def init_weights(self): # initialize weight and bias for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m) elif isinstance(m, nn.BatchNorm2d): uniform_init(m)
def test_uniform_init(): conv_module = nn.Conv2d(3, 16, 3) uniform_init(conv_module, bias=0.1) # TODO: sanity check of weight distribution, e.g. mean, std assert conv_module.bias.allclose(torch.full_like(conv_module.bias, 0.1)) conv_module_no_bias = nn.Conv2d(3, 16, 3, bias=False) uniform_init(conv_module_no_bias)
def init_weights(self, pretrained=None): # initialize weight and bias for m in self.modules(): if isinstance(m, nn.Conv2d): kaiming_init(m) elif isinstance(m, nn.BatchNorm2d): uniform_init(m)
def init_weights(self, pretrained=None): if pretrained is not None: logger = get_root_logger() load_checkpoint(self, pretrained, strict=False, logger=logger) else: for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m) elif isinstance(m, nn.BatchNorm2d): uniform_init(m)
def init_weights(self, pretrained=None): for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m) elif isinstance(m, nn.BatchNorm2d): uniform_init(m)
def init_weights(self): """Initialize the learnable weights.""" uniform_init(self.row_embed) uniform_init(self.col_embed)