Specifications:
- AGX Xavier
- Jetpack 4.6
- TensorRT 8.0.1
- CUDA10.2
- cuDNN 8.2.1
I am basically running a script that runs a face detection model, then runs a tracker, then runs a classifier model on the detected faces. I am facing an issue where:
- When I load the face detection model on its own, and then load the face classifier model and run some tests, I have a stray -1.875 and -1. in my model. My code:
#load in the models
stream = cuda.Stream()
TRT_LOGGER = trt.Logger()
explicit_batch = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
#load in the face detector tensorrt model
with open("weights/yolov5s-face-448x800.trt", "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
fd_engine = runtime.deserialize_cuda_engine(f.read())
for binding in fd_engine:
if fd_engine.binding_is_input(binding):
fd_device_input = cuda.mem_alloc(trt.volume(fd_engine.get_binding_shape(binding)) * fd_engine.max_batch_size * np.dtype(np.float32).itemsize)
else:
fd_host_output = cuda.pagelocked_empty(trt.volume(fd_engine.get_binding_shape(binding)) * fd_engine.max_batch_size, dtype=np.float32)
fd_device_output = cuda.mem_alloc(fd_host_output.nbytes)
fd_context = fd_engine.create_execution_context()
#load in the face classifier tensorrt model
with open("weights/resnet34_fc_fp16.engine", "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
fc_engine = runtime.deserialize_cuda_engine(f.read())
for binding in fc_engine:
if fc_engine.binding_is_input(binding):
fc_device_input = cuda.mem_alloc(abs(trt.volume(fc_engine.get_binding_shape(binding))) * fc_engine.max_batch_size * np.dtype(np.float32).itemsize)
else:
fc_host_output = cuda.pagelocked_empty(abs(trt.volume(fc_engine.get_binding_shape(binding))) * fc_engine.max_batch_size, dtype=np.float32)
fc_device_output = cuda.mem_alloc(fc_host_output.nbytes)
fc_context = fc_engine.create_execution_context()
#Test face classifier
test_batchsize = 3
fc_context.set_binding_shape(0, (test_batchsize, 3, 224, 224))
batch_list = []
for j in range(test_batchsize):
img = Image.open("test_materials/turkish_coffee.jpg")
img = img.resize((224, 224))
if img.mode == "RGBA":
img = img.convert("RGB")
convert_to_tensor = transforms.ToTensor()
normalize = transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
batch_list.append(normalize(convert_to_tensor(img)))
input = torch.stack(batch_list)
fc_host_input = np.array(input.numpy(), dtype=np.float32, order='C')
cuda.memcpy_htod_async(fc_device_input, fc_host_input, stream)
fc_context.execute_async_v2(bindings=[int(fc_device_input), int(fc_device_output)], stream_handle=stream.handle)
cuda.memcpy_dtoh_async(fc_host_output, fc_device_output, stream)
stream.synchronize()
print(fc_host_output)
I get this result:
As can be seen the -1.875 and -1 are strays.
But suppose that I remove the loading of the face detector tensorrt model:
#load in the models
stream = cuda.Stream()
TRT_LOGGER = trt.Logger()
explicit_batch = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
#load in the face classifier tensorrt model
with open("weights/resnet34_fc_fp16.engine", "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
fc_engine = runtime.deserialize_cuda_engine(f.read())
for binding in fc_engine:
if fc_engine.binding_is_input(binding):
fc_device_input = cuda.mem_alloc(abs(trt.volume(fc_engine.get_binding_shape(binding))) * fc_engine.max_batch_size * np.dtype(np.float32).itemsize)
else:
fc_host_output = cuda.pagelocked_empty(abs(trt.volume(fc_engine.get_binding_shape(binding))) * fc_engine.max_batch_size, dtype=np.float32)
fc_device_output = cuda.mem_alloc(fc_host_output.nbytes)
fc_context = fc_engine.create_execution_context()
#Test face classifier
test_batchsize = 3
fc_context.set_binding_shape(0, (test_batchsize, 3, 224, 224))
batch_list = []
for j in range(test_batchsize):
img = Image.open("test_materials/turkish_coffee.jpg")
img = img.resize((224, 224))
if img.mode == "RGBA":
img = img.convert("RGB")
convert_to_tensor = transforms.ToTensor()
normalize = transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
batch_list.append(normalize(convert_to_tensor(img)))
input = torch.stack(batch_list)
fc_host_input = np.array(input.numpy(), dtype=np.float32, order='C')
cuda.memcpy_htod_async(fc_device_input, fc_host_input, stream)
fc_context.execute_async_v2(bindings=[int(fc_device_input), int(fc_device_output)], stream_handle=stream.handle)
cuda.memcpy_dtoh_async(fc_host_output, fc_device_output, stream)
stream.synchronize()
print(fc_host_output)
I instead get this:
Could someone advise why this is happening?
