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Refactor to reduce code duplication

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Rémi Bédard-Couture 2024-05-07 21:15:57 -04:00
parent 408f295416
commit 6c0abe4833
1 changed files with 75 additions and 110 deletions
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185
frigate/detectors/plugins/tensorrt.py
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@ -89,43 +89,50 @@ class TensorRtDetector(DetectionApi):
with open(model_path, "rb") as f, trt.Runtime(self.trt_logger) as runtime: with open(model_path, "rb") as f, trt.Runtime(self.trt_logger) as runtime:
return runtime.deserialize_cuda_engine(f.read()) return runtime.deserialize_cuda_engine(f.read())
def _binding_is_input(self, binding):
if TRT_VERSION < 10:
assert self.engine.binding_is_input(binding)
else:
assert binding == "input"
return True
def _get_binding_dims(self, binding):
if TRT_VERSION < 10:
return self.engine.get_binding_shape(binding)
else:
return self.engine.get_tensor_shape(binding)
def _get_binding_dtype(self, binding):
if TRT_VERSION < 10:
return self.engine.get_binding_dtype(binding)
else:
return self.engine.get_tensor_shape(binding)
def _execute(self):
if TRT_VERSION < 10:
return self.context.execute_async_v2(bindings=self.bindings, stream_handle=self.stream)
else:
return self.context.execute_v2(self.bindings)
def _get_input_shape(self): def _get_input_shape(self):
"""Get input shape of the TensorRT YOLO engine.""" """Get input shape of the TensorRT YOLO engine."""
binding = self.engine[0] binding = self.engine[0]
if TRT_VERSION < 10: assert self._binding_is_input(binding)
assert self.engine.binding_is_input(binding) binding_dims = self._get_binding_dims(binding)
binding_dims = self.engine.get_binding_shape(binding) if len(binding_dims) == 4:
if len(binding_dims) == 4: return (
return ( tuple(binding_dims[2:]),
tuple(binding_dims[2:]), trt.nptype(self._get_binding_dtype(binding)),
trt.nptype(self.engine.get_binding_dtype(binding)), )
) elif len(binding_dims) == 3:
elif len(binding_dims) == 3: return (
return ( tuple(binding_dims[1:]),
tuple(binding_dims[1:]), trt.nptype(self._get_binding_dtype(binding)),
trt.nptype(self.engine.get_binding_dtype(binding)), )
)
else:
raise ValueError(
"bad dims of binding %s: %s" % (binding, str(binding_dims))
)
else: else:
assert binding == "input" raise ValueError(
binding_dims = self.engine.get_tensor_shape("input") "bad dims of binding %s: %s" % (binding, str(binding_dims))
if len(binding_dims) == 4: )
return (
tuple(binding_dims[2:]),
trt.nptype(self.engine.get_tensor_dtype(binding)),
)
elif len(binding_dims) == 3:
return (
tuple(binding_dims[1:]),
trt.nptype(self.engine.get_tensor_dtype(binding)),
)
else:
raise ValueError(
"bad dims of binding %s: %s" % (binding, str(binding_dims))
)
def _allocate_buffers(self): def _allocate_buffers(self):
"""Allocates all host/device in/out buffers required for an engine.""" """Allocates all host/device in/out buffers required for an engine."""
@ -134,75 +141,41 @@ class TensorRtDetector(DetectionApi):
bindings = [] bindings = []
output_idx = 0 output_idx = 0
for binding in self.engine: for binding in self.engine:
if TRT_VERSION < 10: binding_dims = self._get_binding_dims(binding)
binding_dims = self.engine.get_binding_shape(binding) if len(binding_dims) == 4:
if len(binding_dims) == 4: # explicit batch case (TensorRT 7+)
# explicit batch case (TensorRT 7+) size = trt.volume(binding_dims)
size = trt.volume(binding_dims) elif len(binding_dims) == 3:
elif len(binding_dims) == 3: # implicit batch case (TensorRT 6 or older)
# implicit batch case (TensorRT 6 or older) size = trt.volume(binding_dims) * self.engine.max_batch_size
size = trt.volume(binding_dims) * self.engine.max_batch_size
else:
raise ValueError(
"bad dims of binding %s: %s" % (binding, str(binding_dims))
)
nbytes = size * self.engine.get_binding_dtype(binding).itemsize
# Allocate host and device buffers
err, host_mem = cuda.cuMemHostAlloc(
nbytes, Flags=cuda.CU_MEMHOSTALLOC_DEVICEMAP
)
assert err is cuda.CUresult.CUDA_SUCCESS, f"cuMemAllocHost returned {err}"
logger.debug(
f"Allocated Tensor Binding {binding} Memory {nbytes} Bytes ({size} * {self.engine.get_binding_dtype(binding)})"
)
err, device_mem = cuda.cuMemAlloc(nbytes)
assert err is cuda.CUresult.CUDA_SUCCESS, f"cuMemAlloc returned {err}"
# Append the device buffer to device bindings.
bindings.append(int(device_mem))
# Append to the appropriate list.
if self.engine.binding_is_input(binding):
logger.debug(f"Input has Shape {binding_dims}")
inputs.append(HostDeviceMem(host_mem, device_mem, nbytes, size))
else:
# each grid has 3 anchors, each anchor generates a detection
# output of 7 float32 values
assert size % 7 == 0, f"output size was {size}"
logger.debug(f"Output has Shape {binding_dims}")
outputs.append(HostDeviceMem(host_mem, device_mem, nbytes, size))
output_idx += 1
else: else:
binding_dims = self.engine.get_tensor_shape(binding) raise ValueError(
if len(binding_dims) == 4: "bad dims of binding %s: %s" % (binding, str(binding_dims))
# explicit batch case (TensorRT 7+)
size = trt.volume(binding_dims)
else:
raise ValueError(
"bad dims of binding %s: %s" % (binding, str(binding_dims))
)
nbytes = size * self.engine.get_tensor_dtype(binding).itemsize
# Allocate host and device buffers
err, host_mem = cuda.cuMemHostAlloc(
nbytes, Flags=cuda.CU_MEMHOSTALLOC_DEVICEMAP
) )
assert err is cuda.CUresult.CUDA_SUCCESS, f"cuMemAllocHost returned {err}" nbytes = size * self._get_binding_dtype(binding).itemsize
logger.debug( # Allocate host and device buffers
f"Allocated Tensor Binding {binding} Memory {nbytes} Bytes ({size} * {self.engine.get_tensor_dtype(binding)})" err, host_mem = cuda.cuMemHostAlloc(
) nbytes, Flags=cuda.CU_MEMHOSTALLOC_DEVICEMAP
err, device_mem = cuda.cuMemAlloc(nbytes) )
assert err is cuda.CUresult.CUDA_SUCCESS, f"cuMemAlloc returned {err}" assert err is cuda.CUresult.CUDA_SUCCESS, f"cuMemAllocHost returned {err}"
# Append the device buffer to device bindings. logger.debug(
bindings.append(int(device_mem)) f"Allocated Tensor Binding {binding} Memory {nbytes} Bytes ({size} * {self._get_binding_dtype(binding)})"
# Append to the appropriate list. )
if binding == "input": err, device_mem = cuda.cuMemAlloc(nbytes)
logger.debug(f"Input has Shape {binding_dims}") assert err is cuda.CUresult.CUDA_SUCCESS, f"cuMemAlloc returned {err}"
inputs.append(HostDeviceMem(host_mem, device_mem, nbytes, size)) # Append the device buffer to device bindings.
else: bindings.append(int(device_mem))
# each grid has 3 anchors, each anchor generates a detection # Append to the appropriate list.
# output of 7 float32 values if self._binding_is_input(binding):
assert size % 7 == 0, f"output size was {size}" logger.debug(f"Input has Shape {binding_dims}")
logger.debug(f"Output has Shape {binding_dims}") inputs.append(HostDeviceMem(host_mem, device_mem, nbytes, size))
outputs.append(HostDeviceMem(host_mem, device_mem, nbytes, size)) else:
output_idx += 1 # each grid has 3 anchors, each anchor generates a detection
# output of 7 float32 values
assert size % 7 == 0, f"output size was {size}"
logger.debug(f"Output has Shape {binding_dims}")
outputs.append(HostDeviceMem(host_mem, device_mem, nbytes, size))
output_idx += 1
assert len(inputs) == 1, f"inputs len was {len(inputs)}" assert len(inputs) == 1, f"inputs len was {len(inputs)}"
assert len(outputs) == 1, f"output len was {len(outputs)}" assert len(outputs) == 1, f"output len was {len(outputs)}"
return inputs, outputs, bindings return inputs, outputs, bindings
@ -223,16 +196,8 @@ class TensorRtDetector(DetectionApi):
] ]
# Run inference. # Run inference.
if TRT_VERSION < 10: if not self._execute():
if not self.context.execute_async_v2( logger.warn("Execute returned false")
bindings=self.bindings, stream_handle=self.stream
):
logger.warn("Execute returned false")
else:
if not self.context.execute_v2(
self.bindings
):
logger.warn("Execute returned false")
# Transfer predictions back from the GPU. # Transfer predictions back from the GPU.
[ [