in torchbenchmark/models/Background_Matting/networks.py [0:0]
def __init__(self, input_nc, output_nc, ngf=64, nf_part=64,norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks1=7, n_blocks2=3, padding_type='reflect'):
assert(n_blocks1 >= 0); assert(n_blocks2 >= 0)
super(ResnetConditionHR, self).__init__()
self.input_nc = input_nc
self.output_nc = output_nc
self.ngf = ngf
use_bias=True
#main encoder output 256xW/4xH/4
model_enc1 = [nn.ReflectionPad2d(3),nn.Conv2d(input_nc[0], ngf, kernel_size=7, padding=0,bias=use_bias),norm_layer(ngf),nn.ReLU(True)]
model_enc1 += [nn.Conv2d(ngf , ngf * 2, kernel_size=3,stride=2, padding=1, bias=use_bias),norm_layer(ngf * 2),nn.ReLU(True)]
model_enc2 = [nn.Conv2d(ngf*2 , ngf * 4, kernel_size=3,stride=2, padding=1, bias=use_bias),norm_layer(ngf * 4),nn.ReLU(True)]
#back encoder output 256xW/4xH/4
model_enc_back = [nn.ReflectionPad2d(3),nn.Conv2d(input_nc[1], ngf, kernel_size=7, padding=0,bias=use_bias),norm_layer(ngf),nn.ReLU(True)]
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
model_enc_back += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,stride=2, padding=1, bias=use_bias),norm_layer(ngf * mult * 2),nn.ReLU(True)]
#seg encoder output 256xW/4xH/4
model_enc_seg = [nn.ReflectionPad2d(3),nn.Conv2d(input_nc[2], ngf, kernel_size=7, padding=0,bias=use_bias),norm_layer(ngf),nn.ReLU(True)]
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
model_enc_seg += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,stride=2, padding=1, bias=use_bias),norm_layer(ngf * mult * 2),nn.ReLU(True)]
mult = 2**n_downsampling
# #motion encoder output 256xW/4xH/4
model_enc_multi = [nn.ReflectionPad2d(3),nn.Conv2d(input_nc[3], ngf, kernel_size=7, padding=0,bias=use_bias),norm_layer(ngf),nn.ReLU(True)]
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
model_enc_multi += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,stride=2, padding=1, bias=use_bias),norm_layer(ngf * mult * 2),nn.ReLU(True)]
self.model_enc1 = nn.Sequential(*model_enc1)
self.model_enc2 = nn.Sequential(*model_enc2)
self.model_enc_back = nn.Sequential(*model_enc_back)
self.model_enc_seg = nn.Sequential(*model_enc_seg)
self.model_enc_multi = nn.Sequential(*model_enc_multi)
mult = 2**n_downsampling
self.comb_back=nn.Sequential(nn.Conv2d(ngf * mult*2,nf_part,kernel_size=1,stride=1,padding=0,bias=False),norm_layer(ngf),nn.ReLU(True))
self.comb_seg=nn.Sequential(nn.Conv2d(ngf * mult*2,nf_part,kernel_size=1,stride=1,padding=0,bias=False),norm_layer(ngf),nn.ReLU(True))
self.comb_multi=nn.Sequential(nn.Conv2d(ngf * mult*2,nf_part,kernel_size=1,stride=1,padding=0,bias=False),norm_layer(ngf),nn.ReLU(True))
#decoder
model_res_dec=[nn.Conv2d(ngf * mult +3*nf_part,ngf*mult,kernel_size=1,stride=1,padding=0,bias=False),norm_layer(ngf*mult),nn.ReLU(True)]
for i in range(n_blocks1):
model_res_dec += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
model_res_dec_al=[]
for i in range(n_blocks2):
model_res_dec_al += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
model_res_dec_fg=[]
for i in range(n_blocks2):
model_res_dec_fg += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
model_dec_al=[]
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
#model_dec_al += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),kernel_size=3, stride=2,padding=1, output_padding=1,bias=use_bias),norm_layer(int(ngf * mult / 2)),nn.ReLU(True)]
model_dec_al += [nn.Upsample(scale_factor=2,mode='bilinear',align_corners = True),nn.Conv2d(ngf * mult, int(ngf * mult / 2), 3, stride=1,padding=1),norm_layer(int(ngf * mult / 2)),nn.ReLU(True)]
model_dec_al += [nn.ReflectionPad2d(3),nn.Conv2d(ngf, 1, kernel_size=7, padding=0),nn.Tanh()]
model_dec_fg1=[nn.Upsample(scale_factor=2,mode='bilinear',align_corners = True),nn.Conv2d(ngf * 4, int(ngf * 2), 3, stride=1,padding=1),norm_layer(int(ngf * 2)),nn.ReLU(True)]
model_dec_fg2=[nn.Upsample(scale_factor=2,mode='bilinear',align_corners = True),nn.Conv2d(ngf * 4, ngf, 3, stride=1,padding=1),norm_layer(ngf),nn.ReLU(True),nn.ReflectionPad2d(3),nn.Conv2d(ngf, output_nc-1, kernel_size=7, padding=0)]
self.model_res_dec = nn.Sequential(*model_res_dec)
self.model_res_dec_al=nn.Sequential(*model_res_dec_al)
self.model_res_dec_fg=nn.Sequential(*model_res_dec_fg)
self.model_al_out=nn.Sequential(*model_dec_al)
self.model_dec_fg1=nn.Sequential(*model_dec_fg1)
self.model_fg_out = nn.Sequential(*model_dec_fg2)