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model_o_t_gen.py
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model_o_t_gen.py
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import torch
from torchvision.models import vgg19
temp_shape = (0,0)
def calc_padding(h, w, k, s):
h_pad = (((h-1)*s) + k - h)//2
w_pad = (((w-1)*s) + k - w)//2
return (h_pad, w_pad)
def calc_inv_padding(h, w, k, s):
h_pad = (k-h + ((h-1)*s))//2
w_pad = (k-w + ((w-1)*s))//2
return (h_pad, w_pad)
class Conv_bn_block(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self._conv = torch.nn.Conv2d(*args, **kwargs)
self._bn = torch.nn.BatchNorm2d(kwargs['out_channels'])
def forward(self, input):
return torch.nn.functional.leaky_relu(self._bn(self._conv(input)),negative_slope=0.2)
class Res_block(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self._conv1 = torch.nn.Conv2d(in_channels, in_channels//4, kernel_size = 1, stride =1)
self._conv2 = torch.nn.Conv2d(in_channels//4, in_channels//4, kernel_size = 3, stride = 1, padding = 1)
self._conv3 = torch.nn.Conv2d(in_channels//4, in_channels, kernel_size = 1, stride=1)
self._bn = torch.nn.BatchNorm2d(in_channels)
def forward(self, x):
xin = x
x = torch.nn.functional.leaky_relu(self._conv1(x),negative_slope=0.2)
x = torch.nn.functional.leaky_relu(self._conv2(x),negative_slope=0.2)
x = self._conv3(x)
x = torch.add(xin, x)
x = torch.nn.functional.leaky_relu(self._bn(x),negative_slope=0.2)
return x
class encoder_net(torch.nn.Module):
def __init__(self, in_channels, get_feature_map = False):
super().__init__()
self.cnum = 32
self.get_feature_map = get_feature_map
self._conv1_1 = Conv_bn_block(in_channels = in_channels, out_channels = self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._conv1_2 = Conv_bn_block(in_channels =self.cnum, out_channels = self.cnum, kernel_size = 3, stride = 1, padding = 1)
#--------------------------
self._pool1 = torch.nn.Conv2d(in_channels = self.cnum, out_channels = 2 * self.cnum, kernel_size = 3, stride = 2, padding = calc_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv2_1 = Conv_bn_block(in_channels = 2* self.cnum, out_channels = 2*self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._conv2_2 = Conv_bn_block(in_channels = 2* self.cnum, out_channels = 2*self.cnum, kernel_size = 3, stride = 1, padding = 1)
#---------------------------
self._pool2 = torch.nn.Conv2d(in_channels = 2*self.cnum, out_channels = 4* self.cnum, kernel_size = 3, stride = 2, padding = calc_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv3_1 = Conv_bn_block(in_channels = 4* self.cnum, out_channels = 4*self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._conv3_2 = Conv_bn_block(in_channels = 4* self.cnum, out_channels = 4*self.cnum, kernel_size = 3, stride = 1, padding = 1)
#---------------------------
self._pool3 = torch.nn.Conv2d(in_channels = 4*self.cnum, out_channels = 8* self.cnum, kernel_size = 3, stride = 2, padding = calc_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv4_1 = Conv_bn_block(in_channels = 8* self.cnum, out_channels = 8*self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._conv4_2 = Conv_bn_block(in_channels = 8* self.cnum, out_channels = 8*self.cnum, kernel_size = 3, stride = 1, padding = 1)
def forward(self, x):
x = self._conv1_1(x)
x = self._conv1_2(x)
x = torch.nn.functional.leaky_relu(self._pool1(x),negative_slope=0.2)
x = self._conv2_1(x)
x = self._conv2_2(x)
f1 = x
x = torch.nn.functional.leaky_relu(self._pool2(x),negative_slope=0.2)
x = self._conv3_1(x)
x = self._conv3_2(x)
f2 = x
x = torch.nn.functional.leaky_relu(self._pool3(x),negative_slope=0.2)
x = self._conv4_1(x)
x = self._conv4_2(x)
if self.get_feature_map:
return x, [f2, f1]
else:
return x
class build_res_block(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self._block1 = Res_block(in_channels)
self._block2 = Res_block(in_channels)
self._block3 = Res_block(in_channels)
self._block4 = Res_block(in_channels)
def forward(self, x):
x = self._block1(x)
x = self._block2(x)
x = self._block3(x)
x = self._block4(x)
return x
class decoder_net(torch.nn.Module):
def __init__(self, in_channels, get_feature_map = False, mt =1, fn_mt=1):
super().__init__()
self.cnum = 32
self.get_feature_map = get_feature_map
self._conv1_1 = Conv_bn_block(in_channels = fn_mt*in_channels , out_channels = 8*self.cnum, kernel_size = 3, stride =1, padding = 1)
self._conv1_2 = Conv_bn_block(in_channels = 8*self.cnum, out_channels = 8*self.cnum, kernel_size = 3, stride =1, padding = 1)
#-----------------
self._deconv1 = torch.nn.ConvTranspose2d(8*self.cnum, 4*self.cnum, kernel_size = 3, stride = 2, padding = calc_inv_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv2_1 = Conv_bn_block(in_channels = fn_mt*mt*4*self.cnum, out_channels = 4*self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._conv2_2 = Conv_bn_block(in_channels = 4*self.cnum, out_channels = 4*self.cnum, kernel_size = 3, stride = 1, padding = 1)
#-----------------
self._deconv2 = torch.nn.ConvTranspose2d(4*self.cnum, 2*self.cnum, kernel_size =3 , stride = 2, padding = calc_inv_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv3_1 = Conv_bn_block(in_channels = fn_mt*mt*2*self.cnum, out_channels = 2*self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._conv3_2 = Conv_bn_block(in_channels = 2*self.cnum, out_channels = 2*self.cnum, kernel_size = 3, stride = 1, padding = 1)
#----------------
self._deconv3 = torch.nn.ConvTranspose2d(2*self.cnum, self.cnum, kernel_size =3 , stride = 2, padding = calc_inv_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv4_1 = Conv_bn_block(in_channels = self.cnum, out_channels = self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._conv4_2 = Conv_bn_block(in_channels = self.cnum, out_channels = self.cnum, kernel_size = 3, stride = 1, padding = 1)
def forward(self, x, fuse = None):
if fuse and fuse[0] is not None:
x = torch.cat((x, fuse[0]), dim = 1)
x = self._conv1_1(x)
x = self._conv1_2(x)
f1 = x
#----------
x = torch.nn.functional.leaky_relu(self._deconv1(x), negative_slope=0.2)
if fuse and fuse[1] is not None:
x = torch.cat((x, fuse[1]), dim = 1)
x = self._conv2_1(x)
x = self._conv2_2(x)
f2 = x
#----------
x = torch.nn.functional.leaky_relu(self._deconv2(x), negative_slope=0.2)
if fuse and fuse[2] is not None:
x = torch.cat((x, fuse[2]), dim = 1)
x = self._conv3_1(x)
x = self._conv3_2(x)
f3 = x
#----------
x = torch.nn.functional.leaky_relu(self._deconv3(x), negative_slope=0.2)
x = self._conv4_1(x)
x = self._conv4_2(x)
if self.get_feature_map:
return x, [f1, f2, f3]
else:
return x
class text_conversion_net(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.cnum = 32
self._t_encoder = encoder_net(in_channels)
self._t_res = build_res_block(8*self.cnum)
self._s_encoder = encoder_net(in_channels)
self._s_res = build_res_block(8*self.cnum)
self._sk_decoder = decoder_net(16*self.cnum)
self._sk_out = torch.nn.Conv2d(self.cnum, 1, kernel_size =3, stride = 1, padding = 1)
self._t_decoder = decoder_net(16*self.cnum)
self._t_cbr = Conv_bn_block(in_channels = 2*self.cnum, out_channels = 2*self.cnum, kernel_size = 3, stride = 1, padding = 1)
self._t_out = torch.nn.Conv2d(2*self.cnum, 3, kernel_size = 3, stride = 1, padding = 1)
def forward(self, x_t, x_s):
x_t = self._t_encoder(x_t)
x_t = self._t_res(x_t)
x_s = self._s_encoder(x_s)
x_s = self._s_res(x_s)
x = torch.cat((x_t, x_s), dim = 1)
y_sk = self._sk_decoder(x, fuse = None)
y_sk_out = torch.sigmoid(self._sk_out(y_sk))
y_t = self._t_decoder(x, fuse = None)
y_t = torch.cat((y_sk, y_t), dim = 1)
y_t = self._t_cbr(y_t)
y_t_out = torch.tanh(self._t_out(y_t))
return y_sk_out, y_t_out
class fusion_net(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.cnum = 32
self._encoder = encoder_net(in_channels)
self._res = build_res_block(8*self.cnum)
self._decoder = decoder_net(8*self.cnum, fn_mt = 1)
self._out = torch.nn.Conv2d(self.cnum, 3, kernel_size = 3, stride = 1, padding = 1)
def forward(self, x, fuse=None):
x = self._encoder(x)
x = self._res(x)
x = self._decoder(x, fuse = fuse)
x = torch.tanh(self._out(x))
return x
class Generator(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.cnum = 32
self._tcn = text_conversion_net(in_channels)
self._fn = fusion_net(in_channels)
def forward(self, i_t, i_s, gbl_shape):
o_sk, o_t = self._tcn(i_t, i_s)
o_f = self._fn(o_t)
return o_sk, o_t, o_f
class Discriminator(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.cnum = 32
self._conv1 = torch.nn.Conv2d(in_channels, 64, kernel_size = 3, stride = 2, padding = calc_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv2 = torch.nn.Conv2d(64, 128, kernel_size = 3, stride = 2, padding = calc_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv2_bn = torch.nn.BatchNorm2d(128)
self._conv3 = torch.nn.Conv2d(128, 256, kernel_size = 3, stride = 2, padding = calc_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv3_bn = torch.nn.BatchNorm2d(256)
self._conv4 = torch.nn.Conv2d(256, 512, kernel_size = 3, stride = 2, padding = calc_padding(temp_shape[0], temp_shape[1], 3, 2))
self._conv4_bn = torch.nn.BatchNorm2d(512)
self._conv5 = torch.nn.Conv2d(512, 1, kernel_size = 3, stride = 1, padding = 1)
self._conv5_bn = torch.nn.BatchNorm2d(1)
def forward(self, x):
x = torch.nn.functional.leaky_relu(self._conv1(x), negative_slope=0.2)
x = self._conv2(x)
x = torch.nn.functional.leaky_relu(self._conv2_bn(x), negative_slope=0.2)
x = self._conv3(x)
x = torch.nn.functional.leaky_relu(self._conv3_bn(x), negative_slope=0.2)
x = self._conv4(x)
x = torch.nn.functional.leaky_relu(self._conv4_bn(x), negative_slope=0.2)
x = self._conv5(x)
x = self._conv5_bn(x)
return x
class Vgg19(torch.nn.Module):
def __init__(self):
super(Vgg19, self).__init__()
features = list(vgg19(pretrained = True).features)
self.features = torch.nn.ModuleList(features).eval()
def forward(self, x):
results = []
for ii, model in enumerate(self.features):
x = model(x)
if ii in {1, 6, 11, 20, 29}:
results.append(x)
return results