def evaluate(args):
content_image = utils.tensor_load_rgbimage(args.content_image,
size=args.content_size,
keep_asp=True)
content_image = content_image.unsqueeze(0)
style = utils.tensor_load_rgbimage(args.style_image, size=args.style_size)
style = style.unsqueeze(0)
style = utils.preprocess_batch(style)
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
style_model = HangSNetV1()
style_model.load_state_dict(torch.load(args.model))
if args.cuda:
style_model.cuda()
vgg.cuda()
content_image = content_image.cuda()
style = style.cuda()
style_v = Variable(style, volatile=True)
utils.subtract_imagenet_mean_batch(style_v)
features_style = vgg(style_v)
gram_style = [utils.gram_matrix(y) for y in features_style]
content_image = Variable(utils.preprocess_batch(content_image))
target = Variable(gram_style[2].data, requires_grad=False)
style_model.setTarget(target)
output = style_model(content_image)
utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda)
def optimize(args):
""" Gatys et al. CVPR 2017
ref: Image Style Transfer Using Convolutional Neural Networks
"""
# load the content and style target
content_image = utils.tensor_load_rgbimage(args.content_image,
size=args.content_size,
keep_asp=True)
content_image = content_image.unsqueeze(0)
content_image = Variable(utils.preprocess_batch(content_image),
requires_grad=False)
content_image = utils.subtract_imagenet_mean_batch(content_image)
style_image = utils.tensor_load_rgbimage(args.style_image,
size=args.style_size)
style_image = style_image.unsqueeze(0)
style_image = Variable(utils.preprocess_batch(style_image),
requires_grad=False)
style_image = utils.subtract_imagenet_mean_batch(style_image)
# load the pre-trained vgg-16 and extract features
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
if args.cuda:
content_image = content_image.cuda()
style_image = style_image.cuda()
vgg.cuda()
features_content = vgg(content_image)
f_xc_c = Variable(features_content[1].data, requires_grad=False)
features_style = vgg(style_image)
gram_style = [utils.gram_matrix(y) for y in features_style]
# init optimizer
output = Variable(content_image.data, requires_grad=True)
optimizer = Adam([output], lr=args.lr)
mse_loss = torch.nn.MSELoss()
# optimizing the images
for e in range(args.iters):
utils.imagenet_clamp_batch(output, 0, 255)
optimizer.zero_grad()
features_y = vgg(output)
content_loss = args.content_weight * mse_loss(features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_y = utils.gram_matrix(features_y[m])
gram_s = Variable(gram_style[m].data, requires_grad=False)
style_loss += args.style_weight * mse_loss(gram_y, gram_s)
total_loss = content_loss + style_loss
if (e + 1) % args.log_interval == 0:
print(total_loss.data.cpu().numpy()[0])
total_loss.backward()
optimizer.step()
# save the image
output = utils.add_imagenet_mean_batch(output)
utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda)
def train(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 0, 'pin_memory': False}
else:
kwargs = {}
transform = transforms.Compose([
transforms.Scale(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
train_dataset = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
**kwargs)
transformer = TransformerNet()
optimizer = Adam(transformer.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
if args.cuda:
transformer.cuda()
vgg.cuda()
style = utils.tensor_load_rgbimage(args.style_image, size=args.style_size)
style = style.repeat(args.batch_size, 1, 1, 1)
style = utils.preprocess_batch(style)
if args.cuda:
style = style.cuda()
style_v = Variable(style, volatile=True)
utils.subtract_imagenet_mean_batch(style_v)
features_style = vgg(style_v)
gram_style = [utils.gram_matrix(y) for y in features_style]
for e in range(args.epochs):
transformer.train()
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
optimizer.zero_grad()
x = Variable(utils.preprocess_batch(x))
if args.cuda:
x = x.cuda()
y = transformer(x)
xc = Variable(x.data.clone(), volatile=True)
utils.subtract_imagenet_mean_batch(y)
utils.subtract_imagenet_mean_batch(xc)
features_y = vgg(y)
features_xc = vgg(xc)
f_xc_c = Variable(features_xc[1].data, requires_grad=False)
content_loss = args.content_weight * mse_loss(
features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_s = Variable(gram_style[m].data, requires_grad=False)
gram_y = utils.gram_matrix(features_y[m])
style_loss += args.style_weight * mse_loss(
gram_y, gram_s[:n_batch, :, :])
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
agg_content_loss += content_loss.data[0]
agg_style_loss += style_loss.data[0]
if (batch_id + 1) % args.log_interval == 0:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}".format(
time.ctime(), e + 1, count, len(train_dataset),
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss) / (batch_id + 1))
print(mesg)
# save model
transformer.eval()
transformer.cpu()
save_model_filename = "epoch_" + str(args.epochs) + "_" + str(
time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
transform = transforms.Compose([
transforms.Scale(args.image_size), #handle non-square img
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
train_img = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_img, batch_size=args.batch_size, num_workers=4)
n_iter = len(train_loader)
print('=> %d Iter Step of 1 Epoch' % n_iter)
# extract pretrained VGG weight
print('=> Check and Extract pre-trained VGG16 weight')
utils.init_vgg16()
#init model
print('=> Init Model')
style_model = net.StylePart() #empyt model
vgg_model = net.Vgg16Part() # fill pretrained vgg
vgg_model.load_state_dict(torch.load('model/vgg16.weight'))
# Load style_image
print('=> Init Style Image')
style = utils.img2X(args.style_image, args.style_size)
style = style.repeat(args.batch_size, 1, 1, 1)
style = utils.excg_rgb_bgr(style)
# put on GPU
if use_cuda:
def optimize(args):
style_image = utils.tensor_load_rgbimage(args.style_image,
size=args.style_size)
style_image = style_image.unsqueeze(0)
style_image = Variable(utils.preprocess_batch(style_image),
requires_grad=False)
# style_image = utils.subtract_imagenet_mean_batch(style_image)
# generate the vector field that we want to stylize
size = args.content_size
vectors = np.zeros((size, size, 2), dtype=np.float32)
eps = 1e-7
for y in range(size):
for x in range(size):
xx = float(x - size / 2)
yy = float(y - size / 2)
rsq = xx**2 + yy**2
if (rsq == 0):
vectors[y, x, 0] = -1
vectors[y, x, 1] = 1
else:
vectors[y, x, 0] = -yy / rsq if yy != 0 else eps
vectors[y, x, 1] = xx / rsq if xx != 0 else eps
# vectors[y, x, 0] = -1
# vectors[y, x, 1] = 1
# load the pre-trained vgg-16 and extract features
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, 'vgg16.weight')))
if args.cuda:
style_image = style_image.cuda()
vgg.cuda()
features_style = vgg(style_image)
gram_style = [utils.gram_matrix(y) for y in features_style]
# output_size = torch.Size([1, size, size])
# output = torch.randn(output_size) * 80 + 127
# if args.cuda:
# output = output.cuda()
# output = output.expand(3, size, size)
# output = Variable(output, requires_grad=True)
output_size = torch.Size([3, size, size])
output = Variable(torch.randn(output_size, device="cuda") * 80 + 127,
requires_grad=True)
optimizer = Adam([output], lr=args.lr)
mse_loss = torch.nn.MSELoss()
loss = []
tbar = trange(args.iters)
for e in tbar:
utils.clamp_batch(output, 0, 255)
optimizer.zero_grad()
lic_input = output
kernellen = 15
kernel = np.sin(np.arange(kernellen) * np.pi / kernellen)
kernel = kernel.astype(np.float32)
loss.append(args.content_weight * lic.line_integral_convolution(
vectors, lic_input, kernel, args.cuda))
# vgg_input = output.unsqueeze(0)
# features_y = vgg(vgg_input)
# style_loss = 0
# for m in range(len(features_y)):
# gram_y = utils.gram_matrix(features_y[m])
# gram_s = Variable(gram_style[m].data, requires_grad=False)
# style_loss += args.style_weight * mse_loss(gram_y, gram_s)
# style_loss.backward()
# loss[e] += style_loss
loss[e].backward()
optimizer.step()
tbar.set_description(str(loss[e].data.cpu().numpy().item()))
# save the image
if ((e + 1) % args.log_interval == 0):
# print("iter: %d content_loss: %f style_loss %f" % (e, loss[e].item(), style_loss.item()))
utils.tensor_save_bgrimage(output.data,
"output_iter_" + str(e + 1) + ".jpg",
args.cuda)
def optimize(args):
style_image = utils.tensor_load_rgbimage(args.style_image,
size=args.style_size)
style_image = style_image.unsqueeze(0)
style_image = Variable(utils.preprocess_batch(style_image),
requires_grad=False)
style_image = utils.subtract_imagenet_mean_batch(style_image)
# generate the vector field that we want to backward from
size = args.content_size
vectors = np.zeros((size, size, 2), dtype=np.float32)
vortex_spacing = 0.5
extra_factor = 2.
a = np.array([1, 0]) * vortex_spacing
b = np.array([np.cos(np.pi / 3), np.sin(np.pi / 3)]) * vortex_spacing
rnv = int(2 * extra_factor / vortex_spacing)
vortices = [
n * a + m * b for n in range(-rnv, rnv) for m in range(-rnv, rnv)
]
vortices = [(x, y) for (x, y) in vortices
if -extra_factor < x < extra_factor
and -extra_factor < y < extra_factor]
xs = np.linspace(-1, 1, size).astype(np.float32)[None, :]
ys = np.linspace(-1, 1, size).astype(np.float32)[:, None]
for (x, y) in vortices:
rsq = (xs - x)**2 + (ys - y)**2
vectors[..., 0] += (ys - y) / rsq
vectors[..., 1] += -(xs - x) / rsq
# for y in range(size):
# for x in range(size):
# xx = float(x - size / 2)
# yy = float(y - size / 2)
# rsq = xx ** 2 + yy ** 2
# if rsq == 0:
# vectors[y, x, 0] = 1
# vectors[y, x, 1] = 1
# else:
# vectors[y, x, 0] = -yy / rsq
# vectors[y, x, 1] = xx / rsq
# # vectors[y, x, 0] = 1
# # vectors[y, x, 1] = -1
vectors = utils.tensor_load_vector_field(vectors)
# load the pre-trained vgg-16 and extract features
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, 'vgg16.weight')))
if args.cuda:
style_image = style_image.cuda()
vgg.cuda()
features_style = vgg(style_image)
gram_style = [utils.gram_matrix(y) for y in features_style]
# load the sobel network
sobel = Sobel()
if args.cuda:
vectors = vectors.cuda()
sobel.cuda()
# init optimizer
vectors_size = vectors.data.size()
output_size = np.asarray(vectors_size)
output_size[1] = 3
output_size = torch.Size(output_size)
output = Variable(torch.randn(output_size, device="cuda") * 30,
requires_grad=True)
optimizer = Adam([output], lr=args.lr)
cosine_loss = CosineLoss()
mse_loss = torch.nn.MSELoss()
#optimize the images
tbar = trange(args.iters)
for e in tbar:
utils.imagenet_clamp_batch(output, 0, 255)
optimizer.zero_grad()
sobel_input = utils.gray_bgr_batch(output)
sobel_y = sobel(sobel_input)
content_loss = args.content_weight * cosine_loss(vectors, sobel_y)
vgg_input = output
features_y = vgg(vgg_input)
style_loss = 0
for m in range(len(features_y)):
gram_y = utils.gram_matrix(features_y[m])
gram_s = Variable(gram_style[m].data, requires_grad=False)
style_loss += args.style_weight * mse_loss(gram_y, gram_s)
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
if ((e + 1) % args.log_interval == 0):
print("iter: %d content_loss: %f style_loss %f" %
(e, content_loss.item() / args.content_weight,
style_loss.item() / args.style_weight))
tbar.set_description(str(total_loss.data.cpu().numpy().item()))
# save the image
output = utils.add_imagenet_mean_batch_device(output, args.cuda)
utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda)
def train(args):
check_paths(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 0, 'pin_memory': False}
else:
kwargs = {}
transform = transforms.Compose([
transforms.Scale(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
train_dataset = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
**kwargs)
style_model = Net(ngf=args.ngf)
if args.resume is not None:
print('Resuming, initializing using weight from {}.'.format(
args.resume))
style_model.load_state_dict(torch.load(args.resume))
print(style_model)
optimizer = Adam(style_model.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
if args.cuda:
style_model.cuda()
vgg.cuda()
style_loader = utils.StyleLoader(args.style_folder, args.style_size)
tbar = trange(args.epochs)
for e in tbar:
style_model.train()
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
optimizer.zero_grad()
x = Variable(utils.preprocess_batch(x))
if args.cuda:
x = x.cuda()
style_v = style_loader.get(batch_id)
style_model.setTarget(style_v)
style_v = utils.subtract_imagenet_mean_batch(style_v)
features_style = vgg(style_v)
gram_style = [utils.gram_matrix(y) for y in features_style]
y = style_model(x)
xc = Variable(x.data.clone())
y = utils.subtract_imagenet_mean_batch(y)
xc = utils.subtract_imagenet_mean_batch(xc)
features_y = vgg(y)
features_xc = vgg(xc)
f_xc_c = Variable(features_xc[1].data, requires_grad=False)
content_loss = args.content_weight * mse_loss(
features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_y = utils.gram_matrix(features_y[m])
gram_s = Variable(gram_style[m].data,
requires_grad=False).repeat(
args.batch_size, 1, 1, 1)
style_loss += args.style_weight * mse_loss(
gram_y, gram_s[:n_batch, :, :])
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
agg_content_loss += content_loss.data[0]
agg_style_loss += style_loss.data[0]
if (batch_id + 1) % args.log_interval == 0:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}".format(
time.ctime(), e + 1, count, len(train_dataset),
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss) / (batch_id + 1))
tbar.set_description(mesg)
if (batch_id + 1) % (4 * args.log_interval) == 0:
# save model
style_model.eval()
style_model.cpu()
save_model_filename = "Epoch_" + str(e) + "iters_" + str(count) + "_" + \
str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir,
save_model_filename)
torch.save(style_model.state_dict(), save_model_path)
style_model.train()
style_model.cuda()
tbar.set_description("\nCheckpoint, trained model saved at",
save_model_path)
# save model
style_model.eval()
style_model.cpu()
save_model_filename = "Final_epoch_" + str(args.epochs) + "_" + \
str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(style_model.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
def train(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 0, 'pin_memory': False}
else:
kwargs = {}
transform = transforms.Compose([transforms.Scale(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))])
train_dataset = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, **kwargs)
transformer = TransformerNet()
if (args.premodel != ""):
transformer.load_state_dict(torch.load(args.premodel))
print("load pretrain model:"+args.premodel)
optimizer = Adam(transformer.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
if args.cuda:
transformer.cuda()
vgg.cuda()
style = utils.tensor_load_rgbimage(args.style_image, size=args.style_size)
style = style.repeat(args.batch_size, 1, 1, 1)
style = utils.preprocess_batch(style)
if args.cuda:
style = style.cuda()
style_v = Variable(style, volatile=True)
style_v = utils.subtract_imagenet_mean_batch(style_v)
features_style = vgg(style_v)
gram_style = [utils.gram_matrix(y) for y in features_style]
hori=0
writer = SummaryWriter(args.logdir,comment=args.logdir)
for e in range(args.epochs):
transformer.train()
agg_content_loss = 0.
agg_style_loss = 0.
agg_cate_loss = 0.
agg_cam_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
optimizer.zero_grad()
x = Variable(utils.preprocess_batch(x))
if args.cuda:
x = x.cuda()
y = transformer(x)
xc = Variable(x.data.clone(), volatile=True)
#print(y.size()) #(4L, 3L, 224L, 224L)
# Calculate focus loss and category loss
y_cam = utils.depreprocess_batch(y)
y_cam = utils.subtract_mean_std_batch(y_cam)
xc_cam = utils.depreprocess_batch(xc)
xc_cam = utils.subtract_mean_std_batch(xc_cam)
del features_blobs[:]
logit_x = net(xc_cam)
logit_y = net(y_cam)
label=[]
cam_loss = 0
for i in range(len(xc_cam)):
h_x = F.softmax(logit_x[i])
probs_x, idx_x = h_x.data.sort(0, True)
label.append(idx_x[0])
h_y = F.softmax(logit_y[i])
probs_y, idx_y = h_y.data.sort(0, True)
x_cam = returnCAM(features_blobs[0][i], weight_softmax, idx_x[0])
x_cam = Variable(x_cam.data,requires_grad = False)
y_cam = returnCAM(features_blobs[1][i], weight_softmax, idx_y[0])
cam_loss += mse_loss(y_cam, x_cam)
#the focus loss
cam_loss *= 80
#the category loss
label = Variable(torch.LongTensor(label),requires_grad = False).cuda()
cate_loss = 10000 * torch.nn.CrossEntropyLoss()(logit_y,label)
y = utils.subtract_imagenet_mean_batch(y)
xc = utils.subtract_imagenet_mean_batch(xc)
features_y = vgg(y)
features_xc = vgg(xc)
#f_xc_c = Variable(features_xc[1].data, requires_grad=False)
#content_loss = args.content_weight * mse_loss(features_y[1], f_xc_c)
f_xc_c = Variable(features_xc[2].data, requires_grad=False)
content_loss = args.content_weight * mse_loss(features_y[2], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_s = Variable(gram_style[m].data, requires_grad=False)
gram_y = utils.gram_matrix(features_y[m])
style_loss += args.style_weight * mse_loss(gram_y, gram_s[:n_batch, :, :])
#add the total four loss and backward
total_loss = style_loss + content_loss + cam_loss + cate_loss
total_loss.backward()
optimizer.step()
#something for display
agg_content_loss += content_loss.data[0]
agg_style_loss += style_loss.data[0]
agg_cate_loss += cate_loss.data[0]
agg_cam_loss += cam_loss.data[0]
writer.add_scalar("Loss_Cont", agg_content_loss / (batch_id + 1), hori)
writer.add_scalar("Loss_Style", agg_style_loss / (batch_id + 1), hori)
writer.add_scalar("Loss_CAM", agg_cam_loss / (batch_id + 1), hori)
writer.add_scalar("Loss_Cate", agg_cate_loss / (batch_id + 1), hori)
hori += 1
if (batch_id + 1) % args.log_interval == 0:
mesg = "{}Epoch{}:[{}/{}] content:{:.2f} style:{:.2f} cate:{:.2f} cam:{:.2f} total:{:.2f}".format(
time.strftime("%a %H:%M:%S"),e + 1, count, len(train_dataset),
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
agg_cate_loss / (batch_id + 1),
agg_cam_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss + agg_cate_loss + agg_cam_loss ) / (batch_id + 1)
)
print(mesg)
if (batch_id + 1) % 2500 == 0:
transformer.eval()
transformer.cpu()
save_model_filename = "epoch_" + str(e+1) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
transformer.cuda()
transformer.train()
print("saved at ",count)
# save model
transformer.eval()
transformer.cpu()
save_model_filename = "epoch_" + str(args.epochs) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
writer.close()
print("\nDone, trained model saved at", save_model_path)
def train():
check_point_path = ''
transform = transforms.Compose([transforms.Scale(IMAGE_SIZE),
transforms.CenterCrop(IMAGE_SIZE),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))])
train_dataset = datasets.ImageFolder(DATASET_FOLDER, transform)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE)
style_model = Net(ngf=FILTER_CHANNEL, dv=device).to(device)
if RESUME is not None:
print('Resuming, initializing using weight from {}.'.format(RESUME))
style_model.load_state_dict(torch.load(RESUME))
print(style_model)
optimizer = Adam(style_model.parameters(), LEARNING_RATE)
mse_loss = torch.nn.MSELoss()
vgg = Vgg16()
utils.init_vgg16(VGG_DIR)
vgg.load_state_dict(torch.load(os.path.join(VGG_DIR, "vgg16.weight")))
vgg.to(device)
style_loader = utils.StyleLoader(STYLE_FOLDER, IMAGE_SIZE, device)
tbar = tqdm(range(EPOCHS))
for e in tbar:
style_model.train()
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
optimizer.zero_grad()
x = Variable(utils.preprocess_batch(x)).to(device)
style_v = style_loader.get(batch_id)
style_model.setTarget(style_v)
style_v = utils.subtract_imagenet_mean_batch(style_v, device)
features_style = vgg(style_v)
gram_style = [utils.gram_matrix(y) for y in features_style]
y = style_model(x)
xc = Variable(x.data.clone())
y = utils.subtract_imagenet_mean_batch(y, device)
xc = utils.subtract_imagenet_mean_batch(xc, device)
features_y = vgg(y)
features_xc = vgg(xc)
f_xc_c = Variable(features_xc[1].data, requires_grad=False)
content_loss = CONT_WEIGHT * mse_loss(features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_y = utils.gram_matrix(features_y[m])
gram_s = Variable(gram_style[m].data, requires_grad=False).repeat(BATCH_SIZE, 1, 1, 1)
style_loss += STYLE_WEIGHT * mse_loss(gram_y.unsqueeze_(1), gram_s[:n_batch, :, :])
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
agg_content_loss += content_loss.data[0]
agg_style_loss += style_loss.data[0]
if (batch_id + 1) % 100 == 0:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}".format(
time.ctime(), e + 1, count, len(train_dataset),
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss) / (batch_id + 1)
)
tbar.set_description(mesg)
if (batch_id + 1) % (4 * 100) == 0:
# save model
style_model.eval()
style_model.cpu()
save_model_filename = "Epoch_" + str(e) + "iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
CONT_WEIGHT) + "_" + str(STYLE_WEIGHT) + ".model"
save_model_path = os.path.join(SAVE_MODEL_DIR, save_model_filename)
torch.save(style_model.state_dict(), save_model_path)
if check_point_path:
os.remove(check_point_path)
check_point_path = save_model_path
style_model.train()
style_model.cuda()
tbar.set_description("\nCheckpoint, trained model saved at", save_model_path)
# save model
style_model.eval()
style_model.cpu()
save_model_filename = "Final_epoch_" + str(EPOCHS) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
CONT_WEIGHT) + "_" + str(STYLE_WEIGHT) + ".model"
save_model_path = os.path.join(SAVE_MODEL_DIR, save_model_filename)
torch.save(style_model.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
0.02,
gpu_id=device)
net_r = define_G(opt.input_nc_r,
opt.output_nc_r,
opt.ngf,
opt.netG,
'batch',
False,
'normal',
0.02,
gpu_id=device)
# VGG for perceptual loss
if opt.lamb_content > 0:
vgg = Vgg16()
init_vgg16(root_path)
vgg.load_state_dict(torch.load(os.path.join(root_path, "vgg16.weight")))
vgg.to(device)
# define loss
criterionL1 = nn.L1Loss().to(device)
criterionL2 = nn.MSELoss().to(device)
criterionMSE = nn.MSELoss().to(device)
criterionSSIM = SSIM(data_range=255, size_average=True, channel=3)
criterionMSSSIM1 = MS_SSIM(data_range=255, size_average=True, channel=1)
criterionMSSSIM3 = MS_SSIM(data_range=255, size_average=True, channel=3)
# setup optimizer
optimizer_i = optim.Adam(net_i.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
def train(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 0, 'pin_memory': False}
else:
kwargs = {}
training_set = np.loadtxt(args.dataset, dtype=np.float32)
training_set_size = training_set.shape[1]
num_batch = int(training_set_size / args.batch_size)
transformer = TransformerNet()
optimizer = Adam(transformer.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
if args.cuda:
transformer.cuda()
vgg.cuda()
style = np.loadtxt(args.style_image, dtype=np.float32)
style = style.reshape((1, 1, args.style_size_x, args.style_size_y))
style = torch.from_numpy(style)
style = style.repeat(args.batch_size, 3, 1, 1)
if args.cuda:
style = style.cuda()
style_v = Variable(style, volatile=True)
style_v = utils.subtract_imagenet_mean_batch(style_v)
features_style = vgg(style_v)
gram_style = [utils.gram_matrix(y) for y in features_style]
# Hard data
if args.hard_data:
hard_data = np.loadtxt(args.hard_data_file)
# if not isinstance(hard_data[0], list):
# hard_data = [hard_data]
for e in range(args.epochs):
transformer.train()
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
# for batch_id, (x, _) in enumerate(train_loader):
for batch_id in range(num_batch):
x = training_set[:, batch_id * args.batch_size:(batch_id + 1) *
args.batch_size]
n_batch = x.shape[1]
count += n_batch
x = x.transpose()
x = x.reshape((n_batch, 1, args.image_size_x, args.image_size_y))
# plt.imshow(x[0,:,:,:].squeeze(0))
# plt.show()
x = torch.from_numpy(x).float()
optimizer.zero_grad()
x = Variable(x)
if args.cuda:
x = x.cuda()
y = transformer(x)
if args.hard_data:
hard_data_loss = 0
num_hard_data = 0
for hd in hard_data:
hard_data_loss += args.hard_data_weight * (
y[:, 0, hd[1], hd[0]] -
hd[2] * 255.0).norm()**2 / n_batch
num_hard_data += 1
hard_data_loss /= num_hard_data
y = y.repeat(1, 3, 1, 1)
# x = Variable(utils.preprocess_batch(x))
# xc = x.data.clone()
# xc = xc.repeat(1, 3, 1, 1)
# xc = Variable(xc, volatile=True)
y = utils.subtract_imagenet_mean_batch(y)
# xc = utils.subtract_imagenet_mean_batch(xc)
features_y = vgg(y)
# features_xc = vgg(xc)
# f_xc_c = Variable(features_xc[1].data, requires_grad=False)
# content_loss = args.content_weight * mse_loss(features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_s = Variable(gram_style[m].data, requires_grad=False)
gram_y = utils.gram_matrix(features_y[m])
style_loss += args.style_weight * mse_loss(
gram_y, gram_s[:n_batch, :, :])
# total_loss = content_loss + style_loss
total_loss = style_loss
if args.hard_data:
total_loss += hard_data_loss
total_loss.backward()
optimizer.step()
# agg_content_loss += content_loss.data[0]
agg_style_loss += style_loss.data[0]
if (batch_id + 1) % args.log_interval == 0:
if args.hard_data:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\thard_data: {:.6f}\ttotal: {:.6f}".format(
time.ctime(), e + 1, count, num_batch,
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
hard_data_loss.data[0],
(agg_content_loss + agg_style_loss) / (batch_id + 1))
else:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}".format(
time.ctime(), e + 1, count, num_batch,
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss) / (batch_id + 1))
print(mesg)
# save model
transformer.eval()
transformer.cpu()
save_model_filename = "epoch_" + str(args.epochs) + "_" + str(
time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
def train(args):
serialNumFile = "serialNum.txt"
serial = 0
if os.path.isfile(serialNumFile):
with open(serialNumFile, "r") as t:
serial = int(t.read())
serial += 1
with open(serialNumFile, "w") as t:
t.write(str(serial))
if args.mysql:
cnx = mysql.connector.connect(user='******',
database='midburn',
password='******')
cursor = cnx.cursor()
location = args.dataset.split("/")
if location[-1] == "":
location = location[-2]
else:
location = location[-1]
save_model_filename = str(serial) + "_" + extractName(
args.style_image) + "_" + str(args.epochs) + "_" + str(
int(args.content_weight)) + "_" + str(int(
args.style_weight)) + "_size_" + str(
args.image_size) + "_dataset_" + str(location) + ".model"
print(save_model_filename)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
m_epoch = 0
if args.cuda:
torch.cuda.manual_seed(args.seed)
#kwargs = {'num_workers': 0, 'pin_memory': False}
kwargs = {'num_workers': 4, 'pin_memory': True}
else:
kwargs = {}
transform = transforms.Compose([
transforms.Scale(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
train_dataset = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
transformer = TransformerNet()
#transformer = ResNeXtNet()
transformer_type = transformer.__class__.__name__
optimizer = Adam(transformer.parameters(), args.lr)
if args.l1:
loss_criterion = torch.nn.L1Loss()
else:
loss_criterion = torch.nn.MSELoss()
loss_type = loss_criterion.__class__.__name__
if args.visdom:
vis = VisdomLinePlotter("Style Transfer: " + transformer_type)
else:
vis = None
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
vgg.load_state_dict(
torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight")))
if args.cuda:
transformer.cuda()
vgg.cuda()
if args.model is not None:
transformer.load_state_dict(torch.load(args.model))
save_model_filename = save_model_filename + "@@@@@@" + str(
int(getEpoch(args.model)) + int(args.epochs))
m_epoch += int(getEpoch(args.model))
print("loaded model\n")
for param in vgg.parameters():
param.requires_grad = False
with torch.no_grad():
style = utils.tensor_load_rgbimage(args.style_image,
size=args.style_size)
style = style.repeat(args.batch_size, 1, 1, 1)
style = utils.preprocess_batch(style)
if args.cuda:
style = style.cuda()
style = utils.subtract_imagenet_mean_batch(style)
features_style = vgg(style)
gram_style = [utils.gram_matrix(y) for y in features_style]
del features_style
del style
# TODO: scheduler and style-loss criterion unused at the moment
scheduler = StepLR(optimizer, step_size=15000 // args.batch_size)
style_loss_criterion = torch.nn.CosineSimilarity()
total_count = 0
if args.mysql:
q1 = ("REPLACE INTO `images`(`name`) VALUES ('" + args.style_image +
"')")
cursor.execute(q1)
cnx.commit()
imgId = cursor.lastrowid
for e in range(args.epochs):
transformer.train()
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
total_count += n_batch
optimizer.zero_grad()
x = utils.preprocess_batch(x)
if args.cuda:
x = x.cuda()
y = transformer(x)
y = utils.subtract_imagenet_mean_batch(y)
xc = utils.subtract_imagenet_mean_batch(x)
features_y = vgg(y)
f_xc_c = vgg.content_features(xc)
content_loss = args.content_weight * loss_criterion(
features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_s = gram_style[m]
gram_y = utils.gram_matrix(features_y[m])
style_loss += loss_criterion(gram_y, gram_s[:n_batch, :, :])
#style_loss -= style_loss_criterion(gram_y, gram_s[:n_batch, :, :])
style_loss *= args.style_weight
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
# TODO: enable
#scheduler.step()
agg_content_loss += content_loss.item()
agg_style_loss += style_loss.item()
if (batch_id + 1) % args.log_interval == 0:
if args.mysql:
q1 = (
"REPLACE INTO `statistics`(`imgId`,`epoch`, `iteration_id`, `content_loss`, `style_loss`, `loss`) VALUES ("
+ str(imgId) + "," + str(int(e) + m_epoch) + "," +
str(batch_id) + "," + str(agg_content_loss /
(batch_id + 1)) + "," +
str(agg_style_loss / (batch_id + 1)) + "," + str(
(agg_content_loss + agg_style_loss) /
(batch_id + 1)) + ")")
cursor.execute(q1)
cnx.commit()
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}\n".format(
time.ctime(), e + 1, count, len(train_dataset),
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss) / (batch_id + 1))
sys.stdout.flush()
print(mesg)
if vis is not None:
vis.plot(loss_type, "Content Loss", total_count,
content_loss.item())
vis.plot(loss_type, "Style Loss", total_count,
style_loss.item())
vis.plot(loss_type, "Total Loss", total_count,
total_loss.item())
# save model
transformer.eval()
transformer.cpu()
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
