Layer and Weights are Ambiguous

[satabios]

When I try to print the layer and the shape of the weights it is quite ambiguous!

print(pruned_model.conv1, pruned_model.conv1.weight.shape

(Conv2d(3, 4, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False),
 torch.Size([64, 3, 7, 7]))

The complete flow can be found below:

pruned_model = copy.deepcopy(resnet)
import torch_pruning as tp

for layer in conv_list:

    
    pruned_model_device = next(iter(pruned_model.parameters())).device
    DG = tp.DependencyGraph().build_dependency(pruned_model, example_inputs=torch.randn(1,3,32,32).to(pruned_model_device))
    # 2. Group coupled layers for model.conv1
    indices = mapped_layers['layer_pruning_indices'][layer]
    indices_to_prune = indices[:int(len(indices)*0.95)]
    layer_name = layer.replace('model','pruned_model')
    layer_for_pruning = eval(layer_name)
    
    # if(isinstance(layer,nn.Conv2d)):
        
    group = DG.get_pruning_group( layer_for_pruning , tp.prune_conv_out_channels, idxs=indices_to_prune)
    if DG.check_pruning_group(group): # avoid full pruning, i.e., channels=0.
        print(layer_name)
        print("Layer:",eval(layer_name),"Shape:",layer_for_pruning.weight.shape,"Indices:",len(indices_to_prune))
        group.prune()
    pruned_model.zero_grad()
    print("Layer:",eval(layer_name),"Shape:",layer_for_pruning.weight.shape,"Indices:",len(indices_to_prune))



    # _  = pruned_model(torch.randn(1,3,32,32))
   
    break
pruned_model.eval()
pruned_model.zero_grad()
print(pruned_model.conv1.weight.shape)