Get Demo_DFFN conversions working after incorporating tips from:

ethereon#114
ethereon#123
ethereon#104

kaffe/layers.py

@@ -4,6 +4,8 @@
 
 from .shapes import *
 
+import pdb
+
 LAYER_DESCRIPTORS = {
 
     # Caffe Types
@@ -103,6 +105,7 @@ class LayerAdapter(object):
     def __init__(self, layer, kind):
         self.layer = layer
         self.kind = kind
+        self._input_shape = None
 
     @property
     def parameters(self):
@@ -114,7 +117,7 @@ def parameters(self):
             raise NodeDispatchError('Caffe parameters not found for layer kind: %s' % (self.kind))
 
     @staticmethod
-    def get_kernel_value(scalar, repeated, idx, default=None):
+    def get_kernel_value(scalar, repeated, idx, default=None, params=None):
         if scalar:
             return scalar
         if repeated:
@@ -127,15 +130,26 @@ def get_kernel_value(scalar, repeated, idx, default=None):
             # Extract the value for the given spatial dimension
             return repeated[idx]
         if default is None:
+            #pdb.set_trace()
             raise ValueError('Unable to determine kernel parameter!')
         return default
 
+    def set_input_shape(self, input_shape):
+        self._input_shape = input_shape
+
     @property
     def kernel_parameters(self):
         assert self.kind in (NodeKind.Convolution, NodeKind.Pooling)
         params = self.parameters
-        k_h = self.get_kernel_value(params.kernel_h, params.kernel_size, 0)
-        k_w = self.get_kernel_value(params.kernel_w, params.kernel_size, 1)
+        global_pool = hasattr(params, 'global_pooling')
+        if params.kernel_size:
+            k_h = self.get_kernel_value(params.kernel_h, params.kernel_size, 0)
+            k_w = self.get_kernel_value(params.kernel_w, params.kernel_size, 1)
+        elif self._input_shape:
+            k_h, k_w = [self._input_shape.height, self._input_shape.width]
+        else: #errors out in get_kernel_value function
+            k_h = self.get_kernel_value(params.kernel_h, params.kernel_size, 0)
+            k_w = self.get_kernel_value(params.kernel_w, params.kernel_size, 1)
         s_h = self.get_kernel_value(params.stride_h, params.stride, 0, default=1)
         s_w = self.get_kernel_value(params.stride_w, params.stride, 1, default=1)
         p_h = self.get_kernel_value(params.pad_h, params.pad, 0, default=0)

kaffe/shapes.py

@@ -15,6 +15,7 @@ def get_filter_output_shape(i_h, i_w, params, round_func):
 def get_strided_kernel_output_shape(node, round_func):
     assert node.layer is not None
     input_shape = node.get_only_parent().output_shape
+    node.layer.set_input_shape(input_shape)
     o_h, o_w = get_filter_output_shape(input_shape.height, input_shape.width,
                                        node.layer.kernel_parameters, round_func)
     params = node.layer.parameters

kaffe/tensorflow/transformer.py

@@ -146,7 +146,8 @@ def map_dropout(self, node):
         return TensorFlowNode('dropout', node.parameters.dropout_ratio)
 
     def map_batch_norm(self, node):
-        scale_offset = len(node.data) == 4
+        scale_offset = len(node.output_shape) == 4
+        # scale_offset = len(node.data) == 4
         kwargs = {} if scale_offset else {'scale_offset': False}
         return MaybeActivated(node, default=False)('batch_normalization', **kwargs)