def forward(self, x1, x2, x3):
x1 = self.convolutional(x1) # [batch, feature, beta, alpha, gamma]
output1 = so3_integrate(x1) # [batch, feature]
output1 = self.linear(output1)
x2 = self.convolutional(x2) # [batch, feature, beta, alpha, gamma]
output2 = so3_integrate(x2) # [batch, feature]
output2 = self.linear(output2)
x3 = self.convolutional(x3) # [batch, feature, beta, alpha, gamma]
output3 = so3_integrate(x3) # [batch, feature]
output3 = self.linear(output3)
return output1, output2, output3
def forward(self, x):
x = self.seq(x)
x = so3_integrate(x)
x = self.out_layer(x)
return x
def forward(self, x):
x = self.conv1(x)
x = torch.nn.functional.relu(x)
x = self.conv2(x)
x = torch.nn.functional.relu(x)
x = s2cnn.so3_integrate(x)
x = self.out_layer(x)
return x
def forward(self, x):
print('input', x.shape)
x = self.convolutional(x)
print('conv', x.shape)
x = so3_integrate(x)
print('so3', x.shape)
x = self.out_layer(x)
print('linear', x.shape)
return x
def forward(self, x):
x = self.conv1(x)
x = self.norm_layer_2d_1(x)
x = F.relu(x)
x = self.sdo1(x)
x = self.conv2(x)
x = self.norm_layer_2d_2(x)
x = F.relu(x)
x = self.sdo2(x)
x = self.conv3(x)
x = self.norm_layer_2d_3(x)
x = F.relu(x)
x = self.sdo3(x)
x = self.conv4(x)
x = self.norm_layer_2d_4(x)
x = F.relu(x)
x = self.sdo4(x)
x = self.conv5(x)
x = self.norm_layer_2d_5(x)
x = F.relu(x)
x = self.sdo5(x)
x = self.conv6(x)
x = self.norm_layer_2d_6(x)
x = F.relu(x)
x = so3_integrate(x)
x = self.fc_layer(x)
x = self.norm_1d_1(x)
x = F.relu(x)
x = self.do1(x)
x = self.fc_layer_2(x)
x = self.norm_1d_2(x)
x = F.relu(x)
x = self.do2(x)
x = self.fc_layer_3(x)
x = self.norm_1d_3(x)
x = F.relu(x)
x = self.do3(x)
x = self.fc_layer_4(x)
x = self.norm_1d_4(x)
x = F.relu(x)
x = self.do4(x)
x = self.fc_layer_5(x)
x = self.norm_1d_5(x)
x = F.relu(x)
x = self.do5(x)
x = self.fc_layer_6(x)
x = torch.sigmoid(x)
return x
def forward(self, x):
"""
:param x: list( Tensor([B, 2b0, 2b0]) * num_grids )
"""
# S2 Conv
x = [
self.conv0_0(x[0]), # -> [B, f1, 2b1, 2b1, 2b1] * num_grids
self.conv0_1(x[1]),
self.conv0_2(x[2])
]
x = [F.relu(x[0]), F.relu(x[1]), F.relu(x[2])]
x = [self.bn0_0(x[0]), self.bn0_1(x[1]), self.bn0_2(x[2])]
# SO3 Conv
x = [
self.conv1_0(x[0]), # -> [B, f2, 2b2, 2b2, 2b2] * num_grids
self.conv1_1(x[1]),
self.conv1_2(x[2])
]
x = [F.relu(x[i]) for i in range(len(x))]
x = [self.bn1_0(x[0]), self.bn1_1(x[1]), self.bn1_2(x[2])]
x = [
self.conv2_0(x[0]), # -> [B, f3, 2b3, 2b3, 2b3] * num_grids
self.conv2_1(x[1]),
self.conv2_2(x[2])
]
x = [F.relu(x[i]) for i in range(len(x))]
x = [self.bn2_0(x[0]), self.bn2_1(x[1]), self.bn2_2(x[2])]
x = [
self.conv3_0(x[0]), # -> [B, f4, 2b4, 2b4, 2b4] * num_grids
self.conv3_1(x[1]),
self.conv3_2(x[2])
]
x = [F.relu(x[i]) for i in range(len(x))]
x = [self.bn3_0(x[0]), self.bn3_1(x[1]), self.bn3_2(x[2])]
x = [so3_integrate(x[i])
for i in range(len(x))] # -> (B, f4) * num_grids
x = [x[i].unsqueeze(0) for i in range(len(x))]
x = torch.cat(tuple(x), dim=0) # -> (num_grids, B, f4)
N, B, C = x.shape
x = x.permute(1, 2, 0) # -> (B, f4, num_grids)
x = torch.mul(x, torch.sigmoid(self.weights)) # -> (B, f4, num_grids)
x = torch.sum(x, dim=-1, keepdim=False) # -> (B, f4)
x = self.out_layer(x)
return x
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = so3_integrate(x)
x = self.out_layer(x)
return x
def forward(self, x): # pylint: disable=W0221
x = self.sequential(x) # [batch, feature, beta, alpha, gamma]
x = so3_integrate(x) # [batch, feature]
if self.dropout:
x = self.out_layer(self.dropout(x))
else:
x = self.out_layer(x)
#return F.log_softmax(x, dim=1)
"""
if x.shape[0]>1:
x=self.bn(x)#normalization
"""
return x
def forward(self, x):
x = self.norm_layer_2d_1(x)
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = self.conv3(x)
x = F.relu(x)
if (self.if_so3_4):
x = self.conv4(x)
x = F.relu(x)
if (self.if_so3_5):
x = self.conv5(x)
x = F.relu(x)
x = so3_integrate(x)
#print(x.shape)
x = self.fc_layer(x)
x = self.norm_1d_1(x)
x = F.relu(x)
x = self.do1(x)
x = self.fc_layer_2(x)
x = self.norm_1d_2(x)
x = F.relu(x)
x = self.do2(x)
x = self.fc_layer_3(x)
x = self.norm_1d_3(x)
x = F.relu(x)
x = self.do3(x)
if (self.if_fc_4):
x = self.fc_layer_4(x)
x = self.norm_1d_4(x)
x = F.relu(x)
x = self.do4(x)
if (self.if_fc_5):
x = self.fc_layer_5(x)
x = self.norm_1d_5(x)
x = F.relu(x)
x = self.do5(x)
x = self.fc_layer_6(x)
x = self.norm_1d_6(x)
x = F.sigmoid(x)
return x
def forward(self, x):
conv1 = self.conv1(x)
relu1 = F.relu(conv1)
conv2 = self.conv2(relu1)
relu2 = F.relu(conv2) ###
in_data = so3_integrate(
relu2) # -> (B * 512, 40), get rid of the (2b, 2b, 2b)
in_reshape = in_data.reshape(self.batch_size, self.num_points,
self.f2) # (B, 512, L)
in_reshape = in_reshape.transpose_(2, 1) # -> (B, L, 512)
pool3 = self.maxPool(in_reshape) # -> (B, L)
pool3 = pool3.squeeze() # -> (B, L)
# conv3 = self.conv3(in_reshape) # (B, 1, L) -> (B, 10, L'), L' = num_points * f2 - kernel_size + 1
# relu3 = F.relu(conv3)
# bn3 = self.bn3(relu3)
# bn3_reshape = bn3.reshape((self.batch_size, -1)) # (B, 10 * L')
# output = self.out_layer(bn3_reshape)
output = self.out_layer(pool3)
# x = so3_integrate(x)
# x = self.out_layer(x)
# return x
return output
def forward(self, x):
x = self.convolutional(x)
x = so3_integrate(x)
x = self.linear(x)
return x
def forward(self, data):
"""
:param x: list( list( Tensor([B, 2b, 2b]) * num_grids ) * num_centers)
"""
features = []
shells = list([list(), list(), list()])
for x in data:
# S2 Conv
x = [
self.conv0_0(x[0]), # -> [B, f1, 2b1, 2b1, 2b1] * num_grids
self.conv0_1(x[1]),
self.conv0_2(x[2])
]
x = [F.relu(x[0]), F.relu(x[1]), F.relu(x[2])]
x = [self.bn0_0(x[0]), self.bn0_1(x[1]), self.bn0_2(x[2])]
# SO3 Conv
x = [
self.conv1_0(x[0]), # -> [B, f2, 2b2, 2b2, 2b2] * num_grids
self.conv1_1(x[1]),
self.conv1_2(x[2])
]
x = [F.relu(x[i]) for i in range(len(x))]
x = [self.bn1_0(x[0]), self.bn1_1(x[1]), self.bn1_2(x[2])]
x = [
self.conv2_0(x[0]), # -> [B, f3, 2b3, 2b3, 2b3] * num_grids
self.conv2_1(x[1]),
self.conv2_2(x[2])
]
x = [F.relu(x[i]) for i in range(len(x))]
x = [self.bn2_0(x[0]), self.bn2_1(x[1]), self.bn2_2(x[2])]
x = [
self.conv3_0(x[0]), # -> [B, f4, 2b4, 2b4, 2b4] * num_grids
self.conv3_1(x[1]),
self.conv3_2(x[2])
]
x = [F.relu(x[i]) for i in range(len(x))]
x = [self.bn3_0(x[0]), self.bn3_1(x[1]), self.bn3_2(x[2])]
x = [
self.conv4_0(x[0]), # -> [B, f5, 2b5, 2b5, 2b5] * num_grids
self.conv4_1(x[1]),
self.conv4_2(x[2])
]
x = [F.relu(x[i]) for i in range(len(x))]
x = [self.bn4_0(x[0]), self.bn4_1(x[1]), self.bn4_2(x[2])]
x = [so3_integrate(x[i])
for i in range(len(x))] # -> (B, f5) * num_grids
x = [x[i].unsqueeze(0) for i in range(len(x))]
x = torch.cat(tuple(x), dim=0) # -> (num_grids, B, f5)
features.append(x)
x = torch.stack(features, dim=1) # -> [num_grids, num_centers, B, f5]
x = x.permute(2, 3, 0, 1) # -> [B, f5, num_grids, num_centers]
x, _ = torch.max(x, dim=-1) # -> [B, f5, num_grids]
x = torch.mul(x, torch.sigmoid(self.weights)) # -> (B, f5, num_grids)
x = torch.sum(x, dim=-1, keepdim=False) # -> (B, f5)
x = self.out_layer(x)
return x
def forward(self, x): # pylint: disable=W0221
x = self.sequential(x) # [batch, feature, beta, alpha, gamma]
x = so3_integrate(x) # [batch, feature]
return x
def forward(self, x):
# Convolutional part
# Input: PMT hitmap (theta, phi)
# Output: feature map (alpha, beta, gamma)
x = self.conv1(x)
x = self.norm_layer_2d_1(x)
x = F.relu(x)
x = self.sdo1(x)
x = self.conv2(x)
x = self.norm_layer_2d_2(x)
x = F.relu(x)
x = self.sdo2(x)
x = self.conv3(x)
x = self.norm_layer_2d_3(x)
x = F.relu(x)
x = self.sdo3(x)
x = self.conv4(x)
x = self.norm_layer_2d_4(x)
x = F.relu(x)
x = self.sdo4(x)
x = self.conv5(x)
x = self.norm_layer_2d_5(x)
x = F.relu(x)
x = self.sdo5(x)
x = self.conv6(x)
x = self.norm_layer_2d_6(x)
x = F.relu(x)
# Integrate along euler angle space
x = so3_integrate(x)
# Fully connected part
x = self.fc_layer(x)
x = self.norm_1d_1(x)
x = F.relu(x)
x = self.do1(x)
x = self.fc_layer_2(x)
x = self.norm_1d_2(x)
x = F.relu(x)
x = self.do2(x)
x = self.fc_layer_3(x)
x = self.norm_1d_3(x)
x = F.relu(x)
x = self.do3(x)
x = self.fc_layer_4(x)
x = self.norm_1d_4(x)
x = F.relu(x)
x = self.do4(x)
x = self.fc_layer_5(x)
x = self.norm_1d_5(x)
x = F.relu(x)
x = self.do5(x)
x = self.fc_layer_6(x)
x = torch.sigmoid(x)
return x
def forward(self, x): # pylint: disable=W0221
x = self.sequential(x) # [batch, feature, beta, alpha, gamma]
x = so3_integrate(x) # [batch, feature]
x = self.out_layer(x)
return F.log_softmax(x, dim=1)
