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Add NNCF support in OpenVINO export (#4671)

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Co-authored-by: 下北泽miHoMo红茶坊 <39751846+kisaragychihaya@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2023-08-31 15:19:44 +02:00 committed by GitHub
parent d2cf7acce0
commit 45ba99973d
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4 changed files with 72 additions and 45 deletions
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37
.github/workflows/docker.yaml vendored
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@ -8,18 +8,25 @@ on:
branches: [main]
workflow_dispatch:
inputs:
dockerfile:
type: choice
description: Select Dockerfile
options:
- Dockerfile-arm64
- Dockerfile-jetson
- Dockerfile-python
- Dockerfile-cpu
- Dockerfile
Dockerfile:
type: boolean
description: Use Dockerfile
default: true
Dockerfile-cpu:
type: boolean
description: Use Dockerfile-cpu
Dockerfile-arm64:
type: boolean
description: Use Dockerfile-arm64
Dockerfile-jetson:
type: boolean
description: Use Dockerfile-jetson
Dockerfile-python:
type: boolean
description: Use Dockerfile-python
push:
type: boolean
description: Push image to Docker Hub
description: Push images to Docker Hub
default: true
jobs:
@ -93,28 +100,28 @@ jobs:
VERSION_TAG: ${{ steps.get_version.outputs.version_tag }}
- name: Build Image
if: github.event_name == 'push' || github.event.inputs.dockerfile == matrix.dockerfile
if: github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true'
run: |
docker build --platform ${{ matrix.platforms }} -f docker/${{ matrix.dockerfile }} \
-t ultralytics/ultralytics:${{ matrix.tags }} \
-t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} .
- name: Run Tests
if: (github.event_name == 'push' || github.event.inputs.dockerfile == matrix.dockerfile) && matrix.platforms == 'linux/amd64' # arm64 images not supported on GitHub CI runners
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' # arm64 images not supported on GitHub CI runners
run: docker run ultralytics/ultralytics:${{ matrix.tags }} /bin/bash -c "pip install pytest && pytest tests"
- name: Run Benchmarks
# WARNING: Dockerfile (GPU) error on TF.js export 'module 'numpy' has no attribute 'object'.
if: (github.event_name == 'push' || github.event.inputs.dockerfile == matrix.dockerfile) && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile' # arm64 images not supported on GitHub CI runners
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile' # arm64 images not supported on GitHub CI runners
run: docker run ultralytics/ultralytics:${{ matrix.tags }} yolo benchmark model=yolov8n.pt imgsz=160 verbose=0.26
- name: Push Docker Image with Ultralytics version tag
if: (github.event_name == 'push' || (github.event.inputs.dockerfile == matrix.dockerfile && github.event.inputs.push == 'true')) && steps.check_tag.outputs.exists == 'false'
if: (github.event_name == 'push' || (github.event.inputs[matrix.dockerfile] == 'true' && github.event.inputs.push == 'true')) && steps.check_tag.outputs.exists == 'false'
run: |
docker push ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }}
- name: Push Docker Image with latest tag
if: github.event_name == 'push' || (github.event.inputs.dockerfile == matrix.dockerfile && github.event.inputs.push == 'true')
if: github.event_name == 'push' || (github.event.inputs[matrix.dockerfile] == 'true' && github.event.inputs.push == 'true')
run: |
docker push ultralytics/ultralytics:${{ matrix.tags }}
if [[ "${{ matrix.tags }}" == "latest" ]]; then

4
docker/Dockerfile-cpu
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@ -18,8 +18,8 @@ RUN apt update \
WORKDIR /usr/src/ultralytics
# Copy contents
# COPY . /usr/src/app (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
COPY . /usr/src/ultralytics
# RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error

7
ultralytics/data/augment.py
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@ -65,12 +65,7 @@ class Compose:
def __repr__(self):
"""Return string representation of object."""
format_string = f'{self.__class__.__name__}('
for t in self.transforms:
format_string += '\n'
format_string += f' {t}'
format_string += '\n)'
return format_string
return f"{self.__class__.__name__}({', '.join([f'{t}' for t in self.transforms])})"
class BaseMixTransform:

69
ultralytics/engine/exporter.py
View File

@ -58,9 +58,12 @@ from copy import deepcopy
from datetime import datetime
from pathlib import Path
import numpy as np
import torch
from ultralytics.cfg import get_cfg
from ultralytics.data.dataset import YOLODataset
from ultralytics.data.utils import check_det_dataset
from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
from ultralytics.nn.tasks import DetectionModel, SegmentationModel
@ -275,10 +278,11 @@ class Exporter:
f"work. Use export 'imgsz={max(self.imgsz)}' if val is required."
imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(' ', '')
predict_data = f'data={data}' if model.task == 'segment' and format == 'pb' else ''
q = 'int8' if self.args.int8 else 'half' if self.args.half else '' # quantization
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {predict_data}'
f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {s}'
f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {q} {predict_data}'
f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {q} {s}'
f'\nVisualize: https://netron.app')
self.run_callbacks('on_export_end')
@ -367,27 +371,54 @@ class Exporter:
LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
f = str(self.file).replace(self.file.suffix, f'_openvino_model{os.sep}')
fq = str(self.file).replace(self.file.suffix, f'_int8_openvino_model{os.sep}')
f_onnx = self.file.with_suffix('.onnx')
f_ov = str(Path(f) / self.file.with_suffix('.xml').name)
fq_ov = str(Path(fq) / self.file.with_suffix('.xml').name)
def serialize(ov_model, file):
"""Set RT info, serialize and save metadata YAML."""
ov_model.set_rt_info('YOLOv8', ['model_info', 'model_type'])
ov_model.set_rt_info(True, ['model_info', 'reverse_input_channels'])
ov_model.set_rt_info(114, ['model_info', 'pad_value'])
ov_model.set_rt_info([255.0], ['model_info', 'scale_values'])
ov_model.set_rt_info(self.args.iou, ['model_info', 'iou_threshold'])
ov_model.set_rt_info([v.replace(' ', '_') for v in self.model.names.values()], ['model_info', 'labels'])
if self.model.task != 'classify':
ov_model.set_rt_info('fit_to_window_letterbox', ['model_info', 'resize_type'])
ov.serialize(ov_model, file) # save
yaml_save(Path(file).parent / 'metadata.yaml', self.metadata) # add metadata.yaml
ov_model = mo.convert_model(f_onnx,
model_name=self.pretty_name,
framework='onnx',
compress_to_fp16=self.args.half) # export
# Set RT info
ov_model.set_rt_info('YOLOv8', ['model_info', 'model_type'])
ov_model.set_rt_info(True, ['model_info', 'reverse_input_channels'])
ov_model.set_rt_info(114, ['model_info', 'pad_value'])
ov_model.set_rt_info([255.0], ['model_info', 'scale_values'])
ov_model.set_rt_info(self.args.iou, ['model_info', 'iou_threshold'])
ov_model.set_rt_info([v.replace(' ', '_') for k, v in sorted(self.model.names.items())],
['model_info', 'labels'])
if self.model.task != 'classify':
ov_model.set_rt_info('fit_to_window_letterbox', ['model_info', 'resize_type'])
if self.args.int8:
assert self.args.data, "INT8 export requires a data argument for calibration, i.e. 'data=coco8.yaml'"
check_requirements('nncf>=2.5.0')
import nncf
ov.serialize(ov_model, f_ov) # save
yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
def transform_fn(data_item):
"""Quantization transform function."""
im = data_item['img'].numpy().astype(np.float32) / 255.0 # uint8 to fp16/32 and 0 - 255 to 0.0 - 1.0
return np.expand_dims(im, 0) if im.ndim == 3 else im
# Generate calibration data for integer quantization
LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'")
data = check_det_dataset(self.args.data)
dataset = YOLODataset(data['val'], data=data, imgsz=self.imgsz[0], augment=False)
quantization_dataset = nncf.Dataset(dataset, transform_fn)
ignored_scope = nncf.IgnoredScope(types=['Multiply', 'Subtract', 'Sigmoid']) # ignore operation
quantized_ov_model = nncf.quantize(ov_model,
quantization_dataset,
preset=nncf.QuantizationPreset.MIXED,
ignored_scope=ignored_scope)
serialize(quantized_ov_model, fq_ov)
return fq, None
serialize(ov_model, f_ov)
return f, None
@try_export
@ -633,19 +664,13 @@ class Exporter:
if self.args.int8:
verbosity = '--verbosity info'
if self.args.data:
import numpy as np
from ultralytics.data.dataset import YOLODataset
from ultralytics.data.utils import check_det_dataset
# Generate calibration data for integer quantization
LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'")
data = check_det_dataset(self.args.data)
dataset = YOLODataset(data['val'], data=data, imgsz=self.imgsz[0], augment=False)
images = []
n_images = 100 # maximum number of images
for n, batch in enumerate(dataset):
if n >= n_images:
for i, batch in enumerate(dataset):
if i >= 100: # maximum number of calibration images
break
im = batch['img'].permute(1, 2, 0)[None] # list to nparray, CHW to BHWC
images.append(im)