Fixes for failing models

TrainingExtensions/onnx/src/python/aimet_onnx/adaround/adaround_optimizer.py

@@ -149,6 +149,11 @@ def _optimize_rounding(cls, module: ModuleInfo, quantized_input_name,
         use_cache_acts_data = TorchAdaroundOptimizer._can_cache_acts_data(len(cached_dataset), inp_data_torch.shape,
                                                                           out_data_torch.shape)
 
+        attributes = read_attributes_for_op(module)
+        if len(attributes['pad']) > 2:
+            logger.info("Skipping the Convolution layer because padding size of 4 is not supported for optimization")
+            return
+
         if use_cache_acts_data and AdaroundOptimizer.enable_caching_acts_data():
             logger.debug("Caching intermediate activations data for optimization.")
             all_inp_data, all_orig_out_data = act_sampler.sample_and_place_all_acts_on_cpu(cached_dataset)
@@ -266,15 +271,15 @@ def _compute_output_with_adarounded_weights(weights: torch.Tensor, quant_module,
             if 'bias' in quant_module.params:
                 bias = torch.from_numpy(numpy_helper.to_array(quant_module.params['bias'].tensor)).to(device)
             out_data = functional.conv2d(inp_data, adarounded_weights, bias=bias, stride=attributes['strides'],
-                                         dilation=attributes['dilations'], padding=attributes['pads'][0],
+                                         dilation=attributes['dilations'], padding=attributes['pads'],
                                          groups=attributes['group'])
         elif quant_module.type == 'ConvTranspose':
             attributes = read_attributes_for_op(quant_module)
             bias = None
             if 'bias' in quant_module.params:
                 bias = torch.from_numpy(numpy_helper.to_array(quant_module.params['bias'].tensor)).to(device)
             out_data = functional.conv_transpose2d(inp_data, adarounded_weights, bias=bias, stride=attributes['strides'],
-                                                   dilation=attributes['dilations'], padding=attributes['pads'][0],
+                                                   dilation=attributes['dilations'], padding=attributes['pads'],
                                                    groups=attributes['group'])
         elif quant_module.type in ['Gemm', 'MatMul']:
             bias = torch.from_numpy(numpy_helper.to_array(quant_module.params['bias'].tensor)).to(device)

TrainingExtensions/onnx/src/python/aimet_onnx/adaround/adaround_weight.py

@@ -43,6 +43,7 @@
 import json
 from typing import Tuple, Dict, List, Callable
 from onnx import onnx_pb
+from onnxruntime.quantization.onnx_quantizer import ONNXModel
 from tqdm import tqdm
 
 # Import AIMET specific modules
@@ -139,6 +140,8 @@ def apply_adaround(cls, model: onnx_pb.ModelProto, params: AdaroundParameters,
         """
         # pylint: disable=too-many-arguments
         # Create Quant sim with given parameters
+        if not isinstance(model, ONNXModel):
+            model = ONNXModel(model)
         quant_sim = QuantizationSimModel(copy.deepcopy(model), quant_scheme=default_quant_scheme,
                                          default_param_bw=default_param_bw,
                                          config_file=default_config_file)

TrainingExtensions/onnx/src/python/aimet_onnx/adaround/utils.py

@@ -105,7 +105,14 @@ def read_attributes_for_op(module_info: ModuleInfo) -> Dict:
             if attribute.name == 'dilations':
                 attributes['dilations'] = list(attribute.ints)
             elif attribute.name == 'pads':
-                attributes['pads'] = list(attribute.ints)
+                padding = list(attribute.ints)
+                unique_vals = set()
+                new_padding = []
+                for val in padding:
+                    if val not in unique_vals:
+                        unique_vals.add(val)
+                        new_padding.append(val)
+                attributes['pads'] = new_padding
             elif attribute.name == 'strides':
                 attributes['strides'] = list(attribute.ints)
             elif attribute.name == 'group':