77 lines
No EOL
3.2 KiB
Python
77 lines
No EOL
3.2 KiB
Python
import numpy as np
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import random, pdb, os, copy
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import argparse
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import pandas as pd
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import pickle as pkl
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import torch
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from utils import MakeDir, set_seed, str2bool
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from CRT import Model, self_supervised_learning, finetuning
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from base_models import FTDataSet
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--ssl", type=str2bool, default=False, help='Self-supervised learning (pretrain the autoencoder)')
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parser.add_argument("--sl", type=str2bool, default=True, help='Supervised learning (downstream tasks)')
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parser.add_argument("--load", type=str2bool, default=True)
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parser.add_argument("--patch_len", type=int, default=5)
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parser.add_argument("--dim", type=int, default=128)
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parser.add_argument("--depth", type=int, default=6)
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parser.add_argument("--dropout", type=float, default=0.1)
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parser.add_argument("--Headdropout", type=float, default=0.1)
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parser.add_argument("--beta2", type=float, default=1)
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parser.add_argument("--AElearningRate", type=float, default=1e-4)
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parser.add_argument("--HeadlearningRate", type=float, default=1e-3)
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parser.add_argument("--AEbatchSize", type=int, default=512)
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parser.add_argument("--HeadbatchSize", type=int, default=32)
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parser.add_argument("--AEepochs", type=int, default=100)
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parser.add_argument("--Headepochs", type=int, default=50)
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parser.add_argument("--AEwin", type=int, default=5)
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parser.add_argument("--Headwin", type=int, default=5)
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parser.add_argument("--slid", type=int, default=1)
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parser.add_argument("--timeWinFreq", type=int, default=20)
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parser.add_argument("--pretrainDataset", type=str, default='Buffalo_EMAKI')
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parser.add_argument("--testDataset", type=str)
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parser.add_argument("--stage", type=int, choices=[0,1], default=0, help='0: Train the classifier only; 1: Finetune the whole model')
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parser.add_argument("--seed", type=int, default=0)
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opt = parser.parse_args()
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opt.in_dim = 2 # x,y
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opt.seq_len = opt.AEwin * opt.timeWinFreq *2
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opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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set_seed(opt.seed)
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aemodelfile = 'pretrained_model.pkl'
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if opt.ssl:
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'''
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Load dataset for pretraining the Autoencoder
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taskdata: on-screen coordinates (x,y), their magnitude and phase
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'''
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taskdata, taskclicks = loadPretrainDataset(opt)
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model = Model(opt).to(opt.device)
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self_supervised_learning(model, taskdata, taskclicks, opt, modelfile=aemodelfile)
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if opt.load:
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model = torch.load(aemodelfile, map_location=opt.device)
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else:
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model = Model(opt).to(opt.device)
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pdb.set_trace()
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if opt.sl:
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'''
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Load dataset for downstream tasks
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_X: on-screen coordinates (x,y), their magnitude and phase
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_y: labels
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_clicks: clicks
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'''
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train_X, train_y, train_clicks, valid_X, valid_y, valid_clicks = loadDownstreamDataset(opt)
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TrainSet = FTDataSet(train_X, train_y, train_clicks, multi_label=True) # Binary or multi-class
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ValidSet = FTDataSet(valid_X, valid_y, valid_clicks, multi_label=True)
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finetuning(model, TrainSet, ValidSet, opt, aemodelfile.split('.pkl')[0]+'downstream.pkl', multi_label=True) |