def test(network_save_file):
checkpoint = torch.load(network_save_file)
net.load_state_dict(checkpoint['net'])
training_acc = checkpoint['acc']
training_epoch = checkpoint['epoch']
is_bias = bot.group_by_key(('bias' in k, v) for k, v in bot.trainable_params(net).items())
if args.optimizer == 'sgd':
state = {bot.MODEL: net, bot.VALID_MODEL: bot.copy.deepcopy(net), bot.OPTS: [bot.SGD(is_bias[False], opt_params), bot.SGD(is_bias[True], opt_params_bias)]}
else:
state = {bot.MODEL: net, bot.VALID_MODEL: bot.copy.deepcopy(net), bot.OPTS: [bot.SAM1(is_bias[False], opt_params, sam_flag=True), bot.SAM2(is_bias[True], opt_params_bias, sam_flag=True)]}
net.eval()
test_loss = 0
correct = 0
total = len(valid_set['data'])
with torch.no_grad():
res = bot.reduce(0, valid_batches(batch_size, transforms), state, bot.valid_steps)
acc = res['activation_log']['acc']
correct = np.count_nonzero(acc)
#print(acc)
#print(acc.shape)
res2 = bot.epoch_stats(res)
print(res2)
test_loss = np.mean(res['output']['loss'].detach().cpu().numpy())
progress_bar(len(test_dataset), len(test_dataset), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (test_loss, 100.*correct/total, correct, total))
return res2['acc']
'weight_decay':
bot.Const(5e-4 * batch_size),
'momentum':
bot.Const(0.9)
}
opt_params_bias = {
'lr':
lr_schedule([0, epochs / 5, epochs - ema_epochs],
[0.0, 1.0 * 64, 0.1 * 64], batch_size),
'weight_decay':
bot.Const(5e-4 * batch_size / 64),
'momentum':
bot.Const(0.9)
}
is_bias = bot.group_by_key(
('bias' in k, v) for k, v in bot.trainable_params(net).items())
state, timer = {
bot.MODEL:
net,
bot.VALID_MODEL:
bot.copy.deepcopy(net),
bot.OPTS: [
bot.SGD(is_bias[False], opt_params),
bot.SGD(is_bias[True], opt_params_bias)
]
}, bot.Timer(torch.cuda.synchronize)
print('=====> Training...')
train_times = []
train_accs = []