我正在尝试从使用 TensorRT 7.0 优化的基于 resnet50 的 CNN 中提取特征向量。
当向 trt 模型提供单个输入时，我得到了正确的输出。但是，当我向模型提供批量输入时，我只会为该批次的第一个样本获得正确的输出。剩余的输出只是零。
我还使用“builder.max_batch_size = 16”和“EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))”构建了我的 trt 引擎
如何获得批次中所有样本的正确输出？
imgs = np.ones([16,3,256,128]) # batch_size = 16
# output shape = [(16,3072)]
trt_logger = trt.Logger(trt.Logger.INFO)
def load_engine(trt_logger):
TRTbin = 'resnet50_onnx_trt/resnet50mid.model.tar-60.trt'
with open(TRTbin, 'rb') as f, trt.Runtime(trt_logger) as runtime:
return runtime.deserialize_cuda_engine(f.read())
engine = load_engine(trt_logger)
context = engine.create_execution_context()
class HostDeviceMem(object):
#Simple helper data class that's a little nicer to use than a 2-tuple.
def __init__(self, host_mem, device_mem):
self.host = host_mem
self.device = device_mem
def __str__(self):
return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
def __repr__(self):
return self.__str__()
def alloc_buf_N(engine):
"""Allocates all host/device in/out buffers required for an engine."""
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
# size = 1572864 = 16*3*256*128 for inputs
# size = 49152 = 16*3072 for outputs
dtype = trt.nptype(engine.get_binding_dtype(binding))
# dtype = # <class 'numpy.float32'> for both input and output
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype)
# host_mem = [0. 0. 0. ... 0. 0. 0.],
# host_mem.shape) = (1572864,) and (49152,) for inputs and outputs respectively
device_mem = cuda.mem_alloc(host_mem.nbytes)
# Append the device buffer to device bindings.
bindings.append(int(device_mem))
# Append to the appropriate list.
if engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
# print("inputs alloc_buf_N", inputs)
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
# print("outputs alloc_buf_N", outputs)
return inputs, outputs, bindings, stream
def do_inference_v2(engine, context, inputs, bindings, outputs, stream):
"""
Inputs and outputs are expected to be lists of HostDeviceMem objects.
"""
# Transfer input data to the GPU.
cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
context.execute_async(batch_size=16, bindings=bindings, stream_handle=stream.handle)
# Transfer predictions back from the GPU.
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
return [out.host for out in outputs]
inputs = imgs.astype(np.float32)
engine = load_engine(trt_logger)
context = engine.create_execution_context()
inputs_alloc_buf, outputs_alloc_buf, bindings_alloc_buf, stream_alloc_buf = alloc_buf_N(engine)
inputs_alloc_buf[0].host = np.ascontiguousarray(inputs)
trt_feature = do_inference_v2(engine, context, inputs_alloc_buf, bindings_alloc_buf, outputs_alloc_buf, stream_alloc_buf)
print("len(trt_feature)",len(trt_feature))
trt_feature = np.asarray(trt_feature)
trt_feature = trt_feature.reshape(16,3072)
print("trt_feature[0][0:15]",trt_feature[0][0:15])
print("trt_feature[1][0:15]",trt_feature[1][0:15])
print("trt_feature.shape",trt_feature.shape)
这给了我输出
len(trt_feature) 1
trt_feature[0][0:15] [ 0. 0. 0. 23.91 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]
trt_feature[1][0:15] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]
输出应该是长度为 16 的列表（每个维度为 3072），即“len(trt_feature)”= 16。但我得到的输出长度等于 1，即“len(trt_feature)”= 1。