Comments (3)
Hi @1124676457 ,
thanks for reaching out. Could you post your DALI Pipeline code? Or at least some relevant part of it?
from dali_backend.
嗨@1124676457 ,
感谢您伸出援手。你能发布你的DALI管道代码吗?或者至少是其中的一些相关部分?
@dali.pipeline_def(batch_size=1, num_threads=4, device_id=0)
def pipe():
images = dali.fn.external_source(device="cpu",name="DALI_INPUT_0")
images = images / 255
images = dali.fn.decoders.image(images, device="mixed", output_type=types.RGB)
images = dali.fn.resize(images, device='gpu',resize_x=1024, resize_y=576)
images = dali.fn.reshape(images,device='gpu',
src_dims=[2,0,1],
layout="CHW")
images = dali.fn.expand_dims(images, axes=[0], new_axis_names="N")
#images = images / 255
return images
the decode code is this, the worker.frame is the nvdecode's result, shape is (1620,1920):
while True:
if worker.updated:
c += 1
time_connect = time.time()
worker.updated = False
output = triton(worker.frame)
from dali_backend.
your DALI Pipeline looks correct. It suggests, that maybe you're sending the data to the Triton in an improper way. The typical sending pattern for the pipeline you've posted is using either np.fromfile(filename, dtype=np.uint8)
or open(filename, 'rb')
function to open JPEG binary and sending it to the DALI Backend. Should you like to post the client code, I'll take a look and I can point to a problem.
Please refer to these examples that illustrate the usage pattern that is the most common with the pipeline you've presented:
dali_backend/client/dali_grpc_client.py
Line 133 in 3639beb
from dali_backend.
Related Issues (20)
- layout parameter to external_source causes assert error HOT 2
- DALI backend not releasing device memory HOT 8
- How to provide mean & stddev to dali.fn.normalize HOT 3
- Error when executing Mixed operator decoders__Image when sending image binary to dali in triton HOT 9
- Batching does not improve performance with dali HOT 10
- Can dali backend support default values or optional input? HOT 2
- Unexpected large memory needed for gpu resize HOT 4
- Error in thread 31: nvJPEG error (5): The user-provided allocator functions, for either memory allocation or for releasing the memory, returned a non-zero code. HOT 6
- Cannot compile dali_backend with older version of triton HOT 2
- how to provide batch input data for dali pipeline whicn input shapes [-1] HOT 1
- if I want to crop from different start point, how can I build pipe to do this? HOT 2
- Test issue
- Connecting InputOperator with no explicit inputs to Triton HOT 12
- Could not serialize dali.fn.python_function HOT 1
- when using crop_mirror_normalize func, Output layout "CHW" is slower than "HWC" HOT 5
- dlopen libcuda.so failed!. Please install GPU dirverTraceback (most recent call last): HOT 4
- Prefeed multiple input batches to the inference pipeline HOT 7
- Unable to load numpy module in a DALI backend HOT 3
- DALI pipeline in Triton - formatting InferInput batch of images for UINT8 HOT 3
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from dali_backend.