diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..0f945a65a86df2ac702c3f5ce0bd39a90586be73 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.pth.tar filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..1730cce424788e8683cb7cee93d537ae04122a15 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +**/__pycache__/ +**/.ipynb_checkpoints/ +**/data/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..3324643fb5ce32832cd550cc7cd80730e80bea87 --- /dev/null +++ b/LICENSE @@ -0,0 +1,2 @@ +Demo software - Experiments on binary neural networks in computer vision by HyperbeeAI +Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai diff --git a/README.md b/README.md index 5c0d499d78b811d53f0056c53cd1d031d55cdcad..be468d158f4d5d9d395895578f5778cbad122e8e 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,9 @@ ---- -license: other ---- +# Experiments on binary neural networks in computer vision by HyperbeeAI + +Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai + +This repository contains our experiments for quantized and binary neural networks for computer vision tasks, evaluated over the CIFAR100 benchmark dataset. + +See checkpoints/ and associated evaluation scripts. See documentation/ for more information on results: + +![results](./documentation/edited-results-graph.png) diff --git a/checkpoints/maxim000_nas_8b/hardware_checkpoint.pth.tar b/checkpoints/maxim000_nas_8b/hardware_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..34ec6492c8d776389421f7193224712b5f5f66d2 --- /dev/null +++ b/checkpoints/maxim000_nas_8b/hardware_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad65160c4336969064bb46051078022e2dc2ff117201eb4c3204a40a45db426a +size 4229587 diff --git a/checkpoints/maxim000_nas_8b/histograms_hardware_checkpoint/conv1_1.jpg b/checkpoints/maxim000_nas_8b/histograms_hardware_checkpoint/conv1_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8e4c0aca242a1ae81a0a21d69025ecf67fdfa016 --- /dev/null +++ b/checkpoints/maxim000_nas_8b/histograms_hardware_checkpoint/conv1_1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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sha256:3f46937bfeb91cde5856417052cfc47a6d2ff6cc731cf2e8e119a45cffc2e524 +size 239390 diff --git a/checkpoints/maxim000_nas_8b/readme b/checkpoints/maxim000_nas_8b/readme new file mode 100644 index 0000000000000000000000000000000000000000..b55542325b630832f065442bfddda0f453509497 --- /dev/null +++ b/checkpoints/maxim000_nas_8b/readme @@ -0,0 +1 @@ +same ID on deliverable v1 \ No newline at end of file diff --git a/checkpoints/maxim000_nas_8b/statistics_hardware_checkpoint b/checkpoints/maxim000_nas_8b/statistics_hardware_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..3859f993f9fa6319df03ba26ca1ffd6b51223e89 --- /dev/null +++ b/checkpoints/maxim000_nas_8b/statistics_hardware_checkpoint @@ -0,0 +1,179 @@ +Top: + epoch + state_dict + arch + optimizer_state_dict + optimizer_type + compression_sched + extras +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'current_top1': 64.22, 'best_top1': 64.22, 'best_epoch': 254, 'clipping_method': 'MAX_BIT_SHIFT'} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 60 + min, max, mean: -1625.0 , 1508.0 , 104.171875 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 145 + min, max, mean: -108.0 , 127.0 , -0.11921296 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -9670.0 , 7746.0 , 3041.8125 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 200 + min, max, mean: -128.0 , 115.0 , -4.880371 + conv1_3 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 62 + min, max, mean: -10111.0 , 16383.0 , 2751.25 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 211 + min, max, mean: -128.0 , 127.0 , -1.8574761 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 15 + min, max, mean: -16384.0 , 16383.0 , 4184.625 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 241 + min, max, mean: -128.0 , 127.0 , -0.30381945 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -6662.0 , 9154.0 , 423.60938 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 214 + min, max, mean: -128.0 , 127.0 , -3.1674805 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 56 + min, max, mean: -16384.0 , 16383.0 , 4185.4375 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 256 + min, max, mean: -128.0 , 127.0 , -0.86854386 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 120 + min, max, mean: -16384.0 , 16383.0 , 4592.4453 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 247 + min, max, mean: -128.0 , 127.0 , -4.216797 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 23 + min, max, mean: -16384.0 , 16383.0 , -5280.2656 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 256 + min, max, mean: -128.0 , 127.0 , 0.3366021 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 127 + min, max, mean: -12892.0 , 13250.0 , 669.0703 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 255 + min, max, mean: -128.0 , 127.0 , -0.24837239 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -12592.0 , 16346.0 , 3888.5 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 245 + min, max, mean: -128.0 , 127.0 , -0.55474854 + fc + output_shift: [2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 98 + min, max, mean: -1793.0 , 1060.0 , -39.89 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 195 + min, max, mean: -128.0 , 76.0 , -7.866582 diff --git a/checkpoints/maxim000_nas_8b/statistics_training_checkpoint b/checkpoints/maxim000_nas_8b/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..ff16021480335f36eadb962f22251253f6b382ba --- /dev/null +++ b/checkpoints/maxim000_nas_8b/statistics_training_checkpoint @@ -0,0 +1,179 @@ +Top: + epoch + state_dict + arch + optimizer_state_dict + optimizer_type + compression_sched + extras +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'current_top1': 64.22, 'best_top1': 64.22, 'best_epoch': 254} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.09915378 , 0.09204 , 0.0063624056 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.84696245 , 1.1112751 , -0.0008976079 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.2951058 , 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0000000000000000000000000000000000000000..7770eee628cf69d0bb9dddf21bf49c1951ef8c04 --- /dev/null +++ b/checkpoints/maxim001_simplenet_2b4b8b/statistics_hardware_checkpoint @@ -0,0 +1,228 @@ +Top: + epoch + state_dict + arch + optimizer_state_dict + optimizer_type + compression_sched + extras +------------------------------------- +arch: ai85simplenet +------------------------------------- +extras: {'current_top1': 55.76, 'best_top1': 55.76, 'best_epoch': 291, 'clipping_method': 'MAX_BIT_SHIFT'} +------------------------------------- +state_dict: + conv1 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 16 , [16] + # of unique elements: 16 + min, max, mean: -3141.0 , 6035.0 , 1187.375 + weight + total # of elements, shape: 432 , [16, 3, 3, 3] + # of unique elements: 90 + min, max, mean: -103.0 , 95.0 , -0.14583333 + conv2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -882.0 , 968.0 , 307.5 + weight + total # of elements, shape: 2880 , [20, 16, 3, 3] + # of unique elements: 15 + min, max, mean: -8.0 , 7.0 , -0.11736111 + conv3 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 15 + min, max, mean: -165.0 , 255.0 , 114.9 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.05888889 + conv4 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 19 + min, max, mean: -256.0 , 255.0 , 74.95 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.051944446 + conv5 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 15 + min, max, mean: -256.0 , 255.0 , 86.65 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.008611111 + conv6 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 14 + min, max, mean: -204.0 , 255.0 , 120.2 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.018055556 + conv7 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 44 , [44] + # of unique elements: 38 + min, max, mean: -256.0 , 255.0 , 46.636364 + weight + total # of elements, shape: 7920 , [44, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.036237374 + conv8 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 16 + min, max, mean: -256.0 , 255.0 , 83.666664 + weight + total # of elements, shape: 19008 , [48, 44, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.026936026 + conv9 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 17 + min, max, mean: -256.0 , 255.0 , 164.5625 + weight + total # of elements, shape: 20736 , [48, 48, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.042197146 + conv10 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 96 , [96] + # of unique elements: 34 + min, max, mean: -256.0 , 255.0 , -113.583336 + weight + total # of elements, shape: 41472 , [96, 48, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , 0.025583526 + conv11 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 512 , [512] + # of unique elements: 435 + min, max, mean: -1024.0 , 1023.0 , -48.671875 + +[WARNING]: # of unique elements in bias tensor is more than that allowed by bias_bits. + This might be OK, since Maxim deployment repository right shifts these. + + weight + total # of elements, shape: 49152 , [512, 96, 1, 1] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , -0.051452637 + conv12 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 115 + min, max, mean: -1024.0 , 1023.0 , 360.78125 + weight + total # of elements, shape: 65536 , [128, 512, 1, 1] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , -0.100616455 + conv13 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 111 + min, max, mean: -1024.0 , 1023.0 , 317.4922 + weight + total # of elements, shape: 147456 , [128, 128, 3, 3] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , 0.000386556 + conv14 + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 81 + min, max, mean: -146.0 , 131.0 , -3.66 + weight + total # of elements, shape: 12800 , [100, 128, 1, 1] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , -0.8530469 diff --git a/checkpoints/maxim001_simplenet_2b4b8b/statistics_training_checkpoint b/checkpoints/maxim001_simplenet_2b4b8b/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..936a60956a165c7d9ee74b7488ab612b9e9bbdb0 --- /dev/null +++ b/checkpoints/maxim001_simplenet_2b4b8b/statistics_training_checkpoint @@ -0,0 +1,224 @@ +Top: + epoch + state_dict + arch + optimizer_state_dict + optimizer_type + compression_sched + extras +------------------------------------- +arch: ai85simplenet +------------------------------------- +extras: {'current_top1': 55.76, 'best_top1': 55.76, 'best_epoch': 291} +------------------------------------- +state_dict: + conv1 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 16 , [16] + # of unique elements: 16 + min, max, mean: -0.19168334 , 0.36834052 , 0.07247388 + weight + total # of elements, shape: 432 , [16, 3, 3, 3] + # of unique elements: 432 + min, max, mean: -0.802268 , 0.7458835 , -0.0010856437 + conv2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.43049204 , 0.47276396 , 0.15015088 + weight + total # of elements, shape: 2880 , [20, 16, 3, 3] + # of unique elements: 2880 + min, max, mean: -0.6489339 , 0.44373256 , -0.0075482004 + conv3 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.16107927 , 0.64212 , 0.16932115 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3600 + min, max, mean: -0.6594693 , 0.4297285 , -0.0048244996 + conv4 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.2788192 , 0.36745375 , 0.08209655 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3600 + min, max, mean: -0.3781386 , 0.35877833 , -0.004330597 + conv5 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.4778669 , 0.69232625 , 0.1373336 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3600 + min, max, mean: -0.48527744 , 0.3855096 , -2.4955074e-05 + conv6 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.1993689 , 0.68981844 , 0.19056796 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3600 + min, max, mean: -0.32661057 , 0.3896761 , -0.0005324371 + conv7 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 44 , [44] + # of unique elements: 44 + min, max, mean: -0.33314848 , 0.5373695 , 0.05712423 + weight + total # of elements, shape: 7920 , [44, 20, 3, 3] + # of unique elements: 7917 + min, max, mean: -0.29842344 , 0.26807493 , -0.0036170066 + conv8 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 48 + min, max, mean: -0.35770896 , 0.9294356 , 0.1538872 + weight + total # of elements, shape: 19008 , [48, 44, 3, 3] + # of unique elements: 19005 + min, max, mean: -0.25119358 , 0.22448556 , -0.00043370738 + conv9 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 48 + min, max, mean: -0.51157075 , 0.6982333 , 0.19713795 + weight + total # of elements, shape: 20736 , [48, 48, 3, 3] + # of unique elements: 20728 + min, max, mean: -0.19476362 , 0.19595417 , -0.0007558516 + conv10 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 96 , [96] + # of unique elements: 96 + min, max, mean: -0.49737096 , 0.5452776 , -0.09346354 + weight + total # of elements, shape: 41472 , [96, 48, 3, 3] + # of unique elements: 41434 + min, max, mean: -0.1662616 , 0.32571185 , 0.003046196 + conv11 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 512 , [512] + # of unique elements: 512 + min, max, mean: -0.7622132 , 0.6625604 , -0.025022728 + weight + total # of elements, shape: 49152 , [512, 96, 1, 1] + # of unique elements: 49130 + min, max, mean: -0.5251649 , 0.55524755 , -0.0027382441 + conv12 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.276329 , 0.391946 , 0.09177708 + weight + total # of elements, shape: 65536 , [128, 512, 1, 1] + # of unique elements: 65501 + min, max, mean: -0.48823273 , 0.35944995 , -0.0032784953 + conv13 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -1.1315831 , 1.3784926 , 0.32567012 + weight + total # of elements, shape: 147456 , [128, 128, 3, 3] + # of unique elements: 147257 + min, max, mean: -1.3093278 , 1.2955139 , -2.2848448e-05 + conv14 + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.2843461 , 0.25533894 , -0.0070921965 + weight + total # of elements, shape: 12800 , [100, 128, 1, 1] + # of unique elements: 12799 + min, max, mean: -3.512657 , 1.6331301 , -0.21426365 diff --git a/checkpoints/maxim001_simplenet_2b4b8b/training_checkpoint.pth.tar b/checkpoints/maxim001_simplenet_2b4b8b/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..04798d57052f96f8f20a61d40c2c51b01870e41d --- /dev/null +++ b/checkpoints/maxim001_simplenet_2b4b8b/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:972fe3499c3cdd071da48bfbbd06f4b40e7627ebe095be10a9e86cfa9286fac5 +size 4697515 diff --git a/checkpoints/shallow001_nas_8b_SAM/evaluation.py b/checkpoints/shallow001_nas_8b_SAM/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..6ba78e5d9a656fbd5bbabe7a2d61a28c737ccb37 --- /dev/null +++ b/checkpoints/shallow001_nas_8b_SAM/evaluation.py @@ -0,0 +1,66 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Folding BN for:', layer_string) + layer_attribute.configure_layer_base(weight_bits=8, bias_bits=8, shift_quantile=1.0) + layer_attribute.mode_fpt2qat('qat'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + layer_attribute.mode_qat2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow001_nas_8b_SAM/hardware_checkpoint.pth.tar b/checkpoints/shallow001_nas_8b_SAM/hardware_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..12ad2f0529808eea8538f29ce713e78e4d849934 --- /dev/null +++ b/checkpoints/shallow001_nas_8b_SAM/hardware_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39974795a05d7e5213a72d7d4fa4d356fb45f408cd9c51f1c808aa7a36ecfaa5 +size 1432263 diff --git a/checkpoints/shallow001_nas_8b_SAM/histograms_hardware_checkpoint/conv1_1.jpg 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0000000000000000000000000000000000000000..b55542325b630832f065442bfddda0f453509497 --- /dev/null +++ b/checkpoints/shallow001_nas_8b_SAM/readme @@ -0,0 +1 @@ +same ID on deliverable v1 \ No newline at end of file diff --git a/checkpoints/shallow001_nas_8b_SAM/statistics_hardware_checkpoint b/checkpoints/shallow001_nas_8b_SAM/statistics_hardware_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..7f95550e1bb9fbf7b01d4b418ac09ff967cc64ab --- /dev/null +++ b/checkpoints/shallow001_nas_8b_SAM/statistics_hardware_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 64.30999636650085, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 64.30999636650085} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -3312.0 , 3647.0 , 219.67188 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 196 + min, max, mean: -128.0 , 127.0 , -0.13599537 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -15230.0 , 7686.0 , 2532.1875 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 182 + min, max, mean: -128.0 , 112.0 , -3.5986328 + conv1_3 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -12256.0 , 15312.0 , 2155.4844 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 193 + min, max, mean: -128.0 , 121.0 , -1.7572157 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 16 + min, max, mean: -16384.0 , 16383.0 , 5156.5625 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 235 + min, max, mean: -128.0 , 127.0 , -0.35975477 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -6664.0 , 8210.0 , 985.40625 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 198 + min, max, mean: -109.0 , 127.0 , -3.543457 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 54 + min, max, mean: -16384.0 , 16383.0 , 4028.1875 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 253 + min, max, mean: -128.0 , 127.0 , -0.4563395 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 126 + min, max, mean: -16338.0 , 16383.0 , 4410.1484 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 238 + min, max, mean: -128.0 , 127.0 , -3.9754639 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 24 + min, max, mean: -16384.0 , 16383.0 , -4813.4062 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 227 + min, max, mean: -128.0 , 127.0 , 0.47446018 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 127 + min, max, mean: -16384.0 , 16383.0 , 2460.9297 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 250 + min, max, mean: -128.0 , 127.0 , -0.25878906 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 126 + min, max, mean: -13770.0 , 16383.0 , 2701.3594 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 207 + min, max, mean: -101.0 , 127.0 , -0.38433838 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -2465.0 , 2615.0 , -56.6 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 240 + min, max, mean: -128.0 , 127.0 , -11.8649025 diff --git a/checkpoints/shallow001_nas_8b_SAM/statistics_training_checkpoint b/checkpoints/shallow001_nas_8b_SAM/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..15772469adbb001ce947501bf8134a794db93ea8 --- /dev/null +++ b/checkpoints/shallow001_nas_8b_SAM/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.10107696 , 0.11130922 , 0.0067034895 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.60333866 , 0.6379849 , -0.0005045497 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.46478918 , 0.23455882 , 0.07727651 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.731422 , 0.43723717 , -0.014188715 + conv1_3 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.37401438 , 0.46729237 , 0.0657807 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 18431 + min, max, mean: -0.5946365 , 0.47278842 , -0.0068520107 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.36374795 , 0.36529794 , 0.068705976 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 18430 + min, max, mean: -0.20062716 , 0.19888887 , -0.00037256454 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.40673798 , 0.5011109 , 0.06014684 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.8508096 , 1.2500408 , -0.027207367 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.62617564 , 0.6381906 , 0.06749219 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73684 + min, max, mean: -0.16876076 , 0.14783913 , -0.0004468074 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.4986081 , 0.8272092 , 0.13885254 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16378 + min, max, mean: -0.6456978 , 0.60366505 , -0.015519251 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.6772705 , 0.78678006 , -0.07816264 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73688 + min, max, mean: -0.125278 , 0.14437507 , 0.00046636915 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.26326552 , 0.28138405 , 0.03769239 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73683 + min, max, mean: -0.28473294 , 0.33850384 , -0.000497009 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.4202246 , 0.51630175 , 0.08256639 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16380 + min, max, mean: -0.3926522 , 0.5331613 , -0.0014903652 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.30089954 , 0.31922576 , -0.0069139306 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51187 + min, max, mean: -3.3799329 , 2.168811 , -0.18646385 diff --git a/checkpoints/shallow001_nas_8b_SAM/train_log.txt b/checkpoints/shallow001_nas_8b_SAM/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..46ac83cd3331844773f65e70e30bada644fd12ab --- /dev/null +++ b/checkpoints/shallow001_nas_8b_SAM/train_log.txt @@ -0,0 +1,239 @@ +[1] loss: 3.922, train acc: 9.710 test acc: 16.210 19.120 s +[2] loss: 3.306, train acc: 19.734 test acc: 24.700 19.272 s +[3] loss: 2.890, train acc: 27.350 test acc: 30.530 19.156 s +[4] loss: 2.572, train acc: 33.976 test acc: 34.630 18.599 s +[5] loss: 2.351, train acc: 38.444 test acc: 39.340 19.358 s +[6] loss: 2.182, train acc: 42.156 test acc: 41.620 19.322 s +[7] loss: 2.060, train acc: 44.980 test acc: 43.830 18.912 s +[8] loss: 1.946, train acc: 47.628 test acc: 45.620 19.201 s +[9] loss: 1.861, train acc: 49.356 test acc: 47.350 18.739 s +[10] loss: 1.778, train acc: 51.572 test acc: 47.440 19.149 s +[11] loss: 1.720, train acc: 52.758 test acc: 48.690 18.959 s +[12] loss: 1.665, train acc: 54.102 test acc: 50.070 18.651 s +[13] loss: 1.611, train acc: 55.504 test acc: 51.010 18.681 s +[14] loss: 1.565, train acc: 56.742 test acc: 51.310 18.636 s +[15] loss: 1.523, train acc: 57.590 test acc: 50.750 19.178 s +[16] loss: 1.493, train acc: 58.122 test acc: 52.760 18.726 s +[17] loss: 1.456, train acc: 59.148 test acc: 53.310 19.150 s +[18] loss: 1.425, train acc: 60.064 test acc: 53.020 18.625 s +[19] loss: 1.395, train acc: 60.686 test acc: 53.310 18.945 s +[20] loss: 1.366, train acc: 61.512 test acc: 54.200 20.388 s +[21] loss: 1.337, train acc: 62.098 test acc: 54.400 18.636 s +[22] loss: 1.317, train acc: 62.850 test acc: 54.450 18.698 s +[23] loss: 1.288, train acc: 63.556 test acc: 54.980 24.444 s +[24] loss: 1.270, train acc: 63.970 test acc: 54.640 19.223 s +[25] loss: 1.242, train acc: 64.418 test acc: 55.670 19.068 s +[26] loss: 1.228, train acc: 65.022 test acc: 55.390 18.723 s +[27] loss: 1.212, train acc: 65.308 test acc: 56.070 18.621 s +[28] loss: 1.192, train acc: 65.950 test acc: 55.740 18.721 s +[29] loss: 1.172, train acc: 66.610 test acc: 56.360 18.999 s +[30] loss: 1.162, train acc: 66.744 test acc: 56.040 19.265 s +[31] loss: 1.139, train acc: 67.142 test acc: 56.610 18.620 s +[32] loss: 1.127, train acc: 67.530 test acc: 56.350 18.952 s +[33] loss: 1.113, train acc: 67.938 test acc: 56.930 19.421 s +[34] loss: 1.103, train acc: 68.186 test acc: 56.610 19.007 s +[35] loss: 1.081, train acc: 68.868 test acc: 56.850 19.002 s +[36] loss: 1.077, train acc: 68.798 test acc: 57.090 18.931 s +[37] loss: 1.063, train acc: 69.366 test acc: 57.010 18.142 s +[38] loss: 1.048, train acc: 69.726 test acc: 57.600 18.577 s +[39] loss: 1.034, train acc: 70.048 test acc: 57.630 19.337 s +[40] loss: 1.021, train acc: 70.398 test acc: 58.170 18.606 s +[41] loss: 1.013, train acc: 70.720 test acc: 57.340 19.218 s +[42] loss: 1.001, train acc: 71.000 test acc: 58.030 18.656 s +[43] loss: 0.991, train acc: 71.130 test acc: 58.170 18.731 s +[44] loss: 0.982, train acc: 71.388 test acc: 58.150 18.939 s +[45] loss: 0.972, train acc: 71.786 test acc: 57.920 20.176 s +[46] loss: 0.959, train acc: 72.054 test acc: 58.770 19.481 s +[47] loss: 0.946, train acc: 72.474 test acc: 57.930 19.065 s +[48] loss: 0.935, train acc: 72.638 test acc: 57.890 19.334 s +[49] loss: 0.928, train acc: 72.724 test acc: 58.370 18.734 s +[50] loss: 0.925, train acc: 72.930 test acc: 58.690 18.609 s +[51] loss: 0.911, train acc: 73.478 test acc: 58.120 19.188 s +[52] loss: 0.906, train acc: 73.406 test acc: 57.950 18.921 s +[53] loss: 0.896, train acc: 73.732 test acc: 58.300 18.764 s +[54] loss: 0.891, train acc: 73.804 test acc: 58.070 18.855 s +[55] loss: 0.881, train acc: 74.204 test acc: 57.960 18.914 s +[56] loss: 0.873, train acc: 74.446 test acc: 58.690 18.841 s +[57] loss: 0.865, train acc: 74.332 test acc: 58.390 19.063 s +[58] loss: 0.856, train acc: 74.850 test acc: 58.630 19.052 s +[59] loss: 0.849, train acc: 75.136 test acc: 59.100 18.923 s +[60] loss: 0.851, train acc: 74.982 test acc: 58.100 18.426 s +[61] loss: 0.839, train acc: 75.072 test acc: 57.940 19.223 s +[62] loss: 0.828, train acc: 75.610 test acc: 58.210 19.462 s +[63] loss: 0.821, train acc: 75.916 test acc: 57.980 18.999 s +[64] loss: 0.816, train acc: 75.868 test acc: 59.340 18.477 s +[65] loss: 0.806, train acc: 76.154 test acc: 58.640 19.336 s +[66] loss: 0.802, train acc: 76.380 test acc: 59.180 19.209 s +[67] loss: 0.794, train acc: 76.694 test acc: 59.110 18.478 s +[68] loss: 0.792, train acc: 76.544 test acc: 59.230 18.842 s +[69] loss: 0.781, train acc: 77.010 test acc: 58.640 18.791 s +[70] loss: 0.777, train acc: 77.002 test acc: 59.170 19.276 s +[71] loss: 0.773, train acc: 77.146 test acc: 59.250 19.578 s +[72] loss: 0.767, train acc: 77.232 test acc: 59.000 19.281 s +[73] loss: 0.760, train acc: 77.390 test acc: 59.020 18.526 s +[74] loss: 0.762, train acc: 77.430 test acc: 58.650 18.691 s +[75] loss: 0.755, train acc: 77.836 test acc: 59.310 20.628 s +[76] loss: 0.750, train acc: 77.732 test acc: 59.170 18.904 s +[77] loss: 0.745, train acc: 77.560 test acc: 58.820 19.015 s +[78] loss: 0.738, train acc: 78.148 test acc: 58.990 19.101 s +[79] loss: 0.729, train acc: 78.210 test acc: 58.660 18.940 s +[80] loss: 0.728, train acc: 78.240 test acc: 58.870 18.424 s +[81] loss: 0.723, train acc: 78.442 test acc: 58.510 19.399 s +[82] loss: 0.718, train acc: 78.706 test acc: 58.610 18.937 s +[83] loss: 0.712, train acc: 78.724 test acc: 58.560 19.048 s +[84] loss: 0.705, train acc: 78.776 test acc: 58.810 18.905 s +[85] loss: 0.704, train acc: 78.982 test acc: 58.250 19.172 s +[86] loss: 0.698, train acc: 79.308 test acc: 58.380 19.347 s +[87] loss: 0.693, train acc: 79.318 test acc: 58.450 19.214 s +[88] loss: 0.686, train acc: 79.432 test acc: 59.050 19.092 s +[89] loss: 0.683, train acc: 79.574 test acc: 59.140 18.626 s +[90] loss: 0.679, train acc: 79.708 test acc: 58.440 19.234 s +[91] loss: 0.672, train acc: 79.968 test acc: 58.560 18.429 s +[92] loss: 0.669, train acc: 80.088 test acc: 58.820 18.924 s +[93] loss: 0.660, train acc: 80.174 test acc: 58.480 18.966 s +[94] loss: 0.664, train acc: 80.024 test acc: 58.970 18.989 s 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s +[7] loss: 0.486, train acc: 86.110 test acc: 60.160 18.754 s +[8] loss: 0.486, train acc: 86.056 test acc: 60.070 18.997 s +[9] loss: 0.485, train acc: 86.114 test acc: 60.190 18.654 s +[10] loss: 0.484, train acc: 86.144 test acc: 60.130 18.356 s +[11] loss: 0.482, train acc: 86.410 test acc: 59.970 18.743 s +[12] loss: 0.484, train acc: 86.180 test acc: 60.030 19.216 s +[13] loss: 0.482, train acc: 86.230 test acc: 60.250 20.355 s +[14] loss: 0.483, train acc: 86.010 test acc: 60.300 19.104 s +[15] loss: 0.482, train acc: 86.146 test acc: 59.910 18.860 s +[16] loss: 0.484, train acc: 86.202 test acc: 60.070 18.826 s +[17] loss: 0.480, train acc: 86.304 test acc: 60.060 18.555 s +[18] loss: 0.482, train acc: 86.260 test acc: 60.280 19.010 s +[19] loss: 0.481, train acc: 86.156 test acc: 60.300 18.804 s +[20] loss: 0.479, train acc: 86.360 test acc: 60.310 18.998 s +[1] loss: 0.479, train acc: 86.142 test acc: 60.280 18.646 s +[2] loss: 0.476, train acc: 86.300 test acc: 60.320 18.658 s +[3] loss: 0.475, train acc: 86.410 test acc: 60.240 19.096 s +[4] loss: 0.475, train acc: 86.532 test acc: 60.260 18.890 s +[5] loss: 0.476, train acc: 86.228 test acc: 60.250 19.536 s +[6] loss: 0.473, train acc: 86.540 test acc: 60.290 18.323 s +[7] loss: 0.476, train acc: 86.352 test acc: 60.230 19.586 s +[8] loss: 0.473, train acc: 86.520 test acc: 60.230 19.256 s +[9] loss: 0.472, train acc: 86.624 test acc: 60.310 18.598 s +[10] loss: 0.475, train acc: 86.556 test acc: 60.350 18.936 s +[11] loss: 0.475, train acc: 86.476 test acc: 60.380 18.681 s +[12] loss: 0.471, train acc: 86.486 test acc: 60.340 20.621 s +[13] loss: 0.474, train acc: 86.558 test acc: 60.310 18.922 s +[14] loss: 0.470, train acc: 86.620 test acc: 60.290 19.109 s +[15] loss: 0.473, train acc: 86.634 test acc: 60.170 19.187 s +[16] loss: 0.474, train acc: 86.436 test acc: 60.270 18.899 s +[17] loss: 0.471, train acc: 86.656 test acc: 60.280 19.279 s +[18] loss: 0.474, train acc: 86.480 test acc: 60.150 19.134 s +[19] loss: 0.471, train acc: 86.580 test acc: 60.200 18.532 s +[20] loss: 0.473, train acc: 86.662 test acc: 60.170 18.995 s +[1] loss: 1.106, train acc: 76.134 test acc: 62.780 38.125 s +[2] loss: 0.874, train acc: 80.666 test acc: 63.290 39.722 s +[3] loss: 0.838, train acc: 80.908 test acc: 63.320 38.934 s +[4] loss: 0.819, train acc: 81.398 test acc: 63.560 38.463 s +[5] loss: 0.810, train acc: 81.292 test acc: 63.210 38.697 s +[6] loss: 0.803, train acc: 81.268 test acc: 63.530 38.476 s +[7] loss: 0.793, train acc: 81.176 test acc: 63.700 38.083 s +[8] loss: 0.790, train acc: 81.434 test acc: 63.320 38.817 s +[9] loss: 0.787, train acc: 81.242 test acc: 63.570 38.433 s +[10] loss: 0.782, train acc: 81.380 test acc: 63.710 38.234 s +[11] loss: 0.778, train acc: 81.572 test acc: 63.640 39.205 s +[12] loss: 0.773, train acc: 81.422 test acc: 63.700 38.101 s +[13] loss: 0.767, train acc: 81.550 test acc: 63.580 38.276 s +[14] loss: 0.762, train acc: 81.648 test acc: 63.680 38.218 s +[15] loss: 0.766, train acc: 81.220 test acc: 63.710 38.191 s +[16] loss: 0.759, train acc: 81.704 test acc: 63.640 37.920 s +[17] loss: 0.756, train acc: 81.480 test acc: 63.790 38.715 s +[18] loss: 0.758, train acc: 81.528 test acc: 63.760 38.157 s +[19] loss: 0.756, train acc: 81.654 test acc: 63.840 38.704 s +[20] loss: 0.756, train acc: 81.532 test acc: 63.800 38.097 s +[21] loss: 0.752, train acc: 81.542 test acc: 63.900 38.504 s +[22] loss: 0.746, train acc: 81.598 test acc: 63.830 38.281 s +[23] loss: 0.747, train acc: 81.616 test acc: 63.760 38.159 s + +restarting with half the learning rate, zero optimizer state + +[1] loss: 0.742, train acc: 81.706 test acc: 63.920 36.892 s +[2] loss: 0.743, train acc: 81.778 test acc: 63.970 36.748 s +[3] loss: 0.739, train acc: 81.960 test acc: 63.890 36.376 s +[4] loss: 0.737, train acc: 81.954 test acc: 63.770 35.944 s +[5] loss: 0.735, train acc: 81.996 test acc: 64.210 36.866 s +[6] loss: 0.734, train acc: 82.072 test acc: 63.930 36.578 s +[7] loss: 0.734, train acc: 81.916 test acc: 63.930 37.215 s +[8] loss: 0.729, train acc: 81.992 test acc: 63.880 36.817 s +[9] loss: 0.732, train acc: 82.108 test acc: 64.080 36.487 s +[10] loss: 0.728, train acc: 82.142 test acc: 64.070 36.806 s +[11] loss: 0.733, train acc: 81.934 test acc: 63.990 36.853 s +[1] loss: 0.781, train acc: 81.422 test acc: 63.790 37.518 s +[2] loss: 0.821, train acc: 80.904 test acc: 63.350 37.203 s +[3] loss: 0.841, train acc: 80.668 test acc: 63.400 37.730 s +[4] loss: 0.856, train acc: 80.196 test acc: 63.190 37.715 s +[5] loss: 0.866, train acc: 80.016 test acc: 63.070 37.500 s +[6] loss: 0.874, train acc: 79.680 test acc: 63.050 38.076 s +[7] loss: 0.881, train acc: 79.606 test acc: 63.030 37.768 s +[8] loss: 0.882, train acc: 79.624 test acc: 62.860 38.120 s +[9] loss: 0.884, train acc: 79.590 test acc: 62.980 37.331 s +[1] loss: 0.737, train acc: 81.764 test acc: 63.780 39.241 s +[2] loss: 0.706, train acc: 81.852 test acc: 64.160 38.618 s +[3] loss: 0.691, train acc: 82.076 test acc: 64.070 39.309 s +[4] loss: 0.686, train acc: 82.174 test acc: 64.260 38.344 s +[5] loss: 0.674, train acc: 82.528 test acc: 64.100 38.361 s +[6] loss: 0.673, train acc: 82.422 test acc: 64.480 38.350 s +[7] loss: 0.667, train acc: 82.700 test acc: 64.370 38.942 s +[8] loss: 0.665, train acc: 82.792 test acc: 64.400 38.189 s +[9] loss: 0.662, train acc: 82.726 test acc: 64.440 38.667 s +[10] loss: 0.660, train acc: 82.766 test acc: 64.370 39.073 s +[11] loss: 0.660, train acc: 82.808 test acc: 64.400 38.822 s +[12] loss: 0.653, train acc: 83.032 test acc: 64.430 38.702 s +[1] loss: 0.678, train acc: 82.660 test acc: 64.240 37.287 s +[2] loss: 0.688, train acc: 82.854 test acc: 64.310 37.077 s +[3] loss: 0.694, train acc: 82.710 test acc: 64.230 36.969 s +[4] loss: 0.701, train acc: 82.636 test acc: 64.210 36.958 s +[5] loss: 0.702, train acc: 82.640 test acc: 64.300 36.997 s +[6] loss: 0.704, train acc: 82.408 test acc: 64.180 37.049 s +[7] loss: 0.703, train acc: 82.806 test acc: 64.160 37.687 s +[8] loss: 0.710, train acc: 82.334 test acc: 63.980 37.277 s +[9] loss: 0.709, train acc: 82.544 test acc: 64.290 37.380 s +[10] loss: 0.706, train acc: 82.538 test acc: 64.070 37.523 s +[11] loss: 0.712, train acc: 82.400 test acc: 64.020 37.281 s +[12] loss: 0.708, train acc: 82.548 test acc: 63.950 36.890 s +[13] loss: 0.710, train acc: 82.606 test acc: 64.150 36.889 s +[14] loss: 0.709, train acc: 82.514 test acc: 64.210 38.943 s +[15] loss: 0.710, train acc: 82.704 test acc: 64.310 37.126 s +[16] loss: 0.710, train acc: 82.650 test acc: 64.090 36.937 s +[17] loss: 0.712, train acc: 82.526 test acc: 64.180 37.442 s +[18] loss: 0.710, train acc: 82.840 test acc: 64.070 37.089 s +[19] loss: 0.711, train acc: 82.582 test acc: 64.220 37.877 s +[20] loss: 0.710, train acc: 82.668 test acc: 64.150 37.814 s +[21] loss: 0.709, train acc: 82.544 test acc: 64.150 37.165 s diff --git a/checkpoints/shallow001_nas_8b_SAM/training_checkpoint.pth.tar b/checkpoints/shallow001_nas_8b_SAM/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..283e17ee7b6e59a3f2e26b406f730d968af118bd --- /dev/null +++ b/checkpoints/shallow001_nas_8b_SAM/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f6546e8fc470481d7dcc3502152a2243c7b29e812caee7c051837856e62869f +size 1431751 diff --git a/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/evaluation.py b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..1b7f279dc6c815f75c94698811744ceb7bf0c6de --- /dev/null +++ b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/evaluation.py @@ -0,0 +1,80 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat +weight_dictionary = {} +weight_dictionary['conv1_1' ] = 8; +weight_dictionary['conv1_2' ] = 8; +weight_dictionary['conv1_3' ] = 8; +weight_dictionary['conv2_1' ] = 8; +weight_dictionary['conv2_2' ] = 2; +weight_dictionary['conv3_1' ] = 2; +weight_dictionary['conv3_2' ] = 2; +weight_dictionary['conv4_1' ] = 8; +weight_dictionary['conv4_2' ] = 8; +weight_dictionary['conv5_1' ] = 8; +weight_dictionary['fc'] = 8; + +layer_attributes = [] +for layer_string in dir(model): + if(layer_string in weight_dictionary): + layer_attribute = getattr(model, layer_string) + print('Folding BN for:', layer_string) + layer_attribute.configure_layer_base(weight_bits=weight_dictionary[layer_string], bias_bits=8, shift_quantile=0.985) + layer_attribute.mode_fpt2qat('qat'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload to the GPU + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + layer_attribute.mode_qat2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/hardware_checkpoint.pth.tar b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/hardware_checkpoint.pth.tar new 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+1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 61.729997396469116, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 61.729997396469116} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 63 + min, max, mean: -3685.0 , 3388.0 , 52.125 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 217 + min, max, mean: -128.0 , 127.0 , -0.1087963 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -13262.0 , 7488.0 , 2802.0938 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 200 + min, max, mean: -128.0 , 120.0 , -4.1831055 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 57 + min, max, mean: -16384.0 , 16383.0 , 4576.547 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 256 + min, max, mean: -128.0 , 127.0 , -2.108507 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 15 + min, max, mean: -16384.0 , 16383.0 , 3825.4375 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 222 + min, max, mean: -128.0 , 127.0 , -0.1835395 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 54 + min, max, mean: -70.0 , 113.0 , 17.921875 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.056152344 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 38 + min, max, mean: -256.0 , 255.0 , 47.179688 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.005018446 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 103 + min, max, mean: -194.0 , 255.0 , 76.234375 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.055541992 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 25 + min, max, mean: -16384.0 , 16383.0 , -4151.547 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 222 + min, max, mean: -117.0 , 127.0 , 0.27342394 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -16384.0 , 12766.0 , 1170.9844 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 240 + min, max, mean: -128.0 , 127.0 , 0.19311523 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 125 + min, max, mean: -8722.0 , 16383.0 , 3665.7734 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 212 + min, max, mean: -114.0 , 127.0 , -0.04748535 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 98 + min, max, mean: -1474.0 , 1981.0 , -3.73 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 215 + min, max, mean: -128.0 , 107.0 , -10.196386 diff --git a/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/statistics_training_checkpoint b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..585def80baa81c5ca951c23504817d3b8b74151c --- /dev/null +++ b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.11245388 , 0.10340247 , 0.001591533 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.7012884 , 0.76133716 , -0.00029134436 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.40471938 , 0.22852542 , 0.08551041 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.59585965 , 0.46755692 , -0.0164743 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.43269184 , 0.4354972 , 0.075772524 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 18431 + min, max, mean: -0.55321246 , 0.3909786 , -0.004218949 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.3348623 , 0.36877617 , 0.064229615 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 18431 + min, max, mean: -0.23059952 , 0.1768544 , -0.0002033399 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.27203873 , 0.44126716 , 0.070215635 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2048 + min, max, mean: -1.081907 , 1.2835066 , -0.022990013 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.65168923 , 0.6424291 , 0.046436235 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73691 + min, max, mean: -0.1757474 , 0.16606377 , 4.4539163e-05 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.37927154 , 0.72376007 , 0.15195552 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16380 + min, max, mean: -0.61208266 , 0.55124444 , -0.01141741 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.62133706 , 0.669571 , -0.09196919 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73693 + min, max, mean: -0.11380878 , 0.14027645 , 0.00026837227 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.3281726 , 0.19478674 , 0.017257225 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73688 + min, max, mean: -0.2758857 , 0.2754693 , 0.00037729595 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.26617277 , 0.6138764 , 0.112761155 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16382 + min, max, mean: -0.44473115 , 0.56870544 , -0.00018005852 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.17998613 , 0.24179709 , -0.00045737333 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51181 + min, max, mean: -2.0958986 , 1.6769325 , -0.15933196 diff --git a/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/train_log.txt b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..6914eeda46629fb6717aaf06d01fcd3a8ea20988 --- /dev/null +++ b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/train_log.txt @@ -0,0 +1,130 @@ +[1] loss: 3.781, train acc: 12.000 test acc: 19.400 11.920 s +[2] loss: 3.020, train acc: 24.844 test acc: 29.970 11.513 s +[3] loss: 2.578, train acc: 33.148 test acc: 35.450 11.580 s +[4] loss: 2.310, train acc: 38.948 test acc: 40.540 11.579 s +[5] loss: 2.121, train acc: 42.900 test acc: 43.030 11.725 s +[6] loss: 1.977, train acc: 46.418 test acc: 45.500 11.722 s +[7] loss: 1.875, train acc: 48.782 test acc: 46.910 11.510 s +[8] loss: 1.784, train acc: 51.148 test acc: 48.300 11.740 s +[9] loss: 1.717, train acc: 52.452 test acc: 48.540 11.663 s +[10] loss: 1.658, train acc: 53.668 test acc: 49.800 11.841 s +[11] loss: 1.597, train acc: 55.174 test acc: 51.180 11.749 s +[12] loss: 1.553, train acc: 56.304 test acc: 51.370 11.668 s +[13] loss: 1.511, train acc: 57.282 test acc: 51.690 11.893 s +[14] loss: 1.477, train acc: 58.362 test acc: 52.060 11.463 s +[15] loss: 1.429, train acc: 59.400 test acc: 52.610 11.660 s +[16] loss: 1.405, train acc: 60.082 test acc: 53.240 12.190 s +[17] loss: 1.376, train acc: 60.894 test acc: 54.380 11.538 s +[18] loss: 1.346, train acc: 61.432 test acc: 54.320 11.770 s +[19] loss: 1.318, train acc: 62.102 test acc: 54.930 10.889 s +[20] loss: 1.297, train acc: 62.818 test acc: 54.350 11.749 s +[21] loss: 1.272, train acc: 63.690 test acc: 54.710 11.522 s +[22] loss: 1.250, train acc: 63.864 test acc: 54.510 11.644 s +[23] loss: 1.230, train acc: 64.456 test acc: 55.680 11.620 s +[24] loss: 1.209, train acc: 64.628 test acc: 56.550 11.212 s +[25] loss: 1.194, train acc: 65.316 test acc: 55.810 11.542 s +[26] loss: 1.173, train acc: 65.768 test acc: 55.890 11.449 s +[27] loss: 1.156, train acc: 66.234 test acc: 56.300 11.700 s +[28] loss: 1.137, train acc: 66.724 test acc: 55.850 11.935 s +[29] loss: 1.128, train acc: 67.032 test acc: 56.400 11.489 s +[30] loss: 1.112, train acc: 67.424 test acc: 57.370 11.907 s +[31] loss: 1.095, train acc: 67.902 test acc: 56.110 11.797 s +[32] loss: 1.074, train acc: 68.414 test acc: 56.050 11.802 s +[33] loss: 1.064, train acc: 68.930 test acc: 56.690 11.895 s +[34] loss: 1.048, train acc: 69.126 test acc: 57.030 11.455 s +[35] loss: 1.039, train acc: 69.280 test acc: 57.050 11.396 s +[36] loss: 1.023, train acc: 69.842 test acc: 57.050 11.447 s +[37] loss: 1.020, train acc: 69.884 test acc: 56.420 11.296 s +[38] loss: 1.007, train acc: 70.276 test acc: 58.180 11.194 s +[39] loss: 1.000, train acc: 70.444 test acc: 57.770 11.584 s +[40] loss: 0.974, train acc: 71.134 test acc: 57.390 11.918 s +[41] loss: 0.974, train acc: 71.116 test acc: 57.340 11.872 s +[42] loss: 0.966, train acc: 71.256 test acc: 57.460 11.805 s +[43] loss: 0.952, train acc: 71.636 test acc: 56.970 11.734 s +[44] loss: 0.937, train acc: 72.058 test acc: 58.060 11.077 s +[45] loss: 0.932, train acc: 72.180 test acc: 58.100 11.834 s +[46] loss: 0.921, train acc: 72.384 test acc: 57.450 11.649 s +[47] loss: 0.911, train acc: 72.708 test acc: 58.040 12.009 s +[48] loss: 0.903, train acc: 73.076 test acc: 57.820 11.819 s +[49] loss: 0.899, train acc: 73.086 test acc: 57.440 11.746 s +[50] loss: 0.893, train acc: 72.974 test acc: 57.390 11.831 s +[1] loss: 0.776, train acc: 76.970 test acc: 59.040 11.845 s +[2] loss: 0.753, train acc: 77.562 test acc: 59.530 11.773 s +[3] loss: 0.744, train acc: 77.892 test acc: 59.680 11.572 s +[4] loss: 0.738, train acc: 78.150 test acc: 60.010 11.601 s +[5] loss: 0.727, train acc: 78.408 test acc: 59.590 11.594 s +[6] loss: 0.727, train acc: 78.192 test acc: 59.710 11.343 s +[7] loss: 0.711, train acc: 78.714 test acc: 59.260 11.653 s +[8] loss: 0.711, train acc: 78.678 test acc: 59.480 11.274 s +[9] loss: 0.705, train acc: 78.928 test acc: 59.430 11.742 s +[10] loss: 0.700, train acc: 79.190 test acc: 59.730 11.578 s +[1] loss: 0.672, train acc: 79.926 test acc: 59.970 11.180 s +[2] loss: 0.670, train acc: 80.106 test acc: 59.870 11.824 s +[3] loss: 0.668, train acc: 80.148 test acc: 59.950 11.645 s +[4] loss: 0.671, train acc: 79.910 test acc: 59.670 11.816 s +[5] loss: 0.666, train acc: 80.238 test acc: 59.900 11.154 s +[6] loss: 0.658, train acc: 80.456 test acc: 59.650 11.658 s +[7] loss: 0.659, train acc: 80.480 test acc: 59.830 11.740 s +[8] loss: 0.658, train acc: 80.332 test acc: 59.640 11.682 s +[9] loss: 0.654, train acc: 80.538 test acc: 59.870 11.463 s +[10] loss: 0.651, train acc: 80.558 test acc: 59.660 11.752 s +[1] loss: 0.647, train acc: 80.828 test acc: 59.810 11.319 s +[2] loss: 0.646, train acc: 80.748 test acc: 59.860 11.385 s +[3] loss: 0.641, train acc: 80.874 test acc: 59.740 11.732 s +[4] loss: 0.645, train acc: 80.944 test acc: 59.760 11.784 s +[5] loss: 0.647, train acc: 80.850 test acc: 59.780 11.626 s +[6] loss: 0.645, train acc: 80.906 test acc: 59.940 11.401 s +[7] loss: 0.640, train acc: 81.192 test acc: 59.820 11.199 s +[8] loss: 0.644, train acc: 80.864 test acc: 59.760 12.054 s +[9] loss: 0.639, train acc: 80.898 test acc: 59.750 11.888 s +[10] loss: 0.638, train acc: 81.200 test acc: 59.910 11.827 s +[1] loss: 1.557, train acc: 58.494 test acc: 56.130 20.314 s +[2] loss: 1.151, train acc: 67.056 test acc: 58.350 20.333 s +[3] loss: 1.089, train acc: 68.546 test acc: 59.320 20.736 s +[4] loss: 1.057, train acc: 69.342 test acc: 59.090 20.775 s +[5] loss: 1.035, train acc: 69.908 test acc: 60.160 20.459 s +[6] loss: 1.026, train acc: 69.950 test acc: 60.230 20.090 s +[7] loss: 1.012, train acc: 70.302 test acc: 60.220 20.200 s +[8] loss: 1.002, train acc: 70.942 test acc: 60.140 20.565 s +[9] loss: 0.995, train acc: 70.922 test acc: 60.140 20.297 s +[10] loss: 0.984, train acc: 71.250 test acc: 60.460 20.513 s +[11] loss: 0.979, train acc: 71.192 test acc: 60.290 20.487 s +[12] loss: 0.974, train acc: 71.468 test acc: 60.340 20.437 s +[13] loss: 0.963, train acc: 71.580 test acc: 60.350 20.395 s +[14] loss: 0.958, train acc: 71.976 test acc: 60.700 20.438 s +[15] loss: 0.952, train acc: 71.860 test acc: 60.840 20.302 s +[16] loss: 0.959, train acc: 71.804 test acc: 61.730 20.554 s +[17] loss: 0.950, train acc: 72.062 test acc: 60.700 20.760 s +[18] loss: 0.943, train acc: 72.070 test acc: 61.030 20.606 s +[19] loss: 0.943, train acc: 72.066 test acc: 61.020 20.086 s +[20] loss: 0.936, train acc: 72.322 test acc: 60.730 20.996 s +[21] loss: 0.941, train acc: 72.180 test acc: 61.100 20.443 s +[22] loss: 0.935, train acc: 72.570 test acc: 61.340 20.669 s +[23] loss: 0.933, train acc: 72.396 test acc: 60.930 21.823 s +[24] loss: 0.928, train acc: 72.642 test acc: 61.020 20.349 s +[25] loss: 0.925, train acc: 72.692 test acc: 61.020 20.885 s +[26] loss: 0.928, train acc: 72.664 test acc: 60.650 20.422 s +[27] loss: 0.926, train acc: 72.614 test acc: 60.650 20.340 s +[28] loss: 0.923, train acc: 72.576 test acc: 60.840 20.169 s +[29] loss: 0.926, train acc: 72.660 test acc: 60.810 20.450 s +[30] loss: 0.921, train acc: 72.924 test acc: 60.600 20.427 s +[31] loss: 0.919, train acc: 72.918 test acc: 60.690 20.395 s +[32] loss: 0.920, train acc: 72.836 test acc: 60.660 19.916 s +[33] loss: 0.918, train acc: 72.758 test acc: 61.150 20.623 s +[34] loss: 0.917, train acc: 72.802 test acc: 60.180 20.586 s +[35] loss: 0.912, train acc: 73.102 test acc: 61.370 20.284 s +[36] loss: 0.908, train acc: 73.232 test acc: 60.980 20.362 s +[37] loss: 0.910, train acc: 73.072 test acc: 60.710 20.523 s +[38] loss: 0.912, train acc: 72.972 test acc: 60.870 20.359 s +[39] loss: 0.906, train acc: 73.078 test acc: 61.340 20.379 s +[40] loss: 0.905, train acc: 73.060 test acc: 60.020 20.782 s +[41] loss: 0.913, train acc: 72.934 test acc: 60.600 20.586 s +[42] loss: 0.907, train acc: 72.992 test acc: 61.230 20.409 s +[43] loss: 0.909, train acc: 72.978 test acc: 61.710 20.956 s +[44] loss: 0.901, train acc: 73.354 test acc: 60.310 20.364 s +[45] loss: 0.901, train acc: 73.328 test acc: 61.510 20.784 s +[46] loss: 0.905, train acc: 73.192 test acc: 60.980 20.391 s +[47] loss: 0.904, train acc: 72.880 test acc: 60.340 20.712 s +[48] loss: 0.900, train acc: 73.250 test acc: 61.160 20.572 s +[49] loss: 0.903, train acc: 73.184 test acc: 60.910 20.818 s +[50] loss: 0.901, train acc: 73.216 test acc: 61.050 20.780 s diff --git a/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/training_checkpoint.pth.tar b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..9e9c772bb893faa82c11ebaee7ffe700762b5642 --- /dev/null +++ b/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec3625bbf4dd1be313f5942880c50ffac2644cabea19e3f4fd087e819b82606d +size 1431751 diff --git a/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/evaluation.py b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..c8e05fbcb772918cdc65bc2e844b884fa8364fc4 --- /dev/null +++ b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/evaluation.py @@ -0,0 +1,80 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat +weight_dictionary = {} +weight_dictionary['conv1_1' ] = 8; +weight_dictionary['conv1_2' ] = 8; +weight_dictionary['conv1_3' ] = 8; +weight_dictionary['conv2_1' ] = 2; +weight_dictionary['conv2_2' ] = 2; +weight_dictionary['conv3_1' ] = 2; +weight_dictionary['conv3_2' ] = 2; +weight_dictionary['conv4_1' ] = 2; +weight_dictionary['conv4_2' ] = 8; +weight_dictionary['conv5_1' ] = 8; +weight_dictionary['fc'] = 8; + +layer_attributes = [] +for layer_string in dir(model): + if(layer_string in weight_dictionary): + layer_attribute = getattr(model, layer_string) + print('Folding BN for:', layer_string) + layer_attribute.configure_layer_base(weight_bits=weight_dictionary[layer_string], bias_bits=8, shift_quantile=0.985) + layer_attribute.mode_fpt2qat('qat'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload to the GPU + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + layer_attribute.mode_qat2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/hardware_checkpoint.pth.tar b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/hardware_checkpoint.pth.tar new 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+1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 59.950000047683716, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 59.950000047683716} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 63 + min, max, mean: -3430.0 , 2476.0 , 46.09375 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 220 + min, max, mean: -128.0 , 127.0 , -0.30497685 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -12440.0 , 7488.0 , 3157.6562 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 192 + min, max, mean: -128.0 , 123.0 , -4.7963867 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 55 + min, max, mean: -16384.0 , 16383.0 , 4646.7656 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 256 + min, max, mean: -128.0 , 127.0 , -2.926595 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 13 + min, max, mean: -256.0 , 255.0 , 91.09375 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.009114583 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 54 + min, max, mean: -95.0 , 111.0 , 15.515625 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.053222656 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 46 + min, max, mean: -256.0 , 255.0 , 79.58594 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.010064019 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 101 + min, max, mean: -212.0 , 255.0 , 75.24219 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.06500244 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 21 + min, max, mean: -256.0 , 255.0 , -96.0625 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , 0.0077582467 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -14886.0 , 16383.0 , 2551.3594 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 239 + min, max, mean: -128.0 , 127.0 , -0.20049371 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -15764.0 , 16383.0 , 2974.289 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 215 + min, max, mean: -111.0 , 127.0 , -0.0007324219 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 98 + min, max, mean: -1755.0 , 1626.0 , -37.29 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 210 + min, max, mean: -128.0 , 101.0 , -10.281582 diff --git a/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/statistics_training_checkpoint b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..a6d6deb9b72280e67f7e0324eaf222b4200044d7 --- /dev/null +++ b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.10468257 , 0.075563245 , 0.0014071614 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.8909477 , 0.7126475 , -0.0017649194 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.37964904 , 0.22851238 , 0.09636331 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.6206478 , 0.48035166 , -0.018839637 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.25792903 , 0.4632549 , 0.08173839 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 18426 + min, max, mean: -0.5974413 , 0.44250032 , -0.005892037 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.45214012 , 0.36729175 , 0.0695677 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 18428 + min, max, mean: -0.1820527 , 0.17661493 , -5.3904045e-05 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.3718062 , 0.43213025 , 0.06030646 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2048 + min, max, mean: -1.2500217 , 0.9969034 , -0.020823663 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.54298604 , 0.8285807 , 0.08188485 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73684 + min, max, mean: -0.17556906 , 0.23762546 , -0.0002926274 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.4131362 , 0.6732363 , 0.14912458 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16379 + min, max, mean: -0.69071037 , 0.51138675 , -0.013018153 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.62044334 , 0.785705 , -0.09482862 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73663 + min, max, mean: -0.12503509 , 0.13774472 , 0.00063377403 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.22714604 , 0.26259202 , 0.039029084 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73702 + min, max, mean: -0.28169575 , 0.27129906 , -0.00039011563 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.48107234 , 0.52189195 , 0.090940334 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16383 + min, max, mean: -0.43410164 , 0.54937434 , 7.039722e-06 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.21428631 , 0.19852366 , -0.004559571 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51184 + min, max, mean: -2.0640674 , 1.576186 , -0.16064821 diff --git a/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/train_log.txt b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..239d4478b3a977d63b40151447b84a89820a040a --- /dev/null +++ b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/train_log.txt @@ -0,0 +1,130 @@ +[1] loss: 3.748, train acc: 12.484 test acc: 21.510 12.026 s +[2] loss: 2.943, train acc: 25.766 test acc: 30.240 11.710 s +[3] loss: 2.524, train acc: 34.124 test acc: 35.950 11.683 s +[4] loss: 2.263, train acc: 40.030 test acc: 39.590 11.284 s +[5] loss: 2.080, train acc: 43.916 test acc: 43.520 11.613 s +[6] loss: 1.952, train acc: 46.980 test acc: 45.560 11.733 s +[7] loss: 1.841, train acc: 49.742 test acc: 47.210 11.376 s +[8] loss: 1.759, train acc: 51.364 test acc: 48.960 11.933 s +[9] loss: 1.687, train acc: 52.996 test acc: 50.840 11.637 s +[10] loss: 1.620, train acc: 54.474 test acc: 50.740 10.910 s +[11] loss: 1.567, train acc: 56.076 test acc: 51.140 11.652 s +[12] loss: 1.529, train acc: 57.006 test acc: 52.780 11.509 s +[13] loss: 1.476, train acc: 58.156 test acc: 53.590 11.400 s +[14] loss: 1.446, train acc: 58.972 test acc: 53.400 11.674 s +[15] loss: 1.410, train acc: 59.906 test acc: 54.330 11.713 s +[16] loss: 1.377, train acc: 60.740 test acc: 53.670 11.697 s +[17] loss: 1.348, train acc: 61.490 test acc: 54.460 11.352 s +[18] loss: 1.327, train acc: 62.210 test acc: 56.160 11.564 s +[19] loss: 1.293, train acc: 63.050 test acc: 56.710 11.544 s +[20] loss: 1.270, train acc: 63.242 test acc: 56.680 11.762 s +[21] loss: 1.244, train acc: 63.928 test acc: 56.290 11.580 s +[22] loss: 1.227, train acc: 64.548 test acc: 56.300 11.987 s +[23] loss: 1.204, train acc: 65.196 test acc: 56.450 11.768 s +[24] loss: 1.191, train acc: 65.606 test acc: 56.190 11.857 s +[25] loss: 1.165, train acc: 66.212 test acc: 57.000 11.420 s +[26] loss: 1.156, train acc: 66.322 test acc: 57.170 11.718 s +[27] loss: 1.132, train acc: 66.982 test acc: 57.420 11.377 s +[28] loss: 1.119, train acc: 67.288 test acc: 57.880 11.992 s +[29] loss: 1.104, train acc: 67.570 test acc: 57.680 11.782 s +[30] loss: 1.089, train acc: 68.166 test acc: 57.360 11.819 s +[31] loss: 1.076, train acc: 68.394 test acc: 58.090 11.710 s +[32] loss: 1.064, train acc: 68.614 test acc: 57.210 11.920 s +[33] loss: 1.048, train acc: 69.070 test acc: 58.110 11.194 s +[34] loss: 1.032, train acc: 69.422 test acc: 58.240 11.648 s +[35] loss: 1.026, train acc: 69.752 test acc: 58.310 11.994 s +[36] loss: 1.012, train acc: 70.188 test acc: 58.360 11.411 s +[37] loss: 1.000, train acc: 70.522 test acc: 57.900 12.047 s +[38] loss: 0.989, train acc: 70.500 test acc: 58.190 11.538 s +[39] loss: 0.977, train acc: 70.904 test acc: 58.950 11.792 s +[40] loss: 0.958, train acc: 71.470 test acc: 59.150 11.868 s +[41] loss: 0.957, train acc: 71.588 test acc: 59.200 11.879 s +[42] loss: 0.947, train acc: 71.810 test acc: 58.980 11.782 s +[43] loss: 0.939, train acc: 71.964 test acc: 59.180 11.664 s +[44] loss: 0.930, train acc: 72.208 test acc: 59.110 11.918 s +[45] loss: 0.918, train acc: 72.646 test acc: 59.160 11.522 s +[46] loss: 0.915, train acc: 72.562 test acc: 59.180 11.552 s +[47] loss: 0.897, train acc: 73.044 test acc: 58.970 11.787 s +[48] loss: 0.891, train acc: 73.268 test acc: 58.710 11.519 s +[49] loss: 0.885, train acc: 73.220 test acc: 58.890 11.578 s +[50] loss: 0.876, train acc: 73.760 test acc: 59.210 11.502 s +[1] loss: 0.770, train acc: 77.116 test acc: 60.810 11.335 s +[2] loss: 0.737, train acc: 78.020 test acc: 60.940 11.419 s +[3] loss: 0.726, train acc: 78.444 test acc: 60.970 11.618 s +[4] loss: 0.713, train acc: 78.702 test acc: 61.270 11.258 s +[5] loss: 0.715, train acc: 78.808 test acc: 60.660 12.092 s +[6] loss: 0.710, train acc: 78.756 test acc: 61.710 11.643 s +[7] loss: 0.702, train acc: 78.962 test acc: 60.380 11.553 s +[8] loss: 0.697, train acc: 79.136 test acc: 60.410 11.848 s +[9] loss: 0.692, train acc: 79.226 test acc: 60.970 11.657 s +[10] loss: 0.689, train acc: 79.300 test acc: 61.250 11.734 s +[1] loss: 0.658, train acc: 80.298 test acc: 61.470 11.689 s +[2] loss: 0.657, train acc: 80.656 test acc: 61.490 11.420 s +[3] loss: 0.649, train acc: 80.668 test acc: 61.770 11.417 s +[4] loss: 0.648, train acc: 80.640 test acc: 61.540 11.730 s +[5] loss: 0.647, train acc: 80.556 test acc: 61.560 11.314 s +[6] loss: 0.645, train acc: 80.774 test acc: 61.910 11.567 s +[7] loss: 0.647, train acc: 80.736 test acc: 61.510 11.578 s +[8] loss: 0.641, train acc: 80.874 test acc: 61.470 12.034 s +[9] loss: 0.642, train acc: 81.036 test acc: 61.700 11.848 s +[10] loss: 0.635, train acc: 81.292 test acc: 61.410 12.015 s +[1] loss: 0.633, train acc: 81.112 test acc: 61.590 11.310 s +[2] loss: 0.632, train acc: 81.122 test acc: 61.630 11.808 s +[3] loss: 0.628, train acc: 81.314 test acc: 61.540 11.885 s +[4] loss: 0.628, train acc: 81.388 test acc: 61.660 11.753 s +[5] loss: 0.627, train acc: 81.332 test acc: 61.670 12.007 s +[6] loss: 0.627, train acc: 81.364 test acc: 61.550 11.470 s +[7] loss: 0.628, train acc: 81.382 test acc: 61.560 11.791 s +[8] loss: 0.627, train acc: 81.312 test acc: 61.610 11.375 s +[9] loss: 0.621, train acc: 81.582 test acc: 61.600 11.764 s +[10] loss: 0.625, train acc: 81.338 test acc: 61.630 12.091 s +[1] loss: 1.925, train acc: 49.460 test acc: 52.550 20.556 s +[2] loss: 1.402, train acc: 60.186 test acc: 54.660 20.613 s +[3] loss: 1.318, train acc: 61.972 test acc: 54.660 20.445 s +[4] loss: 1.276, train acc: 63.102 test acc: 56.490 20.523 s +[5] loss: 1.233, train acc: 64.180 test acc: 57.600 20.590 s +[6] loss: 1.219, train acc: 64.716 test acc: 57.320 20.672 s +[7] loss: 1.201, train acc: 65.226 test acc: 57.550 20.196 s +[8] loss: 1.180, train acc: 65.520 test acc: 55.960 20.292 s +[9] loss: 1.170, train acc: 65.916 test acc: 57.830 20.603 s +[10] loss: 1.162, train acc: 66.054 test acc: 57.360 20.323 s +[11] loss: 1.146, train acc: 66.522 test acc: 58.240 20.503 s +[12] loss: 1.141, train acc: 66.810 test acc: 58.090 20.671 s +[13] loss: 1.136, train acc: 66.490 test acc: 57.220 20.293 s +[14] loss: 1.123, train acc: 67.188 test acc: 55.900 20.503 s +[15] loss: 1.127, train acc: 66.988 test acc: 58.330 20.268 s +[16] loss: 1.112, train acc: 67.264 test acc: 57.720 20.557 s +[17] loss: 1.114, train acc: 67.136 test acc: 58.960 20.471 s +[18] loss: 1.107, train acc: 67.646 test acc: 57.660 20.380 s +[19] loss: 1.103, train acc: 67.592 test acc: 58.960 20.684 s +[20] loss: 1.101, train acc: 67.398 test acc: 59.200 20.753 s +[21] loss: 1.098, train acc: 67.754 test acc: 58.150 20.839 s +[22] loss: 1.092, train acc: 68.036 test acc: 58.120 20.640 s +[23] loss: 1.101, train acc: 67.590 test acc: 58.100 20.528 s +[24] loss: 1.086, train acc: 67.870 test acc: 58.570 20.597 s +[25] loss: 1.095, train acc: 67.732 test acc: 58.520 20.448 s +[26] loss: 1.091, train acc: 67.630 test acc: 58.680 20.652 s +[27] loss: 1.079, train acc: 68.212 test acc: 58.510 21.863 s +[28] loss: 1.082, train acc: 68.062 test acc: 56.820 20.298 s +[29] loss: 1.081, train acc: 68.120 test acc: 59.230 20.341 s +[30] loss: 1.077, train acc: 68.210 test acc: 58.680 20.776 s +[31] loss: 1.073, train acc: 68.102 test acc: 57.100 20.465 s +[32] loss: 1.071, train acc: 68.396 test acc: 59.510 20.186 s +[33] loss: 1.077, train acc: 68.274 test acc: 58.500 20.350 s +[34] loss: 1.069, train acc: 68.392 test acc: 58.770 20.483 s +[35] loss: 1.061, train acc: 68.798 test acc: 57.540 20.717 s +[36] loss: 1.073, train acc: 68.198 test acc: 59.950 20.426 s +[37] loss: 1.054, train acc: 68.628 test acc: 58.640 20.496 s +[38] loss: 1.065, train acc: 68.460 test acc: 57.900 20.693 s +[39] loss: 1.054, train acc: 68.796 test acc: 58.020 20.547 s +[40] loss: 1.064, train acc: 68.334 test acc: 58.980 20.574 s +[41] loss: 1.060, train acc: 68.674 test acc: 58.490 20.489 s +[42] loss: 1.054, train acc: 68.968 test acc: 59.420 20.531 s +[43] loss: 1.054, train acc: 68.602 test acc: 59.820 20.785 s +[44] loss: 1.062, train acc: 68.442 test acc: 58.470 20.427 s +[45] loss: 1.052, train acc: 68.804 test acc: 59.250 20.547 s +[46] loss: 1.047, train acc: 68.804 test acc: 58.600 20.488 s +[47] loss: 1.056, train acc: 68.814 test acc: 58.970 20.489 s +[48] loss: 1.048, train acc: 68.940 test acc: 58.520 20.624 s +[49] loss: 1.045, train acc: 69.118 test acc: 58.250 20.727 s +[50] loss: 1.044, train acc: 68.990 test acc: 59.240 20.844 s \ No newline at end of file diff --git a/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/training_checkpoint.pth.tar b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..b9f575d37c87369ccc008aa487c3e22dd34cd17f --- /dev/null +++ b/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:beed437e80e19f957b939254c6e64eeb6a27afdcb8ff9c3c97ed0e6d75248420 +size 1431751 diff --git a/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/evaluation.py b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..c5b2f505c34e50d90f5a33a9083c606cec0701e6 --- /dev/null +++ b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/evaluation.py @@ -0,0 +1,80 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat +weight_dictionary = {} +weight_dictionary['conv1_1' ] = 8; +weight_dictionary['conv1_2' ] = 8; +weight_dictionary['conv1_3' ] = 2; +weight_dictionary['conv2_1' ] = 2; +weight_dictionary['conv2_2' ] = 2; +weight_dictionary['conv3_1' ] = 2; +weight_dictionary['conv3_2' ] = 2; +weight_dictionary['conv4_1' ] = 2; +weight_dictionary['conv4_2' ] = 2; +weight_dictionary['conv5_1' ] = 8; +weight_dictionary['fc'] = 8; + +layer_attributes = [] +for layer_string in dir(model): + if(layer_string in weight_dictionary): + layer_attribute = getattr(model, layer_string) + print('Folding BN for:', layer_string) + layer_attribute.configure_layer_base(weight_bits=weight_dictionary[layer_string], bias_bits=8, shift_quantile=0.985) + layer_attribute.mode_fpt2qat('qat'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload to the GPU + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + layer_attribute.mode_qat2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/hardware_checkpoint.pth.tar b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/hardware_checkpoint.pth.tar new 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+1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 57.9800009727478, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 57.9800009727478} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -3211.0 , 2946.0 , 290.875 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 224 + min, max, mean: -128.0 , 127.0 , -0.2181713 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -10399.0 , 7336.0 , 3322.6562 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 198 + min, max, mean: -128.0 , 104.0 , -4.753418 + conv1_3 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 56 + min, max, mean: -119.0 , 255.0 , 42.28125 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.025716146 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 16 + min, max, mean: -256.0 , 255.0 , 71.90625 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.011230469 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 50 + min, max, mean: -135.0 , 145.0 , 13.59375 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.047851562 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 47 + min, max, mean: -256.0 , 255.0 , 76.47656 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.0105523 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 99 + min, max, mean: -193.0 , 255.0 , 78.0 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.064453125 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 22 + min, max, mean: -256.0 , 255.0 , -76.265625 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , 0.004977756 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 107 + min, max, mean: -256.0 , 255.0 , 58.773438 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.008056641 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 123 + min, max, mean: -12583.0 , 16383.0 , 3161.289 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 215 + min, max, mean: -119.0 , 127.0 , -0.15649414 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 99 + min, max, mean: -1628.0 , 1485.0 , -32.65 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 211 + min, max, mean: -128.0 , 89.0 , -10.200137 diff --git a/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/statistics_training_checkpoint b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..3c96b5d404bec9713fb1e5233caaef7946a15865 --- /dev/null +++ b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.09798126 , 0.08990779 , 0.008875869 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.59848505 , 0.75632256 , -0.0006143634 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.31733915 , 0.22386183 , 0.10140069 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.58420384 , 0.40819037 , -0.01869129 + conv1_3 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.23203957 , 0.51034755 , 0.08287333 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 18431 + min, max, mean: -0.59151775 , 0.43220478 , -0.0057644206 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.30094182 , 0.42980048 , 0.070355654 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 18428 + min, max, mean: -0.16991019 , 0.21769144 , 4.4027212e-05 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.5276564 , 0.5653206 , 0.052891113 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.9724338 , 1.1061882 , -0.018025849 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.6170407 , 0.65429825 , 0.078679465 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73688 + min, max, mean: -0.16440398 , 0.16517481 , -0.00033442382 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.3768113 , 0.6656874 , 0.15580902 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16380 + min, max, mean: -0.58836114 , 0.5506579 , -0.013571151 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.6558978 , 0.82889 , -0.069669336 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73680 + min, max, mean: -0.13145642 , 0.13042527 , 0.0003467776 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.32796454 , 0.26320904 , 0.056867614 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73674 + min, max, mean: -0.3363212 , 0.26312655 , -7.0956354e-05 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.38399914 , 0.5454449 , 0.09726812 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16378 + min, max, mean: -0.4663344 , 0.5532854 , -0.0005959581 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.198709 , 0.18128031 , -0.0039853672 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51173 + min, max, mean: -2.3624063 , 1.3867925 , -0.15939271 diff --git a/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/train_log.txt b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..412703126987526b858d88ccd6f65fd5eada0a39 --- /dev/null +++ b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/train_log.txt @@ -0,0 +1,130 @@ +[1] loss: 3.725, train acc: 12.876 test acc: 20.340 11.934 s +[2] loss: 2.955, train acc: 25.568 test acc: 30.140 11.720 s +[3] loss: 2.544, train acc: 33.670 test acc: 36.230 11.755 s +[4] loss: 2.281, train acc: 39.412 test acc: 41.270 11.644 s +[5] loss: 2.097, train acc: 43.638 test acc: 44.000 11.792 s +[6] loss: 1.956, train acc: 46.888 test acc: 46.250 11.513 s +[7] loss: 1.843, train acc: 49.520 test acc: 47.110 11.806 s +[8] loss: 1.762, train acc: 51.488 test acc: 49.410 11.847 s +[9] loss: 1.687, train acc: 53.002 test acc: 50.370 11.886 s +[10] loss: 1.624, train acc: 54.570 test acc: 50.710 12.349 s +[11] loss: 1.579, train acc: 55.596 test acc: 51.740 12.015 s +[12] loss: 1.530, train acc: 56.982 test acc: 51.960 11.661 s +[13] loss: 1.489, train acc: 57.714 test acc: 52.790 11.866 s +[14] loss: 1.448, train acc: 59.076 test acc: 53.500 11.378 s +[15] loss: 1.412, train acc: 59.972 test acc: 53.720 11.698 s +[16] loss: 1.384, train acc: 60.564 test acc: 54.220 11.889 s +[17] loss: 1.352, train acc: 61.314 test acc: 54.960 11.772 s +[18] loss: 1.324, train acc: 62.000 test acc: 55.450 11.538 s +[19] loss: 1.302, train acc: 62.494 test acc: 54.810 11.927 s +[20] loss: 1.272, train acc: 63.292 test acc: 55.320 11.601 s +[21] loss: 1.255, train acc: 64.080 test acc: 56.060 11.772 s +[22] loss: 1.232, train acc: 64.496 test acc: 56.680 11.632 s +[23] loss: 1.208, train acc: 65.080 test acc: 56.780 12.097 s +[24] loss: 1.186, train acc: 65.702 test acc: 57.160 11.887 s +[25] loss: 1.173, train acc: 65.920 test acc: 56.590 12.177 s +[26] loss: 1.154, train acc: 66.582 test acc: 56.800 11.891 s +[27] loss: 1.135, train acc: 66.920 test acc: 57.060 11.884 s +[28] loss: 1.125, train acc: 67.174 test acc: 57.300 11.469 s +[29] loss: 1.106, train acc: 67.826 test acc: 56.830 11.646 s +[30] loss: 1.096, train acc: 67.686 test acc: 57.430 11.957 s +[31] loss: 1.071, train acc: 68.344 test acc: 57.280 12.057 s +[32] loss: 1.060, train acc: 68.814 test acc: 58.270 11.687 s +[33] loss: 1.047, train acc: 69.088 test acc: 57.440 11.788 s +[34] loss: 1.037, train acc: 69.336 test acc: 58.560 11.557 s +[35] loss: 1.030, train acc: 69.578 test acc: 58.240 12.043 s +[36] loss: 1.011, train acc: 69.932 test acc: 58.030 11.783 s +[37] loss: 1.002, train acc: 70.090 test acc: 58.840 12.020 s +[38] loss: 0.992, train acc: 70.720 test acc: 57.640 11.689 s +[39] loss: 0.975, train acc: 70.950 test acc: 57.330 11.762 s +[40] loss: 0.969, train acc: 71.110 test acc: 57.900 12.348 s +[41] loss: 0.953, train acc: 71.416 test acc: 58.160 11.583 s +[42] loss: 0.951, train acc: 71.700 test acc: 58.800 11.698 s +[43] loss: 0.942, train acc: 72.100 test acc: 58.650 11.589 s +[44] loss: 0.926, train acc: 72.392 test acc: 58.080 11.755 s +[45] loss: 0.919, train acc: 72.306 test acc: 58.830 11.901 s +[46] loss: 0.907, train acc: 72.626 test acc: 58.300 11.798 s +[47] loss: 0.902, train acc: 73.014 test acc: 59.660 11.198 s +[48] loss: 0.889, train acc: 73.444 test acc: 58.920 11.829 s +[49] loss: 0.884, train acc: 73.392 test acc: 58.660 12.787 s +[50] loss: 0.869, train acc: 73.854 test acc: 58.150 11.929 s +[1] loss: 0.766, train acc: 77.068 test acc: 60.520 11.787 s +[2] loss: 0.733, train acc: 78.190 test acc: 60.540 11.578 s +[3] loss: 0.723, train acc: 78.478 test acc: 60.780 12.009 s +[4] loss: 0.716, train acc: 78.614 test acc: 60.180 11.910 s +[5] loss: 0.710, train acc: 78.820 test acc: 60.620 12.019 s +[6] loss: 0.708, train acc: 78.908 test acc: 60.940 12.134 s +[7] loss: 0.701, train acc: 78.964 test acc: 60.850 11.963 s +[8] loss: 0.696, train acc: 79.204 test acc: 60.950 11.581 s +[9] loss: 0.694, train acc: 79.240 test acc: 60.860 11.611 s +[10] loss: 0.690, train acc: 79.224 test acc: 60.630 13.505 s +[1] loss: 0.658, train acc: 80.484 test acc: 61.020 11.613 s +[2] loss: 0.653, train acc: 80.624 test acc: 60.990 11.732 s +[3] loss: 0.654, train acc: 80.566 test acc: 61.360 11.470 s +[4] loss: 0.650, train acc: 80.702 test acc: 61.270 12.032 s +[5] loss: 0.646, train acc: 80.778 test acc: 61.150 11.665 s +[6] loss: 0.646, train acc: 80.806 test acc: 61.100 11.779 s +[7] loss: 0.644, train acc: 80.820 test acc: 61.060 11.448 s +[8] loss: 0.645, train acc: 80.758 test acc: 61.260 11.942 s +[9] loss: 0.641, train acc: 81.062 test acc: 61.110 11.808 s +[10] loss: 0.637, train acc: 81.148 test acc: 60.930 11.674 s +[1] loss: 0.635, train acc: 81.108 test acc: 61.020 12.237 s +[2] loss: 0.632, train acc: 81.322 test acc: 60.970 11.973 s +[3] loss: 0.634, train acc: 81.240 test acc: 60.920 12.029 s +[4] loss: 0.630, train acc: 81.314 test acc: 61.080 11.691 s +[5] loss: 0.629, train acc: 81.252 test acc: 60.980 12.092 s +[6] loss: 0.629, train acc: 81.352 test acc: 60.930 11.831 s +[7] loss: 0.630, train acc: 81.376 test acc: 61.050 11.382 s +[8] loss: 0.628, train acc: 81.416 test acc: 61.080 11.456 s +[9] loss: 0.628, train acc: 81.326 test acc: 60.830 11.460 s +[10] loss: 0.629, train acc: 81.278 test acc: 61.110 11.887 s +[1] loss: 2.388, train acc: 38.756 test acc: 47.340 20.252 s +[2] loss: 1.689, train acc: 53.282 test acc: 50.460 20.670 s +[3] loss: 1.573, train acc: 55.438 test acc: 51.290 20.202 s +[4] loss: 1.505, train acc: 57.242 test acc: 52.420 20.842 s +[5] loss: 1.449, train acc: 58.460 test acc: 53.140 20.717 s +[6] loss: 1.418, train acc: 59.188 test acc: 55.010 20.814 s +[7] loss: 1.391, train acc: 59.884 test acc: 54.330 20.396 s +[8] loss: 1.380, train acc: 60.230 test acc: 55.000 20.466 s +[9] loss: 1.357, train acc: 60.782 test acc: 54.990 20.572 s +[10] loss: 1.338, train acc: 61.308 test acc: 54.320 20.385 s +[11] loss: 1.336, train acc: 61.492 test acc: 56.190 20.404 s +[12] loss: 1.326, train acc: 61.564 test acc: 55.110 20.774 s +[13] loss: 1.305, train acc: 61.858 test acc: 55.800 20.572 s +[14] loss: 1.307, train acc: 62.090 test acc: 56.490 20.226 s +[15] loss: 1.283, train acc: 62.782 test acc: 55.640 20.796 s +[16] loss: 1.275, train acc: 62.856 test acc: 55.470 20.782 s +[17] loss: 1.271, train acc: 62.794 test acc: 56.710 20.715 s +[18] loss: 1.271, train acc: 62.776 test acc: 56.300 20.810 s +[19] loss: 1.256, train acc: 63.194 test acc: 55.370 20.562 s +[20] loss: 1.260, train acc: 63.114 test acc: 56.850 22.084 s +[21] loss: 1.253, train acc: 63.302 test acc: 55.220 20.716 s +[22] loss: 1.250, train acc: 63.720 test acc: 55.940 20.301 s +[23] loss: 1.243, train acc: 63.592 test acc: 56.480 20.124 s +[24] loss: 1.249, train acc: 63.560 test acc: 57.520 20.426 s +[25] loss: 1.247, train acc: 63.548 test acc: 55.650 20.574 s +[26] loss: 1.227, train acc: 64.104 test acc: 56.440 20.267 s +[27] loss: 1.239, train acc: 63.804 test acc: 57.000 20.486 s +[28] loss: 1.228, train acc: 63.912 test acc: 55.480 20.198 s +[29] loss: 1.226, train acc: 64.214 test acc: 57.180 20.299 s +[30] loss: 1.227, train acc: 63.888 test acc: 56.210 21.023 s +[31] loss: 1.230, train acc: 64.014 test acc: 57.980 20.605 s +[32] loss: 1.224, train acc: 64.064 test acc: 56.990 20.756 s +[33] loss: 1.211, train acc: 64.474 test acc: 57.410 20.468 s +[34] loss: 1.214, train acc: 64.376 test acc: 56.650 20.321 s +[35] loss: 1.211, train acc: 64.464 test acc: 56.030 20.359 s +[36] loss: 1.212, train acc: 64.414 test acc: 57.280 20.583 s +[37] loss: 1.202, train acc: 64.732 test acc: 57.140 20.407 s +[38] loss: 1.207, train acc: 64.764 test acc: 57.960 20.688 s +[39] loss: 1.202, train acc: 64.834 test acc: 57.670 20.856 s +[40] loss: 1.205, train acc: 64.590 test acc: 56.820 20.451 s +[41] loss: 1.202, train acc: 64.810 test acc: 56.800 20.481 s +[42] loss: 1.196, train acc: 64.856 test acc: 57.330 20.648 s +[43] loss: 1.186, train acc: 65.152 test acc: 57.970 20.571 s +[44] loss: 1.204, train acc: 64.792 test acc: 57.190 20.592 s +[45] loss: 1.202, train acc: 64.742 test acc: 55.780 20.225 s +[46] loss: 1.203, train acc: 64.750 test acc: 56.940 20.661 s +[47] loss: 1.202, train acc: 64.532 test acc: 57.050 20.281 s +[48] loss: 1.200, train acc: 64.664 test acc: 55.670 20.608 s +[49] loss: 1.189, train acc: 65.060 test acc: 55.890 20.534 s +[50] loss: 1.192, train acc: 64.926 test acc: 57.690 20.510 s diff --git a/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/training_checkpoint.pth.tar b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..34bb3766e6dc4f665749e610d6886bdf9a499386 --- /dev/null +++ b/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b13d17fc747a852920e309310d1bb88b0bdc213e0dd1bbca2775647b937c36d +size 1431751 diff --git a/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/evaluation.py b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..73ec1a5fd4e8e6125d7aae1735f3c595a633f0d1 --- /dev/null +++ b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/evaluation.py @@ -0,0 +1,80 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat +weight_dictionary = {} +weight_dictionary['conv1_1' ] = 8; +weight_dictionary['conv1_2' ] = 2; +weight_dictionary['conv1_3' ] = 2; +weight_dictionary['conv2_1' ] = 2; +weight_dictionary['conv2_2' ] = 2; +weight_dictionary['conv3_1' ] = 2; +weight_dictionary['conv3_2' ] = 2; +weight_dictionary['conv4_1' ] = 2; +weight_dictionary['conv4_2' ] = 2; +weight_dictionary['conv5_1' ] = 2; +weight_dictionary['fc'] = 8; + +layer_attributes = [] +for layer_string in dir(model): + if(layer_string in weight_dictionary): + layer_attribute = getattr(model, layer_string) + print('Folding BN for:', layer_string) + layer_attribute.configure_layer_base(weight_bits=weight_dictionary[layer_string], bias_bits=8, shift_quantile=0.985) + layer_attribute.mode_fpt2qat('qat'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload to the GPU + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + layer_attribute.mode_qat2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/hardware_checkpoint.pth.tar b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/hardware_checkpoint.pth.tar new 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+1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 57.339996099472046, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 57.339996099472046} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -3199.0 , 2862.0 , 22.640625 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 223 + min, max, mean: -128.0 , 127.0 , -0.25752315 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 29 + min, max, mean: -197.0 , 139.0 , 53.90625 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.08300781 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 50 + min, max, mean: -256.0 , 255.0 , 79.921875 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.05707465 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 17 + min, max, mean: -256.0 , 255.0 , 35.96875 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , 0.0007052951 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 55 + min, max, mean: -143.0 , 113.0 , 12.59375 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.05126953 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 53 + min, max, mean: -256.0 , 255.0 , 68.40625 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.010633681 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 109 + min, max, mean: -253.0 , 255.0 , 75.14844 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.06890869 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 24 + min, max, mean: -256.0 , 255.0 , -44.453125 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , 0.0019259983 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 100 + min, max, mean: -166.0 , 227.0 , 36.59375 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.01034885 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 107 + min, max, mean: -189.0 , 247.0 , 51.609375 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.0040283203 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 98 + min, max, mean: -1839.0 , 2088.0 , -41.12 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 212 + min, max, mean: -128.0 , 102.0 , -10.25957 diff --git a/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/statistics_training_checkpoint b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..7a721e2002422cce9070fa4574f09575ac4a9bdb --- /dev/null +++ b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.09763674 , 0.08732803 , 0.0006905794 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.7342287 , 0.86711884 , -0.00071328814 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.38424972 , 0.27241862 , 0.10525063 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.59332895 , 0.41311347 , -0.018598543 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.3860237 , 0.5209098 , 0.09245379 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 18427 + min, max, mean: -0.5858764 , 0.47067192 , -0.0042794654 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.5581179 , 0.4162186 , 0.03879378 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 18426 + min, max, mean: -0.20846371 , 0.19582224 , 0.00040720336 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.5586003 , 0.4403127 , 0.049198207 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.9156108 , 1.1265619 , -0.016320027 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.6626083 , 0.7643094 , 0.07738045 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73675 + min, max, mean: -0.15631889 , 0.16410363 , -0.00024500678 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.4948001 , 0.6649789 , 0.15025692 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16380 + min, max, mean: -0.5555891 , 0.5185376 , -0.014057841 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.60154 , 0.678474 , -0.060857046 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73670 + min, max, mean: -0.13421577 , 0.13600616 , 0.000336579 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.16241989 , 0.22186275 , 0.035726417 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73666 + min, max, mean: -0.2782171 , 0.3568763 , -0.0006378573 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.3688353 , 0.4817295 , 0.100752056 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16382 + min, max, mean: -0.45492658 , 0.6571981 , -0.0010069595 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.22445635 , 0.2548512 , -0.0050179153 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51173 + min, max, mean: -2.219185 , 1.5888963 , -0.16028893 diff --git a/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/train_log.txt b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..675b702d07b6e09035f072a241c75f010536f32b --- /dev/null +++ b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/train_log.txt @@ -0,0 +1,130 @@ +[1] loss: 3.758, train acc: 12.210 test acc: 20.400 11.378 s +[2] loss: 2.983, train acc: 24.974 test acc: 29.560 11.949 s +[3] loss: 2.568, train acc: 33.144 test acc: 35.760 11.599 s +[4] loss: 2.305, train acc: 38.934 test acc: 40.890 11.453 s +[5] loss: 2.124, train acc: 42.856 test acc: 43.120 11.548 s +[6] loss: 1.974, train acc: 46.386 test acc: 45.510 11.675 s +[7] loss: 1.871, train acc: 48.858 test acc: 46.080 11.797 s +[8] loss: 1.791, train acc: 50.472 test acc: 48.790 11.844 s +[9] loss: 1.712, train acc: 52.538 test acc: 49.270 11.284 s +[10] loss: 1.659, train acc: 53.592 test acc: 51.050 11.752 s +[11] loss: 1.607, train acc: 54.836 test acc: 50.740 11.534 s +[12] loss: 1.559, train acc: 56.114 test acc: 51.910 11.743 s +[13] loss: 1.507, train acc: 57.634 test acc: 52.030 11.460 s +[14] loss: 1.474, train acc: 58.104 test acc: 52.450 11.520 s +[15] loss: 1.437, train acc: 59.338 test acc: 53.700 11.929 s +[16] loss: 1.408, train acc: 59.828 test acc: 54.210 11.480 s +[17] loss: 1.381, train acc: 60.614 test acc: 54.740 11.500 s +[18] loss: 1.342, train acc: 61.682 test acc: 55.410 11.945 s +[19] loss: 1.322, train acc: 62.176 test acc: 54.790 11.742 s +[20] loss: 1.297, train acc: 62.718 test acc: 55.370 11.303 s +[21] loss: 1.273, train acc: 63.300 test acc: 55.050 11.661 s +[22] loss: 1.251, train acc: 63.596 test acc: 55.690 11.552 s +[23] loss: 1.227, train acc: 64.452 test acc: 56.510 11.237 s +[24] loss: 1.213, train acc: 64.796 test acc: 56.410 11.536 s +[25] loss: 1.190, train acc: 65.416 test acc: 56.880 11.411 s +[26] loss: 1.173, train acc: 66.098 test acc: 56.670 11.392 s +[27] loss: 1.153, train acc: 66.560 test acc: 56.490 10.941 s +[28] loss: 1.141, train acc: 66.658 test acc: 57.660 11.466 s +[29] loss: 1.129, train acc: 66.944 test acc: 57.010 11.666 s +[30] loss: 1.105, train acc: 67.570 test acc: 56.990 11.631 s +[31] loss: 1.092, train acc: 68.068 test acc: 58.110 11.091 s +[32] loss: 1.079, train acc: 68.446 test acc: 57.280 11.564 s +[33] loss: 1.067, train acc: 68.886 test acc: 57.340 12.197 s +[34] loss: 1.053, train acc: 69.060 test acc: 57.380 11.803 s +[35] loss: 1.043, train acc: 69.156 test acc: 57.380 11.677 s +[36] loss: 1.028, train acc: 69.598 test acc: 57.820 11.650 s +[37] loss: 1.014, train acc: 69.900 test acc: 58.080 11.650 s +[38] loss: 1.003, train acc: 70.174 test acc: 58.400 11.619 s +[39] loss: 0.993, train acc: 70.606 test acc: 58.110 11.892 s +[40] loss: 0.982, train acc: 70.740 test acc: 58.260 11.993 s +[41] loss: 0.974, train acc: 70.992 test acc: 58.100 11.644 s +[42] loss: 0.959, train acc: 71.346 test acc: 58.060 11.491 s +[43] loss: 0.950, train acc: 71.732 test acc: 57.900 11.888 s +[44] loss: 0.939, train acc: 71.622 test acc: 58.750 11.739 s +[45] loss: 0.933, train acc: 71.976 test acc: 57.880 11.596 s +[46] loss: 0.925, train acc: 72.316 test acc: 58.370 11.535 s +[47] loss: 0.913, train acc: 72.612 test acc: 58.530 11.291 s +[48] loss: 0.905, train acc: 72.750 test acc: 59.240 11.878 s +[49] loss: 0.888, train acc: 73.406 test acc: 58.770 11.617 s +[50] loss: 0.891, train acc: 73.154 test acc: 58.470 11.751 s +[1] loss: 0.780, train acc: 76.672 test acc: 60.680 11.777 s +[2] loss: 0.751, train acc: 77.650 test acc: 60.810 11.727 s +[3] loss: 0.737, train acc: 77.998 test acc: 60.810 11.870 s +[4] loss: 0.736, train acc: 78.064 test acc: 60.800 11.170 s +[5] loss: 0.727, train acc: 78.344 test acc: 60.710 11.860 s +[6] loss: 0.720, train acc: 78.388 test acc: 60.650 11.440 s +[7] loss: 0.720, train acc: 78.468 test acc: 60.410 11.554 s +[8] loss: 0.715, train acc: 78.718 test acc: 60.240 11.498 s +[9] loss: 0.706, train acc: 78.880 test acc: 60.250 11.620 s +[10] loss: 0.703, train acc: 78.940 test acc: 60.400 11.720 s +[1] loss: 0.674, train acc: 79.932 test acc: 60.800 11.595 s +[2] loss: 0.671, train acc: 79.954 test acc: 60.880 11.029 s +[3] loss: 0.665, train acc: 80.332 test acc: 60.960 11.441 s +[4] loss: 0.666, train acc: 80.088 test acc: 60.700 11.655 s +[5] loss: 0.662, train acc: 80.366 test acc: 60.860 11.867 s +[6] loss: 0.655, train acc: 80.620 test acc: 60.950 11.646 s +[7] loss: 0.657, train acc: 80.368 test acc: 60.860 11.853 s +[8] loss: 0.655, train acc: 80.580 test acc: 60.740 11.275 s +[9] loss: 0.653, train acc: 80.564 test acc: 60.830 11.612 s +[10] loss: 0.655, train acc: 80.474 test acc: 60.980 11.279 s +[1] loss: 0.644, train acc: 80.954 test acc: 60.820 11.920 s +[2] loss: 0.645, train acc: 80.772 test acc: 60.920 11.753 s +[3] loss: 0.643, train acc: 80.888 test acc: 60.930 11.828 s +[4] loss: 0.646, train acc: 80.770 test acc: 60.810 11.803 s +[5] loss: 0.646, train acc: 80.984 test acc: 60.820 11.240 s +[6] loss: 0.641, train acc: 80.980 test acc: 60.750 11.398 s +[7] loss: 0.646, train acc: 80.646 test acc: 60.680 11.805 s +[8] loss: 0.645, train acc: 80.852 test acc: 60.980 11.564 s +[9] loss: 0.641, train acc: 81.142 test acc: 60.810 11.159 s +[10] loss: 0.644, train acc: 80.924 test acc: 60.770 12.116 s +[1] loss: 2.450, train acc: 37.072 test acc: 45.080 20.468 s +[2] loss: 1.786, train acc: 50.514 test acc: 48.780 20.117 s +[3] loss: 1.655, train acc: 53.522 test acc: 50.480 20.066 s +[4] loss: 1.588, train acc: 55.062 test acc: 50.480 20.067 s +[5] loss: 1.544, train acc: 56.050 test acc: 51.440 20.238 s +[6] loss: 1.493, train acc: 57.396 test acc: 53.240 20.372 s +[7] loss: 1.485, train acc: 57.374 test acc: 52.840 20.011 s +[8] loss: 1.463, train acc: 58.102 test acc: 53.240 19.987 s +[9] loss: 1.444, train acc: 58.558 test acc: 54.110 20.409 s +[10] loss: 1.428, train acc: 58.886 test acc: 54.630 20.183 s +[11] loss: 1.414, train acc: 59.196 test acc: 54.260 19.546 s +[12] loss: 1.409, train acc: 59.272 test acc: 53.420 19.960 s +[13] loss: 1.396, train acc: 59.392 test acc: 54.740 20.434 s +[14] loss: 1.378, train acc: 59.892 test acc: 54.730 20.086 s +[15] loss: 1.372, train acc: 60.306 test acc: 54.240 20.044 s +[16] loss: 1.358, train acc: 60.426 test acc: 54.680 20.304 s +[17] loss: 1.357, train acc: 60.894 test acc: 55.070 20.125 s +[18] loss: 1.345, train acc: 61.228 test acc: 55.220 20.601 s +[19] loss: 1.346, train acc: 60.964 test acc: 55.180 20.630 s +[20] loss: 1.340, train acc: 60.866 test acc: 55.790 19.743 s +[21] loss: 1.328, train acc: 61.414 test acc: 54.640 19.763 s +[22] loss: 1.328, train acc: 61.232 test acc: 55.220 20.463 s +[23] loss: 1.329, train acc: 61.366 test acc: 55.190 20.120 s +[24] loss: 1.319, train acc: 61.482 test acc: 55.130 19.579 s +[25] loss: 1.316, train acc: 61.496 test acc: 54.400 20.271 s +[26] loss: 1.319, train acc: 61.558 test acc: 54.950 19.727 s +[27] loss: 1.316, train acc: 61.558 test acc: 55.240 20.357 s +[28] loss: 1.316, train acc: 61.584 test acc: 56.260 20.082 s +[29] loss: 1.307, train acc: 61.810 test acc: 55.600 20.294 s +[30] loss: 1.305, train acc: 61.776 test acc: 55.500 20.095 s +[31] loss: 1.296, train acc: 62.264 test acc: 56.590 19.858 s +[32] loss: 1.296, train acc: 62.298 test acc: 55.580 20.054 s +[33] loss: 1.291, train acc: 62.188 test acc: 55.320 20.039 s +[34] loss: 1.289, train acc: 62.324 test acc: 55.280 20.593 s +[35] loss: 1.287, train acc: 62.298 test acc: 55.600 20.169 s +[36] loss: 1.284, train acc: 62.586 test acc: 56.040 20.441 s +[37] loss: 1.296, train acc: 62.236 test acc: 54.620 20.664 s +[38] loss: 1.281, train acc: 62.502 test acc: 55.140 20.141 s +[39] loss: 1.288, train acc: 62.392 test acc: 57.290 19.608 s +[40] loss: 1.283, train acc: 62.594 test acc: 55.320 19.980 s +[41] loss: 1.280, train acc: 62.652 test acc: 54.900 20.485 s +[42] loss: 1.279, train acc: 62.600 test acc: 55.650 19.912 s +[43] loss: 1.280, train acc: 62.602 test acc: 54.960 20.114 s +[44] loss: 1.277, train acc: 62.884 test acc: 55.420 20.139 s +[45] loss: 1.274, train acc: 62.992 test acc: 57.040 20.589 s +[46] loss: 1.270, train acc: 62.880 test acc: 55.470 20.156 s +[47] loss: 1.273, train acc: 62.858 test acc: 54.940 20.100 s +[48] loss: 1.271, train acc: 62.764 test acc: 55.810 20.489 s +[49] loss: 1.269, train acc: 62.940 test acc: 56.830 20.245 s +[50] loss: 1.269, train acc: 62.924 test acc: 56.930 20.155 s diff --git a/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/training_checkpoint.pth.tar b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..b335f0eb2b0b09831a28004248268dfa057df77f --- /dev/null +++ b/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef03a607656e241dbe1279b707b692e3c6162d43b00a53318bed8bf1dd5f85d8 +size 1431751 diff --git a/checkpoints/shallow006_nas_AP2b8b_earlyexit/evaluation.py b/checkpoints/shallow006_nas_AP2b8b_earlyexit/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..ebebe1d2e2e6ce54120122144f6787a698abe044 --- /dev/null +++ b/checkpoints/shallow006_nas_AP2b8b_earlyexit/evaluation.py @@ -0,0 +1,120 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat but it's Ap +sq = 0.985 + +layer_attribute = getattr(model, 'conv1_1') +layer_attribute.configure_layer_base(weight_bits=8, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv1_1', layer_attribute) + +layer_attribute = getattr(model, 'conv1_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv1_2', layer_attribute) + +layer_attribute = getattr(model, 'conv1_3') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv1_3', layer_attribute) + +layer_attribute = getattr(model, 'conv2_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv2_1', layer_attribute) + +layer_attribute = getattr(model, 'conv2_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv2_2', layer_attribute) + +layer_attribute = getattr(model, 'conv3_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv3_1', layer_attribute) + +layer_attribute = getattr(model, 'conv3_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv3_2', layer_attribute) + +layer_attribute = getattr(model, 'conv4_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv4_1', layer_attribute) + +layer_attribute = getattr(model, 'conv4_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv4_2', layer_attribute) + +layer_attribute = getattr(model, 'conv5_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv5_1', layer_attribute) + +layer_attribute = getattr(model, 'fc') +layer_attribute.configure_layer_base(weight_bits=8, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'fc', layer_attribute) + +model.to(device) # somehow new parameters are left out, so they need a reload to the GPU +######################################################################################### + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + if(layer_attribute.mode == 'qat'): + layer_attribute.mode_qat2hw('eval'); + elif(layer_attribute.mode == 'qat_ap'): + layer_attribute.mode_qat_ap2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow006_nas_AP2b8b_earlyexit/hardware_checkpoint.pth.tar b/checkpoints/shallow006_nas_AP2b8b_earlyexit/hardware_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..f002e081b0142144d69804659ab06d9da701dd92 --- /dev/null +++ b/checkpoints/shallow006_nas_AP2b8b_earlyexit/hardware_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba069904922a8b88e9d2c15ad89a2d85f5ee9095f701f79ab29f4db309a16b41 +size 1431735 diff --git a/checkpoints/shallow006_nas_AP2b8b_earlyexit/readme b/checkpoints/shallow006_nas_AP2b8b_earlyexit/readme new file mode 100644 index 0000000000000000000000000000000000000000..52f86aa5b706cce039c96d48026b351b5bbcbd3a --- /dev/null +++ b/checkpoints/shallow006_nas_AP2b8b_earlyexit/readme @@ -0,0 +1 @@ +not on deliverable v1 \ No newline at end of file diff --git a/checkpoints/shallow006_nas_AP2b8b_earlyexit/train_log.txt b/checkpoints/shallow006_nas_AP2b8b_earlyexit/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..d3b730d05f12d12ccc814d38bf6e4f44faf4997c --- /dev/null +++ b/checkpoints/shallow006_nas_AP2b8b_earlyexit/train_log.txt @@ -0,0 +1,37 @@ +[1] loss: 3.763, train acc: 12.172 test acc: 20.510 11.364 s +[2] loss: 2.991, train acc: 24.978 test acc: 29.630 10.790 s +[3] loss: 2.562, train acc: 33.384 test acc: 36.860 10.984 s +[4] loss: 2.295, train acc: 39.210 test acc: 39.900 11.617 s +[5] loss: 2.110, train acc: 43.296 test acc: 43.690 11.209 s +[6] loss: 1.973, train acc: 46.594 test acc: 44.730 11.260 s +[7] loss: 1.870, train acc: 48.848 test acc: 47.440 11.356 s +[8] loss: 1.781, train acc: 50.596 test acc: 48.870 11.128 s +[9] loss: 1.712, train acc: 52.524 test acc: 51.250 10.447 s +[10] loss: 1.644, train acc: 54.216 test acc: 50.640 11.136 s +[1] loss: 1.477, train acc: 58.468 test acc: 53.050 11.397 s +[2] loss: 1.429, train acc: 59.824 test acc: 53.680 11.101 s +[3] loss: 1.404, train acc: 60.350 test acc: 53.510 11.201 s +[4] loss: 1.384, train acc: 60.914 test acc: 54.080 11.304 s +[5] loss: 1.363, train acc: 61.476 test acc: 54.450 11.185 s +[1] loss: 1.316, train acc: 62.614 test acc: 55.460 11.156 s +[2] loss: 1.304, train acc: 62.868 test acc: 55.270 11.205 s +[1] loss: 3.310, train acc: 19.954 test acc: 31.560 20.231 s +[2] loss: 2.523, train acc: 34.436 test acc: 36.540 20.121 s +[3] loss: 2.342, train acc: 38.188 test acc: 37.980 20.772 s +[4] loss: 2.235, train acc: 40.336 test acc: 38.500 20.740 s +[5] loss: 2.166, train acc: 41.970 test acc: 40.890 20.739 s +[6] loss: 2.126, train acc: 42.726 test acc: 42.450 20.337 s +[7] loss: 2.086, train acc: 43.670 test acc: 42.370 20.509 s +[8] loss: 2.055, train acc: 44.466 test acc: 42.660 20.482 s +[9] loss: 2.031, train acc: 44.904 test acc: 43.890 20.302 s +[10] loss: 2.003, train acc: 45.422 test acc: 44.830 20.555 s +[11] loss: 1.985, train acc: 45.956 test acc: 44.020 20.579 s +[12] loss: 1.968, train acc: 46.420 test acc: 45.190 20.429 s +[13] loss: 1.956, train acc: 46.648 test acc: 45.200 20.367 s +[14] loss: 1.946, train acc: 46.872 test acc: 45.440 20.422 s +[15] loss: 1.926, train acc: 47.066 test acc: 45.990 20.490 s +[16] loss: 1.928, train acc: 47.232 test acc: 45.370 20.343 s +[17] loss: 1.905, train acc: 47.812 test acc: 46.530 20.580 s +[18] loss: 1.903, train acc: 47.764 test acc: 45.280 20.200 s +[19] loss: 1.897, train acc: 47.814 test acc: 46.140 21.832 s +[20] loss: 1.880, train acc: 48.202 test acc: 45.910 20.529 s diff --git a/checkpoints/shallow006_nas_AP2b8b_earlyexit/training_checkpoint.pth.tar b/checkpoints/shallow006_nas_AP2b8b_earlyexit/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..723e20d4ea46bbc51de54d37dc3d4cf2b22c0dbd --- /dev/null +++ b/checkpoints/shallow006_nas_AP2b8b_earlyexit/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53f9a314de1a2f993c7dfc967a1e178a575411c725fb631182046066f62853ef +size 1432263 diff --git a/checkpoints/shallow007_nas_AP2b8b_tuned/evaluation.py b/checkpoints/shallow007_nas_AP2b8b_tuned/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..ebebe1d2e2e6ce54120122144f6787a698abe044 --- /dev/null +++ b/checkpoints/shallow007_nas_AP2b8b_tuned/evaluation.py @@ -0,0 +1,120 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat but it's Ap +sq = 0.985 + +layer_attribute = getattr(model, 'conv1_1') +layer_attribute.configure_layer_base(weight_bits=8, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv1_1', layer_attribute) + +layer_attribute = getattr(model, 'conv1_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv1_2', layer_attribute) + +layer_attribute = getattr(model, 'conv1_3') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv1_3', layer_attribute) + +layer_attribute = getattr(model, 'conv2_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv2_1', layer_attribute) + +layer_attribute = getattr(model, 'conv2_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv2_2', layer_attribute) + +layer_attribute = getattr(model, 'conv3_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv3_1', layer_attribute) + +layer_attribute = getattr(model, 'conv3_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv3_2', layer_attribute) + +layer_attribute = getattr(model, 'conv4_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat_ap'); +setattr(model, 'conv4_1', layer_attribute) + +layer_attribute = getattr(model, 'conv4_2') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv4_2', layer_attribute) + +layer_attribute = getattr(model, 'conv5_1') +layer_attribute.configure_layer_base(weight_bits=2, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'conv5_1', layer_attribute) + +layer_attribute = getattr(model, 'fc') +layer_attribute.configure_layer_base(weight_bits=8, bias_bits=8, shift_quantile=sq) +layer_attribute.mode_fpt2qat('qat'); +setattr(model, 'fc', layer_attribute) + +model.to(device) # somehow new parameters are left out, so they need a reload to the GPU +######################################################################################### + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + if(layer_attribute.mode == 'qat'): + layer_attribute.mode_qat2hw('eval'); + elif(layer_attribute.mode == 'qat_ap'): + layer_attribute.mode_qat_ap2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow007_nas_AP2b8b_tuned/hardware_checkpoint.pth.tar b/checkpoints/shallow007_nas_AP2b8b_tuned/hardware_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..3bda4438daf62981b0a49ae49a83fb56b8259231 --- /dev/null +++ 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b/checkpoints/shallow007_nas_AP2b8b_tuned/histograms_training_checkpoint/fc.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6dd93a10845a7a5fcad6a2463643c6e6b334df63 --- /dev/null +++ b/checkpoints/shallow007_nas_AP2b8b_tuned/histograms_training_checkpoint/fc.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:954c65580503bd52c47b90311d2722937a32a21f78f8dba26fb9538fa8c67d18 +size 245269 diff --git a/checkpoints/shallow007_nas_AP2b8b_tuned/readme b/checkpoints/shallow007_nas_AP2b8b_tuned/readme new file mode 100644 index 0000000000000000000000000000000000000000..347cacf69f176eaa3fe230c52311150bf2185f90 --- /dev/null +++ b/checkpoints/shallow007_nas_AP2b8b_tuned/readme @@ -0,0 +1,5 @@ +we manually tuned the checkpoints here, and then noted the procedure approximately in training.py + +the procedure in training.py reaches ~53.7% whereas the saved models are 54.7%. The difference is reproducible, yet we didn't want to spend time on it since this model will most probably only be reported as activity rather than result + +not on deliverable v1 \ No newline at end of file diff --git a/checkpoints/shallow007_nas_AP2b8b_tuned/statistics_hardware_checkpoint b/checkpoints/shallow007_nas_AP2b8b_tuned/statistics_hardware_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..593c9959f9e0ec56acddde276d096d6b77998c3c --- /dev/null +++ b/checkpoints/shallow007_nas_AP2b8b_tuned/statistics_hardware_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 54.71000075340271, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 54.71000075340271} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 63 + min, max, mean: -3871.0 , 1998.0 , -39.40625 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 214 + min, max, mean: -128.0 , 127.0 , -0.23842593 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -216.0 , 119.0 , 46.40625 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.11035156 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 50 + min, max, mean: -256.0 , 255.0 , 70.484375 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.097601995 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 13 + min, max, mean: -256.0 , 255.0 , 18.5625 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.024305556 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 53 + min, max, mean: -110.0 , 109.0 , 11.515625 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.17285156 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 57 + min, max, mean: -256.0 , 255.0 , 55.242188 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.030978732 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 111 + min, max, mean: -256.0 , 255.0 , 73.92969 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.13757324 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 16 + min, max, mean: -256.0 , 255.0 , -53.171875 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.036512587 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 109 + min, max, mean: -227.0 , 215.0 , 26.53125 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.0400255 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 101 + min, max, mean: -162.0 , 255.0 , 58.023438 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.028259277 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 91 + min, max, mean: -2028.0 , 1904.0 , -19.33 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 210 + min, max, mean: -128.0 , 94.0 , -10.346094 diff --git a/checkpoints/shallow007_nas_AP2b8b_tuned/statistics_training_checkpoint b/checkpoints/shallow007_nas_AP2b8b_tuned/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..b17bf5740d50536bcbecc7aea386140ffb5a3995 --- /dev/null +++ b/checkpoints/shallow007_nas_AP2b8b_tuned/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.11813875 , 0.060958907 , -0.001200831 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.7455433 , 0.671916 , -0.00076643866 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.42276883 , 0.23238021 , 0.09066032 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2029 + min, max, mean: -0.6250661 , 0.5108482 , -0.0215636 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.2868904 , 0.55926675 , 0.08686462 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 18426 + min, max, mean: -0.5628503 , 0.4220557 , -0.008933622 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.44477352 , 0.422497 , 0.024444483 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 18430 + min, max, mean: -0.32489958 , 0.23538375 , -0.0016329655 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.42784423 , 0.42709535 , 0.045108758 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2048 + min, max, mean: -1.1757163 , 1.3093548 , -0.025980728 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.5679986 , 0.7743144 , 0.07456434 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73694 + min, max, mean: -0.19645327 , 0.16555364 , -0.0011221194 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.55822414 , 0.6331916 , 0.14531213 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16380 + min, max, mean: -0.74028367 , 0.57661617 , -0.017740514 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.7003891 , 0.77898175 , -0.042929634 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73707 + min, max, mean: -0.1470842 , 0.17760502 , -0.00078346673 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.22142766 , 0.20997818 , 0.025863137 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73510 + min, max, mean: -0.2756333 , 0.26219037 , -0.0034962785 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.31554478 , 0.72608846 , 0.11502842 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16035 + min, max, mean: -0.4534627 , 0.6127872 , -0.005933266 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.2476085 , 0.23245908 , -0.0023603674 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51176 + min, max, mean: -2.1792614 , 1.4684527 , -0.16165127 diff --git a/checkpoints/shallow007_nas_AP2b8b_tuned/train_log.txt b/checkpoints/shallow007_nas_AP2b8b_tuned/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..522ce77ccdff97e61afbf5b5a9fe7f16ccff7958 --- /dev/null +++ b/checkpoints/shallow007_nas_AP2b8b_tuned/train_log.txt @@ -0,0 +1,400 @@ +[1] loss: 3.775, train acc: 11.670 test acc: 19.060 13.497 s +[2] loss: 3.016, train acc: 24.634 test acc: 29.480 13.057 s +[3] loss: 2.578, train acc: 33.302 test acc: 34.870 13.278 s +[4] loss: 2.310, train acc: 38.806 test acc: 40.000 13.248 s +[5] loss: 2.117, train acc: 43.160 test acc: 42.960 12.961 s +[6] loss: 1.978, train acc: 46.264 test acc: 45.200 12.829 s +[7] loss: 1.871, train acc: 48.764 test acc: 46.700 13.080 s +[8] loss: 1.787, train acc: 50.606 test acc: 47.460 12.889 s +[9] loss: 1.707, train acc: 52.718 test acc: 48.370 13.547 s +[10] loss: 1.650, train acc: 54.024 test acc: 49.550 12.981 s +[11] loss: 1.600, train acc: 55.220 test acc: 50.820 12.874 s +[12] loss: 1.544, train acc: 56.572 test acc: 51.580 13.343 s +[13] loss: 1.504, train acc: 57.696 test acc: 52.590 12.683 s +[14] loss: 1.463, train acc: 58.698 test acc: 53.720 14.001 s +[15] loss: 1.426, train acc: 59.420 test acc: 52.370 13.488 s +[16] loss: 1.398, train acc: 60.090 test acc: 54.500 13.484 s +[17] loss: 1.370, train acc: 60.904 test acc: 55.110 13.209 s +[18] loss: 1.336, train acc: 61.850 test acc: 55.730 13.341 s +[19] loss: 1.308, train acc: 62.582 test acc: 54.920 13.332 s +[20] loss: 1.286, train acc: 62.890 test acc: 56.200 13.329 s +[21] loss: 1.264, train acc: 63.242 test acc: 54.940 13.091 s +[22] loss: 1.246, train acc: 63.884 test acc: 56.180 13.342 s +[23] loss: 1.219, train acc: 64.816 test acc: 56.080 12.938 s +[24] loss: 1.205, train acc: 65.138 test acc: 55.820 13.221 s +[25] loss: 1.181, train acc: 65.486 test acc: 56.390 13.320 s +[26] loss: 1.165, train acc: 66.208 test acc: 56.870 12.899 s +[27] loss: 1.142, train acc: 66.674 test acc: 56.690 13.267 s +[28] loss: 1.130, train acc: 66.896 test acc: 56.830 12.805 s +[29] loss: 1.113, train acc: 67.434 test acc: 57.350 12.939 s +[30] loss: 1.095, train acc: 67.956 test acc: 57.380 12.953 s +[31] loss: 1.080, train acc: 68.242 test acc: 57.730 12.290 s +[32] loss: 1.073, train acc: 68.546 test acc: 57.160 12.162 s +[33] loss: 1.060, train acc: 68.756 test acc: 58.160 11.633 s +[34] loss: 1.043, train acc: 69.286 test acc: 57.610 11.844 s +[35] loss: 1.034, train acc: 69.434 test acc: 57.040 11.950 s +[36] loss: 1.019, train acc: 69.820 test acc: 58.490 12.101 s +[37] loss: 1.010, train acc: 70.064 test acc: 57.820 12.177 s +[38] loss: 0.999, train acc: 70.504 test acc: 58.170 12.269 s +[39] loss: 0.985, train acc: 70.826 test acc: 58.720 12.756 s +[40] loss: 0.974, train acc: 71.006 test acc: 58.340 12.698 s +[41] loss: 0.966, train acc: 71.212 test acc: 58.270 12.601 s +[42] loss: 0.957, train acc: 71.372 test acc: 58.760 12.714 s +[43] loss: 0.946, train acc: 71.754 test acc: 57.800 12.500 s +[44] loss: 0.927, train acc: 72.330 test acc: 57.760 12.742 s +[45] loss: 0.930, train acc: 72.134 test acc: 58.660 12.739 s +[46] loss: 0.917, train acc: 72.612 test acc: 58.240 12.584 s +[47] loss: 0.903, train acc: 72.854 test acc: 58.310 12.920 s +[48] loss: 0.894, train acc: 73.188 test acc: 57.490 12.461 s +[49] loss: 0.892, train acc: 72.964 test acc: 59.380 12.570 s +[50] loss: 0.880, train acc: 73.416 test acc: 58.160 11.878 s +[1] loss: 0.773, train acc: 76.790 test acc: 60.490 12.069 s +[2] loss: 0.745, train acc: 77.862 test acc: 59.900 12.234 s +[3] loss: 0.729, train acc: 78.244 test acc: 60.370 12.291 s +[4] loss: 0.732, train acc: 78.094 test acc: 60.240 11.984 s +[5] loss: 0.719, train acc: 78.422 test acc: 60.370 12.112 s +[6] loss: 0.712, train acc: 78.828 test acc: 60.490 12.707 s +[7] loss: 0.705, train acc: 78.880 test acc: 60.720 12.565 s +[8] loss: 0.701, train acc: 79.120 test acc: 60.100 12.780 s +[9] loss: 0.699, train acc: 78.974 test acc: 60.190 12.942 s +[10] loss: 0.695, train acc: 79.094 test acc: 60.640 12.673 s +[1] loss: 0.669, train acc: 80.152 test acc: 60.690 13.124 s +[2] loss: 0.657, train acc: 80.504 test acc: 60.880 12.664 s +[3] loss: 0.659, train acc: 80.414 test acc: 60.860 12.445 s +[4] loss: 0.654, train acc: 80.726 test acc: 60.810 12.187 s +[5] loss: 0.656, train acc: 80.478 test acc: 60.870 12.881 s +[6] loss: 0.654, train acc: 80.618 test acc: 61.110 12.032 s +[7] loss: 0.650, train acc: 80.648 test acc: 61.180 12.205 s +[8] loss: 0.649, train acc: 80.708 test acc: 61.100 12.120 s +[9] loss: 0.650, train acc: 80.646 test acc: 60.820 11.511 s +[10] loss: 0.645, train acc: 80.884 test acc: 60.990 11.806 s +[1] loss: 4.376, train acc: 5.130 test acc: 8.210 22.072 s +[2] loss: 3.888, train acc: 9.220 test acc: 11.270 22.404 s +[3] loss: 3.700, train acc: 11.936 test acc: 13.980 22.949 s +[4] loss: 3.569, train acc: 14.304 test acc: 15.760 23.155 s +[5] loss: 3.461, train acc: 16.042 test acc: 17.500 22.921 s +[6] loss: 3.372, train acc: 17.336 test acc: 19.070 23.464 s +[7] loss: 3.288, train acc: 19.114 test acc: 20.750 22.851 s +[8] loss: 3.212, train acc: 20.292 test acc: 21.130 22.081 s +[9] loss: 3.159, train acc: 21.056 test acc: 22.760 21.494 s +[10] loss: 3.096, train acc: 22.150 test acc: 24.210 21.747 s +[11] loss: 3.032, train acc: 23.336 test acc: 24.990 21.612 s +[12] loss: 2.966, train acc: 24.648 test acc: 26.210 22.531 s +[13] loss: 2.896, train acc: 25.808 test acc: 27.340 22.902 s +[14] loss: 2.842, train acc: 27.098 test acc: 28.620 22.643 s +[15] loss: 2.798, train acc: 27.870 test acc: 30.450 22.650 s +[16] loss: 2.767, train acc: 28.746 test acc: 29.720 22.510 s +[17] loss: 2.715, train acc: 29.418 test acc: 30.370 22.267 s +[18] loss: 2.661, train acc: 30.788 test acc: 31.770 21.362 s +[19] loss: 2.616, train acc: 31.716 test acc: 32.920 21.652 s +[20] loss: 2.571, train acc: 32.642 test acc: 32.760 20.827 s +[21] loss: 2.535, train acc: 33.250 test acc: 33.560 21.672 s +[22] loss: 2.492, train acc: 34.028 test acc: 35.520 23.240 s +[23] loss: 2.461, train acc: 34.878 test acc: 35.460 22.958 s +[24] loss: 2.421, train acc: 35.666 test acc: 36.000 22.631 s +[25] loss: 2.399, train acc: 36.330 test acc: 35.940 22.832 s +[26] loss: 2.353, train acc: 37.360 test acc: 36.830 22.789 s +[27] loss: 2.321, train acc: 38.068 test acc: 37.970 22.759 s +[28] loss: 2.305, train acc: 38.460 test acc: 38.290 21.691 s +[29] loss: 2.271, train acc: 38.818 test acc: 38.920 21.687 s +[30] loss: 2.255, train acc: 39.390 test acc: 38.990 21.732 s +[31] loss: 2.237, train acc: 39.804 test acc: 40.050 21.561 s +[32] loss: 2.204, train acc: 40.336 test acc: 40.270 22.499 s +[33] loss: 2.190, train acc: 40.842 test acc: 40.510 22.659 s +[34] loss: 2.174, train acc: 41.062 test acc: 41.190 22.453 s +[35] loss: 2.145, train acc: 41.714 test acc: 41.220 22.726 s +[36] loss: 2.128, train acc: 42.342 test acc: 41.200 22.906 s +[37] loss: 2.096, train acc: 43.012 test acc: 41.820 23.100 s +[38] loss: 2.098, train acc: 42.994 test acc: 42.860 22.369 s +[39] loss: 2.069, train acc: 43.320 test acc: 41.930 21.843 s +[40] loss: 2.056, train acc: 43.796 test acc: 42.080 21.897 s +[41] loss: 2.029, train acc: 44.340 test acc: 42.610 21.376 s +[42] loss: 2.032, train acc: 44.526 test acc: 42.990 22.688 s +[43] loss: 2.027, train acc: 44.646 test acc: 41.970 23.134 s +[44] loss: 2.018, train acc: 44.636 test acc: 42.730 23.167 s +[45] loss: 1.996, train acc: 45.372 test acc: 42.710 22.486 s +[46] loss: 1.984, train acc: 45.426 test acc: 45.180 23.428 s +[47] loss: 1.979, train acc: 45.258 test acc: 43.180 22.088 s +[48] loss: 1.972, train acc: 45.812 test acc: 43.630 22.045 s +[49] loss: 1.947, train acc: 46.258 test acc: 43.740 22.917 s +[50] loss: 1.946, train acc: 46.232 test acc: 44.390 21.607 s +[51] loss: 1.928, train acc: 46.760 test acc: 43.690 21.590 s +[52] loss: 1.919, train acc: 46.820 test acc: 44.480 21.304 s +[53] loss: 1.920, train acc: 47.010 test acc: 43.990 21.526 s +[54] loss: 1.899, train acc: 47.498 test acc: 45.350 21.358 s +[55] loss: 1.894, train acc: 47.806 test acc: 44.770 21.367 s +[56] loss: 1.888, train acc: 47.950 test acc: 46.110 21.428 s +[57] loss: 1.886, train acc: 47.732 test acc: 45.180 20.985 s +[58] loss: 1.878, train acc: 47.898 test acc: 46.020 21.595 s +[59] loss: 1.852, train acc: 48.476 test acc: 44.920 21.405 s +[60] loss: 1.861, train acc: 48.112 test acc: 46.120 21.260 s +[61] loss: 1.842, train acc: 48.424 test acc: 45.690 20.774 s +[62] loss: 1.837, train acc: 48.746 test acc: 46.330 21.257 s +[63] loss: 1.832, train acc: 49.350 test acc: 46.680 21.565 s +[64] loss: 1.834, train acc: 48.678 test acc: 46.000 21.163 s +[65] loss: 1.830, train acc: 48.964 test acc: 47.640 21.464 s +[66] loss: 1.822, train acc: 49.314 test acc: 46.320 21.431 s +[67] loss: 1.827, train acc: 49.276 test acc: 46.710 21.134 s +[68] loss: 1.816, train acc: 49.452 test acc: 46.980 21.134 s +[69] loss: 1.817, train acc: 49.354 test acc: 47.180 21.240 s +[70] loss: 1.798, train acc: 49.910 test acc: 47.310 21.443 s +[71] loss: 1.801, train acc: 49.696 test acc: 47.860 21.770 s +[72] loss: 1.795, train acc: 49.882 test acc: 46.390 20.756 s +[73] loss: 1.779, train acc: 50.286 test acc: 47.010 21.809 s +[74] loss: 1.804, train acc: 49.464 test acc: 46.510 21.400 s +[75] loss: 1.777, train acc: 50.312 test acc: 47.200 21.462 s +[76] loss: 1.786, train acc: 50.046 test acc: 47.650 21.212 s +[77] loss: 1.778, train acc: 50.340 test acc: 48.040 21.549 s +[78] loss: 1.772, train acc: 50.416 test acc: 47.960 21.393 s +[79] loss: 1.764, train acc: 50.558 test acc: 46.350 21.659 s +[80] loss: 1.757, train acc: 50.892 test acc: 47.040 21.075 s +[81] loss: 1.764, train acc: 50.630 test acc: 46.770 21.412 s +[82] loss: 1.761, train acc: 50.548 test acc: 47.830 21.574 s +[83] loss: 1.759, train acc: 50.732 test acc: 47.540 21.175 s +[84] loss: 1.750, train acc: 51.028 test acc: 48.180 21.393 s +[85] loss: 1.735, train acc: 51.284 test acc: 47.610 21.429 s +[86] loss: 1.743, train acc: 51.232 test acc: 47.870 21.587 s +[87] loss: 1.732, train acc: 51.320 test acc: 48.240 21.583 s +[88] loss: 1.743, train acc: 50.944 test acc: 47.870 21.466 s +[89] loss: 1.735, train acc: 51.320 test acc: 47.410 21.255 s +[90] loss: 1.728, train acc: 51.464 test acc: 43.970 21.212 s +[91] loss: 1.740, train acc: 51.096 test acc: 48.540 21.329 s +[92] loss: 1.733, train acc: 51.470 test acc: 46.050 21.170 s +[93] loss: 1.722, train acc: 51.688 test acc: 47.570 21.769 s +[94] loss: 1.729, train acc: 51.584 test acc: 47.790 21.251 s +[95] loss: 1.734, train acc: 51.376 test acc: 48.630 21.507 s +[96] loss: 1.733, train acc: 51.470 test acc: 48.060 21.366 s +[97] loss: 1.721, train acc: 51.440 test acc: 47.990 22.998 s +[98] loss: 1.726, train acc: 51.686 test acc: 47.510 21.834 s +[99] loss: 1.730, train acc: 51.500 test acc: 49.240 21.066 s +[100] loss: 1.730, train acc: 51.530 test acc: 48.190 21.647 s +[101] loss: 1.722, train acc: 51.824 test acc: 48.910 21.022 s +[102] loss: 1.716, train acc: 51.748 test acc: 48.350 21.445 s +[103] loss: 1.713, train acc: 51.692 test acc: 49.490 20.782 s +[104] loss: 1.702, train acc: 52.126 test acc: 47.050 21.219 s +[105] loss: 1.704, train acc: 52.050 test acc: 48.050 21.353 s +[106] loss: 1.697, train acc: 52.262 test acc: 48.010 21.526 s +[107] loss: 1.705, train acc: 51.914 test acc: 48.820 21.395 s +[108] loss: 1.710, train acc: 52.000 test acc: 49.900 20.903 s +[109] loss: 1.700, train acc: 52.056 test acc: 47.360 20.987 s +[110] loss: 1.701, train acc: 52.110 test acc: 48.160 21.359 s +[111] loss: 1.698, train acc: 52.482 test acc: 47.340 21.171 s +[112] loss: 1.692, train acc: 52.420 test acc: 48.040 21.418 s +[113] loss: 1.699, train acc: 52.248 test acc: 48.230 21.680 s +[114] loss: 1.696, train acc: 52.482 test acc: 49.150 21.205 s +[115] loss: 1.686, train acc: 52.816 test acc: 48.280 21.505 s +[116] loss: 1.701, train acc: 52.204 test acc: 47.210 21.557 s +[117] loss: 1.706, train acc: 52.018 test acc: 49.440 21.394 s +[118] loss: 1.696, train acc: 52.540 test acc: 50.070 21.291 s +[119] loss: 1.689, train acc: 52.614 test acc: 50.620 21.390 s +[120] loss: 1.699, train acc: 52.202 test acc: 48.000 21.496 s +[121] loss: 1.684, train acc: 52.660 test acc: 48.400 21.490 s +[122] loss: 1.685, train acc: 52.466 test acc: 50.120 21.427 s +[123] loss: 1.689, train acc: 52.446 test acc: 48.620 21.090 s +[124] loss: 1.692, train acc: 52.314 test acc: 49.510 21.055 s +[125] loss: 1.692, train acc: 52.318 test acc: 47.190 21.242 s +[126] loss: 1.677, train acc: 52.916 test acc: 49.250 20.743 s +[127] loss: 1.684, train acc: 52.588 test acc: 49.870 21.799 s +[128] loss: 1.676, train acc: 53.000 test acc: 47.530 20.889 s +[129] loss: 1.676, train acc: 52.890 test acc: 47.340 21.233 s +[130] loss: 1.680, train acc: 52.828 test acc: 49.740 21.577 s +[131] loss: 1.669, train acc: 53.110 test acc: 46.940 21.235 s +[132] loss: 1.671, train acc: 52.898 test acc: 48.580 22.668 s +[133] loss: 1.665, train acc: 53.018 test acc: 48.880 21.589 s +[134] loss: 1.671, train acc: 53.014 test acc: 49.870 21.106 s +[135] loss: 1.670, train acc: 53.022 test acc: 49.040 21.511 s +[136] loss: 1.677, train acc: 52.732 test acc: 48.990 21.361 s +[137] loss: 1.674, train acc: 52.888 test acc: 48.620 21.169 s +[138] loss: 1.672, train acc: 52.856 test acc: 48.650 21.592 s +[139] loss: 1.675, train acc: 52.990 test acc: 49.770 21.970 s +[140] loss: 1.657, train acc: 53.314 test acc: 48.400 21.294 s +[141] loss: 1.667, train acc: 53.214 test acc: 48.830 21.289 s +[142] loss: 1.663, train acc: 53.224 test acc: 50.120 21.145 s +[143] loss: 1.666, train acc: 53.068 test acc: 49.930 21.345 s +[144] loss: 1.653, train acc: 53.314 test acc: 49.290 21.514 s +[145] loss: 1.658, train acc: 53.188 test acc: 48.830 21.474 s +[146] loss: 1.659, train acc: 53.104 test acc: 49.440 21.451 s +[147] loss: 1.672, train acc: 53.156 test acc: 49.270 21.497 s +[148] loss: 1.665, train acc: 53.178 test acc: 49.230 21.339 s +[149] loss: 1.653, train acc: 53.224 test acc: 48.870 21.523 s +[150] loss: 1.678, train acc: 52.876 test acc: 49.760 21.478 s +[1] loss: 1.620, train acc: 54.322 test acc: 50.050 21.053 s +[2] loss: 1.621, train acc: 54.284 test acc: 49.940 21.737 s +[3] loss: 1.633, train acc: 53.854 test acc: 50.020 20.975 s +[4] loss: 1.630, train acc: 53.956 test acc: 50.490 20.789 s +[5] loss: 1.633, train acc: 53.730 test acc: 49.610 21.648 s +[6] loss: 1.634, train acc: 53.850 test acc: 49.540 21.288 s +[7] loss: 1.630, train acc: 53.706 test acc: 49.650 21.565 s +[8] loss: 1.630, train acc: 53.934 test acc: 51.100 22.622 s +[9] loss: 1.638, train acc: 53.940 test acc: 49.500 21.634 s +[10] loss: 1.628, train acc: 53.790 test acc: 50.590 21.560 s +[11] loss: 1.627, train acc: 53.808 test acc: 50.060 21.139 s +[12] loss: 1.644, train acc: 53.484 test acc: 49.160 21.371 s +[13] loss: 1.644, train acc: 53.664 test acc: 50.450 21.350 s +[14] loss: 1.641, train acc: 53.546 test acc: 50.660 22.447 s +[15] loss: 1.633, train acc: 53.714 test acc: 50.020 21.416 s +[16] loss: 1.640, train acc: 53.776 test acc: 49.410 21.473 s +[17] loss: 1.638, train acc: 53.830 test acc: 48.460 20.924 s +[18] loss: 1.633, train acc: 53.570 test acc: 50.410 21.443 s +[19] loss: 1.642, train acc: 53.512 test acc: 50.860 21.562 s +[20] loss: 1.636, train acc: 53.662 test acc: 50.190 21.489 s +[21] loss: 1.644, train acc: 53.716 test acc: 48.760 20.594 s +[22] loss: 1.635, train acc: 53.716 test acc: 50.880 21.548 s +[23] loss: 1.630, train acc: 54.130 test acc: 49.390 21.248 s +[24] loss: 1.642, train acc: 53.498 test acc: 50.470 21.085 s +[25] loss: 1.618, train acc: 54.140 test acc: 49.670 21.256 s +[26] loss: 1.624, train acc: 53.832 test acc: 48.780 21.455 s +[27] loss: 1.635, train acc: 53.704 test acc: 50.400 21.567 s +[28] loss: 1.612, train acc: 54.286 test acc: 49.770 21.141 s +[29] loss: 1.638, train acc: 53.502 test acc: 48.100 21.207 s +[30] loss: 1.624, train acc: 54.276 test acc: 48.910 21.664 s +[1] loss: 1.577, train acc: 55.282 test acc: 51.430 12.897 s +[2] loss: 1.577, train acc: 55.296 test acc: 48.610 12.429 s +[3] loss: 1.599, train acc: 54.982 test acc: 51.800 12.357 s +[4] loss: 1.620, train acc: 54.392 test acc: 50.120 12.411 s +[5] loss: 1.617, train acc: 54.292 test acc: 50.450 12.513 s +[6] loss: 1.609, train acc: 54.556 test acc: 49.630 11.946 s +[7] loss: 1.624, train acc: 54.106 test acc: 50.400 12.504 s +[8] loss: 1.616, train acc: 54.366 test acc: 50.800 12.648 s +[9] loss: 1.604, train acc: 54.552 test acc: 49.490 12.433 s +[10] loss: 1.627, train acc: 54.046 test acc: 49.080 12.691 s +[11] loss: 1.627, train acc: 53.970 test acc: 49.990 12.471 s +[12] loss: 1.625, train acc: 54.212 test acc: 49.680 14.263 s +[13] loss: 1.628, train acc: 53.880 test acc: 50.150 12.580 s +[14] loss: 1.624, train acc: 54.164 test acc: 49.880 12.814 s +[15] loss: 1.621, train acc: 54.232 test acc: 50.840 12.475 s +[16] loss: 1.624, train acc: 54.094 test acc: 49.190 12.299 s +[17] loss: 1.629, train acc: 54.036 test acc: 50.320 12.560 s +[18] loss: 1.623, train acc: 54.082 test acc: 49.520 12.525 s +[19] loss: 1.620, train acc: 54.288 test acc: 50.580 12.745 s +[20] loss: 1.630, train acc: 53.960 test acc: 50.060 13.886 s +[21] loss: 1.625, train acc: 54.040 test acc: 49.330 12.594 s +[22] loss: 1.632, train acc: 54.130 test acc: 48.700 12.771 s +[23] loss: 1.621, train acc: 54.052 test acc: 49.380 12.458 s +[24] loss: 1.616, train acc: 54.160 test acc: 50.410 12.669 s +[25] loss: 1.631, train acc: 53.812 test acc: 50.510 12.462 s +[26] loss: 1.626, train acc: 54.132 test acc: 50.190 12.517 s +[27] loss: 1.628, train acc: 53.936 test acc: 46.760 11.866 s +[28] loss: 1.622, train acc: 54.084 test acc: 50.160 12.710 s +[29] loss: 1.630, train acc: 53.984 test acc: 50.200 12.901 s +[30] loss: 1.623, train acc: 54.118 test acc: 50.330 12.580 s +[1] loss: 1.540, train acc: 56.428 test acc: 52.600 8.532 s +[2] loss: 1.526, train acc: 56.440 test acc: 51.490 8.564 s +[3] loss: 1.537, train acc: 56.622 test acc: 50.950 8.636 s +[4] loss: 1.539, train acc: 56.350 test acc: 52.180 8.534 s +[5] loss: 1.546, train acc: 56.130 test acc: 50.720 8.494 s +[6] loss: 1.571, train acc: 55.680 test acc: 50.660 8.609 s +[7] loss: 1.562, train acc: 55.930 test acc: 50.640 8.589 s +[8] loss: 1.565, train acc: 55.774 test acc: 52.590 8.678 s +[9] loss: 1.568, train acc: 55.628 test acc: 50.570 8.572 s +[10] loss: 1.578, train acc: 55.608 test acc: 50.550 8.511 s +[11] loss: 1.571, train acc: 55.492 test acc: 50.530 8.527 s +[12] loss: 1.584, train acc: 55.344 test acc: 50.000 8.627 s +[13] loss: 1.573, train acc: 55.608 test acc: 51.030 8.653 s +[14] loss: 1.563, train acc: 55.704 test acc: 51.330 8.791 s +[15] loss: 1.579, train acc: 55.632 test acc: 51.120 8.663 s +[16] loss: 1.574, train acc: 55.650 test acc: 50.890 8.682 s +[17] loss: 1.596, train acc: 55.212 test acc: 50.370 8.520 s +[18] loss: 1.576, train acc: 55.534 test acc: 50.310 8.953 s +[19] loss: 1.586, train acc: 55.030 test acc: 50.590 8.643 s +[20] loss: 1.587, train acc: 55.026 test acc: 50.400 8.566 s +[21] loss: 1.573, train acc: 55.546 test acc: 51.630 8.520 s +[22] loss: 1.583, train acc: 55.472 test acc: 49.710 8.573 s +[23] loss: 1.591, train acc: 55.114 test acc: 50.980 8.604 s +[24] loss: 1.602, train acc: 54.914 test acc: 50.150 8.557 s +[25] loss: 1.595, train acc: 54.966 test acc: 50.300 8.660 s +[26] loss: 1.596, train acc: 54.822 test acc: 50.240 8.609 s +[27] loss: 1.590, train acc: 55.174 test acc: 49.950 8.620 s +[28] loss: 1.592, train acc: 55.038 test acc: 50.980 8.549 s +[29] loss: 1.592, train acc: 55.134 test acc: 47.820 8.666 s +[30] loss: 1.597, train acc: 54.958 test acc: 50.460 8.542 s +[1] loss: 1.492, train acc: 57.610 test acc: 53.150 7.693 s +[2] loss: 1.488, train acc: 58.136 test acc: 53.610 7.553 s +[3] loss: 1.495, train acc: 57.788 test acc: 51.860 7.552 s +[4] loss: 1.502, train acc: 57.690 test acc: 52.610 7.583 s +[5] loss: 1.511, train acc: 57.488 test acc: 52.210 7.556 s +[6] loss: 1.508, train acc: 57.476 test acc: 51.540 7.423 s +[7] loss: 1.509, train acc: 57.510 test acc: 53.100 7.495 s +[8] loss: 1.510, train acc: 57.666 test acc: 51.990 7.419 s +[9] loss: 1.515, train acc: 57.592 test acc: 52.460 7.557 s +[10] loss: 1.517, train acc: 57.290 test acc: 52.500 7.605 s +[11] loss: 1.524, train acc: 57.372 test acc: 53.010 7.545 s +[12] loss: 1.516, train acc: 57.408 test acc: 52.920 7.427 s +[13] loss: 1.535, train acc: 56.872 test acc: 51.740 7.596 s +[14] loss: 1.537, train acc: 56.690 test acc: 51.950 7.550 s +[15] loss: 1.534, train acc: 56.956 test acc: 51.280 7.581 s +[16] loss: 1.528, train acc: 56.950 test acc: 52.820 7.650 s +[17] loss: 1.536, train acc: 56.890 test acc: 51.890 7.384 s +[18] loss: 1.535, train acc: 56.956 test acc: 52.580 7.496 s +[19] loss: 1.534, train acc: 56.936 test acc: 52.040 7.623 s +[20] loss: 1.536, train acc: 56.956 test acc: 52.300 7.515 s +[21] loss: 1.540, train acc: 56.718 test acc: 50.130 7.449 s +[22] loss: 1.545, train acc: 56.666 test acc: 51.910 7.561 s +[23] loss: 1.554, train acc: 56.410 test acc: 51.400 7.478 s +[24] loss: 1.546, train acc: 56.692 test acc: 51.440 7.651 s +[25] loss: 1.536, train acc: 57.118 test acc: 52.860 7.478 s +[26] loss: 1.545, train acc: 56.756 test acc: 51.550 7.524 s +[27] loss: 1.563, train acc: 56.222 test acc: 51.340 7.562 s +[28] loss: 1.549, train acc: 56.850 test acc: 51.440 7.522 s +[29] loss: 1.554, train acc: 56.484 test acc: 51.620 7.596 s +[30] loss: 1.536, train acc: 56.888 test acc: 52.460 7.621 s +[1] loss: 1.499, train acc: 58.480 test acc: 53.370 7.723 s +[2] loss: 1.505, train acc: 58.452 test acc: 53.130 7.669 s +[3] loss: 1.511, train acc: 58.296 test acc: 53.660 8.116 s +[4] loss: 1.526, train acc: 57.866 test acc: 52.790 7.706 s +[5] loss: 1.524, train acc: 58.132 test acc: 52.900 7.672 s +[6] loss: 1.520, train acc: 57.810 test acc: 52.730 7.733 s +[7] loss: 1.518, train acc: 58.028 test acc: 52.800 7.527 s +[8] loss: 1.517, train acc: 57.884 test acc: 52.680 7.625 s +[9] loss: 1.533, train acc: 57.774 test acc: 52.990 7.788 s 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output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 133 + min, max, mean: -256.0 , 255.0 , 84.27734 + weight + total # of elements, shape: 294912 , [256, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.028272841 + conv2_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 25 + min, max, mean: -256.0 , 255.0 , 101.859375 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.01031155 + conv2_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 104 + min, max, mean: -256.0 , 255.0 , 54.328125 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.07104492 + conv3_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 10 + min, max, mean: -256.0 , 255.0 , -16.828125 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.0031602648 + conv3_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 121 + min, max, mean: -112.0 , 142.0 , 17.363281 + weight + total # of elements, shape: 16384 , [256, 64, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.0435791 + conv4_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 19 + min, max, mean: -256.0 , 255.0 , 13.25 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.0063035754 + conv4_2 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 35 + min, max, mean: -256.0 , 255.0 , 136.79688 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.020968966 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 107 + min, max, mean: -240.0 , 244.0 , 60.585938 + weight + total # of elements, shape: 8192 , [128, 64, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.010375977 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -2406.0 , 1741.0 , -33.06 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 244 + min, max, mean: -128.0 , 121.0 , -15.069004 diff --git a/checkpoints/shallow008_naslarge_2b8b_noSAM/statistics_training_checkpoint b/checkpoints/shallow008_naslarge_2b8b_noSAM/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..3b93ae7c8048b8c5e12903493755f363166291db --- /dev/null +++ b/checkpoints/shallow008_naslarge_2b8b_noSAM/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.12141815 , 0.10342032 , 0.0030579478 + weight + total # of elements, shape: 3456 , [128, 3, 3, 3] + # of unique elements: 3456 + min, max, mean: -0.9258575 , 1.0867571 , -0.00094597205 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.42035758 , 0.27567905 , 0.1199088 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16363 + min, max, mean: -0.69036406 , 0.46215373 , -0.00803211 + conv1_3 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 256 + min, max, mean: -0.5252101 , 0.76673394 , 0.10533109 + weight + total # of elements, shape: 294912 , [256, 128, 3, 3] + # of unique elements: 294292 + min, max, mean: -0.20648248 , 0.17757419 , -0.0014420272 + conv2_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.4867367 , 0.63989335 , 0.12088616 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 294204 + min, max, mean: -0.08467322 , 0.060850546 , -0.00013407545 + conv2_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.60794014 , 0.7731587 , 0.10950339 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16334 + min, max, mean: -0.588682 , 0.65911394 , -0.0124414 + conv3_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.6709536 , 0.90434617 , -0.046010096 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73625 + min, max, mean: -0.090010956 , 0.12963162 , 0.0004867123 + conv3_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 256 + min, max, mean: -0.43939522 , 0.5542811 , 0.06785342 + weight + total # of elements, shape: 16384 , [256, 64, 1, 1] + # of unique elements: 16169 + min, max, mean: -0.8439546 , 0.8458319 , -0.014337172 + conv4_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.69682604 , 0.707377 , 0.03144107 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 294074 + min, max, mean: -0.0944709 , 0.09537982 , 5.3931428e-05 + conv4_2 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.45413327 , 0.40575495 , 0.101883814 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73538 + min, max, mean: -0.2167601 , 0.2236249 , 0.00013782765 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.4686328 , 0.47734067 , 0.11835486 + weight + total # of elements, shape: 8192 , [128, 64, 1, 1] + # of unique elements: 8094 + min, max, mean: -0.49763885 , 0.9475825 , 0.0046607344 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.2936688 , 0.2124647 , -0.004038828 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51185 + min, max, mean: -3.230919 , 1.8885088 , -0.23695616 diff --git a/checkpoints/shallow008_naslarge_2b8b_noSAM/train_log.txt b/checkpoints/shallow008_naslarge_2b8b_noSAM/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..51ed54de66ca6d26935d8ddaf21a90511506020b --- /dev/null +++ b/checkpoints/shallow008_naslarge_2b8b_noSAM/train_log.txt @@ -0,0 +1,470 @@ +[1] loss: 3.777, train acc: 11.398 test acc: 20.680 17.758 s +[2] loss: 2.954, train acc: 25.604 test acc: 31.010 16.542 s +[3] loss: 2.477, train acc: 35.176 test acc: 37.840 16.391 s +[4] loss: 2.181, train acc: 41.648 test acc: 43.290 16.444 s +[5] loss: 1.972, train acc: 46.462 test acc: 45.600 16.365 s +[6] loss: 1.834, train acc: 49.504 test acc: 49.050 16.455 s +[7] loss: 1.719, train acc: 52.454 test acc: 50.410 16.416 s +[8] loss: 1.620, train acc: 54.918 test acc: 51.630 16.412 s +[9] loss: 1.543, train acc: 56.478 test acc: 52.830 16.442 s +[10] loss: 1.480, train acc: 58.206 test acc: 53.820 16.614 s +[11] loss: 1.421, train acc: 59.730 test acc: 54.910 16.855 s +[12] loss: 1.370, train acc: 60.996 test acc: 55.600 16.466 s +[13] loss: 1.322, train acc: 61.992 test acc: 55.620 16.443 s +[14] loss: 1.285, train acc: 62.960 test acc: 54.910 16.504 s +[15] loss: 1.240, train acc: 64.008 test acc: 56.920 16.419 s +[16] loss: 1.206, train acc: 65.016 test acc: 57.380 16.457 s +[17] loss: 1.175, train acc: 65.972 test acc: 57.640 16.535 s +[18] loss: 1.146, train acc: 66.542 test acc: 58.430 16.475 s +[19] loss: 1.109, train acc: 67.550 test acc: 58.940 16.288 s +[20] loss: 1.092, train acc: 67.916 test acc: 59.660 16.489 s +[21] loss: 1.064, train acc: 68.416 test acc: 58.630 16.363 s +[22] loss: 1.033, train acc: 69.498 test acc: 59.210 16.696 s +[23] loss: 1.012, train acc: 69.918 test acc: 58.860 16.422 s +[24] loss: 0.989, train acc: 70.560 test acc: 58.710 16.415 s +[25] loss: 0.969, train acc: 71.234 test acc: 59.640 16.425 s +[26] loss: 0.945, train acc: 71.794 test acc: 60.030 16.443 s +[27] loss: 0.929, train acc: 72.110 test acc: 60.380 16.436 s +[28] loss: 0.906, train acc: 72.650 test acc: 60.630 16.325 s +[29] loss: 0.894, train acc: 73.002 test acc: 60.570 16.563 s +[30] loss: 0.879, train acc: 73.500 test acc: 60.410 16.472 s +[31] loss: 0.863, train acc: 74.024 test acc: 60.470 16.315 s +[32] loss: 0.847, train acc: 74.450 test acc: 60.680 16.553 s +[33] loss: 0.821, train acc: 75.146 test acc: 60.930 16.657 s +[34] loss: 0.814, train acc: 75.276 test acc: 61.230 16.377 s +[35] loss: 0.803, train acc: 75.550 test acc: 61.610 16.425 s +[36] loss: 0.785, train acc: 76.024 test acc: 60.790 16.352 s +[37] loss: 0.772, train acc: 76.356 test acc: 60.670 16.433 s +[38] loss: 0.755, train acc: 76.716 test acc: 61.970 16.531 s +[39] loss: 0.742, train acc: 77.184 test acc: 62.290 16.424 s +[40] loss: 0.735, train acc: 77.612 test acc: 62.010 16.579 s +[41] loss: 0.719, train acc: 77.836 test acc: 61.600 16.456 s +[42] loss: 0.709, train acc: 78.098 test acc: 61.720 16.391 s +[43] loss: 0.707, train acc: 78.270 test acc: 62.360 16.612 s +[44] loss: 0.687, train acc: 79.048 test acc: 61.460 16.703 s +[45] loss: 0.677, train acc: 79.060 test acc: 61.650 16.368 s +[46] loss: 0.668, train acc: 79.402 test acc: 62.530 16.443 s +[47] loss: 0.658, train acc: 79.558 test acc: 62.150 16.328 s +[48] loss: 0.646, train acc: 80.076 test acc: 62.190 16.384 s +[49] loss: 0.641, train acc: 80.078 test acc: 62.260 16.412 s +[50] loss: 0.625, train acc: 80.346 test acc: 62.120 16.483 s +[51] loss: 0.623, train acc: 80.724 test acc: 61.960 16.462 s +[52] loss: 0.608, train acc: 81.152 test acc: 62.660 16.394 s +[53] loss: 0.601, train acc: 81.154 test acc: 61.770 16.478 s +[54] loss: 0.589, train acc: 81.712 test acc: 62.180 16.490 s +[55] loss: 0.588, train acc: 81.744 test acc: 62.350 16.624 s +[56] loss: 0.577, train acc: 81.934 test acc: 62.320 16.346 s +[57] loss: 0.565, train acc: 82.324 test acc: 62.010 16.422 s +[58] loss: 0.562, train acc: 82.456 test acc: 62.020 16.561 s +[59] loss: 0.554, train acc: 82.410 test acc: 62.640 16.441 s +[60] loss: 0.548, train acc: 82.592 test acc: 61.840 16.474 s +[61] loss: 0.541, train acc: 82.962 test acc: 61.740 16.322 s +[62] loss: 0.535, train acc: 83.114 test acc: 63.390 16.416 s +[63] loss: 0.526, train acc: 83.476 test acc: 62.160 16.488 s +[64] loss: 0.516, train acc: 83.748 test acc: 61.490 16.438 s +[65] loss: 0.510, train acc: 83.786 test acc: 62.370 16.601 s +[66] loss: 0.503, train acc: 84.132 test acc: 63.100 16.539 s +[67] loss: 0.498, train acc: 84.122 test acc: 62.470 16.443 s +[68] loss: 0.496, train acc: 84.158 test acc: 62.770 16.398 s +[69] loss: 0.488, train acc: 84.490 test acc: 62.810 16.487 s +[70] loss: 0.478, train acc: 84.816 test acc: 62.610 16.417 s +[71] loss: 0.475, train acc: 84.812 test acc: 61.880 16.518 s +[72] loss: 0.471, train acc: 84.952 test acc: 63.050 16.523 s +[73] loss: 0.455, train acc: 85.474 test acc: 62.320 16.270 s +[74] loss: 0.458, train acc: 85.284 test acc: 63.170 16.365 s +[75] loss: 0.449, train acc: 85.738 test acc: 62.860 16.493 s +[76] loss: 0.445, train acc: 85.752 test acc: 62.120 16.625 s +[77] loss: 0.441, train acc: 85.958 test acc: 62.020 16.361 s +[78] loss: 0.441, train acc: 85.928 test acc: 62.910 16.462 s +[79] loss: 0.431, train acc: 86.132 test acc: 62.750 16.346 s +[80] loss: 0.427, train acc: 86.318 test acc: 62.680 16.358 s +[81] loss: 0.425, train acc: 86.352 test acc: 62.390 16.462 s +[82] loss: 0.415, train acc: 86.506 test acc: 62.860 16.297 s +[83] loss: 0.417, train acc: 86.596 test acc: 62.400 16.468 s +[84] loss: 0.407, train acc: 86.868 test acc: 63.590 16.437 s +[85] loss: 0.403, train acc: 87.098 test acc: 62.600 16.539 s +[86] loss: 0.392, train acc: 87.488 test acc: 61.950 16.545 s +[87] loss: 0.396, train acc: 87.230 test acc: 62.730 16.640 s +[88] loss: 0.397, train acc: 87.140 test acc: 62.280 16.229 s +[89] loss: 0.384, train acc: 87.562 test acc: 62.660 16.318 s +[90] loss: 0.381, train acc: 87.710 test acc: 61.770 16.562 s +[91] loss: 0.387, train acc: 87.378 test acc: 61.980 16.433 s +[92] loss: 0.373, train acc: 87.856 test acc: 62.910 16.359 s +[93] loss: 0.369, train acc: 88.110 test acc: 61.990 16.472 s +[94] loss: 0.370, train acc: 87.820 test acc: 62.150 16.611 s +[95] loss: 0.362, train acc: 88.212 test acc: 62.470 16.526 s +[96] loss: 0.357, train acc: 88.456 test acc: 62.600 16.550 s +[97] loss: 0.362, train acc: 88.242 test acc: 62.250 16.414 s +[98] loss: 0.355, train acc: 88.432 test acc: 62.920 16.612 s +[99] loss: 0.348, train acc: 88.536 test acc: 62.230 16.480 s +[100] loss: 0.351, train acc: 88.484 test acc: 62.590 16.494 s +[1] loss: 0.259, train acc: 91.922 test acc: 64.180 16.361 s +[2] loss: 0.229, train acc: 93.026 test acc: 64.030 16.546 s +[3] loss: 0.221, train acc: 93.394 test acc: 64.410 16.502 s +[4] loss: 0.216, train acc: 93.578 test acc: 64.400 16.468 s +[5] loss: 0.210, train acc: 93.768 test acc: 63.860 16.547 s +[6] loss: 0.205, train acc: 93.960 test acc: 64.350 16.472 s +[7] loss: 0.200, train acc: 94.090 test acc: 64.070 16.358 s +[8] loss: 0.200, train acc: 94.178 test acc: 64.010 16.496 s +[9] loss: 0.195, train acc: 94.240 test acc: 64.160 16.658 s +[10] loss: 0.193, train acc: 94.234 test acc: 64.280 16.436 s +[1] loss: 0.176, train acc: 94.984 test acc: 64.750 16.522 s +[2] loss: 0.170, train acc: 95.252 test acc: 65.180 16.497 s +[3] loss: 0.169, train acc: 95.238 test acc: 64.830 16.442 s +[4] loss: 0.164, train acc: 95.498 test acc: 64.870 16.480 s +[5] loss: 0.166, train acc: 95.382 test acc: 64.780 16.398 s +[6] loss: 0.164, train acc: 95.488 test acc: 65.040 16.405 s +[7] loss: 0.165, train acc: 95.480 test acc: 64.510 16.459 s +[8] loss: 0.161, train acc: 95.548 test acc: 64.790 16.400 s +[9] loss: 0.160, train acc: 95.534 test acc: 64.820 16.256 s +[10] loss: 0.163, train acc: 95.518 test acc: 64.480 16.707 s +[11] loss: 0.162, train acc: 95.486 test acc: 64.560 16.446 s +[12] loss: 0.159, train acc: 95.604 test acc: 64.590 16.389 s +[13] loss: 0.159, train acc: 95.644 test acc: 64.840 16.473 s +[14] loss: 0.155, train acc: 95.794 test acc: 64.430 16.374 s +[15] loss: 0.157, train acc: 95.676 test acc: 64.810 16.518 s +[16] loss: 0.155, train acc: 95.676 test acc: 64.480 16.321 s +[17] loss: 0.155, train acc: 95.744 test acc: 64.540 16.364 s +[18] loss: 0.154, train acc: 95.814 test acc: 64.600 16.478 s +[19] loss: 0.152, train acc: 95.864 test acc: 64.540 16.510 s +[20] loss: 0.154, train acc: 95.730 test acc: 64.530 16.439 s +[21] loss: 0.153, train acc: 95.884 test acc: 64.470 16.699 s +[22] loss: 0.152, train acc: 95.864 test acc: 64.430 16.430 s +[23] loss: 0.153, train acc: 95.784 test acc: 64.650 16.393 s +[24] loss: 0.151, train acc: 95.870 test acc: 64.620 16.399 s +[25] loss: 0.150, train acc: 95.958 test acc: 64.730 16.477 s +[26] loss: 0.146, train acc: 96.120 test acc: 64.600 16.305 s +[27] loss: 0.148, train acc: 96.006 test acc: 64.510 16.594 s +[28] loss: 0.147, train acc: 96.102 test acc: 64.610 16.273 s +[29] loss: 0.148, train acc: 96.006 test acc: 64.510 16.608 s +[30] loss: 0.146, train acc: 96.058 test acc: 64.720 16.562 s +[1] loss: 0.144, train acc: 96.182 test acc: 64.760 16.527 s +[2] loss: 0.144, train acc: 96.122 test acc: 64.730 16.575 s +[3] loss: 0.140, train acc: 96.404 test acc: 64.510 16.491 s +[4] loss: 0.140, train acc: 96.298 test acc: 64.550 16.380 s +[5] loss: 0.141, train acc: 96.260 test acc: 64.600 16.514 s +[6] loss: 0.141, train acc: 96.320 test acc: 64.580 16.483 s +[7] loss: 0.141, train acc: 96.236 test acc: 64.580 17.928 s +[8] loss: 0.140, train acc: 96.372 test acc: 64.530 16.482 s +[9] loss: 0.140, train acc: 96.334 test acc: 64.540 16.469 s +[10] loss: 0.139, train acc: 96.356 test acc: 64.610 16.482 s +[11] loss: 0.141, train acc: 96.358 test acc: 64.670 16.423 s +[12] loss: 0.139, train acc: 96.288 test acc: 64.670 16.500 s +[13] loss: 0.138, train acc: 96.398 test acc: 64.580 16.804 s +[14] loss: 0.136, train acc: 96.452 test acc: 64.650 16.426 s +[15] loss: 0.137, train acc: 96.508 test acc: 64.790 16.450 s +[16] loss: 0.140, train acc: 96.278 test acc: 64.720 16.395 s +[17] loss: 0.138, train acc: 96.342 test acc: 64.550 16.347 s +[18] loss: 0.136, train acc: 96.442 test acc: 64.490 16.373 s +[19] loss: 0.139, train acc: 96.356 test acc: 64.580 16.531 s +[20] loss: 0.137, train acc: 96.404 test acc: 64.620 16.289 s +[21] loss: 0.136, train acc: 96.430 test acc: 64.560 16.467 s +[22] loss: 0.139, train acc: 96.328 test acc: 64.540 16.368 s +[23] loss: 0.137, train acc: 96.402 test acc: 64.550 16.363 s +[24] loss: 0.138, train acc: 96.446 test acc: 64.690 16.558 s +[25] loss: 0.137, train acc: 96.448 test acc: 64.730 16.395 s +[26] loss: 0.138, train acc: 96.426 test acc: 64.490 16.455 s +[27] loss: 0.136, train acc: 96.452 test acc: 64.670 16.548 s +[28] loss: 0.137, train acc: 96.390 test acc: 64.600 16.525 s +[29] loss: 0.135, train acc: 96.430 test acc: 64.500 16.354 s +[30] loss: 0.137, train acc: 96.470 test acc: 64.560 16.336 s +[31] loss: 0.134, train acc: 96.508 test acc: 64.650 16.520 s +[32] loss: 0.136, train acc: 96.518 test acc: 64.690 16.537 s +[33] loss: 0.136, train acc: 96.448 test acc: 64.660 16.343 s +[34] loss: 0.136, train acc: 96.494 test acc: 64.670 16.482 s +[35] loss: 0.134, train acc: 96.556 test acc: 64.690 16.798 s +[36] loss: 0.135, train acc: 96.528 test acc: 64.730 16.478 s +[37] loss: 0.135, train acc: 96.522 test acc: 64.710 16.493 s +[38] loss: 0.135, train acc: 96.500 test acc: 64.750 16.451 s +[39] loss: 0.134, train acc: 96.426 test acc: 64.830 16.452 s +[40] loss: 0.135, train acc: 96.644 test acc: 64.770 16.549 s +[41] loss: 0.133, train acc: 96.594 test acc: 64.790 16.258 s +[42] loss: 0.136, train acc: 96.368 test acc: 64.770 16.384 s +[43] loss: 0.136, train acc: 96.442 test acc: 64.660 16.475 s +[44] loss: 0.134, train acc: 96.408 test acc: 64.730 17.419 s +[45] loss: 0.134, train acc: 96.496 test acc: 64.730 16.713 s +[46] loss: 0.132, train acc: 96.518 test acc: 64.660 16.565 s +[47] loss: 0.133, train acc: 96.570 test acc: 64.580 16.371 s +[48] loss: 0.131, train acc: 96.630 test acc: 64.730 16.225 s +[49] loss: 0.132, train acc: 96.560 test acc: 64.680 16.505 s +[50] loss: 0.131, train acc: 96.584 test acc: 64.780 16.533 s +[1] loss: 2.162, train acc: 43.954 test acc: 52.540 28.062 s +[2] loss: 1.341, train acc: 61.100 test acc: 55.090 28.011 s +[3] loss: 1.190, train acc: 64.710 test acc: 56.140 27.892 s +[4] loss: 1.110, train acc: 67.132 test acc: 58.250 28.429 s +[5] loss: 1.048, train acc: 68.606 test acc: 59.050 28.169 s +[6] loss: 1.002, train acc: 69.638 test acc: 59.200 28.397 s +[7] loss: 0.970, train acc: 70.350 test acc: 59.750 28.224 s +[8] loss: 0.940, train acc: 71.202 test acc: 59.340 28.095 s +[9] loss: 0.920, train acc: 71.740 test acc: 60.990 27.836 s +[10] loss: 0.900, train acc: 72.484 test acc: 60.450 28.287 s +[11] loss: 0.892, train acc: 72.450 test acc: 61.280 28.160 s +[12] loss: 0.864, train acc: 73.408 test acc: 61.740 28.278 s +[13] loss: 0.865, train acc: 73.144 test acc: 60.870 28.217 s +[14] loss: 0.846, train acc: 73.820 test acc: 61.370 28.250 s +[15] loss: 0.838, train acc: 73.864 test acc: 61.290 28.060 s +[16] loss: 0.823, train acc: 74.468 test acc: 60.970 28.330 s +[17] loss: 0.826, train acc: 74.200 test acc: 59.880 29.227 s +[18] loss: 0.817, train acc: 74.542 test acc: 61.530 28.327 s +[19] loss: 0.809, train acc: 74.656 test acc: 61.110 28.418 s +[20] loss: 0.797, train acc: 75.294 test acc: 61.300 28.109 s +[21] loss: 0.791, train acc: 75.202 test acc: 61.150 28.186 s +[22] loss: 0.784, train acc: 75.550 test acc: 62.420 27.962 s +[23] loss: 0.788, train acc: 75.328 test acc: 62.260 28.127 s +[24] loss: 0.780, train acc: 75.592 test acc: 62.950 28.543 s +[25] loss: 0.772, train acc: 75.756 test acc: 62.600 28.167 s +[26] loss: 0.758, train acc: 76.314 test acc: 62.290 28.155 s +[27] loss: 0.754, train acc: 76.394 test acc: 62.520 28.391 s +[28] loss: 0.751, train acc: 76.484 test acc: 61.340 28.242 s +[29] loss: 0.758, train acc: 76.298 test acc: 62.600 28.318 s +[30] loss: 0.750, train acc: 76.442 test acc: 62.800 28.371 s +[31] loss: 0.747, train acc: 76.440 test acc: 61.230 28.145 s +[32] loss: 0.737, train acc: 76.990 test acc: 61.560 28.127 s +[33] loss: 0.739, train acc: 76.792 test acc: 61.350 28.113 s +[34] loss: 0.740, train acc: 76.892 test acc: 62.440 28.125 s +[35] loss: 0.736, train acc: 76.868 test acc: 61.530 27.900 s +[36] loss: 0.734, train acc: 77.064 test acc: 62.100 28.493 s +[37] loss: 0.732, train acc: 76.940 test acc: 62.350 28.083 s +[38] loss: 0.726, train acc: 77.040 test acc: 61.920 28.176 s +[39] loss: 0.722, train acc: 77.158 test acc: 63.440 28.240 s +[40] loss: 0.731, train acc: 76.982 test acc: 62.370 27.915 s +[41] loss: 0.717, train acc: 77.332 test acc: 62.030 28.030 s +[42] loss: 0.721, train acc: 77.040 test acc: 60.620 28.123 s +[43] loss: 0.722, train acc: 77.142 test acc: 62.060 28.315 s +[44] loss: 0.707, train acc: 77.776 test acc: 62.590 28.504 s +[45] loss: 0.716, train acc: 77.472 test acc: 62.490 27.781 s +[46] loss: 0.711, train acc: 77.516 test acc: 62.180 27.968 s +[47] loss: 0.719, train acc: 77.360 test acc: 62.610 28.189 s +[48] loss: 0.712, train acc: 77.484 test acc: 63.100 28.026 s +[49] loss: 0.712, train acc: 77.472 test acc: 62.680 28.477 s +[50] loss: 0.706, train acc: 77.558 test acc: 62.170 28.168 s +[51] loss: 0.707, train acc: 77.698 test acc: 62.490 28.154 s +[52] loss: 0.707, train acc: 77.544 test acc: 61.740 27.385 s +[53] loss: 0.697, train acc: 77.846 test acc: 61.800 28.032 s +[54] loss: 0.702, train acc: 77.936 test acc: 61.970 28.033 s +[55] loss: 0.706, train acc: 77.666 test acc: 61.290 28.567 s +[56] loss: 0.699, train acc: 77.866 test acc: 62.110 28.337 s +[57] loss: 0.692, train acc: 78.030 test acc: 62.780 28.022 s +[58] loss: 0.701, train acc: 77.600 test acc: 62.360 28.405 s +[59] loss: 0.701, train acc: 77.790 test acc: 61.190 27.909 s +[60] loss: 0.693, train acc: 78.082 test acc: 62.780 28.161 s +[61] loss: 0.696, train acc: 77.914 test acc: 62.610 28.464 s +[62] loss: 0.690, train acc: 78.202 test acc: 62.710 27.688 s +[63] loss: 0.686, train acc: 78.198 test acc: 62.230 28.098 s +[64] loss: 0.689, train acc: 78.044 test acc: 62.340 28.120 s +[65] loss: 0.684, train acc: 78.280 test acc: 62.890 28.404 s +[66] loss: 0.686, train acc: 78.274 test acc: 61.800 28.013 s +[67] loss: 0.688, train acc: 78.540 test acc: 62.300 27.969 s +[68] loss: 0.692, train acc: 78.136 test acc: 63.160 28.461 s +[69] loss: 0.683, train acc: 78.510 test acc: 62.550 28.206 s +[70] loss: 0.684, train acc: 78.252 test acc: 63.320 28.249 s +[71] loss: 0.684, train acc: 78.428 test acc: 62.110 28.179 s +[72] loss: 0.683, train acc: 78.356 test acc: 62.950 28.260 s +[73] loss: 0.678, train acc: 78.310 test acc: 61.540 28.288 s +[74] loss: 0.683, train acc: 78.358 test acc: 62.740 28.467 s +[75] loss: 0.683, train acc: 78.346 test acc: 62.190 28.283 s +[76] loss: 0.678, train acc: 78.324 test acc: 62.080 27.977 s +[77] loss: 0.675, train acc: 78.594 test acc: 62.680 28.262 s +[78] loss: 0.669, train acc: 78.744 test acc: 63.100 27.869 s +[79] loss: 0.667, train acc: 78.884 test acc: 62.610 28.152 s +[80] loss: 0.675, train acc: 78.516 test acc: 62.350 28.102 s +[81] loss: 0.667, train acc: 78.812 test acc: 62.100 28.342 s +[82] loss: 0.671, train acc: 78.596 test acc: 62.400 28.126 s +[83] loss: 0.666, train acc: 78.886 test acc: 63.000 28.116 s +[84] loss: 0.673, train acc: 78.482 test acc: 62.250 28.289 s +[85] loss: 0.667, train acc: 78.760 test acc: 62.370 28.177 s +[86] loss: 0.668, train acc: 78.724 test acc: 62.940 27.992 s +[87] loss: 0.674, train acc: 78.578 test acc: 62.950 28.354 s +[88] loss: 0.665, train acc: 78.846 test acc: 63.100 28.091 s +[89] loss: 0.663, train acc: 78.856 test acc: 62.460 28.168 s +[90] loss: 0.661, train acc: 79.006 test acc: 63.310 28.166 s +[91] loss: 0.671, train acc: 78.608 test acc: 63.470 28.294 s +[92] loss: 0.659, train acc: 78.918 test acc: 62.620 28.139 s +[93] loss: 0.668, train acc: 78.854 test acc: 63.260 28.554 s +[94] loss: 0.661, train acc: 78.764 test acc: 62.650 27.869 s +[95] loss: 0.659, train acc: 78.880 test acc: 61.030 28.446 s +[96] loss: 0.655, train acc: 79.302 test acc: 62.820 28.322 s +[97] loss: 0.651, train acc: 79.228 test acc: 62.660 28.055 s +[98] loss: 0.650, train acc: 79.412 test acc: 62.810 27.941 s +[99] loss: 0.656, train acc: 78.946 test acc: 64.240 28.335 s +[100] loss: 0.649, train acc: 79.378 test acc: 62.110 28.488 s +[1] loss: 0.578, train acc: 81.660 test acc: 63.760 27.965 s +[2] loss: 0.593, train acc: 81.132 test acc: 63.810 28.200 s +[3] loss: 0.602, train acc: 80.612 test acc: 63.730 28.138 s +[4] loss: 0.595, train acc: 80.992 test acc: 63.000 28.046 s +[5] loss: 0.598, train acc: 80.890 test acc: 63.730 28.215 s +[6] loss: 0.597, train acc: 80.918 test acc: 62.200 28.442 s +[7] loss: 0.595, train acc: 80.898 test acc: 62.720 28.484 s +[8] loss: 0.600, train acc: 80.818 test acc: 63.430 28.004 s +[9] loss: 0.594, train acc: 80.768 test acc: 63.370 28.150 s +[10] loss: 0.605, train acc: 80.542 test acc: 63.730 28.261 s +[11] loss: 0.599, train acc: 80.732 test acc: 64.190 28.055 s +[12] loss: 0.596, train acc: 80.728 test acc: 63.300 28.364 s +[13] loss: 0.597, train acc: 80.740 test acc: 63.520 28.337 s +[14] loss: 0.606, train acc: 80.384 test acc: 64.020 28.004 s +[15] loss: 0.600, train acc: 80.854 test acc: 63.220 28.329 s +[16] loss: 0.593, train acc: 80.990 test acc: 62.800 28.061 s +[17] loss: 0.602, train acc: 80.914 test acc: 63.460 28.292 s +[18] loss: 0.599, train acc: 80.732 test acc: 63.980 28.403 s +[19] loss: 0.603, train acc: 80.706 test acc: 62.840 28.448 s +[20] loss: 0.592, train acc: 81.120 test acc: 61.780 27.993 s +[21] loss: 0.602, train acc: 80.672 test acc: 62.950 28.319 s +[22] loss: 0.601, train acc: 80.574 test acc: 63.260 27.895 s +[23] loss: 0.599, train acc: 80.806 test acc: 63.700 28.165 s +[24] loss: 0.597, train acc: 80.760 test acc: 63.090 28.227 s +[25] loss: 0.603, train acc: 80.790 test acc: 62.890 28.320 s +[26] loss: 0.597, train acc: 80.840 test acc: 63.030 28.196 s +[27] loss: 0.603, train acc: 80.674 test acc: 63.050 28.401 s +[28] loss: 0.601, train acc: 80.504 test acc: 63.020 27.846 s +[29] loss: 0.597, train acc: 80.864 test acc: 63.340 28.026 s +[30] loss: 0.596, train acc: 80.922 test acc: 63.010 28.214 s +[1] loss: 0.539, train acc: 82.738 test acc: 64.420 21.937 s +[2] loss: 0.546, train acc: 82.488 test acc: 64.070 22.119 s +[3] loss: 0.551, train acc: 82.356 test acc: 64.160 22.172 s +[4] loss: 0.563, train acc: 82.004 test acc: 62.850 22.135 s +[5] loss: 0.566, train acc: 81.902 test acc: 63.360 21.985 s +[6] loss: 0.563, train acc: 82.000 test acc: 63.830 22.117 s +[7] loss: 0.571, train acc: 81.632 test acc: 64.280 22.237 s +[8] loss: 0.572, train acc: 81.752 test acc: 63.710 22.164 s +[9] loss: 0.566, train acc: 81.772 test acc: 64.330 22.111 s +[10] loss: 0.573, train acc: 81.708 test acc: 63.330 22.268 s +[11] loss: 0.588, train acc: 81.186 test acc: 63.170 22.112 s +[12] loss: 0.584, train acc: 81.318 test acc: 64.780 22.102 s +[13] loss: 0.576, train acc: 81.620 test acc: 62.920 22.103 s +[14] loss: 0.581, train acc: 81.354 test acc: 64.060 22.154 s +[15] loss: 0.567, train acc: 81.998 test acc: 64.150 22.199 s +[16] loss: 0.569, train acc: 81.834 test acc: 64.040 22.011 s +[17] loss: 0.569, train acc: 81.612 test acc: 63.680 22.049 s +[18] loss: 0.585, train acc: 81.232 test acc: 63.000 22.225 s +[19] loss: 0.580, train acc: 81.350 test acc: 63.820 21.999 s +[20] loss: 0.583, train acc: 81.246 test acc: 63.680 22.147 s +[21] loss: 0.576, train acc: 81.570 test acc: 63.000 22.086 s +[22] loss: 0.570, train acc: 81.828 test acc: 63.410 22.052 s +[23] loss: 0.574, train acc: 81.644 test acc: 63.970 22.038 s +[24] loss: 0.579, train acc: 81.472 test acc: 63.130 22.043 s +[25] loss: 0.578, train acc: 81.514 test acc: 63.720 22.128 s +[26] loss: 0.584, train acc: 81.246 test acc: 63.550 22.211 s +[27] loss: 0.583, train acc: 81.274 test acc: 63.590 22.138 s +[28] loss: 0.580, train acc: 81.408 test acc: 63.450 22.090 s +[29] loss: 0.574, train acc: 81.562 test acc: 65.160 21.892 s +[30] loss: 0.582, train acc: 81.402 test acc: 63.710 22.073 s +[1] loss: 0.510, train acc: 83.844 test acc: 65.220 19.601 s +[2] loss: 0.521, train acc: 83.280 test acc: 63.930 19.507 s +[3] loss: 0.524, train acc: 83.330 test acc: 64.560 19.620 s +[4] loss: 0.527, train acc: 83.000 test acc: 63.900 19.541 s +[5] loss: 0.526, train acc: 83.168 test acc: 64.060 19.702 s +[6] loss: 0.539, train acc: 82.844 test acc: 63.700 19.688 s +[7] loss: 0.537, train acc: 82.950 test acc: 63.890 19.613 s +[8] loss: 0.541, train acc: 82.648 test acc: 64.000 19.662 s +[9] loss: 0.536, train acc: 82.764 test acc: 63.570 19.696 s +[10] loss: 0.534, train acc: 82.884 test acc: 64.270 19.569 s +[11] loss: 0.542, train acc: 82.592 test acc: 63.810 19.472 s +[12] loss: 0.538, train acc: 82.802 test acc: 64.110 19.622 s +[13] loss: 0.550, train acc: 82.496 test acc: 62.760 19.532 s +[14] loss: 0.546, train acc: 82.502 test acc: 62.770 19.678 s +[15] loss: 0.548, train acc: 82.506 test acc: 63.750 19.593 s +[16] loss: 0.546, train acc: 82.688 test acc: 63.430 19.606 s +[17] loss: 0.545, train acc: 82.332 test acc: 63.640 19.652 s +[18] loss: 0.536, train acc: 82.714 test acc: 64.170 19.412 s +[19] loss: 0.546, train acc: 82.650 test acc: 63.420 19.696 s +[20] loss: 0.539, train acc: 82.824 test acc: 63.900 19.606 s +[21] loss: 0.542, train acc: 82.854 test acc: 62.560 19.631 s +[22] loss: 0.553, train acc: 82.328 test acc: 63.680 19.625 s +[23] loss: 0.542, train acc: 82.760 test acc: 63.060 19.711 s +[24] loss: 0.547, train acc: 82.466 test acc: 62.380 19.660 s +[25] loss: 0.544, train acc: 82.610 test acc: 63.710 19.759 s +[26] loss: 0.541, train acc: 82.536 test acc: 63.390 19.702 s +[27] loss: 0.539, train acc: 82.740 test acc: 63.440 19.670 s +[28] loss: 0.546, train acc: 82.364 test acc: 64.040 19.645 s +[29] loss: 0.550, train acc: 82.328 test acc: 64.030 19.595 s +[30] loss: 0.543, train acc: 82.680 test acc: 63.650 19.640 s +[1] loss: 0.461, train acc: 85.342 test acc: 65.360 17.687 s +[2] loss: 0.464, train acc: 85.268 test acc: 64.990 17.844 s +[3] loss: 0.473, train acc: 84.846 test acc: 65.350 17.829 s +[4] loss: 0.477, train acc: 84.786 test acc: 64.590 17.737 s +[5] loss: 0.482, train acc: 84.786 test acc: 64.490 17.726 s +[6] loss: 0.488, train acc: 84.502 test acc: 64.150 17.797 s +[7] loss: 0.486, train acc: 84.546 test acc: 64.810 17.787 s +[8] loss: 0.492, train acc: 84.308 test acc: 64.160 17.814 s +[9] loss: 0.491, train acc: 84.224 test acc: 64.300 17.817 s +[10] loss: 0.483, train acc: 84.904 test acc: 64.580 17.735 s +[11] loss: 0.498, train acc: 84.052 test acc: 64.150 17.730 s +[12] loss: 0.494, train acc: 84.222 test acc: 64.130 17.818 s +[13] loss: 0.495, train acc: 84.280 test acc: 64.850 17.852 s +[14] loss: 0.499, train acc: 84.250 test acc: 63.860 17.674 s +[15] loss: 0.497, train acc: 84.138 test acc: 64.560 17.868 s +[16] loss: 0.497, train acc: 84.068 test acc: 64.450 17.724 s +[17] loss: 0.498, train acc: 84.078 test acc: 63.890 17.831 s +[18] loss: 0.496, train acc: 84.104 test acc: 64.720 17.746 s +[19] loss: 0.500, train acc: 83.972 test acc: 64.890 17.674 s +[20] loss: 0.508, train acc: 83.800 test acc: 64.340 17.815 s +[21] loss: 0.504, train acc: 83.884 test acc: 64.410 17.753 s +[22] loss: 0.505, train acc: 83.910 test acc: 65.150 17.893 s +[23] loss: 0.504, train acc: 83.962 test acc: 64.630 17.781 s +[24] loss: 0.502, train acc: 83.986 test acc: 64.140 17.769 s +[25] loss: 0.508, train acc: 83.958 test acc: 64.330 17.836 s +[26] loss: 0.503, train acc: 83.882 test acc: 63.990 17.753 s +[27] loss: 0.504, train acc: 83.954 test acc: 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test acc: 64.530 17.038 s +[30] loss: 0.497, train acc: 84.178 test acc: 63.800 17.131 s +[1] loss: 0.446, train acc: 85.908 test acc: 65.350 17.121 s +[2] loss: 0.453, train acc: 85.846 test acc: 65.730 16.681 s +[3] loss: 0.459, train acc: 85.730 test acc: 65.370 16.804 s +[4] loss: 0.461, train acc: 85.384 test acc: 64.970 16.862 s +[5] loss: 0.463, train acc: 85.482 test acc: 64.920 16.640 s +[6] loss: 0.465, train acc: 85.138 test acc: 64.460 16.697 s +[7] loss: 0.465, train acc: 85.284 test acc: 64.500 16.787 s +[8] loss: 0.471, train acc: 85.244 test acc: 65.590 16.584 s +[9] loss: 0.463, train acc: 85.446 test acc: 64.840 16.716 s +[10] loss: 0.468, train acc: 85.326 test acc: 64.830 16.819 s +[11] loss: 0.472, train acc: 85.100 test acc: 64.360 16.738 s +[12] loss: 0.470, train acc: 85.170 test acc: 64.980 16.732 s +[13] loss: 0.478, train acc: 84.802 test acc: 64.720 16.647 s +[14] loss: 0.479, train acc: 84.944 test acc: 65.220 16.562 s +[15] loss: 0.471, train acc: 85.102 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+------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 27 + min, max, mean: -8704.0 , 1408.0 , -868.0 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 122 + min, max, mean: -73.0 , 118.0 , -0.17418982 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 26 + min, max, mean: -128.0 , 68.0 , 14.0625 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.15136719 + conv1_3 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 40 + min, max, mean: -120.0 , 98.0 , -2.21875 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.11306424 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 20 + min, max, mean: -128.0 , 126.0 , 29.0625 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.035047743 + conv2_2 + output_shift: [0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 47 + min, max, mean: -96.0 , 126.0 , 29.3125 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.2421875 + conv3_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 55 + min, max, mean: -128.0 , 126.0 , 35.21875 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.026529947 + conv3_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 77 + min, max, mean: -128.0 , 126.0 , 20.734375 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.09436035 + conv4_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 35 + min, max, mean: -128.0 , 126.0 , -5.09375 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , 0.0046115452 + conv4_2 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 73 + min, max, mean: -128.0 , 126.0 , -19.015625 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.03000217 + conv5_1 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [1.] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 75 + min, max, mean: -128.0 , 126.0 , 34.890625 + weight + total 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min, max, mean: -2.0 , 1.0 , -0.03479004 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -2166.0 , 2597.0 , -23.7 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 241 + min, max, mean: -128.0 , 125.0 , -14.603789 diff --git a/checkpoints/shallow010_naslarge_2b8b_wSAM/statistics_training_checkpoint b/checkpoints/shallow010_naslarge_2b8b_wSAM/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..04282f469787ce845fac9b6de0980714a16da9bd --- /dev/null +++ b/checkpoints/shallow010_naslarge_2b8b_wSAM/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.11356868 , 0.091928445 , 0.005726372 + weight + total # of elements, shape: 3456 , [128, 3, 3, 3] + # of unique elements: 3456 + min, max, mean: -0.7470972 , 0.8304557 , -0.0008773367 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.41145262 , 0.30299085 , 0.11826816 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16366 + min, max, mean: -0.5303318 , 0.5205089 , -0.009956859 + conv1_3 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 256 + min, max, mean: -0.72582525 , 1.0370457 , 0.08059071 + weight + total # of elements, shape: 294912 , [256, 128, 3, 3] + # of unique elements: 294293 + min, max, mean: -0.20158304 , 0.16796228 , -0.0024090037 + conv2_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.49013856 , 0.5312471 , 0.1016065 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 294202 + min, max, mean: -0.08584305 , 0.060128834 , -0.00024059539 + conv2_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.6671736 , 0.7120095 , 0.08861585 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16371 + min, max, mean: -0.56641173 , 0.64063776 , -0.016870284 + conv3_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.8250142 , 0.74603933 , -0.0037405742 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73571 + min, max, mean: -0.10138782 , 0.11076212 , 0.00093133084 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 256 + min, max, mean: -0.33974662 , 0.44110784 , 0.07637683 + weight + total # of elements, shape: 16384 , [256, 64, 1, 1] + # of unique elements: 16366 + min, max, mean: -0.85561633 , 0.80795157 , -0.020401143 + conv4_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.499701 , 0.9950852 , 0.029063346 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 294007 + min, max, mean: -0.09003077 , 0.09095405 , -0.00023980369 + conv4_2 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.23323293 , 0.33829257 , 0.089144416 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73565 + min, max, mean: -0.23597741 , 0.21488984 , -0.0011587992 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.28117087 , 0.44147873 , 0.086634256 + weight + total # of elements, shape: 8192 , [128, 64, 1, 1] + # of unique elements: 8119 + min, max, mean: -0.5141475 , 0.8037016 , 0.0038255197 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.2644152 , 0.3170493 , -0.002889828 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51181 + min, max, mean: -3.9576318 , 1.9540635 , -0.22963743 diff --git a/checkpoints/shallow010_naslarge_2b8b_wSAM/train_log.txt b/checkpoints/shallow010_naslarge_2b8b_wSAM/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e9fd94b76b5675b3e5a555281d40ba45213ffa5 --- /dev/null +++ b/checkpoints/shallow010_naslarge_2b8b_wSAM/train_log.txt @@ -0,0 +1,470 @@ +[1] loss: 3.794, train acc: 11.314 test acc: 19.980 28.649 s +[2] loss: 2.973, train acc: 25.248 test acc: 29.210 28.386 s +[3] loss: 2.512, train acc: 34.380 test acc: 37.360 28.325 s +[4] loss: 2.216, train acc: 40.850 test acc: 42.340 28.356 s +[5] loss: 2.014, train acc: 45.374 test acc: 45.960 28.438 s +[6] loss: 1.861, train acc: 49.168 test acc: 47.990 28.279 s +[7] loss: 1.739, train acc: 52.020 test acc: 50.610 28.384 s +[8] loss: 1.649, train acc: 54.222 test acc: 52.230 28.304 s +[9] loss: 1.562, train acc: 56.226 test acc: 53.370 28.357 s +[10] loss: 1.498, train acc: 58.022 test acc: 53.630 28.309 s +[11] loss: 1.444, train acc: 58.826 test acc: 55.240 29.686 s +[12] loss: 1.386, train acc: 60.702 test acc: 55.650 28.376 s +[13] loss: 1.343, train acc: 61.484 test acc: 56.510 28.304 s +[14] loss: 1.300, train acc: 62.678 test acc: 57.170 28.336 s +[15] loss: 1.260, train acc: 63.882 test acc: 57.760 28.336 s +[16] loss: 1.222, train acc: 64.736 test acc: 57.530 28.324 s +[17] loss: 1.187, train acc: 65.578 test acc: 57.460 28.353 s +[18] loss: 1.159, train acc: 66.304 test acc: 58.260 28.386 s +[19] loss: 1.134, train acc: 66.972 test acc: 59.360 28.291 s +[20] loss: 1.100, train acc: 67.888 test acc: 58.510 28.352 s +[21] loss: 1.080, train acc: 68.236 test acc: 59.560 28.336 s +[22] loss: 1.053, train acc: 68.996 test acc: 60.050 28.282 s +[23] loss: 1.024, train acc: 69.864 test acc: 59.990 28.355 s +[24] loss: 1.010, train acc: 70.018 test acc: 59.690 28.445 s +[25] loss: 0.990, train acc: 70.714 test acc: 61.140 28.411 s +[26] loss: 0.972, train acc: 71.212 test acc: 61.370 28.295 s +[27] loss: 0.948, train acc: 71.904 test acc: 60.460 28.393 s +[28] loss: 0.929, train acc: 72.322 test acc: 60.280 28.316 s +[29] loss: 0.910, train acc: 72.824 test acc: 60.750 28.273 s +[30] loss: 0.890, train acc: 73.380 test acc: 61.100 28.303 s +[31] loss: 0.878, train acc: 73.954 test acc: 60.840 28.488 s +[32] loss: 0.860, train acc: 74.326 test acc: 60.730 28.333 s +[33] loss: 0.847, train acc: 74.520 test acc: 62.230 28.375 s +[34] loss: 0.831, train acc: 74.982 test acc: 61.950 28.350 s +[35] loss: 0.821, train acc: 75.254 test acc: 61.950 28.284 s +[36] loss: 0.801, train acc: 75.954 test acc: 61.720 29.320 s +[37] loss: 0.792, train acc: 76.140 test acc: 61.990 28.403 s +[38] loss: 0.774, train acc: 76.660 test acc: 62.140 28.331 s +[39] loss: 0.764, train acc: 76.916 test acc: 61.880 28.367 s +[40] loss: 0.749, train acc: 77.284 test acc: 62.350 28.329 s +[41] loss: 0.740, train acc: 77.480 test acc: 62.680 28.290 s +[42] loss: 0.722, train acc: 78.000 test acc: 63.190 28.259 s +[43] loss: 0.716, train acc: 78.110 test acc: 63.250 28.446 s +[44] loss: 0.708, train acc: 78.464 test acc: 61.970 28.400 s +[45] loss: 0.692, train acc: 78.902 test acc: 62.720 28.325 s +[46] loss: 0.684, train acc: 79.270 test acc: 62.810 28.268 s +[47] loss: 0.675, train acc: 79.278 test acc: 62.310 29.411 s +[48] loss: 0.667, train acc: 79.626 test acc: 62.110 28.411 s +[49] loss: 0.647, train acc: 80.156 test acc: 62.520 28.336 s +[50] loss: 0.648, train acc: 79.978 test acc: 62.570 29.862 s +[51] loss: 0.631, train acc: 80.598 test acc: 62.670 28.317 s +[52] loss: 0.626, train acc: 80.732 test acc: 62.530 28.365 s +[53] loss: 0.619, train acc: 80.730 test acc: 63.110 28.362 s +[54] loss: 0.613, train acc: 81.146 test acc: 62.790 28.430 s +[55] loss: 0.605, train acc: 81.116 test acc: 63.030 28.423 s +[56] loss: 0.594, train acc: 81.756 test acc: 63.140 28.416 s +[57] loss: 0.584, train acc: 82.130 test acc: 62.950 28.383 s +[58] loss: 0.578, train acc: 82.098 test acc: 62.990 28.345 s +[59] loss: 0.574, train acc: 82.260 test acc: 62.930 28.286 s +[60] loss: 0.559, train acc: 82.526 test acc: 63.010 28.465 s +[61] loss: 0.549, train acc: 82.688 test acc: 62.730 28.250 s +[62] loss: 0.541, train acc: 83.058 test acc: 62.950 28.412 s +[63] loss: 0.538, train acc: 83.222 test acc: 63.390 28.378 s +[64] loss: 0.532, train acc: 83.452 test acc: 63.100 28.360 s +[65] loss: 0.529, train acc: 83.350 test acc: 62.970 28.408 s +[66] loss: 0.516, train acc: 83.780 test acc: 63.590 28.377 s +[67] loss: 0.510, train acc: 84.062 test acc: 63.590 28.390 s +[68] loss: 0.505, train acc: 84.186 test acc: 62.820 28.325 s +[69] loss: 0.503, train acc: 84.272 test acc: 63.420 28.433 s +[70] loss: 0.496, train acc: 84.420 test acc: 63.630 28.348 s +[71] loss: 0.490, train acc: 84.562 test acc: 63.520 29.374 s +[72] loss: 0.478, train acc: 84.770 test acc: 63.390 28.367 s +[73] loss: 0.474, train acc: 85.018 test acc: 63.410 28.372 s +[74] loss: 0.472, train acc: 85.144 test acc: 63.630 28.389 s +[75] loss: 0.468, train acc: 85.160 test acc: 63.540 28.444 s +[76] loss: 0.464, train acc: 85.244 test acc: 62.660 28.221 s +[77] loss: 0.457, train acc: 85.578 test acc: 63.180 28.342 s +[78] loss: 0.451, train acc: 85.774 test acc: 63.340 28.383 s +[79] loss: 0.449, train acc: 85.718 test acc: 63.030 28.377 s +[80] loss: 0.441, train acc: 86.142 test acc: 63.500 28.411 s +[81] loss: 0.438, train acc: 86.044 test acc: 63.730 28.400 s +[82] loss: 0.428, train acc: 86.570 test acc: 62.710 28.281 s +[83] loss: 0.430, train acc: 86.322 test acc: 63.390 28.343 s +[84] loss: 0.421, train acc: 86.714 test acc: 63.760 28.409 s +[85] loss: 0.415, train acc: 86.826 test acc: 62.490 29.667 s +[86] loss: 0.410, train acc: 87.032 test acc: 62.560 28.359 s +[87] loss: 0.407, train acc: 87.194 test acc: 63.650 28.362 s +[88] loss: 0.407, train acc: 87.148 test acc: 62.980 28.469 s +[89] loss: 0.397, train acc: 87.180 test acc: 63.800 28.305 s +[90] loss: 0.397, train acc: 87.224 test acc: 63.320 28.356 s +[91] loss: 0.381, train acc: 87.956 test acc: 63.420 28.373 s +[92] loss: 0.384, train acc: 87.844 test acc: 63.080 28.319 s +[93] loss: 0.385, train acc: 87.690 test acc: 62.900 28.307 s +[94] loss: 0.379, train acc: 87.868 test acc: 62.320 28.421 s +[95] loss: 0.374, train acc: 88.070 test acc: 63.080 28.350 s +[96] loss: 0.370, train acc: 88.200 test acc: 63.380 28.353 s +[97] loss: 0.361, train acc: 88.494 test acc: 63.780 28.391 s +[98] loss: 0.361, train acc: 88.472 test acc: 63.530 28.384 s +[99] loss: 0.355, train acc: 88.702 test acc: 62.670 28.379 s +[100] loss: 0.357, train acc: 88.516 test acc: 63.270 28.477 s +[1] loss: 0.271, train acc: 91.810 test acc: 65.350 28.323 s +[2] loss: 0.247, train acc: 92.710 test acc: 64.830 28.374 s +[3] loss: 0.241, train acc: 92.850 test acc: 65.590 28.389 s +[4] loss: 0.233, train acc: 93.202 test acc: 65.190 28.360 s +[5] loss: 0.226, train acc: 93.504 test acc: 65.020 28.330 s +[6] loss: 0.221, train acc: 93.620 test acc: 65.150 29.502 s +[7] loss: 0.222, train acc: 93.612 test acc: 64.840 28.418 s +[8] loss: 0.221, train acc: 93.684 test acc: 65.070 28.350 s +[9] loss: 0.216, train acc: 93.858 test acc: 64.960 28.376 s +[10] loss: 0.213, train acc: 93.892 test acc: 64.780 28.359 s +[1] loss: 0.196, train acc: 94.640 test acc: 65.170 28.452 s +[2] loss: 0.190, train acc: 94.756 test acc: 65.180 28.363 s +[3] loss: 0.188, train acc: 94.910 test acc: 65.030 28.463 s +[4] loss: 0.185, train acc: 95.040 test acc: 65.150 28.360 s +[5] loss: 0.185, train acc: 94.954 test acc: 65.210 28.269 s +[6] loss: 0.185, train acc: 95.046 test acc: 65.580 28.365 s +[7] loss: 0.183, train acc: 95.106 test acc: 65.330 28.365 s +[8] loss: 0.182, train acc: 95.074 test acc: 65.210 28.348 s +[9] loss: 0.180, train acc: 95.168 test acc: 65.340 28.460 s +[10] loss: 0.182, train acc: 94.968 test acc: 65.350 28.263 s +[11] loss: 0.179, train acc: 95.218 test acc: 65.320 28.304 s +[12] loss: 0.179, train acc: 95.206 test acc: 65.260 29.589 s +[13] loss: 0.176, train acc: 95.276 test acc: 65.360 29.665 s +[14] loss: 0.175, train acc: 95.280 test acc: 65.290 28.355 s +[15] loss: 0.173, train acc: 95.452 test acc: 65.450 28.395 s +[16] loss: 0.177, train acc: 95.278 test acc: 65.130 28.421 s +[17] loss: 0.175, train acc: 95.400 test acc: 65.310 28.305 s +[18] loss: 0.174, train acc: 95.342 test acc: 65.300 28.328 s +[19] loss: 0.170, train acc: 95.636 test acc: 65.380 28.278 s +[20] loss: 0.174, train acc: 95.384 test acc: 65.510 28.317 s +[21] loss: 0.169, train acc: 95.412 test acc: 65.530 28.333 s +[22] loss: 0.169, train acc: 95.556 test acc: 65.390 28.421 s +[23] loss: 0.173, train acc: 95.312 test acc: 65.310 28.247 s +[24] loss: 0.169, train acc: 95.442 test acc: 65.440 28.473 s +[25] loss: 0.168, train acc: 95.584 test acc: 65.450 28.369 s +[26] loss: 0.167, train acc: 95.576 test acc: 65.450 28.410 s +[27] loss: 0.167, train acc: 95.518 test acc: 65.430 28.366 s +[28] loss: 0.167, train acc: 95.566 test acc: 65.460 28.424 s +[29] loss: 0.167, train acc: 95.584 test acc: 65.300 28.306 s +[30] loss: 0.163, train acc: 95.732 test acc: 65.440 28.288 s +[1] loss: 0.163, train acc: 95.740 test acc: 65.450 28.295 s +[2] loss: 0.160, train acc: 95.808 test acc: 65.650 28.319 s +[3] loss: 0.161, train acc: 95.830 test acc: 65.410 28.342 s +[4] loss: 0.161, train acc: 95.866 test acc: 65.520 28.328 s +[5] loss: 0.160, train acc: 95.922 test acc: 65.410 28.373 s +[6] loss: 0.158, train acc: 96.008 test acc: 65.380 28.353 s +[7] loss: 0.162, train acc: 95.768 test acc: 65.490 28.400 s +[8] loss: 0.159, train acc: 95.872 test acc: 65.500 28.263 s +[9] loss: 0.160, train acc: 95.916 test acc: 65.440 28.378 s +[10] loss: 0.159, train acc: 95.866 test acc: 65.410 28.266 s +[11] loss: 0.161, train acc: 95.764 test acc: 65.400 28.357 s +[12] loss: 0.158, train acc: 95.990 test acc: 65.430 28.365 s +[13] loss: 0.160, train acc: 95.754 test acc: 65.470 28.313 s +[14] loss: 0.160, train acc: 95.864 test acc: 65.360 29.434 s +[15] loss: 0.159, train acc: 95.880 test acc: 65.300 28.343 s +[16] loss: 0.158, train acc: 95.984 test acc: 65.240 28.287 s +[17] loss: 0.159, train acc: 96.034 test acc: 65.290 28.472 s +[18] loss: 0.158, train acc: 95.980 test acc: 65.340 28.310 s +[19] loss: 0.158, train acc: 95.864 test acc: 65.440 28.351 s +[20] loss: 0.158, train acc: 95.916 test acc: 65.390 29.442 s +[21] loss: 0.158, train acc: 95.990 test acc: 65.430 28.330 s +[22] loss: 0.156, train acc: 95.900 test acc: 65.250 28.372 s +[23] loss: 0.156, train acc: 96.012 test acc: 65.450 28.473 s +[24] loss: 0.158, train acc: 95.932 test acc: 65.270 28.501 s +[25] loss: 0.154, train acc: 96.046 test acc: 65.250 28.394 s +[26] loss: 0.158, train acc: 95.918 test acc: 65.220 28.428 s +[27] loss: 0.155, train acc: 96.058 test acc: 65.360 28.399 s +[28] loss: 0.156, train acc: 96.000 test acc: 65.360 28.395 s +[29] loss: 0.155, train acc: 96.002 test acc: 65.240 28.345 s +[30] loss: 0.156, train acc: 95.904 test acc: 65.300 28.425 s +[31] loss: 0.155, train acc: 96.040 test acc: 65.220 28.307 s +[32] loss: 0.154, train acc: 96.118 test acc: 65.350 28.333 s +[33] loss: 0.155, train acc: 96.012 test acc: 65.360 28.264 s +[34] loss: 0.157, train acc: 95.864 test acc: 65.340 28.439 s +[35] loss: 0.158, train acc: 95.930 test acc: 65.290 28.423 s +[36] loss: 0.155, train acc: 95.888 test acc: 65.280 28.437 s +[37] loss: 0.154, train acc: 96.026 test acc: 65.320 28.356 s +[38] loss: 0.154, train acc: 96.058 test acc: 65.330 28.500 s +[39] loss: 0.154, train acc: 96.088 test acc: 65.340 28.476 s +[40] loss: 0.153, train acc: 96.124 test acc: 65.420 28.447 s +[41] loss: 0.154, train acc: 96.076 test acc: 65.340 28.407 s +[42] loss: 0.153, train acc: 96.078 test acc: 65.320 28.328 s +[43] loss: 0.153, train acc: 96.058 test acc: 65.250 28.435 s +[44] loss: 0.153, train acc: 96.060 test acc: 65.200 28.346 s +[45] loss: 0.153, train acc: 96.054 test acc: 65.440 28.386 s +[46] loss: 0.155, train acc: 95.884 test acc: 65.350 28.312 s +[47] loss: 0.153, train acc: 96.076 test acc: 65.290 28.294 s +[48] loss: 0.154, train acc: 96.074 test acc: 65.230 28.312 s +[49] loss: 0.155, train acc: 95.972 test acc: 65.350 28.437 s +[50] loss: 0.152, train acc: 96.080 test acc: 65.400 28.318 s +[1] loss: 2.156, train acc: 47.016 test acc: 55.160 48.729 s +[2] loss: 1.300, train acc: 64.938 test acc: 57.530 49.537 s +[3] loss: 1.169, train acc: 68.278 test acc: 59.230 49.484 s +[4] loss: 1.107, train acc: 69.872 test acc: 59.750 49.527 s +[5] loss: 1.058, train acc: 70.996 test acc: 60.210 49.858 s +[6] loss: 1.025, train acc: 71.816 test acc: 60.640 49.774 s +[7] loss: 0.996, train acc: 72.916 test acc: 61.780 49.952 s +[8] loss: 0.975, train acc: 73.504 test acc: 62.310 49.639 s +[9] loss: 0.970, train acc: 73.346 test acc: 62.330 50.242 s +[10] loss: 0.960, train acc: 73.750 test acc: 62.590 50.355 s +[11] loss: 0.945, train acc: 74.388 test acc: 61.530 50.090 s +[12] loss: 0.939, train acc: 74.392 test acc: 62.160 50.102 s +[13] loss: 0.934, train acc: 74.654 test acc: 62.800 50.238 s +[14] loss: 0.924, train acc: 74.784 test acc: 62.770 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0.161, train acc: 95.826 test acc: 64.470 28.381 s +[34] loss: 0.158, train acc: 96.018 test acc: 64.540 28.315 s +[35] loss: 0.158, train acc: 95.940 test acc: 64.490 28.409 s +[36] loss: 0.159, train acc: 95.954 test acc: 64.600 28.360 s +[37] loss: 0.158, train acc: 95.974 test acc: 64.620 28.357 s +[38] loss: 0.156, train acc: 95.982 test acc: 64.550 28.426 s +[39] loss: 0.157, train acc: 95.856 test acc: 64.600 28.360 s +[40] loss: 0.159, train acc: 95.868 test acc: 64.670 28.323 s +[41] loss: 0.159, train acc: 95.772 test acc: 64.550 28.322 s +[42] loss: 0.158, train acc: 95.954 test acc: 64.570 28.358 s +[43] loss: 0.157, train acc: 96.002 test acc: 64.480 28.404 s +[44] loss: 0.161, train acc: 95.868 test acc: 64.390 28.430 s +[45] loss: 0.157, train acc: 95.910 test acc: 64.560 28.397 s +[46] loss: 0.155, train acc: 96.080 test acc: 64.510 28.428 s +[47] loss: 0.156, train acc: 95.980 test acc: 64.710 28.364 s +[48] loss: 0.155, train acc: 96.058 test acc: 64.660 28.394 s 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acc: 10.596 test acc: 11.010 53.238 s +[16] loss: 3.793, train acc: 10.946 test acc: 11.190 53.182 s +[17] loss: 3.759, train acc: 11.716 test acc: 12.490 53.811 s +[18] loss: 3.713, train acc: 12.260 test acc: 12.070 53.766 s +[19] loss: 3.684, train acc: 12.436 test acc: 12.570 53.642 s +[20] loss: 3.659, train acc: 13.028 test acc: 13.210 53.214 s +[21] loss: 3.630, train acc: 13.340 test acc: 14.360 54.006 s +[22] loss: 3.608, train acc: 14.030 test acc: 14.010 53.434 s +[23] loss: 3.582, train acc: 14.438 test acc: 14.920 53.540 s +[24] loss: 3.553, train acc: 14.958 test acc: 15.300 53.637 s +[25] loss: 3.528, train acc: 15.312 test acc: 15.610 53.872 s +[26] loss: 3.505, train acc: 15.728 test acc: 15.930 53.974 s +[27] loss: 3.478, train acc: 16.126 test acc: 16.410 53.892 s +[28] loss: 3.447, train acc: 16.610 test acc: 17.420 53.782 s +[29] loss: 3.431, train acc: 16.740 test acc: 17.990 53.467 s +[30] loss: 3.411, train acc: 17.194 test acc: 17.170 53.539 s +[31] loss: 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b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/statistics_hardware_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..47a367711b63d5a832da73bd8b6615b1b071ef5d --- /dev/null +++ b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/statistics_hardware_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 61.33999824523926, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 61.33999824523926} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 122 + min, max, mean: -1742.0 , 1909.0 , 69.99219 + weight + total # of elements, shape: 3456 , [128, 3, 3, 3] + # of unique elements: 163 + min, max, mean: -104.0 , 127.0 , -0.13715278 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 84 + min, max, mean: -196.0 , 135.0 , 50.648438 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.06958008 + conv1_3 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 114 + min, max, mean: -256.0 , 255.0 , 67.8125 + weight + total # of elements, shape: 294912 , [256, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.07051934 + conv2_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 25 + min, max, mean: -256.0 , 255.0 , 97.0 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.032714844 + conv2_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 99 + min, max, mean: -256.0 , 255.0 , 47.382812 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.16381836 + conv3_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 8 + min, max, mean: -256.0 , 255.0 , 1.640625 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.013807509 + conv3_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 120 + min, max, mean: -89.0 , 105.0 , 12.6484375 + weight + total # of elements, shape: 16384 , [256, 64, 1, 1] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.11975098 + conv4_1 + output_shift: [-4.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 29 + min, max, mean: -256.0 , 255.0 , 26.671875 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 2 + min, max, mean: -1.0 , 1.0 , -0.037963867 + conv4_2 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 27 + min, max, mean: -256.0 , 255.0 , 129.625 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.06856283 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 105 + min, max, mean: -195.0 , 212.0 , 42.03125 + weight + total # of elements, shape: 8192 , [128, 64, 1, 1] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.072631836 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 99 + min, max, mean: -2073.0 , 1705.0 , -39.07 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 242 + min, max, mean: -128.0 , 127.0 , -14.715996 diff --git a/checkpoints/shallow012_naslarge_AP2b8b_wSAM/statistics_training_checkpoint b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..4bc93d15d4680d16d8a0273e60f2b24404b4287a --- /dev/null +++ b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.1062945 , 0.11649261 , 0.0042711664 + weight + total # of elements, shape: 3456 , [128, 3, 3, 3] + # of unique elements: 3456 + min, max, mean: -0.81091905 , 1.0096906 , -0.0011215414 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.38302064 , 0.2630111 , 0.09883679 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16366 + min, max, mean: -0.6458351 , 0.48061106 , -0.014299535 + conv1_3 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 256 + min, max, mean: -0.44141898 , 0.9098679 , 0.08919213 + weight + total # of elements, shape: 294912 , [256, 128, 3, 3] + # of unique elements: 294335 + min, max, mean: -0.1915816 , 0.16559647 , -0.0038295325 + conv2_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.45334312 , 0.56368154 , 0.108843796 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 294393 + min, max, mean: -0.11026993 , 0.06633544 , -0.0010007931 + conv2_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.7490978 , 0.6358313 , 0.09368026 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16383 + min, max, mean: -0.6281223 , 0.65266085 , -0.021354973 + conv3_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.6982394 , 0.9027271 , 0.0025540655 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73699 + min, max, mean: -0.1486163 , 0.1421723 , -4.3654596e-05 + conv3_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 256 , [256] + # of unique elements: 256 + min, max, mean: -0.34901744 , 0.40962565 , 0.04933401 + weight + total # of elements, shape: 16384 , [256, 64, 1, 1] + # of unique elements: 16381 + min, max, mean: -0.7871786 , 0.8800293 , -0.019785387 + conv4_1 + output_shift: [-4.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.50292665 , 0.59448224 , 0.05066337 + weight + total # of elements, shape: 294912 , [128, 256, 3, 3] + # of unique elements: 294407 + min, max, mean: -0.10920182 , 0.13385801 , -0.00080923137 + conv4_2 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.33529556 , 0.38338777 , 0.091318294 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73652 + min, max, mean: -0.21086846 , 0.19986778 , -0.0023538435 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.3798907 , 0.41461912 , 0.082057945 + weight + total # of elements, shape: 8192 , [128, 64, 1, 1] + # of unique elements: 8136 + min, max, mean: -0.5600442 , 0.8380049 , -0.010083143 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.25300184 , 0.20809306 , -0.00476943 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51186 + min, max, mean: -3.399373 , 2.1379414 , -0.231394 diff --git a/checkpoints/shallow012_naslarge_AP2b8b_wSAM/train_log.txt b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..47cc99f7aaecbc84a6a56afcc82c0837a6353263 --- /dev/null +++ b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/train_log.txt @@ -0,0 +1,470 @@ +[1] loss: 3.777, train acc: 11.234 test acc: 19.550 37.497 s +[2] loss: 2.968, train acc: 25.312 test acc: 30.390 28.272 s +[3] loss: 2.494, train acc: 34.780 test acc: 37.540 28.255 s +[4] loss: 2.200, train acc: 41.278 test acc: 42.710 28.307 s +[5] loss: 1.999, train acc: 45.774 test acc: 46.540 28.373 s +[6] loss: 1.839, train acc: 49.500 test acc: 47.200 28.350 s +[7] loss: 1.734, train acc: 51.982 test acc: 49.930 28.433 s +[8] loss: 1.629, train acc: 54.602 test acc: 51.280 28.296 s +[9] loss: 1.559, train acc: 56.238 test acc: 53.130 28.346 s +[10] loss: 1.490, train acc: 57.902 test acc: 54.150 28.371 s +[11] loss: 1.426, train acc: 59.552 test acc: 53.460 28.275 s +[12] loss: 1.382, train acc: 60.584 test acc: 55.250 28.278 s +[13] loss: 1.330, train acc: 62.006 test acc: 56.610 28.432 s +[14] loss: 1.289, train acc: 63.076 test acc: 56.050 28.280 s +[15] loss: 1.250, train acc: 63.870 test acc: 57.640 28.324 s +[16] loss: 1.216, train acc: 64.494 test acc: 57.580 28.337 s +[17] loss: 1.178, train acc: 65.858 test acc: 57.460 28.283 s +[18] loss: 1.148, train acc: 66.530 test acc: 58.470 28.335 s +[19] loss: 1.115, train acc: 67.348 test acc: 59.130 28.299 s +[20] loss: 1.091, train acc: 67.906 test acc: 59.530 28.283 s +[21] loss: 1.069, train acc: 68.766 test acc: 59.780 28.296 s +[22] loss: 1.045, train acc: 69.270 test acc: 59.790 28.372 s +[23] loss: 1.022, train acc: 69.776 test acc: 59.750 28.301 s +[24] loss: 0.996, train acc: 70.604 test acc: 60.050 28.314 s +[25] loss: 0.980, train acc: 70.830 test acc: 61.030 28.359 s +[26] loss: 0.956, train acc: 71.428 test acc: 60.880 28.338 s +[27] loss: 0.943, train acc: 71.912 test acc: 60.070 28.364 s +[28] loss: 0.923, train acc: 72.374 test acc: 61.000 28.291 s +[29] loss: 0.899, train acc: 72.952 test acc: 61.220 28.305 s +[30] loss: 0.884, train acc: 73.616 test acc: 61.320 28.400 s +[31] loss: 0.867, train acc: 73.976 test acc: 61.310 28.349 s +[32] loss: 0.845, train acc: 74.634 test acc: 61.290 28.398 s +[33] loss: 0.842, train acc: 74.546 test acc: 62.100 28.456 s +[34] loss: 0.820, train acc: 75.276 test acc: 61.600 28.408 s +[35] loss: 0.808, train acc: 75.668 test acc: 62.310 28.364 s +[36] loss: 0.800, train acc: 76.000 test acc: 61.840 28.392 s +[37] loss: 0.779, train acc: 76.380 test acc: 62.500 28.271 s +[38] loss: 0.765, train acc: 76.822 test acc: 62.240 28.351 s +[39] loss: 0.759, train acc: 76.866 test acc: 62.040 28.452 s +[40] loss: 0.749, train acc: 77.172 test acc: 61.560 28.334 s +[41] loss: 0.728, train acc: 77.920 test acc: 62.710 28.275 s +[42] loss: 0.720, train acc: 78.062 test acc: 62.030 28.270 s +[43] loss: 0.707, train acc: 78.506 test acc: 62.810 28.368 s +[44] loss: 0.695, train acc: 78.722 test acc: 62.740 28.426 s +[45] loss: 0.687, train acc: 78.998 test acc: 62.500 28.403 s +[46] loss: 0.678, train acc: 79.176 test acc: 63.100 28.410 s +[47] loss: 0.661, train acc: 79.612 test acc: 62.590 28.377 s +[48] loss: 0.660, train acc: 79.530 test acc: 62.540 28.332 s +[49] loss: 0.648, train acc: 79.898 test acc: 63.290 28.289 s +[50] loss: 0.637, train acc: 80.292 test acc: 63.210 28.342 s +[51] loss: 0.634, train acc: 80.436 test acc: 62.670 28.495 s +[52] loss: 0.616, train acc: 80.872 test acc: 62.190 28.475 s +[53] loss: 0.614, train acc: 81.020 test acc: 62.440 28.389 s +[54] loss: 0.603, train acc: 81.384 test acc: 62.600 28.311 s +[55] loss: 0.596, train acc: 81.492 test acc: 63.080 28.348 s +[56] loss: 0.587, train acc: 81.882 test acc: 62.600 28.377 s +[57] loss: 0.578, train acc: 82.094 test acc: 63.450 28.372 s +[58] loss: 0.571, train acc: 82.314 test acc: 63.110 28.443 s +[59] loss: 0.566, train acc: 82.330 test acc: 63.420 28.360 s +[60] loss: 0.555, train acc: 82.602 test acc: 62.410 28.370 s +[61] loss: 0.549, train acc: 82.994 test acc: 62.600 28.355 s +[62] loss: 0.544, train acc: 82.782 test acc: 63.360 28.355 s +[63] loss: 0.534, train acc: 83.270 test acc: 63.130 28.338 s +[64] loss: 0.527, train acc: 83.584 test acc: 63.340 28.417 s +[65] loss: 0.524, train acc: 83.534 test acc: 62.850 28.455 s +[66] loss: 0.514, train acc: 83.998 test acc: 63.120 28.328 s +[67] loss: 0.506, train acc: 84.336 test acc: 62.820 28.337 s +[68] loss: 0.503, train acc: 84.240 test acc: 62.930 28.366 s +[69] loss: 0.495, train acc: 84.420 test acc: 63.060 28.287 s +[70] loss: 0.497, train acc: 84.240 test acc: 63.160 28.329 s +[71] loss: 0.489, train acc: 84.616 test acc: 63.360 28.556 s +[72] loss: 0.473, train acc: 85.292 test acc: 62.980 28.413 s +[73] loss: 0.470, train acc: 85.118 test acc: 62.730 28.358 s +[74] loss: 0.470, train acc: 85.054 test acc: 62.650 28.349 s +[75] loss: 0.464, train acc: 85.180 test acc: 63.040 28.322 s +[76] loss: 0.455, train acc: 85.570 test acc: 63.460 28.309 s +[77] loss: 0.452, train acc: 85.598 test acc: 62.880 28.351 s +[78] loss: 0.441, train acc: 86.066 test acc: 63.260 28.294 s +[79] loss: 0.441, train acc: 86.012 test acc: 63.350 28.294 s +[80] loss: 0.436, train acc: 86.244 test acc: 62.730 28.340 s +[81] loss: 0.439, train acc: 86.014 test acc: 63.110 28.247 s +[82] loss: 0.428, train acc: 86.386 test acc: 62.940 28.386 s +[83] loss: 0.422, train acc: 86.682 test acc: 63.140 28.421 s +[84] loss: 0.421, train acc: 86.920 test acc: 63.270 28.274 s +[85] loss: 0.411, train acc: 86.916 test acc: 62.950 28.364 s +[86] loss: 0.410, train acc: 87.028 test acc: 63.220 28.405 s +[87] loss: 0.405, train acc: 87.088 test acc: 62.520 28.432 s +[88] loss: 0.399, train acc: 87.214 test acc: 63.100 28.378 s +[89] loss: 0.400, train acc: 87.264 test acc: 62.960 28.349 s +[90] loss: 0.392, train acc: 87.506 test acc: 62.830 28.450 s +[91] loss: 0.385, train acc: 87.726 test acc: 62.290 28.371 s +[92] loss: 0.381, train acc: 87.850 test acc: 63.060 28.380 s +[93] loss: 0.384, train acc: 87.694 test acc: 62.940 28.339 s +[94] loss: 0.377, train acc: 87.814 test acc: 62.660 28.321 s +[95] loss: 0.377, train acc: 87.986 test acc: 62.520 28.385 s +[96] loss: 0.370, train acc: 88.156 test acc: 63.460 28.450 s +[97] loss: 0.368, train acc: 88.260 test acc: 63.180 28.332 s +[98] loss: 0.363, train acc: 88.416 test acc: 62.610 28.292 s +[99] loss: 0.358, train acc: 88.514 test acc: 63.320 28.331 s +[100] loss: 0.347, train acc: 88.900 test acc: 62.710 28.367 s +[1] loss: 0.274, train acc: 91.696 test acc: 64.120 28.370 s +[2] loss: 0.249, train acc: 92.526 test acc: 64.910 28.331 s +[3] loss: 0.243, train acc: 92.834 test acc: 64.760 28.305 s +[4] loss: 0.234, train acc: 93.256 test acc: 64.470 28.372 s +[5] loss: 0.226, train acc: 93.636 test acc: 64.880 28.447 s +[6] loss: 0.228, train acc: 93.342 test acc: 64.900 28.320 s +[7] loss: 0.222, train acc: 93.530 test acc: 64.450 28.337 s +[8] loss: 0.218, train acc: 93.834 test acc: 64.490 28.353 s +[9] loss: 0.215, train acc: 93.892 test acc: 64.310 28.390 s +[10] loss: 0.217, train acc: 93.848 test acc: 64.720 28.336 s +[1] loss: 0.198, train acc: 94.454 test acc: 64.870 28.330 s +[2] loss: 0.189, train acc: 94.754 test acc: 64.820 28.328 s +[3] loss: 0.193, train acc: 94.826 test acc: 64.760 28.275 s +[4] loss: 0.188, train acc: 94.944 test acc: 64.960 28.444 s +[5] loss: 0.185, train acc: 94.978 test acc: 64.610 28.398 s +[6] loss: 0.186, train acc: 94.940 test acc: 64.740 28.407 s +[7] loss: 0.184, train acc: 95.034 test acc: 64.900 28.339 s +[8] loss: 0.183, train acc: 94.926 test acc: 64.950 28.326 s +[9] loss: 0.183, train acc: 95.058 test acc: 64.590 28.311 s +[10] loss: 0.183, train acc: 95.000 test acc: 64.750 28.309 s +[11] loss: 0.180, train acc: 95.060 test acc: 64.890 28.476 s +[12] loss: 0.178, train acc: 95.248 test acc: 64.860 28.314 s +[13] loss: 0.177, train acc: 95.222 test acc: 64.740 28.290 s +[14] loss: 0.179, train acc: 95.276 test acc: 64.880 28.319 s +[15] loss: 0.177, train acc: 95.290 test acc: 64.850 28.378 s +[16] loss: 0.178, train acc: 95.226 test acc: 64.590 28.354 s +[17] loss: 0.174, train acc: 95.390 test acc: 65.000 28.352 s +[18] loss: 0.177, train acc: 95.096 test acc: 64.830 28.467 s +[19] loss: 0.179, train acc: 95.138 test acc: 64.670 28.349 s +[20] loss: 0.170, train acc: 95.496 test acc: 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acc: 64.960 28.382 s +[7] loss: 0.162, train acc: 95.686 test acc: 64.880 28.435 s +[8] loss: 0.159, train acc: 95.984 test acc: 64.940 28.391 s +[9] loss: 0.161, train acc: 95.714 test acc: 65.030 28.344 s +[10] loss: 0.161, train acc: 95.792 test acc: 64.970 28.321 s +[11] loss: 0.158, train acc: 95.940 test acc: 64.930 28.341 s +[12] loss: 0.158, train acc: 95.886 test acc: 65.030 28.435 s +[13] loss: 0.160, train acc: 95.918 test acc: 64.950 28.452 s +[14] loss: 0.163, train acc: 95.712 test acc: 64.900 28.215 s +[15] loss: 0.159, train acc: 95.838 test acc: 64.840 28.125 s +[16] loss: 0.161, train acc: 95.648 test acc: 64.900 28.116 s +[17] loss: 0.159, train acc: 95.780 test acc: 64.830 28.153 s +[18] loss: 0.163, train acc: 95.780 test acc: 64.680 28.172 s +[19] loss: 0.160, train acc: 95.774 test acc: 65.000 28.196 s +[20] loss: 0.159, train acc: 95.892 test acc: 64.900 28.187 s +[21] loss: 0.160, train acc: 95.778 test acc: 64.970 28.193 s +[22] loss: 0.156, train acc: 96.052 test acc: 64.960 28.030 s +[23] loss: 0.159, train acc: 95.762 test acc: 65.050 28.132 s +[24] loss: 0.158, train acc: 95.892 test acc: 65.020 28.100 s +[25] loss: 0.157, train acc: 95.924 test acc: 64.930 28.104 s +[26] loss: 0.157, train acc: 95.884 test acc: 64.850 28.148 s +[27] loss: 0.160, train acc: 95.820 test acc: 64.870 28.220 s +[28] loss: 0.157, train acc: 95.904 test acc: 64.830 28.158 s +[29] loss: 0.156, train acc: 95.968 test acc: 64.760 28.156 s +[30] loss: 0.158, train acc: 95.832 test acc: 64.730 28.133 s +[31] loss: 0.159, train acc: 95.800 test acc: 64.810 28.175 s +[32] loss: 0.156, train acc: 95.850 test acc: 64.850 28.251 s +[33] loss: 0.158, train acc: 95.840 test acc: 64.820 28.152 s +[34] loss: 0.158, train acc: 95.916 test acc: 64.800 28.069 s +[35] loss: 0.157, train acc: 95.900 test acc: 64.780 28.093 s +[36] loss: 0.157, train acc: 95.912 test acc: 64.790 28.170 s +[37] loss: 0.155, train acc: 95.990 test acc: 64.780 28.147 s +[38] loss: 0.157, train acc: 95.964 test acc: 64.860 28.116 s +[39] loss: 0.155, train acc: 95.958 test acc: 64.920 28.149 s +[40] loss: 0.153, train acc: 96.114 test acc: 64.820 28.165 s +[41] loss: 0.155, train acc: 95.980 test acc: 64.780 28.030 s +[42] loss: 0.155, train acc: 95.952 test acc: 64.810 28.107 s +[43] loss: 0.154, train acc: 95.966 test acc: 64.850 28.118 s +[44] loss: 0.154, train acc: 95.976 test acc: 64.830 28.157 s +[45] loss: 0.155, train acc: 96.024 test acc: 64.750 28.295 s +[46] loss: 0.156, train acc: 95.924 test acc: 64.840 28.173 s +[47] loss: 0.152, train acc: 96.100 test acc: 64.830 28.173 s +[48] loss: 0.156, train acc: 95.838 test acc: 64.750 28.185 s +[49] loss: 0.155, train acc: 95.994 test acc: 64.700 28.128 s +[50] loss: 0.155, train acc: 95.978 test acc: 64.800 28.101 s +[1] loss: 4.439, train acc: 4.932 test acc: 8.870 51.360 s +[2] loss: 3.859, train acc: 10.376 test acc: 12.440 52.588 s +[3] loss: 3.644, train acc: 13.520 test acc: 15.310 51.319 s +[4] loss: 3.486, train acc: 16.078 test acc: 19.010 50.775 s +[5] loss: 3.317, train acc: 19.174 test acc: 21.000 51.248 s +[6] loss: 3.152, train acc: 21.796 test acc: 24.410 51.229 s +[7] loss: 2.986, train acc: 24.618 test acc: 27.680 50.793 s +[8] loss: 2.884, train acc: 26.718 test acc: 29.150 51.605 s +[9] loss: 2.783, train acc: 28.992 test acc: 30.710 51.619 s +[10] loss: 2.673, train acc: 31.070 test acc: 33.000 51.464 s +[11] loss: 2.570, train acc: 33.028 test acc: 34.490 51.683 s +[12] loss: 2.486, train acc: 35.006 test acc: 35.740 51.633 s +[13] loss: 2.415, train acc: 36.586 test acc: 37.300 51.499 s +[14] loss: 2.329, train acc: 38.094 test acc: 38.960 51.113 s +[15] loss: 2.260, train acc: 39.896 test acc: 40.580 51.269 s +[16] loss: 2.193, train acc: 41.190 test acc: 41.040 50.374 s +[17] loss: 2.131, train acc: 42.880 test acc: 42.370 51.319 s +[18] loss: 2.074, train acc: 44.034 test acc: 42.560 51.711 s +[19] loss: 2.028, train acc: 45.074 test acc: 44.350 51.415 s +[20] loss: 1.971, train acc: 46.780 test acc: 45.270 50.992 s +[21] loss: 1.921, train acc: 47.760 test acc: 46.080 51.910 s +[22] loss: 1.881, train acc: 48.700 test acc: 46.460 51.337 s +[23] loss: 1.837, train acc: 49.608 test acc: 47.740 50.579 s +[24] loss: 1.795, train acc: 50.674 test acc: 48.550 50.740 s +[25] loss: 1.767, train acc: 51.404 test acc: 48.230 50.506 s +[26] loss: 1.732, train acc: 52.338 test acc: 49.410 51.329 s +[27] loss: 1.704, train acc: 52.904 test acc: 49.640 50.386 s +[28] loss: 1.670, train acc: 53.878 test acc: 50.690 50.979 s +[29] loss: 1.646, train acc: 54.396 test acc: 50.500 50.621 s +[30] loss: 1.624, train acc: 54.770 test acc: 51.160 51.558 s +[31] loss: 1.603, train acc: 55.828 test acc: 51.540 50.957 s +[32] loss: 1.589, train acc: 55.810 test acc: 51.750 51.436 s +[33] loss: 1.563, train acc: 56.600 test acc: 51.840 50.552 s +[34] loss: 1.547, train acc: 56.832 test acc: 52.390 51.087 s +[35] loss: 1.524, train acc: 57.612 test acc: 52.140 50.145 s +[36] loss: 1.512, train acc: 57.860 test acc: 51.940 50.726 s +[37] loss: 1.506, train acc: 58.118 test acc: 53.110 51.175 s +[38] loss: 1.482, train acc: 58.888 test acc: 53.360 50.837 s +[39] loss: 1.475, train acc: 58.814 test acc: 53.360 51.277 s +[40] loss: 1.450, train acc: 59.666 test acc: 53.660 51.893 s +[41] loss: 1.448, train acc: 59.658 test acc: 53.690 51.493 s +[42] loss: 1.430, train acc: 60.090 test acc: 53.770 50.988 s +[43] loss: 1.422, train acc: 60.270 test acc: 54.300 50.975 s +[44] loss: 1.405, train acc: 60.772 test acc: 54.330 51.894 s +[45] loss: 1.388, train acc: 61.292 test acc: 54.960 51.387 s +[46] loss: 1.378, train acc: 61.524 test acc: 54.980 51.073 s +[47] loss: 1.377, train acc: 61.426 test acc: 55.440 51.099 s +[48] loss: 1.362, train acc: 61.798 test acc: 55.410 51.389 s +[49] loss: 1.354, train acc: 62.300 test acc: 55.830 51.176 s +[50] loss: 1.364, train acc: 62.134 test acc: 55.330 50.898 s +[51] loss: 1.348, train acc: 62.430 test acc: 56.350 50.688 s +[52] loss: 1.337, train acc: 62.736 test acc: 56.000 51.724 s +[53] loss: 1.333, train acc: 62.842 test acc: 56.630 51.242 s +[54] loss: 1.326, train acc: 63.176 test acc: 56.410 50.658 s +[55] loss: 1.321, train acc: 63.376 test acc: 56.920 51.349 s +[56] loss: 1.316, train acc: 63.550 test acc: 56.450 50.391 s +[57] loss: 1.305, train acc: 63.726 test acc: 56.650 51.401 s +[58] loss: 1.292, train acc: 64.114 test acc: 57.000 50.782 s +[59] loss: 1.291, train acc: 64.438 test acc: 57.000 51.365 s +[60] loss: 1.283, train acc: 64.414 test acc: 57.350 51.219 s +[61] loss: 1.286, train acc: 64.296 test acc: 57.250 51.540 s +[62] loss: 1.282, train acc: 64.614 test acc: 57.340 50.983 s +[63] loss: 1.275, train acc: 64.812 test acc: 57.670 50.977 s +[64] loss: 1.274, train acc: 64.910 test acc: 57.710 50.763 s +[65] loss: 1.270, train acc: 64.806 test acc: 58.310 51.906 s +[66] loss: 1.270, train acc: 64.906 test acc: 57.740 51.682 s +[67] loss: 1.265, train acc: 65.212 test acc: 58.250 51.591 s +[68] loss: 1.258, train acc: 65.128 test acc: 56.700 51.559 s +[69] loss: 1.252, train acc: 65.618 test acc: 58.050 50.893 s +[70] loss: 1.243, train acc: 65.616 test acc: 58.030 51.991 s +[71] loss: 1.239, train acc: 66.088 test acc: 57.750 51.546 s +[72] loss: 1.237, train acc: 65.940 test acc: 57.790 51.363 s +[73] loss: 1.242, train acc: 65.766 test acc: 57.570 51.367 s +[74] loss: 1.238, train acc: 65.946 test acc: 58.070 51.037 s +[75] loss: 1.236, train acc: 66.316 test acc: 56.820 51.287 s +[76] loss: 1.233, train acc: 66.284 test acc: 58.840 51.384 s +[77] loss: 1.228, train acc: 66.172 test acc: 59.110 50.670 s +[78] loss: 1.224, train acc: 66.596 test acc: 58.680 51.506 s +[79] loss: 1.220, train acc: 66.342 test acc: 57.910 51.112 s +[80] loss: 1.221, train acc: 66.776 test acc: 59.060 50.876 s +[81] loss: 1.220, train acc: 66.752 test acc: 59.170 50.438 s +[82] loss: 1.225, train acc: 66.516 test acc: 56.740 50.475 s +[83] loss: 1.260, train acc: 65.206 test acc: 57.110 51.206 s +[84] loss: 1.249, train acc: 65.462 test acc: 57.100 51.268 s +[85] loss: 1.243, train acc: 65.490 test acc: 57.560 51.147 s +[86] loss: 1.241, train acc: 65.908 test acc: 58.040 51.596 s +[87] loss: 1.242, train acc: 65.536 test acc: 57.490 51.541 s +[88] loss: 1.233, train acc: 65.850 test acc: 57.400 51.410 s +[89] loss: 1.239, train acc: 65.874 test acc: 56.990 50.967 s +[90] loss: 1.243, train acc: 65.742 test acc: 58.550 51.617 s +[91] loss: 1.237, train acc: 65.804 test acc: 58.350 51.103 s +[92] loss: 1.234, train acc: 66.160 test acc: 57.580 51.864 s +[93] loss: 1.229, train acc: 66.258 test acc: 58.080 51.538 s +[94] loss: 1.229, train acc: 66.070 test acc: 58.380 50.878 s +[95] loss: 1.234, train acc: 66.112 test acc: 57.990 51.003 s +[96] loss: 1.235, train acc: 66.072 test acc: 57.950 50.925 s +[97] loss: 1.235, train acc: 66.282 test acc: 58.880 50.555 s +[98] loss: 1.225, train acc: 66.504 test acc: 58.390 51.036 s +[99] loss: 1.221, train acc: 66.584 test acc: 58.680 51.015 s +[100] loss: 1.229, train acc: 66.368 test acc: 58.560 51.669 s +[1] loss: 1.208, train acc: 67.592 test acc: 57.540 51.122 s +[2] loss: 1.238, train acc: 66.336 test acc: 57.560 51.424 s +[3] loss: 1.249, train acc: 65.914 test acc: 57.450 51.370 s +[4] loss: 1.245, train acc: 65.962 test acc: 58.390 51.257 s +[5] loss: 1.251, train acc: 66.114 test acc: 57.510 51.487 s +[6] loss: 1.258, train acc: 65.772 test acc: 57.920 51.970 s +[7] loss: 1.257, train acc: 66.102 test acc: 58.020 51.248 s +[8] loss: 1.260, train acc: 65.786 test acc: 57.900 50.881 s +[9] loss: 1.258, train acc: 65.930 test acc: 57.690 51.151 s +[10] loss: 1.256, train acc: 66.182 test acc: 58.430 50.854 s +[11] loss: 1.260, train acc: 65.616 test acc: 58.320 51.681 s +[12] loss: 1.259, train acc: 66.118 test acc: 57.850 51.395 s +[13] loss: 1.264, train acc: 65.860 test acc: 58.820 51.747 s +[14] loss: 1.255, train acc: 65.888 test acc: 58.000 51.003 s +[15] loss: 1.252, train acc: 66.336 test acc: 58.020 50.659 s +[16] loss: 1.264, train acc: 65.834 test acc: 58.780 51.430 s +[17] loss: 1.259, train acc: 66.054 test acc: 58.220 51.281 s +[18] loss: 1.258, train acc: 66.220 test acc: 58.420 51.714 s +[19] loss: 1.253, train acc: 66.356 test acc: 58.090 51.017 s +[20] loss: 1.260, train acc: 66.046 test acc: 58.390 52.186 s +[21] loss: 1.262, train acc: 66.074 test acc: 58.320 52.246 s +[22] loss: 1.258, train acc: 66.158 test acc: 58.180 51.861 s +[23] loss: 1.257, train acc: 66.014 test acc: 57.910 52.637 s +[24] loss: 1.255, train acc: 66.384 test acc: 58.410 52.714 s +[25] loss: 1.253, train acc: 66.316 test acc: 57.760 52.553 s +[26] loss: 1.269, train acc: 65.826 test acc: 58.430 53.547 s +[27] loss: 1.253, train acc: 66.424 test acc: 57.680 53.264 s +[28] loss: 1.256, train acc: 66.230 test acc: 58.110 52.703 s +[29] loss: 1.269, train acc: 66.054 test acc: 58.680 52.384 s +[30] loss: 1.264, train acc: 65.926 test acc: 58.620 52.402 s +[1] loss: 1.156, train acc: 67.784 test acc: 59.900 41.089 s +[2] loss: 1.152, train acc: 68.306 test acc: 60.480 41.020 s +[3] loss: 1.174, train acc: 67.414 test acc: 58.140 40.703 s +[4] loss: 1.197, train acc: 66.714 test acc: 57.650 40.630 s +[5] loss: 1.187, train acc: 67.014 test acc: 57.650 40.719 s +[6] loss: 1.194, train acc: 66.808 test acc: 58.560 40.971 s +[7] loss: 1.196, train acc: 66.874 test acc: 58.380 40.844 s +[8] loss: 1.198, train acc: 66.710 test acc: 58.540 40.933 s +[9] loss: 1.205, train acc: 66.668 test acc: 58.230 40.368 s +[10] loss: 1.207, train acc: 66.776 test acc: 58.470 40.966 s +[11] loss: 1.204, train acc: 66.680 test acc: 58.410 40.765 s +[12] loss: 1.209, train acc: 66.652 test acc: 58.660 40.988 s +[13] loss: 1.203, train acc: 66.898 test acc: 58.730 40.789 s +[14] loss: 1.198, train acc: 66.904 test acc: 58.420 40.309 s +[15] loss: 1.202, train acc: 66.962 test acc: 58.520 40.683 s +[16] loss: 1.201, train acc: 66.896 test acc: 58.360 40.977 s +[17] loss: 1.204, train acc: 66.948 test acc: 57.900 40.513 s +[18] loss: 1.201, train acc: 66.974 test acc: 58.400 40.704 s +[19] loss: 1.207, train acc: 66.836 test acc: 57.870 40.520 s +[20] loss: 1.207, train acc: 66.860 test acc: 58.060 41.006 s +[21] loss: 1.205, train acc: 66.760 test acc: 58.470 40.918 s +[22] loss: 1.202, train acc: 66.878 test acc: 58.300 40.975 s +[23] loss: 1.207, train acc: 66.738 test acc: 58.670 40.573 s +[24] loss: 1.207, train acc: 66.704 test acc: 58.100 40.545 s +[25] loss: 1.205, train acc: 66.690 test acc: 58.210 40.290 s +[26] loss: 1.201, train acc: 67.166 test acc: 58.660 40.500 s +[27] loss: 1.204, train acc: 66.894 test acc: 58.070 40.686 s +[28] loss: 1.207, train acc: 66.728 test acc: 58.320 40.761 s +[29] loss: 1.202, train acc: 67.046 test acc: 58.780 40.594 s +[30] loss: 1.205, train acc: 66.760 test acc: 58.350 40.566 s +[1] loss: 1.122, train acc: 68.970 test acc: 57.490 35.891 s +[2] loss: 1.179, train acc: 67.144 test acc: 58.400 35.944 s +[3] loss: 1.171, train acc: 67.340 test acc: 58.430 36.373 s +[4] loss: 1.165, train acc: 67.566 test acc: 58.660 36.169 s +[5] loss: 1.171, train acc: 67.356 test acc: 58.790 36.500 s +[6] loss: 1.174, train acc: 67.078 test acc: 58.500 36.054 s +[7] loss: 1.182, train acc: 67.342 test acc: 58.740 36.117 s +[8] loss: 1.173, train acc: 67.352 test acc: 58.570 36.282 s +[9] loss: 1.183, train acc: 67.262 test acc: 57.750 36.059 s +[10] loss: 1.177, train acc: 67.314 test acc: 58.560 35.997 s +[11] loss: 1.182, train acc: 67.440 test acc: 58.370 35.855 s +[12] loss: 1.180, train acc: 67.106 test acc: 58.540 35.963 s +[13] loss: 1.181, train acc: 67.394 test acc: 58.700 36.063 s +[14] loss: 1.185, train acc: 67.098 test acc: 58.750 36.253 s +[15] loss: 1.180, train acc: 67.224 test acc: 58.170 36.141 s +[16] loss: 1.183, train acc: 67.314 test acc: 58.930 35.763 s +[17] loss: 1.196, train acc: 67.000 test acc: 58.340 36.349 s +[18] loss: 1.189, train acc: 66.948 test acc: 58.260 36.178 s +[19] loss: 1.183, train acc: 67.146 test acc: 58.730 36.396 s +[20] loss: 1.191, train acc: 67.110 test acc: 58.490 35.943 s +[21] loss: 1.194, train acc: 67.090 test acc: 58.390 36.191 s +[22] loss: 1.189, train acc: 67.052 test acc: 58.390 36.115 s +[23] loss: 1.197, train acc: 67.292 test acc: 58.820 36.470 s +[24] loss: 1.200, train acc: 66.770 test acc: 58.640 36.356 s +[25] loss: 1.205, train acc: 66.920 test acc: 58.270 36.120 s +[26] loss: 1.191, train acc: 67.160 test acc: 58.600 36.077 s +[27] loss: 1.200, train acc: 66.724 test acc: 58.780 36.474 s +[28] loss: 1.209, train acc: 66.762 test acc: 58.400 36.381 s +[29] loss: 1.207, train acc: 67.078 test acc: 58.100 36.370 s +[30] loss: 1.205, train acc: 66.738 test acc: 58.720 36.039 s +[1] loss: 1.104, train acc: 69.220 test acc: 60.620 32.229 s +[2] loss: 1.078, train acc: 69.546 test acc: 60.820 32.466 s +[3] loss: 1.077, train acc: 69.666 test acc: 60.800 32.183 s +[4] loss: 1.129, train acc: 67.806 test acc: 59.430 32.106 s +[5] loss: 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loss: 1.131, train acc: 68.094 test acc: 59.240 32.130 s +[22] loss: 1.143, train acc: 67.992 test acc: 59.130 32.315 s +[23] loss: 1.143, train acc: 68.122 test acc: 59.380 32.144 s +[24] loss: 1.131, train acc: 68.260 test acc: 59.230 32.152 s +[25] loss: 1.144, train acc: 68.146 test acc: 58.620 32.158 s +[26] loss: 1.144, train acc: 68.150 test acc: 59.110 32.292 s +[27] loss: 1.141, train acc: 68.194 test acc: 59.000 32.335 s +[28] loss: 1.142, train acc: 68.272 test acc: 59.270 32.143 s +[29] loss: 1.141, train acc: 68.022 test acc: 59.180 32.637 s +[30] loss: 1.148, train acc: 68.100 test acc: 58.800 32.590 s +[1] loss: 1.069, train acc: 69.652 test acc: 61.040 31.091 s +[2] loss: 1.066, train acc: 69.760 test acc: 61.190 30.806 s +[3] loss: 1.064, train acc: 69.780 test acc: 60.830 30.925 s +[4] loss: 1.061, train acc: 69.804 test acc: 56.680 30.819 s +[5] loss: 1.060, train acc: 69.902 test acc: 60.750 30.832 s +[6] loss: 1.069, train acc: 69.482 test acc: 60.400 30.536 s +[7] 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+[9] loss: 1.056, train acc: 69.924 test acc: 61.130 30.091 s +[10] loss: 1.055, train acc: 69.840 test acc: 57.170 30.197 s +[11] loss: 1.054, train acc: 70.000 test acc: 61.030 30.219 s +[12] loss: 1.060, train acc: 69.890 test acc: 61.240 30.033 s +[13] loss: 1.055, train acc: 69.974 test acc: 61.230 30.039 s +[14] loss: 1.059, train acc: 69.712 test acc: 60.670 29.992 s +[15] loss: 1.058, train acc: 69.906 test acc: 60.920 30.072 s +[16] loss: 1.055, train acc: 69.826 test acc: 60.760 30.274 s +[17] loss: 1.058, train acc: 70.082 test acc: 61.190 30.175 s +[18] loss: 1.057, train acc: 69.782 test acc: 61.030 29.970 s +[19] loss: 1.061, train acc: 70.000 test acc: 56.850 29.918 s +[20] loss: 1.061, train acc: 69.826 test acc: 57.040 30.185 s +[21] loss: 1.064, train acc: 69.746 test acc: 61.340 30.148 s +[22] loss: 1.060, train acc: 69.766 test acc: 61.110 30.024 s +[23] loss: 1.060, train acc: 69.886 test acc: 61.250 30.193 s +[24] loss: 1.060, train acc: 70.114 test acc: 60.980 30.087 s +[25] loss: 1.063, train acc: 69.768 test acc: 60.980 30.134 s +[26] loss: 1.062, train acc: 69.958 test acc: 61.130 30.019 s +[27] loss: 1.068, train acc: 69.764 test acc: 61.010 29.985 s +[28] loss: 1.066, train acc: 69.914 test acc: 57.060 30.092 s +[29] loss: 1.116, train acc: 67.912 test acc: 59.550 30.006 s +[30] loss: 1.117, train acc: 67.828 test acc: 58.790 30.115 s diff --git a/checkpoints/shallow012_naslarge_AP2b8b_wSAM/training_checkpoint.pth.tar b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..c8478307b586e17b79a224e64b47cf8b4d3cbae1 --- /dev/null +++ b/checkpoints/shallow012_naslarge_AP2b8b_wSAM/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:edb02078eefda6442b089123ab964cf33963e6cb5326c461429e3393aa9072e2 +size 4619975 diff --git a/checkpoints/shallow998_reproduceMaxim000_nas_8b/evaluation.py b/checkpoints/shallow998_reproduceMaxim000_nas_8b/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..6ba78e5d9a656fbd5bbabe7a2d61a28c737ccb37 --- /dev/null +++ b/checkpoints/shallow998_reproduceMaxim000_nas_8b/evaluation.py @@ -0,0 +1,66 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_nas() +model = model.to(device) + +# first, ftp2qat +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Folding BN for:', layer_string) + layer_attribute.configure_layer_base(weight_bits=8, bias_bits=8, shift_quantile=1.0) + layer_attribute.mode_fpt2qat('qat'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + layer_attribute.mode_qat2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git a/checkpoints/shallow998_reproduceMaxim000_nas_8b/hardware_checkpoint.pth.tar b/checkpoints/shallow998_reproduceMaxim000_nas_8b/hardware_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..428e7089318fdac169f81c7b957140b535928d60 --- /dev/null +++ b/checkpoints/shallow998_reproduceMaxim000_nas_8b/hardware_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92a837395278702ea5ffc714a2e774dbe4b1b19a07dc51ffb41cd579134168d5 +size 1431223 diff --git a/checkpoints/shallow998_reproduceMaxim000_nas_8b/histograms_hardware_checkpoint/conv1_1.jpg 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0000000000000000000000000000000000000000..181e3b8713b8c84ab1a4460c4a583456e1d937ac --- /dev/null +++ b/checkpoints/shallow998_reproduceMaxim000_nas_8b/readme @@ -0,0 +1 @@ +not on on deliverable v1 \ No newline at end of file diff --git a/checkpoints/shallow998_reproduceMaxim000_nas_8b/statistics_hardware_checkpoint b/checkpoints/shallow998_reproduceMaxim000_nas_8b/statistics_hardware_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..45970ec48d89fccc69154a0cd1e054140a9b90d4 --- /dev/null +++ b/checkpoints/shallow998_reproduceMaxim000_nas_8b/statistics_hardware_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 64.78999853134155, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 64.78999853134155} +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 63 + min, max, mean: -1568.0 , 1831.0 , 38.640625 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 155 + min, max, mean: -105.0 , 109.0 , -0.122106485 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -15481.0 , 7800.0 , 3626.5625 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 200 + min, max, mean: -128.0 , 127.0 , -5.270508 + conv1_3 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 63 + min, max, mean: -7690.0 , 16383.0 , 3146.8281 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 184 + min, max, mean: -116.0 , 103.0 , -1.2205946 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 10 + min, max, mean: -16384.0 , 16383.0 , 1596.0625 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 232 + min, max, mean: -128.0 , 127.0 , 0.12641059 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -3721.0 , 11407.0 , 964.0 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 209 + min, max, mean: -128.0 , 127.0 , -2.3925781 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 47 + min, max, mean: -16384.0 , 16383.0 , 3604.875 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 250 + min, max, mean: -128.0 , 127.0 , -0.31487358 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 122 + min, max, mean: -10467.0 , 16383.0 , 4923.797 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 234 + min, max, mean: -128.0 , 127.0 , -3.060669 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 16 + min, max, mean: -16384.0 , 16383.0 , -2837.8125 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 228 + min, max, mean: -125.0 , 127.0 , 0.3362766 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -15940.0 , 16383.0 , 3565.0312 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 244 + min, max, mean: -128.0 , 127.0 , -0.21457249 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -9980.0 , 16383.0 , 3269.8203 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 212 + min, max, mean: -108.0 , 127.0 , 0.20965576 + fc + output_shift: [1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 99 + min, max, mean: -1737.0 , 1383.0 , -2.73 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 212 + min, max, mean: -128.0 , 115.0 , -10.220196 diff --git a/checkpoints/shallow998_reproduceMaxim000_nas_8b/statistics_training_checkpoint b/checkpoints/shallow998_reproduceMaxim000_nas_8b/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..59372a82a1831d0db19cf31163b8778e1d2b6c7d --- /dev/null +++ b/checkpoints/shallow998_reproduceMaxim000_nas_8b/statistics_training_checkpoint @@ -0,0 +1,176 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1_1 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.09569145 , 0.11176931 , 0.0023545348 + weight + total # of elements, shape: 1728 , [64, 3, 3, 3] + # of unique elements: 1728 + min, max, mean: -0.82078534 , 0.8478423 , -0.0009090822 + conv1_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.47243807 , 0.23804682 , 0.11067512 + weight + total # of elements, shape: 2048 , [32, 64, 1, 1] + # of unique elements: 2048 + min, max, mean: -0.6935298 , 0.5647582 , -0.020792957 + conv1_3 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.23467612 , 0.58637875 , 0.097503155 + weight + total # of elements, shape: 18432 , [64, 32, 3, 3] + # of unique elements: 18429 + min, max, mean: -0.4526815 , 0.4040971 , -0.004771175 + conv2_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 32 , [32] + # of unique elements: 32 + min, max, mean: -0.39697582 , 0.48210716 , 0.027005818 + weight + total # of elements, shape: 18432 , [32, 64, 3, 3] + # of unique elements: 18430 + min, max, mean: -0.2135905 , 0.15751368 , 0.00010535153 + conv2_2 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.22709861 , 0.6962397 , 0.0588381 + weight + total # of elements, shape: 2048 , [64, 32, 1, 1] + # of unique elements: 2048 + min, max, mean: -1.1051985 , 1.338855 , -0.01851481 + conv3_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.6500221 , 0.7398894 , 0.050455317 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73688 + min, max, mean: -0.17812 , 0.18307836 , -0.00030776308 + conv3_2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.31943882 , 0.59705323 , 0.15301093 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16381 + min, max, mean: -0.5597776 , 0.6652047 , -0.01194022 + conv4_1 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 64 , [64] + # of unique elements: 64 + min, max, mean: -0.6996244 , 1.0072677 , -0.068444364 + weight + total # of elements, shape: 73728 , [64, 128, 3, 3] + # of unique elements: 73693 + min, max, mean: -0.12233131 , 0.15938132 , 0.00032919308 + conv4_2 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.24322651 , 0.2781469 , 0.05461803 + weight + total # of elements, shape: 73728 , [128, 64, 3, 3] + # of unique elements: 73706 + min, max, mean: -0.27082616 , 0.2800041 , -0.00041949743 + conv5_1 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.30457166 , 0.6364847 , 0.100854084 + weight + total # of elements, shape: 16384 , [128, 128, 1, 1] + # of unique elements: 16381 + min, max, mean: -0.4237072 , 0.58368826 , 0.00083749543 + fc + output_shift: [1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.995] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.21204573 , 0.16882493 , -0.00033210413 + weight + total # of elements, shape: 51200 , [100, 512] + # of unique elements: 51184 + min, max, mean: -2.0832171 , 1.7930893 , -0.15969671 diff --git a/checkpoints/shallow998_reproduceMaxim000_nas_8b/train_log.txt b/checkpoints/shallow998_reproduceMaxim000_nas_8b/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..3cb329ae110efff3b07670406d6b07cbf27c0408 --- /dev/null +++ b/checkpoints/shallow998_reproduceMaxim000_nas_8b/train_log.txt @@ -0,0 +1,80 @@ +[1] loss: 0.762, train acc: 77.196 test acc: 60.750 11.810 s +[2] loss: 0.729, train acc: 78.344 test acc: 60.910 11.491 s +[3] loss: 0.720, train acc: 78.434 test acc: 60.710 10.884 s +[4] loss: 0.716, train acc: 78.674 test acc: 61.090 10.968 s +[5] loss: 0.713, train acc: 78.672 test acc: 61.100 11.055 s +[6] loss: 0.704, train acc: 78.902 test acc: 60.720 11.461 s +[7] loss: 0.694, train acc: 79.244 test acc: 60.920 11.213 s +[8] loss: 0.693, train acc: 79.240 test acc: 61.120 11.522 s +[9] loss: 0.686, train acc: 79.674 test acc: 60.810 11.768 s +[10] loss: 0.685, train acc: 79.330 test acc: 60.940 11.511 s +[1] loss: 0.657, train acc: 80.576 test acc: 61.300 11.377 s +[2] loss: 0.653, train acc: 80.706 test acc: 61.420 11.543 s +[3] loss: 0.645, train acc: 80.588 test acc: 61.300 11.366 s +[4] loss: 0.646, train acc: 80.844 test acc: 61.330 11.641 s +[5] loss: 0.642, train acc: 81.022 test acc: 61.100 11.457 s +[6] loss: 0.646, train acc: 80.834 test acc: 61.440 12.158 s +[7] loss: 0.642, train acc: 80.812 test acc: 61.570 11.364 s +[8] loss: 0.638, train acc: 81.120 test acc: 61.420 11.412 s +[9] loss: 0.639, train acc: 80.940 test acc: 61.370 11.735 s +[10] loss: 0.639, train acc: 80.880 test acc: 61.200 11.217 s +[1] loss: 0.632, train acc: 81.256 test acc: 61.340 11.085 s +[2] loss: 0.623, train acc: 81.544 test acc: 61.440 11.539 s +[3] loss: 0.630, train acc: 81.212 test acc: 61.310 11.818 s +[4] loss: 0.629, train acc: 81.214 test acc: 61.290 11.577 s +[5] loss: 0.625, train acc: 81.432 test acc: 61.390 11.470 s +[6] loss: 0.628, train acc: 81.430 test acc: 61.340 10.953 s +[7] loss: 0.627, train acc: 81.346 test acc: 61.370 11.500 s +[8] loss: 0.622, train acc: 81.486 test acc: 61.280 11.401 s +[9] loss: 0.624, train acc: 81.470 test acc: 61.460 11.208 s +[10] loss: 0.626, train acc: 81.468 test acc: 61.480 11.027 s +[1] loss: 1.011, train acc: 73.166 test acc: 62.560 20.878 s +[2] loss: 0.814, train acc: 77.122 test acc: 63.810 20.481 s +[3] loss: 0.787, train acc: 77.598 test acc: 63.990 19.871 s +[4] loss: 0.771, train acc: 77.892 test acc: 64.050 20.514 s +[5] loss: 0.765, train acc: 78.046 test acc: 64.270 20.631 s +[6] loss: 0.753, train acc: 78.334 test acc: 64.190 20.031 s +[7] loss: 0.748, train acc: 78.512 test acc: 63.940 20.496 s +[8] loss: 0.744, train acc: 78.402 test acc: 64.170 20.405 s +[9] loss: 0.740, train acc: 78.518 test acc: 64.120 20.454 s +[10] loss: 0.732, train acc: 78.788 test acc: 64.440 20.533 s +[11] loss: 0.729, train acc: 78.948 test acc: 64.280 20.391 s +[12] loss: 0.728, train acc: 78.934 test acc: 64.200 20.250 s +[13] loss: 0.722, train acc: 79.098 test acc: 64.490 20.663 s +[14] loss: 0.721, train acc: 79.006 test acc: 64.280 20.591 s +[15] loss: 0.718, train acc: 79.172 test acc: 64.400 20.625 s +[16] loss: 0.717, train acc: 79.194 test acc: 64.260 19.724 s +[17] loss: 0.710, train acc: 79.256 test acc: 64.610 20.744 s +[18] loss: 0.710, train acc: 79.402 test acc: 64.010 20.262 s +[19] loss: 0.709, train acc: 79.332 test acc: 64.570 20.961 s +[20] loss: 0.710, train acc: 79.380 test acc: 64.390 20.473 s +[21] loss: 0.706, train acc: 79.244 test acc: 64.530 20.469 s +[22] loss: 0.708, train acc: 79.272 test acc: 64.740 20.316 s +[23] loss: 0.703, train acc: 79.422 test acc: 64.720 20.441 s +[24] loss: 0.699, train acc: 79.612 test acc: 64.650 20.398 s +[25] loss: 0.696, train acc: 79.650 test acc: 64.490 20.347 s +[26] loss: 0.696, train acc: 79.656 test acc: 64.610 20.415 s +[27] loss: 0.696, train acc: 79.682 test acc: 64.410 20.771 s +[28] loss: 0.693, train acc: 79.874 test acc: 64.280 20.261 s +[29] loss: 0.688, train acc: 79.860 test acc: 64.580 20.493 s +[30] loss: 0.691, train acc: 79.910 test acc: 64.540 20.766 s +[31] loss: 0.691, train acc: 79.752 test acc: 64.330 20.218 s +[32] loss: 0.685, train acc: 80.072 test acc: 64.510 20.685 s +[33] loss: 0.689, train acc: 79.918 test acc: 64.320 20.564 s +[34] loss: 0.685, train acc: 79.940 test acc: 64.460 19.978 s +[35] loss: 0.682, train acc: 80.030 test acc: 64.630 20.616 s +[36] loss: 0.684, train acc: 79.808 test acc: 64.710 21.065 s +[37] loss: 0.678, train acc: 80.006 test acc: 64.740 20.225 s +[38] loss: 0.677, train acc: 80.270 test acc: 64.820 20.539 s +[39] loss: 0.677, train acc: 79.870 test acc: 64.480 20.851 s +[40] loss: 0.677, train acc: 79.984 test acc: 64.310 20.533 s +[41] loss: 0.673, train acc: 80.250 test acc: 64.500 20.294 s +[42] loss: 0.674, train acc: 80.392 test acc: 64.260 20.478 s +[43] loss: 0.675, train acc: 80.076 test acc: 64.060 20.404 s +[44] loss: 0.675, train acc: 80.044 test acc: 64.700 20.230 s +[45] loss: 0.672, train acc: 80.274 test acc: 64.590 20.728 s +[46] loss: 0.667, train acc: 80.496 test acc: 64.720 20.321 s +[47] loss: 0.669, train acc: 80.374 test acc: 64.510 19.969 s +[48] loss: 0.667, train acc: 80.512 test acc: 64.520 20.483 s +[49] loss: 0.667, train acc: 80.336 test acc: 64.700 20.293 s +[50] loss: 0.662, train acc: 80.538 test acc: 64.340 20.316 s diff --git a/checkpoints/shallow998_reproduceMaxim000_nas_8b/training_checkpoint.pth.tar b/checkpoints/shallow998_reproduceMaxim000_nas_8b/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..2f5a1bc6f0179c7d051237a400b2e3d54c46bbcc --- /dev/null +++ b/checkpoints/shallow998_reproduceMaxim000_nas_8b/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51b70b418927083040b18e392d6dedc6ee74610965fb98bbcc72ebb7f369b340 +size 1431751 diff --git a/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/evaluation.py b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..f7da74072d31732d3236c287eece21fbc431491d --- /dev/null +++ b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/evaluation.py @@ -0,0 +1,82 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys, time +import torch.nn as nn +import torch.optim as optim + +# bizden +import layers, models, dataloader +from library.utils import compute_batch_accuracy, compute_set_accuracy + +bs = 100; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False); + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model = models.maxim_simplenet() +model = model.to(device) + +weight_dictionary = {} +weight_dictionary['conv1' ] = 8; +weight_dictionary['conv2' ] = 4; +weight_dictionary['conv3' ] = 2; +weight_dictionary['conv4' ] = 2; +weight_dictionary['conv5' ] = 2; +weight_dictionary['conv6' ] = 2; +weight_dictionary['conv7' ] = 2; +weight_dictionary['conv8' ] = 2; +weight_dictionary['conv9' ] = 2; +weight_dictionary['conv10'] = 2; +weight_dictionary['conv11'] = 4; +weight_dictionary['conv12'] = 4; +weight_dictionary['conv13'] = 4; +weight_dictionary['conv14'] = 4; + +layer_attributes = [] +for layer_string in dir(model): + if(layer_string in weight_dictionary): + layer_attribute = getattr(model, layer_string) + print('Folding BN for:', layer_string) + layer_attribute.configure_layer_base(weight_bits=weight_dictionary[layer_string], bias_bits=8, shift_quantile=0.985) + layer_attribute.mode_fpt2qat('qat'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload to the GPU + +# then, load trained checkpoint +checkpoint = torch.load('training_checkpoint.pth.tar'); +model.load_state_dict(checkpoint['state_dict']) + +print('') +print('Computing test set accuracy, training checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +print('') +print('Test accuracy:', test_acc*100.0) +print('') + +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True); + +# then, qat2hw +model = model.to(device) +for layer_string in dir(model): + layer_attribute = getattr(model, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + print('Generating HW parameters for:', layer_string) + layer_attribute.mode_qat2hw('eval'); + setattr(model, layer_string, layer_attribute) +model.to(device) # somehow new parameters are left out, so they need a reload + +print('') +print('Computing test set accuracy, hardware checkpoint') +test_acc = compute_set_accuracy(model, test_loader) + +torch.save({ + 'epoch': 123456789, + 'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()}, + 'state_dict': model.state_dict(), + 'arch': 'ai85nascifarnet' + }, 'hardware_checkpoint.pth.tar') + +print('') +print('Test accuracy:', test_acc*100.0) diff --git 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b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/readme @@ -0,0 +1 @@ +not on on deliverable v1 \ No newline at end of file diff --git a/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/statistics_hardware_checkpoint b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/statistics_hardware_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..a71840beaad48f3076ac8e0b28c73705b3b03928 --- /dev/null +++ b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/statistics_hardware_checkpoint @@ -0,0 +1,225 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: {'best epoch': 123456789, 'best_top1': 53.65999937057495, 'clipping_method': 'MAX_BIT_SHIFT', 'current_top1': 53.65999937057495} +------------------------------------- +state_dict: + conv1 + output_shift: [-0.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 16 , [16] + # of unique elements: 16 + min, max, mean: -1407.0 , 1368.0 , 81.0 + weight + total # of elements, shape: 432 , [16, 3, 3, 3] + # of unique elements: 99 + min, max, mean: -77.0 , 81.0 , -0.011574074 + conv2 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -577.0 , 551.0 , 124.1 + weight + total # of elements, shape: 2880 , [20, 16, 3, 3] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , -0.090625 + conv3 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 19 + min, max, mean: -51.0 , 206.0 , 93.85 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.045 + conv4 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 19 + min, max, mean: -72.0 , 232.0 , 48.45 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.031666666 + conv5 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 15 + min, max, mean: -256.0 , 255.0 , -5.3 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , 0.0055555557 + conv6 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 17 + min, max, mean: -256.0 , 255.0 , 81.6 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.03277778 + conv7 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 44 , [44] + # of unique elements: 37 + min, max, mean: -220.0 , 255.0 , 93.27273 + weight + total # of elements, shape: 7920 , [44, 20, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.06792929 + conv8 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 17 + min, max, mean: -256.0 , 255.0 , 88.291664 + weight + total # of elements, shape: 19008 , [48, 44, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.021569865 + conv9 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 30 + min, max, mean: -256.0 , 255.0 , 102.729164 + weight + total # of elements, shape: 20736 , [48, 48, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , -0.02425733 + conv10 + output_shift: [-3.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 96 , [96] + # of unique elements: 26 + min, max, mean: -256.0 , 255.0 , -125.552086 + weight + total # of elements, shape: 41472 , [96, 48, 3, 3] + # of unique elements: 4 + min, max, mean: -2.0 , 1.0 , 0.030864198 + conv11 + output_shift: [-1.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 512 , [512] + # of unique elements: 432 + min, max, mean: -1024.0 , 1023.0 , 161.24219 + +[WARNING]: # of unique elements in bias tensor is more than that allowed by bias_bits. + This might be OK, since Maxim deployment repository right shifts these. + + weight + total # of elements, shape: 49152 , [512, 96, 1, 1] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , -0.040649414 + conv12 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 99 + min, max, mean: -1024.0 , 1023.0 , 235.99219 + weight + total # of elements, shape: 65536 , [128, 512, 1, 1] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , -0.012710571 + conv13 + output_shift: [-2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 113 + min, max, mean: -1024.0 , 1023.0 , 212.21094 + weight + total # of elements, shape: 147456 , [128, 128, 3, 3] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , 0.0015123156 + conv14 + output_shift: [2.] + adjust_output_shift: [0.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 78 + min, max, mean: -152.0 , 129.0 , -2.28 + weight + total # of elements, shape: 12800 , [100, 128, 1, 1] + # of unique elements: 16 + min, max, mean: -8.0 , 7.0 , -1.1907812 diff --git a/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/statistics_training_checkpoint b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/statistics_training_checkpoint new file mode 100644 index 0000000000000000000000000000000000000000..8103197b64624f5d975654cec3cbe6c45df6047d --- /dev/null +++ b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/statistics_training_checkpoint @@ -0,0 +1,221 @@ +Top: + epoch + extras + state_dict + arch +------------------------------------- +arch: ai85nascifarnet +------------------------------------- +extras: None +------------------------------------- +state_dict: + conv1 + output_shift: [-0.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [8.] + bias_bits: [8.] + bias + total # of elements, shape: 16 , [16] + # of unique elements: 16 + min, max, mean: -0.08590559 , 0.0834875 , 0.0049372935 + weight + total # of elements, shape: 432 , [16, 3, 3, 3] + # of unique elements: 432 + min, max, mean: -0.6041579 , 0.6299337 , -0.0001223148 + conv2 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.28180933 , 0.2689574 , 0.060624152 + weight + total # of elements, shape: 2880 , [20, 16, 3, 3] + # of unique elements: 2880 + min, max, mean: -0.6084496 , 0.5516144 , -0.0053155622 + conv3 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.10017363 , 0.40167764 , 0.18330546 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3599 + min, max, mean: -0.55548435 , 0.40462005 , -0.012197269 + conv4 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.14059089 , 0.4525638 , 0.09453481 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3600 + min, max, mean: -0.4925906 , 0.34693444 , -0.009273671 + conv5 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.49907166 , 0.47305846 , -0.002226038 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3600 + min, max, mean: -0.38474175 , 0.445418 , 0.0020036458 + conv6 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 20 , [20] + # of unique elements: 20 + min, max, mean: -0.269071 , 0.3368378 , 0.087997034 + weight + total # of elements, shape: 3600 , [20, 20, 3, 3] + # of unique elements: 3600 + min, max, mean: -0.4536345 , 0.4365145 , -0.0017370804 + conv7 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 44 , [44] + # of unique elements: 44 + min, max, mean: -0.21474119 , 0.4481558 , 0.10638161 + weight + total # of elements, shape: 7920 , [44, 20, 3, 3] + # of unique elements: 7920 + min, max, mean: -0.36363983 , 0.37674007 , -0.0059674215 + conv8 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 48 + min, max, mean: -0.4592532 , 0.73961246 , 0.12518513 + weight + total # of elements, shape: 19008 , [48, 44, 3, 3] + # of unique elements: 19007 + min, max, mean: -0.19524875 , 0.20283978 , -0.00013639091 + conv9 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 48 , [48] + # of unique elements: 48 + min, max, mean: -0.5065948 , 0.6203854 , 0.132932 + weight + total # of elements, shape: 20736 , [48, 48, 3, 3] + # of unique elements: 20731 + min, max, mean: -0.31671003 , 0.28223878 , -0.0026333057 + conv10 + output_shift: [-3.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [2.] + bias_bits: [8.] + bias + total # of elements, shape: 96 , [96] + # of unique elements: 96 + min, max, mean: -0.48270226 , 0.6064789 , -0.12257254 + weight + total # of elements, shape: 41472 , [96, 48, 3, 3] + # of unique elements: 41456 + min, max, mean: -0.206253 , 0.21709208 , 0.00348483 + conv11 + output_shift: [-1.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 512 , [512] + # of unique elements: 512 + min, max, mean: -0.75687575 , 0.76118356 , 0.081021115 + weight + total # of elements, shape: 49152 , [512, 96, 1, 1] + # of unique elements: 49129 + min, max, mean: -0.5242623 , 0.537267 , -0.002486636 + conv12 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.36233884 , 0.36437505 , 0.0642186 + weight + total # of elements, shape: 65536 , [128, 512, 1, 1] + # of unique elements: 65509 + min, max, mean: -0.36360282 , 0.29257432 , -0.00038944746 + conv13 + output_shift: [-2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 128 , [128] + # of unique elements: 128 + min, max, mean: -0.25512248 , 0.4903532 , 0.06255462 + weight + total # of elements, shape: 147456 , [128, 128, 3, 3] + # of unique elements: 147305 + min, max, mean: -0.4534711 , 0.7435867 , 0.00025575177 + conv14 + output_shift: [2.] + adjust_output_shift: [1.] + quantize_activation: [1.] + shift_quantile: [0.985] + weight bits: [4.] + bias_bits: [8.] + bias + total # of elements, shape: 100 , [100] + # of unique elements: 100 + min, max, mean: -0.59363127 , 0.5039009 , -0.008943005 + weight + total # of elements, shape: 12800 , [100, 128, 1, 1] + # of unique elements: 12800 + min, max, mean: -5.000554 , 3.3605664 , -0.5948116 diff --git a/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/train_log.txt b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..589eda4ddd46172c90f9a188363006089b11b2a5 --- /dev/null +++ b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/train_log.txt @@ -0,0 +1,274 @@ +[1] loss: 4.132, train acc: 7.430 test acc: 11.730 13.903 s +[2] loss: 3.623, train acc: 14.194 test acc: 17.620 12.943 s +[3] loss: 3.298, train acc: 19.808 test acc: 23.210 13.231 s +[4] loss: 3.028, train acc: 24.882 test acc: 27.190 13.394 s +[5] loss: 2.792, train acc: 29.182 test acc: 29.600 13.789 s +[6] loss: 2.609, train acc: 32.506 test acc: 32.780 13.826 s +[7] loss: 2.471, train acc: 35.470 test acc: 34.900 13.702 s +[8] loss: 2.353, train acc: 37.946 test acc: 38.120 13.349 s +[9] loss: 2.254, train acc: 40.004 test acc: 40.570 13.531 s +[10] loss: 2.169, train acc: 42.000 test acc: 40.980 13.797 s +[11] loss: 2.096, train acc: 43.676 test acc: 42.450 13.225 s +[12] loss: 2.036, train acc: 44.962 test acc: 43.010 13.460 s +[13] loss: 1.981, train acc: 46.216 test acc: 43.770 13.911 s +[14] loss: 1.920, train acc: 47.630 test acc: 44.260 13.368 s +[15] loss: 1.880, train acc: 48.538 test acc: 45.750 13.929 s +[16] loss: 1.834, train acc: 49.710 test acc: 45.580 14.080 s +[17] loss: 1.806, train acc: 50.450 test acc: 47.590 13.822 s +[18] loss: 1.763, train acc: 51.200 test acc: 47.530 13.839 s +[19] loss: 1.736, train acc: 51.976 test acc: 47.870 13.653 s +[20] loss: 1.709, train acc: 52.506 test acc: 48.650 13.470 s +[21] loss: 1.672, train acc: 53.416 test acc: 49.200 14.063 s +[22] loss: 1.652, train acc: 53.574 test acc: 49.380 14.819 s +[23] loss: 1.626, train acc: 54.546 test acc: 50.120 13.910 s +[24] loss: 1.600, train acc: 55.248 test acc: 49.820 13.814 s +[25] loss: 1.576, train acc: 55.686 test acc: 49.650 13.854 s +[26] loss: 1.555, train acc: 56.290 test acc: 51.200 13.639 s +[27] loss: 1.536, train acc: 56.784 test acc: 49.770 13.942 s +[28] loss: 1.516, train acc: 57.534 test acc: 51.150 14.169 s +[29] loss: 1.501, train acc: 57.348 test acc: 50.920 13.316 s +[30] loss: 1.486, train acc: 58.038 test acc: 50.960 13.426 s +[31] loss: 1.464, train acc: 58.600 test acc: 51.840 13.739 s +[32] loss: 1.445, train acc: 58.918 test acc: 51.470 13.761 s +[33] loss: 1.435, train acc: 59.110 test acc: 51.390 13.338 s +[34] loss: 1.418, train acc: 59.652 test acc: 51.100 13.552 s +[35] loss: 1.398, train acc: 60.198 test acc: 51.670 13.431 s +[36] loss: 1.384, train acc: 60.588 test acc: 51.550 13.718 s +[37] loss: 1.372, train acc: 60.574 test acc: 52.690 13.790 s +[38] loss: 1.359, train acc: 61.090 test acc: 52.140 13.313 s +[39] loss: 1.345, train acc: 61.364 test acc: 52.400 13.400 s +[40] loss: 1.328, train acc: 61.808 test acc: 52.760 13.478 s +[41] loss: 1.321, train acc: 62.012 test acc: 52.330 13.782 s +[42] loss: 1.308, train acc: 62.314 test acc: 53.810 13.411 s +[43] loss: 1.292, train acc: 62.628 test acc: 52.990 13.653 s +[44] loss: 1.286, train acc: 62.820 test acc: 53.000 13.369 s +[45] loss: 1.274, train acc: 63.350 test acc: 53.290 15.053 s +[46] loss: 1.260, train acc: 63.444 test acc: 53.700 13.788 s +[47] loss: 1.245, train acc: 64.062 test acc: 52.820 13.438 s +[48] loss: 1.240, train acc: 64.088 test acc: 53.320 13.367 s +[49] loss: 1.233, train acc: 64.092 test acc: 53.830 13.719 s +[50] loss: 1.220, train acc: 64.588 test acc: 53.190 13.290 s +[51] loss: 1.217, train acc: 64.566 test acc: 53.130 13.342 s +[52] loss: 1.199, train acc: 65.012 test acc: 53.270 13.361 s +[53] loss: 1.194, train acc: 64.886 test acc: 52.910 13.136 s +[54] loss: 1.185, train acc: 65.334 test acc: 53.520 13.873 s +[55] loss: 1.172, train acc: 65.762 test acc: 53.160 13.907 s +[56] loss: 1.167, train acc: 65.826 test acc: 53.130 12.982 s +[57] loss: 1.157, train acc: 66.208 test acc: 53.930 13.709 s +[58] loss: 1.144, train acc: 66.444 test acc: 54.250 13.373 s +[59] loss: 1.144, train acc: 66.548 test acc: 54.290 13.366 s +[60] loss: 1.135, train acc: 66.618 test acc: 53.840 13.250 s +[61] loss: 1.124, train acc: 66.982 test acc: 53.660 13.526 s +[62] loss: 1.116, train acc: 67.352 test acc: 54.280 13.308 s +[63] loss: 1.111, train acc: 67.394 test acc: 53.990 13.394 s +[64] loss: 1.104, train acc: 67.588 test acc: 54.190 13.618 s +[65] loss: 1.098, train acc: 67.582 test acc: 53.900 13.500 s +[66] loss: 1.089, train acc: 67.728 test acc: 54.800 13.317 s +[67] loss: 1.077, train acc: 68.192 test acc: 54.170 13.748 s +[68] loss: 1.071, train acc: 68.202 test acc: 54.450 13.357 s +[69] loss: 1.064, train acc: 68.718 test acc: 53.150 13.057 s +[70] loss: 1.066, train acc: 68.494 test acc: 53.750 13.454 s +[71] loss: 1.057, train acc: 68.632 test acc: 53.810 13.775 s +[72] loss: 1.052, train acc: 68.882 test acc: 54.060 13.336 s +[73] loss: 1.041, train acc: 69.096 test acc: 54.520 13.280 s +[74] loss: 1.031, train acc: 69.376 test acc: 54.410 13.249 s +[75] loss: 1.034, train acc: 69.168 test acc: 55.070 13.232 s +[76] loss: 1.022, train acc: 69.584 test acc: 53.530 13.445 s +[77] loss: 1.015, train acc: 70.026 test acc: 53.640 13.248 s +[78] loss: 1.006, train acc: 70.012 test acc: 53.980 13.200 s +[79] loss: 1.002, train acc: 70.118 test acc: 53.990 13.616 s +[80] loss: 0.997, train acc: 70.142 test acc: 54.460 13.761 s +[81] loss: 0.996, train acc: 70.388 test acc: 54.320 13.802 s +[82] loss: 0.985, train acc: 70.488 test acc: 54.010 13.466 s +[83] loss: 0.982, train acc: 70.516 test acc: 53.950 13.777 s +[84] loss: 0.971, train acc: 70.808 test acc: 54.300 13.377 s +[85] loss: 0.966, train acc: 70.790 test acc: 54.670 13.311 s +[86] loss: 0.964, train acc: 71.052 test acc: 55.320 13.046 s +[87] loss: 0.960, train acc: 71.136 test acc: 54.790 13.881 s +[88] loss: 0.953, train acc: 71.372 test acc: 54.120 13.122 s +[89] loss: 0.947, train acc: 71.368 test acc: 54.040 13.542 s +[90] loss: 0.938, train acc: 71.606 test acc: 54.640 13.392 s +[91] loss: 0.937, train acc: 71.752 test acc: 53.480 13.234 s +[92] loss: 0.932, train acc: 71.972 test acc: 54.820 13.599 s +[93] loss: 0.925, train acc: 71.932 test acc: 54.300 13.339 s +[94] loss: 0.929, train acc: 72.010 test acc: 54.430 13.869 s +[95] loss: 0.917, train acc: 72.342 test acc: 54.230 13.096 s +[96] loss: 0.915, train acc: 72.226 test acc: 54.190 13.228 s +[97] loss: 0.918, train acc: 72.320 test acc: 54.770 13.671 s +[98] loss: 0.907, train acc: 72.578 test acc: 54.420 13.656 s +[99] loss: 0.905, train acc: 72.674 test acc: 53.820 13.484 s +[100] loss: 0.896, train acc: 73.058 test acc: 55.050 13.630 s +[1] loss: 0.795, train acc: 76.000 test acc: 56.300 13.214 s +[2] loss: 0.767, train acc: 76.972 test acc: 55.760 13.424 s +[3] loss: 0.762, train acc: 76.904 test acc: 55.710 13.724 s +[4] loss: 0.757, train acc: 77.200 test acc: 55.600 13.515 s +[5] loss: 0.754, train acc: 77.298 test acc: 55.450 13.385 s +[6] loss: 0.741, train acc: 77.826 test acc: 55.620 12.975 s +[7] loss: 0.737, train acc: 77.736 test acc: 55.790 13.148 s +[8] loss: 0.738, train acc: 77.796 test acc: 55.560 13.796 s +[9] loss: 0.724, train acc: 77.978 test acc: 55.960 13.661 s +[10] loss: 0.721, train acc: 78.066 test acc: 55.300 13.507 s +[11] loss: 0.727, train acc: 77.960 test acc: 55.850 13.343 s +[12] loss: 0.718, train acc: 78.394 test acc: 55.860 13.741 s +[13] loss: 0.721, train acc: 77.910 test acc: 55.400 13.255 s +[14] loss: 0.715, train acc: 78.440 test acc: 55.680 13.438 s +[15] loss: 0.711, train acc: 78.476 test acc: 55.960 13.364 s +[16] loss: 0.714, train acc: 78.512 test acc: 55.280 13.313 s +[17] loss: 0.710, train acc: 78.660 test acc: 55.030 13.155 s +[18] loss: 0.702, train acc: 78.870 test acc: 55.590 13.645 s +[19] loss: 0.706, train acc: 78.560 test acc: 55.500 13.711 s +[20] loss: 0.695, train acc: 78.872 test acc: 55.960 13.496 s +[21] loss: 0.703, train acc: 78.864 test acc: 55.140 13.718 s +[22] loss: 0.694, train acc: 79.094 test acc: 55.230 13.298 s +[23] loss: 0.697, train acc: 78.980 test acc: 55.220 13.581 s +[24] loss: 0.679, train acc: 79.420 test acc: 55.250 13.293 s +[25] loss: 0.689, train acc: 79.282 test acc: 55.100 13.283 s +[26] loss: 0.693, train acc: 78.836 test acc: 55.210 13.561 s +[27] loss: 0.681, train acc: 79.298 test acc: 54.900 13.503 s +[28] loss: 0.680, train acc: 79.252 test acc: 55.390 13.369 s +[29] loss: 0.684, train acc: 79.352 test acc: 55.670 13.509 s +[30] loss: 0.680, train acc: 79.284 test acc: 55.560 13.629 s +[31] loss: 0.676, train acc: 79.414 test acc: 55.410 13.720 s +[32] loss: 0.679, train acc: 79.328 test acc: 55.030 13.696 s +[33] loss: 0.674, train acc: 79.378 test acc: 54.860 13.463 s +[34] loss: 0.674, train acc: 79.622 test acc: 55.280 14.089 s +[35] loss: 0.671, train acc: 79.524 test acc: 55.060 13.532 s +[36] loss: 0.663, train acc: 79.730 test acc: 55.360 13.366 s +[37] loss: 0.669, train acc: 79.540 test acc: 55.230 13.339 s +[38] loss: 0.668, train acc: 79.814 test acc: 55.150 13.306 s +[39] loss: 0.668, train acc: 79.714 test acc: 55.040 13.406 s +[40] loss: 0.666, train acc: 79.724 test acc: 54.910 13.737 s +[41] loss: 0.660, train acc: 79.806 test acc: 54.700 13.535 s +[42] loss: 0.656, train acc: 79.948 test acc: 55.090 13.263 s +[43] loss: 0.658, train acc: 79.800 test acc: 55.060 13.274 s +[44] loss: 0.652, train acc: 80.134 test acc: 55.330 13.394 s +[45] loss: 0.654, train acc: 80.038 test acc: 55.230 13.584 s +[46] loss: 0.655, train acc: 79.978 test acc: 55.440 13.331 s +[47] loss: 0.649, train acc: 80.220 test acc: 55.250 13.440 s +[48] loss: 0.652, train acc: 79.954 test acc: 55.190 13.946 s +[49] loss: 0.648, train acc: 80.220 test acc: 55.210 13.544 s +[50] loss: 0.646, train acc: 80.476 test acc: 54.680 13.493 s +[1] loss: 0.623, train acc: 81.016 test acc: 55.170 13.423 s +[2] loss: 0.620, train acc: 81.046 test acc: 55.180 13.231 s +[3] loss: 0.614, train acc: 81.350 test acc: 55.320 13.522 s +[4] loss: 0.612, train acc: 81.450 test acc: 55.350 13.753 s +[5] loss: 0.609, train acc: 81.418 test acc: 55.380 13.710 s +[6] loss: 0.611, train acc: 81.338 test acc: 55.380 13.418 s +[7] loss: 0.610, train acc: 81.308 test acc: 55.310 13.533 s +[8] loss: 0.607, train acc: 81.606 test acc: 55.140 13.087 s +[9] loss: 0.610, train acc: 81.398 test acc: 55.480 13.179 s +[10] loss: 0.609, train acc: 81.640 test acc: 55.120 13.593 s +[11] loss: 0.608, train acc: 81.380 test acc: 55.400 14.122 s +[12] loss: 0.612, train acc: 81.404 test acc: 55.170 13.414 s +[13] loss: 0.605, train acc: 81.522 test acc: 55.400 13.118 s 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test acc: 55.020 13.482 s +[28] loss: 0.571, train acc: 82.662 test acc: 55.160 13.722 s +[29] loss: 0.574, train acc: 82.540 test acc: 55.050 12.904 s +[30] loss: 0.570, train acc: 82.918 test acc: 54.980 13.396 s +[31] loss: 0.572, train acc: 82.634 test acc: 54.970 13.703 s +[32] loss: 0.567, train acc: 82.712 test acc: 54.930 13.629 s +[33] loss: 0.570, train acc: 82.648 test acc: 55.050 13.803 s +[34] loss: 0.576, train acc: 82.420 test acc: 55.200 13.372 s +[35] loss: 0.575, train acc: 82.392 test acc: 55.090 13.609 s +[36] loss: 0.576, train acc: 82.552 test acc: 55.060 13.562 s +[37] loss: 0.565, train acc: 82.882 test acc: 54.970 13.039 s +[38] loss: 0.565, train acc: 82.746 test acc: 55.010 13.535 s +[39] loss: 0.567, train acc: 82.880 test acc: 55.040 13.770 s +[40] loss: 0.568, train acc: 82.724 test acc: 55.130 13.450 s +[1] loss: 2.906, train acc: 28.238 test acc: 40.670 25.236 s +[2] loss: 1.937, train acc: 46.946 test acc: 45.700 24.150 s +[3] loss: 1.763, train acc: 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acc: 59.390 test acc: 52.930 24.896 s +[20] loss: 1.387, train acc: 59.740 test acc: 50.880 24.918 s +[21] loss: 1.379, train acc: 60.120 test acc: 51.940 24.929 s +[22] loss: 1.382, train acc: 59.954 test acc: 51.250 24.573 s +[23] loss: 1.380, train acc: 59.836 test acc: 53.840 24.796 s +[24] loss: 1.369, train acc: 60.292 test acc: 52.730 24.533 s +[25] loss: 1.360, train acc: 60.362 test acc: 52.150 24.865 s +[26] loss: 1.362, train acc: 60.340 test acc: 50.730 24.467 s +[27] loss: 1.362, train acc: 60.406 test acc: 52.160 24.830 s +[28] loss: 1.362, train acc: 60.466 test acc: 52.030 24.392 s +[29] loss: 1.362, train acc: 60.180 test acc: 51.750 24.828 s +[30] loss: 1.347, train acc: 60.782 test acc: 52.280 25.078 s +[31] loss: 1.362, train acc: 60.374 test acc: 51.550 24.690 s +[32] loss: 1.355, train acc: 60.672 test acc: 51.870 24.865 s +[33] loss: 1.363, train acc: 60.266 test acc: 52.340 24.797 s +[34] loss: 1.351, train acc: 60.644 test acc: 51.120 25.981 s diff --git a/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/training_checkpoint.pth.tar b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/training_checkpoint.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..af69b3e5b9895a7785ca6a5d5e61ba2413f82f1f --- /dev/null +++ b/checkpoints/shallow999_reproduceMaxim001_simplenet_2b4b8b/training_checkpoint.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7449d3d530d4f0840fe6a7bb65199ab0a8f20105967f5f39f2caa3baf473eb3 +size 1578207 diff --git a/dataloader.py b/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..e761d48eb7cf76c9513a5fcf28a63fe5e90338b9 --- /dev/null +++ b/dataloader.py @@ -0,0 +1,87 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import os +import torch +import torchvision +from torchvision import transforms +import random +import numpy as np + +class ai85_normalize: + def __init__(self, act_8b_mode): + self.act_8b_mode = act_8b_mode + + def __call__(self, img): + if(self.act_8b_mode): + return img.sub(0.5).mul(256.).round().clamp(min=-128, max=127) + return img.sub(0.5).mul(256.).round().clamp(min=-128, max=127).div(128.) + +def load_cifar100(batch_size=128, num_workers=1, shuffle=True, act_8b_mode=False): + """ + Maxim's data augmentation: 4 pixels are padded on each side, and a 32x32 crop is randomly sampled + from the padded image or its horizontal flip. + """ + train_transform = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + ai85_normalize(act_8b_mode=act_8b_mode) + ]) + + train_dataset = torchvision.datasets.CIFAR100(root='data', train=True, download=True, transform=train_transform) + + test_transform = transforms.Compose([ + transforms.ToTensor(), + ai85_normalize(act_8b_mode=act_8b_mode) + ]) + + test_dataset = torchvision.datasets.CIFAR100(root='data', train=False, download=True, transform=test_transform) + + train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle) + test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False) + + return train_loader, test_loader + +def load_cifar100_p(batch_size=128, num_workers=1, shuffle=True, act_8b_mode=False, partial=20.0): + """ + Maxim's data augmentation: 4 pixels are padded on each side, and a 32x32 crop is randomly sampled + from the padded image or its horizontal flip. + """ + dataset_size=50000 + # Do an error check since we added the parameter, it's not relayed to torch or something + if((partial > 100.0) or (partial < 0.0)): + print('') + print('Argument partial can only be between 0 and 100') + print('Exiting.') + print('') + sys.exit() + + # Train dataset transform # disabled augmentation to compare optimization performance + train_transform = transforms.Compose([ +# transforms.RandomCrop(32, padding=4), +# transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + ai85_normalize(act_8b_mode=act_8b_mode) + ]) + + # Load complete training dataset to use as a base for partial dataset + train_dataset = torchvision.datasets.CIFAR100(root='data', train=True, download=True, transform=train_transform) + # Get subset of training dataset + num_elements_to_load = np.round(50000*partial/100.0) + indices_from_dataset = createRandomSortedList(int(num_elements_to_load), 0, dataset_size) + partial_dataset = torch.utils.data.Subset(train_dataset, indices_from_dataset) + print('Loaded',partial,'% of the training dataset, corresponding to', len(indices_from_dataset) ,'image/label tuples') + batch_loader = torch.utils.data.DataLoader(partial_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle) + return batch_loader + +def createRandomSortedList(num, start = 1, end = 100): + arr = [] + tmp = random.randint(start, end) + for x in range(num): + while tmp in arr: + tmp = random.randint(start, end) + arr.append(tmp) + arr.sort() + return arr diff --git a/documentation/benchmark.png b/documentation/benchmark.png new file mode 100644 index 0000000000000000000000000000000000000000..bbdd56c6e12760405f265d2b1700c00ffaad050d --- /dev/null +++ b/documentation/benchmark.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1bf3d8ab6d233f9fd3edf06238db5a0de4c30a6488baab12a69f5c12de293cf6 +size 206869 diff --git a/documentation/edited-results-graph.png b/documentation/edited-results-graph.png new file mode 100644 index 0000000000000000000000000000000000000000..0e8f246b36cad332f58be28fcd77d43b668885a2 --- /dev/null +++ b/documentation/edited-results-graph.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73cbdf7cd5732c3303062418ca4a52c3bff367a3c38c7450f0e98d6a767af795 +size 90907 diff --git a/documentation/generated-leaderboard.txt b/documentation/generated-leaderboard.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e7dce52666f0b58df54bade2985c37b883efb4a --- /dev/null +++ b/documentation/generated-leaderboard.txt @@ -0,0 +1,77 @@ +buraksoner@buraksoner-XPS-15-7590:~/sai-tr/workspace_maxim_ai85_cifar100$ python tools/generate_leaderboard.py + +Found checkpoints (except reproduce checkpoints) at these locations: +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/maxim000_nas_8b +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/maxim001_simplenet_2b4b8b +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow001_nas_8b_SAM +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow002_nas_2b8b_noSAM_4lyrDeep +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow003_nas_2b8b_noSAM_3lyrDeep +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow004_nas_2b8b_noSAM_2lyrDeep +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow005_nas_2b8b_noSAM_1lyrDeep +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow006_nas_AP2b8b_earlyexit +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow007_nas_AP2b8b_tuned +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow008_naslarge_2b8b_noSAM +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow009_nas_aha_AP8b_7bBias +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow010_naslarge_2b8b_wSAM +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow011_naslarge_AP8b_wSAM_failed +/home/buraksoner/sai-tr/workspace_maxim_ai85_cifar100/checkpoints/shallow012_naslarge_AP2b8b_wSAM + +Gathering hardware-mode top-1 accuracy and size info from each checkpoint + +Leaderboard +-------------------------------- +Name : maxim000_nas_8b +Top-1 accuracy: 64.22 +Size (KBytes) : 348.772 + +Name : maxim001_simplenet_2b4b8b +Top-1 accuracy: 55.76 +Size (KBytes) : 166.448 + +Name : shallow001_nas_8b_SAM +Top-1 accuracy: 64.31 +Size (KBytes) : 348.772 + +Name : shallow002_nas_2b8b_noSAM_4lyrDeep +Top-1 accuracy: 61.73 +Size (KBytes) : 279.652 + +Name : shallow003_nas_2b8b_noSAM_3lyrDeep +Top-1 accuracy: 59.95 +Size (KBytes) : 210.532 + +Name : shallow004_nas_2b8b_noSAM_2lyrDeep +Top-1 accuracy: 57.98 +Size (KBytes) : 141.412 + +Name : shallow005_nas_2b8b_noSAM_1lyrDeep +Top-1 accuracy: 57.34 +Size (KBytes) : 127.588 + +Name : shallow006_nas_AP2b8b_earlyexit +Top-1 accuracy: 46.51 +Size (KBytes) : 127.588 , but has "-1/+1 only" 2b layers, so this would be: 104.548 KBytes on MAX78002 + +Name : shallow007_nas_AP2b8b_tuned +Top-1 accuracy: 54.71 +Size (KBytes) : 127.588 , but has "-1/+1 only" 2b layers, so this would be: 104.548 KBytes on MAX78002 + +Name : shallow008_naslarge_2b8b_noSAM +Top-1 accuracy: 65.88 +Size (KBytes) : 328.548 + +Name : shallow009_nas_aha_AP8b_7bBias +Top-1 accuracy: 55.06 +Size (KBytes) : 127.588 , but has "-1/+1 only" 2b layers, so this would be: 90.724 KBytes on MAX78002 + +Name : shallow010_naslarge_2b8b_wSAM +Top-1 accuracy: 66.38 +Size (KBytes) : 328.548 + +Name : shallow011_naslarge_AP8b_wSAM_failed +Top-1 accuracy: 36.09 +Size (KBytes) : 328.548 , but has "-1/+1 only" 2b layers, so this would be: 192.356 KBytes on MAX78002 + +Name : shallow012_naslarge_AP2b8b_wSAM +Top-1 accuracy: 61.34 +Size (KBytes) : 328.548 , but has "-1/+1 only" 2b layers, so this would be: 241.508 KBytes on MAX78002 \ No newline at end of file diff --git a/documentation/generated-results-graph.png b/documentation/generated-results-graph.png new file mode 100644 index 0000000000000000000000000000000000000000..b166c76335c68f60a4ca6783ebb99a434a727230 --- /dev/null +++ b/documentation/generated-results-graph.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:606b4405258a08cc8523f30c456893959e3b41010c792ad460041de88e6d4f87 +size 35040 diff --git a/documentation/readme b/documentation/readme new file mode 100644 index 0000000000000000000000000000000000000000..f2e7ad933a9cd3a03dd4244b0209736fe6606aa1 --- /dev/null +++ b/documentation/readme @@ -0,0 +1 @@ +generalized_cnn_aha.pdf is for formulating the next version of this repo. That file should probably be moved out, or a cleaner version of it should be created at the place where this file can be moved to. \ No newline at end of file diff --git a/documentation/workflow-training-modes.png b/documentation/workflow-training-modes.png new file mode 100644 index 0000000000000000000000000000000000000000..8bf672fdd51951ac594d89a46c22ceb4adfe8934 --- /dev/null +++ b/documentation/workflow-training-modes.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c442ff6e964e39bc5af4ea18d2613f928d902c18fb1cb8cb97c0cfd50019f53f +size 1368342 diff --git a/functions.py b/functions.py new file mode 100644 index 0000000000000000000000000000000000000000..c51d6826e816502b312c85b795abfd4eea4555f9 --- /dev/null +++ b/functions.py @@ -0,0 +1,137 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys +import torch.nn as nn +from torch.autograd import Function + +################################################### +### Quantization Functions +### backward passes are straight through + +## Up-Down (ud) quantization for wide last layer ("bigdata"). Used in QAT +class Q_ud_wide(Function): + @staticmethod + def forward(_, x, xb, extrab): + up_factor = 2**(xb-extrab-1) + down_factor = 2**(xb-1) + return x.mul(up_factor).add(.5).floor().div(down_factor) + + @staticmethod + def backward(_, x): + return x, None, None + +## Up-Down (ud) quantization. Used in QAT +class Q_ud(Function): + @staticmethod + def forward(_, x, xb): + updown_factor = 2**(xb-1) + return x.mul(updown_factor).add(.5).floor().div(updown_factor) + + @staticmethod + def backward(_, x): + return x, None + +## Up-Down (ud) quantization for antipodal binary. Used in qat-ap +class Q_ud_ap(Function): + @staticmethod + def forward(_, x): + x = torch.sign(x).div(2.0) # antipodal (-1,+1) weights @HW correspond to (-0.5,+0.5) in qat + mask = (x == 0) + return x - mask.type(torch.FloatTensor).to(x.device).div(2.0) + + @staticmethod + def backward(_, x): + return x + +## Up (u) quantization. Used in Eval/hardware +class Q_u(Function): + @staticmethod + def forward(_, x, xb): + up_factor = 2**(8-xb) + return x.mul(up_factor).add(.5).floor() ### Burak: maxim has a .add(0.5) at the beginning, I think that's wrong + + @staticmethod + def backward(_, x): + return x, None + +## Down (d) quantization. Used in Eval/hardware +class Q_d(Function): + @staticmethod + def forward(_, x, xb): + down_factor = 2**(xb-1) + return x.div(down_factor).add(.5).floor() ### Burak: maxim has a .add(0.5) at the beginning, I think that's wrong + + @staticmethod + def backward(_, x): + return x, None + + +################################################### +### Quantization module +### ("umbrella" for Functions) +class quantization(nn.Module): + def __init__(self, xb = 8, mode='updown', wide=False): + super().__init__() + self.xb = xb + self.mode = mode + self.wide = wide + + def forward(self, x): + if(self.mode=='updown'): + if(self.wide): + return Q_ud_wide.apply(x, self.xb, 1) + else: + return Q_ud.apply(x, self.xb) + elif(self.mode=='down'): + if(self.wide): + return Q_d.apply(x, self.xb + 1) + else: + return Q_d.apply(x, self.xb) + elif(self.mode=='up'): + return Q_u.apply(x, self.xb) + elif(self.mode=='updown_ap'): + return Q_ud_ap.apply(x) + else: + print('wrong quantization mode. exiting') + sys.exit() + +################################################### +### Clamping modules +### (doesn't need Functions since backward passes are well-defined) +class clamping_qa(nn.Module): + def __init__(self, xb = 8, wide=False): + super().__init__() + if(wide): + self.min_val = -16384.0 + self.max_val = 16383.0 + else: + self.min_val = -1.0 + self.max_val = (2**(xb-1)-1)/(2**(xb-1)) + + def forward(self, x): + return x.clamp(min=self.min_val, max=self.max_val) + +class clamping_hw(nn.Module): + def __init__(self, xb = 8, wide=False): + super().__init__() + if(wide): + self.min_val = -2**(30-1) + self.max_val = 2**(30-1)-1 + else: + self.min_val = -2**(xb-1) + self.max_val = 2**(xb-1)-1 + + def forward(self, x): + return x.clamp(min=self.min_val, max=self.max_val) + + +################################################### +### Computing output_shift, i.e., "los" +def calc_out_shift(weight, bias, shift_quantile): + bias_r = torch.flatten(bias) + weight_r = torch.flatten(weight) + params_r = torch.cat((weight_r, bias_r)) + limit = torch.quantile(params_r.abs(), shift_quantile) + return -(1./limit).log2().floor().clamp(min=-15., max=15.) diff --git a/layers.py b/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c0b669d698512ff77b75c2d02bdd87ea998d78 --- /dev/null +++ b/layers.py @@ -0,0 +1,392 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, sys +import torch.nn as nn +import numpy as np +from torch.autograd import Function +from functions import quantization, clamping_qa, clamping_hw, calc_out_shift + +################################################### +### Base layer for conv/linear, +### enabling quantization-related mechanisms +class shallow_base_layer(nn.Module): + def __init__( + self, + quantization_mode = 'fpt', # 'fpt', 'qat', 'qat_ap' and 'eval' + pooling_flag = None, # boolean flag for now, only maxpooling of 2-pools with stride 2 + operation_module = None, # torch nn module for keeping and updating conv/linear parameters + operation_fcnl = None, # torch nn.functional for actually doing the operation + activation_module = None, # torch nn module for relu/abs + batchnorm_module = None, # torch nn module for batchnorm, see super + output_width_30b = False # boolean flag that chooses between "bigdata" (32b) and normal (8b) activation modes for MAX78000 + ): + super().__init__() + + ############################################################################### + # Initialize stuff that won't change throughout the model's lifetime here + # since this place will only be run once (first time the model is declared) + if(pooling_flag==True): + self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) + else: + self.pool = None + + ### Burak: we have to access and change (forward pass) and also train (backward pass) parameters .weight and .bias for the operations + ### therefore we keep both a functional and a module for Conv2d/Linear. The name "op" is mandatory for keeping params in Maxim + ### checkpoint format. + self.op = operation_module + self.op_fcn = operation_fcnl + self.act = activation_module + self.bn = batchnorm_module + self.wide = output_width_30b + + ############################################################################### + # Initialize stuff that will change during mode progression (FPT->QAT->Eval/HW). + self.mode = quantization_mode; + self.quantize_Q_ud_8b = None + self.quantize_Q_ud_wb = None + self.quantize_Q_ud_bb = None + self.quantize_Q_ud_ap = None + self.quantize_Q_d_8b = None + self.quantize_Q_u_wb = None + self.quantize_Q_ud_wide = None + self.quantize_Q_d_wide = None + self.clamp_C_qa_8b = None + self.clamp_C_qa_bb = None + self.clamp_C_qa_wb = None + self.clamp_C_hw_8b = None + self.clamp_C_qa_wide = None + self.clamp_C_hw_wide = None + + ### Burak: these aren't really trainable parameters, but they're logged in the Maxim checkpoint format. It seems they marked + ### them as "non-trainable parameters" to get them automatically saved in the state_dict + self.output_shift = nn.Parameter(torch.Tensor([ 0 ]), requires_grad=False) ### Burak: we called this los, this varies, default:0 + self.weight_bits = nn.Parameter(torch.Tensor([ 8 ]), requires_grad=False) ### Burak: we called this wb, this varies, default:8 + self.bias_bits = nn.Parameter(torch.Tensor([ 8 ]), requires_grad=False) ### Burak: this is always 8 + self.quantize_activation = nn.Parameter(torch.Tensor([ 0 ]), requires_grad=False) ### Burak: this is 0 in FPT, 1 in QAT & eval/hardware, default: fpt + self.adjust_output_shift = nn.Parameter(torch.Tensor([ 1 ]), requires_grad=False) ### Burak: this is 1 in FPT & QAT, 0 in eval/hardware, default: fpt + self.shift_quantile = nn.Parameter(torch.Tensor([ 1 ]), requires_grad=False) ### Burak: this varies, default:1 (naive) + + ############################################################################### + # Do first mode progression (to the default) + ### Burak: this recognizes that layer configuration is done via a function, + ### thus, can be done again in training time for mode progression + weight_bits = self.weight_bits + bias_bits = self.bias_bits + shift_quantile = self.shift_quantile + self.configure_layer_base( weight_bits, bias_bits, shift_quantile ) + + # This will be called during mode progression to set fields, + # check workflow-training-modes.png in doc for further info. + # sets functions for all modes though, not just the selected mode + def configure_layer_base(self, weight_bits, bias_bits, shift_quantile): + # quantization operators + self.quantize_Q_ud_8b = quantization(xb = 8, mode ='updown' , wide=False) # 8 here is activation bits + self.quantize_Q_ud_wb = quantization(xb = weight_bits, mode ='updown' , wide=False) + self.quantize_Q_ud_bb = quantization(xb = bias_bits, mode ='updown' , wide=False) + self.quantize_Q_ud_ap = quantization(xb = 2, mode ='updown_ap' , wide=False) # 2 here is dummy, mode antipodal overrides xb + self.quantize_Q_d_8b = quantization(xb = 8, mode ='down' , wide=False) # 8 here is activation bits + self.quantize_Q_u_wb = quantization(xb = weight_bits, mode ='up' , wide=False) + self.quantize_Q_ud_wide = quantization(xb = 8, mode ='updown' , wide=True) # 8 here is activation bits, but its wide, so check inside + self.quantize_Q_d_wide = quantization(xb = 8, mode ='down' , wide=True) # 8 here is activation bits, but its wide, so check inside + + # clamping operators + self.clamp_C_qa_8b = clamping_qa(xb = 8, wide=False) # 8 here is activation bits + self.clamp_C_qa_bb = clamping_qa(xb = bias_bits, wide=False) + self.clamp_C_qa_wb = clamping_qa(xb = weight_bits, wide=False) + self.clamp_C_hw_8b = clamping_hw(xb = 8, wide=False) # 8 here is activation bits + self.clamp_C_qa_wide = clamping_qa(xb = None, wide=True) # None to avoid misleading info on the # of bits, check inside + self.clamp_C_hw_wide = clamping_hw(xb = None, wide=True) # None to avoid misleading info on the # of bits, check inside + + # state variables + self.weight_bits = nn.Parameter(torch.Tensor([ weight_bits ]), requires_grad=False) + self.bias_bits = nn.Parameter(torch.Tensor([ bias_bits ]), requires_grad=False) + self.shift_quantile = nn.Parameter(torch.Tensor([ shift_quantile ]), requires_grad=False) + + # This will be called during mode progression, during training + def mode_fpt2qat(self, quantization_mode): + # just fold batchnorms + if(self.bn is not None): + w_fp = self.op.weight.data + b_fp = self.op.bias.data + + running_mean_mu = self.bn.running_mean + running_var = self.bn.running_var + running_stdev_sigma = torch.sqrt(running_var + 1e-20) + + w_hat = w_fp * (1.0 / (running_stdev_sigma*4.0)).reshape((w_fp.shape[0],) + (1,) * (len(w_fp.shape) - 1)) + b_hat = (b_fp - running_mean_mu)/(running_stdev_sigma*4.0) + + self.op.weight.data = w_hat + self.op.bias.data = b_hat + self.bn = None + else: + pass + #print('This layer does not have batchnorm') + self.mode = quantization_mode; + self.quantize_activation = nn.Parameter(torch.Tensor([ 1 ]), requires_grad=False) ### Burak: this is 0 in FPT, 1 in QAT & eval/hardware + self.adjust_output_shift = nn.Parameter(torch.Tensor([ 1 ]), requires_grad=False) ### Burak: this is 1 in FPT & QAT, 0 in eval/hardware + + # This will be called during mode progression after training, for eval + def mode_qat2hw(self, quantization_mode): + w_hat = self.op.weight.data + b_hat = self.op.bias.data + + shift = -self.output_shift.data; + s_o = 2**(shift) + wb = self.weight_bits.data.cpu().numpy()[0] + + w_clamp = [-2**(wb-1) , 2**(wb-1)-1 ] + b_clamp = [-2**(wb+8-2), 2**(wb+8-2)-1] # 8 here is activation bits + + w = w_hat.mul(2**(wb -1)).mul(s_o).add(0.5).floor() + w = w.clamp(min=w_clamp[0],max=w_clamp[1]) + + b = b_hat.mul(2**(wb -1 + 7)).mul(s_o).add(0.5).floor() + b = b.clamp(min=b_clamp[0],max=b_clamp[1]) + + self.op.weight.data = w + self.op.bias.data = b + self.mode = quantization_mode; + self.quantize_activation = nn.Parameter(torch.Tensor([ 1 ]), requires_grad=False) ### Burak: this is 0 in FPT, 1 in QAT & eval/hardware + self.adjust_output_shift = nn.Parameter(torch.Tensor([ 0 ]), requires_grad=False) ### Burak: this is 1 in FPT & QAT, 0 in eval/hardware + + def mode_qat_ap2hw(self, quantization_mode): + w_hat = self.op.weight.data + b_hat = self.op.bias.data + + shift = -self.output_shift.data; + s_o = 2**(shift) + wb = self.weight_bits.data.cpu().numpy()[0] + + if(wb==2): + w = self.quantize_Q_ud_ap(w_hat).mul(2.0) + else: + w_clamp = [-2**(wb-1) , 2**(wb-1)-1 ] + w = w_hat.mul(2**(wb -1)).mul(s_o).add(0.5).floor() + w = w.clamp(min=w_clamp[0],max=w_clamp[1]) + + b_clamp = [-2**(wb+8-2), 2**(wb+8-2)-1] # 8 here is activation bits + b = b_hat.mul(2**(wb -1 + 7)).mul(s_o).add(0.5).floor() + b = b.clamp(min=b_clamp[0],max=b_clamp[1]) + + self.op.weight.data = w + self.op.bias.data = b + self.mode = quantization_mode; + self.quantize_activation = nn.Parameter(torch.Tensor([ 1 ]), requires_grad=False) ### Burak: this is 0 in FPT, 1 in QAT & eval/hardware + self.adjust_output_shift = nn.Parameter(torch.Tensor([ 0 ]), requires_grad=False) ### Burak: this is 1 in FPT & QAT, 0 in eval/hardware + + + def forward(self, x): + if(self.pool is not None): + x = self.pool(x) + + if(self.mode == 'fpt'): + # pre-compute stuff + w_fp = self.op.weight + b_fp = self.op.bias + + # actual forward pass + x = self.op_fcn(x, w_fp, b_fp, self.op.stride, self.op.padding) + if(self.bn is not None): + x = self.bn(x) # make sure var=1 and mean=0 + x = x / 4.0 # since BN is only making sure var=1 and mean=0, 1/4 is to keep everything within [-1,1] w/ hi prob. + if(self.act is not None): + x = self.act(x) + if((self.wide) and (self.act is None)): + x = self.clamp_C_qa_wide(x) + else: + x = self.clamp_C_qa_8b(x) + + # save stuff (los is deactivated in fpt) + self.output_shift = nn.Parameter(torch.Tensor([ 0 ]), requires_grad=False) # functional, used in Maxim-friendly checkpoints + self.quantize_activation = nn.Parameter(torch.Tensor([ 0 ]), requires_grad=False) # ceremonial, for Maxim-friendly checkpoints + self.adjust_output_shift = nn.Parameter(torch.Tensor([ 1 ]), requires_grad=False) # ceremonial, for Maxim-friendly checkpoints + + elif(self.mode == 'qat'): + ############################################################################### + ## ASSUMPTION: batchnorms are already folded before coming here. Check doc, ## + ## the parameters with _fp and with _hat are of different magnitude ## + ############################################################################### + + # pre-compute stuff + w_hat = self.op.weight + b_hat = self.op.bias + los = calc_out_shift(w_hat.detach(), b_hat.detach(), self.shift_quantile.detach()) + s_w = 2**(-los) + s_o = 2**(los) + w_hat_q = self.clamp_C_qa_wb(self.quantize_Q_ud_wb(w_hat*s_w)); + b_hat_q = self.clamp_C_qa_bb(self.quantize_Q_ud_bb(b_hat*s_w)); + + # actual forward pass + x = self.op_fcn(x, w_hat_q, b_hat_q, self.op.stride, self.op.padding) + x = x*s_o + if(self.act is not None): + x = self.act(x) + if((self.wide) and (self.act is None)): + x = self.quantize_Q_ud_wide(x) + x = self.clamp_C_qa_wide(x) + else: + x = self.quantize_Q_ud_8b(x) + x = self.clamp_C_qa_8b(x) + + # save stuff + self.output_shift = nn.Parameter(torch.Tensor([ los ]), requires_grad=False) # functional, used in Maxim-friendly checkpoints + + elif(self.mode == 'qat_ap'): + ############################################################################### + ## ASSUMPTION: batchnorms are already folded before coming here. Check doc, ## + ## the parameters with _fp and with _hat are of different magnitude ## + ############################################################################### + + # pre-compute stuff + w_hat = self.op.weight + b_hat = self.op.bias + los = calc_out_shift(w_hat.detach(), b_hat.detach(), self.shift_quantile.detach()) + s_w = 2**(-los) + s_o = 2**(los) + ############################################## + # This is the only difference from qat + if(self.weight_bits.data==2): + w_hat_q = self.quantize_Q_ud_ap(w_hat*s_w); + else: + w_hat_q = self.clamp_C_qa_wb(self.quantize_Q_ud_wb(w_hat*s_w)); + ############################################## + b_hat_q = self.clamp_C_qa_bb(self.quantize_Q_ud_bb(b_hat*s_w)); + + # actual forward pass + x = self.op_fcn(x, w_hat_q, b_hat_q, self.op.stride, self.op.padding) + x = x*s_o + if(self.act is not None): + x = self.act(x) + if((self.wide) and (self.act is None)): + x = self.quantize_Q_ud_wide(x) + x = self.clamp_C_qa_wide(x) + else: + x = self.quantize_Q_ud_8b(x) + x = self.clamp_C_qa_8b(x) + + # save stuff + self.output_shift = nn.Parameter(torch.Tensor([ los ]), requires_grad=False) # functional, used in Maxim-friendly checkpoints + + elif(self.mode == 'eval'): + ##################################################################################### + ## ASSUMPTION: parameters are already converted to HW before coming here.Check doc ## + ##################################################################################### + + # pre-compute stuff + w = self.op.weight + b = self.op.bias + los = self.output_shift + s_o = 2**(los) + w_q = self.quantize_Q_u_wb(w); + b_q = self.quantize_Q_u_wb(b); # yes, wb, not a typo, they need to be on the same scale + + # actual forward pass + x = self.op_fcn(x, w_q, b_q, self.op.stride, self.op.padding) # convolution / linear + x = x*s_o + if(self.act is not None): + x = self.act(x) + if((self.wide) and (self.act is None)): + x = self.quantize_Q_d_wide(x) + x = self.clamp_C_hw_wide(x) + else: + x = self.quantize_Q_d_8b(x) + x = self.clamp_C_hw_8b(x) + + # nothing to save, this was a hardware-emulated evaluation pass + else: + print('wrong quantization mode. should have been one of {fpt, qat, eval}. exiting') + sys.exit() + + return x + + +class conv(shallow_base_layer): + def __init__( + self, + C_in_channels = None, # number of input channels + D_out_channels = None, # number of output channels + K_kernel_dimension = None, # square kernel dimension + padding = None, # amount of pixels to pad on one side (other side is symmetrically padded too) + pooling = False, # boolean flag for now, only maxpooling of 2-pools with stride 2 + batchnorm = False, # boolean flag for now, no trainable affine parameters + activation = None, # 'relu' is the only choice for now + output_width_30b = False # boolean flag that chooses between "bigdata" (32b) and normal (8b) activation modes for MAX78000 + ): + pooling_flag = pooling + + if(activation is None): + activation_fcn = None; + elif(activation == 'relu'): + activation_fcn = nn.ReLU(inplace=True); + else: + print('wrong activation type in model. only {relu} is acceptable. exiting') + sys.exit() + + ### Burak: only a module is enough for BN since we neither need to access internals in forward pass, nor train anything (affine=False) + if(batchnorm): + batchnorm_mdl = nn.BatchNorm2d(D_out_channels, eps=1e-05, momentum=0.05, affine=False) + else: + batchnorm_mdl = None; + + operation_mdl = nn.Conv2d(C_in_channels, D_out_channels, kernel_size=K_kernel_dimension, stride=1, padding=padding, bias=True); + operation_fcn = nn.functional.conv2d + + super().__init__( + pooling_flag = pooling_flag, + activation_module = activation_fcn, + operation_module = operation_mdl, + operation_fcnl = operation_fcn, + batchnorm_module = batchnorm_mdl, + output_width_30b = output_width_30b + ) + +def linear_functional(x, weight, bias, _stride, _padding): + # dummy linear function that has same arguments as conv + return nn.functional.linear(x, weight, bias) + +class fullyconnected(shallow_base_layer): + def __init__( + self, + in_features = None, # number of output features + out_features = None, # number of output features + pooling = False, # boolean flag for now, only maxpooling of 2-pools with stride 2 + batchnorm = False, # boolean flag for now, no trainable affine parameters + activation = None, # 'relu' is the only choice for now + output_width_30b = False # boolean flag that chooses between "bigdata" (32b) and normal (8b) activation modes for MAX78000 + ): + + pooling_flag = pooling + + if(activation is None): + activation_fcn = None; + elif(activation == 'relu'): + activation_fcn = nn.ReLU(inplace=True); + else: + print('wrong activation type in model. only {relu} is acceptable. exiting') + sys.exit() + + ### Burak: only a module is enough for BN since we neither need to access internals in forward pass, nor train anything (affine=False) + if(batchnorm): + batchnorm_mdl = nn.BatchNorm2d(out_features, eps=1e-05, momentum=0.05, affine=False) + else: + batchnorm_mdl = None; + + operation_mdl = nn.Linear(in_features, out_features, bias=True); + operation_fcn = linear_functional + + super().__init__( + pooling_flag = pooling_flag, + activation_module = activation_fcn, + operation_module = operation_mdl, + operation_fcnl = operation_fcn, + batchnorm_module = batchnorm_mdl, + output_width_30b = output_width_30b + ) + + # Define dummy arguments to make Linear and conv compatible in shallow_base_layer. + # the name "op" here refers to op in super, i.e., in base_layer + self.op.stride = None + self.op.padding = None diff --git a/library/readme b/library/readme new file mode 100644 index 0000000000000000000000000000000000000000..8f9ebce07a4bcc0220ff5d4a1ee8b08dfa04fcb5 --- /dev/null +++ b/library/readme @@ -0,0 +1 @@ +project-independent algorithms, methods, functions \ No newline at end of file diff --git a/library/sam.py b/library/sam.py new file mode 100644 index 0000000000000000000000000000000000000000..58f955ade92ed21d205850165f2b2d91866ff9cd --- /dev/null +++ b/library/sam.py @@ -0,0 +1,62 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch + +# ref: https://github.com/davda54/sam +class SAM(torch.optim.Optimizer): + def __init__(self, params, base_optimizer, rho=0.05, **kwargs): + assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}" + + defaults = dict(rho=rho, **kwargs) + super(SAM, self).__init__(params, defaults) + + self.base_optimizer = base_optimizer(self.param_groups, **kwargs) + self.param_groups = self.base_optimizer.param_groups + + @torch.no_grad() + def first_step(self, zero_grad=False): + grad_norm = self._grad_norm() + for group in self.param_groups: + scale = group["rho"] / (grad_norm + 1e-12) + + for p in group["params"]: + if p.grad is None: continue + e_w = p.grad * scale.to(p) + p.add_(e_w) # climb to the local maximum "w + e(w)" + self.state[p]["e_w"] = e_w + + if zero_grad: self.zero_grad() + + @torch.no_grad() + def second_step(self, zero_grad=False): + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: continue + p.sub_(self.state[p]["e_w"]) # get back to "w" from "w + e(w)" + + self.base_optimizer.step() # do the actual "sharpness-aware" update + + if zero_grad: self.zero_grad() + + @torch.no_grad() + def step(self, closure=None): + assert closure is not None, "Sharpness Aware Minimization requires closure, but it was not provided" + closure = torch.enable_grad()(closure) # the closure should do a full forward-backward pass + + self.first_step(zero_grad=True) + closure() + self.second_step() + + def _grad_norm(self): + shared_device = self.param_groups[0]["params"][0].device # put everything on the same device, in case of model parallelism + norm = torch.norm( + torch.stack([ + p.grad.norm(p=2).to(shared_device) + for group in self.param_groups for p in group["params"] + if p.grad is not None + ]), + p=2 + ) + return norm diff --git a/library/utils.py b/library/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..29f315f91a6694c2ba886ef2bbc9f89e50e96432 --- /dev/null +++ b/library/utils.py @@ -0,0 +1,27 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch + +def compute_batch_accuracy(pred, label): + correct = (pred == label).sum() + return correct,label.size(0) + +def compute_set_accuracy(model, test_loader): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + correct = 0 + total = 0 + with torch.no_grad(): + for data in test_loader: + inputs, labels = data + + inputs = inputs.to(device) + labels = labels.to(device) + outputs = model(inputs) + + correct_batch, total_batch = compute_batch_accuracy(torch.argmax(outputs, dim=1), labels) + correct += correct_batch + total += total_batch + + return correct/total \ No newline at end of file diff --git a/models.py b/models.py new file mode 100644 index 0000000000000000000000000000000000000000..ad458e112c664ac403cf095e460ab0c2c0d58c16 --- /dev/null +++ b/models.py @@ -0,0 +1,136 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch.nn as nn +import layers + +class maxim_nas(nn.Module): + def __init__( + self, + num_classes =100, + num_channels =3, + dimensions =(32, 32), + bias =True, + **kwargs + ): + super().__init__() + + ### Burak: disable word wrap in your editor to see this "table" for conv layers properly + ### mark that all layers are 'same' padding. + ### input ch | out ch | kernel dim | padding + self.conv1_1 = layers.conv( num_channels, 64, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv1_2 = layers.conv( 64, 32, 1, 0, pooling=False, batchnorm=True, activation='relu') + self.conv1_3 = layers.conv( 32, 64, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv2_1 = layers.conv( 64, 32, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv2_2 = layers.conv( 32, 64, 1, 0, pooling=False, batchnorm=True, activation='relu') + self.conv3_1 = layers.conv( 64, 128, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv3_2 = layers.conv( 128, 128, 1, 0, pooling=False, batchnorm=True, activation='relu') + self.conv4_1 = layers.conv( 128, 64, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv4_2 = layers.conv( 64, 128, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv5_1 = layers.conv( 128, 128, 1, 0, pooling=True , batchnorm=True, activation='relu') + self.fc = layers.fullyconnected(512, num_classes, output_width_30b=True, pooling=False, batchnorm=False, activation=None) + + def forward(self, x): + x = self.conv1_1(x) + x = self.conv1_2(x) + x = self.conv1_3(x) + x = self.conv2_1(x) + x = self.conv2_2(x) + x = self.conv3_1(x) + x = self.conv3_2(x) + x = self.conv4_1(x) + x = self.conv4_2(x) + x = self.conv5_1(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + return x + +class maxim_nas_large(nn.Module): + def __init__( + self, + num_classes =100, + num_channels =3, + dimensions =(32, 32), + bias =True, + **kwargs + ): + super().__init__() + + ### Burak: disable word wrap in your editor to see this "table" for conv layers properly + ### mark that all layers are 'same' padding. + ### input ch | out ch | kernel dim | padding + self.conv1_1 = layers.conv( num_channels, 128, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv1_2 = layers.conv( 128, 128, 1, 0, pooling=False, batchnorm=True, activation='relu') + self.conv1_3 = layers.conv( 128, 256, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv2_1 = layers.conv( 256, 128, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv2_2 = layers.conv( 128, 128, 1, 0, pooling=False, batchnorm=True, activation='relu') + self.conv3_1 = layers.conv( 128, 64, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv3_2 = layers.conv( 64, 256, 1, 0, pooling=False, batchnorm=True, activation='relu') + self.conv4_1 = layers.conv( 256, 128, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv4_2 = layers.conv( 128, 64, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv5_1 = layers.conv( 64, 128, 1, 0, pooling=True , batchnorm=True, activation='relu') + self.fc = layers.fullyconnected(512, num_classes, output_width_30b=True, pooling=False, batchnorm=False, activation=None) + + def forward(self, x): + x = self.conv1_1(x) + x = self.conv1_2(x) + x = self.conv1_3(x) + x = self.conv2_1(x) + x = self.conv2_2(x) + x = self.conv3_1(x) + x = self.conv3_2(x) + x = self.conv4_1(x) + x = self.conv4_2(x) + x = self.conv5_1(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + return x + + +class maxim_simplenet(nn.Module): + def __init__( + self, + num_classes =100, + num_channels =3, + dimensions =(32, 32), + bias =True, + **kwargs + ): + super().__init__() + + ### Burak: disable word wrap in your editor to see this "table" for conv layers properly + ### mark that all layers are 'same' padding. + ### input ch | out ch | kernel dim | padding + self.conv1 = layers.conv( num_channels, 16, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv2 = layers.conv( 16, 20, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv3 = layers.conv( 20, 20, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv4 = layers.conv( 20, 20, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv5 = layers.conv( 20, 20, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv6 = layers.conv( 20, 20, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv7 = layers.conv( 20, 44, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv8 = layers.conv( 44, 48, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv9 = layers.conv( 48, 48, 3, 1, pooling=False, batchnorm=True, activation='relu') + self.conv10= layers.conv( 48, 96, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv11= layers.conv( 96, 512, 1, 0, pooling=True , batchnorm=True, activation='relu') + self.conv12= layers.conv( 512, 128, 1, 0, pooling=False, batchnorm=True, activation='relu') + self.conv13= layers.conv( 128, 128, 3, 1, pooling=True , batchnorm=True, activation='relu') + self.conv14= layers.conv( 128, num_classes, 1, 0, output_width_30b=True, pooling=False, batchnorm=False, activation=None) + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = self.conv4(x) + x = self.conv5(x) + x = self.conv6(x) + x = self.conv7(x) + x = self.conv8(x) + x = self.conv9(x) + x = self.conv10(x) + x = self.conv11(x) + x = self.conv12(x) + x = self.conv13(x) + x = self.conv14(x) + x = x.view(x.size(0), -1) + return x \ No newline at end of file diff --git a/tests/hardware_checkpoint_maximVerification.py b/tests/hardware_checkpoint_maximVerification.py new file mode 100644 index 0000000000000000000000000000000000000000..dff81c97738a73a8403cb2035cab7eca86b1e839 --- /dev/null +++ b/tests/hardware_checkpoint_maximVerification.py @@ -0,0 +1,94 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import sys, torch +sys.path.append(".") ## works only when running from repo top layer +import layers +import models +import dataloader + +bs = 250; +train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=1, shuffle=True, act_8b_mode=True); + +print('') +print('Check: maxim checkpoints loaded into our model definitions, see test accuracy.') +print(' We expect approx. 64.32 for NAS, 55.76 for simplenet') + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +print('') +print('Device:', device) + +print('') +print('NAS Model') +mm = models.maxim_nas() +mm = mm.to(device) + +## make mode eval here (easy since we set all layer weights to 8b) +for layer_string in dir(mm): + layer_attribute = getattr(mm, layer_string) + if isinstance(layer_attribute, layers.shallow_base_layer): + layer_attribute.configure_layer_base(weight_bits=8, bias_bits=8, shift_quantile=0.99) + layer_attribute.mode_fpt2qat('qat'); + layer_attribute.mode_qat2hw('eval') + setattr(mm, layer_string, layer_attribute) + +checkpoint = torch.load('checkpoints/maxim000_nas_8b/hardware_checkpoint.pth.tar') +mm.load_state_dict(checkpoint['state_dict']) +mm = mm.to(device) + +dataiter = iter(test_loader) +ma = 0; +for i in range(0,int(10000/bs)): + images , labels = dataiter.next() + images = images.to(device) + labels =labels.to(device) + out = mm(images) + ma += torch.sum(torch.argmax(out,dim=1)==labels) +print('Test Accuracy:', (ma)/10000*100) + +print('') +print('Simplenet Mixed Precision Model') +mm = models.maxim_simplenet() +mm = mm.to(device) + +## make mode eval here (not that easy, layers are 2b/4b/8b) +# replace that weird policy thing here with explicit settings +weight_dictionary = {} +weight_dictionary['conv1' ] = 8; +weight_dictionary['conv2' ] = 4; +weight_dictionary['conv3' ] = 2; +weight_dictionary['conv4' ] = 2; +weight_dictionary['conv5' ] = 2; +weight_dictionary['conv6' ] = 2; +weight_dictionary['conv7' ] = 2; +weight_dictionary['conv8' ] = 2; +weight_dictionary['conv9' ] = 2; +weight_dictionary['conv10'] = 2; +weight_dictionary['conv11'] = 4; +weight_dictionary['conv12'] = 4; +weight_dictionary['conv13'] = 4; +weight_dictionary['conv14'] = 4; + +layer_attributes = [] +for layer_string in dir(mm): + if(layer_string in weight_dictionary): + layer_attribute = getattr(mm, layer_string) + layer_attribute.configure_layer_base(weight_bits=weight_dictionary[layer_string], bias_bits=8, shift_quantile=1.0) + layer_attribute.mode_fpt2qat('qat'); + layer_attribute.mode_qat2hw('eval') + setattr(mm, layer_string, layer_attribute) + +checkpoint = torch.load('checkpoints/maxim001_simplenet_2b4b8b/hardware_checkpoint.pth.tar') +mm.load_state_dict(checkpoint['state_dict'], strict=False) +mm = mm.to(device) + +dataiter = iter(test_loader) +ma = 0; +for i in range(0,int(10000/bs)): + images , labels = dataiter.next() + images = images.to(device) + labels =labels.to(device) + out = mm(images) + ma += torch.sum(torch.argmax(out,dim=1)==labels) +print('Test Accuracy:', (ma)/10000*100) diff --git a/tests/max78000_bnn_with_Q1numbers.py b/tests/max78000_bnn_with_Q1numbers.py new file mode 100644 index 0000000000000000000000000000000000000000..1e2dd9edc75573dcd9a28c45f0d7a4c1d3c8cc7e --- /dev/null +++ b/tests/max78000_bnn_with_Q1numbers.py @@ -0,0 +1,80 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch + +batch_size = 1 +num_rows = 32 +num_cols = 32 +input_channels = 30 +output_channels = 100 +kernel_dim = 3 +same_padding = kernel_dim // 2 + +## input activations, random, 8-bit [-128,+127] +u = (1/4)*torch.randn((batch_size, input_channels, num_rows, num_cols)) ## somewhat close to the [-1,1] range +u = u.mul((2**7)) ## expand to the [-128, +128] range (not quantized) +min_act = -(2**(8-1)) ## compute min value for quantized activation +max_act = 2**(8-1)-1 ## compute max value for quantized activation +u = u.add(0.5).floor().clamp(min=min_act, max=max_act) ## quantize to 8-bit, 2s complement, clamp to [-128, +127] + + +## weight, random, -1/+1 +x = (1/4)*torch.rand((output_channels, input_channels, kernel_dim, kernel_dim)) ## somewhat close to the [-1,1] range +for kr in range(0, kernel_dim): + for kc in range(0, kernel_dim): + for ki in range(0, input_channels): + for ko in range(0, output_channels): + element = x[ko,ki,kr,kc] + if(element > 0.0): + x[ko,ki,kr,kc] = 1 ## quantize to +1 + else: + x[ko,ki,kr,kc] = -1 ## quantize to -1 + + +## bias, random, 8-bit [-128,+127] +b = (1/4)*torch.randn((output_channels)) ## somewhat close to the [-1,1] range +b = b.mul((2**7)) ## expand to the [-128, +128] range (not quantized) +b = b.add(0.5).floor().clamp(min=min_act, max=max_act) ## quantize to 8-bit, 2s complement, clamp to [-128, +127] + + +## output with -1/+1 +y_act = torch.nn.functional.conv2d(u,x,bias=b, padding=same_padding) ## operation +y_act = y_act.mul(128) ## apply s_q +y_act = y_act.mul(2**(0)) ## apply s_o +y_act = y_act.div(128).add(0.5).floor() ## apply f +#y_act = y_act.clamp(min=min_act, max=max_act) ## apply 8-bit clamp +y_act = torch.nn.functional.relu(y_act) ## apply relu + +## output emulation with -1/0 dictionary +allm1 = -torch.abs(x) ## generate all -1 kernel +zeta = x.add(-1).div(2.0) ## generate zeta kernel + +y_emu1 = torch.nn.functional.conv2d(2*u,zeta, bias=b,padding=same_padding) ## operation +y_emu1 = y_emu1.mul(128) ## apply s_q +y_emu1 = y_emu1.mul(2**(0)) ## apply s_o +y_emu1 = y_emu1.div(128).add(0.5).floor() ## apply f +#y_emu1 = y_emu1.clamp(min=min_act, max=max_act) ## apply 8-bit clamp + +y_emu2 = torch.nn.functional.conv2d(u , allm1 , padding=same_padding) ## operation +y_emu2 = y_emu2.mul(128) ## apply s_q +y_emu2 = y_emu2.mul(2**(0)) ## apply s_o +y_emu2 = y_emu2.div(128).add(0.5).floor() ## apply f +#y_emu2 = y_emu2.clamp(min=min_act, max=max_act) ## apply 8-bit clamp + +y_emu = y_emu1 - y_emu2 ## residual subtract +y_emu = y_emu.add(0.5).floor() ## apply f +#y_emu = y_emu.clamp(min=min_act, max=max_act) ## apply 8-bit clamp + +y_emu = torch.nn.functional.relu(y_emu) ## apply relu + +print('actual output:') +print('') +print(y_act) +print('') +print('emulated output:') +print('') +print(y_emu) +print('') +print('difference:', torch.sum(torch.abs(y_act-y_emu)).numpy()) \ No newline at end of file diff --git a/tests/readme b/tests/readme new file mode 100644 index 0000000000000000000000000000000000000000..4f3331edb3a829af8f657e8bcca66a52f615a468 --- /dev/null +++ b/tests/readme @@ -0,0 +1 @@ +these are python3-runnable tests, but from the top folder \ No newline at end of file diff --git a/tools/generate_checkpoint_statistics.py b/tools/generate_checkpoint_statistics.py new file mode 100644 index 0000000000000000000000000000000000000000..d601a348537216cfda47bf45e7bea1d99e4709d9 --- /dev/null +++ b/tools/generate_checkpoint_statistics.py @@ -0,0 +1,201 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, matplotlib, os, sys, argparse +import numpy as np +import matplotlib.pyplot as plt +matplotlib.use('Agg') + +def generate_histogram_for_quantized_layer(layer_key, layer_weight, layer_bias, checkpoint_type, histograms_folderpath): + histogram_folder_exists = os.path.isdir(histograms_folderpath) + if not histogram_folder_exists: + os.makedirs(histograms_folderpath) + + matplotlib.rcParams.update({'font.size': 16}) + fig, axs = plt.subplots(1, 2, tight_layout=True, figsize=(20, 10)) + ww = layer_weight.flatten(); + bb = layer_bias.flatten(); + + ww_max = np.amax(ww) + ww_min = np.amin(ww) + ww_unq = len(np.unique(ww)) + + bb_max = np.amax(bb) + bb_min = np.amin(bb) + bb_unq = len(np.unique(bb)) + + if checkpoint_type=='hardware': + ww_num_bins = ww_unq*3 + bb_num_bins = bb_unq*3 + ww_max_lim = ww_max+1; + bb_max_lim = bb_max+1/16384; + elif checkpoint_type=='training': + ww_num_bins = min(ww_unq*3,800) + bb_num_bins = min(bb_unq*3,800) + ww_max_lim = ww_max+1/128; + bb_max_lim = bb_max+1/128; + + axs[0].grid(True) + axs[0].set_title('weight', fontdict={'fontsize': 22, 'fontweight': 'medium'}) + axs[0].hist(ww, range=(ww_min, ww_max_lim), bins=ww_num_bins, align='left') + + axs[1].grid(True) + axs[1].set_title('bias', fontdict={'fontsize': 22, 'fontweight': 'medium'}) + axs[1].hist(bb, range=(bb_min, bb_max_lim), bins=bb_num_bins, align='left') + + filename = os.path.join(histograms_folderpath,layer_key + '.jpg') + plt.savefig(filename) + plt.close() + +def main(): + parser = argparse.ArgumentParser(description='Print out model statistics file and optionally also save weight/bias histogram figures for each layer') + parser.add_argument('-c','--checkpoint-name', help='Name of folder under the checkpoints folder for which you want to generate a model statistics file', required=True) + parser.add_argument('-q','--checkpoint-type', help='checkpoint type can be either a hardware or training checkpoint.', required=True) + parser.add_argument('-g','--generate-histograms', help='Add this flag if you want to save jpg figures inside the checkpoint folder for histograms of bias and weight values of each layer in the network', action='store_true', default=False, required=False) + args = vars(parser.parse_args()) + + checkpoint_folder = os.path.join('checkpoints',args['checkpoint_name']); + if(os.path.isdir(checkpoint_folder)): + print('') + print('Found checkpoint folder') + else: + print('') + print('Could not find checkpoint folder. Please check that:') + print('1- you are running this script from the top level of the repository, and') + print('2- the checkpoint folder you gave the name for exists (needs to be created manually)') + sys.exit(); + + checkpoint_type = args['checkpoint_type'] + if(checkpoint_type=='hardware'): + print('') + print('Searching for a hardware_checkpoint.pth.tar') + print('') + check_for_bit_errors = True; + elif(checkpoint_type=='training'): + print('') + print('Searching for a training_checkpoint.pth.tar') + print('') + check_for_bit_errors = False; + else: + print('') + print('Something is wrong, we dont know of a',checkpoint_type, 'checkpoint. Perhaps a misspelling?' ) + print('') + sys.exit() + + checkpoint_filename = checkpoint_type+'_checkpoint.pth.tar'; + + a = torch.load(os.path.join(checkpoint_folder,checkpoint_filename)) + + flag_generate_histograms = args['generate_histograms'] + if(flag_generate_histograms): + print('[INFO]: Will generate histograms') + + with open(os.path.join(checkpoint_folder,'statistics_'+checkpoint_type+'_checkpoint'), 'w') as f: + print('[INFO]: Generating statistics file') + print('Top:', file=f) + for key in a.keys(): + print(' ', key, file=f) + + if( 'arch' not in a.keys()): + print('[ERROR]: there is no key named arch in this checkpoint', file=f) + print('[ERROR]: there is no key named arch in this checkpoint') + #sys.exit() + if( 'state_dict' not in a.keys()): + print('[ERROR]: there is no key named state_dict in this checkpoint', file=f) + print('[ERROR]: there is no key named state_dict in this checkpoint') + #sys.exit() + if( 'extras' not in a.keys()): + print('[ERROR]: there is no key named extras in this checkpoint', file=f) + print('[ERROR]: there is no key named extras in this checkpoint') + #sys.exit() + + print('-------------------------------------', file=f) + print('arch:', a['arch'], file=f) + + print('-------------------------------------', file=f) + print('extras:', a['extras'], file=f) + + print('-------------------------------------', file=f) + print('state_dict:', file=f) + + layer_keys = [] + layers = [] + for key in a['state_dict'].keys(): + fields = key.split('.') + if(fields[0] not in layer_keys): + layer_keys.append(fields[0]) + layers.append({'key': fields[0], + 'weight_bits':None, + 'bias_bits':None, + 'adjust_output_shift':None, + 'output_shift':None, + 'quantize_activation':None, + 'shift_quantile':None, + 'weight': None, + 'bias':None }) + idx = -1 + else: + idx = layer_keys.index(fields[0]) + + if((fields[1]=='weight_bits') or \ + (fields[1]=='output_shift') or \ + (fields[1]=='bias_bits') or \ + (fields[1]=='quantize_activation') \ + or (fields[1]=='adjust_output_shift') \ + or (fields[1]=='shift_quantile')): + layers[idx][fields[1]] = a['state_dict'][key].cpu().numpy(); + elif(fields[1]=='op'): + layers[idx][fields[2]] = a['state_dict'][key].cpu().numpy(); + else: + print('[ERROR]: Unknown field. Exiting', file=f) + print('[ERROR]: Unknown field. Exiting') + sys.exit() + + for layer in layers: + print(' ', layer['key'], file=f) + print(' output_shift: ', layer['output_shift'], file=f) + print(' adjust_output_shift: ', layer['adjust_output_shift'], file=f) + print(' quantize_activation: ', layer['quantize_activation'], file=f) + print(' shift_quantile: ', layer['shift_quantile'], file=f) + print(' weight bits: ', layer['weight_bits'], file=f) + print(' bias_bits: ', layer['bias_bits'], file=f) + + print(' bias', file=f) + print(' total # of elements, shape:', np.size(layer['bias']), ',', list(layer['bias'].shape), file=f) + print(' # of unique elements: ', len(np.unique(layer['bias'])), file=f) + print(' min, max, mean:', np.amin(layer['bias']), ', ', np.amax(layer['bias']), ', ', np.mean(layer['bias']), file=f) + if((len(np.unique(layer['bias'])) > 2**layer['bias_bits']) and (check_for_bit_errors)): + print('', file=f) + print('[WARNING]: # of unique elements in bias tensor is more than that allowed by bias_bits.', file=f) + print(' This might be OK, since Maxim deployment repository right shifts these.', file=f) + print('', file=f) + print('') + print('[WARNING]: # of unique elements in bias tensor is more than that allowed by bias_bits.') + print(' This might be OK, since Maxim deployment repository right shifts these.') + print(' Check stats file for details.') + print('') + print(' weight', file=f) + print(' total # of elements, shape:', np.size(layer['weight']), ',', list(layer['weight'].shape), file=f) + print(' # of unique elements: ', len(np.unique(layer['weight'])), file=f) + print(' min, max, mean:', np.amin(layer['weight']), ', ', np.amax(layer['weight']), ', ', np.mean(layer['weight']), file=f) + + if((len(np.unique(layer['weight'])) > 2**layer['weight_bits']) and (check_for_bit_errors)): + print('', file=f) + print('[ERROR]: # of unique elements in weight tensor is more than that allowed by weight_bits.', file=f) + print(' This is definitely not OK, weights are used in HW as is.', file=f) + print(' Exiting.', file=f) + print('', file=f) + print('') + print('[ERROR]: # of unique elements in weight tensor is more than that allowed by weight_bits.') + print(' This is definitely not OK, weights are used in HW as is.') + print(' Exiting.') + print('') + sys.exit() + if(flag_generate_histograms): + generate_histogram_for_quantized_layer(layer['key'], layer['weight'], layer['bias'], checkpoint_type, os.path.join(checkpoint_folder, 'histograms_'+checkpoint_type+'_checkpoint')) + print('[INFO]: saved histograms for layer', layer['key']) + + +if __name__ == '__main__': + main() diff --git a/tools/generate_leaderboard.py b/tools/generate_leaderboard.py new file mode 100644 index 0000000000000000000000000000000000000000..d5b3cf8321d61f1a56f702dc89709f4612def382 --- /dev/null +++ b/tools/generate_leaderboard.py @@ -0,0 +1,136 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, matplotlib, os, sys, argparse +import numpy as np +import matplotlib.pyplot as plt +matplotlib.use('Agg') + +def get_checkpoints_except_reproduce(): + # get a list of all available checkpoint folder names except the ones that have reproduce in it + temporary = sorted(os.listdir("./checkpoints")) + checkpoint_full_paths = [] + checkpoint_name_list = [] + for name in temporary: + fullname = os.path.join("./checkpoints",name) + if ((os.path.isdir(fullname)) and ('reproduce' not in fullname)): + checkpoint_full_paths.append(os.path.abspath(fullname)) + checkpoint_name_list.append(name) + + return checkpoint_full_paths, checkpoint_name_list + +def get_checkpoint_top1s_sizes(checkpoint_full_paths, checkpoint_name_list): + checkpoint_best_top1s = [] + checkpoint_sizes_in_bytes = [] + checkpoint_sizes_in_bytes_max78000 = [] + checkpoint_sizes_antipodal = [] + for i, cp in enumerate(checkpoint_full_paths): + nn = os.path.join(cp, 'hardware_checkpoint.pth.tar') + if(os.path.isfile(nn)): + a = torch.load(nn) + else: + print("Hardware checkpoint does not exist for:", checkpoint_name_list[i]) + checkpoint_best_top1s.append(None) + checkpoint_sizes_in_bytes.append(None) + checkpoint_sizes_in_bytes_max78000.append(None) + checkpoint_sizes_antipodal.append(None) + continue + + checkpoint_best_top1s.append(a['extras']['best_top1']) + + layer_keys = [] + layers = [] + for key in a['state_dict'].keys(): + fields = key.split('.') + if(fields[0] not in layer_keys): + layer_keys.append(fields[0]) + layers.append({'key': fields[0], + 'weight_bits':None, + 'bias_bits':None, + 'adjust_output_shift':None, + 'output_shift':None, + 'quantize_activation':None, + 'shift_quantile':None, + 'weight': None, + 'bias':None }) + idx = -1 + else: + idx = layer_keys.index(fields[0]) + + if((fields[1]=='weight_bits') or \ + (fields[1]=='output_shift') or \ + (fields[1]=='bias_bits') or \ + (fields[1]=='quantize_activation') \ + or (fields[1]=='adjust_output_shift') \ + or (fields[1]=='shift_quantile')): + layers[idx][fields[1]] = a['state_dict'][key].cpu().numpy(); + elif(fields[1]=='op'): + layers[idx][fields[2]] = a['state_dict'][key].cpu().numpy(); + else: + print('[ERROR]: Unknown field. Exiting', file=f) + print('[ERROR]: Unknown field. Exiting') + sys.exit() + + size_in_bytes = 0.0 + size_in_bytes_max78000 = 0.0 ## this keeps track of antipodal layers as 2b, bad hack + + ## info flag that tells if there are any antipodal layers in the network, + ## this triggers viewing size_in_bytes_max78000 rather than size_in_bytes + ## bad hack, needs to change at some point + antipodal = False + + for layer in layers: + ### Burak: handle antipodal layers + ### Burak: implicit assumption -> all networks have bias + if(layer['weight_bits'][0]==2): + # antipodal 2-bit, count these as 1-bit + if(len(np.unique(layer['weight'])) == 2): + size_in_bytes += (layer['weight_bits'][0]/(2.0*8.0))*layer['weight'].size + (layer['bias_bits'][0]/8.0)*layer['bias'].size + size_in_bytes_max78000 += (layer['weight_bits'][0]/8.0)*layer['weight'].size + (layer['bias_bits'][0]/8.0)*layer['bias'].size + antipodal = True + continue + + newsize = (layer['weight_bits'][0]/8.0)*layer['weight'].size + (layer['bias_bits'][0]/8.0)*layer['bias'].size + size_in_bytes += newsize + size_in_bytes_max78000 += newsize; + + checkpoint_sizes_in_bytes.append(size_in_bytes) + checkpoint_sizes_in_bytes_max78000.append(size_in_bytes_max78000) + checkpoint_sizes_antipodal.append(antipodal) + + return checkpoint_best_top1s, checkpoint_sizes_in_bytes, checkpoint_sizes_in_bytes_max78000, checkpoint_sizes_antipodal + +def main(): + + cp_full_paths, cp_name_list = get_checkpoints_except_reproduce() + print('') + print('Found checkpoints (except reproduce checkpoints) at these locations:') + for cp_path in cp_full_paths: + print(cp_path) + + print('') + print('Gathering hardware-mode top-1 accuracy and size info from each checkpoint') + cp_best_top1s, cp_sizes_in_bytes, cp_sizes_in_bytes_max78000, cp_sizes_antipodal = get_checkpoint_top1s_sizes(cp_full_paths, cp_name_list) + + print("") + print('Leaderboard') + print('--------------------------------') + for i, cp in enumerate(cp_name_list): + print("Name : ", cp) + if(cp_best_top1s[i] is not None): + print("Top-1 accuracy: ", np.round(100*cp_best_top1s[i])/100) + if(cp_sizes_antipodal[i]): + print("Size (KBytes) : ", cp_sizes_in_bytes_max78000[i]/1000.0, ', but has "-1/+1 only" 2b layers, so this would be:', cp_sizes_in_bytes[i]/1000.0, 'KBytes on MAX78002') + else: + print("Size (KBytes) : ", cp_sizes_in_bytes[i]/1000.0) + else: + print("Top-1 accuracy: ", cp_best_top1s[i]) + if(cp_sizes_antipodal[i]): + print("Size (KBytes) : ", cp_sizes_in_bytes_max78000[i], ', MARK: has some antipodal 2b layers') + else: + print("Size (KBytes) : ", cp_sizes_in_bytes[i]) + print("") + +if __name__ == '__main__': + main() diff --git a/tools/generate_results_graph.py b/tools/generate_results_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..81584ba96401a41569ad013d388f0cf2425ca128 --- /dev/null +++ b/tools/generate_results_graph.py @@ -0,0 +1,86 @@ +########################################################################### +# Computer vision - Binary neural networks demo software by HyperbeeAI. # +# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. main@shallow.ai # +########################################################################### +import torch, matplotlib, os, sys, argparse +sys.path.append('..') + +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +matplotlib.use('Agg') + +from datetime import date + +# bizden +from generate_leaderboard import get_checkpoints_except_reproduce, get_checkpoint_top1s_sizes + +def main(): + cp_full_paths, cp_name_list = get_checkpoints_except_reproduce() + print('') + print('Found checkpoints (except reproduce checkpoints) at these locations:') + for cp_path in cp_full_paths: + print(cp_path) + + print('') + print('Gathering hardware-mode top-1 accuracy and size info from each checkpoint') + cp_best_top1s, cp_sizes_in_bytes, cp_sizes_in_bytes_max78000, cp_sizes_antipodal = get_checkpoint_top1s_sizes(cp_full_paths, cp_name_list) + + print('') + print('Generating results graph under documentation, with timestamp') + + ############################################### + ### Hardcoded + fig, ax = plt.subplots(figsize=(9, 5)) + ax.set_xlim((80,400)) + ax.set_ylim((53,68.0)) + ax.grid(True) + ax.set_axisbelow(True) + ax.set_xlabel('Size [KBytes]', fontsize=15) + ax.set_ylabel('Validation set accuracy [%]', fontsize=15) + ############################################### + + color_maxim = np.asarray([30,30,255])/256 + color_shallow = np.asarray([255,30,30])/256 + for i, name in enumerate(cp_name_list): + if('maxim' in name): + color = color_maxim + annot = 'm'+name[5:8] + elif('shallow' in name): + color = color_shallow + annot = 's'+name[7:10] + else: + print('') + print('whose model is this?! ->', name) + print('exiting') + print('') + sys.exit() + + + if(cp_sizes_antipodal[i]): + ax.scatter(cp_sizes_in_bytes[i]/1000.0, cp_best_top1s[i], color = color, s = 70, linestyle='None', alpha=0.2) + ax.scatter(cp_sizes_in_bytes_max78000[i]/1000.0, cp_best_top1s[i], color = color, s = 70, linestyle='None', alpha=0.8) + ax.plot([cp_sizes_in_bytes[i]/1000.0, cp_sizes_in_bytes_max78000[i]/1000.0], [cp_best_top1s[i], cp_best_top1s[i]], color = color, linestyle='dashed') + else: + ax.scatter(cp_sizes_in_bytes[i]/1000.0, cp_best_top1s[i], color = color, s = 70, linestyle='None', alpha=0.8) + + #annot_position_x = cp_sizes_in_bytes[i]/1000.0-10 + #annot_position_y = cp_best_top1s[i]+0.6 + #ax.text(annot_position_x, annot_position_y, annot, fontsize=11, color=color) + + custom_lines = [Line2D([0], [0], color=color_maxim, lw=4), + Line2D([0], [0], color=color_shallow, lw=4)] + ax.legend(custom_lines, ['maxim', 'shallow'], loc='upper left', fontsize=12) + plt.title('Models for CIFAR-100', fontsize=15) + + today = date.today() + dd = today.strftime("%Y-%m-%d") + graph_path = 'documentation/'+dd+'-results-graph.png' + plt.savefig(graph_path) + + print('') + print('Saved graph under', graph_path) + print('') + +if __name__ == '__main__': + main() diff --git a/tools/readme b/tools/readme new file mode 100644 index 0000000000000000000000000000000000000000..b74bfdbf2c629d56f3877b3505ddbf42b5869933 --- /dev/null +++ b/tools/readme @@ -0,0 +1 @@ +these are python3-runnable tools, but from the top folder \ No newline at end of file