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Commit 1f92d285 authored by Maciej Wielgosz's avatar Maciej Wielgosz
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updated shape in the model for dgcnn classification on shapeNet

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.gitignore
+ 3
− 1
View file @ 1f92d285
......@@ -28,4 +28,6 @@ lightning_logs
cifar-10-batches-py
*.ckpt
*.gz
MNIST
\ No newline at end of file
MNIST
results
ModelNet10
\ No newline at end of file
dgcnn/dgcnn_main.py
+ 0
− 1
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......@@ -16,7 +16,6 @@ def main():
print(input_tensor.shape)
out = dgcnn(input_tensor)
print(out.shape)
print(out)
if __name__ == '__main__':
main()
dgcnn/edge_conv_layer_run.ipynb
+ 7
− 7
View file @ 1f92d285
%% Cell type:code id: tags:
``` python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
class EdgeConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(EdgeConv, self).__init__()
self.in_channels = in_channels
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
nn.Conv2d(2*in_channels, out_channels, kernel_size=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
def forward(self, x, k=20):
#batch_size, num_points, in_channels
batch_size, num_points, feature_dim = x.shape
x = x.view(batch_size, num_points,feature_dim )
knn_indices = self.knn(x, k)
knn_gathered = self.gather_neighbors(x, knn_indices)
edge_features = torch.cat((knn_gathered - x.unsqueeze(2).repeat(1,1,k,1), knn_gathered), dim=1)
edge_features = edge_features.view(batch_size, feature_dim, 2*num_points, k)
edge_features = edge_features.view(batch_size, 2*feature_dim, num_points, k)
return self.conv(edge_features).transpose(2, 1)
@staticmethod
def knn(x, k):
"""Find the indices of the k nearest neighbors for each point in the input tensor."""
batch_size, num_points, _ = x.shape
x_expanded = x.unsqueeze(2).expand(-1, -1, num_points, -1)
x_transposed = x.unsqueeze(1).expand(-1, num_points, -1, -1)
distances = torch.norm(x_expanded - x_transposed, dim=-1)
_, indices = distances.topk(k=k, dim=-1, largest=False)
return indices
@staticmethod
def gather_neighbors(x, knn_indices):
def gather_neighbors(self, x, knn_indices):
batch_size, num_points, _ = x.shape
_, _, k = knn_indices.shape
x_expanded = x.unsqueeze(2).repeat(1, 1, num_points, 1)
neighbors = torch.gather(x_expanded, 2, knn_indices.view(batch_size, num_points, k, 1).repeat(1, 1, 1, 3))
neighbors = torch.gather(x_expanded, 2, knn_indices.view(batch_size, num_points, k, 1).repeat(1, 1, 1, self.in_channels))
return neighbors
# Generate a sample input vector
batch_size = 1
in_channels = 3
num_points = 1024
point_cloud = torch.randn(batch_size, in_channels, num_points)
# Create an EdgeConv layer
out_channels = 32 # You can choose the desired number of output channels
edge_conv_layer = EdgeConv(in_channels, out_channels)
edge_conv_layer.eval()
# Pass the sample input vector through the EdgeConv layer
k = 10
output = edge_conv_layer(point_cloud.view(batch_size, num_points, in_channels), k)
print("Output shape:", output.shape)
```
%% Output
Output shape: torch.Size([1, 2048, 32, 10])
Output shape: torch.Size([1, 1024, 32, 10])
%% Cell type:code id: tags:
``` python
def knn(x, k):
inner = -2 * torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x ** 2, dim=1, keepdim=True)
pairwise_distance = -xx - inner - xx.transpose(2, 1)
knn_indices = pairwise_distance.topk(k=k, dim=-1)[1]
return knn_indices
import torch
# Set sample input parameters
batch_size = 2
num_points = 1024
feature_dim = 3
k = 20
# Create a random input tensor
x = torch.randn(batch_size, num_points, feature_dim)
print("Sample input vector x:")
print(x.shape)
# Compute the k nearest neighbors
knn_indices = knn(x, k)
print("k nearest neighbors indices:")
print(knn_indices.shape)
```
%% Cell type:code id: tags:
``` python
import torch
def knn(x, k):
"""Find the indices of the k nearest neighbors for each point in the input tensor."""
batch_size, num_points, _ = x.shape
x_expanded = x.unsqueeze(2).expand(-1, -1, num_points, -1)
x_transposed = x.unsqueeze(1).expand(-1, num_points, -1, -1)
distances = torch.norm(x_expanded - x_transposed, dim=-1)
_, indices = distances.topk(k=k, dim=-1, largest=False)
return indices
# Set sample input parameters
batch_size = 1
num_points = 1024
feature_dim = 3
k = 20
# Create a random input tensor
x = torch.randn(batch_size, num_points, feature_dim)
print("Sample input vector x:")
print(x.shape)
# Compute the k nearest neighbors
knn_indices = knn(x, k)
print("k nearest neighbors indices:")
print(knn_indices.shape)
```
%% Output
Sample input vector x:
torch.Size([1, 1024, 3])
k nearest neighbors indices:
torch.Size([1, 1024, 20])
%% Cell type:code id: tags:
``` python
x_expanded = x.unsqueeze(2).repeat(1, 1, num_points, 1)
print("x_expanded:", x_expanded.shape)
print(x_expanded[0, 0, 0, :])
print(x[0, 0, :])
```
%% Output
x_expanded: torch.Size([1, 1024, 1024, 3])
tensor([-0.7148, -0.1634, -1.3527])
tensor([-0.7148, -0.1634, -1.3527])
%% Cell type:code id: tags:
``` python
print('knn_indices:', knn_indices.shape)
# Gather the neighbors
neighbors = torch.gather(x_expanded, 2, knn_indices.view(batch_size, num_points, k, 1).repeat(1, 1, 1, 3))
print("neighbors:", neighbors.shape)
```
%% Output
knn_indices: torch.Size([1, 1024, 20])
neighbors: torch.Size([1, 1024, 20, 3])
......
dgcnn/model.py
+ 26
− 7
View file @ 1f92d285
......@@ -11,20 +11,21 @@ class EdgeConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(EdgeConv, self).__init__()
self.in_channels = in_channels
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
nn.Conv2d(2*in_channels, out_channels, kernel_size=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU() # TODO: replace with leaky relu
nn.ReLU()
)
def forward(self, x, k=20):
#batch_size, num_points, in_channels
batch_size, num_points, feature_dim = x.shape
x = x.view(batch_size, num_points,feature_dim )
knn_indices = self.knn(x, k)
knn_gathered = self.gather_neighbors(x, knn_indices)
edge_features = torch.cat((knn_gathered - x.unsqueeze(2).repeat(1,1,k,1), knn_gathered), dim=1)
edge_features = edge_features.view(batch_size, feature_dim, 2*num_points, k)
edge_features = edge_features.view(batch_size, 2*feature_dim, num_points, k)
return self.conv(edge_features).transpose(2, 1)
@staticmethod
......@@ -93,6 +94,12 @@ class DGCNN(nn.Module):
self.edge_conv2 = EdgeConv(64, 128)
self.edge_conv3 = EdgeConv(128, 256)
self.edge_conv4 = EdgeConv(256, 512)
self.bn5 = nn.BatchNorm1d(1024)
self.conv5 = nn.Sequential(nn.Conv1d(512, 1024, kernel_size=1, bias=False),
self.bn5,
nn.LeakyReLU(negative_slope=0.2))
self.linear1 = nn.Linear(2048, 512, bias=False)
self.fc = nn.Sequential(
nn.Linear(512, 256),
nn.BatchNorm1d(256),
......@@ -107,17 +114,29 @@ class DGCNN(nn.Module):
def forward(self, x):
# Apply Transform_Net on input point cloud
batch_size = x.size(0)
trans_matrix = self.transform_net(x)
x = torch.bmm(x, trans_matrix)
x1 = self.edge_conv1(x)
x1 = x1.max(dim=-1, keepdim=False)[0]
# print("x1 shape: ", x1.shape)
x2 = self.edge_conv2(x1)
x2 = x2.max(dim=-1, keepdim=False)[0]
# print("x2 shape: ", x2.shape)
x3 = self.edge_conv3(x2)
x3 = x3.max(dim=-1, keepdim=False)[0]
# print("x3 shape: ", x3.shape)
x4 = self.edge_conv4(x3)
x4 = x4.max(dim=-1, keepdim=False)[0]
x = torch.max(x4, dim=1, keepdim=True)[0]
x = self.fc(x.squeeze(1))
return x
\ No newline at end of file
# print("x4 shape: ", x4.shape)
# x5 = torch.cat((x1, x2, x3, x4), dim=1) # (batch_size, 64+64+128+256, num_points)
# x6 = F.adaptive_max_pool1d(x5, 1).view(batch_size, -1) # (batch_size, emb_dims, num_points) -> (batch_size, emb_dims)
# x7 = F.adaptive_avg_pool1d(x5, 1).view(batch_size, -1) # (batch_size, emb_dims, num_points) -> (batch_size, emb_dims)
# x8 = torch.cat((x6, x7), 1) # (batch_size, emb_dims*2)
# x8 = F.leaky_relu(self.bn6(self.linear1(x8)), negative_slope=0.2) # (batch_size, emb_dims*2) -> (batch_size, 512)
x9 = torch.max(x4, dim=1, keepdim=True)[0]
x10 = self.fc(x9.squeeze(1))
return x10
\ No newline at end of file
point_net_autoencoder/shapenet_data.py
+ 14
− 4
View file @ 1f92d285
......@@ -16,7 +16,7 @@ class ShapenetData(object):
small_data=False,
small_data_size=10,
just_one_class=False,
norm=True,
norm=False,
data_augmentation=False
) -> None:
......@@ -115,6 +115,9 @@ class ShapenetData(object):
elif self.split == 'val':
point_set = np.loadtxt(self.val_data_file[index]).astype(np.float32)
# get labels
labels = point_set[:, 6]
# get just the points
point_set = point_set[:, 0:3]
......@@ -122,14 +125,18 @@ class ShapenetData(object):
if self.norm:
point_set = self.normalize(point_set)
# choice = np.random.choice(len(point_set), self.npoints, replace=True)
choice = np.random.choice(len(point_set), self.npoints, replace=True)
# chose the first npoints
choice = np.arange(self.npoints)
point_set = point_set[choice, :]
point_set = point_set.astype(np.float32)
return point_set
# get the labels
labels = labels[choice]
labels = labels.astype(np.int64)
return point_set, labels
def __len__(self):
if self.split == 'train':
......@@ -142,7 +149,10 @@ class ShapenetData(object):
if __name__ == "__main__":
shapenet_data = ShapenetData(root='/home/nibio/mutable-outside-world/code/oracle_gpu_runs/data/shapenet', split='train')
shapenet_data = ShapenetData(
root='/home/nibio/mutable-outside-world/code/oracle_gpu_runs/data/shapenet',
split='train'
)
# print(shapenet_data.train_file_list)
print(shapenet_data.get_seg_classes('Car'))
print(shapenet_data.get_class_names())
......
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