diff --git a/forward_UpDimV2_long.py b/forward_UpDimV2_long.py
index 945056c3e0307740066aff175d7f63957e1790cc..f941d4f0aa1f882552ad4f33e55215becf70c664 100644
--- a/forward_UpDimV2_long.py
+++ b/forward_UpDimV2_long.py
@@ -17,131 +17,130 @@ from tqdm import tqdm, trange
from math import ceil
+class UpDimV2(torch.nn.Module):
+ def __init__(self, num_class):
+ super(UpDimV2, self).__init__()
+ self.activation = torch.nn.LeakyReLU(0.001, inplace=True)
+
+ # Block 1D 1
+ self.conv11 = torch.nn.Conv1d(1, 32, 3, 1, 1)
+ self.norm11 = torch.nn.BatchNorm1d(32)
+ self.conv21 = torch.nn.Conv1d(32, 32, 3, 2, 1)
+ self.norm21 = torch.nn.BatchNorm1d(32)
+ self.skip11 = torch.nn.Conv1d(1, 32, 1, 2)
+
+ # Block 1D 2
+ self.conv12 = torch.nn.Conv1d(32, 64, 3, 2, 1)
+ self.norm12 = torch.nn.BatchNorm1d(64)
+ self.conv22 = torch.nn.Conv1d(64, 128, 3, 2, 1)
+ self.norm22 = torch.nn.BatchNorm1d(128)
+ self.skip12 = torch.nn.Conv1d(32, 128, 1, 4)
+
+ # Block 2D 1
+ self.conv31 = torch.nn.Conv2d(1, 32, 3, 1, 1)
+ self.norm31 = torch.nn.BatchNorm2d(32)
+ self.conv41 = torch.nn.Conv2d(32, 32, 3, 2, 1)
+ self.norm41 = torch.nn.BatchNorm2d(32)
+ self.skip21 = torch.nn.Conv2d(1, 32, 1, 2)
+
+ # Block 2D 2
+ self.conv32 = torch.nn.Conv2d(32, 64, 3, 2, 1)
+ self.norm32 = torch.nn.BatchNorm2d(64)
+ self.conv42 = torch.nn.Conv2d(64, 128, 3, 2, 1)
+ self.norm42 = torch.nn.BatchNorm2d(128)
+ self.skip22 = torch.nn.Conv2d(32, 128, 1, 4)
+
+ # Block 3D 1
+ self.conv51 = torch.nn.Conv3d(1, 32, 3, (1, 2, 1), 1)
+ self.norm51 = torch.nn.BatchNorm3d(32)
+ self.conv61 = torch.nn.Conv3d(32, 64, 3, 2, 1)
+ self.norm61 = torch.nn.BatchNorm3d(64)
+ self.skip31 = torch.nn.Conv3d(1, 64, 1, (2, 4, 2))
+
+ # Block 3D 2
+ self.conv52 = torch.nn.Conv3d(64, 128, 3, 2, 1)
+ self.norm52 = torch.nn.BatchNorm3d(128)
+ self.conv62 = torch.nn.Conv3d(128, 256, 3, 2, 1)
+ self.norm62 = torch.nn.BatchNorm3d(256)
+ self.skip32 = torch.nn.Conv3d(64, 256, 1, 4)
+
+ # Fully connected
+ self.soft_max = torch.nn.Softmax(-1) # If the time stride is too big, the softmax will be done on a singleton
+ # which always ouput a 1
+ self.fc1 = torch.nn.Linear(4096, 1024)
+ self.fc2 = torch.nn.Linear(1024, 512)
+ self.fc3 = torch.nn.Linear(512, num_class)
+
+ def forward(self, x):
+ # Block 1D 1
+ out = self.conv11(x)
+ out = self.norm11(out)
+ out = self.activation(out)
+ out = self.conv21(out)
+ out = self.norm21(out)
+ skip = self.skip11(x)
+ out = self.activation(out + skip)
+
+ # Block 1D 2
+ skip = self.skip12(out)
+ out = self.conv12(out)
+ out = self.norm12(out)
+ out = self.activation(out)
+ out = self.conv22(out)
+ out = self.norm22(out)
+ out = self.activation(out + skip)
+
+ # Block 2D 1
+ out = out.reshape((lambda b, c, h: (b, 1, c, h))(*out.shape))
+ skip = self.skip21(out)
+ out = self.conv31(out)
+ out = self.norm31(out)
+ out = self.activation(out)
+ out = self.conv41(out)
+ out = self.norm41(out)
+ out = self.activation(out + skip)
+
+ # Block 2D 2
+ skip = self.skip22(out)
+ out = self.conv32(out)
+ out = self.norm32(out)
+ out = self.activation(out)
+ out = self.conv42(out)
+ out = self.norm42(out)
+ out = self.activation(out + skip)
+
+ # Block 3D 1
+ out = out.reshape((lambda b, c, w, h: (b, 1, c, w, h))(*out.shape))
+ skip = self.skip31(out)
+ out = self.conv51(out)
+ out = self.norm51(out)
+ out = self.activation(out)
+ out = self.conv61(out)
+ out = self.norm61(out)
+ out = self.activation(out + skip)
+
+ # Block 3D 2
+ skip = self.skip32(out)
+ out = self.conv52(out)
+ out = self.norm52(out)
+ out = self.activation(out)
+ out = self.conv62(out)
+ out = self.norm62(out)
+ out = self.activation(out + skip)
+
+ # Fully connected
+ out = torch.max(self.soft_max(out), -1)[0].reshape(-1, 4096)
+ out = self.activation(self.fc1(out))
+ out = self.activation(self.fc2(out))
+ return self.fc3(out)
+
+
def main(args):
batch_size = 64
num_feature = 4096
num_classes = 10
rng = np.random.RandomState(42)
- class UpDimV2(torch.nn.Module):
-
- def __init__(self, num_class):
- super(UpDimV2, self).__init__()
- self.activation = torch.nn.LeakyReLU(0.001, inplace=True)
-
- # Block 1D 1
- self.conv11 = torch.nn.Conv1d(1, 32, 3, 1, 1)
- self.norm11 = torch.nn.BatchNorm1d(32)
- self.conv21 = torch.nn.Conv1d(32, 32, 3, 2, 1)
- self.norm21 = torch.nn.BatchNorm1d(32)
- self.skip11 = torch.nn.Conv1d(1, 32, 1, 2)
-
- # Block 1D 2
- self.conv12 = torch.nn.Conv1d(32, 64, 3, 2, 1)
- self.norm12 = torch.nn.BatchNorm1d(64)
- self.conv22 = torch.nn.Conv1d(64, 128, 3, 2, 1)
- self.norm22 = torch.nn.BatchNorm1d(128)
- self.skip12 = torch.nn.Conv1d(32, 128, 1, 4)
-
- # Block 2D 1
- self.conv31 = torch.nn.Conv2d(1, 32, 3, 1, 1)
- self.norm31 = torch.nn.BatchNorm2d(32)
- self.conv41 = torch.nn.Conv2d(32, 32, 3, 2, 1)
- self.norm41 = torch.nn.BatchNorm2d(32)
- self.skip21 = torch.nn.Conv2d(1, 32, 1, 2)
-
- # Block 2D 2
- self.conv32 = torch.nn.Conv2d(32, 64, 3, 2, 1)
- self.norm32 = torch.nn.BatchNorm2d(64)
- self.conv42 = torch.nn.Conv2d(64, 128, 3, 2, 1)
- self.norm42 = torch.nn.BatchNorm2d(128)
- self.skip22 = torch.nn.Conv2d(32, 128, 1, 4)
-
- # Block 3D 1
- self.conv51 = torch.nn.Conv3d(1, 32, 3, (1, 2, 1), 1)
- self.norm51 = torch.nn.BatchNorm3d(32)
- self.conv61 = torch.nn.Conv3d(32, 64, 3, 2, 1)
- self.norm61 = torch.nn.BatchNorm3d(64)
- self.skip31 = torch.nn.Conv3d(1, 64, 1, (2, 4, 2))
-
- # Block 3D 2
- self.conv52 = torch.nn.Conv3d(64, 128, 3, 2, 1)
- self.norm52 = torch.nn.BatchNorm3d(128)
- self.conv62 = torch.nn.Conv3d(128, 256, 3, 2, 1)
- self.norm62 = torch.nn.BatchNorm3d(256)
- self.skip32 = torch.nn.Conv3d(64, 256, 1, 4)
-
- # Fully connected
- self.soft_max = torch.nn.Softmax(-1) # If the time stride is too big, the softmax will be done on a singleton
- # which always ouput a 1
- self.fc1 = torch.nn.Linear(4096, 1024)
- self.fc2 = torch.nn.Linear(1024, 512)
- self.fc3 = torch.nn.Linear(512, num_class)
-
- def forward(self, x):
- # Block 1D 1
- out = self.conv11(x)
- out = self.norm11(out)
- out = self.activation(out)
- out = self.conv21(out)
- out = self.norm21(out)
- skip = self.skip11(x)
- out = self.activation(out + skip)
-
- # Block 1D 2
- skip = self.skip12(out)
- out = self.conv12(out)
- out = self.norm12(out)
- out = self.activation(out)
- out = self.conv22(out)
- out = self.norm22(out)
- out = self.activation(out + skip)
-
- # Block 2D 1
- out = out.reshape((lambda b, c, h: (b, 1, c, h))(*out.shape))
- skip = self.skip21(out)
- out = self.conv31(out)
- out = self.norm31(out)
- out = self.activation(out)
- out = self.conv41(out)
- out = self.norm41(out)
- out = self.activation(out + skip)
-
- # Block 2D 2
- skip = self.skip22(out)
- out = self.conv32(out)
- out = self.norm32(out)
- out = self.activation(out)
- out = self.conv42(out)
- out = self.norm42(out)
- out = self.activation(out + skip)
-
- # Block 3D 1
- out = out.reshape((lambda b, c, w, h: (b, 1, c, w, h))(*out.shape))
- skip = self.skip31(out)
- out = self.conv51(out)
- out = self.norm51(out)
- out = self.activation(out)
- out = self.conv61(out)
- out = self.norm61(out)
- out = self.activation(out + skip)
-
- # Block 3D 2
- skip = self.skip32(out)
- out = self.conv52(out)
- out = self.norm52(out)
- out = self.activation(out)
- out = self.conv62(out)
- out = self.norm62(out)
- out = self.activation(out + skip)
-
- # Fully connected
- out = torch.max(self.soft_max(out), -1)[0].reshape(-1, 4096)
- out = self.activation(self.fc1(out))
- out = self.activation(self.fc2(out))
- return self.fc3(out)
-
-
model = torch.nn.DataParallel(UpDimV2(num_classes))
model.load_state_dict((torch.load(args.weight)['model']))
model.to('cuda')