Merge pull request #5388 from vrv/master
Revert IDCT jpeg change and nvcc flag change
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commit
a507438361
@ -139,11 +139,8 @@ uint8* UncompressLow(const void* srcdata, FewerArgsForCompiler* argball) {
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cinfo.do_fancy_upsampling = boolean(flags.fancy_upscaling);
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cinfo.scale_num = 1;
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cinfo.scale_denom = ratio;
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// Activating this has a quality/speed trade-off implication.
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// However, most JPEGs are already compressed, and so the faster IDCT
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// should have no effect on training. The fast setting speeds up training on the
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// GPU, e.g. by about 30% for smaller networks such as AlexNet.
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cinfo.dct_method = JDCT_IFAST;
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// Activating this has a quality/speed trade-off implication:
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// cinfo.dct_method = JDCT_IFAST;
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jpeg_start_decompress(&cinfo);
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@ -165,7 +165,7 @@ TEST(JpegMemTest, Jpeg2) {
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// Compare the two images
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const int totalerr = ComputeSumAbsoluteDifference(
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imgdata1.get(), refdata1.get(), in_w, in_h, stride1, stride1);
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CHECK_LE(totalerr, 120000);
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CHECK_LE(totalerr, 85000);
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}
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// check the second image too. Should be bitwise identical to the first.
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@ -1803,10 +1803,10 @@ class JpegTest(test_util.TensorFlowTestCase):
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jpeg0, image0, image1, image2 = sess.run([jpeg0, image0, image1, image2])
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# The decoded-encoded image should be similar to the input
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self.assertLess(self.averageError(image0, image1), 0.7)
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self.assertLess(self.averageError(image0, image1), 0.6)
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# We should be very close to a fixpoint
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self.assertLess(self.averageError(image1, image2), 0.6)
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self.assertLess(self.averageError(image1, image2), 0.02)
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# Smooth ramps compress well (input size is 153600)
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self.assertGreaterEqual(len(jpeg0), 5000)
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@ -416,7 +416,7 @@ def _cuda_copts():
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"@local_config_cuda//cuda:using_nvcc": (
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common_cuda_opts +
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[
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"-nvcc_options=expt-relaxed-constexpr",
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"-nvcc_options=relaxed-constexpr",
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"-nvcc_options=ftz=true",
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]
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),
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