ultralytics 8.0.149 add Open Images V7 training (#4178)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: AdiEcho <30563671+AdiEcho@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = '8.0.148'
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__version__ = '8.0.149'
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from ultralytics.hub import start
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from ultralytics.models import RTDETR, SAM, YOLO
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661
ultralytics/cfg/datasets/open-images-v7.yaml
Normal file
661
ultralytics/cfg/datasets/open-images-v7.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
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# Example usage: yolo train data=open-images-v7.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── open-images-v7 ← downloads here (561 GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/open-images-v7 # dataset root dir
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train: images/train # train images (relative to 'path') 1743042 images
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val: images/val # val images (relative to 'path') 41620 images
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test: # test images (optional)
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# Classes
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names:
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0: Accordion
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1: Adhesive tape
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2: Aircraft
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3: Airplane
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4: Alarm clock
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5: Alpaca
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6: Ambulance
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7: Animal
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8: Ant
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9: Antelope
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10: Apple
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11: Armadillo
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12: Artichoke
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13: Auto part
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14: Axe
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15: Backpack
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16: Bagel
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17: Baked goods
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18: Balance beam
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19: Ball
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20: Balloon
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21: Banana
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22: Band-aid
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23: Banjo
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||||
24: Barge
|
||||
25: Barrel
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26: Baseball bat
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27: Baseball glove
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28: Bat (Animal)
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29: Bathroom accessory
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30: Bathroom cabinet
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31: Bathtub
|
||||
32: Beaker
|
||||
33: Bear
|
||||
34: Bed
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35: Bee
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36: Beehive
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37: Beer
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||||
38: Beetle
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39: Bell pepper
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40: Belt
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41: Bench
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42: Bicycle
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43: Bicycle helmet
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44: Bicycle wheel
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45: Bidet
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46: Billboard
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47: Billiard table
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48: Binoculars
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49: Bird
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50: Blender
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51: Blue jay
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52: Boat
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||||
53: Bomb
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54: Book
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55: Bookcase
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56: Boot
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57: Bottle
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58: Bottle opener
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59: Bow and arrow
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60: Bowl
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61: Bowling equipment
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62: Box
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63: Boy
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64: Brassiere
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65: Bread
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66: Briefcase
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67: Broccoli
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68: Bronze sculpture
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69: Brown bear
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||||
70: Building
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||||
71: Bull
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72: Burrito
|
||||
73: Bus
|
||||
74: Bust
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75: Butterfly
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76: Cabbage
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77: Cabinetry
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78: Cake
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79: Cake stand
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80: Calculator
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81: Camel
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82: Camera
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83: Can opener
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84: Canary
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85: Candle
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86: Candy
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87: Cannon
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88: Canoe
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89: Cantaloupe
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90: Car
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91: Carnivore
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92: Carrot
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93: Cart
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94: Cassette deck
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95: Castle
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96: Cat
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97: Cat furniture
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98: Caterpillar
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99: Cattle
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100: Ceiling fan
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101: Cello
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102: Centipede
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103: Chainsaw
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104: Chair
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105: Cheese
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106: Cheetah
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107: Chest of drawers
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108: Chicken
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109: Chime
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110: Chisel
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111: Chopsticks
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112: Christmas tree
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113: Clock
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114: Closet
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115: Clothing
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116: Coat
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117: Cocktail
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118: Cocktail shaker
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119: Coconut
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120: Coffee
|
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121: Coffee cup
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122: Coffee table
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123: Coffeemaker
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124: Coin
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125: Common fig
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126: Common sunflower
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127: Computer keyboard
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128: Computer monitor
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129: Computer mouse
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130: Container
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131: Convenience store
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132: Cookie
|
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133: Cooking spray
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134: Corded phone
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135: Cosmetics
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136: Couch
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137: Countertop
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138: Cowboy hat
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139: Crab
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140: Cream
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141: Cricket ball
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142: Crocodile
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143: Croissant
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144: Crown
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145: Crutch
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146: Cucumber
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147: Cupboard
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148: Curtain
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149: Cutting board
|
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150: Dagger
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151: Dairy Product
|
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152: Deer
|
||||
153: Desk
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154: Dessert
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155: Diaper
|
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156: Dice
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157: Digital clock
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158: Dinosaur
|
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159: Dishwasher
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160: Dog
|
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161: Dog bed
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162: Doll
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163: Dolphin
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164: Door
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165: Door handle
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166: Doughnut
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167: Dragonfly
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168: Drawer
|
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169: Dress
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170: Drill (Tool)
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171: Drink
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172: Drinking straw
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173: Drum
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174: Duck
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175: Dumbbell
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176: Eagle
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177: Earrings
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178: Egg (Food)
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179: Elephant
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180: Envelope
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181: Eraser
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182: Face powder
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183: Facial tissue holder
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184: Falcon
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185: Fashion accessory
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186: Fast food
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187: Fax
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188: Fedora
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189: Filing cabinet
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190: Fire hydrant
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191: Fireplace
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192: Fish
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193: Flag
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194: Flashlight
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195: Flower
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196: Flowerpot
|
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197: Flute
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198: Flying disc
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199: Food
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200: Food processor
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201: Football
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202: Football helmet
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203: Footwear
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204: Fork
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205: Fountain
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206: Fox
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207: French fries
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208: French horn
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209: Frog
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210: Fruit
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211: Frying pan
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212: Furniture
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213: Garden Asparagus
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214: Gas stove
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215: Giraffe
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||||
216: Girl
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217: Glasses
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218: Glove
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219: Goat
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220: Goggles
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221: Goldfish
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222: Golf ball
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223: Golf cart
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224: Gondola
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225: Goose
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226: Grape
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227: Grapefruit
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228: Grinder
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229: Guacamole
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230: Guitar
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231: Hair dryer
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232: Hair spray
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233: Hamburger
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234: Hammer
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235: Hamster
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236: Hand dryer
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237: Handbag
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238: Handgun
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239: Harbor seal
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240: Harmonica
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241: Harp
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242: Harpsichord
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243: Hat
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244: Headphones
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245: Heater
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246: Hedgehog
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247: Helicopter
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248: Helmet
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249: High heels
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250: Hiking equipment
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251: Hippopotamus
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252: Home appliance
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253: Honeycomb
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254: Horizontal bar
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255: Horse
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256: Hot dog
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257: House
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258: Houseplant
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259: Human arm
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260: Human beard
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261: Human body
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262: Human ear
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263: Human eye
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264: Human face
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265: Human foot
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266: Human hair
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267: Human hand
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268: Human head
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269: Human leg
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270: Human mouth
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271: Human nose
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272: Humidifier
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273: Ice cream
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274: Indoor rower
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275: Infant bed
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276: Insect
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277: Invertebrate
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278: Ipod
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279: Isopod
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280: Jacket
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281: Jacuzzi
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282: Jaguar (Animal)
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283: Jeans
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284: Jellyfish
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285: Jet ski
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286: Jug
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287: Juice
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288: Kangaroo
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289: Kettle
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290: Kitchen & dining room table
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291: Kitchen appliance
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292: Kitchen knife
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293: Kitchen utensil
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294: Kitchenware
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295: Kite
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296: Knife
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297: Koala
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298: Ladder
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299: Ladle
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300: Ladybug
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301: Lamp
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302: Land vehicle
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303: Lantern
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304: Laptop
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305: Lavender (Plant)
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306: Lemon
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307: Leopard
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308: Light bulb
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309: Light switch
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310: Lighthouse
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311: Lily
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312: Limousine
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313: Lion
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314: Lipstick
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315: Lizard
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316: Lobster
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317: Loveseat
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318: Luggage and bags
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319: Lynx
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320: Magpie
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321: Mammal
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322: Man
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323: Mango
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324: Maple
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325: Maracas
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326: Marine invertebrates
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327: Marine mammal
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328: Measuring cup
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329: Mechanical fan
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330: Medical equipment
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331: Microphone
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332: Microwave oven
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333: Milk
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334: Miniskirt
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335: Mirror
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336: Missile
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337: Mixer
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338: Mixing bowl
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339: Mobile phone
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340: Monkey
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341: Moths and butterflies
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342: Motorcycle
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343: Mouse
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344: Muffin
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345: Mug
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346: Mule
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347: Mushroom
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348: Musical instrument
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349: Musical keyboard
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350: Nail (Construction)
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351: Necklace
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352: Nightstand
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353: Oboe
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354: Office building
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355: Office supplies
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356: Orange
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357: Organ (Musical Instrument)
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358: Ostrich
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359: Otter
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360: Oven
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361: Owl
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362: Oyster
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363: Paddle
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364: Palm tree
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365: Pancake
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366: Panda
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367: Paper cutter
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368: Paper towel
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369: Parachute
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370: Parking meter
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371: Parrot
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372: Pasta
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373: Pastry
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374: Peach
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375: Pear
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376: Pen
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377: Pencil case
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378: Pencil sharpener
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379: Penguin
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380: Perfume
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381: Person
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382: Personal care
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383: Personal flotation device
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384: Piano
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385: Picnic basket
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386: Picture frame
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387: Pig
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388: Pillow
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389: Pineapple
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390: Pitcher (Container)
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391: Pizza
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392: Pizza cutter
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393: Plant
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394: Plastic bag
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395: Plate
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396: Platter
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397: Plumbing fixture
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398: Polar bear
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399: Pomegranate
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400: Popcorn
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401: Porch
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402: Porcupine
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403: Poster
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404: Potato
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405: Power plugs and sockets
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406: Pressure cooker
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407: Pretzel
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408: Printer
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409: Pumpkin
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||||
410: Punching bag
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||||
411: Rabbit
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||||
412: Raccoon
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413: Racket
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414: Radish
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415: Ratchet (Device)
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416: Raven
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417: Rays and skates
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418: Red panda
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419: Refrigerator
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||||
420: Remote control
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421: Reptile
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422: Rhinoceros
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423: Rifle
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424: Ring binder
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425: Rocket
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426: Roller skates
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427: Rose
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428: Rugby ball
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429: Ruler
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430: Salad
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431: Salt and pepper shakers
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432: Sandal
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||||
433: Sandwich
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||||
434: Saucer
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||||
435: Saxophone
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||||
436: Scale
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||||
437: Scarf
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||||
438: Scissors
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439: Scoreboard
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||||
440: Scorpion
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||||
441: Screwdriver
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||||
442: Sculpture
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||||
443: Sea lion
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444: Sea turtle
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||||
445: Seafood
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||||
446: Seahorse
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||||
447: Seat belt
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||||
448: Segway
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||||
449: Serving tray
|
||||
450: Sewing machine
|
||||
451: Shark
|
||||
452: Sheep
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||||
453: Shelf
|
||||
454: Shellfish
|
||||
455: Shirt
|
||||
456: Shorts
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||||
457: Shotgun
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||||
458: Shower
|
||||
459: Shrimp
|
||||
460: Sink
|
||||
461: Skateboard
|
||||
462: Ski
|
||||
463: Skirt
|
||||
464: Skull
|
||||
465: Skunk
|
||||
466: Skyscraper
|
||||
467: Slow cooker
|
||||
468: Snack
|
||||
469: Snail
|
||||
470: Snake
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||||
471: Snowboard
|
||||
472: Snowman
|
||||
473: Snowmobile
|
||||
474: Snowplow
|
||||
475: Soap dispenser
|
||||
476: Sock
|
||||
477: Sofa bed
|
||||
478: Sombrero
|
||||
479: Sparrow
|
||||
480: Spatula
|
||||
481: Spice rack
|
||||
482: Spider
|
||||
483: Spoon
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||||
484: Sports equipment
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||||
485: Sports uniform
|
||||
486: Squash (Plant)
|
||||
487: Squid
|
||||
488: Squirrel
|
||||
489: Stairs
|
||||
490: Stapler
|
||||
491: Starfish
|
||||
492: Stationary bicycle
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||||
493: Stethoscope
|
||||
494: Stool
|
||||
495: Stop sign
|
||||
496: Strawberry
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||||
497: Street light
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||||
498: Stretcher
|
||||
499: Studio couch
|
||||
500: Submarine
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||||
501: Submarine sandwich
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||||
502: Suit
|
||||
503: Suitcase
|
||||
504: Sun hat
|
||||
505: Sunglasses
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||||
506: Surfboard
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||||
507: Sushi
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||||
508: Swan
|
||||
509: Swim cap
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||||
510: Swimming pool
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||||
511: Swimwear
|
||||
512: Sword
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||||
513: Syringe
|
||||
514: Table
|
||||
515: Table tennis racket
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||||
516: Tablet computer
|
||||
517: Tableware
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||||
518: Taco
|
||||
519: Tank
|
||||
520: Tap
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||||
521: Tart
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||||
522: Taxi
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||||
523: Tea
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||||
524: Teapot
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||||
525: Teddy bear
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||||
526: Telephone
|
||||
527: Television
|
||||
528: Tennis ball
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||||
529: Tennis racket
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||||
530: Tent
|
||||
531: Tiara
|
||||
532: Tick
|
||||
533: Tie
|
||||
534: Tiger
|
||||
535: Tin can
|
||||
536: Tire
|
||||
537: Toaster
|
||||
538: Toilet
|
||||
539: Toilet paper
|
||||
540: Tomato
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||||
541: Tool
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||||
542: Toothbrush
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||||
543: Torch
|
||||
544: Tortoise
|
||||
545: Towel
|
||||
546: Tower
|
||||
547: Toy
|
||||
548: Traffic light
|
||||
549: Traffic sign
|
||||
550: Train
|
||||
551: Training bench
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||||
552: Treadmill
|
||||
553: Tree
|
||||
554: Tree house
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||||
555: Tripod
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||||
556: Trombone
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||||
557: Trousers
|
||||
558: Truck
|
||||
559: Trumpet
|
||||
560: Turkey
|
||||
561: Turtle
|
||||
562: Umbrella
|
||||
563: Unicycle
|
||||
564: Van
|
||||
565: Vase
|
||||
566: Vegetable
|
||||
567: Vehicle
|
||||
568: Vehicle registration plate
|
||||
569: Violin
|
||||
570: Volleyball (Ball)
|
||||
571: Waffle
|
||||
572: Waffle iron
|
||||
573: Wall clock
|
||||
574: Wardrobe
|
||||
575: Washing machine
|
||||
576: Waste container
|
||||
577: Watch
|
||||
578: Watercraft
|
||||
579: Watermelon
|
||||
580: Weapon
|
||||
581: Whale
|
||||
582: Wheel
|
||||
583: Wheelchair
|
||||
584: Whisk
|
||||
585: Whiteboard
|
||||
586: Willow
|
||||
587: Window
|
||||
588: Window blind
|
||||
589: Wine
|
||||
590: Wine glass
|
||||
591: Wine rack
|
||||
592: Winter melon
|
||||
593: Wok
|
||||
594: Woman
|
||||
595: Wood-burning stove
|
||||
596: Woodpecker
|
||||
597: Worm
|
||||
598: Wrench
|
||||
599: Zebra
|
||||
600: Zucchini
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
|
||||
from ultralytics.utils.checks import check_requirements, check_version
|
||||
|
||||
check_requirements('fiftyone')
|
||||
if is_ubuntu() and check_version(get_ubuntu_version(), '>=22.04'):
|
||||
# Ubuntu>=22.04 patch https://github.com/voxel51/fiftyone/issues/2961#issuecomment-1666519347
|
||||
check_requirements('fiftyone-db-ubuntu2204')
|
||||
|
||||
import fiftyone as fo
|
||||
import fiftyone.zoo as foz
|
||||
import warnings
|
||||
|
||||
name = 'open-images-v7'
|
||||
fraction = 1.0 # fraction of full dataset to use
|
||||
LOGGER.warning('WARNING ⚠️ Open Images V7 dataset requires at least **561 GB of free space. Starting download...')
|
||||
for split in 'train', 'validation': # 1743042 train, 41620 val images
|
||||
train = split == 'train'
|
||||
|
||||
# Load Open Images dataset
|
||||
dataset = foz.load_zoo_dataset(name,
|
||||
split=split,
|
||||
label_types=['detections'],
|
||||
dataset_dir=Path(SETTINGS['datasets_dir']) / 'fiftyone' / name,
|
||||
max_samples=round((1743042 if train else 41620) * fraction))
|
||||
|
||||
# Define classes
|
||||
if train:
|
||||
classes = dataset.default_classes # all classes
|
||||
# classes = dataset.distinct('ground_truth.detections.label') # only observed classes
|
||||
|
||||
# Export to YOLO format
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
|
||||
dataset.export(export_dir=str(Path(SETTINGS['datasets_dir']) / name),
|
||||
dataset_type=fo.types.YOLOv5Dataset,
|
||||
label_field='ground_truth',
|
||||
split='val' if split == 'validation' else split,
|
||||
classes=classes,
|
||||
overwrite=train)
|
||||
@ -209,8 +209,12 @@ def check_det_dataset(dataset, autodownload=True):
|
||||
# Checks
|
||||
for k in 'train', 'val':
|
||||
if k not in data:
|
||||
raise SyntaxError(
|
||||
emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs."))
|
||||
if k == 'val' and 'validation' in data:
|
||||
LOGGER.info("WARNING ⚠️ renaming data YAML 'validation' key to 'val' to match YOLO format.")
|
||||
data['val'] = data.pop('validation') # replace 'validation' key with 'val' key
|
||||
else:
|
||||
raise SyntaxError(
|
||||
emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs."))
|
||||
if 'names' not in data and 'nc' not in data:
|
||||
raise SyntaxError(emojis(f"{dataset} key missing ❌.\n either 'names' or 'nc' are required in all data YAMLs."))
|
||||
if 'names' in data and 'nc' in data and len(data['names']) != data['nc']:
|
||||
@ -251,14 +255,14 @@ def check_det_dataset(dataset, autodownload=True):
|
||||
m += f"\nNote dataset download directory is '{DATASETS_DIR}'. You can update this in '{SETTINGS_YAML}'"
|
||||
raise FileNotFoundError(m)
|
||||
t = time.time()
|
||||
r = None # success
|
||||
if s.startswith('http') and s.endswith('.zip'): # URL
|
||||
safe_download(url=s, dir=DATASETS_DIR, delete=True)
|
||||
r = None # success
|
||||
elif s.startswith('bash '): # bash script
|
||||
LOGGER.info(f'Running {s} ...')
|
||||
r = os.system(s)
|
||||
else: # python script
|
||||
r = exec(s, {'yaml': data}) # return None
|
||||
exec(s, {'yaml': data})
|
||||
dt = f'({round(time.time() - t, 1)}s)'
|
||||
s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌'
|
||||
LOGGER.info(f'Dataset download {s}\n')
|
||||
|
||||
@ -214,8 +214,8 @@ class Exporter:
|
||||
self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else \
|
||||
tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y)
|
||||
self.pretty_name = Path(self.model.yaml.get('yaml_file', self.file)).stem.replace('yolo', 'YOLO')
|
||||
trained_on = f'trained on {Path(self.args.data).name}' if self.args.data else '(untrained)'
|
||||
description = f'Ultralytics {self.pretty_name} model {trained_on}'
|
||||
data = model.args['data'] if hasattr(model, 'args') and isinstance(model.args, dict) else ''
|
||||
description = f'Ultralytics {self.pretty_name} model {f"trained on {data}" if data else ""}'
|
||||
self.metadata = {
|
||||
'description': description,
|
||||
'author': 'Ultralytics',
|
||||
@ -269,13 +269,12 @@ class Exporter:
|
||||
s = '' if square else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not " \
|
||||
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(' ', '')
|
||||
data = f'data={self.args.data}' if model.task == 'segment' and format == 'pb' else ''
|
||||
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} {data}'
|
||||
f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={self.args.data} {s}'
|
||||
f'\nVisualize: https://netron.app')
|
||||
predict_data = f'data={data}' if model.task == 'segment' and format == 'pb' else ''
|
||||
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'\nVisualize: https://netron.app')
|
||||
|
||||
self.run_callbacks('on_export_end')
|
||||
return f # return list of exported files/dirs
|
||||
@ -612,7 +611,7 @@ class Exporter:
|
||||
for n, batch in enumerate(dataset):
|
||||
if n >= n_images:
|
||||
break
|
||||
im = batch['img'].permute(1, 2, 0)[None] # list to nparray, CHW to BHWC,
|
||||
im = batch['img'].permute(1, 2, 0)[None] # list to nparray, CHW to BHWC
|
||||
images.append(im)
|
||||
f.mkdir()
|
||||
images = torch.cat(images, 0).float()
|
||||
|
||||
@ -34,6 +34,9 @@ def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=Fals
|
||||
Args:
|
||||
box1 (torch.Tensor): (4, )
|
||||
boxes (torch.Tensor): (n, 4)
|
||||
iou_thres (float): IoU threshold
|
||||
image_shape (tuple): (height, width)
|
||||
raw_output (bool): If True, return the raw IoU values instead of the indices
|
||||
|
||||
Returns:
|
||||
high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
|
||||
|
||||
@ -161,6 +161,7 @@ class BaseModel(nn.Module):
|
||||
Prints model information
|
||||
|
||||
Args:
|
||||
detailed (bool): if True, prints out detailed information about the model. Defaults to False
|
||||
verbose (bool): if True, prints out the model information. Defaults to False
|
||||
imgsz (int): the size of the image that the model will be trained on. Defaults to 640
|
||||
"""
|
||||
@ -168,11 +169,10 @@ class BaseModel(nn.Module):
|
||||
|
||||
def _apply(self, fn):
|
||||
"""
|
||||
`_apply()` is a function that applies a function to all the tensors in the model that are not
|
||||
parameters or registered buffers
|
||||
Applies a function to all the tensors in the model that are not parameters or registered buffers.
|
||||
|
||||
Args:
|
||||
fn: the function to apply to the model
|
||||
fn (function): the function to apply to the model
|
||||
|
||||
Returns:
|
||||
A model that is a Detect() object.
|
||||
@ -186,7 +186,8 @@ class BaseModel(nn.Module):
|
||||
return self
|
||||
|
||||
def load(self, weights, verbose=True):
|
||||
"""Load the weights into the model.
|
||||
"""
|
||||
Load the weights into the model.
|
||||
|
||||
Args:
|
||||
weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
|
||||
|
||||
@ -359,6 +359,19 @@ DEFAULT_CFG_KEYS = DEFAULT_CFG_DICT.keys()
|
||||
DEFAULT_CFG = IterableSimpleNamespace(**DEFAULT_CFG_DICT)
|
||||
|
||||
|
||||
def is_ubuntu() -> bool:
|
||||
"""
|
||||
Check if the OS is Ubuntu.
|
||||
|
||||
Returns:
|
||||
(bool): True if OS is Ubuntu, False otherwise.
|
||||
"""
|
||||
with contextlib.suppress(FileNotFoundError):
|
||||
with open('/etc/os-release') as f:
|
||||
return 'ID=ubuntu' in f.read()
|
||||
return False
|
||||
|
||||
|
||||
def is_colab():
|
||||
"""
|
||||
Check if the current script is running inside a Google Colab notebook.
|
||||
@ -550,6 +563,19 @@ def get_default_args(func):
|
||||
return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}
|
||||
|
||||
|
||||
def get_ubuntu_version():
|
||||
"""
|
||||
Retrieve the Ubuntu version if the OS is Ubuntu.
|
||||
|
||||
Returns:
|
||||
(str): Ubuntu version or None if not an Ubuntu OS.
|
||||
"""
|
||||
with contextlib.suppress(FileNotFoundError, AttributeError):
|
||||
with open('/etc/os-release') as f:
|
||||
return re.search(r'VERSION_ID="(\d+\.\d+)"', f.read())[1]
|
||||
return None
|
||||
|
||||
|
||||
def get_user_config_dir(sub_dir='Ultralytics'):
|
||||
"""
|
||||
Get the user config directory.
|
||||
|
||||
@ -51,6 +51,7 @@ def check_imgsz(imgsz, stride=32, min_dim=1, max_dim=2, floor=0):
|
||||
imgsz (int | cList[int]): Image size.
|
||||
stride (int): Stride value.
|
||||
min_dim (int): Minimum number of dimensions.
|
||||
max_dim (int): Maximum number of dimensions.
|
||||
floor (int): Minimum allowed value for image size.
|
||||
|
||||
Returns:
|
||||
|
||||
Reference in New Issue
Block a user