SYMBOL INDEX (107 symbols across 11 files)

FILE: LayoutGAN/MNIST/mnist_modules.py
  class RelationModule (line 12) | class RelationModule(tf.keras.Model):
    method __init__ (line 13) | def __init__(self, channels=128, output_dim=128, key_dim=128, **kwargs):
    method call (line 27) | def call(self, inputs):
  class Discriminator (line 45) | class Discriminator(tf.keras.Model):
    method __init__ (line 46) | def __init__(self, n_filters=32, n_hidden=128, layout_dim=(28, 28), re...
    method call (line 65) | def call(self, inputs):
  class Generator (line 75) | class Generator(tf.keras.Model):
    method __init__ (line 76) | def __init__(self, n_filters=128, output_dim=2, n_component=128, n_cla...
    method call (line 124) | def call(self, x):

FILE: LayoutGAN/MNIST/mnist_train.py
  class LayoutGAN (line 19) | class LayoutGAN(object):
    method __init__ (line 20) | def __init__(self, geometric_dim=2, n_class=1, batch_size=64, n_compon...
    method build_model (line 37) | def build_model(self, dlr, g_lr):
    method step (line 43) | def step(self, real_data, noise, training=True):
    method train (line 65) | def train(self):
    method render (line 122) | def render(self):
    method build_discriminator (line 125) | def build_discriminator(self):
    method build_generator (line 128) | def build_generator(self):
    method gradient_penalty (line 131) | def gradient_penalty(self, real, fake):
    method generator_loss (line 144) | def generator_loss(self, z):
    method discriminator_loss (line 151) | def discriminator_loss(self, x, z):

FILE: LayoutGAN/MNIST/mnist_utils.py
  function merge (line 6) | def merge(images, size):
  function image_manifold_size (line 25) | def image_manifold_size(num_images):
  function layout_point (line 32) | def layout_point(final_pred, output_height, output_width):

FILE: LayoutGAN/Publaynet/modules.py
  class RelationModule (line 10) | class RelationModule(tf.keras.Model):
    method __init__ (line 11) | def __init__(self, channels=128, output_dim=128, key_dim=128, **kwargs):
    method call (line 25) | def call(self, inputs):
  class Discriminator (line 43) | class Discriminator(tf.keras.Model):
    method __init__ (line 44) | def __init__(self, n_filters=32, n_hidden=128, layout_dim=(28, 28), re...
    method call (line 67) | def call(self, inputs):
  class Generator (line 77) | class Generator(tf.keras.Model):
    method __init__ (line 78) | def __init__(self, n_filters=128, output_dim=2, n_component=128, n_cla...
    method call (line 152) | def call(self, x):

FILE: LayoutGAN/Publaynet/train.py
  class LayoutGAN (line 17) | class LayoutGAN(object):
    method __init__ (line 18) | def __init__(self, geometric_dim=2, n_class=1, batch_size=64, n_compon...
    method build_model (line 34) | def build_model(self, d_lr, g_lr):
    method step (line 43) | def step(self, real_data, noise, training=True, step=0):
    method train (line 63) | def train(self):
    method render (line 109) | def render(self):
    method build_discriminator (line 112) | def build_discriminator(self):
    method build_generator (line 115) | def build_generator(self):
    method generator_loss (line 118) | def generator_loss(self, z):
    method discriminator_loss (line 125) | def discriminator_loss(self, x, z):

FILE: LayoutGAN/Publaynet/utils.py
  function convert_to_cxywh (line 9) | def convert_to_cxywh(data):
  function generate_colors (line 22) | def generate_colors(class_names=None, n_class=50):
  function plot_layouts (line 41) | def plot_layouts(pred, colors, class_names, path=""):
  function layout_bbox (line 95) | def layout_bbox(final_pred, output_height, output_width):

FILE: LayoutVAE/Source/bboxvae.py
  class BboxVAE (line 11) | class BboxVAE(T.nn.Module):
    method __init__ (line 12) | def __init__(self,n_class,n_dim,max_box,latent_dim=32):
    method forward (line 28) | def forward(self,inputs,isTrain=True):

FILE: LayoutVAE/Source/countvae.py
  class CountVAE (line 11) | class CountVAE(T.nn.Module):
    method __init__ (line 13) | def __init__(self,n_class,max_box=9):
    method forward (line 38) | def forward(self, inputs, isTrain = False):

FILE: LayoutVAE/Source/layoutvae.py
  class LayoutVAE (line 19) | class LayoutVAE(T.nn.Module):
    method __init__ (line 21) | def __init__(self, n_class = 6, max_box = 9,bboxvae_latent_dim = 32,bb...
    method forward (line 37) | def forward(self,input):
    method load_data (line 84) | def load_data(self, path, frac = 0.5, train_test_split = 0.1):
    method train (line 122) | def train(self, optim, train_mode = 'bboxvae', epochs = 100, bsize = 2...
    method load_countvae_weights (line 229) | def load_countvae_weights(self,path):
    method load_bboxvae_weights (line 237) | def load_bboxvae_weights(self,path):
    method train_bboxvae (line 245) | def train_bboxvae(self,epochs=30, bsize=256, validation_split=0.1, opt...
    method train_countvae (line 260) | def train_countvae(self,epochs=30, bsize=256, validation_split=0.1, op...
    method pred_countvae (line 276) | def pred_countvae(self,data=None):
    method pred_bboxvae (line 290) | def pred_bboxvae(self,Data=None):
    method countvae_pred_grpah (line 336) | def countvae_pred_grpah(self,path,epoch = 0):
    method convert_to_cxywh (line 360) | def convert_to_cxywh(self,data):
    method save_model (line 389) | def save_model(self,path):
    method save_history (line 396) | def save_history(self,path):

FILE: LayoutVAE/Source/modelblocks.py
  class fcblock (line 12) | class fcblock(T.nn.Module):
    method __init__ (line 13) | def __init__(self, n_class):
    method forward (line 21) | def forward(self,inputs):
  class Embeder (line 25) | class Embeder(T.nn.Module):
    method __init__ (line 26) | def __init__(self,n_class):
    method forward (line 35) | def forward(self,inputs):
  class Encoder (line 44) | class Encoder(T.nn.Module):
    method __init__ (line 45) | def __init__(self, in_dim=1 ,latent_dim=32):
    method forward (line 54) | def forward(self,inputs):
  class Prior (line 67) | class Prior(T.nn.Module):
    method __init__ (line 68) | def __init__(self,latent_dim=32):
    method forward (line 76) | def forward(self,inputs):
  class Decoder (line 84) | class Decoder(T.nn.Module):
    method __init__ (line 85) | def __init__(self,output_dim,latent_dim=32):
    method forward (line 92) | def forward(self,inputs):
  class ELBOLoss (line 104) | class ELBOLoss(T.nn.Module):
    method __init__ (line 106) | def __init__(self):
    method forward (line 109) | def forward(self,inputs):
  class EmbedBbox (line 127) | class EmbedBbox(T.nn.Module):
    method __init__ (line 129) | def __init__(self,n_class):
    method forward (line 143) | def forward(self,inputs):
  class ELBOLoss_Bbox (line 159) | class ELBOLoss_Bbox(T.nn.Module):
    method __init__ (line 161) | def __init__(self):
    method forward (line 164) | def forward(self,inputs):
  class Reparamatrize_bvae (line 177) | class Reparamatrize_bvae(T.nn.Module):
    method __init__ (line 179) | def __init__(self):
    method forward (line 182) | def forward(self,inputs):
  class ReparamatrizeMulti (line 191) | class ReparamatrizeMulti(T.nn.Module):
    method __init__ (line 193) | def __init__(self):
    method forward (line 196) | def forward(self,inputs):
  class Reparamatrize_cvae (line 204) | class Reparamatrize_cvae(T.nn.Module):
    method __init__ (line 206) | def __init__(self):
    method forward (line 209) | def forward(self,inputs):
  class Sampling (line 224) | class Sampling(T.nn.Module):
    method __init__ (line 226) | def __init__(self,MAX_BOX):
    method forward (line 230) | def forward(self,lamda):

FILE: LayoutVAE/Source/utils.py
  function plot_history (line 15) | def plot_history(history,title = 'Training Statistics', path =""):
  function generate_colors (line 49) | def generate_colors(class_names = None,n_class=6):
  function plot_layouts (line 83) | def plot_layouts(pred,colors,class_names,title="Predictions", path=""):
  function countvae_pred_graph (line 147) | def countvae_pred_graph(model,path=""):