def reset_parameters(self):
self.apply(weights_init)
relu_gain = nn.init.calculate_gain('relu')
self.conv1.weight.data.mul_(relu_gain)
self.conv2.weight.data.mul_(relu_gain)
self.conv3.weight.data.mul_(relu_gain)
self.conv4.weight.data.mul_(relu_gain)
self.conv5.weight.data.mul_(relu_gain)
self.conv6.weight.data.mul_(relu_gain)
self.conv7.weight.data.mul_(relu_gain)
self.conv8.weight.data.mul_(relu_gain)
self.conv9.weight.data.mul_(relu_gain)
self.conv10.weight.data.mul_(relu_gain)
self.conv11.weight.data.mul_(relu_gain)
self.conv12.weight.data.mul_(relu_gain)
self.fc1.weight.data.mul_(relu_gain)
self.fc2.weight.data.mul_(relu_gain)
if hasattr(self, 'gru'):
orthogonal(self.gru.weight_ih.data)
orthogonal(self.gru.weight_hh.data)
self.gru.bias_ih.data.fill_(0)
self.gru.bias_hh.data.fill_(0)
if self.dist.__class__.__name__ == "DiagGaussian":
self.dist.fc_mean.weight.data.mul_(0.01)
def weight_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1 or classname.find('Linear') != -1:
orthogonal(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0)
def reset_parameters(self):
self.apply(weights_init_mlp)
orthogonal(self.gru.weight_ih.data)
orthogonal(self.gru.weight_hh.data)
self.gru.bias_ih.data.fill_(0)
self.gru.bias_hh.data.fill_(0)
if self.dist.__class__.__name__ == "DiagGaussian":
self.dist.fc_mean.weight.data.mul_(0.01)
def reset_parameters(self):
self.apply(weights_init)
relu_gain = nn.init.calculate_gain('leaky_relu')
self.linear1.weight.data.mul_(relu_gain)
if hasattr(self, 'gru'):
orthogonal(self.gru.weight_ih.data)
orthogonal(self.gru.weight_hh.data)
self.gru.bias_ih.data.fill_(0)
self.gru.bias_hh.data.fill_(0)
if self.dist.__class__.__name__ == "DiagGaussian":
self.dist.fc_mean.weight.data.mul_(0.01)
def reset_parameters(self):
self.apply(weights_init)
self.head.apply(weights_init_head)
if hasattr(self, 'icm'):
self.icm.head.apply(weights_init_head)
if hasattr(self, 'gru'):
orthogonal(self.gru.weight_ih.data)
orthogonal(self.gru.weight_hh.data)
self.gru.bias_ih.data.fill_(0)
self.gru.bias_hh.data.fill_(0)
if self.dist.__class__.__name__ == "DiagGaussian":
self.dist.fc_mean.weight.data.mul_(0.01)
def reset_parameters(self):
self.apply(weights_init)
relu_gain = nn.init.calculate_gain('relu')
self.conv1.weight.data.mul_(relu_gain)
self.conv2.weight.data.mul_(relu_gain)
self.conv3.weight.data.mul_(relu_gain)
self.linear1.weight.data.mul_(relu_gain)
self.linear2.weight.data.mul_(relu_gain)
if hasattr(self, 'gru'):
orthogonal(self.gru.weight_ih.data)
orthogonal(self.gru.weight_hh.data)
self.gru.bias_ih.data.fill_(0)
self.gru.bias_hh.data.fill_(0)
def add_item(self, item):
u = utils.vector(self.point, item.point)
direction = utils.orthogonal(u)
center = (.5 * (self.point[0] + item.point[0]),
.5 * (self.point[1] + item.point[1]))
line = shapes.InfiniteLine(center, direction)
new_item = OtherItem(item, line)
keep_new_item = True
others_to_remove = set()
for other in self.others:
other.line.constrain(new_item, self.point)
new_item.line.constrain(other, self.point)
if not other.is_valid():
others_to_remove |= {other}
if not new_item.is_valid():
keep_new_item = False
break
self.others -= others_to_remove
if keep_new_item:
self.others |= {new_item}
return new_item
return None
def item_that_contains(self, point):
for point_item in self.point_items:
if not point_item.is_bounded():
continue
skip = False
for other in point_item.others:
edge_normal = utils.orthogonal(other.line.direction)
A, B = other.vertices()
AO = utils.vector(A, point_item.point)
AP = utils.vector(A, point)
if utils.dot(AO, edge_normal) * utils.dot(AP, edge_normal) < 0:
skip = True
break
if not skip:
return point_item
raise Exception("PointSet.item_that_contains: not found")
def reset_parameters(self):
orthogonal(self.gru.weight_ih.data)
orthogonal(self.gru.weight_hh.data)
self.gru.bias_ih.data.fill_(0)
self.gru.bias_hh.data.fill_(0)
self.critic_linear.bias.data.fill_(0)
def __init__(self, point, direction):
self.point = point
self.direction = direction
self.normal = utils.orthogonal(self.direction)