Detect.py supports running against a Triton container (#9228)
* update coco128-seg comments * Enables detect.py to use Triton for inference Triton Inference Server is an open source inference serving software that streamlines AI inferencing. https://github.com/triton-inference-server/server The user can now provide a "--triton-url" argument to detect.py to use a local or remote Triton server for inference. For e.g., http://localhost:8000 will use http over port 8000 and grpc://localhost:8001 will use grpc over port 8001. Note, it is not necessary to specify a weights file to use Triton. A Triton container can be created by first exporting the Yolov5 model to a Triton supported runtime. Onnx, Torchscript, TensorRT are supported by both Triton and the export.py script. The exported model can then be containerized via the OctoML CLI. See https://github.com/octoml/octo-cli#getting-started for a guide. * added triton client to requirements * fixed support for TFSavedModels in Triton * reverted change * Test CoreML update Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update ci-testing.yml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Use pathlib Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Refacto DetectMultiBackend to directly accept triton url as --weights http://... Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Deploy category Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update detect.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update common.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update common.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update predict.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update predict.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update predict.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update triton.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update triton.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add printout and requirements check * Cleanup Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * triton fixes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixed triton model query over grpc * Update check_requirements('tritonclient[all]') * group imports * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix likely remote URL bug * update comment * Update is_url() * Fix 2x download attempt on http://path/to/model.pt Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: glennjocher <glenn.jocher@ultralytics.com> Co-authored-by: Gaz Iqbal <giqbal@octoml.ai> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@@ -10,6 +10,7 @@ import warnings
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from collections import OrderedDict, namedtuple
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from copy import copy
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from pathlib import Path
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from urllib.parse import urlparse
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import cv2
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import numpy as np
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@@ -327,11 +328,13 @@ class DetectMultiBackend(nn.Module):
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super().__init__()
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w = str(weights[0] if isinstance(weights, list) else weights)
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pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = self._model_type(w) # type
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w = attempt_download(w) # download if not local
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pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
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fp16 &= pt or jit or onnx or engine # FP16
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nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
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stride = 32 # default stride
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cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
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if not (pt or triton):
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w = attempt_download(w) # download if not local
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if pt: # PyTorch
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model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
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@@ -342,7 +345,7 @@ class DetectMultiBackend(nn.Module):
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elif jit: # TorchScript
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LOGGER.info(f'Loading {w} for TorchScript inference...')
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extra_files = {'config.txt': ''} # model metadata
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model = torch.jit.load(w, _extra_files=extra_files)
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model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
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model.half() if fp16 else model.float()
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if extra_files['config.txt']: # load metadata dict
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d = json.loads(extra_files['config.txt'],
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@@ -472,6 +475,12 @@ class DetectMultiBackend(nn.Module):
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predictor = pdi.create_predictor(config)
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input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
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output_names = predictor.get_output_names()
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elif triton: # NVIDIA Triton Inference Server
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LOGGER.info(f'Using {w} as Triton Inference Server...')
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check_requirements('tritonclient[all]')
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from utils.triton import TritonRemoteModel
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model = TritonRemoteModel(url=w)
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nhwc = model.runtime.startswith("tensorflow")
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else:
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raise NotImplementedError(f'ERROR: {w} is not a supported format')
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@@ -488,6 +497,8 @@ class DetectMultiBackend(nn.Module):
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b, ch, h, w = im.shape # batch, channel, height, width
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if self.fp16 and im.dtype != torch.float16:
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im = im.half() # to FP16
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if self.nhwc:
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im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)
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if self.pt: # PyTorch
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y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
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@@ -517,7 +528,7 @@ class DetectMultiBackend(nn.Module):
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self.context.execute_v2(list(self.binding_addrs.values()))
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y = [self.bindings[x].data for x in sorted(self.output_names)]
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elif self.coreml: # CoreML
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im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
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im = im.cpu().numpy()
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im = Image.fromarray((im[0] * 255).astype('uint8'))
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# im = im.resize((192, 320), Image.ANTIALIAS)
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y = self.model.predict({'image': im}) # coordinates are xywh normalized
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@@ -532,8 +543,10 @@ class DetectMultiBackend(nn.Module):
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self.input_handle.copy_from_cpu(im)
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self.predictor.run()
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y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
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elif self.triton: # NVIDIA Triton Inference Server
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y = self.model(im)
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else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
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im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
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im = im.cpu().numpy()
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if self.saved_model: # SavedModel
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y = self.model(im, training=False) if self.keras else self.model(im)
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elif self.pb: # GraphDef
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@@ -566,8 +579,8 @@ class DetectMultiBackend(nn.Module):
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def warmup(self, imgsz=(1, 3, 640, 640)):
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# Warmup model by running inference once
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warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb
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if any(warmup_types) and self.device.type != 'cpu':
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warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
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if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
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im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input
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for _ in range(2 if self.jit else 1): #
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self.forward(im) # warmup
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@@ -575,14 +588,17 @@ class DetectMultiBackend(nn.Module):
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@staticmethod
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def _model_type(p='path/to/model.pt'):
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# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
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# types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
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from export import export_formats
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sf = list(export_formats().Suffix) + ['.xml'] # export suffixes
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check_suffix(p, sf) # checks
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p = Path(p).name # eliminate trailing separators
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pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, xml2 = (s in p for s in sf)
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xml |= xml2 # *_openvino_model or *.xml
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tflite &= not edgetpu # *.tflite
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return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle
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from utils.downloads import is_url
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sf = list(export_formats().Suffix) # export suffixes
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if not is_url(p, check=False):
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check_suffix(p, sf) # checks
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url = urlparse(p) # if url may be Triton inference server
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types = [s in Path(p).name for s in sf]
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types[8] &= not types[9] # tflite &= not edgetpu
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triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
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return types + [triton]
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@staticmethod
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def _load_metadata(f=Path('path/to/meta.yaml')):
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