我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用torch.nn.ConvTranspose3d()。
def weights_init(m): classname = m.__class__.__name__ if classname.find('Linear') != -1: torch.nn.init.xavier_uniform( m.weight.data, gain=1 ) m.bias.data.fill_(0.1) # elif classname.find('Conv3d') != -1: # torch.nn.init.xavier_uniform( # m.weight.data, # gain=1 # ) # elif classname.find('ConvTranspose3d') != -1: # torch.nn.init.xavier_uniform( # m.weight.data, # gain=1 # )
def __init__(self, inChans, outChans, nConvs, elu, dropout=False): super(UpTransition, self).__init__() self.up_conv = nn.ConvTranspose3d(inChans, outChans // 2, kernel_size=2, stride=2) self.bn1 = ContBatchNorm3d(outChans // 2) self.do1 = passthrough self.do2 = nn.Dropout3d() self.relu1 = ELUCons(elu, outChans // 2) self.relu2 = ELUCons(elu, outChans) if dropout: self.do1 = nn.Dropout3d() self.ops = _make_nConv(outChans, nConvs, elu)
def conv_transpose(in_ch, out_ch, kernel_size, stride=1, padding=0, out_padding=0, dilation=1, groups=1, bias=True, dim=2): #TODO: in the future some preprocessing goes here in_dim = dim if in_dim == 1: return nn.ConvTranspose1d(in_ch, out_ch, kernel_size, stride=stride, padding=padding, output_padding=out_padding, dilation=dilation, groups=groups, bias=bias) elif in_dim == 2: return nn.ConvTranspose2d(in_ch, out_ch, kernel_size, stride=stride, padding=padding, output_padding=out_padding, dilation=dilation, groups=groups, bias=bias) elif in_dim == 3: return nn.ConvTranspose3d(in_ch, out_ch, kernel_size, stride=stride, padding=padding, output_padding=out_padding, dilation=dilation, groups=groups, bias=bias) # pooling
def test_conv_modules_raise_error_on_incorrect_input_size(self): modules = [nn.Conv1d(3, 8, 3), nn.ConvTranspose1d(3, 8, 3), nn.Conv2d(3, 8, 3), nn.ConvTranspose2d(3, 8, 3), nn.Conv3d(3, 8, 3), nn.ConvTranspose3d(3, 8, 3)] invalid_input_dims = [(2, 4), (2, 4), (3, 5), (3, 5), (4, 6), (4, 6)] for invalid_dims, module in zip(invalid_input_dims, modules): for dims in invalid_dims: input = Variable(torch.Tensor(torch.Size((3, ) * dims))) self.assertRaises(ValueError, lambda: module(input))
def is_sparseable(m): return True if hasattr(m, 'weight') and isinstance(m, ( nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d, nn.Linear)) else False
