dchauhan-arm commented on a change in pull request #9209:
URL: https://github.com/apache/tvm/pull/9209#discussion_r724888277
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File path: tests/python/contrib/test_ethosu/test_codegen.py
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@@ -168,5 +166,95 @@ def create_graph_activation(input_tensor_name,
input_tensor_shape, input_tensor_
infra.verify_source(compiled_models, accel_type)
[email protected]("accel_type", ACCEL_TYPES)
[email protected]("ifm_shape", [(1, 55, 55, 3), (1, 23, 32, 7)])
[email protected](
+ "kernel_shape, activation",
+ [((3, 3), "relu"), ((1, 2), None)],
+)
[email protected]("padding", ["SAME", "VALID"])
[email protected]("strides, dilation", [((1, 1), (2, 2)), ((3, 2), (1,
1))])
+def test_tflite_depthwise_conv2d(
+ accel_type,
+ ifm_shape,
+ kernel_shape,
+ padding,
+ strides,
+ dilation,
+ activation,
+):
+ dtype = "int8"
+
+ def create_tflite_graph():
+ tf.config.run_functions_eagerly(True)
+
+ class Model(tf.Module):
+ @tf.function
+ def depthwise_conv2d(self, x):
+ weight_shape = [kernel_shape[0], kernel_shape[1],
ifm_shape[3], 1]
+ weight = tf.constant(np.random.uniform(size=weight_shape),
dtype=tf.float32)
+ # The input strides to the TensorFlow API needs to be of shape
1x4
+ tf_strides = [1, strides[0], strides[1], 1]
+ op = tf.nn.depthwise_conv2d(
+ x, weight, strides=tf_strides, padding=padding,
dilations=dilation
+ )
+ if activation:
+ op = tf.nn.relu(op)
+ return op
+
Review comment:
LGTM regarding tf.nn.depthwise_conv2d usage. Others' comments cover
everything else.
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