def get_model_based_on_rho(rho,
arch,
config_only=False,
model_config_overrides={}):
# extra receptive checking
extra_kernal_rf = rho - 7
model_config = {
"arch": arch,
"base_channels": 128,
"block_type": "basic",
"depth": 26,
"input_shape": [10, 2, -1, -1],
"multi_label": False,
"n_classes": 10,
"prediction_threshold": 0.4,
"stage1": {
"maxpool": [1, 2, 4],
"k1s": [
3, 3 - (-extra_kernal_rf > 6) * 2,
3 - (-extra_kernal_rf > 4) * 2, 3 - (-extra_kernal_rf > 2) * 2
],
"k2s": [
1, 3 - (-extra_kernal_rf > 5) * 2,
3 - (-extra_kernal_rf > 3) * 2, 3 - (-extra_kernal_rf > 1) * 2
]
},
"stage2": {
"maxpool": [],
"k1s": [
3 - (-extra_kernal_rf > 0) * 2, 1 + (extra_kernal_rf > 1) * 2,
1 + (extra_kernal_rf > 3) * 2, 1 + (extra_kernal_rf > 5) * 2
],
"k2s": [
1 + (extra_kernal_rf > 0) * 2, 1 + (extra_kernal_rf > 2) * 2,
1 + (extra_kernal_rf > 4) * 2, 1 + (extra_kernal_rf > 6) * 2
]
},
"stage3": {
"maxpool": [],
"k1s": [
1 + (extra_kernal_rf > 7) * 2, 1 + (extra_kernal_rf > 9) * 2,
1 + (extra_kernal_rf > 11) * 2, 1 + (extra_kernal_rf > 13) * 2
],
"k2s": [
1 + (extra_kernal_rf > 8) * 2, 1 + (extra_kernal_rf > 10) * 2,
1 + (extra_kernal_rf > 12) * 2, 1 + (extra_kernal_rf > 14) * 2
]
},
"block_type": "basic",
"use_bn": True,
"weight_init": "fixup"
}
# override model_config
model_config = update_dict(model_config, model_config_overrides)
if config_only:
return model_config
return Network(model_config)
'the pre-trained model path to load, in this case the model is only evaluated'
)
args = parser.parse_args()
if args.load is None:
with open("configs/cp_resnet.json", "r") as text_file:
default_conf = json.load(text_file)
else:
with open("configs/cp_resnet_eval.json", "r") as text_file:
default_conf = json.load(text_file)
# overriding the database config
print(f"\nSelected training dataset is configs/datasets{args.dataset} ...\n")
with open("configs/datasets/" + args.dataset, "r") as text_file:
dataset_conf = json.load(text_file)
default_conf = utils_funcs.update_dict(default_conf, dataset_conf)
default_conf['out_dir'] = default_conf['out_dir'].replace(
"cp_resnet", args.arch) + str(
datetime.datetime.now().strftime('%b%d_%H.%M.%S'))
print("The experiment outputs will be found at: ", default_conf['out_dir'])
tensorboard_write_path = default_conf['out_dir'].replace("out", "runs", 1)
print("The experiment tesnorboard can be accessed: tensorboard --logdir ",
tensorboard_write_path)
print("Rho value : ", args.rho)
print("Use Mix-up : ", args.mixup)
arch = importlib.import_module('models.{}'.format(args.arch))