forked from codeproject/CodeProject.AI-Server
/
options.py
369 lines (332 loc) · 28 KB
/
options.py
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import os
import platform
try:
from module_options import ModuleOptions
except ImportError:
print("Unable to import ModuleOptions, running with defaults")
class ModuleOptions:
module_path = '.'
def getEnvVariable(a, b):
return b
class Settings:
def __init__(self, model_name: str, model_name_pattern: str, std_model_name: str,
tpu_model_name: str, labels_name: str):
self.model_name = model_name
self.model_name_pattern = model_name_pattern
self.cpu_model_name = std_model_name
self.tpu_model_name = tpu_model_name
self.labels_name = labels_name
self.MODEL_SEGMENTS = {
'tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq': {
# 104.2 ms per inference
2: ['all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_1_of_2_edgetpu.tflite'],
# 67.5 ms per inference
3: ['all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_0_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_1_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_2_of_3_edgetpu.tflite'],
# 49.1 ms per inference
4: ['all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_1_of_2_edgetpu.tflite'],
# 43.5 ms per inference
5: ['all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_0_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_1_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_2_of_3_edgetpu.tflite'],
# 37.0 ms per inference
6: ['all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_0_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_1_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_2_of_3_edgetpu.tflite'],
# 31.1 ms per inference
7: ['all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_1_of_2_edgetpu.tflite'],
# 27.1 ms per inference
8: ['all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_0_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_1_of_3_edgetpu.tflite', 'all_segments_tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_segment_2_of_3_edgetpu.tflite'],
},
'efficientdet_lite2_448_ptq': {
# 32.1 ms per inference
2: ['all_segments_efficientdet_lite2_448_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_efficientdet_lite2_448_ptq_segment_1_of_2_edgetpu.tflite'],
# 19.5 ms per inference
3: ['166x_first_seg_efficientdet_lite2_448_ptq_segment_0_of_2_edgetpu.tflite', '166x_first_seg_efficientdet_lite2_448_ptq_segment_1_of_2_edgetpu.tflite'],
# 16.5 ms per inference
4: ['15x_first_seg_efficientdet_lite2_448_ptq_segment_0_of_3_edgetpu.tflite', '15x_first_seg_efficientdet_lite2_448_ptq_segment_1_of_3_edgetpu.tflite', '15x_first_seg_efficientdet_lite2_448_ptq_segment_2_of_3_edgetpu.tflite'],
# 13.6 ms per inference
5: ['15x_first_seg_efficientdet_lite2_448_ptq_segment_0_of_2_edgetpu.tflite', '15x_first_seg_efficientdet_lite2_448_ptq_segment_1_of_2_edgetpu.tflite'],
# 11.5 ms per inference
7: ['166x_first_seg_efficientdet_lite2_448_ptq_segment_0_of_2_edgetpu.tflite', '166x_first_seg_efficientdet_lite2_448_ptq_segment_1_of_2_edgetpu.tflite'],
# 11.3 ms per inference
8: ['15x_first_seg_efficientdet_lite2_448_ptq_segment_0_of_2_edgetpu.tflite', '15x_first_seg_efficientdet_lite2_448_ptq_segment_1_of_2_edgetpu.tflite'],
},
'efficientdet_lite3_512_ptq': {
# 20.9 ms per inference
4: ['15x_last_seg_efficientdet_lite3_512_ptq_segment_0_of_2_edgetpu.tflite', '15x_last_seg_efficientdet_lite3_512_ptq_segment_1_of_2_edgetpu.tflite'],
},
'efficientdet_lite3x_640_ptq': {
# 95.0 ms per inference
2: ['all_segments_efficientdet_lite3x_640_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_efficientdet_lite3x_640_ptq_segment_1_of_2_edgetpu.tflite'],
# 70.6 ms per inference
3: ['all_segments_efficientdet_lite3x_640_ptq_segment_0_of_3_edgetpu.tflite', 'all_segments_efficientdet_lite3x_640_ptq_segment_1_of_3_edgetpu.tflite', 'all_segments_efficientdet_lite3x_640_ptq_segment_2_of_3_edgetpu.tflite'],
# 47.9 ms per inference
4: ['2x_first_seg_efficientdet_lite3x_640_ptq_segment_0_of_3_edgetpu.tflite', '2x_first_seg_efficientdet_lite3x_640_ptq_segment_1_of_3_edgetpu.tflite', '2x_first_seg_efficientdet_lite3x_640_ptq_segment_2_of_3_edgetpu.tflite'],
# 38.7 ms per inference
5: ['15x_first_seg_efficientdet_lite3x_640_ptq_segment_0_of_2_edgetpu.tflite', '15x_first_seg_efficientdet_lite3x_640_ptq_segment_1_of_2_edgetpu.tflite'],
# 35.1 ms per inference
6: ['all_segments_efficientdet_lite3x_640_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_efficientdet_lite3x_640_ptq_segment_1_of_2_edgetpu.tflite'],
# 30.6 ms per inference
7: ['all_segments_efficientdet_lite3x_640_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_efficientdet_lite3x_640_ptq_segment_1_of_2_edgetpu.tflite'],
# 27.3 ms per inference
8: ['all_segments_efficientdet_lite3x_640_ptq_segment_0_of_2_edgetpu.tflite', 'all_segments_efficientdet_lite3x_640_ptq_segment_1_of_2_edgetpu.tflite'],
},
'yolov5m-int8': {
# 56.3 ms per inference
2: ['all_segments_yolov5m-int8_segment_0_of_2_edgetpu.tflite', 'all_segments_yolov5m-int8_segment_1_of_2_edgetpu.tflite'],
# 32.2 ms per inference
3: ['15x_first_seg_yolov5m-int8_segment_0_of_2_edgetpu.tflite', '15x_first_seg_yolov5m-int8_segment_1_of_2_edgetpu.tflite'],
# 25.9 ms per inference
4: ['2x_last_seg_yolov5m-int8_segment_0_of_4_edgetpu.tflite', '2x_last_seg_yolov5m-int8_segment_1_of_4_edgetpu.tflite', '2x_last_seg_yolov5m-int8_segment_2_of_4_edgetpu.tflite', '2x_last_seg_yolov5m-int8_segment_3_of_4_edgetpu.tflite'],
# 21.2 ms per inference
5: ['all_segments_yolov5m-int8_segment_0_of_2_edgetpu.tflite', 'all_segments_yolov5m-int8_segment_1_of_2_edgetpu.tflite'],
# 18.8 ms per inference
6: ['15x_last_seg_yolov5m-int8_segment_0_of_3_edgetpu.tflite', '15x_last_seg_yolov5m-int8_segment_1_of_3_edgetpu.tflite', '15x_last_seg_yolov5m-int8_segment_2_of_3_edgetpu.tflite'],
# 14.7 ms per inference
7: ['all_segments_yolov5m-int8_segment_0_of_4_edgetpu.tflite', 'all_segments_yolov5m-int8_segment_1_of_4_edgetpu.tflite', 'all_segments_yolov5m-int8_segment_2_of_4_edgetpu.tflite', 'all_segments_yolov5m-int8_segment_3_of_4_edgetpu.tflite'],
# 14.6 ms per inference
8: ['all_segments_yolov5m-int8_segment_0_of_3_edgetpu.tflite', 'all_segments_yolov5m-int8_segment_1_of_3_edgetpu.tflite', 'all_segments_yolov5m-int8_segment_2_of_3_edgetpu.tflite'],
},
'yolov5l-int8': {
# 61.1 ms per inference
3: ['all_segments_yolov5l-int8_segment_0_of_3_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_1_of_3_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_2_of_3_edgetpu.tflite'],
# 48.0 ms per inference
4: ['all_segments_yolov5l-int8_segment_0_of_4_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_1_of_4_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_2_of_4_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_3_of_4_edgetpu.tflite'],
# 39.0 ms per inference
5: ['all_segments_yolov5l-int8_segment_0_of_5_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_1_of_5_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_2_of_5_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_3_of_5_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_4_of_5_edgetpu.tflite'],
# 31.5 ms per inference
6: ['all_segments_yolov5l-int8_segment_0_of_3_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_1_of_3_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_2_of_3_edgetpu.tflite'],
# 26.7 ms per inference
7: ['dumb_yolov5l-int8_segment_0_of_6_edgetpu.tflite', 'dumb_yolov5l-int8_segment_1_of_6_edgetpu.tflite', 'dumb_yolov5l-int8_segment_2_of_6_edgetpu.tflite', 'dumb_yolov5l-int8_segment_3_of_6_edgetpu.tflite', 'dumb_yolov5l-int8_segment_4_of_6_edgetpu.tflite', 'dumb_yolov5l-int8_segment_5_of_6_edgetpu.tflite'],
# 24.4 ms per inference
8: ['all_segments_yolov5l-int8_segment_0_of_4_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_1_of_4_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_2_of_4_edgetpu.tflite', 'all_segments_yolov5l-int8_segment_3_of_4_edgetpu.tflite'],
},
'yolov8s_416_640px': {
# 25.6 ms per inference
3: ['166x_first_seg_yolov8s_416_640px_segment_0_of_2_edgetpu.tflite', '166x_first_seg_yolov8s_416_640px_segment_1_of_2_edgetpu.tflite'],
},
'yolov8m_416_640px': {
# 114.4 ms per inference
2: ['all_segments_yolov8m_416_640px_segment_0_of_2_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_1_of_2_edgetpu.tflite'],
# 71.9 ms per inference
3: ['all_segments_yolov8m_416_640px_segment_0_of_3_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_1_of_3_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_2_of_3_edgetpu.tflite'],
# 53.0 ms per inference
4: ['2x_first_seg_yolov8m_416_640px_segment_0_of_3_edgetpu.tflite', '2x_first_seg_yolov8m_416_640px_segment_1_of_3_edgetpu.tflite', '2x_first_seg_yolov8m_416_640px_segment_2_of_3_edgetpu.tflite'],
# 43.5 ms per inference
5: ['166x_first_seg_yolov8m_416_640px_segment_0_of_4_edgetpu.tflite', '166x_first_seg_yolov8m_416_640px_segment_1_of_4_edgetpu.tflite', '166x_first_seg_yolov8m_416_640px_segment_2_of_4_edgetpu.tflite', '166x_first_seg_yolov8m_416_640px_segment_3_of_4_edgetpu.tflite'],
# 31.8 ms per inference
6: ['2x_first_seg_yolov8m_416_640px_segment_0_of_5_edgetpu.tflite', '2x_first_seg_yolov8m_416_640px_segment_1_of_5_edgetpu.tflite', '2x_first_seg_yolov8m_416_640px_segment_2_of_5_edgetpu.tflite', '2x_first_seg_yolov8m_416_640px_segment_3_of_5_edgetpu.tflite', '2x_first_seg_yolov8m_416_640px_segment_4_of_5_edgetpu.tflite'],
# 29.5 ms per inference
7: ['all_segments_yolov8m_416_640px_segment_0_of_4_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_1_of_4_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_2_of_4_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_3_of_4_edgetpu.tflite'],
# 26.0 ms per inference
8: ['all_segments_yolov8m_416_640px_segment_0_of_3_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_1_of_3_edgetpu.tflite', 'all_segments_yolov8m_416_640px_segment_2_of_3_edgetpu.tflite'],
},
'yolov8l_416_640px': {
# 169.6 ms per inference
2: ['all_segments_yolov8l_416_640px_segment_0_of_2_edgetpu.tflite', 'all_segments_yolov8l_416_640px_segment_1_of_2_edgetpu.tflite'],
# 115.8 ms per inference
3: ['15x_first_seg_yolov8l_416_640px_segment_0_of_2_edgetpu.tflite', '15x_first_seg_yolov8l_416_640px_segment_1_of_2_edgetpu.tflite'],
# 89.7 ms per inference
4: ['all_segments_yolov8l_416_640px_segment_0_of_2_edgetpu.tflite', 'all_segments_yolov8l_416_640px_segment_1_of_2_edgetpu.tflite'],
# 77.7 ms per inference
5: ['4x_first_seg_yolov8l_416_640px_segment_0_of_2_edgetpu.tflite', '4x_first_seg_yolov8l_416_640px_segment_1_of_2_edgetpu.tflite'],
# 64.2 ms per inference
6: ['15x_first_seg_yolov8l_416_640px_segment_0_of_2_edgetpu.tflite', '15x_first_seg_yolov8l_416_640px_segment_1_of_2_edgetpu.tflite'],
# 57.3 ms per inference
7: ['3x_first_seg_yolov8l_416_640px_segment_0_of_3_edgetpu.tflite', '3x_first_seg_yolov8l_416_640px_segment_1_of_3_edgetpu.tflite', '3x_first_seg_yolov8l_416_640px_segment_2_of_3_edgetpu.tflite'],
# 52.2 ms per inference
8: ['166x_first_seg_yolov8l_416_640px_segment_0_of_3_edgetpu.tflite', '166x_first_seg_yolov8l_416_640px_segment_1_of_3_edgetpu.tflite', '166x_first_seg_yolov8l_416_640px_segment_2_of_3_edgetpu.tflite'],
},
'ipcam-general-v8': {
# 53.4 ms per inference
2: ['2x_last_seg_ipcam-general-v8_segment_0_of_2_edgetpu.tflite', '2x_last_seg_ipcam-general-v8_segment_1_of_2_edgetpu.tflite'],
# 24.3 ms per inference
3: ['all_segments_ipcam-general-v8_segment_0_of_2_edgetpu.tflite', 'all_segments_ipcam-general-v8_segment_1_of_2_edgetpu.tflite'],
# 19.9 ms per inference
4: ['15x_first_seg_ipcam-general-v8_segment_0_of_3_edgetpu.tflite', '15x_first_seg_ipcam-general-v8_segment_1_of_3_edgetpu.tflite', '15x_first_seg_ipcam-general-v8_segment_2_of_3_edgetpu.tflite'],
# 15.6 ms per inference
5: ['15x_last_seg_ipcam-general-v8_segment_0_of_3_edgetpu.tflite', '15x_last_seg_ipcam-general-v8_segment_1_of_3_edgetpu.tflite', '15x_last_seg_ipcam-general-v8_segment_2_of_3_edgetpu.tflite'],
# 15.2 ms per inference
6: ['15x_last_seg_ipcam-general-v8_segment_0_of_3_edgetpu.tflite', '15x_last_seg_ipcam-general-v8_segment_1_of_3_edgetpu.tflite', '15x_last_seg_ipcam-general-v8_segment_2_of_3_edgetpu.tflite'],
# 12.3 ms per inference
7: ['15x_first_seg_ipcam-general-v8_segment_0_of_3_edgetpu.tflite', '15x_first_seg_ipcam-general-v8_segment_1_of_3_edgetpu.tflite', '15x_first_seg_ipcam-general-v8_segment_2_of_3_edgetpu.tflite'],
# 10.9 ms per inference
8: ['2x_last_seg_ipcam-general-v8_segment_0_of_3_edgetpu.tflite', '2x_last_seg_ipcam-general-v8_segment_1_of_3_edgetpu.tflite', '2x_last_seg_ipcam-general-v8_segment_2_of_3_edgetpu.tflite'],
},
}
self.tpu_segments_lists = {}
if model_name_pattern in self.MODEL_SEGMENTS:
self.tpu_segments_lists = self.MODEL_SEGMENTS[model_name_pattern]
class Options:
def __init__(self):
# ----------------------------------------------------------------------
# Setup constants
# Models at:
# https://coral.ai/models/object-detection/
# https://github.com/MikeLud/CodeProject.AI-Custom-IPcam-Models/
# https://github.com/ultralytics/ultralytics
#
# YOLOv8 benchmarked with 3 CPU cores and 6 PCIe TPUs
self.MODEL_SETTINGS = {
"yolov8": {
# 59.88 ms throughput / 855.40 ms inference
"large": Settings('YOLOv8', 'yolov8l_416_640px',
'yolov8l_416_640px.tflite', # 46Mb CPU
'yolov8l_416_640px_edgetpu.tflite', # 48Mb TPU
'coco_labels.txt'),
# 53.72 ms throughput / 762.86 ms inference
"medium": Settings('YOLOv8', 'yolov8m_416_640px', \
'yolov8m_416_640px.tflite', # 21Mb CPU
'yolov8m_416_640px_edgetpu.tflite', # 22Mb TPU
'coco_labels.txt'),
# 21.52 ms throughput / 291.35 ms inference
"small": Settings('YOLOv8', 'yolov8s_416_640px',
'yolov8s_416_640px.tflite', # 11Mb CPU
'yolov8s_416_640px_edgetpu.tflite', # 12Mb TPU
'coco_labels.txt'),
# 10.35 ms throughput / 123.35 ms inference
"tiny": Settings('YOLOv8', 'yolov8n_416_640px',
'yolov8n_416_640px.tflite', # 4Mb CPU
'yolov8n_416_640px_edgetpu.tflite', # 3Mb TPU
'coco_labels.txt')
},
"yolov5": {
"large": Settings('YOLOv5', 'yolov5l-int8',
'yolov5l-int8.tflite', # 46Mb CPU
'yolov5l-int8_edgetpu.tflite', # 48Mb TPU
'coco_labels.txt'),
"medium": Settings('YOLOv5', 'yolov5m-int8',
'yolov5m-int8.tflite', # 21Mb CPU
'yolov5m-int8_edgetpu.tflite', # 22Mb TPU
'coco_labels.txt'),
"small": Settings('YOLOv5', 'yolov5s-int8',
'yolov5s-int8.tflite', # 7Mb CPU
'yolov5s-int8_edgetpu.tflite', # 8Mb TPU
'coco_labels.txt'),
"tiny": Settings('YOLOv5', 'yolov5n-int8',
'yolov5n-int8.tflite', # 2Mb CPU
'yolov5n-int8_edgetpu.tflite', # 2Mb TPU
'coco_labels.txt')
},
"efficientdet-lite": {
# Large: EfficientDet-Lite3x 90 objects COCO 640x640x3 2 197.0 ms 43.9% mAP
"large": Settings('EfficientDet-Lite', 'efficientdet_lite3x_640_ptq', \
'efficientdet_lite3x_640_ptq.tflite', # 14Mb CPU
'efficientdet_lite3x_640_ptq_edgetpu.tflite', # 20Mb TPU
'coco_labels.txt'),
# Medium: EfficientDet-Lite3 90 objects 512x512x3 2 107.6 ms 39.4% mAP
"medium": Settings('EfficientDet-Lite', 'efficientdet_lite3_512_ptq', \
'efficientdet_lite3_512_ptq.tflite', # CPU
'efficientdet_lite3_512_ptq_edgetpu.tflite', # TPU
'coco_labels.txt'),
# Small: EfficientDet-Lite2 90 objects COCO 448x448x3 2 104.6 ms 36.0% mAP
"small": Settings('EfficientDet-Lite', 'efficientdet_lite2_448_ptq', \
'efficientdet_lite2_448_ptq.tflite', # 10Mb CPU
'efficientdet_lite2_448_ptq_edgetpu.tflite', # TPU
'coco_labels.txt'),
# Tiny: EfficientDet-Lite1 90 objects COCO 384x384x3 2 56.3 ms 34.3% mAP
"tiny": Settings('EfficientDet-Lite', 'efficientdet_lite1_384_ptq', \
'efficientdet_lite1_384_ptq.tflite', # 7Mb CPU
'efficientdet_lite1_384_ptq_edgetpu.tflite', # TPU
'coco_labels.txt')
},
"mobilenet ssd": {
# Large: SSD/FPN MobileNet V1 90 objects, COCO 640x640x3 TF-lite v2 229.4 ms 31.1% mAP
"large": Settings('MobileNet SSD', 'tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq', \
'tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq.tflite', # CPU
'tf2_ssd_mobilenet_v1_fpn_640x640_coco17_ptq_edgetpu.tflite', # TPU
'coco_labels.txt'),
# Medium: SSDLite MobileDet 90 objects, COCO 320x320x3 TF-lite v1 9.1 ms 32.9% mAP
"medium": Settings('MobileNet SSD', 'ssdlite_mobiledet_coco_', \
'ssdlite_mobiledet_coco_qat_postprocess.tflite', # 5Mb CPU
'ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite', # TPU
'coco_labels.txt'),
# Small: SSD MobileNet V2 90 objects, COCO 300x300x3 TF-lite v2 7.6 ms 22.4% mAP
"small": Settings('MobileNet SSD', 'tf2_ssd_mobilenet_v2', \
'tf2_ssd_mobilenet_v2_coco17_ptq.tflite', # 6.7Mb CPU
'tf2_ssd_mobilenet_v2_coco17_ptq_edgetpu.tflite', # TPU
'coco_labels.txt'),
# Tiny: MobileNet V2 90 objects, COCO 300x300x3 TF-lite v2 Quant
"tiny": Settings('MobileNet SSD', 'ssd_mobilenet_v2_coco_', \
'ssd_mobilenet_v2_coco_quant_postprocess.tflite', # 6.6Mb CPU
'ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite', # TPU
'coco_labels.txt')
}
}
self.ENABLE_MULTI_TPU = True
self.MIN_CONFIDENCE = 0.5
self.INTERPRETER_LIFESPAN_SECONDS = 3600.0
self.WATCHDOG_IDLE_SECS = 5.0 # To be added to non-multi code
self.MAX_IDLE_SECS_BEFORE_RECYCLE = 60.0 # To be added to non-multi code
self.WARN_TEMPERATURE_THRESHOLD_CELSIUS = 80 # PCIe && Linux only
self.MAX_PIPELINE_QUEUE_LEN = 1000 # Multi-only
self.TILE_OVERLAP = 15 # Multi-only.
self.DOWNSAMPLE_BY = 6.0 # Multi-only. Smaller number results in more tiles generated
self.IOU_THRESHOLD = 0.1 # Multi-only
# ----------------------------------------------------------------------
# Setup values
self._show_env_variables = True
self.module_path = ModuleOptions.module_path
self.models_dir = os.path.normpath(ModuleOptions.getEnvVariable("MODELS_DIR", f"{self.module_path}/assets"))
self.model_name = os.path.normpath(ModuleOptions.getEnvVariable("CPAI_CORAL_MODEL_NAME", "MobileNet SSD"))
self.model_size = ModuleOptions.getEnvVariable("MODEL_SIZE", "Small") # small, medium, large
# custom_models_dir = os.path.normpath(ModuleOptions.getEnvVariable("CUSTOM_MODELS_DIR", f"{module_path}/custom-models"))
self.use_multi_tpu = ModuleOptions.getEnvVariable("CPAI_CORAL_MULTI_TPU", str(self.ENABLE_MULTI_TPU)).lower() == "true"
self.min_confidence = float(ModuleOptions.getEnvVariable("MIN_CONFIDENCE", self.MIN_CONFIDENCE))
self.sleep_time = 0.01
# For multi-TPU tiling. Smaller number results in more tiles generated
self.downsample_by = float(ModuleOptions.getEnvVariable("CPAI_CORAL_DOWNSAMPLE_BY", self.DOWNSAMPLE_BY))
self.tile_overlap = int(ModuleOptions.getEnvVariable("CPAI_CORAL_TILE_OVERLAP", self.TILE_OVERLAP))
self.iou_threshold = float(ModuleOptions.getEnvVariable("CPAI_CORAL_IOU_THRESHOLD", self.IOU_THRESHOLD))
# Maybe - perhaps! - we need shorter var names
self.watchdog_idle_secs = float(ModuleOptions.getEnvVariable("CPAI_CORAL_WATCHDOG_IDLE_SECS", self.WATCHDOG_IDLE_SECS))
self.interpreter_lifespan_secs = float(ModuleOptions.getEnvVariable("CPAI_CORAL_INTERPRETER_LIFESPAN_SECONDS", self.INTERPRETER_LIFESPAN_SECONDS))
self.max_idle_secs_before_recycle = float(ModuleOptions.getEnvVariable("CPAI_CORAL_MAX_IDLE_SECS_BEFORE_RECYCLE", self.MAX_IDLE_SECS_BEFORE_RECYCLE))
self.max_pipeline_queue_length = int(ModuleOptions.getEnvVariable("CPAI_CORAL_MAX_PIPELINE_QUEUE_LEN", self.MAX_PIPELINE_QUEUE_LEN))
self.warn_temperature_thresh_C = int(ModuleOptions.getEnvVariable("CPAI_CORAL_WARN_TEMPERATURE_THRESHOLD_CELSIUS", self.WARN_TEMPERATURE_THRESHOLD_CELSIUS))
# finalise settings
if platform.system() == 'Darwin':
self.ENABLE_MULTI_TPU = False
else:
self.ENABLE_MULTI_TPU = True
self.set_model(self.model_name)
# ----------------------------------------------------------------------
# dump the important variables
if self._show_env_variables:
print(f"Debug: MODULE_PATH: {self.module_path}")
print(f"Debug: MODELS_DIR: {self.models_dir}")
print(f"Debug: CPAI_CORAL_MODEL_NAME: {self.model_name}")
print(f"Debug: MODEL_SIZE: {self.model_size}")
print(f"Debug: CPU_MODEL_NAME: {self.cpu_model_name}")
print(f"Debug: TPU_MODEL_NAME: {self.tpu_model_name}")
def set_model(self, model_name):
# Normalise input
self.model_name = model_name.lower()
if self.model_name not in [ "mobilenet ssd", "efficientdet-lite", "yolov5", "yolov8"]: # 'yolov5' - no sense including v5 anymore
self.model_name = "mobilenet ssd"
self.model_size = self.model_size.lower()
"""
With models MobileNet SSD, EfficientDet-Lite, and YOLOv5/v8, we have
three classes of model. The first is basically designed to work in concert
with the Edge TPU and are compatible with the Dev Board Micro. They are
very fast and don't require additional CPU resources. The YOLOv5/v8 models
should be directly comparable with other CPAI modules running YOLOv5/v8.
They should be high-quality, but are not designed with the Edge TPU in
mind and rely more heavily on the CPU. The EfficientDet-Lite models are
in between: not as modern as YOLOv5/v8, but less reliant on the CPU.
Each class of model is broken into four sizes depending on the
intensity of the workload.
"""
model_valid = self.model_size in [ "tiny", "small", "medium", "large" ]
if not model_valid:
self.model_size = "small"
# Get settings
# Note: self.model_name and self.model_size are lowercase to ensure dict lookup works
settings = self.MODEL_SETTINGS[self.model_name][self.model_size]
self.cpu_model_name = settings.cpu_model_name
self.tpu_model_name = settings.tpu_model_name
self.labels_name = settings.labels_name
# pre-chew
self.model_cpu_file = os.path.normpath(os.path.join(self.models_dir, self.cpu_model_name))
self.model_tpu_file = os.path.normpath(os.path.join(self.models_dir, self.tpu_model_name))
self.label_file = os.path.normpath(os.path.join(self.models_dir, self.labels_name))
self.tpu_segments_lists = {}
for tpu_cnt, name_list in settings.tpu_segments_lists.items():
self.tpu_segments_lists[tpu_cnt] = \
[os.path.normpath(os.path.join(self.models_dir, name)) for name in name_list]