experiments/STT-tensorflow
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Special case 1D resizes and no-op resizes.

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Bilinear resize from [X, Y] -> [X, Y] is a no-op, but previously, ResizeBilinear would generated a 1x1 convolution.

Bilinear resize from [X, A] -> [X, B] or [A, Y] -> [B, Y] are one dimensional, but previously the code used the two dimensional general case if the kernel size was sufficiently small.

PiperOrigin-RevId: 251713592
This commit is contained in:
A. Unique TensorFlower 2019-06-05 14:02:34 -07:00 committed by TensorFlower Gardener
parent 50abb98b7d
commit ac034fd4e6
2 changed files with 7 additions and 2 deletions
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2
tensorflow/compiler/tests/image_ops_test.py
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@ -560,6 +560,8 @@ class ResizeBilinearTest(parameterized.TestCase, xla_test.XLATestCase):
("72x72To456x456", 72, 72, 456, 456), ("72x72To456x456", 72, 72, 456, 456),
("86x86To456x456", 86, 86, 456, 456), ("86x86To456x456", 86, 86, 456, 456),
("100x100To456x456", 100, 100, 456, 456), ("100x100To456x456", 100, 100, 456, 456),
("64x64To224x224", 64, 64, 224, 224),
("224x224To224x224", 224, 224, 224, 224),
# This test is disabled because it is very slow. It is slow because # This test is disabled because it is very slow. It is slow because
# 383 is prime, 383 and 2047 are coprime, and 2048 is large. # 383 is prime, 383 and 2047 are coprime, and 2048 is large.
# ("Disabled_384x72To2048x384", 384, 72, 2048, 384), # ("Disabled_384x72To2048x384", 384, 72, 2048, 384),

7
tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc
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@ -312,8 +312,11 @@ xla::XlaOp ResizeUsingDilationAndConvolution(
} }
// Split convolutions into independent dimensions if they would be a very // Split convolutions into independent dimensions if they would be a very
// large kernel. // large kernel or if one or more of the dimensions are already equal.
if (dims.kernel_size[0] * dims.kernel_size[1] < kMax2DKernelSize) { bool decompose_resize =
in_size[0] == out_size[0] || in_size[1] == out_size[1] ||
dims.kernel_size[0] * dims.kernel_size[1] >= kMax2DKernelSize;
if (!decompose_resize) {
xla::XlaOp kernel = MakeGeneralResizeKernel(builder, type, dims.kernel_size, xla::XlaOp kernel = MakeGeneralResizeKernel(builder, type, dims.kernel_size,
channels, is_kernel_bilinear); channels, is_kernel_bilinear);
output = output =