add ImageNet example

bayesian_torch/ao/quantization/quantize.py

@@ -38,6 +38,7 @@
 from typing import Any, List, Optional, Type, Union
 from torch import Tensor
 from bayesian_torch.models.bnn_to_qbnn import bnn_to_qbnn
+from torch.nn import BatchNorm2d
 # import copy
 
 __all__ = [
@@ -140,6 +141,14 @@ def enable_prepare(m):
             if callable(prepare):
                 m._modules[name].prepare()
                 m._modules[name].dnn_to_bnn_flag=True
+        elif "BatchNorm2dLayer" in m._modules[name].__class__.__name__: # replace BatchNorm2dLayer with BatchNorm2d in downsample
+            layer_fn = BatchNorm2d  # Get QBNN layer
+            bn_layer = layer_fn(
+                num_features=m._modules[name].num_features
+            )
+            bn_layer.__dict__.update(m._modules[name].__dict__)
+            setattr(m, name, bn_layer)
+
 
 
 def prepare(model):
@@ -149,7 +158,7 @@ def prepare(model):
     3. run torch.quantize.prepare()
     """
     qmodel = QuantizableResNet(QuantizableBottleneck, [3, 4, 6, 3])
-    qmodel.load_state_dict(model.state_dict())
+    qmodel.load_state_dict(model.module.state_dict())
     qmodel.eval()
     enable_prepare(qmodel)
     qmodel.qconfig = torch.quantization.get_default_qconfig("onednn")

bayesian_torch/layers/batchnorm.py

@@ -15,6 +15,7 @@ def __init__(self,
                  affine=True,
                  track_running_stats=True):
         super(BatchNorm2dLayer, self).__init__()
+        self.num_features = num_features
         self.eps = eps
         self.momentum = momentum
         self.affine = affine

bayesian_torch/scripts/quantize_bayesian_imagenet.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+
+model=resnet50
+mode='test'
+val_batch_size=1
+num_monte_carlo=1
+
+python examples/main_bayesian_imagenet_bnn2qbnn.py --mode=$mode --val_batch_size=$val_batch_size --num_monte_carlo=$num_monte_carlo ../../datasets