Default FP16 TensorRT export

detect.py

@@ -97,6 +97,10 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     half &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16 supported on limited backends with CUDA
     if pt or jit:
         model.model.half() if half else model.model.float()
+    elif engine and model.trt_fp16_input != half:
+        LOGGER.info('model ' + (
+            'requires' if model.trt_fp16_input else 'incompatible with') + ' --half. Adjusting automatically.')
+        half = model.trt_fp16_input
 
     # Dataloader
     if webcam:

export.py

@@ -233,9 +233,8 @@ def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=F
         for out in outputs:
             LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
 
-        half &= builder.platform_has_fast_fp16
-        LOGGER.info(f'{prefix} building FP{16 if half else 32} engine in {f}')
-        if half:
+        LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 else 32} engine in {f}')
+        if builder.platform_has_fast_fp16:
             config.set_flag(trt.BuilderFlag.FP16)
         with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
             t.write(engine.serialize())

models/common.py

@@ -338,6 +338,7 @@ def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):
             import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
             check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
             Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
+            trt_fp16_input = False
             logger = trt.Logger(trt.Logger.INFO)
             with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
                 model = runtime.deserialize_cuda_engine(f.read())
@@ -348,6 +349,8 @@ def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):
                 shape = tuple(model.get_binding_shape(index))
                 data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device)
                 bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr()))
+                if model.binding_is_input(index) and dtype == np.float16:
+                    trt_fp16_input = True
             binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
             context = model.create_execution_context()
             batch_size = bindings['images'].shape[0]

val.py

@@ -144,6 +144,10 @@ def run(data,
             model.model.half() if half else model.model.float()
         elif engine:
             batch_size = model.batch_size
+            if model.trt_fp16_input != half:
+                LOGGER.info('model ' + (
+                    'requires' if model.trt_fp16_input else 'incompatible with') + ' --half. Adjusting automatically.')
+                half = model.trt_fp16_input
         else:
             half = False
             batch_size = 1  # export.py models default to batch-size 1