def define_module(self):
from layers import DynamicFilterLayer
ninput = self.motion_dim + self.content_dim
ngf = self.gf_dim
# TEXT.DIMENSION -> GAN.CONDITION_DIM
self.ca_net = CA_NET()
# -> ngf x 4 x 4
self.fc = nn.Sequential(
nn.Linear(ninput, int(ngf * 4 * 4 / 2), bias=False),
nn.BatchNorm1d(ngf * 4 * 2), nn.ReLU(True))
# ngf x 4 x 4 -> ngf/2 x 8 x 8
self.upsample1 = upBlock(ngf, ngf // 2)
# -> ngf/4 x 16 x 16
self.upsample2 = upBlock(ngf // 2, ngf // 4)
# -> ngf/8 x 32 x 32
self.upsample3 = upBlock(ngf // 4, ngf // 8)
# -> ngf/16 x 64 x 64
self.upsample4 = upBlock(ngf // 8, ngf // 16)
# -> 3 x 64 x 64
self.img = nn.Sequential(conv3x3(ngf // 16, 3), nn.Tanh())
self.filter_net = nn.Sequential(
nn.Linear(self.content_dim, self.filter_size**2, bias=False),
nn.BatchNorm1d(self.filter_size**2),
#nn.Softmax()
)
self.image_net = nn.Sequential(
nn.Linear(self.motion_dim, self.r_image_size**2, bias=False),
nn.BatchNorm1d(self.r_image_size**2))
self.dfn_layer = DynamicFilterLayer(
(self.filter_size, self.filter_size, 1),
pad=(self.filter_size // 2, self.filter_size // 2),
grouping=False)
self.downsamples = nn.Sequential(
nn.Conv2d(
1, ngf, 3, 2, 1, bias=False
), #spectral_norm(nn.Conv2d(1, ngf, 3, 2, 1, bias=False)),
nn.BatchNorm2d(ngf),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(
ngf, ngf // 2, 4, 2, 1, bias=False
), #spectral_norm(nn.Conv2d(ngf, ngf//2, 4, 2, 1, bias=False)),
nn.BatchNorm2d(ngf // 2),
nn.LeakyReLU(0.2, inplace=True),
)
def define_module(self):
from layers import DynamicFilterLayer1D as DynamicFilterLayer
ninput = self.motion_dim + self.content_dim + self.image_size # (365+124+124=613)
ngf = self.gf_dim # 128*8=1024
self.ca_net = CA_NET()
# -> ngf x 4 x 4
self.filter_net = nn.Sequential(
nn.Linear(self.content_dim,
self.filter_size * self.filter_num * self.out_num),
nn.BatchNorm1d(self.filter_size * self.filter_num * self.out_num))
self.image_net = nn.Sequential(
nn.Linear(self.motion_dim, self.image_size * self.filter_num),
nn.BatchNorm1d(self.image_size * self.filter_num), nn.Tanh())
# For generate final image
self.fc = nn.Sequential(nn.Linear(ninput, ngf * 4 * 4, bias=False),
nn.BatchNorm1d(ngf * 4 * 4), nn.ReLU(True))
self.upsample1 = upBlock(ngf, ngf // 2)
# -> ngf/4 x 16 x 16
self.upsample2 = upBlock(ngf // 2, ngf // 4)
# -> ngf/8 x 32 x 32
self.upsample3 = upBlock(ngf // 4, ngf // 8)
# -> ngf/16 x 64 x 64
self.upsample4 = upBlock(ngf // 8, ngf // 16)
# -> 3 x 64 x 64
self.img = nn.Sequential(conv3x3(ngf // 16, 3), nn.Tanh())
if self.use_segment:
ngf_seg = self.gf_dim_seg
self.seg_c = conv3x3(ngf_seg, ngf)
self.seg_c1 = conv3x3(ngf_seg // 2, ngf // 2)
# self.seg_c2 = conv3x3(ngf_seg//4, ngf//4)
# self.seg_c3 = conv3x3(ngf_seg//8, ngf//8)
# self.seg_c4 = conv3x3(ngf_seg//16, ngf//16)
# For generate seg and img v4 and v5 and v6
self.fc_seg = nn.Sequential(
nn.Linear(ninput, ngf_seg * 4 * 4, bias=False),
nn.BatchNorm1d(ngf_seg * 4 * 4), nn.ReLU(True))
# ngf x 4 x 4 -> ngf/2 x 8 x 8
self.upsample1_seg = upBlock(ngf_seg, ngf_seg // 2)
# -> ngf/4 x 16 x 16
self.upsample2_seg = upBlock(ngf_seg // 2, ngf_seg // 4)
# -> ngf/8 x 32 x 32
self.upsample3_seg = upBlock(ngf_seg // 4, ngf_seg // 8)
# -> ngf/16 x 64 x 64
self.upsample4_seg = upBlock(ngf_seg // 8, ngf_seg // 16)
# -> 3 x 64 x 64
self.img_seg = nn.Sequential(conv3x3(ngf_seg // 16, 1), nn.Tanh())
self.m_net = nn.Sequential(nn.Linear(self.motion_dim, self.motion_dim),
nn.BatchNorm1d(self.motion_dim))
self.c_net = nn.Sequential(
nn.Linear(self.content_dim, self.content_dim),
nn.BatchNorm1d(self.content_dim))
self.dfn_layer = DynamicFilterLayer(self.filter_size,
pad=self.filter_size // 2)