view model training

models/view_encoder.py

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+import os
+import sys
+
+os.environ["CUDA_VISIBLE_DEVICES"] = '0'
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+
+import torch
+import numpy as np
+from torch import nn, Tensor
+from torch.nn import functional as F
+from functools import partial
+from typing import Any, Callable, List, Optional, Sequence
+
+torch.set_float32_matmul_precision('high')
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+
+
+class ViewEncoder(nn.Module):
+    def __init__(self, bottleneck_size):
+        super(ViewEncoder, self).__init__()
+        self.bottleneck_size = bottleneck_size
+        
+        # Encoder
+        self.encoder = nn.Sequential(
+            nn.Linear(3*3, 64),
+            nn.ReLU(),
+            nn.Linear(64, 128),
+            nn.ReLU(),
+            nn.Linear(128, 256),
+            nn.ReLU(),
+            nn.Linear(256, 512),
+            nn.ReLU(),
+            nn.Linear(512, bottleneck_size),
+            nn.ReLU()
+        )
+
+        # Decoder
+        self.decoder = nn.Sequential(
+            nn.Linear(bottleneck_size, 512),
+            nn.ReLU(),
+            nn.Linear(512, 256),
+            nn.ReLU(),
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Linear(128, 64),
+            nn.ReLU(),
+            nn.Linear(64, 3*3),
+            nn.Tanh(),
+        )
+    
+    def forward(self, x):
+        # Encode
+        matrix = self.encoder(x.view(-1, 3*3))
+        
+        # Decode
+        x = self.decoder(matrix)
+        x = x * np.pi
+        return x.view(-1, 3, 3), matrix
+    
+
+if __name__ == '__main__':
+    # test the model
+    model = ViewEncoder(bottleneck_size=128).to('cuda')
+    # model = torch.compile(model)
+    print(model)
+    print(sum(p.numel() for p in model.parameters() if p.requires_grad))
+    print(sum(p.numel() for p in model.parameters()))
+
+    x = torch.rand((4, 3, 3)).to('cuda')
+    output = model(x)
+
+    # from torchviz import make_dot
+    # graph = make_dot(output, params=dict(model.named_parameters()))
+    # graph.render("model_graph", format="pdf")
+
+    pass