| import os |
| import numpy as np |
| import mxnet as mx |
| from mxnet.test_utils import * |
| |
| def _get_model(): |
| if not os.path.exists('model/Inception-7-symbol.json'): |
| download('http://data.mxnet.io/models/imagenet/inception-v3.tar.gz', dirname='model') |
| os.system("cd model; tar -xf inception-v3.tar.gz --strip-components 1") |
| |
| def _dump_images(shape): |
| import skimage.io |
| import skimage.transform |
| img_list = [] |
| for img in sorted(os.listdir('data/test_images/')): |
| img = skimage.io.imread('data/test_images/'+img) |
| short_egde = min(img.shape[:2]) |
| yy = int((img.shape[0] - short_egde) / 2) |
| xx = int((img.shape[1] - short_egde) / 2) |
| img = img[yy : yy + short_egde, xx : xx + short_egde] |
| img = skimage.transform.resize(img, shape) |
| img_list.append(img) |
| imgs = np.asarray(img_list, dtype=np.float32).transpose((0, 3, 1, 2)) - 128 |
| np.save('data/test_images_%d_%d.npy'%shape, imgs) |
| |
| def _get_data(shape): |
| download("http://data.mxnet.io/data/test_images_%d_%d.npy" % (shape), dirname='data') |
| download("http://data.mxnet.io/data/inception-v3-dump.npz", dirname="data") |
| |
| def test_consistency(dump=False): |
| shape = (299, 299) |
| _get_model() |
| _get_data(shape) |
| if dump: |
| _dump_images(shape) |
| gt = None |
| else: |
| gt = {n: mx.nd.array(a) for n, a in np.load('data/inception-v3-dump.npz').items()} |
| data = np.load('data/test_images_%d_%d.npy'%shape) |
| sym, arg_params, aux_params = mx.model.load_checkpoint('model/Inception-7', 1) |
| arg_params['data'] = data |
| arg_params['softmax_label'] = np.random.randint(low=1, high=1000, size=(data.shape[0],)) |
| ctx_list = [{'ctx': mx.gpu(0), 'data': data.shape, 'type_dict': {'data': data.dtype}}, |
| {'ctx': mx.cpu(0), 'data': data.shape, 'type_dict': {'data': data.dtype}}] |
| gt = check_consistency(sym, ctx_list, arg_params=arg_params, aux_params=aux_params, |
| tol=1e-3, grad_req='null', raise_on_err=False, ground_truth=gt) |
| if dump: |
| np.savez('data/inception-v3-dump.npz', **{n: a.asnumpy() for n, a in gt.items()}) |
| |
| if __name__ == '__main__': |
| test_consistency(False) |
