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io_utils.py
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import sys
from pathlib import Path
import neptune.new as neptune
import numpy as np
import os
import glob
import argparse
from neptune.new import Run
import backbone
import configs
import hn_args
from methods.hypernets import hypernet_types
model_dict = dict(
Conv4 = backbone.Conv4,
Conv4Pool = backbone.Conv4Pool,
Conv4S = backbone.Conv4S,
Conv6 = backbone.Conv6,
ResNet10 = backbone.ResNet10,
ResNet18 = backbone.ResNet18,
ResNet34 = backbone.ResNet34,
ResNet50 = backbone.ResNet50,
ResNet101 = backbone.ResNet101,
Conv4WithKernel = backbone.Conv4WithKernel,
ResNetWithKernel = backbone.ResNetWithKernel,
)
class ParamHolder:
"""A class for checking which script arguments were actually used at any time"""
def __init__(self, params):
self.params = params
self.history = []
def __getattr__(self, item):
it = getattr(self.params, item)
if item not in self.history:
print("Getting", item, "=", it)
self.history.append(item)
return it
def get_ignored_args(self):
return sorted([
k for k in vars(self.params).keys() if k not in self.history
])
def parse_args(script):
parser = argparse.ArgumentParser(description= 'few-shot script %s' %(script), formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument('--seed' , default=0, type=int, help='Seed for Numpy and pyTorch. Default: 0 (None)')
parser.add_argument('--dataset' , default='CUB', help='CUB/miniImagenet/cross/omniglot/cross_char')
parser.add_argument('--model' , default='Conv4', help='model: Conv{4|6}{Pool} / ResNet{10|18|34|50|101}', choices=sorted(model_dict.keys())) # 50 and 101 are not used in the paper
parser.add_argument('--method', default='baseline', choices=['baseline', 'baseline++', 'DKT', 'protonet', 'matchingnet', 'relationnet', 'relationnet_softmax', 'maml', 'maml_approx', 'hyper_maml'] + list(hypernet_types.keys()),
help='baseline/baseline++/protonet/matchingnet/relationnet{_softmax}/maml{_approx}/hn_poc/hyper_maml') #relationnet_softmax replace L2 norm with softmax to expedite training, maml_approx use first-order approximation in the gradient for efficiency
parser.add_argument('--train_n_way' , default=5, type=int, help='class num to classify for training') #baseline and baseline++ would ignore this parameter
parser.add_argument('--test_n_way' , default=5, type=int, help='class num to classify for testing (validation) ') #baseline and baseline++ only use this parameter in finetuning
parser.add_argument('--n_shot' , default=5, type=int, help='number of labeled data in each class, same as n_support') #baseline and baseline++ only use this parameter in finetuning
parser.add_argument('--train_aug' , action='store_true', help='perform data augmentation or not during training ') #still required for save_features.py and test.py to find the model path correctly
parser.add_argument("--checkpoint_suffix", type=str,default="", help="Suffix for custom experiment differentiation" )
parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate")
parser.add_argument("--optim", type=str, choices=["adam", "sgd"], help="Optimizer", default="adam")
parser.add_argument("--n_val_perms", type=int, default=1, help="Number of task permutations in evaluation.")
parser.add_argument("--lr_scheduler", type=str, help="LR scheduler", default="none", choices=[
"multisteplr", "none", "cosine", "reducelronplateau"
])
parser.add_argument("--milestones", nargs='+', type=int, default=None, help="Milestones for multisteplr")
parser.add_argument("--maml_save_feature_network", action="store_true", help="if to save feature net used in MAML")
parser.add_argument("--maml_adapt_classifier", action="store_true", help="Adapt only the classifier during second gradient calculation")
parser.add_argument("--evaluate_model", action="store_true", help="Skip train phase and perform final test")
if script == 'train':
parser.add_argument('--num_classes' , default=200, type=int, help='total number of classes in softmax, only used in baseline') #make it larger than the maximum label value in base class
parser.add_argument('--save_freq' , default=50, type=int, help='Save frequency')
parser.add_argument('--start_epoch' , default=0, type=int,help ='Starting epoch')
parser.add_argument('--stop_epoch' , default=-1, type=int, help ='Stopping epoch') #for meta-learning methods, each epoch contains 100 episodes. The default epoch number is dataset dependent. See train.py
parser.add_argument('--resume' , action='store_true', help='continue from previous trained model with largest epoch')
parser.add_argument('--warmup' , action='store_true', help='continue from baseline, neglected if resume is true') #never used in the paper
parser.add_argument("--es_epoch", type=int, default=250,
help="Check if val accuracy threshold achieved at this epoch, stop if not.")
parser.add_argument("--es_threshold", type=float, default=70.0,
help="Val accuracy threshold for early stopping")
parser.add_argument("--eval_freq", type=int, default=1, help="Evaluation frequency")
elif script == 'save_features':
parser.add_argument('--split' , default='novel', help='base/val/novel') #default novel, but you can also test base/val class accuracy if you want
parser.add_argument('--save_iter', default=-1, type=int,help ='save feature from the model trained in x epoch, use the best model if x is -1')
elif script == 'test':
parser.add_argument('--split' , default='novel', help='base/val/novel') #default novel, but you can also test base/val class accuracy if you want
parser.add_argument('--save_iter', default=-1, type=int,help ='saved feature from the model trained in x epoch, use the best model if x is -1')
parser.add_argument('--adaptation' , action='store_true', help='further adaptation in test time or not')
parser.add_argument('--repeat', default=5, type=int, help ='Repeat the test N times with different seeds and take the mean. The seeds range is [seed, seed+repeat]')
else:
raise ValueError('Unknown script')
parser = hn_args.add_hn_args_to_parser(parser)
return ParamHolder(parser.parse_args())
def parse_args_regression(script):
parser = argparse.ArgumentParser(description= 'few-shot script %s' %(script))
parser.add_argument('--seed' , default=0, type=int, help='Seed for Numpy and pyTorch. Default: 0 (None)')
parser.add_argument('--model' , default='Conv3', help='model: Conv{3} / MLP{2}')
parser.add_argument('--method' , default='DKT', help='DKT / transfer')
parser.add_argument('--dataset' , default='QMUL', help='QMUL / sines')
parser.add_argument('--spectral', action='store_true', help='Use a spectral covariance kernel function')
if script == 'train_regression':
parser.add_argument('--start_epoch' , default=0, type=int,help ='Starting epoch')
parser.add_argument('--stop_epoch' , default=100, type=int, help ='Stopping epoch') #for meta-learning methods, each epoch contains 100 episodes. The default epoch number is dataset dependent. See train.py
parser.add_argument('--resume' , action='store_true', help='continue from previous trained model with largest epoch')
elif script == 'test_regression':
parser.add_argument('--n_support', default=5, type=int, help='Number of points on trajectory to be given as support points')
parser.add_argument('--n_test_epochs', default=10, type=int, help='How many test people?')
return parser.parse_args()
def get_assigned_file(checkpoint_dir,num):
assign_file = os.path.join(checkpoint_dir, '{:d}.tar'.format(num))
return assign_file
def get_resume_file(checkpoint_dir):
filelist = glob.glob(os.path.join(checkpoint_dir, '*.tar'))
if len(filelist) == 0:
return None
last_model_files = [x for x in filelist if os.path.basename(x) == 'last_model.tar' ]
if len(last_model_files) == 1:
return last_model_files[0]
filelist = [ x for x in filelist if os.path.basename(x) != 'best_model.tar' ]
epochs = np.array([int(os.path.splitext(os.path.basename(x))[0]) for x in filelist])
max_epoch = np.max(epochs)
resume_file = os.path.join(checkpoint_dir, '{:d}.tar'.format(max_epoch))
return resume_file
def get_best_file(checkpoint_dir):
best_file = os.path.join(checkpoint_dir, 'best_model.tar')
if os.path.isfile(best_file):
return best_file
else:
return get_resume_file(checkpoint_dir)
def setup_neptune(params) -> Run:
try:
run_name = Path(params.checkpoint_dir).relative_to(Path(configs.save_dir) / "checkpoints").name
run_file = Path(params.checkpoint_dir) / "NEPTUNE_RUN.txt"
run_id = None
if params.resume and run_file.exists():
with run_file.open("r") as f:
run_id = f.read()
print("Resuming neptune run", run_id)
run = neptune.init(
name=run_name,
source_files="**/*.py",
tags=[params.checkpoint_suffix] if params.checkpoint_suffix != "" else [],
run=run_id
)
with run_file.open("w") as f:
f.write(run._short_id)
print("Starting neptune run", run._short_id)
run["params"] = vars(params.params)
run["cmd"] = f"python {' '.join(sys.argv)}"
return run
except Exception as e:
print("Cannot initialize neptune because of", e)
pass