def __call__(self, x, t=None, train=True):
h1 = self.conv1(x)
h2 = F.relu(h1)
h3 = F.max_pooling_2d(h2, 2)
h4 = self.linear1(h3)
return F.softmax_cross_entropy(h4, t) if train else F.Softmax(h4)
def check_backward(self, x_data, gy_data, use_cudnn=True):
gradient_check.check_backward(functions.Softmax(use_cudnn), x_data,
gy_data, **self.check_backward_options)
def main():
parser = argparse.ArgumentParser(description='predict for submit:')
parser.add_argument('--batchsize', '-b', type=int, default=128,
help='Number of images in each mini-batch')
parser.add_argument('--gpu', '-g', type=int, default=0,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--model', '-m', default='pyramid', choices=['resnet50', 'pyramid'], required=True,
help='model')
parser.add_argument('--weight', '-w', default='', required=True
help='load pretrained model')
parser.add_argument('--start_channel', default=16,
type=int, help='start channel')
parser.add_argument('--depth', default=16, type=int,
help='depth')
parser.add_argument('--alpha', default=90, type=int,
help='alpha')
parser.add_argument('--class_weight', default=None, type=str)
parser.add_argument('--data_dir', default='data')
parser.add_argument('--tta', default=False, help='test time argument')
args = parser.parse_args()
print(args)
print('')
class_labels = 14951
if args.model == 'resnet50':
predictor = ResNet('auto')
predictor.fc6 = L.Linear(2048, class_labels)
elif args.model == 'pyramid':
predictor = shaked_pyramid_net.PyramidNet(
skip=True, num_class=class_labels, depth=args.depth, alpha=args.alpha, start_channel=args.start_channel)
if not args.weight == '':
chainer.serializers.load_npz(args.weight, model)
data_file = join(args.data_dir, 'test.txt')
test = chainer.datasets.ImageDataset(
data_file, root=args.data_dir, dtype=np.uint8)
test = skip_trainsform.SkipTransform(
test, augmentor_transformer.AugmentorTransform(train=False))
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
with open(test_file, 'r') as f:
ids = list(map(lambda x: Path(x.strip()).trim, f.readlines()))
labels = []
scores = []
for i, batch in enumerate(test_iter):
x = chainer.dataset.concat_examples(batch)
y = F.Softmax(model(batch), axis=1)
y = chainer.backends.cuda.to_cpu(y.array)
labels.append(np.argmax(y, axis=1))
scores.append(np.max(y, axis=1))
labels = np.concatenate(labels)
scores = np.concatenate(scores)
df = pd.DataFrame([ids, labels, scores], columns=['id', 'labels', 'score'])
def check_backward(self, x_data, gy_data, use_cudnn='always'):
with chainer.using_config('use_cudnn', use_cudnn):
gradient_check.check_backward(functions.Softmax(axis=self.axis),
x_data, gy_data,
**self.check_backward_options)
