"Class conv_transpose2d is not found" when exporting a pruning-optimized model

I tried pruning optimization (pruning only) for my detection model. I got following error when calling compression_ctrl.export_model()

  File "nncf_pytorch/nncf/compression_method_api.py", line 213, in export_model
    self.prepare_for_export()
  File "nncf_pytorch/nncf/pruning/filter_pruning/algo.py", line 204, in prepare_for_export
    model_pruner.prune_model()
  File "nncf_pytorch/nncf/pruning/export_helpers.py", line 392, in prune_model
    self.mask_propagation()
  File "nncf_pytorch/nncf/pruning/export_helpers.py", line 315, in mask_propagation
    cls = self.get_class_by_type_name(node_type)()
  File "nncf_pytorch/nncf/pruning/export_helpers.py", line 303, in get_class_by_type_name
    raise RuntimeError("Class {} is not found".format(type_name))
RuntimeError: Class conv_transpose2d is not found

Is it a bug or torch.nn.ConvTranspose2d not supported?

Pruning itself seems working judging from the training log of Mask zero %, PR, Filter PR columns printed by print_statistics function is above 0.

[vshampor]

@mkaglins @vanyalzr

[nsk-lab]

@vshampor @vanyalzr

Yes. I could export onnx without error. thx.

But the final pruning rate was low. my setting:

    "params": {
        "schedule": "baseline", // The type of scheduling to use for adjusting the target pruning level. Either `exponential`, `exponential_with_bias`,  or `baseline`, by default it is `baseline`"
        "pruning_init": 0.1, // Initial value of the pruning level applied to the model. 0.0 by default.
        "pruning_target": 0.4, // Target value of the pruning level for the model. 0.5 by default.
        "num_init_steps": 1, // Number of epochs for model pretraining before starting filter pruning. 0 by default.
        "pruning_steps": 4, // Number of epochs during which the pruning rate is increased from `pruning_init` to `pruning_target` value.
        "weight_importance": "L2", // The type of filter importance metric. Can be one of `L1`, `L2`, `geometric_median`. `L2` by default.
        "all_weights": false, // Whether to prune layers independently (choose filters with the smallest importance in each layer separately) or not. `False` by default.
        "prune_first_conv": false, // Whether to prune first Convolutional layers or not. First means that it is a convolutional layer such that there is a path from model input to this layer such that there are no other convolution operations on it. `False` by default.
        "prune_last_conv": false, // Whether to prune last Convolutional layers or not.  Last means that it is a Convolutional layer such that there is a path from this layer to the model output such that there are no other convolution operations on it. `False` by default.
        "prune_downsample_convs": false, // Whether to prune downsample Convolutional layers (with stride > 1) or not. `False` by default.
        "prune_batch_norms": false, // Whether to nullifies parameters of Batch Norm layer corresponds to zeroed filters of convolution corresponding to this Batch Norm. `False` by default.
        "zero_grad": true // Whether to setting gradients corresponding to zeroed filters to zero during training, `True` by default.
    },```

and the log at 5 epoch is

.
. (ommited)
.

+--------+--------+--------+--------+--------+--------+--------+-------+-------+
| XXXXXX | [64,   | [40,   | [64]   | [40]   | 147520 | 92200  | 0.375 | 0.375 |
| XXXX/Y | 256,   | 256,   |        |        |        |        |       |       |
| YYYYYY | 3, 3]  | 3, 3]  |        |        |        |        |       |       |
| YYY[ZZ |        |        |        |        |        |        |       |       |
| Z]/NNC |        |        |        |        |        |        |       |       |
| FConv2 |        |        |        |        |        |        |       |       |
| d[0]   |        |        |        |        |        |        |       |       |
+--------+--------+--------+--------+--------+--------+--------+-------+-------+
| XXXXXX | [64,   | [40,   | [64]   | [40]   | 147520 | 92200  | 0.375 | 0.375 |
| XXXX/Y | 256,   | 256,   |        |        |        |        |       |       |
| YYYYYY | 3, 3]  | 3, 3]  |        |        |        |        |       |       |
| YYY[ZZ |        |        |        |        |        |        |       |       |
| ]/NNCF |        |        |        |        |        |        |       |       |
| Conv2d |        |        |        |        |        |        |       |       |
| [0]    |        |        |        |        |        |        |       |       |
+--------+--------+--------+--------+--------+--------+--------+-------+-------+
INFO:nncf:Final Model Pruning Rate = 0.165
INFO:nncf:Total MAC pruning level = 0.124

0.165 is much lower than pruning target 0.4 Is it due to uncompressed ConvTranspose2D layer?

if so, i really want the support of pruning ConvTranspose2D