Python utils.tensor_load_rgbimage() Examples
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Example #1
Source File: main.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 6 votes |
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)
Example #2
Source File: main.py From training_results_v0.6 with Apache License 2.0 | 6 votes |
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)
Example #3
Source File: main.py From MXNet-Gluon-Style-Transfer with MIT License | 6 votes |
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)
Example #4
Source File: main.py From SNIPER-mxnet with Apache License 2.0 | 6 votes |
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)
Example #5
Source File: neural_style.py From fast-neural-style with MIT License | 5 votes |
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)
Example #6
Source File: main.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 4 votes |
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)
Example #7
Source File: main.py From training_results_v0.6 with Apache License 2.0 | 4 votes |
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)
Example #8
Source File: main.py From MXNet-Gluon-Style-Transfer with MIT License | 4 votes |
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)
Example #9
Source File: main.py From SNIPER-mxnet with Apache License 2.0 | 4 votes |
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)