Update on "Improve interpolate() speed for channels_last images and masks"

NicolasHug · NicolasHug · commit 84f80937cb9b · 2022-10-06T11:32:29.000Z
This PR improves the speed of `interpolate()`: - on images and masks (`num_channels < 4`, `channels_last=True`) - for the following modes: linear (antialias=False), nearest (int and float), and nearest-exact (int and float) - for both upsampling and downsampling The actual speed-up ranges from 1.1X to 110X, but this depends on various factors like number of threads and of course input_size/output_size. In a typical torchvision training job (where num_threads=1 because of DataLoader multi-processing), the following speed-ups should be expected (I ran much more benchmarks than this one, see below for more details): ``` (1, 3, 600, 400) -> (224, 224) linear float32 num_threads=1 1.0X 1.0ms vs 1.0ms (1, 3, 600, 400) -> (224, 224) nearest float32 num_threads=1 1.9X 0.9ms vs 0.5ms (1, 3, 600, 400) -> (224, 224) nearest uint8 num_threads=1 1.7X 0.9ms vs 0.5ms (1, 3, 600, 400) -> (224, 224) nearest-exact float32 num_threads=1 2.1X 1.0ms vs 0.5ms (1, 3, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=1 1.8X 0.9ms vs 0.5ms (1, 1, 600, 400) -> (224, 224) linear float32 num_threads=1 7X 0.8ms vs 0.1ms (1, 1, 600, 400) -> (224, 224) nearest float32 num_threads=1 14X 0.852ms vs 0.061ms (1, 1, 600, 400) -> (224, 224) nearest uint8 num_threads=1 9X 0.828ms vs 0.087ms (1, 1, 600, 400) -> (224, 224) nearest-exact float32 num_threads=1 15X 0.922ms vs 0.061ms (1, 1, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=1 10X 0.897ms vs 0.087ms ``` An immediate follow-up to this PR would be to do the same changes for the 3D kernels. Thanks a ton fmassa for the help! ### Speedup benchmarks: Results: <details> ``` ---------------------------------------------------------------------------------------------------- (1, 3, 64, 64) -> (224, 224) linear float32 num_threads=1 0.9X 0.9ms vs 1.1ms (1, 3, 64, 64) -> (224, 224) nearest float32 num_threads=1 1.6X 0.9ms vs 0.5ms (1, 3, 64, 64) -> (224, 224) nearest uint8 num_threads=1 1.7X 0.9ms vs 0.5ms (1, 3, 64, 64) -> (224, 224) nearest-exact float32 num_threads=1 1.7X 1.0ms vs 0.5ms (1, 3, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=1 1.9X 0.9ms vs 0.5ms (1, 1, 64, 64) -> (224, 224) linear float32 num_threads=1 8X 0.806ms vs 0.097ms (1, 1, 64, 64) -> (224, 224) nearest float32 num_threads=1 15X 0.848ms vs 0.056ms (1, 1, 64, 64) -> (224, 224) nearest uint8 num_threads=1 10X 0.828ms vs 0.084ms (1, 1, 64, 64) -> (224, 224) nearest-exact float32 num_threads=1 16X 0.914ms vs 0.057ms (1, 1, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=1 10X 0.900ms vs 0.086ms (1, 3, 64, 64) -> (224, 224) linear float32 num_threads=2 1.6X 1.1ms vs 0.7ms (1, 3, 64, 64) -> (224, 224) nearest float32 num_threads=2 1.6X 0.6ms vs 0.4ms (1, 3, 64, 64) -> (224, 224) nearest uint8 num_threads=2 1.7X 0.4ms vs 0.3ms (1, 3, 64, 64) -> (224, 224) nearest-exact float32 num_threads=2 1.7X 0.6ms vs 0.4ms (1, 3, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=2 1.7X 0.5ms vs 0.3ms (1, 1, 64, 64) -> (224, 224) linear float32 num_threads=2 9X 0.800ms vs 0.088ms (1, 1, 64, 64) -> (224, 224) nearest float32 num_threads=2 11X 0.459ms vs 0.043ms (1, 1, 64, 64) -> (224, 224) nearest uint8 num_threads=2 7X 0.424ms vs 0.064ms (1, 1, 64, 64) -> (224, 224) nearest-exact float32 num_threads=2 12X 0.503ms vs 0.043ms (1, 1, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=2 8X 0.461ms vs 0.059ms (1, 3, 64, 64) -> (224, 224) linear float32 num_threads=12 3X 1.1ms vs 0.3ms (1, 3, 64, 64) -> (224, 224) nearest float32 num_threads=12 1.6X 0.3ms vs 0.2ms (1, 3, 64, 64) -> (224, 224) nearest uint8 num_threads=12 1.5X 0.2ms vs 0.1ms (1, 3, 64, 64) -> (224, 224) nearest-exact float32 num_threads=12 1.5X 0.3ms vs 0.2ms (1, 3, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=12 1.5X 0.2ms vs 0.1ms (1, 1, 64, 64) -> (224, 224) linear float32 num_threads=12 5X 0.8ms vs 0.2ms (1, 1, 64, 64) -> (224, 224) nearest float32 num_threads=12 10X 0.445ms vs 0.047ms (1, 1, 64, 64) -> (224, 224) nearest uint8 num_threads=12 7X 0.432ms vs 0.062ms (1, 1, 64, 64) -> (224, 224) nearest-exact float32 num_threads=12 10X 0.478ms vs 0.046ms (1, 1, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=12 7X 0.470ms vs 0.063ms (1, 3, 64, 64) -> (224, 224) linear float32 num_threads=32 3X 1.1ms vs 0.4ms (1, 3, 64, 64) -> (224, 224) nearest float32 num_threads=32 1.8X 0.3ms vs 0.2ms (1, 3, 64, 64) -> (224, 224) nearest uint8 num_threads=32 1.5X 0.2ms vs 0.1ms (1, 3, 64, 64) -> (224, 224) nearest-exact float32 num_threads=32 1.4X 0.3ms vs 0.2ms (1, 3, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=32 1.5X 0.2ms vs 0.1ms (1, 1, 64, 64) -> (224, 224) linear float32 num_threads=32 11X 0.815ms vs 0.074ms (1, 1, 64, 64) -> (224, 224) nearest float32 num_threads=32 10X 0.443ms vs 0.045ms (1, 1, 64, 64) -> (224, 224) nearest uint8 num_threads=32 7X 0.436ms vs 0.061ms (1, 1, 64, 64) -> (224, 224) nearest-exact float32 num_threads=32 10X 0.478ms vs 0.046ms (1, 1, 64, 64) -> (224, 224) nearest-exact uint8 num_threads=32 8X 0.470ms vs 0.061ms ---------------------------------------------------------------------------------------------------- (1, 3, 128, 128) -> (224, 224) linear float32 num_threads=1 0.9X 0.9ms vs 1.1ms (1, 3, 128, 128) -> (224, 224) nearest float32 num_threads=1 1.5X 0.9ms vs 0.6ms (1, 3, 128, 128) -> (224, 224) nearest uint8 num_threads=1 1.7X 0.9ms vs 0.5ms (1, 3, 128, 128) -> (224, 224) nearest-exact float32 num_threads=1 1.6X 1.0ms vs 0.6ms (1, 3, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=1 1.8X 0.9ms vs 0.5ms (1, 1, 128, 128) -> (224, 224) linear float32 num_threads=1 8X 0.808ms vs 0.099ms (1, 1, 128, 128) -> (224, 224) nearest float32 num_threads=1 15X 0.848ms vs 0.058ms (1, 1, 128, 128) -> (224, 224) nearest uint8 num_threads=1 9X 0.820ms vs 0.087ms (1, 1, 128, 128) -> (224, 224) nearest-exact float32 num_threads=1 16X 0.909ms vs 0.059ms (1, 1, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=1 10X 0.898ms vs 0.088ms (1, 3, 128, 128) -> (224, 224) linear float32 num_threads=2 1.4X 0.9ms vs 0.7ms (1, 3, 128, 128) -> (224, 224) nearest float32 num_threads=2 1.5X 0.5ms vs 0.3ms (1, 3, 128, 128) -> (224, 224) nearest uint8 num_threads=2 1.7X 0.4ms vs 0.3ms (1, 3, 128, 128) -> (224, 224) nearest-exact float32 num_threads=2 1.5X 0.5ms vs 0.4ms (1, 3, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=2 1.8X 0.5ms vs 0.3ms (1, 1, 128, 128) -> (224, 224) linear float32 num_threads=2 9X 0.799ms vs 0.090ms (1, 1, 128, 128) -> (224, 224) nearest float32 num_threads=2 10X 0.459ms vs 0.045ms (1, 1, 128, 128) -> (224, 224) nearest uint8 num_threads=2 7X 0.427ms vs 0.059ms (1, 1, 128, 128) -> (224, 224) nearest-exact float32 num_threads=2 11X 0.501ms vs 0.044ms (1, 1, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=2 8X 0.460ms vs 0.060ms (1, 3, 128, 128) -> (224, 224) linear float32 num_threads=12 2.9X 1.0ms vs 0.3ms (1, 3, 128, 128) -> (224, 224) nearest float32 num_threads=12 1.2X 0.2ms vs 0.2ms (1, 3, 128, 128) -> (224, 224) nearest uint8 num_threads=12 1.5X 0.2ms vs 0.1ms (1, 3, 128, 128) -> (224, 224) nearest-exact float32 num_threads=12 1.1X 0.2ms vs 0.2ms (1, 3, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=12 1.6X 0.2ms vs 0.1ms (1, 1, 128, 128) -> (224, 224) linear float32 num_threads=12 12X 0.809ms vs 0.068ms (1, 1, 128, 128) -> (224, 224) nearest float32 num_threads=12 11X 0.438ms vs 0.041ms (1, 1, 128, 128) -> (224, 224) nearest uint8 num_threads=12 8X 0.432ms vs 0.055ms (1, 1, 128, 128) -> (224, 224) nearest-exact float32 num_threads=12 12X 0.480ms vs 0.041ms (1, 1, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=12 8X 0.464ms vs 0.056ms (1, 3, 128, 128) -> (224, 224) linear float32 num_threads=32 3X 1.1ms vs 0.3ms (1, 3, 128, 128) -> (224, 224) nearest float32 num_threads=32 1.3X 0.3ms vs 0.2ms (1, 3, 128, 128) -> (224, 224) nearest uint8 num_threads=32 1.5X 0.2ms vs 0.1ms (1, 3, 128, 128) -> (224, 224) nearest-exact float32 num_threads=32 1.4X 0.3ms vs 0.2ms (1, 3, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=32 1.6X 0.2ms vs 0.1ms (1, 1, 128, 128) -> (224, 224) linear float32 num_threads=32 11X 0.813ms vs 0.075ms (1, 1, 128, 128) -> (224, 224) nearest float32 num_threads=32 10X 0.443ms vs 0.046ms (1, 1, 128, 128) -> (224, 224) nearest uint8 num_threads=32 7X 0.433ms vs 0.061ms (1, 1, 128, 128) -> (224, 224) nearest-exact float32 num_threads=32 10X 0.478ms vs 0.046ms (1, 1, 128, 128) -> (224, 224) nearest-exact uint8 num_threads=32 8X 0.470ms vs 0.062ms ---------------------------------------------------------------------------------------------------- (1, 3, 224, 224) -> (600, 400) linear float32 num_threads=1 0.9X 4.5ms vs 5.2ms (1, 3, 224, 224) -> (600, 400) nearest float32 num_threads=1 1.5X 4.2ms vs 2.8ms (1, 3, 224, 224) -> (600, 400) nearest uint8 num_threads=1 1.8X 4.1ms vs 2.3ms (1, 3, 224, 224) -> (600, 400) nearest-exact float32 num_threads=1 1.6X 4.5ms vs 2.8ms (1, 3, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=1 1.9X 4.4ms vs 2.3ms (1, 1, 224, 224) -> (600, 400) linear float32 num_threads=1 9X 3.8ms vs 0.4ms (1, 1, 224, 224) -> (600, 400) nearest float32 num_threads=1 17X 4.0ms vs 0.2ms (1, 1, 224, 224) -> (600, 400) nearest uint8 num_threads=1 11X 3.9ms vs 0.4ms (1, 1, 224, 224) -> (600, 400) nearest-exact float32 num_threads=1 19X 4.4ms vs 0.2ms (1, 1, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=1 12X 4.3ms vs 0.4ms (1, 3, 224, 224) -> (600, 400) linear float32 num_threads=2 1.5X 4.5ms vs 3.1ms (1, 3, 224, 224) -> (600, 400) nearest float32 num_threads=2 1.4X 2.3ms vs 1.6ms (1, 3, 224, 224) -> (600, 400) nearest uint8 num_threads=2 1.7X 2.1ms vs 1.2ms (1, 3, 224, 224) -> (600, 400) nearest-exact float32 num_threads=2 1.6X 2.5ms vs 1.6ms (1, 3, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=2 1.8X 2.2ms vs 1.2ms (1, 1, 224, 224) -> (600, 400) linear float32 num_threads=2 15X 3.8ms vs 0.3ms (1, 1, 224, 224) -> (600, 400) nearest float32 num_threads=2 15X 2.2ms vs 0.1ms (1, 1, 224, 224) -> (600, 400) nearest uint8 num_threads=2 7X 2.0ms vs 0.3ms (1, 1, 224, 224) -> (600, 400) nearest-exact float32 num_threads=2 16X 2.4ms vs 0.1ms (1, 1, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=2 8X 2.2ms vs 0.3ms (1, 3, 224, 224) -> (600, 400) linear float32 num_threads=12 8X 5.2ms vs 0.7ms (1, 3, 224, 224) -> (600, 400) nearest float32 num_threads=12 1.3X 0.6ms vs 0.4ms (1, 3, 224, 224) -> (600, 400) nearest uint8 num_threads=12 1.7X 0.4ms vs 0.2ms (1, 3, 224, 224) -> (600, 400) nearest-exact float32 num_threads=12 1.4X 0.6ms vs 0.4ms (1, 3, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=12 1.8X 0.4ms vs 0.2ms (1, 1, 224, 224) -> (600, 400) linear float32 num_threads=12 36X 3.9ms vs 0.1ms (1, 1, 224, 224) -> (600, 400) nearest float32 num_threads=12 10X 0.526ms vs 0.051ms (1, 1, 224, 224) -> (600, 400) nearest uint8 num_threads=12 7X 0.514ms vs 0.069ms (1, 1, 224, 224) -> (600, 400) nearest-exact float32 num_threads=12 11X 0.569ms vs 0.052ms (1, 1, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=12 8X 0.557ms vs 0.070ms (1, 3, 224, 224) -> (600, 400) linear float32 num_threads=32 9X 4.5ms vs 0.5ms (1, 3, 224, 224) -> (600, 400) nearest float32 num_threads=32 0.5X 0.2ms vs 0.5ms (1, 3, 224, 224) -> (600, 400) nearest uint8 num_threads=32 1.5X 0.2ms vs 0.1ms (1, 3, 224, 224) -> (600, 400) nearest-exact float32 num_threads=32 1.0X 0.5ms vs 0.5ms (1, 3, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=32 1.6X 0.2ms vs 0.1ms (1, 1, 224, 224) -> (600, 400) linear float32 num_threads=32 44X 3.864ms vs 0.087ms (1, 1, 224, 224) -> (600, 400) nearest float32 num_threads=32 10X 0.527ms vs 0.053ms (1, 1, 224, 224) -> (600, 400) nearest uint8 num_threads=32 7X 0.516ms vs 0.070ms (1, 1, 224, 224) -> (600, 400) nearest-exact float32 num_threads=32 10X 0.567ms vs 0.055ms (1, 1, 224, 224) -> (600, 400) nearest-exact uint8 num_threads=32 8X 0.558ms vs 0.072ms ---------------------------------------------------------------------------------------------------- (1, 3, 256, 256) -> (320, 320) linear float32 num_threads=1 1.0X 1.9ms vs 1.9ms (1, 3, 256, 256) -> (320, 320) nearest float32 num_threads=1 2.0X 1.8ms vs 0.9ms (1, 3, 256, 256) -> (320, 320) nearest uint8 num_threads=1 1.7X 1.8ms vs 1.0ms (1, 3, 256, 256) -> (320, 320) nearest-exact float32 num_threads=1 2.1X 1.9ms vs 0.9ms (1, 3, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=1 1.9X 1.9ms vs 1.0ms (1, 1, 256, 256) -> (320, 320) linear float32 num_threads=1 9X 1.6ms vs 0.2ms (1, 1, 256, 256) -> (320, 320) nearest float32 num_threads=1 16X 1.7ms vs 0.1ms (1, 1, 256, 256) -> (320, 320) nearest uint8 num_threads=1 10X 1.7ms vs 0.2ms (1, 1, 256, 256) -> (320, 320) nearest-exact float32 num_threads=1 17X 1.9ms vs 0.1ms (1, 1, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=1 11X 1.8ms vs 0.2ms (1, 3, 256, 256) -> (320, 320) linear float32 num_threads=2 1.7X 1.9ms vs 1.1ms (1, 3, 256, 256) -> (320, 320) nearest float32 num_threads=2 2.0X 1.0ms vs 0.5ms (1, 3, 256, 256) -> (320, 320) nearest uint8 num_threads=2 1.7X 0.9ms vs 0.5ms (1, 3, 256, 256) -> (320, 320) nearest-exact float32 num_threads=2 2.3X 1.1ms vs 0.5ms (1, 3, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=2 1.8X 1.0ms vs 0.5ms (1, 1, 256, 256) -> (320, 320) linear float32 num_threads=2 8X 1.6ms vs 0.2ms (1, 1, 256, 256) -> (320, 320) nearest float32 num_threads=2 14X 0.931ms vs 0.067ms (1, 1, 256, 256) -> (320, 320) nearest uint8 num_threads=2 7X 0.9ms vs 0.1ms (1, 1, 256, 256) -> (320, 320) nearest-exact float32 num_threads=2 15X 1.016ms vs 0.069ms (1, 1, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=2 9X 0.9ms vs 0.1ms (1, 3, 256, 256) -> (320, 320) linear float32 num_threads=12 8X 1.9ms vs 0.3ms (1, 3, 256, 256) -> (320, 320) nearest float32 num_threads=12 1.7X 0.2ms vs 0.1ms (1, 3, 256, 256) -> (320, 320) nearest uint8 num_threads=12 1.5X 0.2ms vs 0.1ms (1, 3, 256, 256) -> (320, 320) nearest-exact float32 num_threads=12 1.9X 0.2ms vs 0.1ms (1, 3, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=12 1.6X 0.2ms vs 0.1ms (1, 1, 256, 256) -> (320, 320) linear float32 num_threads=12 20X 1.630ms vs 0.081ms (1, 1, 256, 256) -> (320, 320) nearest float32 num_threads=12 10X 0.457ms vs 0.044ms (1, 1, 256, 256) -> (320, 320) nearest uint8 num_threads=12 7X 0.439ms vs 0.060ms (1, 1, 256, 256) -> (320, 320) nearest-exact float32 num_threads=12 11X 0.485ms vs 0.045ms (1, 1, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=12 8X 0.474ms vs 0.061ms (1, 3, 256, 256) -> (320, 320) linear float32 num_threads=32 8X 1.9ms vs 0.3ms (1, 3, 256, 256) -> (320, 320) nearest float32 num_threads=32 2.0X 0.2ms vs 0.1ms (1, 3, 256, 256) -> (320, 320) nearest uint8 num_threads=32 1.6X 0.2ms vs 0.1ms (1, 3, 256, 256) -> (320, 320) nearest-exact float32 num_threads=32 1.4X 0.2ms vs 0.2ms (1, 3, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=32 1.4X 0.2ms vs 0.1ms (1, 1, 256, 256) -> (320, 320) linear float32 num_threads=32 21X 1.628ms vs 0.078ms (1, 1, 256, 256) -> (320, 320) nearest float32 num_threads=32 9X 0.453ms vs 0.048ms (1, 1, 256, 256) -> (320, 320) nearest uint8 num_threads=32 7X 0.445ms vs 0.063ms (1, 1, 256, 256) -> (320, 320) nearest-exact float32 num_threads=32 11X 0.535ms vs 0.048ms (1, 1, 256, 256) -> (320, 320) nearest-exact uint8 num_threads=32 8X 0.502ms vs 0.063ms ---------------------------------------------------------------------------------------------------- (1, 3, 500, 500) -> (800, 800) linear float32 num_threads=1 1.0X 13.8ms vs 14.0ms (1, 3, 500, 500) -> (800, 800) nearest float32 num_threads=1 1.8X 13.1ms vs 7.4ms (1, 3, 500, 500) -> (800, 800) nearest uint8 num_threads=1 1.8X 11.1ms vs 6.1ms (1, 3, 500, 500) -> (800, 800) nearest-exact float32 num_threads=1 1.9X 13.9ms vs 7.4ms (1, 3, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=1 1.9X 11.8ms vs 6.1ms (1, 1, 500, 500) -> (800, 800) linear float32 num_threads=1 10X 10.2ms vs 1.1ms (1, 1, 500, 500) -> (800, 800) nearest float32 num_threads=1 19X 10.8ms vs 0.6ms (1, 1, 500, 500) -> (800, 800) nearest uint8 num_threads=1 11X 10.4ms vs 0.9ms (1, 1, 500, 500) -> (800, 800) nearest-exact float32 num_threads=1 20X 11.6ms vs 0.6ms (1, 1, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=1 12X 11.4ms vs 0.9ms (1, 3, 500, 500) -> (800, 800) linear float32 num_threads=2 1.8X 13.7ms vs 7.7ms (1, 3, 500, 500) -> (800, 800) nearest float32 num_threads=2 2.6X 7.3ms vs 2.8ms (1, 3, 500, 500) -> (800, 800) nearest uint8 num_threads=2 1.8X 5.6ms vs 3.1ms (1, 3, 500, 500) -> (800, 800) nearest-exact float32 num_threads=2 1.9X 7.9ms vs 4.1ms (1, 3, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=2 1.9X 6.0ms vs 3.1ms (1, 1, 500, 500) -> (800, 800) linear float32 num_threads=2 18X 10.1ms vs 0.6ms (1, 1, 500, 500) -> (800, 800) nearest float32 num_threads=2 19X 5.8ms vs 0.3ms (1, 1, 500, 500) -> (800, 800) nearest uint8 num_threads=2 10X 5.3ms vs 0.5ms (1, 1, 500, 500) -> (800, 800) nearest-exact float32 num_threads=2 20X 6.3ms vs 0.3ms (1, 1, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=2 11X 5.7ms vs 0.5ms (1, 3, 500, 500) -> (800, 800) linear float32 num_threads=12 8X 13.8ms vs 1.6ms (1, 3, 500, 500) -> (800, 800) nearest float32 num_threads=12 2.9X 1.5ms vs 0.5ms (1, 3, 500, 500) -> (800, 800) nearest uint8 num_threads=12 1.7X 1.0ms vs 0.5ms (1, 3, 500, 500) -> (800, 800) nearest-exact float32 num_threads=12 1.5X 1.5ms vs 1.0ms (1, 3, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=12 1.8X 1.0ms vs 0.6ms (1, 1, 500, 500) -> (800, 800) linear float32 num_threads=12 80X 10.1ms vs 0.1ms (1, 1, 500, 500) -> (800, 800) nearest float32 num_threads=12 13X 0.928ms vs 0.072ms (1, 1, 500, 500) -> (800, 800) nearest uint8 num_threads=12 8X 0.9ms vs 0.1ms (1, 1, 500, 500) -> (800, 800) nearest-exact float32 num_threads=12 13X 1.001ms vs 0.074ms (1, 1, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=12 9X 1.0ms vs 0.1ms (1, 3, 500, 500) -> (800, 800) linear float32 num_threads=32 18X 14.0ms vs 0.8ms (1, 3, 500, 500) -> (800, 800) nearest float32 num_threads=32 1.9X 1.0ms vs 0.6ms (1, 3, 500, 500) -> (800, 800) nearest uint8 num_threads=32 2.9X 0.7ms vs 0.2ms (1, 3, 500, 500) -> (800, 800) nearest-exact float32 num_threads=32 1.7X 0.9ms vs 0.6ms (1, 3, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=32 1.8X 0.4ms vs 0.2ms (1, 1, 500, 500) -> (800, 800) linear float32 num_threads=32 111X 10.254ms vs 0.092ms (1, 1, 500, 500) -> (800, 800) nearest float32 num_threads=32 14X 0.784ms vs 0.056ms (1, 1, 500, 500) -> (800, 800) nearest uint8 num_threads=32 7X 0.551ms vs 0.075ms (1, 1, 500, 500) -> (800, 800) nearest-exact float32 num_threads=32 11X 0.607ms vs 0.057ms (1, 1, 500, 500) -> (800, 800) nearest-exact uint8 num_threads=32 8X 0.596ms vs 0.076ms ---------------------------------------------------------------------------------------------------- (1, 3, 224, 224) -> (64, 64) linear float32 num_threads=1 1.0X 0.084ms vs 0.084ms (1, 3, 224, 224) -> (64, 64) nearest float32 num_threads=1 1.0X 0.077ms vs 0.078ms (1, 3, 224, 224) -> (64, 64) nearest uint8 num_threads=1 1.0X 0.076ms vs 0.076ms (1, 3, 224, 224) -> (64, 64) nearest-exact float32 num_threads=1 1.0X 0.083ms vs 0.083ms (1, 3, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=1 1.0X 0.081ms vs 0.082ms (1, 1, 224, 224) -> (64, 64) linear float32 num_threads=1 1.0X 0.071ms vs 0.071ms (1, 1, 224, 224) -> (64, 64) nearest float32 num_threads=1 1.0X 0.074ms vs 0.074ms (1, 1, 224, 224) -> (64, 64) nearest uint8 num_threads=1 1.0X 0.072ms vs 0.072ms (1, 1, 224, 224) -> (64, 64) nearest-exact float32 num_threads=1 1.0X 0.080ms vs 0.080ms (1, 1, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=1 0.9X 0.078ms vs 0.084ms (1, 3, 224, 224) -> (64, 64) linear float32 num_threads=2 1.0X 0.083ms vs 0.084ms (1, 3, 224, 224) -> (64, 64) nearest float32 num_threads=2 1.0X 0.076ms vs 0.077ms (1, 3, 224, 224) -> (64, 64) nearest uint8 num_threads=2 1.0X 0.075ms vs 0.074ms (1, 3, 224, 224) -> (64, 64) nearest-exact float32 num_threads=2 1.0X 0.082ms vs 0.083ms (1, 3, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=2 1.0X 0.080ms vs 0.083ms (1, 1, 224, 224) -> (64, 64) linear float32 num_threads=2 1.0X 0.070ms vs 0.071ms (1, 1, 224, 224) -> (64, 64) nearest float32 num_threads=2 1.0X 0.073ms vs 0.075ms (1, 1, 224, 224) -> (64, 64) nearest uint8 num_threads=2 1.0X 0.071ms vs 0.072ms (1, 1, 224, 224) -> (64, 64) nearest-exact float32 num_threads=2 1.0X 0.079ms vs 0.080ms (1, 1, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=2 1.0X 0.077ms vs 0.079ms (1, 3, 224, 224) -> (64, 64) linear float32 num_threads=12 1.0X 0.083ms vs 0.084ms (1, 3, 224, 224) -> (64, 64) nearest float32 num_threads=12 1.0X 0.080ms vs 0.078ms (1, 3, 224, 224) -> (64, 64) nearest uint8 num_threads=12 1.0X 0.077ms vs 0.075ms (1, 3, 224, 224) -> (64, 64) nearest-exact float32 num_threads=12 1.0X 0.083ms vs 0.083ms (1, 3, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=12 1.0X 0.083ms vs 0.082ms (1, 1, 224, 224) -> (64, 64) linear float32 num_threads=12 1.0X 0.071ms vs 0.071ms (1, 1, 224, 224) -> (64, 64) nearest float32 num_threads=12 1.0X 0.076ms vs 0.074ms (1, 1, 224, 224) -> (64, 64) nearest uint8 num_threads=12 1.0X 0.073ms vs 0.071ms (1, 1, 224, 224) -> (64, 64) nearest-exact float32 num_threads=12 1.0X 0.080ms vs 0.080ms (1, 1, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=12 1.0X 0.080ms vs 0.078ms (1, 3, 224, 224) -> (64, 64) linear float32 num_threads=32 1.0X 0.084ms vs 0.084ms (1, 3, 224, 224) -> (64, 64) nearest float32 num_threads=32 1.0X 0.078ms vs 0.077ms (1, 3, 224, 224) -> (64, 64) nearest uint8 num_threads=32 1.0X 0.076ms vs 0.076ms (1, 3, 224, 224) -> (64, 64) nearest-exact float32 num_threads=32 1.0X 0.083ms vs 0.083ms (1, 3, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=32 1.0X 0.081ms vs 0.082ms (1, 1, 224, 224) -> (64, 64) linear float32 num_threads=32 1.0X 0.072ms vs 0.072ms (1, 1, 224, 224) -> (64, 64) nearest float32 num_threads=32 1.0X 0.074ms vs 0.075ms (1, 1, 224, 224) -> (64, 64) nearest uint8 num_threads=32 1.0X 0.072ms vs 0.072ms (1, 1, 224, 224) -> (64, 64) nearest-exact float32 num_threads=32 1.0X 0.077ms vs 0.080ms (1, 1, 224, 224) -> (64, 64) nearest-exact uint8 num_threads=32 1.0X 0.076ms vs 0.079ms ---------------------------------------------------------------------------------------------------- (1, 3, 224, 224) -> (128, 128) linear float32 num_threads=1 1.0X 0.3ms vs 0.3ms (1, 3, 224, 224) -> (128, 128) nearest float32 num_threads=1 1.8X 0.3ms vs 0.2ms (1, 3, 224, 224) -> (128, 128) nearest uint8 num_threads=1 1.6X 0.3ms vs 0.2ms (1, 3, 224, 224) -> (128, 128) nearest-exact float32 num_threads=1 2.0X 0.3ms vs 0.2ms (1, 3, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=1 1.7X 0.3ms vs 0.2ms (1, 1, 224, 224) -> (128, 128) linear float32 num_threads=1 6X 0.265ms vs 0.044ms (1, 1, 224, 224) -> (128, 128) nearest float32 num_threads=1 10X 0.280ms vs 0.028ms (1, 1, 224, 224) -> (128, 128) nearest uint8 num_threads=1 7X 0.273ms vs 0.037ms (1, 1, 224, 224) -> (128, 128) nearest-exact float32 num_threads=1 11X 0.303ms vs 0.028ms (1, 1, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=1 8X 0.297ms vs 0.038ms (1, 3, 224, 224) -> (128, 128) linear float32 num_threads=2 1.5X 0.3ms vs 0.2ms (1, 3, 224, 224) -> (128, 128) nearest float32 num_threads=2 1.8X 0.163ms vs 0.093ms (1, 3, 224, 224) -> (128, 128) nearest uint8 num_threads=2 1.5X 0.2ms vs 0.1ms (1, 3, 224, 224) -> (128, 128) nearest-exact float32 num_threads=2 1.9X 0.180ms vs 0.096ms (1, 3, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=2 1.6X 0.2ms vs 0.1ms (1, 1, 224, 224) -> (128, 128) linear float32 num_threads=2 6X 0.264ms vs 0.044ms (1, 1, 224, 224) -> (128, 128) nearest float32 num_threads=2 10X 0.278ms vs 0.028ms (1, 1, 224, 224) -> (128, 128) nearest uint8 num_threads=2 7X 0.270ms vs 0.037ms (1, 1, 224, 224) -> (128, 128) nearest-exact float32 num_threads=2 11X 0.298ms vs 0.028ms (1, 1, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=2 8X 0.293ms vs 0.037ms (1, 3, 224, 224) -> (128, 128) linear float32 num_threads=12 1.5X 0.3ms vs 0.2ms (1, 3, 224, 224) -> (128, 128) nearest float32 num_threads=12 1.7X 0.158ms vs 0.095ms (1, 3, 224, 224) -> (128, 128) nearest uint8 num_threads=12 1.5X 0.2ms vs 0.1ms (1, 3, 224, 224) -> (128, 128) nearest-exact float32 num_threads=12 1.7X 0.170ms vs 0.100ms (1, 3, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=12 1.6X 0.2ms vs 0.1ms (1, 1, 224, 224) -> (128, 128) linear float32 num_threads=12 6X 0.269ms vs 0.043ms (1, 1, 224, 224) -> (128, 128) nearest float32 num_threads=12 11X 0.291ms vs 0.027ms (1, 1, 224, 224) -> (128, 128) nearest uint8 num_threads=12 8X 0.281ms vs 0.037ms (1, 1, 224, 224) -> (128, 128) nearest-exact float32 num_threads=12 11X 0.305ms vs 0.028ms (1, 1, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=12 8X 0.306ms vs 0.038ms (1, 3, 224, 224) -> (128, 128) linear float32 num_threads=32 1.5X 0.3ms vs 0.2ms (1, 3, 224, 224) -> (128, 128) nearest float32 num_threads=32 1.6X 0.160ms vs 0.098ms (1, 3, 224, 224) -> (128, 128) nearest uint8 num_threads=32 1.5X 0.2ms vs 0.1ms (1, 3, 224, 224) -> (128, 128) nearest-exact float32 num_threads=32 1.7X 0.171ms vs 0.099ms (1, 3, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=32 1.6X 0.2ms vs 0.1ms (1, 1, 224, 224) -> (128, 128) linear float32 num_threads=32 6X 0.269ms vs 0.044ms (1, 1, 224, 224) -> (128, 128) nearest float32 num_threads=32 10X 0.282ms vs 0.028ms (1, 1, 224, 224) -> (128, 128) nearest uint8 num_threads=32 7X 0.276ms vs 0.037ms (1, 1, 224, 224) -> (128, 128) nearest-exact float32 num_threads=32 11X 0.305ms vs 0.028ms (1, 1, 224, 224) -> (128, 128) nearest-exact uint8 num_threads=32 8X 0.299ms vs 0.038ms ---------------------------------------------------------------------------------------------------- (1, 3, 320, 320) -> (256, 256) linear float32 num_threads=1 1.0X 1.2ms vs 1.3ms (1, 3, 320, 320) -> (256, 256) nearest float32 num_threads=1 2.0X 1.2ms vs 0.6ms (1, 3, 320, 320) -> (256, 256) nearest uint8 num_threads=1 1.7X 1.1ms vs 0.7ms (1, 3, 320, 320) -> (256, 256) nearest-exact float32 num_threads=1 2.1X 1.2ms vs 0.6ms (1, 3, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=1 1.9X 1.2ms vs 0.7ms (1, 1, 320, 320) -> (256, 256) linear float32 num_threads=1 8X 1.1ms vs 0.1ms (1, 1, 320, 320) -> (256, 256) nearest float32 num_threads=1 15X 1.109ms vs 0.073ms (1, 1, 320, 320) -> (256, 256) nearest uint8 num_threads=1 10X 1.1ms vs 0.1ms (1, 1, 320, 320) -> (256, 256) nearest-exact float32 num_threads=1 16X 1.192ms vs 0.074ms (1, 1, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=1 11X 1.2ms vs 0.1ms (1, 3, 320, 320) -> (256, 256) linear float32 num_threads=2 1.7X 1.2ms vs 0.7ms (1, 3, 320, 320) -> (256, 256) nearest float32 num_threads=2 2.0X 0.6ms vs 0.3ms (1, 3, 320, 320) -> (256, 256) nearest uint8 num_threads=2 1.7X 0.6ms vs 0.3ms (1, 3, 320, 320) -> (256, 256) nearest-exact float32 num_threads=2 2.2X 0.7ms vs 0.3ms (1, 3, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=2 1.8X 0.6ms vs 0.3ms (1, 1, 320, 320) -> (256, 256) linear float32 num_threads=2 9X 1.0ms vs 0.1ms (1, 1, 320, 320) -> (256, 256) nearest float32 num_threads=2 11X 0.598ms vs 0.052ms (1, 1, 320, 320) -> (256, 256) nearest uint8 num_threads=2 8X 0.556ms vs 0.072ms (1, 1, 320, 320) -> (256, 256) nearest-exact float32 num_threads=2 12X 0.649ms vs 0.053ms (1, 1, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=2 8X 0.598ms vs 0.073ms (1, 3, 320, 320) -> (256, 256) linear float32 num_threads=12 5X 1.2ms vs 0.3ms (1, 3, 320, 320) -> (256, 256) nearest float32 num_threads=12 1.5X 0.2ms vs 0.1ms (1, 3, 320, 320) -> (256, 256) nearest uint8 num_threads=12 1.3X 0.2ms vs 0.1ms (1, 3, 320, 320) -> (256, 256) nearest-exact float32 num_threads=12 1.6X 0.2ms vs 0.1ms (1, 3, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=12 1.4X 0.2ms vs 0.1ms (1, 1, 320, 320) -> (256, 256) linear float32 num_threads=12 9X 1.0ms vs 0.1ms (1, 1, 320, 320) -> (256, 256) nearest float32 num_threads=12 12X 0.572ms vs 0.048ms (1, 1, 320, 320) -> (256, 256) nearest uint8 num_threads=12 8X 0.560ms vs 0.068ms (1, 1, 320, 320) -> (256, 256) nearest-exact float32 num_threads=12 13X 0.617ms vs 0.049ms (1, 1, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=12 9X 0.604ms vs 0.068ms (1, 3, 320, 320) -> (256, 256) linear float32 num_threads=32 5X 1.2ms vs 0.3ms (1, 3, 320, 320) -> (256, 256) nearest float32 num_threads=32 1.5X 0.2ms vs 0.1ms (1, 3, 320, 320) -> (256, 256) nearest uint8 num_threads=32 1.4X 0.2ms vs 0.1ms (1, 3, 320, 320) -> (256, 256) nearest-exact float32 num_threads=32 1.6X 0.2ms vs 0.1ms (1, 3, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=32 1.4X 0.2ms vs 0.1ms (1, 1, 320, 320) -> (256, 256) linear float32 num_threads=32 13X 1.042ms vs 0.081ms (1, 1, 320, 320) -> (256, 256) nearest float32 num_threads=32 12X 0.586ms vs 0.050ms (1, 1, 320, 320) -> (256, 256) nearest uint8 num_threads=32 8X 0.562ms vs 0.069ms (1, 1, 320, 320) -> (256, 256) nearest-exact float32 num_threads=32 12X 0.621ms vs 0.051ms (1, 1, 320, 320) -> (256, 256) nearest-exact uint8 num_threads=32 9X 0.609ms vs 0.070ms ---------------------------------------------------------------------------------------------------- (1, 3, 600, 400) -> (224, 224) linear float32 num_threads=1 1.0X 1.0ms vs 1.0ms (1, 3, 600, 400) -> (224, 224) nearest float32 num_threads=1 1.9X 0.9ms vs 0.5ms (1, 3, 600, 400) -> (224, 224) nearest uint8 num_threads=1 1.7X 0.9ms vs 0.5ms (1, 3, 600, 400) -> (224, 224) nearest-exact float32 num_threads=1 2.1X 1.0ms vs 0.5ms (1, 3, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=1 1.8X 0.9ms vs 0.5ms (1, 1, 600, 400) -> (224, 224) linear float32 num_threads=1 7X 0.8ms vs 0.1ms (1, 1, 600, 400) -> (224, 224) nearest float32 num_threads=1 14X 0.852ms vs 0.061ms (1, 1, 600, 400) -> (224, 224) nearest uint8 num_threads=1 9X 0.828ms vs 0.087ms (1, 1, 600, 400) -> (224, 224) nearest-exact float32 num_threads=1 15X 0.922ms vs 0.061ms (1, 1, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=1 10X 0.897ms vs 0.087ms (1, 3, 600, 400) -> (224, 224) linear float32 num_threads=2 1.6X 0.9ms vs 0.6ms (1, 3, 600, 400) -> (224, 224) nearest float32 num_threads=2 1.9X 0.5ms vs 0.2ms (1, 3, 600, 400) -> (224, 224) nearest uint8 num_threads=2 1.7X 0.4ms vs 0.3ms (1, 3, 600, 400) -> (224, 224) nearest-exact float32 num_threads=2 2.1X 0.5ms vs 0.3ms (1, 3, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=2 1.8X 0.5ms vs 0.3ms (1, 1, 600, 400) -> (224, 224) linear float32 num_threads=2 10X 0.808ms vs 0.084ms (1, 1, 600, 400) -> (224, 224) nearest float32 num_threads=2 10X 0.462ms vs 0.046ms (1, 1, 600, 400) -> (224, 224) nearest uint8 num_threads=2 7X 0.429ms vs 0.062ms (1, 1, 600, 400) -> (224, 224) nearest-exact float32 num_threads=2 12X 0.504ms vs 0.044ms (1, 1, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=2 7X 0.461ms vs 0.063ms (1, 3, 600, 400) -> (224, 224) linear float32 num_threads=12 4X 1.0ms vs 0.2ms (1, 3, 600, 400) -> (224, 224) nearest float32 num_threads=12 1.7X 0.2ms vs 0.1ms (1, 3, 600, 400) -> (224, 224) nearest uint8 num_threads=12 1.5X 0.2ms vs 0.1ms (1, 3, 600, 400) -> (224, 224) nearest-exact float32 num_threads=12 1.9X 0.2ms vs 0.1ms (1, 3, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=12 1.6X 0.2ms vs 0.1ms (1, 1, 600, 400) -> (224, 224) linear float32 num_threads=12 12X 0.820ms vs 0.067ms (1, 1, 600, 400) -> (224, 224) nearest float32 num_threads=12 11X 0.438ms vs 0.041ms (1, 1, 600, 400) -> (224, 224) nearest uint8 num_threads=12 8X 0.431ms vs 0.056ms (1, 1, 600, 400) -> (224, 224) nearest-exact float32 num_threads=12 12X 0.482ms vs 0.041ms (1, 1, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=12 8X 0.467ms vs 0.056ms (1, 3, 600, 400) -> (224, 224) linear float32 num_threads=32 4X 1.0ms vs 0.3ms (1, 3, 600, 400) -> (224, 224) nearest float32 num_threads=32 1.7X 0.2ms vs 0.1ms (1, 3, 600, 400) -> (224, 224) nearest uint8 num_threads=32 1.5X 0.2ms vs 0.1ms (1, 3, 600, 400) -> (224, 224) nearest-exact float32 num_threads=32 1.8X 0.2ms vs 0.1ms (1, 3, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=32 1.6X 0.2ms vs 0.1ms (1, 1, 600, 400) -> (224, 224) linear float32 num_threads=32 12X 0.824ms vs 0.070ms (1, 1, 600, 400) -> (224, 224) nearest float32 num_threads=32 10X 0.443ms vs 0.044ms (1, 1, 600, 400) -> (224, 224) nearest uint8 num_threads=32 7X 0.438ms vs 0.059ms (1, 1, 600, 400) -> (224, 224) nearest-exact float32 num_threads=32 11X 0.479ms vs 0.045ms (1, 1, 600, 400) -> (224, 224) nearest-exact uint8 num_threads=32 8X 0.470ms vs 0.059ms ---------------------------------------------------------------------------------------------------- (1, 3, 800, 800) -> (500, 500) linear float32 num_threads=1 1.0X 4.7ms vs 4.7ms (1, 3, 800, 800) -> (500, 500) nearest float32 num_threads=1 2.0X 4.4ms vs 2.2ms (1, 3, 800, 800) -> (500, 500) nearest uint8 num_threads=1 1.8X 4.3ms vs 2.5ms (1, 3, 800, 800) -> (500, 500) nearest-exact float32 num_threads=1 2.1X 4.7ms vs 2.2ms (1, 3, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=1 1.9X 4.6ms vs 2.5ms (1, 1, 800, 800) -> (500, 500) linear float32 num_threads=1 9X 4.0ms vs 0.4ms (1, 1, 800, 800) -> (500, 500) nearest float32 num_threads=1 17X 4.2ms vs 0.2ms (1, 1, 800, 800) -> (500, 500) nearest uint8 num_threads=1 11X 4.1ms vs 0.4ms (1, 1, 800, 800) -> (500, 500) nearest-exact float32 num_threads=1 19X 4.6ms vs 0.2ms (1, 1, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=1 12X 4.5ms vs 0.4ms (1, 3, 800, 800) -> (500, 500) linear float32 num_threads=2 1.7X 4.7ms vs 2.7ms (1, 3, 800, 800) -> (500, 500) nearest float32 num_threads=2 2.1X 2.4ms vs 1.1ms (1, 3, 800, 800) -> (500, 500) nearest uint8 num_threads=2 1.8X 2.2ms vs 1.3ms (1, 3, 800, 800) -> (500, 500) nearest-exact float32 num_threads=2 2.3X 2.6ms vs 1.1ms (1, 3, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=2 1.9X 2.3ms vs 1.3ms (1, 1, 800, 800) -> (500, 500) linear float32 num_threads=2 15X 4.0ms vs 0.3ms (1, 1, 800, 800) -> (500, 500) nearest float32 num_threads=2 16X 2.3ms vs 0.1ms (1, 1, 800, 800) -> (500, 500) nearest uint8 num_threads=2 9X 2.1ms vs 0.2ms (1, 1, 800, 800) -> (500, 500) nearest-exact float32 num_threads=2 17X 2.5ms vs 0.1ms (1, 1, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=2 10X 2.3ms vs 0.2ms (1, 3, 800, 800) -> (500, 500) linear float32 num_threads=12 10X 4.7ms vs 0.5ms (1, 3, 800, 800) -> (500, 500) nearest float32 num_threads=12 1.9X 0.4ms vs 0.2ms (1, 3, 800, 800) -> (500, 500) nearest uint8 num_threads=12 1.7X 0.4ms vs 0.2ms (1, 3, 800, 800) -> (500, 500) nearest-exact float32 num_threads=12 1.9X 0.4ms vs 0.2ms (1, 3, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=12 1.8X 0.4ms vs 0.2ms (1, 1, 800, 800) -> (500, 500) linear float32 num_threads=12 41X 3.969ms vs 0.096ms (1, 1, 800, 800) -> (500, 500) nearest float32 num_threads=12 11X 0.545ms vs 0.051ms (1, 1, 800, 800) -> (500, 500) nearest uint8 num_threads=12 8X 0.532ms vs 0.070ms (1, 1, 800, 800) -> (500, 500) nearest-exact float32 num_threads=12 11X 0.590ms vs 0.052ms (1, 1, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=12 8X 0.578ms vs 0.071ms (1, 3, 800, 800) -> (500, 500) linear float32 num_threads=32 17X 4.7ms vs 0.3ms (1, 3, 800, 800) -> (500, 500) nearest float32 num_threads=32 1.8X 0.2ms vs 0.1ms (1, 3, 800, 800) -> (500, 500) nearest uint8 num_threads=32 2.0X 0.3ms vs 0.1ms (1, 3, 800, 800) -> (500, 500) nearest-exact float32 num_threads=32 1.9X 0.2ms vs 0.1ms (1, 3, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=32 1.6X 0.2ms vs 0.1ms (1, 1, 800, 800) -> (500, 500) linear float32 num_threads=32 45X 4.028ms vs 0.090ms (1, 1, 800, 800) -> (500, 500) nearest float32 num_threads=32 10X 0.549ms vs 0.053ms (1, 1, 800, 800) -> (500, 500) nearest uint8 num_threads=32 7X 0.536ms vs 0.072ms (1, 1, 800, 800) -> (500, 500) nearest-exact float32 num_threads=32 11X 0.592ms vs 0.055ms (1, 1, 800, 800) -> (500, 500) nearest-exact uint8 num_threads=32 8X 0.581ms vs 0.074ms ``` </details> Code: <details> I used this file which is adapted from https://github.com/pytorch/pytorch/blob/master/benchmarks/operator_benchmark/pt/interpolate_test.py ```py import operator_benchmark as op_bench import torch """Microbenchmarks for interpolate operator.""" class InterpolateBenchmark(op_bench.TorchBenchmarkBase): def init(self, input_size, output_size, channels_last=False, mode='linear', dtype=torch.float): input_image = torch.randint(0, 256, size=input_size, dtype=dtype, device='cpu', requires_grad=self.auto_set()) if channels_last: if input_image.ndim == 4: input_image = input_image.contiguous(memory_format=torch.channels_last) elif input_image.ndim == 5: input_image = input_image.contiguous(memory_format=torch.channels_last_3d) else: raise ValueError( f"Can not set channels_last to the input of {input_image.ndim} dims" ) align_corners = None if "nearest" in mode else False if mode == "linear": mode = { 3: 'linear', 4: 'bilinear', 5: 'trilinear', }[input_image.ndim] self.inputs = { "input_image": input_image, "output_size": output_size, "mode": mode, "align_corners": align_corners, } self.set_module_name("interpolate") def forward(self, input_image, output_size, mode, align_corners): return torch.nn.functional.interpolate(input_image, size=output_size, mode=mode, align_corners=align_corners) def make_config(): sizes = ( ((224, 224), (64, 64)), ((224, 224), (128, 128)), ((600, 400), (224, 224)), ((320, 320), (256, 256)), ((800, 800), (500, 500)), ) attrs = [] for (HW1, HW2) in sizes: attrs.append([(1, 3, *HW1), HW2]) # 3 channels attrs.append([(1, 1, *HW1), HW2]) # 1 channel attrs.append([(1, 3, *HW2), HW1]) # 3 channels attrs.append([(1, 1, *HW2), HW1]) # 1 channel config = op_bench.config_list( attr_names=["input_size", "output_size"], attrs=attrs, cross_product_configs={ 'channels_last': [True], 'mode': ["linear", "nearest", "nearest-exact"], 'dtype': [torch.float, torch.uint8] }, tags=["short"], ) # Need to remove instances with both torch.int and linear # Note: this is naaaasty def get_mode(l): for d in l: if "mode" in d: return d["mode"] def get_dtype(l): for d in l: if "dtype" in d: return d["dtype"] config = [l for l in config if not(get_mode(l) == "linear" and get_dtype(l) == torch.uint8)] return config config = make_config() op_bench.generate_pt_test(config, InterpolateBenchmark) if __name__ == "__main__": op_bench.benchmark_runner.main() ``` with ``` for num_threads in 1 2 12 32; do echo "num_threads=$num_threads" && python -m pt.my_interpolate_test --iterations 1000 --omp_num_threads $num_threads ; done > $out_file ``` and this very ugly helper ```py import re with open("main") as f: main = f.readlines() with open("new") as f: new = f.readlines() out = [] for main_line, new_line in zip(main, new): if main_line.startswith("num_threads="): num_threads = int(main_line.split("=")[-1]) if main_line.startswith("# Input"): deets = f"{main_line.strip()}, {num_threads=}" if main_line.startswith("Forward"): main_time = float(main_line.split()[-1]) new_time = float(new_line.split()[-1]) ratio = main_time / new_time fmt = ".1f" if ratio < 3 else ".0f" improv = f"{ratio:{fmt}}X" time_fmt = ",.3f" if new_time < 100 else ",.1f" deets = deets.strip().replace("# Input: ", "") deets = deets.replace(": ", "=") deets = deets.replace("input_size=", "") deets = deets.replace(", output_size=", " -> ") deets = deets.replace("dtype=torch.", "") deets = deets.replace("mode=", "") deets = deets.replace("channels_last=True, ", "") split = deets.split(",") size = ','.join(split[:-3]) mode, dtype, threads = split[-3:] deets = f"{size:<30} {mode:<15} {dtype:<10} {threads:<15}" l = f"{deets} {improv:<5} {main_time / 1000:{time_fmt}}ms vs {new_time / 1000:{time_fmt}}ms" out.append(l) def key(s): # s = ''.join(s.split()[1:]) # remove "N.nX" part num_threads = (int(re.findall(r"num_threads=(\d+)", s)[0]),) input_shape, output_shape = re.findall("$.*?$", s) input_shape = input_shape[1:-1] # remove parenthesis input_HW = tuple(int(x) for x in input_shape.split(",")[-2:]) input_C = (-int(input_shape.split(",")[1]),) output_HW = tuple(int(x) for x in output_shape[1:-1].split(",")) is_downsample = (output_HW[0] < input_HW[0],) if "linear" in s: mode = "linear" elif "nearest-exact" in s: mode = "nearest-exact" else: assert "nearest" in s mode = "nearest" mode = (mode,) return is_downsample + input_HW + output_HW + num_threads + input_C + mode for i, l in enumerate(sorted(out, key=key)): if i % 10 == 0 and i % 40 != 0: print() if i % 40 == 0: print("-" * 100) print(l) ``` </details> Closes #83840 When this is merged we should be able to remove some hack in vision as well pytorch/vision#6661 (CC vfdev-5 datumbox ) [ghstack-poisoned]
diff --git a/aten/src/ATen/native/cpu/UpSampleKernel.cpp b/aten/src/ATen/native/cpu/UpSampleKernel.cpp
@@ -1260,22 +1260,26 @@ void _upsample_nearest_exact1d_kernel_impl(
     output, input, false, {scales_w});
 }
 
-// This condition is used to know whether we should dispatch to a vectorized
-// kernel, or to the more general upsample_generic_Nd_kernel_impl(). For now,
-// the vectorized kernels are only optimized for channels_last and when C >= 4
-// (shape = NCHW). For a very wide range of use-cases (typically image or mask
-// resizing where we have C < 4), using upsample_generic_Nd_kernel_impl() is
-// actually faster. On top of that, bencharmks showed that this also depends on
-// the *output* size (output_H + output_W) , for both upsampling and
-// downsampling. The current 128 threshold was determined through benchmarks.
-#define USE_VECTORIZED_KERNEL_COND (((input.is_contiguous(at::MemoryFormat::ChannelsLast)) && (input.size(-3) > 3)) || ((output.size(-2) + output.size(-1)) <= 128))
+int _use_vectorized_kernel_cond(
+    const Tensor& output,
+    const Tensor& input) {
+      // This condition is used to know whether we should dispatch to a vectorized
+      // kernel, or to the more general upsample_generic_Nd_kernel_impl(). For now,
+      // the vectorized kernels are only optimized for channels_last and when C >= 4
+      // (shape = NCHW). For a very wide range of use-cases (typically image or mask
+      // resizing where we have C < 4), using upsample_generic_Nd_kernel_impl() is
+      // actually faster. On top of that, bencharmks showed that this also depends on
+      // the *output* size (output_H + output_W) , for both upsampling and
+      // downsampling. The current 128 threshold was determined through benchmarks.
+      return ((input.is_contiguous(at::MemoryFormat::ChannelsLast)) && (input.size(-3) > 3)) || ((output.size(-2) + output.size(-1)) <= 128);
+}
 
 void upsample_nearest2d_kernel_impl(
     const Tensor& output,
     const Tensor& input,
     c10::optional<double> scales_h,
     c10::optional<double> scales_w) {
-  if (USE_VECTORIZED_KERNEL_COND) {
+  if (_use_vectorized_kernel_cond(output, input)) {
     AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Byte, at::ScalarType::BFloat16,
         input.scalar_type(), "upsample_nearest2d_channels_last", [&] {
       cpu_upsample_nearest_channels_last<scalar_t, scale_t, nearest_idx>(output, input, {scales_h, scales_w});
@@ -1291,7 +1295,7 @@ void _upsample_nearest_exact2d_kernel_impl(
     const Tensor& input,
     c10::optional<double> scales_h,
     c10::optional<double> scales_w) {
-  if (USE_VECTORIZED_KERNEL_COND) {
+  if (_use_vectorized_kernel_cond(output, input)) {
     AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::Byte, input.scalar_type(), "upsample_nearest2d_channels_last", [&] {
       cpu_upsample_nearest_channels_last<scalar_t, scale_t, nearest_exact_idx>(output, input, {scales_h, scales_w});
     });
@@ -1350,12 +1354,12 @@ void upsample_bilinear2d_kernel_impl(
     c10::optional<double> scales_h,
     c10::optional<double> scales_w) {
 
-  // See note above about USE_VECTORIZED_KERNEL_COND. The extra cond is present
+  // See note above about _use_vectorized_kernel_cond(output, input). The extra cond is present
   // because benchmarks showed that with only 1 thread, images (C == 3) were
   // slightly faster with the vectorized kernel than with the generic one.
   // That's not the case for masks though (C == 1), which strongly benefit from
   // using the generic kernel.
-  if ((USE_VECTORIZED_KERNEL_COND) || (at::get_num_threads() == 1 && input.size(-3) == 3)) {
+  if ((_use_vectorized_kernel_cond(output, input)) || (at::get_num_threads() == 1 && input.size(-3) == 3)) {
     AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, input.scalar_type(), "upsample_bilinear2d_channels_last", [&] {
       cpu_upsample_linear_channels_last<scalar_t, scale_t>(output, input, align_corners, {scales_h, scales_w});
     });
diff --git a/benchmarks/operator_benchmark/pt/interpolate_test.py b/benchmarks/operator_benchmark/pt/interpolate_test.py
@@ -49,6 +49,10 @@ def forward(self, input_image, output_size, mode, align_corners):
         [(1, 3, 60, 40), (24, 24)],
         [(1, 3, 600, 400), (240, 240)],
         [(1, 3, 320, 320), (256, 256)],
+
+        [(1, 1, 60, 40), (24, 24)],
+        [(1, 1, 600, 400), (240, 240)],
+        [(1, 1, 320, 320), (256, 256)],
     ],
     cross_product_configs={
         'channels_last': [True, False],
@@ -63,6 +67,10 @@ def forward(self, input_image, output_size, mode, align_corners):
         [(1, 3, 60, 40), (24, 24)],
         [(1, 3, 600, 400), (240, 240)],
         [(1, 3, 320, 320), (256, 256)],
+
+        [(1, 1, 60, 40), (24, 24)],
+        [(1, 1, 600, 400), (240, 240)],
+        [(1, 1, 320, 320), (256, 256)],
     ],
     cross_product_configs={
         'channels_last': [True, False],
@@ -80,6 +88,10 @@ def forward(self, input_image, output_size, mode, align_corners):
         [(1, 3, 500, 500), (256, 256)],
         [(1, 3, 500, 500), (800, 800)],
 
+        [(1, 1, 320, 320), (512, 512)],
+        [(1, 1, 500, 500), (256, 256)],
+        [(1, 1, 500, 500), (800, 800)],
+
         # vectorization test-case
         [(2, 128, 64, 46), (128, 128)],
         [(2, 128, 64, 46), (32, 24)],
