ML_RealBogus_Rubin

This model has been pushed to the Hub using the PytorchModelHubMixin integration:

• Code:

  How to load each version of the model?

  Each version tag (v0.1, v0.2, v0.3) match the versioning schema introduced and described in DMTN-337

  import torch
  import torch.nn as nn
  from huggingface_hub import PyTorchModelHubMixin
  
  class TACCNN(nn.Module,PyTorchModelHubMixin):
    def __init__(self, input_shape=(3, 51, 51)):
        super(TACCNN, self).__init__()
        self.conv1 = nn.Conv2d(input_shape[0], 16, kernel_size=5, stride=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=5, stride=1)
        self.dropout1 = nn.Dropout(0.4)
        self.dropout2 = nn.Dropout(0.4)
        self.dropout3 = nn.Dropout(0.4)
  
        dummy_input = torch.randn(1, input_shape[0], input_shape[1], input_shape[2])
        self.forward_conv(dummy_input)
        self.fc1 = nn.Linear(self.num_shape[1], 32)
        self.fc2 = nn.Linear(32, 1)
  
    def forward_conv(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = torch.flatten(x, 1)
        self.num_shape = x.size()
  
    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.dropout1(x)
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.dropout2(x)
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = self.dropout3(x)
        x = torch.flatten(x, 1)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.sigmoid(x).squeeze(1)
  
  # load v01
  print('v01')
  model = TACCNN.from_pretrained("taceroc/test_rubin_model", revision='v0.1')
  model.eval()
  print(model)
  torch.manual_seed(2)
  print(model(torch.rand(1,3, 51, 51)))
  
  # load v02
  print('v02')
  model = TACCNN.from_pretrained("taceroc/test_rubin_model", revision='v0.2')
  model.eval()
  print(model)
  torch.manual_seed(2)
  print(model(torch.rand(1,3, 51, 51)))
  
  # load v03
  print('v03')
  model = TACCNN.from_pretrained("taceroc/test_rubin_model", revision='v0.3')
  model.eval()
  print(model)
  torch.manual_seed(2)
  print(model(torch.rand(1,3, 51, 51)))
  

• Paper: [More Information Needed]

• Docs: [More Information Needed]