsampler([1] + dim[1:])
image_downsampler = Downsampler(dim)
blender = Blender(dim)
extrator = Extractor(dim)
train_loss, train_op, train_summ_op = \
train(FLAGS.data_dir, image_upsampler, wm_upsampler, blender, image_downsampler, extrator, summ)
val_summ_op = val(FLAGS.data_dir, image_upsampler, wm_upsampler, blender, image_downsampler, extrator, summ)
print('Constructing saver.')
saver = tf.train.Saver()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to as some of the ops do not have GPU implementations.
config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False)
assert (FLAGS.gpus != ''), 'invalid GPU specification'
config.gpu_options.visible_device_list = FLAGS.gpus
# Build an initialization operation to run below.
init = [tf.global_variables_initializer(), tf.local_variables_initializer()]
with tf.Session(config = config) as sess:
sess.run(init)
writer = tf.summary.FileWriter(event_log_dir, graph = sess.graph)
# Run training.
for itr in range(FLAGS.num_iterations):
cost, _, train_summ_str = sess.run([train_loss, train_op, train_summ_op])
# Print info: iteration #, cost.
print(str(itr) + ' ' + str(cost))
writer.add_summary(train_summ_str, itr)
if itr % FLAGS.validation_interval == 1:
# Run through validation set.
val_summ_str = sess.run(val_summ_op)
writer.add_summary(val_summ_str, itr)
tf.logging.info('Saving model.')
saver.save(sess, checkpoint_path)
tf.logging.info('Training complete')
source_training_liquid_labels = source_training_liquid_labels / math.sqrt(1000)
source_validation_liquid_labels = source_validation_liquid_labels / math.sqrt(
1000)
target_liquid_labels = target_liquid_labels / math.sqrt(1000)
length_source_training = source_training_gas_labels.size()[0]
length_source_validation = source_validation_gas_labels.size()[0]
length_target = target_gas_labels.size()[0]
#Parameters
Batch_size_s = int(length_source_training / 25) + 1
Batch_size_t = int(length_target / 25) + 1
num_epochs = 200
m = 1
n = 10**(-7)
E = Extractor()
D = Discriminator()
R = Regressor()
E.apply(weights_init)
D.apply(weights_init)
R.apply(weights_init)
e_learning_rate = 0.00003
d_learning_rate = 0.00015
r_learning_rate = 0.0000001
e_optimizer = optim.RMSprop(E.parameters(), lr=e_learning_rate, alpha=0.9)
d_optimizer = optim.RMSprop(D.parameters(), lr=d_learning_rate, alpha=0.9)
r_optimizer = optim.RMSprop(R.parameters(), lr=r_learning_rate, alpha=0.9)
e_steps = 1
d_steps = 1
r_steps = 1
shuffle=shuffle, num_workers=num_workers)
s2_loader_raw = torch.utils.data.DataLoader(s2_set, batch_size=batch_size,
shuffle=shuffle, num_workers=num_workers)
t_loader_raw = torch.utils.data.DataLoader(t_set, batch_size=batch_size,
shuffle=shuffle, num_workers=num_workers)
t_loader_test = torch.utils.data.DataLoader(t_set_test, batch_size=batch_size,
shuffle=False, num_workers=num_workers)
s1_loader_raw1 = torch.utils.data.DataLoader(s1_set, batch_size=1,
shuffle=shuffle, pin_memory=True)
s2_loader_raw1 = torch.utils.data.DataLoader(s2_set, batch_size=1,
shuffle=shuffle, pin_memory=True)
t_loader_raw1 = torch.utils.data.DataLoader(t_set, batch_size=1,
shuffle=shuffle,pin_memory=True)
extractor = Extractor().cpu()
extractor.load_state_dict(torch.load("/Users/bytedabce/PycharmProjects/mix_net/train_eval/pre_train_model/bvlc_extractor.pth"))
s1_classifier = Classifier(num_classes=num_classes).cpu()
s2_classifier = Classifier(num_classes=num_classes).cpu()
s1_classifier.load_state_dict(torch.load("/Users/bytedabce/PycharmProjects/mix_net/train_eval/pre_train_model/bvlc_s1_cls.pth"))
s2_classifier.load_state_dict(torch.load("/Users/bytedabce/PycharmProjects/mix_net/train_eval/pre_train_model/bvlc_s2_cls.pth"))
s1_t_discriminator = Discriminator().cpu()
s2_t_discriminator = Discriminator().cpu()
def print_log(step, epoch, epoches, lr, l1, l2, l3, l4, l5, l6, l7, l8, flag, ploter, count):
print ("Step [%d/%d] Epoch [%d/%d] lr: %f, s1_cls_loss: %.4f, s2_cls_loss: %.4f, s1_t_dis_loss: %.4f, " \
"s2_t_dis_loss: %.4f, s1_t_confusion_loss_s1: %.4f, s1_t_confusion_loss_t: %.4f, " \
"s2_t_confusion_loss_s2: %.4f, s2_t_confusion_loss_t: %.4f, selected_source: %s" \
shuffle=shuffle,
num_workers=num_workers)
s2_loader_raw = torch.utils.data.DataLoader(s2_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
t_loader_raw = torch.utils.data.DataLoader(t_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
t_loader_test = torch.utils.data.DataLoader(t_set_test,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
extractor = Extractor().cuda(gpu_id)
extractor.load_state_dict(
torch.load(
"/home/xuruijia/ZJY/ADW/bvlc_A_W_2_D/pretrain/bvlc_extractor.pth"))
s1_classifier = Classifier(num_classes=num_classes).cuda(gpu_id)
s2_classifier = Classifier(num_classes=num_classes).cuda(gpu_id)
s1_classifier.load_state_dict(
torch.load("/home/xuruijia/ZJY/ADW/bvlc_A_W_2_D/pretrain/bvlc_s1_cls.pth"))
s2_classifier.load_state_dict(
torch.load("/home/xuruijia/ZJY/ADW/bvlc_A_W_2_D/pretrain/bvlc_s2_cls.pth"))
s1_t_discriminator = Discriminator().cuda(gpu_id)
s2_t_discriminator = Discriminator().cuda(gpu_id)
def print_log(step, epoch, epoches, lr, l1, l2, l3, l4, l5, l6, l7, l8, flag,
ploter, count):
shuffle=shuffle,
num_workers=num_workers)
s2_loader_raw = torch.utils.data.DataLoader(s2_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
t_loader_raw = torch.utils.data.DataLoader(t_set,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
t_loader_test = torch.utils.data.DataLoader(t_set_test,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
extractor = Extractor()
s1_classifier = Classifier(num_classes=num_classes)
s2_classifier = Classifier(num_classes=num_classes)
s1_t_discriminator = Discriminator()
s2_t_discriminator = Discriminator()
extractor.load_state_dict(
torch.load(osp.join(MAIN_DIR, "MSDA/pretrain/office/bvlc_extractor.pth")))
extractor = nn.DataParallel(extractor)
extractor = extractor.cuda()
s1_classifier.load_state_dict(
torch.load(osp.join(MAIN_DIR, "MSDA/pretrain/office/bvlc_s1_cls.pth")))
s2_classifier.load_state_dict(
torch.load(osp.join(MAIN_DIR, "MSDA/pretrain/office/bvlc_s2_cls.pth")))
s1_classifier = nn.DataParallel(s1_classifier)
source_validation_features, source_validation_gas_labels, source_validation_liquid_labels = info_transfer2FloatTensor(
source_validation_features, source_validation_labels)
target_features, target_gas_labels, target_liquid_labels = info_transfer2FloatTensor(target_features,
target_labels)
source_training_liquid_labels = source_training_liquid_labels / math.sqrt(1000)
source_validation_liquid_labels = source_validation_liquid_labels / math.sqrt(1000)
target_liquid_labels =target_liquid_labels / math.sqrt(1000)
length_target = target_gas_labels.size()[0]
target_dataset = Data.TensorDataset(target_features,target_liquid_labels)
Batch_size = 128
target_loader = Data.DataLoader(dataset=target_dataset, batch_size=Batch_size,
shuffle=False, num_workers=2)
E = Extractor()
R = Regressor()
E.load_state_dict(torch.load('E_l2.pkl'))
R.load_state_dict(torch.load('R_l2.pkl'))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
E.to(device)
R.to(device)
prediction_target, reference_target = predict(target_loader, E, R)
#plot
standard_line_x = [0, 6500]
standard_line_y = [0, 6500]
error_line_x_p500 = [0, 6000]
error_line_y_p500 = [500, 6500]
error_line_x_n500 = [500, 6500]
error_line_y_n500 = [0, 6000]
def main(unused_argv):
if FLAGS.checkpoint_dir == '' or not os.path.exists(FLAGS.checkpoint_dir):
raise ValueError('invalid checkpoint directory {}'.format(FLAGS.checkpoint_dir))
checkpoint_dir = os.path.join(FLAGS.checkpoint_dir, '')
if FLAGS.output_dir == '':
raise ValueError('invalid output directory {}'.format(FLAGS.output_dir))
elif not os.path.exists(FLAGS.output_dir):
assert FLAGS.output_dir != FLAGS.checkpoint_dir
os.makedirs(FLAGS.output_dir)
print('reconstructing models and inputs.')
image = Image('/data/yuming/watermark-data/image_paths.mat', FLAGS.image_seq)()
wm = Watermark('/data/yuming/watermark-data/watermark.mat')()
dim = [1, FLAGS.img_height, FLAGS.img_width, FLAGS.num_chans]
image_upsampler = Upsampler(dim)
wm_upsampler = Upsampler([1] + dim[1:])
downsampler = Downsampler(dim)
blender = Blender(dim)
extrator = Extractor(dim)
image_upsampled = image_upsampler(image)
wm_upsampled = wm_upsampler(wm)
image_blended = blender(image_upsampled, wm_upsampled)
image_downsampled = downsampler(image_blended)
wm_extracted = extrator(image_downsampled)
# Calculate the psnr of the model.
psnr = PSNR()
image_psnr = psnr(image, image_downsampled)
wm_psnr = psnr(wm, wm_extracted)
summ_psnr_op = tf.summary.merge([tf.summary.text('image_psnr', tf.as_string(image_psnr)),
tf.summary.text('wm_psnr', tf.as_string(wm_psnr))])
saver = tf.train.Saver()
writer = tf.summary.FileWriter(FLAGS.output_dir, tf.get_default_graph())
config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False)
assert (FLAGS.gpus != ''), 'invalid GPU specification'
config.gpu_options.visible_device_list = FLAGS.gpus
with tf.Session(config = config) as sess:
sess.run(tf.local_variables_initializer())
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
print('No checkpoint file found')
return
summ_psnr_str, image_val, image_downsampled_val = \
sess.run([summ_psnr_op, image, image_downsampled])
writer.add_summary(summ_psnr_str, global_step = 0)
'''
images = [{'data': np.squeeze(image_val[0, :, :, :].astype(np.uint8)), 'title': "original image"},
{'data': np.squeeze(image_downsampled_val[0, :, :, :].astype(np.uint8)), 'title': "watermarked image"}]
'''
images = [{'data': np.squeeze(image_val[0, :, :, :].astype(np.uint8)), 'title': ""},
{'data': np.squeeze(image_downsampled_val[0, :, :, :].astype(np.uint8)), 'title': ""}]
image_str = draw_image(images)
writer.add_summary(image_str, global_step = 0)
