Merge branch 'master' into 'master'

Fold batchnorm (scale, offset)

See merge request !447

mace/python/tools/tf_converter_lib.py

@@ -75,7 +75,8 @@ def get_input_tensor(op, index):
 
 
 class TFConverter(object):
-    def __init__(self, tf_ops, net_def, dt, device, winograd):
+    def __init__(self, graph, tf_ops, net_def, dt, device, winograd):
+        self.graph = graph
         self.net_def = net_def
         self.tf_ops = tf_ops
         self.dt = dt
@@ -494,7 +495,12 @@ class TFConverter(object):
         self.resolved_ops[op.name] = 1
 
         if len(self.tf_graph.get(op.name, [])) == 1 and \
-           self.tf_graph[op.name][0].type == 'BiasAdd':
+            self.tf_graph[op.name][0].type == 'BiasAdd' or \
+                (len(self.tf_graph[op.name]) == 1 and
+                    self.tf_graph[op.name][0].type == 'Add' and
+                    len(self.tf_graph[op.name][0].inputs) == 2 and
+                    len(self.graph.get_tensor_by_name(
+                        self.tf_graph[op.name][0].inputs[1].name).shape) == 1):
             bias_add_op = self.tf_graph[op.name][0]
             if self.device == 'gpu':
                 output_name = self.add_buffer_to_image(
@@ -650,61 +656,52 @@ class TFConverter(object):
         self.net_def.op.extend([op_def])
 
     def convert_batchnorm(self, op):
-        bn_ops = []
-        bn_ops.append(op)
-        for i in range(1, 3):
-            if len(self.tf_graph[bn_ops[i-1].name]) == 1 and \
-               self.tf_graph[bn_ops[i-1].name][0].type == BATCH_NORM_ORDER[i]:
-                bn_ops.append(self.tf_graph[bn_ops[i - 1].name][0])
-            else:
-                raise Exception('Invalid BatchNorm Op')
-        if len(self.tf_graph[bn_ops[2].name]) == 2 and \
-                self.tf_graph[bn_ops[2].name][0].type == \
-                BATCH_NORM_ORDER[3] and \
-                self.tf_graph[bn_ops[2].name][1].type == BATCH_NORM_ORDER[4]:
-            bn_ops.append(self.tf_graph[bn_ops[2].name][0])
-            bn_ops.append(self.tf_graph[bn_ops[2].name][1])
-        else:
-            raise Exception('Invalid BatchNorm Op')
-        bn_ops.append(self.tf_graph[bn_ops[4].name][0])
-        bn_ops.append(self.tf_graph[bn_ops[3].name][0])
-
         op_def = mace_pb2.OperatorDef()
         arg = op_def.arg.add()
         arg.name = 'T'
         arg.i = self.dt
-
-        input_name = get_input_tensor(bn_ops[3], 0).name
-        gamma = get_input_tensor(bn_ops[2], 1).name
-        beta = get_input_tensor(bn_ops[5], 0).name
-        mean = get_input_tensor(bn_ops[4], 0).name
-        variance = get_input_tensor(bn_ops[0], 0).name
-
-        op_def.name = op.name[:-4]  # remove /add
-        op_def.type = 'BatchNorm'
-        if self.device == 'gpu':
-            op_def.input.extend([input_name])
-            for tensor_name in [gamma, beta, mean, variance]:
-                output_name = self.add_buffer_to_image(tensor_name, "ARGUMENT")
-                op_def.input.extend([output_name])
-        else:
-            op_def.input.extend([input_name, gamma, beta, mean, variance])
-        op_def.output.extend([output.name for output in bn_ops[6].outputs])
-        self.add_output_shape(bn_ops[6].outputs, op_def)
-        epsilon_arg = op_def.arg.add()
-        epsilon_arg.name = 'epsilon'
-        epsilon_arg.f = get_input_tensor(op, 1).eval().astype(np.float)
         data_format_arg = op_def.arg.add()
         data_format_arg.name = 'data_format'
         if self.device == 'cpu':
             data_format_arg.s = 'NCHW'
         else:
             data_format_arg.s = 'NHWC'
-        self.unused_tensor.add(get_input_tensor(op, 1).name)
+        op_def.name = op.name
+        op_def.type = 'FoldedBatchNorm'
+
+        add_op = self.tf_graph[op.name][0]
+        scale_tensor = get_input_tensor(op, 1)
+        offset_tensor = get_input_tensor(add_op, 1)
+        input_names = [scale_tensor.name, offset_tensor.name]
 
+        op_def.input.extend([op.inputs[0].name])
+        if self.device == 'gpu':
+            for name in input_names:
+                output_name = self.add_buffer_to_image(name, "ARGUMENT")
+                op_def.input.extend([output_name])
+        else:
+            op_def.input.extend([name for name in input_names])
+
+        self.resolved_ops[op.name] = 1
+        self.resolved_ops[add_op.name] = 1
+        final_op = add_op
+
+        if len(self.tf_graph[op.name]) == 1 \
+                and self.tf_graph[op.name][0].type in activation_name_map:
+            activation_op = self.tf_graph[op.name][0]
+            fused_act_arg = op_def.arg.add()
+            fused_act_arg.name = 'activation'
+            fused_act_arg.s = activation_name_map[activation_op.type]
+            if activation_op.type == 'Relu6':
+                max_limit_arg = op_def.arg.add()
+                max_limit_arg.name = 'max_limit'
+                max_limit_arg.f = 6
+            final_op = activation_op
+            self.resolved_ops[activation_op.name] = 1
+
+        op_def.output.extend([final_op.outputs[0].name])
+        self.add_output_shape([final_op.outputs[0]], op_def)
         self.net_def.op.extend([op_def])
-        for i in range(0, 7):
-            self.resolved_ops[bn_ops[i].name] = 1
 
     def convert_pooling(self, op):
         op_def = self.net_def.op.add()
@@ -1155,7 +1152,7 @@ class TFConverter(object):
                     self.convert_conv2d(op)
             elif op.type == 'FusedBatchNorm':
                 self.convert_fused_batchnorm(op)
-            elif op.type == 'Add' and op.name.endswith('batchnorm/add'):
+            elif op.type == 'Mul' and op.name.find('batchnorm/mul') != -1:
                 self.convert_batchnorm(op)
             elif op.type == 'AvgPool' or op.type == 'MaxPool':
                 self.convert_pooling(op)
@@ -1382,7 +1379,7 @@ def convert_to_mace_pb(model_file, input_node, input_shape, output_node,
         with session.graph.as_default() as graph:
             tf.import_graph_def(input_graph_def, name="")
             ops = graph.get_operations()
-            converter = TFConverter(ops, net_def, dt, device, winograd)
+            converter = TFConverter(graph, ops, net_def, dt, device, winograd)
             converter.convert(input_nodes, output_nodes)
             optimizer = Optimizer(net_def, device)
             net_def = optimizer.optimize()