def __init__(self,
q_size=10,
encoding_size=128,
timesteps=10,
training=True,
use_attention=False,
grid_size=5):
super(DRAW, self).__init__()
self.training = training
self.encoding_size = encoding_size
self.q_size = q_size
self.use_attention = use_attention
self.timesteps = timesteps
# use equal encoding and decoding size
self.encoder_rnn = BasicRNN(hstate_size=self.encoding_size,
output_size=self.encoding_size)
self.decoder_rnn = BasicRNN(hstate_size=self.encoding_size,
output_size=self.encoding_size)
self.register_parameter('decoder_linear_weights', None)
self.register_parameter('encoding_mu_weights', None)
self.register_parameter('encoding_logvar_weights', None)
self.filter_linear_layer = nn.Linear(self.encoding_size, 5)
self.grid_size = grid_size
self.minclamp = 1e-8
self.maxclamp = 1e8
def __init__(self,
num_classes,
inchans=3,
att_encoding_size=128,
timesteps=10,
attn_grid_size=50):
super(AttentionSegmenter, self).__init__()
self.num_classes = num_classes
self.att_encoding_size = att_encoding_size
self.timesteps = timesteps
self.attn_grid_size = attn_grid_size
self.encoder = ConvolutionStack(inchans, final_relu=False, padding=0)
self.encoder.append(32, 3, 1)
self.encoder.append(32, 3, 2)
self.encoder.append(64, 3, 1)
self.encoder.append(64, 3, 2)
self.encoder.append(96, 3, 1)
self.encoder.append(96, 3, 2)
self.decoder = TransposedConvolutionStack(96,
final_relu=False,
padding=0)
self.decoder.append(96, 3, 2)
self.decoder.append(64, 3, 1)
self.decoder.append(64, 3, 2)
self.decoder.append(32, 3, 1)
self.decoder.append(32, 3, 2)
self.decoder.append(self.num_classes, 3, 1)
self.attn_reader = GaussianAttentionReader()
self.attn_writer = GaussianAttentionWriter()
self.att_rnn = BasicRNN(hstate_size=att_encoding_size, output_size=5)
self.register_parameter('att_decoder_weights', None)
def __init__(self,
q_size=10,
encoding_size=128,
timesteps=10,
training=True,
use_attention=False):
super(DRAW, self).__init__()
self.training = training
self.encoding_size = encoding_size
self.q_size = q_size
self.use_attention = use_attention
self.timesteps = timesteps
# use equal encoding and decoding size
self.encoder_rnn = BasicRNN(output_size=self.encoding_size)
self.decoder_rnn = BasicRNN(output_size=self.encoding_size)
self.register_parameter('decoder_linear_weights', None)
self.register_parameter('encoding_mu_weights', None)
self.register_parameter('encoding_logvar_weights', None)