entities: make read_entities return entities

Signed-off-by: Niv Sardi <[email protected]>

python/entity.py

@@ -7,7 +7,7 @@ from common import defaults
 def read_entities(fn = defaults.MAIN_CSV_PATH):
     with open(fn, newline='') as csvfile:
         reader = csv.DictReader(csvfile)
-        bcos = { d['bco']:d for  d in reader}
+        bcos = { d['bco']:Entity.from_dict(d) for d in reader}
     return bcos
 
 class Entity(NamedTuple):

python/write_data.py

@@ -5,7 +5,7 @@ import argparse
 from common import defaults
 
 def gen_data_yaml(bcos, datapath='../data'):
-    names = [f"{d['name']}" for d in bcos.values()]
+    names = [f"{d.name}" for d in bcos.values()]
     return f'''
 # this file is autogenerated by write_data.py
 

train.sh

@@ -0,0 +1,60 @@
+#!/bin/sh
+
+set -e
+
+PY=python3
+DATA_PATH=${PWD}/data
+
+${PY} ./python/write_data.py ./data/entities.csv --data ${DATA_PATH} > ${DATA_PATH}/data.yaml
+grep nc ${DATA_PATH}/data.yaml > ${DATA_PATH}/custom_yolov5s.yaml
+cat <<EOF >> ${DATA_PATH}/custom_yolov5s.yaml
+# parameters
+nc: {num_classes}  # number of classes  # CHANGED HERE
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.50  # layer channel multiple
+
+# anchors
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# YOLOv5 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, BottleneckCSP, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, BottleneckCSP, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, BottleneckCSP, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
+   [-1, 3, BottleneckCSP, [1024, False]],  # 9
+  ]
+
+# YOLOv5 head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, BottleneckCSP, [512, False]],  # 13
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]
+EOF
+(cd yolov5; ${PY} train.py --img 416 --batch 80 --epochs 1000 --data ${DATA_PATH}/data.yaml --cfg ${DATA_PATH}/custom_yolov5s.yaml --weights '')