def build_activation(act_func, inplace=True): if act_func == 'relu': return nn.ReLU(inplace=inplace) elif act_func == 'relu6': return nn.ReLU6(inplace=inplace) elif act_func == 'tanh': return nn.Tanh() elif act_func == 'sigmoid': return nn.Sigmoid() elif act_func is None: return None else: raise ValueError('do not support: %s' % act_func)
def __init__(self, nz, ngf, nc): super(DCGANGenerator, self).__init__() self.main = nn.Sequential( # input is Z, going into a convolution nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False), nn.BatchNorm2d(ngf * 8), nn.ReLU(True), # state size. (ngf*8) x 4 x 4 nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True), # state size. (ngf*4) x 8 x 8 nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 2), nn.ReLU(True), # state size. (ngf*2) x 16 x 16 nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf), nn.ReLU(True), # state size. (ngf) x 32 x 32 nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False), nn.Tanh() # state size. (nc) x 64 x 64 )