Python utils.preprocess_batch() Examples

The following are 7 code examples of utils.preprocess_batch(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: main.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 6 votes vote down vote up 
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 vote down vote up 
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 vote down vote up 
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 vote down vote up 
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: trainer.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def _init_inputs(self):
        preproc_func = self.preproc_func
        input_shape = self.input_shape
        # Define input TF placeholder
        with tf.device('/gpu:0'):
            x_pre = tf.placeholder(tf.float32, shape=input_shape, name='x')
            x = preprocess_batch(x_pre, preproc_func)
            y = tf.placeholder(tf.float32, shape=(self.batch_size, 10),
                               name='y')

        self.g0_inputs = {'x_pre': x_pre, 'x': x, 'y': y}
Example #6
Source File: neural_style.py    From fast-neural-style with MIT License 5 votes vote down vote up 
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 #7
Source File: trainer.py    From cleverhans with MIT License 5 votes vote down vote up 
def _init_inputs(self):
    preproc_func = self.preproc_func
    input_shape = self.input_shape
    # Define input TF placeholder
    with tf.device('/gpu:0'):
      x_pre = tf.placeholder(tf.float32, shape=input_shape, name='x')
      x = preprocess_batch(x_pre, preproc_func)
      y = tf.placeholder(tf.float32, shape=(self.batch_size, 10),
                         name='y')

    self.g0_inputs = {'x_pre': x_pre, 'x': x, 'y': y}