def __init__(self,
feat_stride=16,
init_filter_reg=1e-2,
init_gauss_sigma=1.0,
num_dist_bins=5,
bin_displacement=1.0,
mask_init_factor=4.0,
score_act='bentpar',
act_param=None,
mask_act='sigmoid'):
super().__init__()
self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1))
self.feat_stride = feat_stride
self.distance_map = DistanceMap(num_dist_bins, bin_displacement)
# Distance coordinates
d = torch.arange(num_dist_bins, dtype=torch.float32).reshape(
1, -1, 1, 1) * bin_displacement
if init_gauss_sigma == 0:
init_gauss = torch.zeros_like(d)
init_gauss[0, 0, 0, 0] = 1
else:
init_gauss = torch.exp(-1 / 2 * (d / init_gauss_sigma)**2)
self.label_map_predictor = nn.Conv2d(num_dist_bins,
1,
kernel_size=1,
bias=False)
self.label_map_predictor.weight.data = init_gauss - init_gauss.min()
mask_layers = [nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)]
if mask_act == 'sigmoid':
mask_layers.append(nn.Sigmoid())
init_bias = 0.0
elif mask_act == 'linear':
init_bias = 0.5
else:
raise ValueError('Unknown activation')
self.target_mask_predictor = nn.Sequential(*mask_layers)
self.target_mask_predictor[
0].weight.data = mask_init_factor * torch.tanh(2.0 - d) + init_bias
self.spatial_weight_predictor = nn.Conv2d(num_dist_bins,
1,
kernel_size=1,
bias=False)
self.spatial_weight_predictor.weight.data.fill_(1.0)
if score_act == 'bentpar':
self.score_activation = activation.BentIdentPar(act_param)
elif score_act == 'relu':
self.score_activation = activation.LeakyReluPar()
else:
raise ValueError('Unknown activation')
def __init__(self, num_iter=1, feat_stride=16, init_step_length=1.0,
init_filter_reg=1e-2, init_gauss_sigma=1.0, num_dist_bins=5, bin_displacement=1.0, mask_init_factor=4.0,
score_act='relu', act_param=None, min_filter_reg=1e-3, mask_act='sigmoid',
detach_length=float('Inf'), alpha_eps=0):
super().__init__()
self.num_iter = num_iter
self.feat_stride = feat_stride
self.log_step_length = nn.Parameter(math.log(init_step_length) * torch.ones(1))
self.filter_reg = nn.Parameter(init_filter_reg * torch.ones(1))
self.distance_map = DistanceMap(num_dist_bins, bin_displacement)
self.min_filter_reg = min_filter_reg
self.detach_length = detach_length
self.alpha_eps = alpha_eps
# Distance coordinates
d = torch.arange(num_dist_bins, dtype=torch.float32).reshape(1,-1,1,1) * bin_displacement
if init_gauss_sigma == 0:
init_gauss = torch.zeros_like(d)
init_gauss[0,0,0,0] = 1
else:
init_gauss = torch.exp(-1/2 * (d / init_gauss_sigma)**2)
# Module that predicts the target label function (y in the paper)
self.label_map_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)
self.label_map_predictor.weight.data = init_gauss - init_gauss.min()
# Module that predicts the target mask (m in the paper)
mask_layers = [nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)]
if mask_act == 'sigmoid':
mask_layers.append(nn.Sigmoid())
init_bias = 0.0
elif mask_act == 'linear':
init_bias = 0.5
else:
raise ValueError('Unknown activation')
self.target_mask_predictor = nn.Sequential(*mask_layers)
self.target_mask_predictor[0].weight.data = mask_init_factor * torch.tanh(2.0 - d) + init_bias
# Module that predicts the residual weights (v in the paper)
self.spatial_weight_predictor = nn.Conv2d(num_dist_bins, 1, kernel_size=1, bias=False)
self.spatial_weight_predictor.weight.data.fill_(1.0)
# The score actvation and its derivative
if score_act == 'bentpar':
self.score_activation = activation.BentIdentPar(act_param)
self.score_activation_deriv = activation.BentIdentParDeriv(act_param)
elif score_act == 'relu':
self.score_activation = activation.LeakyReluPar()
self.score_activation_deriv = activation.LeakyReluParDeriv()
else:
raise ValueError('Unknown score activation')