[GitHub] [tvm] ashutosh-arm commented on a diff in pull request #10939: [CMSIS-NN] Moved TFLite model making to common area

[GitBox <gi...@apache.org>]

ashutosh-arm commented on code in PR #10939:
URL: https://github.com/apache/tvm/pull/10939#discussion_r856117243


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python/tvm/relay/testing/tflite.py:
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@@ -0,0 +1,152 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Common utilities for creating TFLite models"""
+from distutils.version import LooseVersion
+import numpy as np
+import pytest
+import tvm
+
+pytest.importorskip("tflite")
+pytest.importorskip("tensorflow")
+import tflite.Model  # pylint: disable=wrong-import-position
+import tensorflow as tf  # pylint: disable=wrong-import-position
+
+
+class TFLiteModel:
+    """Creates TFLite Model and facilitates reference data generation"""
+
+    def __init__(self, dtype):
+        self.serial_model = None  # This is what TFLite convert() provides
+        self.dtype = dtype  # This is the dtype of graph inputs
+        self.shape_dict = {}
+        self.dtype_dict = {}
+
+    @tf.function
+    def conv2d_single_function(self, ifm_tensor, args):
+        """Returns TFLite Conv2d layer"""
+        assert len(args) == 6, "Conv2D needs (ifm_shape, kernel_shape, strides, padding, dilation)"
+        _, kernel_shape, strides, padding, dilation, activation = args
+        op = tf.nn.conv2d(
+            ifm_tensor,
+            filters=tf.constant(
+                np.random.uniform(size=[kernel_shape[0], kernel_shape[1], 3, 3]),
+                dtype=tf.float32,
+            ),
+            strides=[1, strides[0], strides[1], 1],
+            padding=padding,
+            dilations=dilation,
+        )
+        if activation == "RELU":
+            op = tf.nn.relu(op)
+        elif activation == "NONE":
+            pass
+        else:
+            assert False, "Unsupported activation {}".format(activation)
+        return op
+
+    def create_tflite_model(self, op_type, *args):
+        """Returns TFLite serial graph, Relay module, Relay params based on op_type"""
+        concrete_func = None
+        input_shape = None
+        if op_type == "conv2d_single":
+            input_shape = args[0]
+            ifm_tensor = tf.TensorSpec(input_shape, dtype=tf.float32, name="input")
+            concrete_func = self.conv2d_single_function.get_concrete_function(ifm_tensor, args)
+        else:
+            assert False, "Unsupported op_type {}".format(op_type)
+
+        def representative_dataset():
+            for _ in range(100):
+                data = np.random.rand(*tuple(input_shape))

Review Comment:
   ACK.



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