bugfix

main.py

@@ -320,7 +320,7 @@ def train(self, epoch, save_model=False):
             k: '{:02d}%'.format(int(round(v * 100 / sum(timer.values()))))
             for k, v in timer.items()
         }
-        self.print_log(f'\tTraining loss: {np.mean(loss_value):.4f}.  Training acc: {np.mean(acc_value)*100):.2f}%.')
+        self.print_log(f'\tTraining loss: {np.mean(loss_value):.4f}.  Training acc: {np.mean(acc_value)*100:.2f}%.')
         self.print_log(f'\tTime consumption: [Data]{proportion["dataloader"]}, [Network]{proportion["model"]}')
 
         if save_model:

model/infogcn.py

@@ -9,9 +9,6 @@
 from torch import linalg as LA
 
 from model.ms_tcn import MultiScale_TemporalConv as MS_TCN
-from model.ms_gcn import MultiScale_GraphConv as MS_GCN
-from model.ms_gcn import MultiHead_GraphConv as MH_GCN
-from model.port import MORT
 from einops import rearrange, repeat
 
 from utils import set_parameter_requires_grad, get_vector_property
@@ -37,15 +34,15 @@ def __init__(self, num_class=60, num_point=25, num_person=2, graph=None, in_chan
         self.to_joint_embedding = nn.Linear(in_channels, base_channel)
         self.pos_embedding = nn.Parameter(torch.randn(1, self.num_point, base_channel))
 
-        self.l2 = EncodingBlock(base_channel, base_channel,A, adaptive=adaptive)
-        self.l3 = EncodingBlock(base_channel, base_channel,A, adaptive=adaptive)
-        self.l4 = EncodingBlock(base_channel, base_channel,A, adaptive=adaptive)
-        self.l5 = EncodingBlock(base_channel, base_channel*2, A, stride=2, adaptive=adaptive)
-        self.l6 = EncodingBlock(base_channel*2, base_channel*2, A, adaptive=adaptive)
-        self.l7 = EncodingBlock(base_channel*2, base_channel*2, A, adaptive=adaptive)
-        self.l8 = EncodingBlock(base_channel*2, base_channel*4, A, stride=2, adaptive=adaptive)
-        self.l9 = EncodingBlock(base_channel*4, base_channel*4, A, adaptive=adaptive)
-        self.l10 = EncodingBlock(base_channel*4, base_channel*4, A, adaptive=adaptive)
+        self.l1 = EncodingBlock(base_channel, base_channel,A)
+        self.l2 = EncodingBlock(base_channel, base_channel,A)
+        self.l3 = EncodingBlock(base_channel, base_channel,A)
+        self.l4 = EncodingBlock(base_channel, base_channel*2, A, stride=2)
+        self.l5 = EncodingBlock(base_channel*2, base_channel*2, A)
+        self.l6 = EncodingBlock(base_channel*2, base_channel*2, A)
+        self.l7 = EncodingBlock(base_channel*2, base_channel*4, A, stride=2)
+        self.l8 = EncodingBlock(base_channel*4, base_channel*4, A)
+        self.l9 = EncodingBlock(base_channel*4, base_channel*4, A)
         self.fc = nn.Linear(base_channel*4, base_channel*4)
         self.fc_mu = nn.Linear(base_channel*4, base_channel*4)
         self.fc_logvar = nn.Linear(base_channel*4, base_channel*4)
@@ -89,6 +86,7 @@ def forward(self, x):
 
         x = self.data_bn(x)
         x = rearrange(x, 'n (m v c) t -> (n m) c t v', m=M, v=V).contiguous()
+        x = self.l1(x)
         x = self.l2(x)
         x = self.l3(x)
         x = self.l4(x)
@@ -97,7 +95,6 @@ def forward(self, x):
         x = self.l7(x)
         x = self.l8(x)
         x = self.l9(x)
-        x = self.l10(x)
 
         # N*M,C,T,V
         c_new = x.size(1)

model/modules.py

@@ -120,8 +120,8 @@ def forward(self, x, attn=None):
         A = attn * self.shared_topology.unsqueeze(0)
         for h in range(self.num_head):
             A_h = A[:, h, :, :] # (nt)vv
-            x = rearrange(x, 'n c t v -> (n t) v c')
-            z = A_h@x
+            feature = rearrange(x, 'n c t v -> (n t) v c')
+            z = A_h@feature
             z = rearrange(z, '(n t) v c-> n c t v', t=T).contiguous()
             z = self.conv_d[h](z)
             out = z + out if out is not None else z
@@ -132,9 +132,9 @@ def forward(self, x, attn=None):
 
         return out
 
-class EncodingBlcok(nn.Module):
+class EncodingBlock(nn.Module):
     def __init__(self, in_channels, out_channels, A, stride=1, residual=True):
-        super(EncodingBlcok, self).__init__()
+        super(EncodingBlock, self).__init__()
         self.agcn = SA_GC(in_channels, out_channels, A)
         self.tcn = MS_TCN(out_channels, out_channels, kernel_size=5, stride=stride,
                          dilations=[1, 2], residual=False)