def __init__(self, hidden_dim, **kwargs):
super().__init__()
# conv feature
self.conv = nn.Sequential(
nn.Conv2d(COLOR_CHANNELS * 2,
NUM_CHANNELS_2 // 2,
kernel_size=4,
stride=2,
padding=1), nn.LeakyReLU(LEAKY), nn.Dropout2d(DROPOUT),
BasicBlocks.block_conv_k3s2p1_BN_RELU(NUM_CHANNELS_2 // 2,
NUM_CHANNELS_1 // 2,
leaky=LEAKY),
nn.Dropout2d(DROPOUT),
BasicBlocks.block_conv_k3s2p1_BN_RELU(NUM_CHANNELS_1 // 2,
NUM_CHANNELS_1 // 2 * 2,
leaky=LEAKY),
nn.Dropout2d(DROPOUT))
self.fc = BasicBlocks.block_linear_BN_RELU(NUM_CHANNELS_1 // 2 * 2 *
4 * 4,
NUM_HIDDEN_1,
leaky=LEAKY)
self.last_layer = nn.Linear(NUM_HIDDEN_1, hidden_dim)
def __init__(self, hidden_dim, **kwargs):
super(Discriminator, self).__init__()
num_channels = list(map(lambda x: x // 2, NUM_CHANNELS))
# b * NUM_CHANNELS[0] * 32 * 32
self.first_layer = nn.Sequential(
nn.Conv2d(COLOUR_CHANNELS * 2,
num_channels[0],
kernel_size=3,
stride=2,
padding=1), nn.LeakyReLU(LEAKY, inplace=True))
# b * NUM_CHANNELS[-1] * 4 * 4
list_layers = [
BasicBlocks.block_conv_k3s2p1_BN_RELU(num_channels[i0],
num_channels[i0 + 1],
leaky=LEAKY)
for i0 in range(len(num_channels) - 1)
]
self.intermediate_layer = nn.Sequential(*list_layers)
# b * hidden_dim * 1 * 1
self.last_layer = nn.Conv2d(num_channels[-1],
hidden_dim,
kernel_size=4,
stride=1,
padding=0)
def __init__(self, num_PAC=1, **kwargs):
super(Discriminator, self).__init__()
# conv feature
self.conv = nn.Sequential(
nn.Conv2d(COLOR_CHANNELS * num_PAC,
NUM_CHANNELS_2,
kernel_size=4,
stride=2,
padding=1), nn.LeakyReLU(LEAKY),
BasicBlocks.block_conv_k3s2p1_BN_RELU(NUM_CHANNELS_2,
NUM_CHANNELS_1,
leaky=LEAKY),
BasicBlocks.block_conv_k3s2p1_BN_RELU(NUM_CHANNELS_1,
NUM_CHANNELS_1 * 2,
leaky=LEAKY))
self.fc = BasicBlocks.block_linear_BN_RELU(NUM_CHANNELS_1 * 2 * 4 * 4,
NUM_HIDDEN_1,
leaky=LEAKY)
self.last_layer = nn.Linear(NUM_HIDDEN_1, 1)