giuseros commented on a change in pull request #6899:
URL: https://github.com/apache/incubator-tvm/pull/6899#discussion_r528801570
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -1124,53 +1126,124 @@ def _test_transpose_conv(tensor_in_sizes,
filter_in_sizes, output_shape, strides
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)]
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")
- strides = [1] + strides + [1]
- # in_filter layout is HWOI
- out = nn_ops.conv2d_transpose(
- in_data, in_filter, output_shape=output_shape, strides=strides,
padding=padding
- )
- data_array = np.reshape(data_array, tensor_in_sizes).astype("float32")
- compare_tflite_with_tvm(data_array, "Placeholder:0", [in_data], [out])
+ if quantized:
+ # Initializes the input tensor with array containing incrementing
+ # numbers from 1.
+ data_array = [max(f, 255) for f in range(1, total_size_1 + 1)]
+ filter_array = [max(f, 255) for f in range(1, total_size_2 + 1)]
+ data_array = np.reshape(data_array,
tensor_in_sizes).astype("uint8")
+ filter_array = np.reshape(filter_array,
filter_in_sizes).astype("uint8")
+
+ in_data = array_ops.placeholder(shape=tensor_in_sizes,
dtype="float32", name="in_data")
+ inq_data = tf.quantization.fake_quant_with_min_max_args(
+ in_data, min=-100, max=100, name="q_data"
+ )
+ input_range = {"q_data": (-100, 100)}
+
+ in_filter = constant_op.constant(
+ filter_array, shape=filter_in_sizes, dtype="float32",
name="in_filter"
+ )
+ inq_filter = tf.quantization.fake_quant_with_min_max_args(
+ in_filter, min=-100, max=100, name="q_filter"
+ )
+
+ strides = [1] + strides + [1]
+
+ out = nn_ops.conv2d_transpose(
+ inq_data, inq_filter, output_shape=output_shape,
strides=strides, padding=padding
+ )
+ out = tf.quantization.fake_quant_with_min_max_args(out, min=-100,
max=100, name="out")
+ compare_tflite_with_tvm(
+ [data_array], ["q_data"], [inq_data], [out], quantized=True,
input_range=input_range
+ )
+ else:
+ # 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)]
+
+ in_data = array_ops.placeholder(shape=tensor_in_sizes,
dtype="float32", name="in_data")
+ in_filter = constant_op.constant(
+ filter_array, shape=filter_in_sizes, dtype="float32",
name="in_filter"
+ )
+ strides = [1] + strides + [1]
+ # in_filter layout is HWOI
+ out = nn_ops.conv2d_transpose(
+ in_data, in_filter, output_shape=output_shape,
strides=strides, padding=padding
+ )
+ data_array = np.reshape(data_array,
tensor_in_sizes).astype("float32")
+ compare_tflite_with_tvm([data_array], ["in_data"], [in_data],
[out])
def test_forward_transpose_conv():
- # kernel 3x3, padding VALID
- _test_transpose_conv([4, 32, 32, 16], [3, 3, 5, 16], [4, 34, 34, 5], [1,
1], "VALID")
- _test_transpose_conv([1, 32, 32, 16], [3, 3, 5, 16], [1, 65, 65, 5], [2,
2], "VALID")
- _test_transpose_conv([1, 32, 32, 16], [3, 3, 5, 16], [1, 65, 34, 5], [2,
1], "VALID")
-
- # kernel 3x3, padding SAME
- _test_transpose_conv([4, 32, 32, 16], [3, 3, 5, 16], [4, 32, 32, 5], [1,
1], "SAME")
- _test_transpose_conv([1, 32, 32, 16], [3, 3, 5, 16], [1, 64, 64, 5], [2,
2], "SAME")
- _test_transpose_conv([1, 32, 32, 16], [3, 3, 5, 16], [1, 64, 32, 5], [2,
1], "SAME")
-
- # kernel 2x2, padding VALID
- _test_transpose_conv([4, 32, 32, 16], [2, 2, 5, 16], [4, 33, 33, 5], [1,
1], "VALID")
- _test_transpose_conv([1, 32, 32, 16], [2, 2, 5, 16], [1, 64, 64, 5], [2,
2], "VALID")
- _test_transpose_conv([1, 32, 32, 16], [2, 2, 5, 16], [1, 64, 33, 5], [2,
1], "VALID")
-
- # kernel 2x2, padding SAME
- _test_transpose_conv([4, 32, 32, 16], [2, 2, 5, 16], [4, 32, 32, 5], [1,
1], "SAME")
- _test_transpose_conv([1, 32, 32, 16], [2, 2, 5, 16], [1, 64, 64, 5], [2,
2], "SAME")
- _test_transpose_conv([1, 32, 32, 16], [2, 2, 5, 16], [1, 64, 32, 5], [2,
1], "SAME")
-
- # kernel 1x1, padding VALID
- _test_transpose_conv([4, 32, 32, 16], [1, 1, 5, 16], [4, 32, 32, 5], [1,
1], "VALID")
- _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 63, 5], [2,
2], "VALID")
- _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 32, 5], [2,
1], "VALID")
-
- # kernel 1x1, padding SAME
- _test_transpose_conv([4, 32, 32, 16], [1, 1, 5, 16], [4, 32, 32, 5], [1,
1], "SAME")
- _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 63, 5], [2,
2], "SAME")
- _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 32, 5], [2,
1], "SAME")
+ for quantized in [True, False]:
Review comment:
There is a test for per-channel quantization in
`test_op_qnn_conv2d_transpose.py`. `test_forward.py` for TFlite does not seem
to contain any per-channel quantized tests for any of the operators under test
(while there are some per-channel tests in the pytorch front-end).
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