Check e2e tests

.dockerignore

@@ -15,4 +15,6 @@ __pycache__
 .pytest_cache
 .git
 *.md
-scripts
+scripts/*
+!scripts/jetson/
+!scripts/jetson/generate_int8_cache.py

.woodpecker/02-build-push.yml

@@ -36,15 +36,25 @@ steps:
         from_secret: registry_token
     commands:
       - echo "$REGISTRY_TOKEN" | docker login "$REGISTRY_HOST" -u "$REGISTRY_USER" --password-stdin
-      - export TAG=${CI_COMMIT_BRANCH}-${TAG_SUFFIX}
-      - export BUILD_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
       - |
+        RAW_BRANCH="$CI_COMMIT_BRANCH"
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="$WOODPECKER_COMMIT_BRANCH"; fi
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="$CI_COMMIT_REF"; fi
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="$WOODPECKER_COMMIT_REF"; fi
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="manual"; fi
+        SAFE_BRANCH=$(printf '%s' "$RAW_BRANCH" | sed 's#^refs/heads/##; s#^refs/tags/##; s#[^A-Za-z0-9_.-]#-#g; s#^-*##; s#-*$##')
+        if [ -z "$SAFE_BRANCH" ]; then SAFE_BRANCH="manual"; fi
+        TAG="$SAFE_BRANCH-$TAG_SUFFIX"
+        BUILD_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
+        echo "RAW_BRANCH=$RAW_BRANCH"
+        echo "SAFE_BRANCH=$SAFE_BRANCH"
+        echo "TAG=$TAG"
         docker build -f ${DOCKERFILE} \
           --build-arg CI_COMMIT_SHA=$CI_COMMIT_SHA \
           --label org.opencontainers.image.revision=$CI_COMMIT_SHA \
           --label org.opencontainers.image.created=$BUILD_DATE \
           --label org.opencontainers.image.source=$CI_REPO_URL \
           -t $REGISTRY_HOST/azaion/detections:$TAG .
-      - docker push $REGISTRY_HOST/azaion/detections:$TAG
+        docker push $REGISTRY_HOST/azaion/detections:$TAG
     volumes:
       - /var/run/docker.sock:/var/run/docker.sock

.woodpecker/build-push-jetson-trt104.yml

@@ -0,0 +1,41 @@
+when:
+  - event: [manual]
+    evaluate: 'BUILD_JETSON_TRT104 == "1"'
+
+labels:
+  platform: arm64
+
+steps:
+  - name: build-push-jetson-trt104
+    image: docker
+    environment:
+      REGISTRY_HOST:
+        from_secret: registry_host
+      REGISTRY_USER:
+        from_secret: registry_user
+      REGISTRY_TOKEN:
+        from_secret: registry_token
+    commands:
+      - echo "$REGISTRY_TOKEN" | docker login "$REGISTRY_HOST" -u "$REGISTRY_USER" --password-stdin
+      - |
+        RAW_BRANCH="$CI_COMMIT_BRANCH"
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="$WOODPECKER_COMMIT_BRANCH"; fi
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="$CI_COMMIT_REF"; fi
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="$WOODPECKER_COMMIT_REF"; fi
+        if [ -z "$RAW_BRANCH" ]; then RAW_BRANCH="manual"; fi
+        SAFE_BRANCH=$(printf '%s' "$RAW_BRANCH" | sed 's#^refs/heads/##; s#^refs/tags/##; s#[^A-Za-z0-9_.-]#-#g; s#^-*##; s#-*$##')
+        if [ -z "$SAFE_BRANCH" ]; then SAFE_BRANCH="manual"; fi
+        TAG="$SAFE_BRANCH-arm-trt104"
+        BUILD_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
+        echo "RAW_BRANCH=$RAW_BRANCH"
+        echo "SAFE_BRANCH=$SAFE_BRANCH"
+        echo "TAG=$TAG"
+        docker build -f Dockerfile.jetson.trt104 \
+          --build-arg CI_COMMIT_SHA=$CI_COMMIT_SHA \
+          --label org.opencontainers.image.revision=$CI_COMMIT_SHA \
+          --label org.opencontainers.image.created=$BUILD_DATE \
+          --label org.opencontainers.image.source=$CI_REPO_URL \
+          -t $REGISTRY_HOST/azaion/detections:$TAG .
+        docker push $REGISTRY_HOST/azaion/detections:$TAG
+    volumes:
+      - /var/run/docker.sock:/var/run/docker.sock

.woodpecker/e2e-convert-jetson.yml

@@ -34,7 +34,11 @@ steps:
           echo "ERROR: fixtures/models/azaion.onnx is missing; cannot convert Jetson engine"
           exit 1
         fi
-        ls -lh fixtures/image_small.jpg fixtures/models/azaion.onnx
+        if [ ! -f fixtures/models/azaion.int8_calib.cache ]; then
+          echo "ERROR: fixtures/models/azaion.int8_calib.cache is missing; cannot verify INT8 Jetson conversion"
+          exit 1
+        fi
+        ls -lh fixtures/image_small.jpg fixtures/models/azaion.onnx fixtures/models/azaion.int8_calib.cache
 
         E2E_PROFILE=jetson \
         E2E_WAIT_FOR_ENGINE_ENABLED=1 \
@@ -42,12 +46,16 @@ steps:
         E2E_LOG_TAIL=300 \
           bash run_test.sh tests/test_health_engine.py::TestHealthEngineStep03Warmed -rs
 
+        if [ ! -f results/report.csv ]; then
+          echo "ERROR: results/report.csv is missing; cannot verify Jetson conversion warm-up result"
+          exit 1
+        fi
         if grep -qi 'skipped' results/report.csv; then
           echo "ERROR: Jetson conversion warm-up tests were skipped; no engine was produced"
           cat results/report.csv
           exit 1
         fi
 
-        bash scripts/publish_jetson_engine.sh
+        REQUIRE_INT8_ENGINE=1 bash scripts/publish_jetson_engine.sh
     volumes:
       - /var/run/docker.sock:/var/run/docker.sock

Dockerfile.jetson

@@ -1,4 +1,4 @@
-FROM nvcr.io/nvidia/l4t-jetpack:r36.2.0
+FROM nvcr.io/nvidia/l4t-jetpack:r36.4.0
 ARG CI_COMMIT_SHA=unknown
 ENV AZAION_REVISION=$CI_COMMIT_SHA
 

Dockerfile.jetson.trt104

@@ -0,0 +1,21 @@
+FROM nvcr.io/nvidia/tensorrt:24.09-py3-igpu
+ARG CI_COMMIT_SHA=unknown
+ENV AZAION_REVISION=$CI_COMMIT_SHA
+
+RUN apt-get update && apt-get install -y \
+    python3 python3-pip python3-dev gcc \
+    libgl1 libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY requirements.txt requirements-jetson.txt ./
+RUN pip3 install --no-cache-dir -r requirements-jetson.txt
+COPY . .
+RUN BUILD_TENSORRT_EXTENSIONS=1 python3 setup.py build_ext --inplace
+ENV PYTHONPATH=/app/src
+RUN adduser --disabled-password --no-create-home --gecos "" appuser \
+    && mkdir -p /app/Logs \
+    && chown -R appuser /app
+USER appuser
+EXPOSE 8080
+CMD ["python3", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]

_docs/02_tasks/done/AZ-180_jetson_orin_nano_support.md

@@ -2,7 +2,7 @@
 
 **Task**: AZ-180_jetson_orin_nano_support
 **Name**: Jetson Orin Nano Support
-**Description**: Run the detection service on NVIDIA Jetson Orin Nano with a JetPack 6.x container image, INT8 engine conversion using a pre-generated calibration cache, and docker-compose configuration.
+**Description**: Run the detection service on NVIDIA Jetson Orin Nano with a JetPack 6.2.x-compatible container image, INT8 engine conversion using a pre-generated calibration cache, and docker-compose configuration.
 **Complexity**: 5 points
 **Dependencies**: None
 **Component**: Deployment + Inference Engine
@@ -18,7 +18,7 @@ The detection service cannot run on NVIDIA Jetson Orin Nano for two reasons:
 ## Outcome
 
 - A `Dockerfile.jetson` that builds and runs on Jetson Orin Nano (aarch64, JetPack 6.x)
-- A `requirements-jetson.txt` that installs Python dependencies without pip-installing tensorrt or pycuda
+- A `requirements-jetson.txt` that installs Python dependencies without pip-installing tensorrt
 - A `docker-compose.jetson.yml` with NVIDIA Container Runtime configuration
 - `convert_from_source()` in `tensorrt_engine.pyx` extended to accept an optional INT8 calibration cache path — if the cache is present, INT8 is used; otherwise FP16 fallback
 - `init_ai()` in `inference.pyx` extended to try downloading the calibration cache from the Loader service before starting the conversion thread
@@ -28,7 +28,7 @@ The detection service cannot run on NVIDIA Jetson Orin Nano for two reasons:
 
 ### Included
 - `Dockerfile.jetson` using a JetPack 6.x L4T base image with pre-installed TensorRT and PyCUDA
-- `requirements-jetson.txt` derived from `requirements.txt`, excluding tensorrt and pycuda
+- `requirements-jetson.txt` derived from `requirements.txt`, excluding tensorrt and installing PyCUDA via pip where the JetPack apt package is unavailable
 - `docker-compose.jetson.yml` with `runtime: nvidia`
 - `tensorrt_engine.pyx`: extend `convert_from_source(bytes onnx_model, str calib_cache_path=None)` — set `INT8` flag and load cache when path is provided; fall back to FP16 when not
 - `inference.pyx`: extend `init_ai()` to attempt download of `azaion.int8_calib.cache` from Loader before spawning the conversion thread; pass the local path to `convert_from_source()`
@@ -75,7 +75,7 @@ Then the detections service is reachable on port 8080
 ## Non-Functional Requirements
 
 **Compatibility**
-- JetPack 6.x (CUDA 12.2, TensorRT 10.x)
+- JetPack 6.2.x-compatible container (`nvcr.io/nvidia/l4t-jetpack:r36.4.0`, CUDA 12.6 / TensorRT 10.3 compute stack)
 - Jetson Orin Nano (aarch64, SM 8.7)
 
 **Reliability**
@@ -101,7 +101,7 @@ Note: AC-2, AC-5, AC-6 require physical Jetson hardware and cannot run in standa
 
 ## Constraints
 
-- TensorRT and PyCUDA must NOT be pip-installed — provided by JetPack in the base image
+- TensorRT must NOT be pip-installed — provided by JetPack in the base image. PyCUDA may be pip-installed on `l4t-jetpack:r36.4.0` because `python3-pycuda` is unavailable in the apt repositories.
 - Base image must be a JetPack 6.x L4T image — not a generic CUDA image
 - Calibration cache download failure must be non-fatal — log a warning and fall back to FP16
 - INT8 conversion and FP16 conversion produce different engine files (different filenames) so cached engines are not confused
@@ -114,7 +114,7 @@ Note: AC-2, AC-5, AC-6 require physical Jetson hardware and cannot run in standa
 
 **Risk 2: PyCUDA availability in base image**
 - *Risk*: Some L4T images do not include pycuda
-- *Mitigation*: Fall back to `apt-get install python3-pycuda` or source build with `CUDA_ROOT` set
+- *Mitigation*: Fall back to pip source build with `CUDA_ROOT` set when no `python3-pycuda` apt package is available
 
 **Risk 3: INT8 accuracy degradation**
 - *Risk*: Without a well-representative calibration dataset, mAP may drop >1 point

_docs/04_deploy/containerization.md

@@ -119,9 +119,9 @@ Already exists: `e2e/docker-compose.test.yml`. No changes needed — supports bo
 
 | Aspect | Specification |
 |--------|--------------|
-| Base image | `nvcr.io/nvidia/l4t-base:r36.3.0` (JetPack 6.x, aarch64) |
+| Base image | `nvcr.io/nvidia/l4t-jetpack:r36.4.0` (JetPack 6.2.x-compatible, aarch64) |
 | TensorRT | Pre-installed via JetPack — `python3-libnvinfer` apt package (NOT pip) |
-| PyCUDA | Pre-installed via JetPack — `python3-pycuda` apt package (NOT pip) |
+| PyCUDA | Installed via pip in `requirements-jetson.txt` because `python3-pycuda` is not available in the `l4t-jetpack:r36.4.0` apt repositories |
 | Build stages | Single stage (Cython compile requires gcc) |
 | Non-root user | `adduser --disabled-password --gecos '' appuser` + `USER appuser` |
 | Exposed ports | 8080 |
@@ -129,7 +129,7 @@ Already exists: `e2e/docker-compose.test.yml`. No changes needed — supports bo
 | Runtime | Requires NVIDIA Container Runtime (`runtime: nvidia` in docker-compose) |
 
 **Jetson-specific behaviour**:
-- `requirements-jetson.txt` derives from `requirements.txt` — `tensorrt` and `pycuda` are excluded from pip and provided by JetPack
+- `requirements-jetson.txt` derives from `requirements.txt` — `tensorrt` is excluded from pip and installed from the JetPack/L4T apt packages in `Dockerfile.jetson`; PyCUDA is installed via pip on this image line because the apt package is unavailable
 - Engine filename auto-encodes CC+SM (e.g. `azaion.cc_8.7_sm_16.engine` for Orin Nano), ensuring the Jetson engine is distinct from any x86-cached engine
 - INT8 is used when `azaion.int8_calib.cache` is available on the Loader service; precision suffix appended to engine filename (`*.int8.engine`); FP16 fallback when cache is absent
 - `docker-compose.jetson.yml` uses `runtime: nvidia` for the NVIDIA Container Runtime
@@ -139,6 +139,21 @@ Already exists: `e2e/docker-compose.test.yml`. No changes needed — supports bo
 docker compose -f docker-compose.jetson.yml up
 ```
 
+### detections-jetson-trt104 (Dockerfile.jetson.trt104)
+
+Experimental INT8 calibration image for Jetson Orin devices. It uses NVIDIA's TensorRT iGPU container:
+
+| Aspect | Specification |
+|--------|--------------|
+| Base image | `nvcr.io/nvidia/tensorrt:24.09-py3-igpu` |
+| TensorRT | TensorRT 10.4.0.26 |
+| Purpose | Work around TensorRT 10.3 INT8 calibration failures such as `checkSanity.cpp::checkLinks` / `Graph::regions` internal errors |
+
+Build/tag example:
+```bash
+docker build -f Dockerfile.jetson.trt104 -t <registry>/azaion/detections:dev-arm-trt104 .
+```
+
 ## Image Tagging Strategy
 
 | Context | Tag Format | Example |

e2e/docker-compose.test.yml

@@ -65,7 +65,7 @@ services:
       - jetson
     build:
       context: ..
-      dockerfile: Dockerfile.jetson
+      dockerfile: ${JETSON_DOCKERFILE:-Dockerfile.jetson}
     runtime: nvidia
     depends_on:
       - mock-loader

e2e/fixtures/models/azaion.int8_calib.cache

@@ -0,0 +1,524 @@
+TRT-100400-EntropyCalibration2
+images: 3c010a14
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+/model/model.2/Split_output_1: 3d73e151
+/model/model.2/m.0/cv1/conv/Conv_output_0: 3dcab8c2
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+/Slice_1_output_0: 3be038d4
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+/Reshape_6_output_0: 41273e0c
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+/Gather_10_output_0: 41273e0c
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+/model/model.23/cv3.1/cv3.1.0/cv3.1.0.1/act/Sigmoid_output_0: 3c021329
+ONNXTRT_Broadcast_108_output: 3b810a14
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+ONNXTRT_Broadcast_110_output: 3b810a14
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+/model/model.23/cv2.1/cv2.1.1/act/Mul_output_0: 3d91002d
+/model/model.23/cv3.1/cv3.1.0/cv3.1.0.1/act/Mul_output_0: 3d118ec2
+(Unnamed Layer* 457) [Constant]_output: 3e010a14
+ONNXTRT_ShapeShuffle_113_output: 3e010a14
+/model/model.23/ConstantOfShape_1_output_0: 3e010a14
+/model/model.23/cv3.2/cv3.2.2/Conv_output_0: 3e172ed8
+/Gather_11_output_0: 3be2fe01
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+/model/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv_output_0: 3d798e51
+/model/model.22/Split_output_0: 3d037465
+/model/model.22/Split_output_1: 3cb0b046
+/model/model.23/Reshape_1_output_0: 3db881cc
+/model/model.23/cv3.1/cv3.1.1/cv3.1.1.0/act/Sigmoid_output_0: 3c010a14
+ONNXTRT_ShapeShuffle_382_output: 0
+/model/model.23/dfl/Reshape_1_output_0: 3df78149
+/model/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv_output_0: 3d3888f4
+/model/model.23/Expand_2_output_0: 3f204a85
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+/model/model.23/cv3.2/cv3.2.1/cv3.2.1.1/act/Mul_output_0: 3d0fbe3e
+/Cast_output_0: 3d214c99
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+/model/model.22/m.0/cv1/conv/Conv_output_0: 3d223bd1
+/model/model.22/m.0/cv2/conv/Conv_output_0: 3d08f193
+/model/model.23/cv3.1/cv3.1.1/cv3.1.1.0/act/Mul_output_0: 3d01f178
+/model/model.23/Unsqueeze_9_output_0: 3f204a85
+/model/model.23/Unsqueeze_10_output_0: 3f204a85
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+/model/model.22/m.0/cv2/act/Sigmoid_output_0: 3c005708
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e2e/scripts/publish_jetson_engine.sh

@@ -5,7 +5,9 @@ COMPOSE="${COMPOSE:-docker compose -f docker-compose.test.yml --profile jetson}"
 REGISTRY_HOST="${REGISTRY_HOST:?REGISTRY_HOST is required}"
 ENGINE_REPOSITORY="${JETSON_ENGINE_REPOSITORY:-$REGISTRY_HOST/azaion/detections-jetson-engine}"
 BRANCH="${CI_COMMIT_BRANCH:-local}"
-ENGINE_TAG="${JETSON_ENGINE_TAG:-$(printf '%s' "$BRANCH" | tr -c 'A-Za-z0-9_.-' '-')}"
+default_tag="$(printf '%s' "$BRANCH" | sed 's#^refs/heads/##; s#^refs/tags/##; s#[^A-Za-z0-9_.-]#-#g; s#^-*##; s#-*$##')"
+default_tag="${default_tag:-local}"
+ENGINE_TAG="${JETSON_ENGINE_TAG:-$default_tag}"
 OUT_DIR="${JETSON_ENGINE_OUT_DIR:-results/jetson-engine}"
 
 mkdir -p "$OUT_DIR/models"
@@ -26,6 +28,15 @@ if [[ "$engine_count" == "0" ]]; then
   exit 1
 fi
 
+if [[ "${REQUIRE_INT8_ENGINE:-0}" == "1" ]]; then
+  int8_engine_count="$(find "$OUT_DIR/models" -maxdepth 1 -type f -name 'azaion*.int8.engine' | wc -l | tr -d ' ')"
+  if [[ "$int8_engine_count" == "0" ]]; then
+    echo "ERROR: INT8 engine is required, but no azaion*.int8.engine file was produced"
+    find "$OUT_DIR/models" -maxdepth 1 -type f -name 'azaion*.engine' -print -exec ls -lh {} \;
+    exit 1
+  fi
+fi
+
 echo "--- Converted TensorRT engine files:"
 find "$OUT_DIR/models" -maxdepth 1 -type f -name 'azaion*.engine' -print -exec ls -lh {} \;
 

e2e/scripts/pull_jetson_engine.sh

@@ -8,7 +8,9 @@ fi
 
 ENGINE_REPOSITORY="${JETSON_ENGINE_REPOSITORY:-$REGISTRY_HOST/azaion/detections-jetson-engine}"
 BRANCH="${CI_COMMIT_BRANCH:-local}"
-ENGINE_TAG="${JETSON_ENGINE_TAG:-$(printf '%s' "$BRANCH" | tr -c 'A-Za-z0-9_.-' '-')}"
+default_tag="$(printf '%s' "$BRANCH" | sed 's#^refs/heads/##; s#^refs/tags/##; s#[^A-Za-z0-9_.-]#-#g; s#^-*##; s#-*$##')"
+default_tag="${default_tag:-local}"
+ENGINE_TAG="${JETSON_ENGINE_TAG:-$default_tag}"
 TARGET_DIR="${JETSON_ENGINE_TARGET_DIR:-fixtures/models}"
 image="$ENGINE_REPOSITORY:$ENGINE_TAG"
 

e2e/tests/test_video.py

@@ -73,7 +73,7 @@ def video_events(warm_engine, http_client, auth_headers):
             "X-Channel-Id": channel_id,
             "X-Filename": "video_test01.mp4",
             "X-Config": json.dumps(
-                {"model_batch_size": 1, "frame_period_recognition": 100}
+                {"model_batch_size": 1, "frame_period_recognition": 4}
             ),
             "Content-Type": "application/octet-stream",
         },

scripts/jetson/generate_int8_cache.py

@@ -30,6 +30,13 @@ def parse_args():
     parser.add_argument("--output", default="azaion.int8_calib.cache")
     parser.add_argument("--input-size", type=int, default=1280, help="Model input H=W (default 1280)")
     parser.add_argument("--num-samples", type=int, default=500)
+    parser.add_argument("--workspace-gb", type=float, default=4.0)
+    parser.add_argument("--no-fp16", action="store_true", help="Do not enable FP16 fallback during INT8 calibration")
+    parser.add_argument(
+        "--softmax-fp32",
+        action="store_true",
+        help="Force TensorRT SoftMax layers to FP32 as a workaround for Jetson INT8 calibration failures",
+    )
     return parser.parse_args()
 
 
@@ -66,7 +73,7 @@ def main():
     if not images:
         print(f"No images found in {args.images_dir}", file=sys.stderr)
         sys.exit(1)
-    print(f"Using {len(images)} calibration images")
+    print(f"Using {len(images)} calibration images", flush=True)
 
     H = W = args.input_size
 
@@ -98,6 +105,13 @@ def main():
             print(f"Cache written → {args.output}")
 
     onnx_data = Path(args.onnx).read_bytes()
+    try:
+        from engines.onnx_tensorrt_compat import prepare_for_tensorrt
+
+        onnx_data = prepare_for_tensorrt(onnx_data)
+        print("Prepared ONNX model for TensorRT static Jetson build", flush=True)
+    except Exception as e:
+        print(f"WARNING: ONNX TensorRT compatibility preparation failed: {e}", file=sys.stderr)
     logger = trt.Logger(trt.Logger.INFO)
     explicit_batch = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
 
@@ -107,9 +121,11 @@ def main():
         trt.OnnxParser(network, logger) as parser,
         builder.create_builder_config() as config,
     ):
-        config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 4 * 1024 ** 3)
+        config.set_memory_pool_limit(
+            trt.MemoryPoolType.WORKSPACE, int(args.workspace_gb * 1024 ** 3)
+        )
         config.set_flag(trt.BuilderFlag.INT8)
-        if builder.platform_has_fast_fp16:
+        if not args.no_fp16 and builder.platform_has_fast_fp16:
             config.set_flag(trt.BuilderFlag.FP16)
 
         calibrator = _ImageCalibrator()
@@ -120,6 +136,23 @@ def main():
                 print(parser.get_error(i), file=sys.stderr)
             sys.exit(1)
 
+        if args.softmax_fp32:
+            constrained = 0
+            for i in range(network.num_layers):
+                layer = network.get_layer(i)
+                if layer.type == trt.LayerType.SOFTMAX:
+                    layer.precision = trt.float32
+                    for j in range(layer.num_outputs):
+                        layer.set_output_type(j, trt.float32)
+                    constrained += 1
+            if constrained:
+                for flag_name in ("PREFER_PRECISION_CONSTRAINTS", "OBEY_PRECISION_CONSTRAINTS"):
+                    flag = getattr(trt.BuilderFlag, flag_name, None)
+                    if flag is not None:
+                        config.set_flag(flag)
+                        break
+            print(f"Forced {constrained} SoftMax layers to FP32", flush=True)
+
         inp = network.get_input(0)
         shape = inp.shape
         C = shape[1]
@@ -128,7 +161,7 @@ def main():
             profile.set_shape(inp.name, (1, C, H, W), (1, C, H, W), (1, C, H, W))
             config.add_optimization_profile(profile)
 
-        print("Building TensorRT engine with INT8 calibration (several minutes on Jetson)…")
+        print("Building TensorRT engine with INT8 calibration (several minutes on Jetson)...", flush=True)
         plan = builder.build_serialized_network(network, config)
         if plan is None:
             print("Engine build failed", file=sys.stderr)