def __init__(self, hidden_dim, Tanh, **kwargs):
super().__init__()
# parameter
self.hidden_dim = hidden_dim
# first dconv layers
num_channels = list(reversed(NUM_CHANNELS))
# b * NUM_CHANNELS[-1] * 4 * 4
self.first_layer = nn.Sequential(
nn.ConvTranspose2d(hidden_dim,
num_channels[0],
kernel_size=4,
stride=1,
padding=0,
bias=False),
nn.BatchNorm2d(num_channels[0]),
nn.ReLU(inplace=True),
)
# b * NUM_CHANNELS[0] * 32 * 32
list_layers = [
BasicBlocks.block_deconv_k4s2p1_BN_RELU(num_channels[0],
num_channels[1]),
BasicBlocks.block_deconv_k4s2p1_BN_RELU(num_channels[1],
num_channels[2]),
BasicBlocks.block_deconv_k4s2p1_BN_RELU(num_channels[2],
num_channels[3]),
]
self.dconvs = nn.Sequential(*list_layers)
# last dconv layer
# b * 3 * 64 * 64
self.last_layer = nn.ConvTranspose2d(num_channels[-1],
COLOUR_CHANNELS,
kernel_size=4,
stride=2,
padding=1,
bias=False)
# out layer
self.out = nn.Tanh() if Tanh else nn.Sigmoid()
logging.debug(f'Generator out {self.out}')
def __init__(self, hidden_dim, Tanh, **kwargs):
super().__init__()
self.fc = nn.Sequential(
BasicBlocks.block_linear_BN_RELU(hidden_dim,
NUM_HIDDEN_1,
leaky=LEAKY),
BasicBlocks.block_linear_BN_RELU(NUM_HIDDEN_1,
NUM_CHANNELS_1 * 49,
leaky=LEAKY))
# transpose convolution layers.
self.dconv = nn.Sequential(
BasicBlocks.block_deconv_k4s2p1_BN_RELU(NUM_CHANNELS_1,
NUM_CHANNELS_2,
leaky=LEAKY),
nn.ConvTranspose2d(in_channels=NUM_CHANNELS_2,
out_channels=1,
kernel_size=4,
stride=2,
padding=1))
self.out = nn.Tanh() if Tanh else nn.Sigmoid()
logging.debug(f'Generator out {self.out}')