/
test.py
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/
test.py
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### execute this function to train and test the vae-model
from test_model import Model
import numpy as np
import pickle
import torch
import os
import argparse
import time
import warnings
warnings.filterwarnings("ignore")
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser()
parser.add_argument('--dataset')
parser.add_argument('--latent_size',type=int)
parser.add_argument('--generalized', type = str2bool)
args = parser.parse_args()
########################################
# the basic hyperparameters
########################################
hyperparameters = {
'num_shots': 0,
'device': 'cuda',
'model_specifics': {'cross_reconstruction': True,
'name': 'CADA',
'distance': 'wasserstein',
'warmup': {'beta': {'factor': 0.25,
'end_epoch': 90,
'start_epoch': 0},
'cross_reconstruction': {'factor': 2.37,
'end_epoch': 75,
'start_epoch': 21},
'distance': {'factor': 8.0,
'end_epoch': 25,
'start_epoch': 0}}},
'lr_gen_model': 0.00015,
'generalized': True,
'batch_size': 50,
'samples_per_class': {'SUN': (200, 0, 400, 0),
'APY': (200, 0, 400, 0),
'CUB': (200, 0, 400, 0),
'AWA2': (200, 0, 400, 0),
'FLO': (200, 0, 400, 0),
'AWA1': (200, 0, 400, 0)},
'epochs': 200,
'loss': 'l1',
'auxiliary_data_source' : 'attributes',
'lr_cls': 0.001,
'dataset': 'CUB',
'hidden_size_rule': {'resnet_features': (4096, 4096),
'attributes': (4096, 4096),
'sentences': (4096, 4096) },
'coarse_latent_size': 2048,
'latent_size': 64,
'recon_x_cyc_w': 0.5,
'adapt_mode': 'SWD', #MCD or SWD
'classifier': 'softmax', #knn or softmax
'result_root': '/home/shimingchen/ZSL/Feature-Matching/model/result'
}
# The training epochs for the final classifier, for early stopping,
# as determined on the validation spit
cls_train_steps = [
{'dataset': 'SUN', 'latent_size': 128, 'generalized': True, 'cls_train_steps': 40},
{'dataset': 'SUN', 'latent_size': 128, 'generalized': False, 'cls_train_steps': 30},
{'dataset': 'AWA1', 'latent_size': 64, 'generalized': True, 'cls_train_steps': 33},
{'dataset': 'AWA1', 'latent_size': 64, 'generalized': False, 'cls_train_steps': 25},
{'dataset': 'CUB', 'latent_size': 64, 'generalized': True, 'cls_train_steps': 23},
{'dataset': 'CUB', 'latent_size': 64, 'generalized': False, 'cls_train_steps': 22},
{'dataset': 'AWA2', 'latent_size': 64, 'generalized': True, 'cls_train_steps': 50},
{'dataset': 'AWA2', 'latent_size': 64, 'generalized': False, 'cls_train_steps': 39},
]
##################################
# change some hyperparameters here
##################################
hyperparameters['dataset'] = args.dataset
hyperparameters['latent_size']= args.latent_size
hyperparameters['generalized']= args.generalized
hyperparameters['cls_train_steps'] = [x['cls_train_steps'] for x in cls_train_steps
if all([hyperparameters['dataset']==x['dataset'],
hyperparameters['latent_size']==x['latent_size'],
hyperparameters['generalized']==x['generalized'] ])][0]
print('***')
print(hyperparameters['cls_train_steps'] )
if hyperparameters['generalized']:
if hyperparameters['num_shots']==0:
hyperparameters['samples_per_class'] = {'CUB': (200, 0, 400, 0), 'SUN': (200, 0, 400, 0),
'APY': (200, 0, 400, 0), 'AWA1': (200, 0, 400, 0),
'AWA2': (200, 0, 400, 0), 'FLO': (200, 0, 400, 0)}
else:
hyperparameters['samples_per_class'] = {'CUB': (200, 0, 200, 200), 'SUN': (200, 0, 200, 200),
'APY': (200, 0, 200, 200), 'AWA1': (200, 0, 200, 200),
'AWA2': (200, 0, 200, 200), 'FLO': (200, 0, 200, 200)}
else:
if hyperparameters['num_shots']==0:
hyperparameters['samples_per_class'] = {'CUB': (0, 0, 400, 0), 'SUN': (0, 0, 200, 0),
'APY': (0, 0, 200, 0), 'AWA1': (0, 0, 800, 0),
'AWA2': (0, 0, 200, 0), 'FLO': (0, 0, 200, 0)}
else:
hyperparameters['samples_per_class'] = {'CUB': (0, 0, 200, 200), 'SUN': (0, 0, 200, 200),
'APY': (0, 0, 200, 200), 'AWA1': (0, 0, 200, 200),
'AWA2': (0, 0, 200, 200), 'FLO': (0, 0, 200, 200)}
model = Model( hyperparameters)
model.to(hyperparameters['device'])
model.test()
print(time.strftime('ending time:%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
print("dataset", args.dataset)
print(hyperparameters['classifier'])
print("**********END*******************")