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jblitzar
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github-python

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logger.info("final best loss test performance (at epoch %d)" % best_epoch_loss)
print 'test loss: %.4f, corr: %d, total: %d, acc: %.2f%%' % (
best_loss_test_err / test_inst, best_loss_test_corr, test_total, best_loss_test_corr * 100 / test_total)
logger.info("final best acc test performance (at epoch %d)" % best_epoch_acc)
print 'test loss: %.4f, corr: %d, total: %d, acc: %.2f%%' % (
best_acc_test_err / test_inst, best_acc_test_corr, test_total, best_acc_test_corr * 100 / test_total)
def test():
energies_var = T.tensor4('energies', dtype=theano.config.floatX)
targets_var = T.imatrix('targets')
masks_var = T.matrix('masks', dtype=theano.config.floatX)
layer_input = lasagne.layers.InputLayer([2, 2, 3, 3], input_var=energies_var)
out = lasagne.layers.get_output(layer_input)
loss = crf_loss(out, targets_var, masks_var)
prediction, acc = crf_accuracy(energies_var, targets_var)
fn = theano.function([energies_var, targets_var, masks_var], [loss, prediction, acc])
energies = np.array([[[[10, 15, 20], [5, 10, 15], [3, 2, 0]], [[5, 10, 1], [5, 10, 1], [5, 10, 1]]],
[[[5, 6, 7], [2, 3, 4], [2, 1, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]]]], dtype=np.float32)
targets = np.array([[0, 1], [0, 2]], dtype=np.int32)
masks = np.array([[1, 1], [1, 0]], dtype=np.float32)
l, p, a = fn(energies, targets, masks)
print l
print p
print a
if __name__ == '__main__':
main()
# <FILESEP>
import warnings
warnings.filterwarnings("ignore")
# from apex import amp
import numpy as np
import torch.utils.data as data
from torchvision import transforms
import os
import torch
import argparse
from data_preprocessing.dataset_raf import RafDataSet
from data_preprocessing.dataset_affectnet import Affectdataset
from data_preprocessing.dataset_affectnet_8class import Affectdataset_8class
from sklearn.metrics import f1_score, confusion_matrix
from time import time
from utils import *
from data_preprocessing.sam import SAM
from models.emotion_hyp import pyramid_trans_expr
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='rafdb', help='dataset')
parser.add_argument('-c', '--checkpoint', type=str, default=None, help='Pytorch checkpoint file path')
parser.add_argument('--batch_size', type=int, default=200, help='Batch size.')
parser.add_argument('--val_batch_size', type=int, default=32, help='Batch size for validation.')
parser.add_argument('--modeltype', type=str, default='large', help='small or base or large')
parser.add_argument('--optimizer', type=str, default="adam", help='Optimizer, adam or sgd.')
parser.add_argument('--lr', type=float, default=0.00004, help='Initial learning rate for sgd.')
parser.add_argument('--momentum', default=0.9, type=float, help='Momentum for sgd')
parser.add_argument('--workers', default=2, type=int, help='Number of data loading workers (default: 4)')
parser.add_argument('--epochs', type=int, default=300, help='Total training epochs.')
parser.add_argument('--gpu', type=str, default='0,1', help='assign multi-gpus by comma concat')
return parser.parse_args()
def run_training():
args = parse_args()
torch.manual_seed(123)
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
print("Work on GPU: ", os.environ['CUDA_VISIBLE_DEVICES'])
data_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
transforms.RandomErasing(scale=(0.02, 0.1)),
])
data_transforms_val = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
num_classes = 7
if args.dataset == "rafdb":