import mxnet as mx ?import pdb ?def load_checkpoint(): ?????""" ????Load model checkpoint from file. ????:param prefix: Prefix of model name. ????:param epoch: Epoch number of model we would like to load. ????:return: (arg_params, aux_params) ????arg_params : dict of str to NDArray ????????Model parameter, dict of name to NDArray of net‘s weights. ????aux_params : dict of str to NDArray ????????Model parameter, dict of name to NDArray of net‘s auxiliary states. ????""" ?????save_dict = mx.nd.load(‘model-0000.params‘) ?????arg_params = {} ?????aux_params = {} ?????for k, v in save_dict.items(): ?????????tp, name = k.split(‘:‘, 1) ?????????if tp == ‘arg‘: ?????????????arg_params[name] = v ?????????if tp == ‘aux‘: ?????????????aux_params[name] = v ?????return arg_params, aux_params ?????def convert_context(params, ctx): ?????""" ????:param params: dict of str to NDArray ????:param ctx: the context to convert to ????:return: dict of str of NDArray with context ctx ????""" ?????new_params = dict() ?????for k, v in params.items(): ?????????new_params[k] = v.as_in_context(ctx) ?????#print new_params[0] ?????return new_params ?????def load_param(convert=False, ctx=None): ?????""" ????wrapper for load checkpoint ????:param prefix: Prefix of model name. ????:param epoch: Epoch number of model we would like to load. ????:param convert: reference model should be converted to GPU NDArray first ????:param ctx: if convert then ctx must be designated. ????:return: (arg_params, aux_params) ????""" ?????arg_params, aux_params = load_checkpoint() ?????if convert: ?????????if ctx is None: ?????????????ctx = mx.cpu() ?????????arg_params = convert_context(arg_params, ctx) ?????????aux_params = convert_context(aux_params, ctx) ?????return arg_params, aux_params ?????if __name__==‘__main__‘: ?????????result = ?load_param(); ?????????#pdb.set_trace() ?????????print ‘result is‘ ?????????#print result ???????for dic in result: ???????????for key in dic: ???????????????print(key,dic[key].shape) ???????# print ‘one of results is:‘ ?????????# print result[0][‘fc2_weight‘].asnumpy() ? python showmxmodel.py 2>&1 | tee log.txt
result is
(‘stage3_unit2_bn1_beta‘, (256L,))
(‘stage3_unit2_bn3_beta‘, (256L,))
(‘stage3_unit11_bn1_gamma‘, (256L,))
(‘stage3_unit5_bn3_gamma‘, (256L,))
(‘stage3_unit3_conv1_weight‘, (256L, 256L, 3L, 3L))
(‘stage2_unit1_bn3_gamma‘, (128L,))
(‘stage3_unit4_conv1_weight‘, (256L, 256L, 3L, 3L))
(‘stage3_unit12_bn3_beta‘, (256L,))
(‘stage2_unit2_bn3_beta‘, (128L,))
(‘conv0_weight‘, (64L, 3L, 3L, 3L))
(‘stage3_unit11_relu1_gamma‘, (256L,))
(‘stage4_unit1_conv1sc_weight‘, (512L, 256L, 1L, 1L))
(‘stage3_unit1_conv1sc_weight‘, (256L, 128L, 1L, 1L))
(‘bn1_beta‘, (512L,))
(‘stage1_unit2_bn2_beta‘, (64L,))
(‘stage3_unit2_conv2_weight‘, (256L, 256L, 3L, 3L))
(‘stage1_unit2_conv1_weight‘, (64L, 64L, 3L, 3L))
(‘stage3_unit14_bn2_beta‘, (256L,))
(‘stage4_unit2_bn3_beta‘, (512L,))
(‘stage3_unit8_bn1_gamma‘, (256L,))
(‘stage3_unit7_bn1_gamma‘, (256L,))
(‘stage2_unit3_bn1_beta‘, (128L,))
(‘stage2_unit4_conv1_weight‘, (128L, 128L, 3L, 3L))
(‘stage3_unit2_bn2_gamma‘, (256L,))
(‘stage1_unit1_conv1_weight‘, (64L, 64L, 3L, 3L))
(‘stage3_unit9_conv2_weight‘, (256L, 256L, 3L, 3L))
(‘stage3_unit13_conv1_weight‘, (256L, 256L, 3L, 3L))
(‘stage3_unit1_relu1_gamma‘, (256L,))
(‘stage4_unit1_bn3_beta‘, (512L,))
(‘stage2_unit1_bn2_beta‘, (128L,))
(‘stage3_unit14_conv1_weight‘, (256L, 256L, 3L, 3L))
(‘stage3_unit8_bn1_beta‘, (256L,))
(‘stage3_unit11_conv1_weight‘, (256L, 256L, 3L, 3L))
(‘stage1_unit1_bn3_gamma‘, (64L,))
(‘stage2_unit2_conv2_weight‘, (128L, 128L, 3L, 3L))
(‘stage4_unit2_bn1_gamma‘, (512L,))
(‘stage3_unit3_bn1_gamma‘, (256L,))
(‘stage1_unit3_bn2_gamma‘, (64L,))
(‘stage1_unit3_bn3_gamma‘, (64L,))
(‘stage4_unit2_relu1_gamma‘, (512L,))
(‘stage3_unit10_conv2_weight‘, (256L, 256L, 3L, 3L))
(‘stage3_unit12_conv1_weight‘, (256L, 256L, 3L, 3L))
(‘stage3_unit2_relu1_gamma‘, (256L,))
(‘stage3_unit10_bn2_beta‘, (256L,))
(‘stage2_unit3_bn3_gamma‘, (128L,))
(‘stage2_unit3_bn2_beta‘, (128L,))
(‘stage3_unit8_bn3_beta‘, (256L,))
(‘fc1_gamma‘, (512L,))
(‘stage3_unit14_bn3_gamma‘, (256L,))
(‘stage3_unit9_bn3_gamma‘, (256L,))
(‘stage2_unit3_bn3_beta‘, (128L,))
(‘stage3_unit1_sc_gamma‘, (256L,))
(‘stage3_unit7_bn1_beta‘, (256L,))
(‘stage1_unit2_bn3_beta‘, (64L,))
(‘stage3_unit14_relu1_gamma‘, (256L,))
(‘stage3_unit13_bn2_beta‘, (256L,))
(‘stage2_unit1_conv1sc_weight‘, (128L, 64L, 1L, 1L))
(‘bn0_beta‘, (64L,))
(‘stage3_unit12_bn1_gamma‘, (256L,))
(‘stage2_unit1_sc_gamma‘, (128L,))
(‘relu0_gamma‘, (64L,))
(‘stage2_unit2_bn2_gamma‘, (128L,))
(‘stage3_unit4_relu1_gamma‘, (256L,))
Mxnet 查看模型params的网络结构
原文地址：https://www.cnblogs.com/adong7639/p/9173854.html