u99127 commented on a change in pull request #4788: [FRONTEND][TFLITE]Gather,
StridedSlice op support added
URL: https://github.com/apache/incubator-tvm/pull/4788#discussion_r374498740
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File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -244,6 +244,74 @@ def test_forward_slice():
_test_slice(np.arange(8, dtype=np.int32).reshape((2, 4)), begin=[0,
1], size=[-1, -1])
_test_slice(np.arange(5, dtype=np.int32).reshape((5, )), begin=[4],
size=[-1])
+#######################################################################
+# Gather
+# ------
+
+def _test_gather(dshape, indices, axis, dtype):
+ """ One iteration of Gather """
+ data = np.random.uniform(1, 10, size=dshape).astype(dtype)
+ indices = np.asarray(indices).astype('int32')
+
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
+ out = array_ops.gather(in_data, indices, axis=axis)
+ compare_tflite_with_tvm(data, 'Placeholder:0', [in_data], [out])
+
+ #Test quantized input
+ data = np.random.uniform(1, 10, size=dshape).astype(np.uint8)
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype,
name="in_data")
+ out = array_ops.gather(in_data, indices, axis=axis)
+ compare_tflite_with_tvm([data], ['in_data:0'], [in_data], [out],
quantized=True)
+
+def test_forward_gather():
+ """ GATHER """
+ _test_gather((4,), [1], 0, 'float32')
+ _test_gather((1, 4), [0], 0, 'int32')
+ _test_gather((4,), [[[1, 0], [0, 1]]], 0, 'float32')
+ _test_gather((2, 2), [[[1, 0], [0, 1]]], 0, 'int32')
+ _test_gather((2, 2), [[[1, 0], [0, 1]]], 1, 'int32')
+ _test_gather((2, 2), [[[1, 0], [0, 1]]], 0, 'float32')
+ _test_gather((3, 3, 3), [[[1, 0]]], 0, 'int32')
+ _test_gather((3, 3, 3), [[[1, 0]]], 2, 'int32')
+ _test_gather((4, 3, 5, 6), [[2, 1, 0, 0]], 0, 'float32')
+
+#######################################################################
+# StridedSlice
+# ------------
+
+def _test_stridedslice(ip_shape, begin, end, stride, dtype,
+ begin_mask=0, end_mask=0, new_axis_mask=0,
+ shrink_axis_mask=0, ellipsis_mask=0):
+ """ One iteration of a Stridedslice """
+ data = np.random.uniform(size=ip_shape).astype(dtype)
+ with tf.Graph().as_default():
+ in_data = tf.placeholder(dtype, ip_shape, name="in_data")
+ out = array_ops.strided_slice(in_data, begin, end, stride,
+ begin_mask=begin_mask,
+ end_mask=end_mask,
new_axis_mask=new_axis_mask,
+ shrink_axis_mask=shrink_axis_mask,
+ ellipsis_mask=ellipsis_mask)
+ compare_tflite_with_tvm(data, 'in_data:0', [in_data], [out])
+
+ #Test with quantized inputs
+ data = np.random.uniform(size=ip_shape).astype(np.uint8)
+ with tf.Graph().as_default():
+ in_data = tf.placeholder(dtype, ip_shape, name="in_data")
+ out = array_ops.strided_slice(in_data, begin, end, stride,
+ begin_mask=begin_mask,
+ end_mask=end_mask,
new_axis_mask=new_axis_mask,
+ shrink_axis_mask=shrink_axis_mask,
+ ellipsis_mask=ellipsis_mask)
+ compare_tflite_with_tvm([data], ['in_data:0'], [in_data], [out],
quantized=True)
Review comment:
Can we instead change the caller to call _test_stridedslice with a uint8
data type and avoid duplication of code ? If the data type can't be passed, can
the data be created in the caller with an appropriate type and passed in.
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