add IS_CROP and comments to params
"source": [
"params = train.Hyperparameters()\n",
"\n",
- "params.MODEL = \"pilot_net\"\n",
- "params.POLICY = \"autopilot\"\n",
+ "params.MODEL = \"pilot_net\" # choices: \"pilot_net\",\"cil_mobile\",\"cil_mobile_fast\",\"cil\"\n",
+ "params.POLICY = \"autopilot\" # choices: \"autopilot\",\"point_goal_nav\"\n",
"params.TRAIN_BATCH_SIZE = 128\n",
"params.TEST_BATCH_SIZE = 16\n",
"params.LEARNING_RATE = 0.0003\n",
"params.NUM_EPOCHS = 100\n",
- "params.BATCH_NORM = True\n",
- "params.FLIP_AUG = False\n",
- "params.CMD_AUG = False\n",
- "params.USE_LAST = False\n",
- "params.WANDB = False"
+ "params.BATCH_NORM = True # use batch norm (recommended)\n",
+ "params.FLIP_AUG = False # flip image and controls as augmentation (only autopilot)\n",
+ "params.CMD_AUG = False # randomize high-level command as augmentation (only autopilot)\n",
+ "params.USE_LAST = False # resume training from last checkpoint\n",
+ "params.WANDB = False\n",
+ "# policy = \"autopilot\": images are expected to be 256x96 - no cropping required\n",
+ "# policy = \"point_goal_nav\": images are expected to be 160x120 - cropping to 160x90\n",
+ "params.IS_CROP = params.POLICY == \"point_goal_nav\""
]
},
{
