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launch_utils.py
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launch_utils.py
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import argparse
import os
import pickle
import shutil
import random
from typing import Tuple, Dict
from avalanche.benchmarks import GenericCLScenario
from avalanche.benchmarks.classic import SplitCIFAR10, SplitCIFAR100, SplitFMNIST, SplitMNIST
from avalanche.benchmarks.generators import benchmark_with_validation_stream, nc_benchmark
from avalanche.benchmarks.datasets import EMNIST
import numpy as np
import torch
from torch.backends import cudnn
from torchvision import transforms
import model_config
DATASET_PATH = os.path.join(os.path.abspath('..'), 'datasets')
LOG_PATH = os.path.join(os.path.abspath('.'), 'Logs')
def set_seeds(seed: int) -> None:
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.use_deterministic_algorithms(True)
cudnn.benchmark = False
cudnn.deterministic = True
def get_argument_parser() -> argparse.Namespace:
parser = argparse.ArgumentParser(description='Experiment')
# Logging params
parser.add_argument('--experiment_name', type=str, default = 'Test_CE')
parser.add_argument("--gpu_prefetch", type=int, default = 1)
# LTM Memory Params
parser.add_argument('--ltm_per_class', type=int, default = 25)
parser.add_argument('--ltm_k_nearest', type=int, default = 5)
# STM Memory Params
parser.add_argument('--stm_per_class', type=int, default = 50)
parser.add_argument('--stm_num_tasks', type=int, default = 1) # 1 (current) + 2 (most recent)
parser.add_argument('--memory_mode', type=str, default="internal", choices=["raw", "internal"])
# Dataset params
parser.add_argument('--dataset', type=str, default = 'EMNIST')
parser.add_argument('--number_of_tasks', type=int, default = 13)
# Architectural params
parser.add_argument('--model', type=str, default = 'MNISTLIKE')
parser.add_argument('--prune_perc', type=float, default=60.0)
# Learning params
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--permute_seed', type=int, default=0)
parser.add_argument('--deterministic', type=int, default=1)
# Anything under torch.optim works. e.g., 'SGD' and 'Adam'
parser.add_argument('--optimizer', type=str, default = 'Adadelta')
parser.add_argument('--sgd_momentum', type=float, default = 0.90)
parser.add_argument('--learning_rate', type=float, default = 1.0)
parser.add_argument('--batch_size', type=int, default = 256)
parser.add_argument('--batch_size_memory', type=int, default = 256)
parser.add_argument('--weight_decay', type=float, default =0.0)
parser.add_argument('--supcon_temperature', type=float, default = 0.1)
parser.add_argument('--reinit', type=int, default = 1)
# Algortihm params
parser.add_argument('--phase_epochs', type=int, default = 3)
parser.add_argument("--min_activation_perc", type=float, default=60.0)
parser.add_argument("--max_phases", type=int, default=10)
parser.add_argument('--tau_param', type=float, default = 30)
# Pretrain load and freeze
parser.add_argument('--pretrain_load_path', type=str, default = "")
parser.add_argument('--pretrain_freeze', type=int, default = 0) # 0 = no freezing, 1 = freeze all early conv
# parser.add_argument('--last_layer_bn', type=int, default = 0) Not used anymore
# parser.add_argument('--lr_decay_phase', type=float, default = 1.0)
# parser.add_argument('--early_stop_coef', type=float, default=2.0) Not used anymore
parser.add_argument('--verbose_logging', type=int, default = '4', choices=[0, 1, 2, 3, 4, 5, 6])
return parser.parse_args()
def get_model_config_dict(args: argparse.Namespace) -> dict:
return getattr(model_config, args.model)
def get_log_param_dict(args: argparse.Namespace) -> dict:
return {
"LogPath": LOG_PATH,
"DirName": args.experiment_name,
"save_activations_task": args.verbose_logging in [3, 4, 5, 6],
"save_activations_phases": args.verbose_logging in [6],
"save_model_phase": args.verbose_logging in [6],
"eval_model_phase": args.verbose_logging in [5, 6],
"save_model_task": args.verbose_logging in [3, 4, 5, 6],
"write_phase_log": args.verbose_logging in [4, 5, 6],
"write_task_log": args.verbose_logging in [2, 3, 4, 5, 6],
"write_sequence_log": args.verbose_logging in [1, 2, 3, 4, 5, 6],
"no_log": args.verbose_logging == 0
}
def create_log_dirs(args: argparse.Namespace, log_params: dict) -> None:
dirpath = os.path.join(log_params["LogPath"], log_params["DirName"])
# Remove existing files/dirs
if os.path.exists(dirpath) and os.path.isdir(dirpath):
shutil.rmtree(dirpath)
if log_params["no_log"]:
return
# Create log dirs and save experiment args
os.makedirs(dirpath)
with open(os.path.join(dirpath, 'args.pkl'), 'wb') as file:
pickle.dump(args, file)
if log_params["write_task_log"]:
for task_id in range(1, args.number_of_tasks +1):
os.makedirs(os.path.join(dirpath, "Task_{}".format(task_id)))
def get_experience_streams(args: argparse.Namespace) -> Tuple[GenericCLScenario, int, int, Dict]:
if args.dataset == "EMNIST":
emnist_train = EMNIST(root=DATASET_PATH, train = True, split='letters', download= True)
emnist_train.targets = emnist_train.targets - 1
emnist_test = EMNIST(root=DATASET_PATH, train = False, split='letters', download= True)
emnist_test.targets = emnist_test.targets - 1
stream = nc_benchmark(train_dataset=emnist_train,test_dataset=emnist_test, # type: ignore
n_experiences=args.number_of_tasks, task_labels = False, shuffle = False,
seed = args.seed, fixed_class_order=list(map(lambda x: int(x), emnist_train.targets.unique())),
train_transform = transforms.ToTensor(),
eval_transform=transforms.ToTensor())
stream_with_val = benchmark_with_validation_stream(stream, validation_size=0.01, output_stream="val", shuffle=True)
task2classes = dict((index, stream.classes_in_this_experience)
for index, stream in enumerate(stream_with_val.train_stream, 1))
return (stream_with_val, 784, 46, task2classes)
if args.dataset == "EMNIST_P":
emnist_train = EMNIST(root=DATASET_PATH, train = True, split='letters', download= True)
emnist_train.targets = emnist_train.targets - 1
emnist_test = EMNIST(root=DATASET_PATH, train = False, split='letters', download= True)
emnist_test.targets = emnist_test.targets - 1
local_random = random.Random()
local_random.seed(args.permute_seed)
original_list = list(map(lambda x: int(x), emnist_train.targets.unique()))
if args.permute_seed != 0:
original_list = local_random.sample(original_list, len(original_list))
stream = nc_benchmark(train_dataset=emnist_train,test_dataset=emnist_test, # type: ignore
n_experiences=args.number_of_tasks, task_labels = False, shuffle = False,
seed = args.seed, fixed_class_order=original_list,
train_transform = transforms.ToTensor(),
eval_transform=transforms.ToTensor())
stream_with_val = benchmark_with_validation_stream(stream, validation_size=0.01, output_stream="val", shuffle=True)
task2classes = dict((index, stream.classes_in_this_experience)
for index, stream in enumerate(stream_with_val.train_stream, 1))
return (stream_with_val, 784, 46, task2classes)
if args.dataset == "SplitMNIST":
stream = SplitMNIST(n_experiences = args.number_of_tasks,
seed = args.seed, dataset_root=DATASET_PATH, fixed_class_order=list(range(10)))
stream_with_val = benchmark_with_validation_stream(stream, validation_size=0.01, output_stream="val", shuffle=True)
task2classes = dict((index, stream.classes_in_this_experience)
for index, stream in enumerate(stream_with_val.train_stream, 1))
return (stream_with_val, 784, 10, task2classes)
if args.dataset == "SplitFMNIST":
stream = SplitFMNIST(n_experiences = args.number_of_tasks,
seed = args.seed, dataset_root=DATASET_PATH, fixed_class_order=list(range(10)))
stream_with_val = benchmark_with_validation_stream(stream, validation_size=0.01, output_stream="val", shuffle=True)
task2classes = dict((index, stream.classes_in_this_experience)
for index, stream in enumerate(stream_with_val.train_stream, 1))
return (stream_with_val, 784, 10, task2classes)
if args.dataset == "SplitCIFAR10":
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
stream = SplitCIFAR10(n_experiences = args.number_of_tasks,
seed = args.seed, dataset_root=DATASET_PATH, fixed_class_order=list(range(10)),
train_transform=transform, eval_transform=transform)
stream_with_val = benchmark_with_validation_stream(stream, validation_size=0.01, output_stream="val", shuffle=True)
task2classes = dict((index, stream.classes_in_this_experience)
for index, stream in enumerate(stream_with_val.train_stream, 1))
return (stream_with_val, 3, 10, task2classes)
if args.dataset == "SplitCIFAR100":
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762))])
stream = SplitCIFAR100(n_experiences = args.number_of_tasks,
seed = args.seed, dataset_root=DATASET_PATH, fixed_class_order=list(range(100)),
train_transform=transform, eval_transform=transform)
stream_with_val = benchmark_with_validation_stream(stream, validation_size=0.01, output_stream="val", shuffle=True)
task2classes = dict((index, stream.classes_in_this_experience)
for index, stream in enumerate(stream_with_val.train_stream, 1))
return (stream_with_val, 3, 100, task2classes)
raise Exception("Dataset {} is not defined!".format(args.dataset))