Python utils.download() Examples

The following are 7 code examples of utils.download(). 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: process_data.py    From GPPVAE with Apache License 2.0 6 votes vote down vote up 
def main():

    # 1. download and unzip data
    download_data(data_dir)

    # 2. load data
    RV = import_data()

    # 3. split train, validation and test
    RV = split_data(RV)

    # 4. export
    out_file = os.path.join(data_dir, "data_faces.h5")
    fout = h5py.File(out_file, "w")
    for key in RV.keys():
        fout.create_dataset(key, data=RV[key])
    fout.close()
Example #2
Source File: audio_downloads.py    From Looking-to-Listen-at-the-Cocktail-Party with MIT License 5 votes vote down vote up 
def make_audio(location, name, d_csv, start_idx, end_idx):
    for i in range(start_idx,end_idx):
        f_name = name + str(i)
        link = "https://www.youtube.com/watch?v="+d_csv.loc[i][0]
        start_time = d_csv.loc[i][1]
        end_time = start_time+3.0
        utils.download(location,f_name,link)
        utils.cut(location,f_name,start_time,end_time)
        print("\r Process audio... ".format(i) + str(i), end="")
    print("\r Finish !!", end="")
Example #3
Source File: ukbench.py    From SoTu with MIT License 5 votes vote down vote up 
def __init__(self, root):
        self.root = root
        if not posixpath.exists(posixpath.join(self.root, self.ukbench_dir)):
            download(self.root, self.filename, self.url)
            unzip(self.root, self.filename, self.ukbench_dir)
        self.uris = sorted(list_files(root=posixpath.join(self.root,
                                                          self.ukbench_dir,
                                                          'full'),
                                      suffix=('png', 'jpg', 'jpeg', 'gif')))
Example #4
Source File: load_vgg_sol.py    From stanford-tensorflow-tutorials with MIT License 5 votes vote down vote up 
def __init__(self, input_img):
        utils.download(VGG_DOWNLOAD_LINK, VGG_FILENAME, EXPECTED_BYTES)
        self.vgg_layers = scipy.io.loadmat(VGG_FILENAME)['layers']
        self.input_img = input_img
        self.mean_pixels = np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3))
Example #5
Source File: load_vgg.py    From stanford-tensorflow-tutorials with MIT License 5 votes vote down vote up 
def __init__(self, input_img):
        utils.download(VGG_DOWNLOAD_LINK, VGG_FILENAME, EXPECTED_BYTES)
        self.vgg_layers = scipy.io.loadmat(VGG_FILENAME)['layers']
        self.input_img = input_img
        self.mean_pixels = np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3))
Example #6
Source File: style_transfer.py    From stanford-tensorflow-tutorials with MIT License 5 votes vote down vote up 
def main():
    with tf.variable_scope('input') as scope:
        # use variable instead of placeholder because we're training the intial image to make it
        # look like both the content image and the style image
        input_image = tf.Variable(np.zeros([1, IMAGE_HEIGHT, IMAGE_WIDTH, 3]), dtype=tf.float32)
    
    utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
    utils.make_dir('checkpoints')
    utils.make_dir('outputs')
    model = vgg_model.load_vgg(VGG_MODEL, input_image)
    model['global_step'] = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
    
    content_image = utils.get_resized_image(CONTENT_IMAGE, IMAGE_HEIGHT, IMAGE_WIDTH)
    content_image = content_image - MEAN_PIXELS
    style_image = utils.get_resized_image(STYLE_IMAGE, IMAGE_HEIGHT, IMAGE_WIDTH)
    style_image = style_image - MEAN_PIXELS

    model['content_loss'], model['style_loss'], model['total_loss'] = _create_losses(model, 
                                                    input_image, content_image, style_image)
    ###############################
    ## TO DO: create optimizer
    ## model['optimizer'] = ...
    ###############################
    model['summary_op'] = _create_summary(model)

    initial_image = utils.generate_noise_image(content_image, IMAGE_HEIGHT, IMAGE_WIDTH, NOISE_RATIO)
    train(model, input_image, initial_image)
Example #7
Source File: style_transfer.py    From stanford-tensorflow-tutorials with MIT License 5 votes vote down vote up 
def main():
    with tf.variable_scope('input') as scope:
        # use variable instead of placeholder because we're training the intial image to make it
        # look like both the content image and the style image
        input_image = tf.Variable(np.zeros([1, IMAGE_HEIGHT, IMAGE_WIDTH, 3]), dtype=tf.float32)
    
    utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
    utils.make_dir('checkpoints')
    utils.make_dir('outputs')
    model = vgg_model.load_vgg(VGG_MODEL, input_image)
    model['global_step'] = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')

    content_image = utils.get_resized_image(CONTENT_IMAGE, IMAGE_HEIGHT, IMAGE_WIDTH)
    content_image = content_image - MEAN_PIXELS
    style_image = utils.get_resized_image(STYLE_IMAGE, IMAGE_HEIGHT, IMAGE_WIDTH)
    style_image = style_image - MEAN_PIXELS

    model['content_loss'], model['style_loss'], model['total_loss'] = _create_losses(model, 
                                                    input_image, content_image, style_image)
    ###############################
    ## TO DO: create optimizer
    model['optimizer'] = tf.train.AdamOptimizer(LR).minimize(model['total_loss'], 
                                                            global_step=model['global_step'])
    ###############################
    model['summary_op'] = _create_summary(model)

    initial_image = utils.generate_noise_image(content_image, IMAGE_HEIGHT, IMAGE_WIDTH, NOISE_RATIO)
    train(model, input_image, initial_image)