25

conv = model[0]
                
if (batch_normalize):
   bn = model[1]
                    
   #Get the number of weights of Batch Norm Layer
   num_bn_biases = bn.bias.numel()
                    
   #Load the weights
   bn_biases = torch.from_numpy(weights[ptr:ptr + num_bn_biases])
   ptr += num_bn_biases
                    
   bn_weights = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
   ptr  += num_bn_biases
                    
   bn_running_mean = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
   ptr  += num_bn_biases
                    
   bn_running_var = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
   ptr  += num_bn_biases
                    
   #Cast the loaded weights into dims of model weights. 
   bn_biases = bn_biases.view_as(bn.bias.data)
   bn_weights = bn_weights.view_as(bn.weight.data)
   bn_running_mean = bn_running_mean.view_as(bn.running_mean)
   bn_running_var = bn_running_var.view_as(bn.running_var)

   #Copy the data to model
   bn.bias.data.copy_(bn_biases)
   bn.weight.data.copy_(bn_weights)
   bn.running_mean.copy_(bn_running_mean)
   bn.running_var.copy_(bn_running_var)