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server.py
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server.py
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#!/usr/bin/env python3
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import numpy as np # pytype: disable=import-error
import nvidia.dali.fn as fn # pytype: disable=import-error
import nvidia.dali.types as types # pytype: disable=import-error
import torch # pytype: disable=import-error
from model_inference import SegmentationPyTorch # pytype: disable=import-error
from nvidia.dali import pipeline_def # pytype: disable=import-error
from pytriton.decorators import batch
from pytriton.model_config import DynamicBatcher, ModelConfig, Tensor
from pytriton.triton import Triton, TritonConfig
MAX_BATCH_SIZE = 32
LOGGER = logging.getLogger("examples.dali_resnet101_pytorch.server")
@pipeline_def(batch_size=MAX_BATCH_SIZE, num_threads=4, device_id=0, prefetch_queue_depth=1)
def dali_preprocessing_pipe():
"""
DALI pre-processing pipeline definition.
"""
encoded = fn.external_source(name="encoded")
decoded = fn.experimental.decoders.video(encoded, device="mixed")
preprocessed = fn.resize(decoded, resize_x=224, resize_y=224)
preprocessed = fn.crop_mirror_normalize(
preprocessed,
dtype=types.FLOAT,
output_layout="FCHW",
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
)
return decoded, preprocessed
@pipeline_def(batch_size=MAX_BATCH_SIZE, num_threads=4, device_id=0, prefetch_queue_depth=1)
def dali_postprocessing_pipe(class_idx=0, prob_threshold=0.6):
"""
DALI post-processing pipeline definition
Args:
class_idx: Index of the class that shall be segmented. Shall be correlated with `seg_class_name` argument
in the Model instance.
prob_threshold: Probability threshold, at which the class affiliation is determined.
Returns:
Segmented images.
"""
image = fn.external_source(device="gpu", name="image", layout="HWC")
width = fn.cast(fn.external_source(device="cpu", name="width"), dtype=types.FLOAT)
height = fn.cast(fn.external_source(device="cpu", name="height"), dtype=types.FLOAT)
prob = fn.external_source(device="gpu", name="probabilities", layout="CHW")
prob = fn.expand_dims(prob[class_idx], axes=[2], new_axis_names="C")
prob = fn.resize(prob, resize_x=width, resize_y=height, interp_type=types.DALIInterpType.INTERP_NN)
mask = fn.cast(prob > prob_threshold, dtype=types.UINT8)
return image * mask
# Initialize DALI Pipelines. This step is put outside of `infer_func` so it is performed during Triton initialization.
preprocessing_pipe = dali_preprocessing_pipe()
preprocessing_pipe.build()
postprocessing_pipe = dali_postprocessing_pipe()
postprocessing_pipe.build()
def preprocess(images):
"""
Setting DALI pipeline inputs and running the pre-processing.
"""
preprocessing_pipe.feed_input("encoded", images)
imgs, preprocessed = preprocessing_pipe.run()
# DALI's TensorListGpu to Torch's Tensor conversion is conducted with the help of the CuPy.
import cupy as cp # pytype: disable=import-error
return torch.as_tensor(cp.asarray(imgs.as_tensor()), device=torch.device("cuda")), torch.as_tensor(
cp.asarray(preprocessed.as_tensor()), device=torch.device("cuda")
)
def postprocess(images, probabilities):
"""
Setting DALI pipeline inputs and running the post-processing.
"""
postprocessing_pipe.feed_input("image", images, layout="HWC")
postprocessing_pipe.feed_input("probabilities", probabilities, layout="CHW")
postprocessing_pipe.feed_input("width", np.full(images.shape[0], images.shape[2]))
postprocessing_pipe.feed_input("height", np.full(images.shape[0], images.shape[1]))
(img,) = postprocessing_pipe.run()
return img
# Initializing ResNet101. This step is put outside of `infer_func` so it is performed during Triton initialization.
segmentation = SegmentationPyTorch(
seg_class_name="__background__",
device_id=0,
)
@batch
def _infer_fn(**inputs):
encoded_video = inputs["video"]
image, input = preprocess(encoded_video)
batch_size, frames_num = image.shape[:2]
input = input.reshape(-1, *input.shape[-3:]) # NFCHW to NCHW (flattening first two dimensions)
image = image.reshape(-1, *image.shape[-3:]) # NFHWC to NHWC (flattening first two dimensions)
prob = segmentation(input)
out = postprocess(image, prob)
return {
"original": image.cpu().numpy().reshape(batch_size, frames_num, *image.shape[-3:]),
"segmented": out.as_cpu().as_array().reshape(batch_size, frames_num, *image.shape[-3:]),
}
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--verbose",
action="store_true",
default=False,
)
return parser.parse_args()
def main():
args = parse_args()
log_verbose = 1 if args.verbose else 0
log_level = logging.DEBUG if args.verbose else logging.INFO
logging.basicConfig(level=log_level, format="%(asctime)s - %(levelname)s - %(name)s: %(message)s")
with Triton(config=TritonConfig(log_verbose=log_verbose)) as triton:
triton.bind(
model_name="ResNet101",
infer_func=_infer_fn,
inputs=[
Tensor(name="video", dtype=np.uint8, shape=(-1,)), # Encoded video
],
outputs=[
Tensor(name="original", dtype=np.uint8, shape=(-1, -1, -1, -1)), # FHWC
Tensor(name="segmented", dtype=np.uint8, shape=(-1, -1, -1, -1)), # FHWC
],
config=ModelConfig(
max_batch_size=MAX_BATCH_SIZE,
batcher=DynamicBatcher(max_queue_delay_microseconds=5000),
),
strict=True,
)
triton.serve()
if __name__ == "__main__":
main()