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Fix issue #80: model architecture #85

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Fix issue #80: model architecture #85

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16 changes: 10 additions & 6 deletions pointnet/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,11 +89,13 @@ def __init__(self, global_feat = True, feature_transform = False):
super(PointNetfeat, self).__init__()
self.stn = STN3d()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.conv2 = torch.nn.Conv1d(64, 64, 1)
self.conv3 = torch.nn.Conv1d(64, 128, 1)
self.conv4 = torch.nn.Conv1d(128, 1024, 1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.bn2 = nn.BatchNorm1d(64)
self.bn3 = nn.BatchNorm1d(128)
self.bn4 = nn.BatchNorm1d(1024)
self.global_feat = global_feat
self.feature_transform = feature_transform
if self.feature_transform:
Expand All @@ -106,6 +108,7 @@ def forward(self, x):
x = torch.bmm(x, trans)
x = x.transpose(2, 1)
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))

if self.feature_transform:
trans_feat = self.fstn(x)
Expand All @@ -116,8 +119,8 @@ def forward(self, x):
trans_feat = None

pointfeat = x
x = F.relu(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
x = F.relu(self.bn3(self.conv3(x)))
x = self.bn4(self.conv4(x))
x = torch.max(x, 2, keepdim=True)[0]
x = x.view(-1, 1024)
if self.global_feat:
Expand Down Expand Up @@ -211,3 +214,4 @@ def feature_transform_regularizer(trans):
seg = PointNetDenseCls(k = 3)
out, _, _ = seg(sim_data)
print('seg', out.size())