def __init__(self):
test_noisy_image = utils.imread(utils.get_image_path(False, 64, 4003))
test_noisy_image = utils.scale_image(test_noisy_image,
2.0) # Image size 128x128
test_noisy_image /= 255.0
test_noisy_image = test_noisy_image.reshape(128, 128, 1)
self.noisy_img1 = test_noisy_image
test_noisy_image = utils.imread(utils.get_image_path(False, 64, 19983))
test_noisy_image = utils.scale_image(test_noisy_image,
2.0) # Image size 128x128
test_noisy_image /= 255.0
test_noisy_image = test_noisy_image.reshape(128, 128, 1)
self.noisy_img2 = test_noisy_image
def step(self, x, y, log=False):
y_prob = self.model(x)
loss = self.bce_loss(y_prob, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.global_step += 1
if log:
step = self.global_step
tfwriter = self.tfwriter
tfwriter.add_scalar("losses/loss", loss, step)
# create grid of images
x_grid = torchvision.utils.make_grid(x[:, [2, 1, 0]])
y_grid = torchvision.utils.make_grid(y)
seg_grid = torchvision.utils.make_grid(y_prob)
# write to tensorboard
tfwriter.add_image('inputs', scale_image(x_grid), step)
tfwriter.add_image('labels', y_grid, step)
tfwriter.add_image('segmentation', seg_grid, step)
tfwriter.add_histogram('segmentation', y, step)
return loss
def run(args):
global use_cuda
print('Loading Generator')
model = Generator()
model.load_state_dict(torch.load(args.weights))
# Generate latent vector
x = torch.randn(1, 512, 1, 1)
if use_cuda:
model = model.cuda()
x = x.cuda()
x = Variable(x)
print('Executing forward pass')
images = model(x)
if use_cuda:
images = images.cpu()
images_np = images.data.numpy().transpose(0, 2, 3, 1)
image_np = scale_image(images_np[0, ...])
print('Output')
plt.figure()
plt.imshow(image_np)
plt.show(block=True)
def run(args, x, frame):
global use_cuda
#print('Loading Generator') # modified for readability
model = Generator()
model.load_state_dict(torch.load(args.weights))
# Generate latent vector
# x = torch.randn(1, 512, 1, 1) # now generated in main and passed as an argument
if use_cuda:
model = model.cuda()
x = x.cuda()
x = Variable(x, volatile=True)
#print('Executing forward pass')
images = model(x)
if use_cuda:
images = images.cpu()
#
images_np = images.data.numpy().transpose(0, 2, 3, 1)
image_np = scale_image(images_np[0, ...])
#print('Output')
#plt.figure()
#plt.imshow(image_np)
cv2image_np = image_np[..., ::-1]
#cv2.imshow("test", cv2image_np)
cv2.imwrite(
"/home/ubuntu/prog_gans_pytorch_inference/test" + str(frame) + ".png",
cv2image_np)
def load_map_info(self):
image = Image.open(self.path + "/" + self.name + ".png")
image = utils.scale_image(self.scale, image)
with open(self.path + "/" + self.name + "_info.txt") as file:
map_info = file.read()
self.image_png = image
self.image_pygame = utils.image_to_pygame(image)
self.map_info = map_info
def dis_update(self, x_dict, log=False):
if self.distribute:
gen = self.gen.module
dis = self.dis.module
else:
gen = self.gen
dis = self.dis
if gen.skip_dim:
x_skip = {name: x[:, gen.skip_dim] for name, x in x_dict.items()}
else:
x_skip = {name: None for name in x_dict.keys()}
z_dict = {name: gen.encode(x, name) for name, x in x_dict.items()}
keys = list(x_dict.keys())
# for each discriminator, show it examples of true examples + all other false examples
dis_losses = []
for name, x in x_dict.items():
cross_keys = copy.copy(list(gen.decoders.keys()))
cross_keys.remove(name)
x_cross = {
kk: gen.decode(z_dict[kk][0] + z_dict[kk][1],
name,
skip_x=x_skip[kk])
for kk in z_dict.keys()
}
dis_losses += [
dis.models[name].calc_dis_loss(xc.detach(), x_dict[name])
for kk, xc in x_cross.items()
]
loss_dis = torch.mean(torch.stack(dis_losses))
loss = loss_dis * self.params['gan_w']
self.optimizer_dis.zero_grad()
loss.backward()
self.optimizer_dis.step()
if log:
tfwriter = self.tfwriter
tfwriter.add_scalar('losses/dis/total', loss_dis, self.global_step)
x_12 = gen.decode(z_dict[keys[0]][0],
keys[1],
skip_x=x_skip[keys[0]])
for c in range(x_12.shape[1]):
tfwriter.add_image(f"images/cross_reconstruction/channel_{c}",
utils.scale_image(x_12[0, c:c + 1]),
self.global_step)
return loss
def step(self, x, y, log=False):
y_reg = self.model(x)
loss = self.mse_loss(y, y_reg)
#print('loss', loss)
#print('null inputs', torch.mean((x != x).type(torch.FloatTensor)))
if loss != loss:
print(f"Loss Regression: {loss.item()}")
print('x', x[0,0,:,:].cpu().detach().numpy())
print('y', y[0,0,:,:].cpu().detach().numpy())
print('regression', y_reg[0,0,:,:].cpu().detach().numpy())
print('probs', y_prob[0,0,:,:].cpu().detach().numpy())
print('mask', mask[0,0,:,:].cpu().detach().numpy())
print('sq_err', sq_err[0,0,:,:].cpu().detach().numpy())
if loss != loss:
sys.exit()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.global_step += 1
#print('next output', self.model(x)[0])
if log:
step = self.global_step
tfwriter = self.tfwriter
tfwriter.add_scalar("losses/loss", loss, step)
# create grid of images
x_grid = torchvision.utils.make_grid(x[:,[2,1,0]], nrow=2)
y_grid = torchvision.utils.make_grid(y[:,[0,]], nrow=2)
y_reg_grid = torchvision.utils.make_grid(y_reg[:,[0,]], nrow=2)
# write to tensorboard
tfwriter.add_image('inputs', scale_image(x_grid), step)
tfwriter.add_image('label', scale_image(y_grid), step)
tfwriter.add_image('regression', scale_image(y_reg_grid), step)
return loss
def _update_scaled_image(self):
if self.cvImage is None:
self.qtImage = None
return
scaled_image = scale_image(self.cvImage, self.scale)
if len(scaled_image.shape) == 2:
scaled_image = cv2.cvtColor(scaled_image, cv2.COLOR_GRAY2BGR)
height, width, byteValue = scaled_image.shape
byteValue = byteValue * width
cv2.cvtColor(scaled_image, cv2.COLOR_BGR2RGB, scaled_image)
self.qtImage = QtGui.QImage(scaled_image, width, height, byteValue,
QtGui.QImage.Format_RGB888)
self.resize(width, height)
def run(model, path):
global use_cuda
for i in xrange(0, 50):
# Generate latent vector
x = torch.randn(1, 512, 1, 1)
if use_cuda:
model = model.cuda()
x = x.cuda()
x = Variable(x, volatile=True)
image = model(x)
if use_cuda:
image = image.cpu()
image_np = image.data.numpy().transpose(0, 2, 3, 1)
image_np = scale_image(image_np[0, ...])
image = Image.fromarray(image_np)
fname = os.path.join(path, '_gen{}.jpg'.format(i))
image.save(fname)
print("{}th image".format(i))
def test(self):
self.load_best_model()
saved_dir = os.path.join(os.getcwd(), "xray_images")
saved_dir = os.path.join(saved_dir, "test_images_128x128")
if not os.path.exists(saved_dir):
os.mkdir(saved_dir)
for i in range(1, 4000):
if os.path.exists(utils.get_image_path(True, 64, i)):
test_noisy_128 = utils.imread(utils.get_image_path(
True, 64, i))
test_noisy_128 = utils.scale_image(test_noisy_128,
2.0) # Image size 128x128
test_noisy_128 /= 255.0
test_noisy_128 = test_noisy_128.reshape(128, 128, 1)
prediction = self.model.predict(np.array([test_noisy_128]))[0]
prediction = prediction * 255
prediction = prediction.astype('uint8').reshape((128, 128))
predicted_img = Image.fromarray(prediction)
clean_image_path = utils.get_image_path(True, 128, i)
predicted_img.save(clean_image_path)
def stage_PNet(self, model, img):
h, w, _ = img.shape
img_size = (w, h)
boxes_tot = np.empty((0, 5))
reg_offsets = np.empty((0, 4))
scales = self.get_image_pyramid_scales(self.min_face_size, img_size)
print(scales)
for scale in scales:
resized = utils.scale_image(img, scale)
normalized = utils.normalize_image(resized)
net_input = np.expand_dims(normalized, 0)
cls_map, reg_map, _ = model.predict(net_input)
cls_map = cls_map.squeeze()[:, :, 1] # here
reg_map = reg_map.squeeze()
boxes, indices = self.generate_bboxes_with_scores(cls_map,
scale,
threshold=0.7)
reg_deltas = reg_map[indices]
indices = self.non_maximum_suppression(boxes, 0.5, 'union')
boxes_tot = np.append(boxes_tot, boxes[indices], axis=0)
reg_offsets = np.append(reg_offsets, reg_deltas[indices], axis=0)
indices = self.non_maximum_suppression(boxes_tot, 0.7, 'union')
boxes_tot = boxes_tot[indices]
reg_offsets = reg_offsets[indices]
# refine bounding boxes
refined_boxes = self.refine_bboxes(boxes_tot, reg_offsets)
return refined_boxes
STYLE_WEIGHT = 1e10
TV_WEIGHT = 0.0001
transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.ToTensor(),
])
style_transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.ToTensor(),
])
vgg = Vgg16(requires_grad=False).cuda() # vgg16 model
I = utils.scale_image(filename=STYLE_IMG_PATH, size=128, scale=512)
I = np.array(I)
plt.imshow(I)
plt.show()
style_img = utils.load_image(filename=STYLE_IMG_PATH, size=IMAGE_SIZE)
# style_img = utils.image_compose(IMG=style_img, IMAGE_ROW=4, IMAGE_COLUMN=4, IMAGE_SIZE=128)
content_img = utils.load_image(filename=CONTENT_IMG_PATH, size=IMAGE_SIZE)
style_img = style_transform(style_img)
content_img = transform(content_img)
style_img = style_img.repeat(BATCH_SIZE, 1, 1, 1).cuda() # make fake batch
content_img = content_img.repeat(BATCH_SIZE, 1, 1, 1).cuda()
def gen_update(self, x_dict, log=False):
keys = list(x_dict.keys())
if self.distribute:
gen = self.gen.module
dis = self.dis.module
else:
gen = self.gen
dis = self.dis
z_dict = {name: gen.encode(x, name) for name, x in x_dict.items()}
if gen.skip_dim:
x_skip = {name: x[:, gen.skip_dim] for name, x in x_dict.items()}
else:
x_skip = {name: None for name in x_dict.keys()}
x_recon = {
name: gen.decode(z + noise, name, skip_x=x_skip[name])
for name, (z, noise) in z_dict.items()
}
key0 = keys[0]
_, _, h_hat, w_hat = x_recon[key0].shape
# decode all cross pairs
loss_gan = 0.
data_losses = dict()
loss_shared_recon = 0.
loss_cycle_z = 0.
loss_cycle_recon = 0.
loss_cycle_z_recon = 0.
cycles = 1 + self.global_step // self.cycle_step
gan_losses = []
cycle_recon_losses = []
shared_losses = []
cycle_zkl_losses = []
cycle_zrecon_losses = []
for k in keys:
x_k = x_dict[k]
z_k, n_k = z_dict[k]
cross_keys = list(gen.decoders.keys()) #copy.copy(keys)
cross_keys.remove(k)
cross_recon = {
kk: gen.decode(z_k + n_k, kk, skip_x=x_skip[k])
for kk in cross_keys
}
# GAN Loss
for kk in cross_keys:
gan_losses.append(dis.models[kk].calc_gen_loss(
cross_recon[kk]))
# Reconstruction loss
data_losses[k] = self.mse_loss(x_k, x_recon[k])
# Shared reconstruction loss
if hasattr(self, 'shared') and (k in self.shared):
shared_losses = [
self.mse_loss(x_k[:, self.shared[k][kk]],
cross_recon[kk][:, self.shared[kk][k]])
for kk in cross_keys
]
xk_1 = x_k
zk_1 = z_k
for c in range(cycles):
xk_cycle = {
kk: gen.decode(zk_1 + n_k, kk, skip_x=x_skip[k])
for kk in keys
} # decode each domain
z_cycle = {
kk: gen.encode(_x, kk)
for kk, _x in xk_cycle.items()
} # encode back to z
x2_cycle = {
kk: gen.decode(z_cycle[kk][0] + n_k, k, skip_x=x_skip[k])
for kk in keys
} # decode back to cross domains
cycle_recon_losses += [
self.mse_loss(x_k, x2_cycle[kk]) for kk in keys
]
cycle_zrecon_losses = [
self.mse_loss(z_k, z_cycle[kk][0]) for kk in keys
]
if log and (len(cross_keys) > 0):
kshow = cross_keys[0]
#self.tfwriter.add_image(f"images/cross_{c}_recon_{kshow}", utils.scale_image(x2_cycle[kshow][0,:1]), self.global_step)
#self.tfwriter.add_histogram(f"channel0/cross_reconstruct_{kshow}", x2_cycle[kshow][0,0], self.global_step)
cycle_zkl_losses = [
self._compute_kl(z2) for name, (z2, _) in z_cycle.items()
]
if c > 0:
x1_k = x2_cycle[k]
zk_1, _ = gen.encode(x1_k, k)
#z1_cycle = {name: self.gen.encode(x, name) for name, x in x2_cycle.items()}
loss_recon = torch.mean(
torch.stack([x for _, x in data_losses.items()]))
loss_gan = torch.mean(torch.stack(gan_losses))
loss_cycle_recon = cycles * torch.mean(torch.stack(cycle_recon_losses))
if len(shared_losses) > 0:
loss_shared_recon += torch.mean(torch.stack(shared_losses))
loss_cycle_z = cycles * torch.mean(torch.stack(cycle_zkl_losses))
loss_cycle_z_recon = cycles * torch.mean(
torch.stack(cycle_zrecon_losses))
loss = self.params['gan_w'] * loss_gan + \
self.params['recon_x_w'] * loss_recon + \
self.params['recon_x_cyc_w'] * loss_cycle_recon + \
self.params['recon_z_cyc_w'] * loss_cycle_z_recon + \
self.params['recon_kl_cyc_w'] * loss_cycle_z + \
self.params['recon_shared_w'] * loss_shared_recon
self.optimizer_gen.zero_grad()
loss.backward()
self.optimizer_gen.step()
self.scheduler.step()
step = self.global_step
if log:
tfwriter = self.tfwriter
#print(f"Gen: Step {self.global_step} -- Loss={loss.item()}")
tfwriter.add_scalar("losses/gen/total", loss, step)
tfwriter.add_scalar("losses/gen/recon", loss_recon, step)
tfwriter.add_scalar("losses/gen/gan", loss_gan, step)
tfwriter.add_scalar("losses/gen/cycle_recon", loss_cycle_recon,
step)
tfwriter.add_scalar("losses/gen/shared_recon", loss_shared_recon,
step)
#tfwriter.add_scalar("losses/gen/cycle_kl", loss_cc_z, step)
tfwriter.add_scalar("losses/gen/cycle_z_recon", loss_cycle_z_recon,
step)
for name in x_dict:
x = x_dict[name]
self.tfwriter.add_image(f"images/{name}/input",
utils.scale_image(x[0, :1]), step)
self.tfwriter.add_image(
f"images/{name}/reconstruction",
utils.scale_image(x_recon[name][0, :1]), step)
self.tfwriter.add_scalar(f"losses/data/{name}",
data_losses[name], step)
for j in range(x.shape[1]):
self.tfwriter.add_histogram(
f"channel{j}/Observed_Domain{name}", x[:, j], step)
self.tfwriter.add_histogram(
f"channel{j}/reconstructed_Domain{name}",
x_recon[name][:, j], step)
return loss
def load_sprites(self):
self.sprite_png = Image.open(self.path + "/" + self.name + ".png")
self.sprite_png = utils.scale_image(res["scale"], self.sprite_png)
self.sprite_pygame = utils.image_to_pygame(self.sprite_png)
def __call__(self):
seed = 9
self.batchsize = batch_size = 5
start_images = 0
number_of_images = 1400
train_ratio = 0.7
self.reset_graph(seed)
self.build_model(seed, batch_size)
# Set up a saver to load and save the model and a tensorboard graph
self.saver = tf.train.Saver(max_to_keep=5)
writer = tf.summary.FileWriter("graphs", tf.get_default_graph())
# Check if we should load a model, and if we succeded doing so
if (True):
could_load, checkpoint_counter = self.load()
else:
could_load = False
if could_load:
#
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
#self.export_for_web()
# Load the data (both train and test)
data_neg, data_pos = get_data(start_images, number_of_images,
train_ratio, seed)
n_epochs = 51
for epoch in range(n_epochs):
print(f'epoch{epoch}')
for i in range(
int(
np.floor(
float(number_of_images * train_ratio) /
batch_size))):
# Get the current batch
batch_pos = data_pos['train_data'][(
i * batch_size):((i * batch_size) + batch_size)]
batch_neg = data_neg['train_data'][(
i * batch_size):((i * batch_size) + batch_size)]
labels_pos = data_pos['train_labels'][(
i * batch_size):((i * batch_size) + batch_size)]
labels_neg = data_neg['train_labels'][(
i * batch_size):((i * batch_size) + batch_size)]
# Update the weights
self.sess.run(self.train_d,
feed_dict={
self.X_neg: batch_neg,
self.X_pos: batch_pos
})
self.sess.run(self.train_g0,
feed_dict={
self.X_neg: batch_neg,
self.X_pos: batch_pos
})
self.sess.run(self.train_g1,
feed_dict={
self.X_neg: batch_neg,
self.X_pos: batch_pos
})
if i % 10 == 0:
[l_pix0, l_pix1, l_per0, l_per1, l_gan0, l_gan1, l_dual0, l_dual1, l_D, l_g0, l_g1] = \
self.sess.run(
[self.l_pix0, self.l_pix1, self.l_per0, self.l_per1, self.l_gan0, self.l_gan1, self.l_dual0, self.l_dual1, self.l_cls, self.l_g0, self.l_g1],
feed_dict = {
self.X_neg: batch_neg,
self.X_pos: batch_pos
}
)
print(
"epoch: %d batch: %d l_pix0: %g l_pix1: %g l_per0: %g l_per1: %g l_gan0: %g l_gan1: %g l_dual0: %g l_dual1: %g l_g0: %g l_g1: %g l_D: %g"
% (epoch, i, l_pix0, l_pix1, l_per0, l_per1, l_gan0,
l_gan1, l_dual0, l_dual1, l_g0, l_g1, l_D))
if (epoch % 10 == 0):
self.save(epoch)
all_images = self.sess.run(self.all_images,
feed_dict={
self.X_neg: batch_neg,
self.X_pos: batch_pos,
})
for key, img_collection in all_images.items():
for j, img in enumerate(img_collection):
im = Image.fromarray(scale_image(img).astype('uint8'))
im.save(f'images/{key}-{str(j)}-epoch{str(epoch)}.png')
im.close()
import model
import mtcnn
import utils
import numpy as np
import cv2
if __name__ == "__main__":
net = model.make_pnet()
img = cv2.imread(
'dataset/wider_images/0--Parade/0_Parade_marchingband_1_6.jpg')
scale = 1
resized = utils.scale_image(img, scale)
normalized = utils.normalize_image(resized)
net_input = np.expand_dims(normalized, 0)
print(net_input.shape)
outputs = net.predict(net_input)
pass
def step(self, x, y, mask, log=False):
y[mask == 1] = 0.
eps = 1e-7
y_hat, logvar, y_prob, reg_losses = self.model(x, train=True)
y_cond = torch.masked_select(y, mask == 0)
y_hat_cond = torch.masked_select(y_hat, mask == 0)
y_logvar_cond = torch.masked_select(logvar, mask == 0)
y_precision_cond = torch.exp(-y_logvar_cond) + eps
cond_logprob = y_precision_cond * (y_cond -
y_hat_cond)**2 + y_logvar_cond
cond_logprob *= -1
cond_logprob = torch.mean(cond_logprob)
logprob_classifier = mask * torch.log(y_prob + eps) + (
1 - mask) * torch.log(1 - y_prob + eps)
logprob_classifier = torch.mean(logprob_classifier)
logprob = logprob_classifier + cond_logprob
neglogloss = -logprob
loss = neglogloss + 1e-2 * reg_losses
#print('loss', loss)
#print('null inputs', torch.mean((x != x).type(torch.FloatTensor)))
if loss != loss:
print(
f"Loss binary: {-logprob_classifier.item()}, Loss Regression: {-cond_logprob.item()}"
)
print('x', x[0, 0, :, :].cpu().detach().numpy())
print('y', y[0, 0, :, :].cpu().detach().numpy())
print('regression', y_reg[0, 0, :, :].cpu().detach().numpy())
print('probs', y_prob[0, 0, :, :].cpu().detach().numpy())
print('mask', mask[0, 0, :, :].cpu().detach().numpy())
print('sq_err', sq_err[0, 0, :, :].cpu().detach().numpy())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.global_step += 1
#print('next output', self.model(x)[0])
if log:
step = self.global_step
tfwriter = self.tfwriter
tfwriter.add_scalar("losses/binary", -logprob_classifier, step)
tfwriter.add_scalar("losses/regression", -cond_logprob, step)
tfwriter.add_scalar("losses/regularizer", -reg_losses, step)
tfwriter.add_scalar("losses/loss", loss, step)
y_hat *= 1 - mask
# create grid of images
x_grid = torchvision.utils.make_grid(x[:8, [2, 1, 0]])
y_grid = torchvision.utils.make_grid(y[:8, [2, 1, 0]])
mask_grid = torchvision.utils.make_grid(mask[:8, :1])
seg_grid = torchvision.utils.make_grid(y_prob[:8])
y_reg_grid = torchvision.utils.make_grid(y_hat[:8, [2, 1, 0]])
# write to tensorboard
tfwriter.add_image('inputs', scale_image(x_grid), step)
tfwriter.add_image('label', scale_image(y_grid), step)
tfwriter.add_image('regression', scale_image(y_reg_grid), step)
tfwriter.add_image('mask', mask_grid, step)
tfwriter.add_image('segmentation', seg_grid, step)
tfwriter.add_histogram('segmentation', y, step)
tfwriter.add_histogram('cond_observed', y_cond, step)
tfwriter.add_histogram('cond_regression', y_hat_cond, step)
return loss
