You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/11/23 15:48:12 UTC
[GitHub] [incubator-tvm] giuseros commented on a change in pull request #6899: Add initial support for quantized transpose convolution in Relay
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).
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org