本文整理汇总了Python中utils.tensor_load_rgbimage方法的典型用法代码示例。如果您正苦于以下问题：Python utils.tensor_load_rgbimage方法的具体用法？Python utils.tensor_load_rgbimage怎么用？Python utils.tensor_load_rgbimage使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utils的用法示例。
在下文中一共展示了utils.tensor_load_rgbimage方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: evaluate
# 需要导入模块: import utils [as 别名]
# 或者: from utils import tensor_load_rgbimage [as 别名]
def evaluate(args):
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# images
content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)
style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)
style_image = utils.preprocess_batch(style_image)
# model
style_model = net.Net(ngf=args.ngf)
style_model.load_parameters(args.model, ctx=ctx)
# forward
style_model.set_target(style_image)
output = style_model(content_image)
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda) 示例2: evaluate
# 需要导入模块: import utils [as 别名]
# 或者: from utils import tensor_load_rgbimage [as 别名]
def evaluate(args):
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# images
content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)
style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)
style_image = utils.preprocess_batch(style_image)
# model
style_model = net.Net(ngf=args.ngf)
style_model.load_params(args.model, ctx=ctx)
# forward
style_model.set_target(style_image)
output = style_model(content_image)
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda) 示例3: evaluate
# 需要导入模块: import utils [as 别名]
# 或者: from utils import tensor_load_rgbimage [as 别名]
def evaluate(args):
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# images
content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)
style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)
style_image = utils.preprocess_batch(style_image)
# model
style_model = net.Net(ngf=args.ngf)
style_model.load_params(args.model, ctx=ctx)
# forward
style_model.setTarget(style_image)
output = style_model(content_image)
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda) 示例4: stylize
# 需要导入模块: import utils [as 别名]
# 或者: from utils import tensor_load_rgbimage [as 别名]
def stylize(args):
content_image = utils.tensor_load_rgbimage(args.content_image, scale=args.content_scale)
content_image = content_image.unsqueeze(0)
if args.cuda:
content_image = content_image.cuda()
content_image = Variable(utils.preprocess_batch(content_image), volatile=True)
style_model = TransformerNet()
style_model.load_state_dict(torch.load(args.model))
if args.cuda:
style_model.cuda()
output = style_model(content_image)
utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda) 示例5: optimize
# 需要导入模块: import utils [as 别名]
# 或者: from utils import tensor_load_rgbimage [as 别名]
def optimize(args):
""" Gatys et al. CVPR 2017
ref: Image Style Transfer Using Convolutional Neural Networks
"""
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# load the content and style target
content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True)
content_image = utils.subtract_imagenet_mean_preprocess_batch(content_image)
style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size)
style_image = utils.subtract_imagenet_mean_preprocess_batch(style_image)
# load the pre-trained vgg-16 and extract features
vgg = net.Vgg16()
utils.init_vgg_params(vgg, 'models', ctx=ctx)
# content feature
f_xc_c = vgg(content_image)[1]
# style feature
features_style = vgg(style_image)
gram_style = [net.gram_matrix(y) for y in features_style]
# output
output = Parameter('output', shape=content_image.shape)
output.initialize(ctx=ctx)
output.set_data(content_image)
# optimizer
trainer = gluon.Trainer([output], 'adam',
{'learning_rate': args.lr})
mse_loss = gluon.loss.L2Loss()
# optimizing the images
for e in range(args.iters):
utils.imagenet_clamp_batch(output.data(), 0, 255)
# fix BN for pre-trained vgg
with autograd.record():
features_y = vgg(output.data())
content_loss = 2 * args.content_weight * mse_loss(features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_y = net.gram_matrix(features_y[m])
gram_s = gram_style[m]
style_loss = style_loss + 2 * args.style_weight * mse_loss(gram_y, gram_s)
total_loss = content_loss + style_loss
total_loss.backward()
trainer.step(1)
if (e + 1) % args.log_interval == 0:
print('loss:{:.2f}'.format(total_loss.asnumpy()[0]))
# save the image
output = utils.add_imagenet_mean_batch(output.data())
utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda)