chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
@@ -0,0 +1,22 @@
+## Scripts for TRT Deployment
+For both baseline and QAT, change:
+- `RESNET_DEPTH` for 50 or 101,
+- `RESNET_VERSION` for v1 or v2,
+- `BS` for which batch sizes you wish to evaluate the engine on.
+
+#### Baseline
+```
+./scripts/deploy_engine_baseline.sh
+```
+> Change `ROOT_DIR` to where your ONNX file is.
+
+#### QAT
+```
+./scripts/deploy_engine_qat.sh
+```
+> Change `QAT_SUBDIR` and `ROOT_DIR` to where your ONNX file is.
+
+### Only accuracy
+```
+./scripts/infer_engine.sh
+```
@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+
+# Single run:
+# ../../engine_builder/build_engine_single.py --root_dir=/home/nvidia/PycharmProjects/tensorflow-quantization/examples/resnet/weights/resnet50v1 --onnx=model_baseline_dynamic.onnx --engine=model_baseline_dynamic.engine --input=224,224,3 --min_bs=1 --max_bs=1 --opt_bs=1 --precision=fp32
+#
+
+RESNET_DEPTH=50
+RESNET_VERSION=v1
+ROOT_DIR=../weights/resnet${RESNET_DEPTH}${RESNET_VERSION}
+LOGS_SUBDIR=baseline_engines_trtSource
+LOGS_DIR=${ROOT_DIR}/${LOGS_SUBDIR}
+mkdir $LOGS_DIR
+
+echo "1/3. Building engine"
+# bs=32 OOM in workstation
+ONNX=model_baseline_dynamic.onnx
+ENGINE=${LOGS_SUBDIR}/model_baseline_dynamic_bs{min1,opt8,max16}.engine
+python ../../../engine_builder/build_engine_single.py --root_dir=$ROOT_DIR \
+                                                      --onnx=$ONNX \
+                                                      --engine=$ENGINE \
+                                                      --input=224,224,3 \
+                                                      --min_bs=1 --opt_bs=8 --max_bs=16 \
+                                                      --precision=fp32
+wait
+
+for BS in 8 16;  do # 8 32 128; do
+  echo "Model evaluation..."
+  echo "############### bs=${BS} ###############"
+
+  # Latency calculation from built engine
+  echo "2/3. Latency evaluation"
+  trtexec --device=0 \
+          --loadEngine=${ROOT_DIR}/${ENGINE} \
+          --shapes=input_1:0:${BS}x224x224x3 \
+          --workspace=2048 \
+          --separateProfileRun \
+          --dumpProfile \
+          --explicitBatch &> ${LOGS_DIR}/trtexec_latency_bs${BS}.log
+  wait
+
+  echo "3/3. Accuracy evaluation"
+  python ../infer_engine.py --engine=${ROOT_DIR}/${ENGINE} \
+                            --log_file=engine_accuracy_bs${BS}.log \
+                            --model_name=resnet_$RESNET_VERSION \
+                            -b=$BS
+  wait
+done
@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+
+# Single run:
+# ../../engine_builder/build_engine_single.py --root_dir=/home/nvidia/PycharmProjects/tensorflow-quantization/examples/resnet/weights/resnet50v1 --onnx=model_baseline_dynamic.onnx --engine=model_baseline_dynamic.engine --input=224,224,3 --min_bs=1 --max_bs=1 --opt_bs=1 --precision=fp32
+#
+
+RESNET_DEPTH=50
+RESNET_VERSION=v1
+QAT_SUBDIR=qat_tfrecord_ep10_steps500_l2False_baselr0.0001_piecewise_sgd_bs128
+ROOT_DIR=../weights/resnet${RESNET_DEPTH}${RESNET_VERSION}/${QAT_SUBDIR}
+LOGS_SUBDIR=engines_trtSource
+LOGS_DIR=${ROOT_DIR}/${LOGS_SUBDIR}
+mkdir $LOGS_DIR
+
+echo "1/3. Building engine"
+# bs=32 OOM in workstation
+ONNX=model_dynamic.onnx
+ENGINE=${LOGS_SUBDIR}/model_baseline_bs{min1,opt8,max128}.engine
+python ../../../engine_builder/build_engine_single.py --root_dir=$ROOT_DIR \
+                                                      --onnx=$ONNX \
+                                                      --engine=$ENGINE \
+                                                      --input=224,224,3 \
+                                                      --min_bs=1 --opt_bs=8 --max_bs=128 \
+                                                      --precision=int8
+wait
+
+for BS in 1 8 128;  do # 8 32 128; do
+  echo "Model evaluation..."
+  echo "############### bs=${BS} ###############"
+
+  # Latency calculation from built engine
+  echo "2/3. Latency evaluation"
+  trtexec --device=0 \
+          --loadEngine=${ROOT_DIR}/${ENGINE} \
+          --shapes=input_1:0:${BS}x224x224x3 \
+          --workspace=1024 \
+          --separateProfileRun \
+          --dumpProfile \
+          --explicitBatch \
+          --int8 &> ${LOGS_DIR}/trtexec_latency_bs${BS}.log
+  wait
+
+  echo "3/3. Accuracy evaluation"
+  python ../infer_engine.py --engine=${ROOT_DIR}/${ENGINE} \
+                            --log_file=engine_accuracy_bs${BS}.log \
+                            --model_name=resnet_$RESNET_VERSION \
+                            -b=$BS
+  wait
+done
@@ -0,0 +1,17 @@
+ROOT_DIR="/home/nvidia/PycharmProjects/tensorrt_qat/examples/resnet/"
+
+RESNET_DEPTH="50"
+RESNET_VERSION="v1"
+MODEL_TYPE="baseline"  # "qat"
+PRECISION="fp32"  # "int8"
+
+ENGINES_DIR="engines_gtc_trt8.4_gittrt/${MODEL_TYPE}"
+LOGS_DIR="logs_gtc_trt8.4_gittrt/${MODEL_TYPE}"
+
+for BS in 1; do
+  SUBDIR="resnet${RESNET_DEPTH}${RESNET_VERSION}_${PRECISION}_${BS}_sparsity_disable_DLA_disabled"
+
+  python ../infer_engine.py --engine=${ROOT_DIR}/${ENGINES_DIR}/${SUBDIR}.plan \
+                            --log_file=${ROOT_DIR}/${LOGS_DIR}/${SUBDIR}_accuracy.log \
+                            --model_name=resnet_$RESNET_VERSION -b=1
+done