[DARKNET FRONTEND]Batchnorm added as part of Dense op for running rnn model for next wo… (#1385)

nnvm/python/nnvm/frontend/darknet.py

@@ -226,13 +226,18 @@ def _darknet_dense(inputs, attrs):
     """Process the dense operation."""
     op_name, new_attrs = 'dense', {}
     new_attrs['units'] = _darknet_required_attr(attrs, 'num_hidden')
-
+    out_name = {}
     if attrs.get('use_bias', False) is True:
         new_attrs['use_bias'] = True
     if attrs.get('use_flatten', False) is True:
         inputs[0] = _sym.flatten(inputs[0])
     sym = _darknet_get_nnvm_op(op_name)(*inputs, **new_attrs)
-    out_name = sym.list_output_names()[0].replace('_output', '')
+    out_name[0] = sym.list_output_names()[0].replace('_output', '')
+    if 'use_batchNorm' in attrs:
+        op_name, new_attrs = 'batch_norm', {}
+        new_attrs['epsilon'] = 0.000001
+        sym = _darknet_get_nnvm_op(op_name)(*sym, **new_attrs)
+        out_name[1] = sym.list_output_names()[0].replace('_output', '')
     if 'activation' in attrs:
         new_attrs = {}
         new_attrs['activation'] = attrs['activation']
@@ -430,13 +435,16 @@ def _get_connected_weights(layer, opname, params, dtype):
     weights = _read_memory_buffer((layer.outputs, layer.inputs), layer.weights, dtype)
     biases = _read_memory_buffer((layer.outputs, ), layer.biases, dtype)
 
-    k = _get_tvm_params_name(opname, 'weight')
+    k = _get_tvm_params_name(opname[0], 'weight')
     params[k] = tvm.nd.array(weights)
-    k = _get_tvm_params_name(opname, 'bias')
-    params[k] = tvm.nd.array(biases)
 
     if layer.batch_normalize == 1 and layer.dontloadscales != 1:
-        _get_batchnorm_weights(layer, opname, params, layer.outputs, dtype)
+        _get_batchnorm_weights(layer, opname[1], params, layer.outputs, dtype)
+        k = _get_tvm_params_name(opname[1], 'beta')
+        params[k] = tvm.nd.array(biases)
+    else:
+        k = _get_tvm_params_name(opname[0], 'bias')
+        params[k] = tvm.nd.array(biases)
 
 def _get_batchnorm_weights(layer, opname, params, size, dtype):
     """Parse the weights for batchnorm, which includes, scales, moving mean

nnvm/tests/python/frontend/darknet/test_forward.py

@@ -169,6 +169,20 @@ def test_forward_dense():
     test_forward(net)
     LIB.free_network(net)
 
+def test_forward_dense_batchnorm():
+    '''test fully connected layer with batchnorm'''
+    net = LIB.make_network(1)
+    layer = LIB.make_connected_layer(1, 12, 2, 1, 1, 0)
+    for i in range(5):
+        layer.rolling_mean[i] = np.random.rand(1)
+        layer.rolling_variance[i] = np.random.rand(1)
+        layer.scales[i] = np.random.rand(1)
+    net.layers[0] = layer
+    net.w = net.h = 2
+    LIB.resize_network(net, 2, 2)
+    test_forward(net)
+    LIB.free_network(net)
+
 def test_forward_maxpooling():
     '''test maxpooling layer'''
     net = LIB.make_network(1)
@@ -264,6 +278,7 @@ if __name__ == '__main__':
     test_forward_batch_norm()
     test_forward_shortcut()
     test_forward_dense()
+    test_forward_dense_batchnorm()
     test_forward_reorg()
     test_forward_region()
     test_forward_elu()