Thank you. Is this what you are asking for?
for param_tensor in model.state_dict():
            print(param_tensor, "\t", model.state_dict()[param_tensor].size())

Model's state_dict:
layer1.0.weight 	 torch.Size([64, 3, 3, 3])
layer1.0.bias 	 torch.Size([64])
layer1.2.weight 	 torch.Size([64])
layer1.2.bias 	 torch.Size([64])
layer1.2.running_mean 	 torch.Size([64])
layer1.2.running_var 	 torch.Size([64])
layer1.2.num_batches_tracked 	 torch.Size([])
layer2.0.weight 	 torch.Size([128, 64, 3, 3])
layer2.0.bias 	 torch.Size([128])
layer2.2.weight 	 torch.Size([128])
layer2.2.bias 	 torch.Size([128])
layer2.2.running_mean 	 torch.Size([128])
layer2.2.running_var 	 torch.Size([128])
layer2.2.num_batches_tracked 	 torch.Size([])
layer3.0.weight 	 torch.Size([128, 128, 3, 3])
layer3.0.bias 	 torch.Size([128])
layer3.2.weight 	 torch.Size([128])
layer3.2.bias 	 torch.Size([128])
layer3.2.running_mean 	 torch.Size([128])
layer3.2.running_var 	 torch.Size([128])
layer3.2.num_batches_tracked 	 torch.Size([])
fc1.weight 	 torch.Size([1000, 2408448])
fc1.bias 	 torch.Size([1000])
fc2.weight 	 torch.Size([10, 1000])
fc2.bias 	 torch.Size([10])