Python get_args Exemples

Exemple #1

Fichier : fsp_kd.py Projet : Turing311/stage-kd

import warnings
warnings.filterwarnings("ignore",
                        category=UserWarning,
                        module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *
from trainer import *

args = get_args(description='Flow of Solution Procedure KD', mode='train')
expt = 'fsp-kd'

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    args.gpu = int(args.gpu)
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "stage": 0,
    "num_classes": 10,
    "batch_size": 64,
    "num_epochs": args.epoch,

Exemple #2

import warnings
warnings.filterwarnings("ignore",
                        category=UserWarning,
                        module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *
from trainer import *

args = get_args(description='Simultaneous KD', mode='train')
expt = 'simultaneous-kd'

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    args.gpu = int(args.gpu)
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "stage": 0,
    "num_classes": 10,
    "batch_size": 64,
    "num_epochs": args.epoch,

Exemple #3

Fichier : attention_transfer_kd.py Projet : Turing311/stage-kd

warnings.filterwarnings("ignore",
                        category=UserWarning,
                        module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *
from trainer import *

args = get_args(description='Attention Transfer KD', mode='train')
expt = 'attention-kd'

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    args.gpu = int(args.gpu)
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "stage": 0,
    "num_classes": 10,
    "batch_size": 64,
    "num_epochs": args.epoch,

Exemple #4

Fichier : no_teacher.py Projet : Turing311/stage-kd

import warnings
warnings.filterwarnings("ignore",
                        category=UserWarning,
                        module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *
from trainer import *

args = get_args(description='No Teacher', mode='train')
expt = 'no-teacher'

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    args.gpu = int(args.gpu)
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "stage": 0,
    "num_classes": 10,
    "batch_size": 64,
    "num_epochs": args.epoch,

Exemple #5

Fichier : hinton_kd.py Projet : Turing311/stage-kd

import warnings
warnings.filterwarnings("ignore",
                        category=UserWarning,
                        module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *
from trainer import *

args = get_args(description='Hinton KD', mode='train')
expt = 'hinton-kd'

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    args.gpu = int(args.gpu)
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "num_classes": 10,
    "batch_size": 64,
    "num_epochs": args.epoch,
    "learning_rate": 1e-4,

Exemple #6

Fichier : create_dataset.py Projet : Turing311/stage-kd

import os
from pathlib import Path
from fastai.vision import *
import shutil
import random
import argparse
import sys
from image_classification.arguments import get_args

# parser = argparse.ArgumentParser(description = 'Creating dataset for less data')
# parser.add_argument('-d', choices = ['imagenette', 'imagewoof', 'cifar10'], help = 'Give the dataset name from the choices')
# parser.add_argument('-p', type = int, help = 'Give percentage of dataset')
# args = parser.parse_args()

args = get_args(description='Creating dataset for less data experiments',
                mode='data')
random.seed(args.seed)

if args.dataset == 'imagenette' or args.dataset == 'imagewoof':
    NUM_ = int(args.percentage * 13)
    if args.dataset == 'imagenette':
        data = untar_data(
            'https://s3.amazonaws.com/fast-ai-imageclas/imagenette')
    else:
        data = untar_data(
            'https://s3.amazonaws.com/fast-ai-imageclas/imagewoof')
elif args.dataset == 'cifar10':
    NUM_ = int(args.percentage * 50)
    data = untar_data(URLs.CIFAR)
else:
    print('Give dataset from choices only')

Exemple #7

import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *
from trainer import *


args = get_args(description='Traditional KD', mode='train')
expt = 'traditional-kd'

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    args.gpu = int(args.gpu)
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "stage": 0,
    "num_classes": 10,
    "batch_size": 64,
    "num_epochs": args.epoch,
    "learning_rate": 1e-4,

Exemple #8

import warnings
warnings.filterwarnings("ignore",
                        category=UserWarning,
                        module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *

args = get_args(description='Evaluation Script', mode='eval')

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "stage": 0,
    "num_classes": 10,
    "batch_size": 64,
    "learning_rate": 1e-4,
    "seed": args.seed,
    "percentage": args.percentage,
    "gpu": args.gpu,

Exemple #9

Fichier : stagewise_kd.py Projet : Turing311/stage-kd

import warnings
warnings.filterwarnings("ignore",
                        category=UserWarning,
                        module="torch.nn.functional")
from comet_ml import Experiment
from fastai.vision import *
import torch
import argparse
import os
from image_classification.arguments import get_args
from image_classification.datasets.dataset import get_dataset
from image_classification.utils.utils import *
from image_classification.models.custom_resnet import *
from trainer import *

args = get_args(description='Stagewise KD', mode='train')
expt = 'stagewise-kd'

torch.manual_seed(args.seed)
if args.gpu != 'cpu':
    args.gpu = int(args.gpu)
    torch.cuda.set_device(args.gpu)
    torch.cuda.manual_seed(args.seed)

hyper_params = {
    "dataset": args.dataset,
    "model": args.model,
    "stage": 0,
    "num_classes": 10,
    "batch_size": 64,
    "num_epochs": args.epoch,