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eval_curve.py
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eval_curve.py
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import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='/tmp/eval', metavar='DIR',
help='training directory (default: /tmp/eval)')
parser.add_argument('--num_points', type=int, default=61, metavar='N',
help='number of points on the curve (default: 61)')
parser.add_argument('--dataset', type=str, default='CIFAR10', metavar='DATASET',
help='dataset name (default: CIFAR10)')
parser.add_argument('--use_test', action='store_true',
help='switches between validation and test set (default: validation)')
parser.add_argument('--transform', type=str, default='VGG', metavar='TRANSFORM',
help='transform name (default: VGG)')
parser.add_argument('--data_path', type=str, default=None, metavar='PATH',
help='path to datasets location (default: None)')
parser.add_argument('--batch_size', type=int, default=128, metavar='N',
help='input batch size (default: 128)')
parser.add_argument('--num_workers', type=int, default=4, metavar='N',
help='number of workers (default: 4)')
parser.add_argument('--model', type=str, default=None, metavar='MODEL',
help='model name (default: None)')
parser.add_argument('--curve', type=str, default=None, metavar='CURVE',
help='curve type to use (default: None)')
parser.add_argument('--num_bends', type=int, default=3, metavar='N',
help='number of curve bends (default: 3)')
parser.add_argument('--ckpt', type=str, default=None, metavar='CKPT',
help='checkpoint to eval (default: None)')
parser.add_argument('--wd', type=float, default=1e-4, metavar='WD',
help='weight decay (default: 1e-4)')
args = parser.parse_args()
os.makedirs(args.dir, exist_ok=True)
torch.backends.cudnn.benchmark = True
loaders, num_classes = data.loaders(
args.dataset,
args.data_path,
args.batch_size,
args.num_workers,
args.transform,
args.use_test,
shuffle_train=False
)
architecture = getattr(models, args.model)
curve = getattr(curves, args.curve)
model = curves.CurveNet(
num_classes,
curve,
architecture.curve,
args.num_bends,
architecture_kwargs=architecture.kwargs,
)
model.cuda()
checkpoint = torch.load(args.ckpt)
model.load_state_dict(checkpoint['model_state'])
criterion = F.cross_entropy
regularizer = curves.l2_regularizer(args.wd)
T = args.num_points
ts = np.linspace(0.0, 1.0, T)
tr_loss = np.zeros(T)
tr_nll = np.zeros(T)
tr_acc = np.zeros(T)
te_loss = np.zeros(T)
te_nll = np.zeros(T)
te_acc = np.zeros(T)
tr_err = np.zeros(T)
te_err = np.zeros(T)
dl = np.zeros(T)
previous_weights = None
columns = ['t', 'Train loss', 'Train nll', 'Train error (%)', 'Test nll', 'Test error (%)']
t = torch.FloatTensor([0.0]).cuda()
for i, t_value in enumerate(ts):
t.data.fill_(t_value)
weights = model.weights(t)
if previous_weights is not None:
dl[i] = np.sqrt(np.sum(np.square(weights - previous_weights)))
previous_weights = weights.copy()
utils.update_bn(loaders['train'], model, t=t)
tr_res = utils.test(loaders['train'], model, criterion, regularizer, t=t)
te_res = utils.test(loaders['test'], model, criterion, regularizer, t=t)
tr_loss[i] = tr_res['loss']
tr_nll[i] = tr_res['nll']
tr_acc[i] = tr_res['accuracy']
tr_err[i] = 100.0 - tr_acc[i]
te_loss[i] = te_res['loss']
te_nll[i] = te_res['nll']
te_acc[i] = te_res['accuracy']
te_err[i] = 100.0 - te_acc[i]
values = [t, tr_loss[i], tr_nll[i], tr_err[i], te_nll[i], te_err[i]]
table = tabulate.tabulate([values], columns, tablefmt='simple', floatfmt='10.4f')
if i % 40 == 0:
table = table.split('\n')
table = '\n'.join([table[1]] + table)
else:
table = table.split('\n')[2]
print(table)
def stats(values, dl):
min = np.min(values)
max = np.max(values)
avg = np.mean(values)
int = np.sum(0.5 * (values[:-1] + values[1:]) * dl[1:]) / np.sum(dl[1:])
return min, max, avg, int
tr_loss_min, tr_loss_max, tr_loss_avg, tr_loss_int = stats(tr_loss, dl)
tr_nll_min, tr_nll_max, tr_nll_avg, tr_nll_int = stats(tr_nll, dl)
tr_err_min, tr_err_max, tr_err_avg, tr_err_int = stats(tr_err, dl)
te_loss_min, te_loss_max, te_loss_avg, te_loss_int = stats(te_loss, dl)
te_nll_min, te_nll_max, te_nll_avg, te_nll_int = stats(te_nll, dl)
te_err_min, te_err_max, te_err_avg, te_err_int = stats(te_err, dl)
print('Length: %.2f' % np.sum(dl))
print(tabulate.tabulate([
['train loss', tr_loss[0], tr_loss[-1], tr_loss_min, tr_loss_max, tr_loss_avg, tr_loss_int],
['train error (%)', tr_err[0], tr_err[-1], tr_err_min, tr_err_max, tr_err_avg, tr_err_int],
['test nll', te_nll[0], te_nll[-1], te_nll_min, te_nll_max, te_nll_avg, te_nll_int],
['test error (%)', te_err[0], te_err[-1], te_err_min, te_err_max, te_err_avg, te_err_int],
], [
'', 'start', 'end', 'min', 'max', 'avg', 'int'
], tablefmt='simple', floatfmt='10.4f'))
np.savez(
os.path.join(args.dir, 'curve.npz'),
ts=ts,
dl=dl,
tr_loss=tr_loss,
tr_loss_min=tr_loss_min,
tr_loss_max=tr_loss_max,
tr_loss_avg=tr_loss_avg,
tr_loss_int=tr_loss_int,
tr_nll=tr_nll,
tr_nll_min=tr_nll_min,
tr_nll_max=tr_nll_max,
tr_nll_avg=tr_nll_avg,
tr_nll_int=tr_nll_int,
tr_acc=tr_acc,
tr_err=tr_err,
tr_err_min=tr_err_min,
tr_err_max=tr_err_max,
tr_err_avg=tr_err_avg,
tr_err_int=tr_err_int,
te_loss=te_loss,
te_loss_min=te_loss_min,
te_loss_max=te_loss_max,
te_loss_avg=te_loss_avg,
te_loss_int=te_loss_int,
te_nll=te_nll,
te_nll_min=te_nll_min,
te_nll_max=te_nll_max,
te_nll_avg=te_nll_avg,
te_nll_int=te_nll_int,
te_acc=te_acc,
te_err=te_err,
te_err_min=te_err_min,
te_err_max=te_err_max,
te_err_avg=te_err_avg,
te_err_int=te_err_int,
)