def train_net(x,y,w,h,c,H=200,d=2,niter=200000,bz=256,traj_iter=1000):
bw = (y.shape[-1] == 1)
net = ReLUNet(H=H,d=d,bw=bw).cuda()
net.apply(weight_init)
x = Variable(torch.from_numpy(x)).cuda()
y = Variable(torch.from_numpy(y)).cuda()
optim = torch.optim.Adam(net.parameters(), lr=1e-3) #lr=1e-4)
mse = nn.MSELoss().cuda()
T = []
for itr in range(niter):
optim.zero_grad()
b = np.random.randint(0,x.shape[0],bz)
y_ = net(x[b])
loss = mse(y_,y[b])
#loss = torch.sum((y_-y)**2)
loss.backward(retain_graph=True)
optim.step()
if (itr%traj_iter == 0):
#import pdb; pdb.set_trace();
out_np = net(x).detach().cpu().data.numpy().reshape((w,h,c))
if bw:
out_np = out_np[:,:,0]
utils.imwrite(os.path.join(output_dir,'itr'+str(itr)+'.png'),out_np)
T.append(out_np)
print('Iteration '+str(itr)+': '+str(loss.data))
del out_np
return T
def make_denoise_small_ds(source_dir, target_dir):
factor = 0.25
interp = cv2.INTER_AREA # for downsampling
if not os.path.exists(target_dir):
os.system('mkdir ' + target_dir)
for img_dir in os.listdir(source_dir):
img_name = os.path.split(img_dir)[-1]
if len(img_name) == 0:
img_name = os.path.split(img_dir)[-2]
ext = '.png'
clean_img = os.path.join(source_dir, img_dir, img_name + ext)
img = utils.imread(clean_img)
size = (int(img.shape[1] * factor), int(img.shape[0] * factor))
img = cv2.resize(img, size, interpolation=interp)
target_img_dir = os.path.join(target_dir, img_name)
if not os.path.exists(target_img_dir):
os.system('mkdir ' + target_img_dir)
target_clean = os.path.join(target_img_dir, img_name + ext)
utils.imwrite(target_clean, img)
for sigma in [5, 10, 20, 25, 30, 35, 40, 50, 60, 70, 75, 80, 90, 100]:
output_file = os.path.join(target_dir, img_dir,
img_name + '_s' + str(sigma) + ext)
sigma = sigma / 255.
noisy_img = utils.get_noisy_image(img, sigma)
utils.imwrite(output_file, noisy_img)
def renderframe(modeltest, outname, sess, upsample_method):
# TODO finish this
print("Model: " + modeltest + ' saved test file ' + outname)
# load test image
input_img_path = '/home/kth/deepstuff/frames/bk01.jpg'
testimg = utils.imread2(input_img_path)
testimg = utils.imresize(testimg, 1)
#testimg = utils.imresize_xy(testimg,256,256)
testimg_4d = testimg[np.newaxis, :] # .astype(np.float32)
# tf.reset_default_graph()
with tf.variable_scope('img_t_net_test', reuse=tf.AUTO_REUSE):
Xtest = tf.placeholder(tf.float32,
shape=testimg_4d.shape,
name='input')
Ytest = create_net(Xtest, upsample_method)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
print("Evaluating test image...")
with tf.Session() as sesstest:
sesstest.run(init_op)
img_out = sesstest.run(Ytest, feed_dict={Xtest: testimg_4d})
img_out = np.squeeze(img_out)
utils.imwrite(outname, img_out)
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
tf_config = {'rnd.np_random_seed': 1}
tflib.init_tf(tf_config)
assert os.path.exists(args.restore_path)
_, _, _, Gs, _ = load_pkl(args.restore_path)
latent_dim = Gs.components.synthesis.input_shape[2]
print(f'Latent dimension shape: {latent_dim}')
# Building graph
Z = tf.placeholder('float32', [None, latent_dim], name='Gaussian')
print(f'Z in tensorflow graph: {Z.shape}')
sampling_from_z = Gs.get_output_for(Z, None, randomize_noise=True)
sess = tf.get_default_session()
save_dir = args.output_dir or './outputs/sampling'
os.makedirs(save_dir, exist_ok=True)
print('Sampling...')
for it in tqdm(range(args.total_nums)):
samples = sess.run(
sampling_from_z,
{Z: np.random.randn(args.batch_size * 2, latent_dim)})
samples = samples.transpose(0, 2, 3, 1)
print(f'shape of output: {samples.shape}')
imwrite(immerge(samples, 2, args.batch_size),
'%s/sampling_%06d_newseed.png' % (save_dir, it))
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
tf_config = {'rnd.np_random_seed': 1000}
tflib.init_tf(tf_config)
assert os.path.exists(args.restore_path)
_, _, _, Gs, _ = load_pkl(args.restore_path)
num_layers = Gs.components.synthesis.input_shape[1]
batch_codes = np.load(args.data_dir_encode)
print(batch_codes.shape)
latent_dim = batch_codes.shape[1]
print(f'Latent dimension shape: {latent_dim}')
# Building graph
w_vec = tf.placeholder('float32', [None, latent_dim], name='w_codes')
print(f'W in tensorflow graph: {w_vec.shape}')
encoder_w_tile = tf.tile(w_vec[:, np.newaxis], [1, num_layers, 1])
print(f'encoder_w_tile size: {encoder_w_tile.shape}')
reconstructor = Gs.components.synthesis.get_output_for(
encoder_w_tile, randomize_noise=False)
sess = tf.get_default_session()
save_dir = args.output_dir or './outputs/rebuild_encodings'
os.makedirs(save_dir, exist_ok=True)
print('Creating Images...')
samples = sess.run(reconstructor, {w_vec: batch_codes})
samples = samples.transpose(0, 2, 3, 1)
print(f'shape of output: {samples.shape}')
imwrite(immerge(samples, 4, args.batch_size),
'%s/decode_00000_new1.png' % (save_dir))
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
tf_config = {'rnd.np_random_seed': 1000}
tflib.init_tf(tf_config)
assert os.path.exists(args.restore_path)
E, _, _, Gs, _ = load_pkl(args.restore_path)
num_layers = Gs.components.synthesis.input_shape[1]
# Building graph
real = tf.placeholder('float32', [None, 3, args.image_size, args.image_size], name='real_image')
encoder_w = E.get_output_for(real, phase=False)
encoder_w_tile = tf.tile(encoder_w[:, np.newaxis], [1, num_layers, 1])
reconstructor = Gs.components.synthesis.get_output_for(encoder_w_tile, randomize_noise=False)
sess = tf.get_default_session()
# Preparing data
input_images, _ = preparing_data(im_path=args.data_dir_test, img_type=args.img_type)
save_dir = args.output_dir or './outputs/reconstruction'
os.makedirs(save_dir, exist_ok=True)
print('Reconstructing...')
for it, image_id in tqdm(enumerate(range(0, input_images.shape[0], args.batch_size))):
batch_images = input_images[image_id:image_id+args.batch_size]
rec = sess.run(reconstructor, feed_dict={real: batch_images})
orin_recon = np.concatenate([batch_images, rec], axis=0)
orin_recon = orin_recon.transpose(0, 2, 3, 1)
imwrite(immerge(orin_recon, 2, batch_images.shape[0]),
'%s/reconstruction_%06d.png' % (save_dir, it))
def on_epoch_end(self, val_loss=None, logs=None):
# save model
global num_epoch
global dataset_name
global img_rows
global img_cols
global mbllen
num_epoch += 1
modelname = './models/' + str(num_epoch) + '_' + dataset_name + '_base.h5'
mbllen.save_weights(modelname)
# test val data
path = glob('../dataset/test/*.jpg')
number = 0
psnr_ave = 0
for i in range(len(path)):
if i>15:
break
img_B_path = path[i]
img_B = utils.imread_color(img_B_path)
path_mid = os.path.split(img_B_path)
path_A_1 = path_mid[0] + '_' + dataset_name
path_A = os.path.join(path_A_1, path_mid[1])
img_A = utils.imread_color(path_A)
nw = random.randint(0, img_A.shape[0] - img_rows)
nh = random.randint(0, img_A.shape[1] - img_cols)
crop_img_A = img_A[nw:nw + img_rows, nh:nh + img_cols, :]
crop_img_B = img_B[nw:nw + img_rows, nh:nh + img_cols, :]
crop_img_A = crop_img_A[np.newaxis, :]
crop_img_B = crop_img_B[np.newaxis, :]
fake_B = mbllen.predict(crop_img_A)
identy_B = mbllen.predict(crop_img_B)
out_img = np.concatenate([crop_img_A, fake_B, crop_img_B, identy_B], axis=2)
out_img = out_img[0, :, :, :]
fake_B = fake_B[0, :, :, :]
img_B = crop_img_B[0, :, :, :]
clean_psnr = utils.psnr_cau(fake_B, img_B)
L_psnr = ("%.4f" % clean_psnr)
number += 1
psnr_ave += clean_psnr
filename = os.path.basename(path[i])
img_name = './val_images/' + str(num_epoch) + '_' + L_psnr + '_' + filename
utils.imwrite(img_name, out_img)
psnr_ave /= number
print('------------------------------------------------')
print("[Epoch %d] [PSNR_AVE :%f]" % (num_epoch, psnr_ave))
print('------------------------------------------------')
def _inject_summary(self, tag, feed_dict, step):
summaries = self.sess.run(self.summary_ops[tag], feed_dict)
self.summary_writer.add_summary(summaries['summary'], step)
path = os.path.join(
self.config.sample_model_dir, "{}.png".format(step))
imwrite(path, img_tile(summaries['output'],
tile_shape=self.config.sample_image_grid)[:, :, 0])
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
tf_config = {'rnd.np_random_seed': 1000}
tflib.init_tf(tf_config)
assert os.path.exists(args.restore_path)
assert os.path.exists(args.boundary)
E, _, _, Gs, _ = load_pkl(args.restore_path)
num_layers, latent_dim = Gs.components.synthesis.input_shape[1:3]
# Building graph
real = tf.placeholder('float32',
[None, 3, args.image_size, args.image_size],
name='real_image')
W = tf.placeholder('float32', [None, num_layers, latent_dim],
name='Gaussian')
encoder_w = E.get_output_for(real, phase=False)
reconstruction_from_w = Gs.components.synthesis.get_output_for(
W, randomize_noise=False)
sess = tf.get_default_session()
# Preparing data
input_images, images_name = preparing_data(im_path=args.data_dir_test,
img_type=args.img_type)
boundary = np.load(args.boundary)
boundary_name = args.boundary.split('/')[-1].split('_')[0]
save_dir = args.output_dir or './outputs/manipulation'
os.makedirs(save_dir, exist_ok=True)
print('manipulation in w space on %s' % (boundary_name))
for i in tqdm(range(input_images.shape[0])):
input_image = input_images[i:i + 1]
im_name = images_name[i]
latent_code = sess.run(encoder_w, feed_dict={real: input_image})
codes = manipulate(latent_code,
boundary,
num_layers=num_layers,
step=args.step,
start_distance=args.start_distance,
end_distance=args.end_distance)
inputs = np.zeros((args.batch_size, num_layers, latent_dim),
np.float32)
output_images = []
for idx in range(0, args.step, args.batch_size):
batch = codes[idx:idx + args.batch_size]
inputs[0:len(batch)] = batch
images = sess.run(reconstruction_from_w, feed_dict={W: inputs})
output_images.append(images[0:len(batch)])
output_images = np.concatenate(output_images, axis=0)
output_images = np.concatenate([input_image, output_images], axis=0)
output_images = output_images.transpose(0, 2, 3, 1)
imwrite(immerge(output_images, 1, args.step + 1),
'%s/%s_%s.png' % (save_dir, im_name, boundary_name))
def make_denoise_ds(data_dir):
for img_dir in os.listdir(data_dir):
img_name = os.path.split(img_dir)[-1]
if len(img_name) == 0:
img_name = os.path.split(img_dir)[-2]
ext = '.png'
clean_img = os.path.join(data_dir, img_dir, img_name + ext)
img = utils.imread(clean_img)
for sigma in [5, 10, 20, 25, 30, 35, 40, 50, 60, 70, 75, 80, 90, 100]:
output_file = os.path.join(data_dir, img_dir,
img_name + '_s' + str(sigma) + ext)
sigma = sigma / 255.
noisy_img = utils.get_noisy_image(img, sigma)
utils.imwrite(output_file, noisy_img)
def main():
"""Main function."""
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
tf_config = {'rnd.np_random_seed': 1000}
tflib.init_tf(tf_config)
assert os.path.exists(args.restore_path)
_, _, _, Gs, _ = load_pkl(args.restore_path)
num_layers = Gs.components.synthesis.input_shape[1]
w_codes = np.load(args.data_dir_encode)
boundary = np.load(args.boundary_path)
print(w_codes.shape)
latent_dim = w_codes.shape[1]
total_num = w_codes.shape[0]
# Building graph
w_vec = tf.placeholder('float32', [None, latent_dim], name='w_codes')
print(f'W in tensorflow graph: {w_vec.shape}')
encoder_w_tile = tf.tile(w_vec[:, np.newaxis], [1, num_layers, 1])
print(f'encoder_w_tile size: {encoder_w_tile.shape}')
reconstructor = Gs.components.synthesis.get_output_for(encoder_w_tile, randomize_noise=False)
sess = tf.get_default_session()
save_dir = args.output_dir or './outputs/edited_images'
os.makedirs(save_dir, exist_ok=True)
for sample_id in tqdm(range(total_num), leave=True):
#get edited codes for one image
interpolations = linear_interpolate(w_codes[sample_id:sample_id+1],
boundary,
start_distance=args.start_distance,
end_distance=args.end_distance,
steps=args.steps)
print(f'Interpolations= {interpolations.shape}')
if interpolations[0].all == interpolations[1].all:
print('STOP')
sys.exit(1)
interpolation_id = 0
samples = sess.run(reconstructor, {w_vec: interpolations})
samples = samples.transpose(0, 2, 3, 1)
print(f'Samples= {samples.shape}')
for image in samples:
save_path = os.path.join(args.output_dir, f'{sample_id:03d}_{interpolation_id:03d}.jpg')
imwrite(image, save_path)
interpolation_id += 1
print(f'interpolation_id: {interpolation_id}, steps: {args.steps}')
assert interpolation_id == args.steps
def reconstruction(sess, images, real, reconstructor, save_dir):
it = 0
for image_id in tqdm(range(0, len(images), args.batch_size)):
images_name = images[image_id:image_id+args.batch_size]
batch_images = []
for im_name in images_name:
batch_images.append(cv2.imread(im_name)[:, :, ::-1])
batch_images = np.asarray(batch_images)
batch_images = adjust_dynamic_range(batch_images.astype(np.float32), [0, 255], [-1., 1.])
rec = sess.run(reconstructor, feed_dict={real: batch_images.transpose(0, 3, 1, 2)})
rec = rec.transpose(0, 2, 3, 1)
rec = np.clip(rec, -1., 1.)
orin_recon = np.concatenate([batch_images, rec], axis=0)
imwrite(immerge(orin_recon, 2, len(images_name)), '%s/iter_%06d.png' % (save_dir, it))
it += 1
def test(self):
batch_size = self.data_loader.batch_size
num_epoch = len(self.data_loader.synthetic_data_paths) / batch_size
for idx in trange(num_epoch, desc="Refine all synthetic images"):
feed_dict = {
self.model.synthetic_batch_size: batch_size,
}
res = self.model.test_refiner(self.sess,
feed_dict,
None,
with_output=True)
for image, filename in zip(res['output'], res['filename']):
basename = os.path.basename(filename).replace(
"_cropped", "_refined")
path = os.path.join(self.config.output_model_dir, basename)
imwrite(path, image[:, :, 0])
def manipulation(sess, images, real, Z, get_w_from_img, get_img_from_w, boundaries, save_dir, steps=11, start_distance=-5., end_distance=5.):
linspace = np.linspace(start_distance, end_distance, steps)
linspace = linspace.reshape(-1, 1).astype(np.float32)
for boundary in boundaries:
attr = boundary.split('/')[-1].split('_')[1]
print('manipulating on %s' % (attr))
boundary_ = np.load(boundary)
boundary_ = boundary_ * linspace
for image in tqdm(images):
img_1 = cv2.imread(image)[:, :, ::-1][np.newaxis]
img_1_name = image.split('/')[-1].split('.')[0]
img_1 = adjust_dynamic_range(img_1.astype(np.float32), [0, 255], [-1., 1.])
latent_w = sess.run(get_w_from_img, feed_dict={real: img_1.transpose(0, 3, 1, 2)})
inter = latent_w + boundary_
mid_res = sess.run(get_img_from_w, feed_dict={Z: inter})
mid_res = mid_res.transpose(0, 2, 3, 1)
mid_res = np.concatenate([img_1, mid_res], axis=0)
imwrite(immerge(mid_res, 1, steps + 1), '%s/%s_attr_%s.png' % (save_dir, img_1_name, attr))
def test(self):
batch_size = self.data_loader.batch_size
num_epoch = len(self.data_loader.synthetic_data_paths) // batch_size
path = "tested-images"
os.mkdir(path)
for idx in trange(num_epoch, desc="Refine all synthetic images"):
feed_dict = {
self.model.synthetic_batch_size: batch_size,
}
res = self.model.test_refiner(
self.sess, feed_dict, None, with_output=True)
counter = 0
for image, filename in zip(res['output'], res['filename']):
# basename = os.path.basename(filename).replace(".jpg", "_refined.jpg")
# path = os.path.join(self.config.output_model_dir, basename)
write_path = os.path.join(path, "%d.jpg" % counter)
imwrite(write_path, image[:, :, 0])
counter+= 1
def load(config, data_path, sample_path, rng):
if not os.path.exists(data_path):
print('creating folder', data_path)
os.makedirs(data_path)
maybe_download_and_extract(config, data_path)
synthetic_image_path = maybe_preprocess(config, data_path, sample_path)
gaze_data = np.load(os.path.join(data_path, DATA_FNAME))
real_data = gaze_data['real']
if not os.path.exists(sample_path):
os.makedirs(sample_path)
print("[*] Save samples images in {}".format(data_path))
random_idxs = rng.choice(len(real_data), 100)
for idx, random_idx in enumerate(random_idxs):
image_path = os.path.join(sample_path, "real_{}.png".format(idx))
imwrite(image_path, real_data[random_idx])
return real_data, synthetic_image_path
def interpolation_on_w(sess, images, real, Z, get_w_from_img, get_img_from_w, step, save_dir):
for image1 in tqdm(images):
img_1 = cv2.imread(image1)[:, :, ::-1][np.newaxis]
img_1 = adjust_dynamic_range(img_1.astype(np.float32), [0, 255], [-1., 1.])
img_1_name = image1.split('/')[-1].split('.')[0]
for image2 in images:
img_2 = cv2.imread(image2)[:, :, ::-1][np.newaxis]
img_2 = adjust_dynamic_range(img_2.astype(np.float32), [0, 255], [-1., 1.])
img_2_name = image2.split('/')[-1].split('.')[0]
latent_1 = sess.run(get_w_from_img, feed_dict={real: img_1.transpose(0, 3, 1, 2)})
latent_2 = sess.run(get_w_from_img, feed_dict={real: img_2.transpose(0, 3, 1, 2)})
linspace = np.linspace(0.0, 1.0, step)[:, np.newaxis].astype(np.float32)
mid_res = latent_1 + linspace * (latent_2 - latent_1)
mid_res = sess.run(get_img_from_w, feed_dict={Z: mid_res})
mid_res = mid_res.transpose(0, 2, 3, 1)
mid_res = np.clip(mid_res, -1., 1.)
mid_res = np.concatenate([img_1, mid_res, img_2], axis=0)
imwrite(immerge(mid_res, 1, step + 2), '%s/%s_%s.png' % (save_dir, img_1_name, img_2_name))
def generate_to_art(param):
print('generate_to_art')
input_img_path = param['input_img_path']
output_img_path = param['output_img_path']
model_path = param['model_path']
upsample_method = param['upsample_method']
content_target_resize = param['content_target_resize']
# Read + preprocess input image.
img = utils.imread(input_img_path)
img = utils.imresize(img, content_target_resize)
img_4d = img[np.newaxis, :]
tf.reset_default_graph()
# Create the graph.
with tf.variable_scope('img_t_net'):
X = tf.placeholder(tf.float32, shape=img_4d.shape, name='input')
Y = create_net(X, upsample_method)
# Filter the input image.
with tf.Session() as sess:
print 'Loading up model...'
# Saver used to restore the model to the session.
saver = tf.train.Saver()
saver.restore(sess, model_path)
print 'Evaluating...'
img_out = sess.run(Y, feed_dict={X: img_4d})
sess.close()
# Postprocess + save the output image.
print 'Saving image.'
img_out = np.squeeze(img_out)
utils.imwrite(output_img_path, img_out)
print 'Done.'
def make_inpaint_ds(data_dir):
for img_dir in os.listdir(data_dir):
img_name = os.path.split(img_dir)[-1]
print(img_name)
if len(img_name) == 0:
img_name = os.path.split(img_dir)[-2]
ext = '.png'
img = utils.imread(os.path.join(data_dir, img_dir, img_name + ext))
# read mask, if available. otherwise, generate maske dropping 50% pixels
mask_path = os.path.join(data_dir, img_dir, img_name + '_mask' + ext)
if os.path.exists(mask_path):
mask = utils.imread(mask_path)
else:
mask = np.ones((img.shape[0], img.shape[1]))
mask = utils.add_mask_noise(mask)
utils.imwrite(mask_path, mask)
masked_img = utils.mask_img(img, mask)
output_file = os.path.join(data_dir, img_dir,
img_name + '_noisy' + ext)
utils.imwrite(output_file, masked_img)
def load(config, data_path, sample_path, rng):
if not os.path.exists(data_path):
print('creating folder', data_path)
os.makedirs(data_path)
synthetic_image_path = maybe_preprocess(config, data_path, sample_path)
supervised_image_path = glob(
os.path.join(data_path, config.synthetic_image_sup_dir, '*.png'))
nuclei_data = np.load(os.path.join(data_path, DATA_FNAME))
real_data = nuclei_data['real']
ref_real_data = nuclei_data['ref_real']
if not os.path.exists(sample_path):
os.makedirs(sample_path)
print("[*] Save samples images in {}".format(data_path))
random_idxs = rng.choice(len(real_data), 100)
for idx, random_idx in enumerate(random_idxs):
image_path = os.path.join(sample_path, "real_{}.png".format(idx))
imwrite(image_path, real_data[random_idx])
return real_data, synthetic_image_path, ref_real_data, supervised_image_path
def imwrite_fork(dert_, param, Ave, fork_history):
"""Output fork's gradient image."""
# Select dert_ to draw:
if param == 'i':
o = dert_[0]
elif param == 'g':
o = dert_[1]
elif param == 'm':
assert len(dert_) in (5, 12)
o = dert_[2]
elif param == 'ga':
o = dert_[7] if len(dert_) == 12 else dert_[6]
else:
o = None
if OUTPUT_BIN:
if fork_history[-1] == "a":
Ave = ANGLE_AVE
imwrite(OUTPUT_PATH + fork_history, (o > Ave) * 255)
elif OUTPUT_NORMALIZE:
imwrite(OUTPUT_PATH + fork_history,
255 * (o - o.min()) / (o.max() - o.min()))
else:
imwrite(OUTPUT_PATH + fork_history, o)
model_path = args.model_path
upsample_method = args.upsample_method
content_target_resize = args.content_target_resize
# Read + preprocess input image.
img = utils.imread(input_img_path)
img = utils.imresize(img, content_target_resize)
img_4d = img[np.newaxis, :]
# Create the graph.
with tf.variable_scope('img_t_net'):
X = tf.placeholder(tf.float32, shape=img_4d.shape, name='input')
Y = create_net(X, upsample_method)
# Saver used to restore the model to the session.
saver = tf.train.Saver()
# Filter the input image.
with tf.Session() as sess:
print 'Loading up model...'
saver.restore(sess, model_path)
print 'Evaluating...'
img_out = sess.run(Y, feed_dict={X: img_4d})
# Postprocess + save the output image.
print 'Saving image.'
img_out = np.squeeze(img_out)
utils.imwrite(output_img_path, img_out)
print 'Done.'
g_summary_opt, _ = sess.run([g_summary, g_step], feed_dict={z: z_ipt})
summary_writer.add_summary(g_summary_opt, it)
# display
if it % 1 == 0:
print("Epoch: (%3d) (%5d/%5d)" % (epoch, it_epoch, batch_epoch))
# save
if (it + 1) % 1000 == 0:
save_path = saver.save(
sess, '%s/Epoch_(%d)_(%dof%d).ckpt' %
(ckpt_dir, epoch, it_epoch, batch_epoch))
print('Model saved in file: % s' % save_path)
# sample
if (it + 1) % 100 == 0:
f_sample_opt = sess.run(f_sample, feed_dict={z: z_ipt_sample})
save_dir = './sample_images_while_training/celeba_wgan'
utils.mkdir(save_dir + '/')
utils.imwrite(
utils.immerge(f_sample_opt, 10,
10), '%s/Epoch_(%d)_(%dof%d).jpg' %
(save_dir, epoch, it_epoch, batch_epoch))
except Exception as e:
traceback.print_exc()
finally:
print(" [*] Close main session!")
sess.close()
def saveSampleImgs(imgs, full_path, row, column):
utils.imwrite(utils.immerge(imgs, row, column),full_path)
def train():
alpha_span = 800000
batch_size = 32
ckpt_dir = './checkpoints/wgp'
n_gen = 1
n_critic = 1
it_start = 0
#epoch = 20*(alpha_span * 2 // (2*4936)) # 4936 is number of images
def preprocess_fn(img):
img = tf.image.resize_images(img, [target_size, target_size], method=tf.image.ResizeMethod.AREA) / 127.5 -1
return img
def preprocess_fn_dummy(img):
img = tf.image.resize_images(img, [final_size, final_size], method=tf.image.ResizeMethod.AREA) / 127.5 -1
return img
# dataset
img_paths = glob.glob('./imgs/faces/*.png')
data_pool = utils.DiskImageData(5, img_paths, batch_size//2, shape=[640, 640, 3], preprocess_fn=preprocess_fn)
data_pool_dummy = utils.DiskImageData(7, img_paths, 1, shape=[640, 640, 3], preprocess_fn=preprocess_fn_dummy)
batch_epoch = len(data_pool) // (batch_size * 1)#n_critic
# build graph
print('Building a graph ...')
nodes = build(batch_size)
# session
sess = utils.session()
saver = tf.train.Saver()
# summary
summary_writer = tf.summary.FileWriter('./summaries/wgp/', sess.graph)
utils.mkdir(ckpt_dir + '/')
print('Initializing all variables ...')
sess.run(tf.global_variables_initializer())
# run final size session for storing all variables to be used into the optimizer
print('Running final size dummy session ...')
#if target_size == initial_size and len(sys.argv) <= 3:
# _ = sess.run([nodes['dummy']['d']], feed_dict=get_ipt(2, final_size, 1.0, data_pool_dummy ,z_dim, nodes['dummy']['input'] ))
# _ = sess.run([nodes['dummy']['g']], feed_dict=get_ipt(2, final_size, 1.0, data_pool_dummy ,z_dim, nodes['dummy']['input'] ))
# load checkpoint
if len(sys.argv)>3 and sys.argv[2]=='resume':
print ('Loading the checkpoint ...')
saver.restore(sess, ckpt_dir+'/model.ckpt')
it_start = 1 + int(sys.argv[3])
last_saved_iter = it_start - 1
''' train '''
for it in range(it_start, 9999999999):
# fade alpha
alpha_ipt = it / (alpha_span / batch_size)
if alpha_ipt > 1 or target_size == initial_size:
alpha_ipt = 1.0
print('Alpha : %f' % alpha_ipt)
alpha_ipt = 1.0
# train D
for i in range(n_critic):
d_summary_opt, _ = sess.run([nodes['summaries']['d'], nodes['product']['d']],\
feed_dict=get_ipt(batch_size, target_size, alpha_ipt, data_pool, z_dim, nodes['product']['input']))
summary_writer.add_summary(d_summary_opt, it)
# train G
for i in range(n_gen):
g_summary_opt, _ = sess.run([nodes['summaries']['g'], nodes['product']['g']],\
feed_dict=get_ipt(batch_size, target_size, alpha_ipt, data_pool, z_dim, nodes['product']['input']))
summary_writer.add_summary(g_summary_opt, it)
# display
epoch = it // batch_epoch
it_epoch = it % batch_epoch + 1
if it % 1 == 0:
print("iter : %8d, epoch : (%3d) (%5d/%5d) _ resume point : %d" % (it, epoch, it_epoch, batch_epoch,last_saved_iter))
# sample
if (it + 1) % batch_epoch == 0:
f_sample_opt = sess.run(nodes['sample'], feed_dict=get_ipt_for_sample(batch_size, z_dim, nodes['product']['input']))
f_sample_opt = np.clip(f_sample_opt, -1, 1)
save_dir = './sample_images_while_training/wgp/'
utils.mkdir(save_dir + '/')
osz = int(math.sqrt(batch_size))+1
utils.imwrite(utils.immerge(f_sample_opt, osz, osz), '%s/iter_(%d).png' % (save_dir, it))
# save
if (it + 1) % batch_epoch == 0:
last_saved_iter = it
save_path = saver.save(sess, '%s/model.ckpt' % (ckpt_dir))
print('Model saved in file: %s' % save_path)
def main():
with tf.name_scope('input'):
real = tf.placeholder(
'float32', [args.batch_size, 3, args.image_size, args.image_size],
name='real_image')
z = tf.placeholder('float32', [args.batch_size, args.z_dim],
name='Gaussian')
lr_g = tf.placeholder(tf.float32, None, name='learning_rate_g')
lr_d = tf.placeholder(tf.float32, None, name='learning_rate_d')
G_x = generator_x(input_z=z, reuse=False)
G_z = generator_z(input_x=real, reuse=False)
dis_fake, dis_fake_logit = discriminator(input_x=G_x,
input_z=z,
reuse=False)
dis_real, dis_real_logit = discriminator(input_x=real,
input_z=G_z,
reuse=True)
Reconstruction = generator_x(generator_z(real, reuse=True), reuse=True)
with tf.variable_scope('generator_loss'):
G_loss_img = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=dis_fake_logit, labels=tf.ones_like(dis_fake_logit)))
G_loss_z = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=dis_real_logit, labels=tf.zeros_like(dis_real_logit)))
G_loss = G_loss_img + G_loss_z
with tf.variable_scope('discriminator_loss'):
D_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=dis_real_logit, labels=tf.ones_like(dis_real_logit)))
D_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=dis_fake_logit, labels=tf.zeros_like(dis_fake_logit)))
D_loss = D_loss_real + D_loss_fake
Genx_vars = [
v for v in tf.global_variables() if v.name.startswith("generator_x")
]
Genz_vars = [
v for v in tf.global_variables() if v.name.startswith("generator_z")
]
Dis_vars = [
v for v in tf.global_variables() if v.name.startswith("discriminator")
]
G_solver = tf.train.AdamOptimizer(lr_g, args.beta1, args.beta2).minimize(
G_loss, var_list=Genx_vars + Genz_vars)
D_solver = tf.train.AdamOptimizer(lr_d, args.beta1,
args.beta2).minimize(D_loss,
var_list=Dis_vars)
saver = tf.train.Saver(max_to_keep=5)
sess = tensorflow_session()
if args.restore_path != '':
print('resotre weights from {}'.format(args.restore_path))
saver.restore(sess, tf.train.latest_checkpoint(args.restore_path))
print('Load weights finished!!!')
else:
sess.run(tf.global_variables_initializer())
print('Getting training HQ data...')
image_batch_train = get_train_data(sess,
data_dir=args.data_dir_train,
batch_size=args.batch_size)
image_batch_test = get_train_data(sess,
data_dir=args.data_dir_test,
batch_size=args.batch_size)
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
for it in range(120000):
latent_z = np.random.randn(args.batch_size,
args.z_dim).astype(np.float32)
batch_images_train = sess.run(image_batch_train)
batch_images_train = adjust_dynamic_range(
batch_images_train.astype(np.float32), [0, 255], [0., 1.])
feed_dict_1 = {
real: batch_images_train,
z: latent_z,
lr_g: args.learning_rate * 10,
lr_d: args.learning_rate * 0.1
}
_, d_loss_real, d_loss_fake = sess.run(
[D_solver, D_loss_real, D_loss_fake], feed_dict=feed_dict_1)
_, g_loss_img, g_loss_z = sess.run([G_solver, G_loss_img, G_loss_z],
feed_dict=feed_dict_1)
if it % 50 == 0:
print(
'Iter: {} g_loss_img: {} g_loss_z: {} d_r_loss: {} d_f_loss_: {}'
.format(it, g_loss_img, g_loss_z, d_loss_real, d_loss_fake))
if it % 1000 == 0:
samples1 = sess.run(G_x, feed_dict={z: latent_z})
samples1 = adjust_dynamic_range(samples1.transpose(0, 2, 3, 1),
drange_in=[0, 1],
drange_out=[-1, 1])
imwrite(immerge(samples1[:36, :, :, :], 6, 6),
'%s/epoch_%d_sampling.png' % (args.log_dir, it))
batch_images_test = sess.run(image_batch_test)
batch_images_test = adjust_dynamic_range(
batch_images_test.astype(np.float32), [0, 255], [0., 1.])
recon = sess.run(Reconstruction,
feed_dict={real: batch_images_test})
recon = adjust_dynamic_range(recon.transpose(0, 2, 3, 1),
drange_in=[0, 1],
drange_out=[-1, 1])
imwrite(immerge(recon[:36, :, :, :], 6, 6),
'%s/epoch_%d_recon.png' % (args.log_dir, it))
batch_images = adjust_dynamic_range(
batch_images_test.transpose(0, 2, 3, 1),
drange_in=[0, 1],
drange_out=[-1, 1])
imwrite(immerge(batch_images[:36, :, :, :], 6, 6),
'%s/epoch_%d_orin.png' % (args.log_dir, it))
if np.mod(it, 10000) == 0 and it > 50000:
saver.save(sess, args.checkpoint_dir, global_step=it)
def train():
graph = tf.Graph()
with graph.as_default():
z = tf.placeholder(tf.float32, shape=[64, 100], name='z')
img_batch = read_records.read_and_decode(
'tf_records/cartoon.tfrecords', batch_size=batch_size)
#generator
# fake=models.generator(z, stddev=0.02, alpha=alpha, name='generator', reuse=False)
#
# #discriminator
# dis_real=models.discriminator(img_batch , alpha=alpha, batch_size=batch_size)
# dis_fake=models.discriminator(fake, alpha=alpha, reuse=True)
#generator
fake = models.generator(z, reuse=False) #, is_training=True
#discriminator
dis_real = models.discriminator(img_batch,
reuse=False) #is_training=True
dis_fake = models.discriminator(fake, reuse=True) #, is_training=True
# #losses
# gene_loss = tf.reduce_mean(tf.squared_difference(dis_fake, 0.9))
# dis_loss = (tf.reduce_mean(tf.squared_difference(dis_real, 0.9))
# + tf.reduce_mean(tf.square(dis_fake))) / 2
gene_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(dis_fake) * 0.9, logits=dis_fake))
d_f_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(dis_fake), logits=dis_fake))
d_r_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(dis_real) * 0.9, logits=dis_real))
dis_loss = d_f_loss + d_r_loss
gen_loss_sum = tf.summary.scalar("gen_loss", gene_loss)
dis_loss_sum = tf.summary.scalar("dis_loss", dis_loss)
merge_sum_gen = tf.summary.merge([gen_loss_sum])
merge_sum_dis = tf.summary.merge([dis_loss_sum])
#variables
gene_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='generator')
dis_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='discriminator')
gene_opt = tf.train.AdamOptimizer(
learning_rate=0.0002, beta1=0.3).minimize(gene_loss,
var_list=gene_var)
dis_opt = tf.train.AdamOptimizer(learning_rate=0.0002,
beta1=0.3).minimize(dis_loss,
var_list=dis_var)
test_sample = models.generator(z, reuse=True) #, is_training=False
test_out = tf.add(test_sample, 0, 'test_out')
init = tf.global_variables_initializer()
print('t')
with tf.Session(graph=graph) as sess:
sess.run(init) # 初始化全局变量
z_ipt_sample = np.random.normal(size=[batch_size, 100])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
writer = tf.summary.FileWriter('./tensorboard', sess.graph)
saver = tf.train.Saver()
try:
for i in range(run_nums):
z_ipt = np.random.normal(size=[batch_size, 100])
#train D
#_, dis_loss1 = sess.run([dis_opt,dis_loss],feed_dict={real:img_batch,z:z_ipt})
sum_dis, _, dis_loss1 = sess.run(
[merge_sum_dis, dis_opt, dis_loss], feed_dict={z: z_ipt})
#train G
sum_gen, _, gen_loss1 = sess.run(
[merge_sum_gen, gene_opt, gene_loss], feed_dict={z: z_ipt})
if i % 400 == 0:
print(i)
test_sample_opt = sess.run(test_sample,
feed_dict={z: z_ipt_sample})
#print(type(test_sample_opt),test_sample_opt.shape)
utils.mkdir('out_cartoon')
utils.imwrite(utils.immerge(test_sample_opt, 10, 10),
'out_cartoon/' + str(i) + '.jpg')
# writer.add_summary(sum_dis, i)
#writer.add_summary(sum_gen, i)
print("train end!!!")
except tf.errors.OutOfRangeError:
print('out of range')
finally:
coord.request_stop()
coord.request_stop()
coord.join(threads)
writer.close()
saver.save(sess, "./checkpoints/DCGAN")
all_luts = []
for image_file in input_files:
res_map = safe_decoder(text_detector, image_file, res_type)
if (res_map is not None):
file_bname = os.path.basename(image_file).rsplit('.')[0]
lut = {'input_file': image_file}
blut = from_res_map_to_bbox(res_map,
th_size=th_size,
th_prob=th_prob,
border_perc=.16)
lut.update(blut)
# write response map if necessary
if (not no_map):
output_res_file = os.path.join(output_dir, file_bname + '.png')
lut['response_map'] = output_res_file
imwrite(
np.round(res_map * 255).astype('uint8'), output_res_file)
verbose_print(['INFO: dump response map to', output_res_file],
verbose, 1)
# write output detection json file if necessary
if (not no_box):
output_box_json = os.path.join(output_dir,
file_bname + '.json')
json.dump(lut, open(output_box_json, 'w'), indent=4)
verbose_print(['INFO: dump text bbox to', output_box_json],
verbose, 1)
all_luts.append(lut)
if (no_box):
output_mega_json = os.path.join(output_dir, 'decoded_bbox.json')
json.dump(all_luts, open(output_mega_json, 'w'), indent=4)
verbose_print(['INFO: dump mega text bbox to', output_mega_json],
verbose, 1)
root_fcr, fig)
blob['Dert']['M'] = ave - blob['Dert']['M']
blob['dert__'], fga, root_fcr, fig = layer_2(blob['dert__'],
blob['Dert']['G'],
blob['Dert']['M'], fga,
root_fcr, fig)
blob['Dert']['M'] = ave - blob['Dert']['M']
blob['dert__'], fga, root_fcr, fig = layer_3(blob['dert__'],
blob['Dert']['G'],
blob['Dert']['M'], fga,
root_fcr, fig)
frame = map_frame_binary(frame, sign_map={1: 'white', 0: 'black'})
imwrite("./images/test_intra_comp.bmp", frame)
print('Done!')
'''
# Recursive comps:
recursive_comp(dert_=dert_,
rng=1,
fca=1,
nI=2,
Ave=ave,
fork_history="i",
depth=MAX_DEPTH)
print('Done!')
print('Terminating...')'''
def imwrite_fork(dert_, param, Ave, fork_history):
# save img and fft trajectories
fft_dir = os.path.join(output_dir, 'fft' + hyp_str)
if not os.path.exists(fft_dir):
os.system('mkdir ' + fft_dir)
traj_dir = os.path.join(output_dir, 'traj' + hyp_str)
if not os.path.exists(traj_dir):
os.system('mkdir ' + traj_dir)
fft_var = [fft(t[:, :]) for t in traj1]
for i in range(0, min(50, len(fft_var) * traj_iter), 2):
cv2.imwrite(os.path.join(fft_dir,
str(i) + '_fft.png'), 20 * fft_var[i])
utils.imwrite(
os.path.join(traj_dir, str(i)) + '_img.png',
traj1[i, :, :])
plt.cla()
plt.clf()
plt.close()
plt.plot([np.sum(f) for f in fft_var])
plt.savefig(os.path.join(output_dir, 'fft_var.png'),
bbox_inches='tight')
plt.cla()
plt.clf()
plt.close()
traj_iter = cfg.TRAJ_ITER
for err_type in ['err_traj', 'err_true_t1', 'err_true_t2']:
# compare across varying num channels
