Shell script for batch-running post-train quantization command line examples

examples/quantization/post_train_quant/run_all_ptq_cmdline_samples.sh

+#!/bin/bash
+
+# This script was used to generate the results shown in post-training quantization command line readme at:
+# <distiller_root>/examples/quantization/post_train_quant/command_line.md
+# Note that the readme shows only a subset of the tests run by this script, especially the 6-bits tests.
+# This scripts also runs the conversion to "native" PyTorch post-train quant feature.
+#
+# IMPORTANT: 
+#  * It is assumed that the script is run from the following directory:
+#     <distiller_root>/examples/classifier_compression
+#    Some of the paths used are relative to this directory.
+
+model="resnet50"
+dataset_path="$MACHINE_HOME/datasets/imagenet"
+stats_file="../quantization/post_train_quant/stats/${model}_quant_stats.yaml"
+out_dir="logs/${model}_ptq_pytorch_convert_latest"
+num_workers=22
+
+base_args="--arch ${model} ${dataset_path} --pretrained -j ${num_workers} --evaluate --quantize-eval --qe-stats-file ${stats_file} -o ${out_dir}"
+
+for engine in distiller pytorch; do
+  convert_flag=""
+  if [ "$engine" = pytorch ]; then
+    convert_flag="--qe-convert-pytorch"
+  fi
+  for n_bits in 8 6; do
+    for acts_mode in sym asym_u; do
+      for wts_mode in sym asym_u; do
+        for per_ch in per_tensor per_channel; do
+          per_ch_flag=""
+          if [ "$per_ch" = per_channel ]; then
+            per_ch_flag="--qe-per-channel"
+          fi
+          exp_name="acts_${acts_mode}_wts_${wts_mode}_${per_ch}_${engine}"
+          set -x
+          python compress_classifier.py ${base_args} --qe-mode-acts ${acts_mode} --qe-mode-wts ${wts_mode} --qe-bits-acts ${n_bits} --qe-bits-wts ${n_bits} ${per_ch_flag} ${convert_flag} --name ${exp_name}
+          set +x
+        done
+      done
+    done
+  done
+done