[FRONTEND][TFLITE] Add FULLY_CONNECTED op into tflite frontend, support Inception V4 (#3019)

* Add FULLY_CONNECTED op into tflite frontend, support Inception V4

* Fix comment style in TF Lite tests.

python/tvm/relay/frontend/tflite.py

@@ -63,7 +63,8 @@ class OperatorConverter(object):
             'SQUEEZE': self.convert_squeeze,
             'MAX_POOL_2D': self.convert_max_pool2d,
             'CONCATENATION': self.convert_concatenation,
-            'ADD': self.convert_add
+            'ADD': self.convert_add,
+            'FULLY_CONNECTED': self.convert_fully_connected,
         }
 
     def check_unsupported_ops(self):
@@ -352,6 +353,71 @@ class OperatorConverter(object):
         out = _op.add(lhs_expr, rhs_expr)
         return out
 
+    def convert_fully_connected(self, op):
+        """Convert TFLite fully connected"""
+        try:
+            from tflite.Operator import Operator
+            from tflite.FullyConnectedOptions import FullyConnectedOptions
+            from tflite.BuiltinOptions import BuiltinOptions
+            from tflite.TensorType import TensorType
+            from tflite.ActivationFunctionType import ActivationFunctionType
+        except ImportError:
+            raise ImportError("The tflite package must be installed")
+
+        assert isinstance(op, Operator)
+        input_tensors = self.get_input_tensors(op)
+        assert len(input_tensors) >= 2, "input tensors length should be >= 2"
+
+        input_tensor = input_tensors[0]
+        input_tensor_idx = input_tensor.tensor_idx
+        weight_tensor = input_tensors[1]
+
+        input_tensor_shape = input_tensor.tensor.ShapeAsNumpy()
+        weight_tensor_shape = weight_tensor.tensor.ShapeAsNumpy()
+
+        # reshape input tensor from N H W C to N H*W*C
+        input_size_per_batch = 1
+        for s in range(1, len(input_tensor_shape)):
+            input_size_per_batch *= input_tensor_shape[s]
+        assert input_size_per_batch == weight_tensor_shape[1], \
+            "input size and weight size are mismatched"
+        target_shape = tuple((input_tensor_shape[0], input_size_per_batch))
+        in_expr = self.get_expr(input_tensor_idx)
+        in_expr = _op.reshape(in_expr, target_shape)
+
+        assert op.BuiltinOptionsType() == BuiltinOptions.FullyConnectedOptions
+        op_options = op.BuiltinOptions()
+        fully_connected_options = FullyConnectedOptions()
+        fully_connected_options.Init(op_options.Bytes, op_options.Pos)
+        fused_activation_fn = fully_connected_options.FusedActivationFunction()
+
+        # weight tensor type should be UINT8 (quantization) or FLOAT32
+        weight_tensor_type = weight_tensor.tensor.Type()
+        assert weight_tensor_type in (TensorType.UINT8, TensorType.FLOAT32)
+        weight_tensor_type_str = self.get_tensor_type_str(weight_tensor_type)
+
+        weight_value = self.get_tensor_value(weight_tensor)
+        weight_expr = self.exp_tab.new_const(weight_value, dtype=weight_tensor_type_str)
+
+        out = _op.nn.dense(in_expr, weight_expr)
+
+        # if we have bias
+        if len(input_tensors) == 3:
+            bias_tensor = input_tensors[2]
+            bias_tensor_type = bias_tensor.tensor.Type()
+            # bias tensor type should be INT32 (quantization) or FLOAT32
+            assert bias_tensor_type in (TensorType.INT32, TensorType.FLOAT32)
+            bias_tensor_type_str = self.get_tensor_type_str(bias_tensor_type)
+            bias_expr = self.exp_tab.new_const(self.get_tensor_value(bias_tensor),
+                                               dtype=bias_tensor_type_str)
+            out = _op.nn.bias_add(out, bias_expr)
+
+        # If we have fused activations
+        if fused_activation_fn != ActivationFunctionType.NONE:
+            out = self.convert_fused_activation_function(out, fused_activation_fn)
+
+        return out
+
     def convert_squeeze(self, op):
         """Convert TFLite squeeze"""
         try:

tests/python/frontend/tflite/test_forward.py

@@ -459,12 +459,63 @@ def test_forward_softmax():
     """ Softmax """
     _test_softmax(np.arange(6.0, dtype=np.float32).reshape((1, 6)))
 
+
+#######################################################################
+# Fully Connected
+# -------
+
+def _test_fully_connected(tensor_in_sizes, filter_in_sizes, bias_in_size=None):
+    """ One iteration of fully connected """
+
+    total_size_1 = 1
+    total_size_2 = 1
+    for s in tensor_in_sizes:
+        total_size_1 *= s
+    for s in filter_in_sizes:
+        total_size_2 *= s
+    # Initializes the input tensor with array containing incrementing
+    # numbers from 1.
+    data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
+    filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
+    assert int(total_size_1 / tensor_in_sizes[0]) == filter_in_sizes[0], \
+        "input size and filter size are mismatched"
+
+    with tf.Graph().as_default():
+        in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
+        in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
+
+        # reshape N H W C into N H*W*C
+        in_data_reshape = array_ops.reshape(in_data, [tensor_in_sizes[0], -1])
+
+        out = math_ops.mat_mul(in_data_reshape, in_filter)
+
+        # if we have bias
+        if bias_in_size:
+            assert bias_in_size[0] == filter_in_sizes[1], "bias and filter size are mismatched"
+            bias_array = [f * 1.0 for f in range(1, bias_in_size[0] + 1)]
+            in_bias = constant_op.constant(bias_array, shape=bias_in_size, dtype='float32')
+            out = nn_ops.bias_add(out, in_bias)
+
+        tflite_data_array = np.reshape(data_array, tensor_in_sizes).astype('float32')
+        tvm_data_array = np.transpose(tflite_data_array, axes=(0, 3, 1, 2))
+        compare_tflite_with_tvm(tflite_data_array, tvm_data_array,
+                                'Placeholder:0', [in_data], [out])
+
+
+def test_forward_fully_connected():
+    """ Fully Connected """
+    _test_fully_connected([1, 1, 1, 150], [150, 100])
+    _test_fully_connected([1, 1, 1, 150], [150, 100], [100])
+    _test_fully_connected([5, 1, 1, 150], [150, 100])
+    _test_fully_connected([5, 1, 1, 150], [150, 100], [100])
+
+
 #######################################################################
 # Mobilenet
 # ---------
 
 def test_forward_mobilenet_v1():
-    '''test mobilenet v1 tflite model'''
+    """Test the Mobilenet V1 TF Lite model."""
     # MobilenetV1
     tflite_model_file = tf_testing.get_workload_official(
         "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz",
@@ -479,7 +530,7 @@ def test_forward_mobilenet_v1():
                                 rtol=1e-5, atol=1e-5)
 
 def test_forward_mobilenet_v2():
-    '''test mobilenet v2 tflite model'''
+    """Test the Mobilenet V2 TF Lite model."""
     # MobilenetV2
     tflite_model_file = tf_testing.get_workload_official(
         "http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224.tgz",
@@ -494,11 +545,11 @@ def test_forward_mobilenet_v2():
                                 rtol=1e-5, atol=1e-5)
 
 #######################################################################
-# Inception V3
+# Inception
 # ------------
 
 def test_forward_inception_v3_net():
-    '''test inception v3 tflite model'''
+    """Test the Inception V3 TF Lite model."""
     # InceptionV3
     tflite_model_file = tf_testing.get_workload_official(
         "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz",
@@ -512,6 +563,21 @@ def test_forward_inception_v3_net():
     tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]),
                                 rtol=1e-5, atol=1e-5)
 
+def test_forward_inception_v4_net():
+    """Test the Inception V4 TF Lite model."""
+    # InceptionV4
+    tflite_model_file = tf_testing.get_workload_official(
+        "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz",
+        "inception_v4.tflite")
+    with open(tflite_model_file, "rb") as f:
+        tflite_model_buf = f.read()
+    data = np.random.uniform(size=(1, 299, 299, 3)).astype('float32')
+    tvm_data = np.transpose(data, axes=(0, 3, 1, 2))
+    tflite_output = run_tflite_graph(tflite_model_buf, data)
+    tvm_output = run_tvm_graph(tflite_model_buf, tvm_data, 'input')
+    tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]),
+                                rtol=1e-5, atol=1e-5)
+
 #######################################################################
 # Main
 # ----
@@ -525,6 +591,7 @@ if __name__ == '__main__':
     test_forward_convolution()
     test_forward_pooling()
     test_forward_softmax()
+    test_forward_fully_connected()
 
     # Math
     test_forward_add()
@@ -533,3 +600,4 @@ if __name__ == '__main__':
     test_forward_mobilenet_v1()
     test_forward_mobilenet_v2()
     test_forward_inception_v3_net()
+    test_forward_inception_v4_net()