elemwise_op_common.h:133: Check failed: assign(&dattr, (*vec)[i]) Incompatible attr in node at 0-th output: expected [2048], got [512]

[EpilogueCc]

how to deal with this problem

[EpilogueCc]

Traceback (most recent call last):
File "/root/workspace/tasn-master/tasn-mxnet/example/tasn/train.py", line 65, in
eval_metric = evaluate.Multi_Accuracy(num=6))
File "/root/workspace/tasn-master/tasn-mxnet/example/tasn/common/fit.py", line 196, in fit
monitor = monitor)
File "/root/workspace/mxnet_python/python/mxnet/module/base_module.py", line 503, in fit
allow_missing=allow_missing, force_init=force_init)
File "/root/workspace/mxnet_python/python/mxnet/module/module.py", line 309, in init_params
_impl(desc, arr, arg_params)
File "/root/workspace/mxnet_python/python/mxnet/module/module.py", line 297, in _impl
cache_arr.copyto(arr)
File "/root/workspace/mxnet_python/python/mxnet/ndarray/ndarray.py", line 2074, in copyto
return _internal._copyto(self, out=other)
File "", line 25, in _copyto
File "/root/workspace/mxnet_python/python/mxnet/_ctypes/ndarray.py", line 92, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/root/workspace/mxnet_python/python/mxnet/base.py", line 252, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [17:10:58] /v-helzhe/code/test/last/TASN/tasn-mxnet/3rdparty/mshadow/../../src/operator/tensor/../elemwise_op_common.h:133: Check failed: assign(&dattr, (*vec)[i]) Incompatible attr in node at 0-th output: expected [2048], got [512]

[EpilogueCc]

"""
Adapted from https://github.com/phunterlau/iNaturalist.git

by Heliang Zheng
03/28/2019

"""
import random
import os
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
from common import fit, evaluate
import common.cub_data as data
import mxnet as mx
import numpy as np
import os, urllib
import model

if name == "main":

os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "1"
random.seed(0)
np.random.seed(0)
mx.random.seed(0)
# parse args
parser = argparse.ArgumentParser(description="fine-tune a dataset",
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
train = fit.add_fit_args(parser)
data.add_data_args(parser)
aug = data.add_data_aug_args(parser)
data.set_data_aug_level(parser, 1)
parser.set_defaults(image_shape = '3,512,512', num_epochs=300,
                    lr=0.1, lr_step_epochs='100,200', wd=0, mom=0)

args = parser.parse_args()
# args.gpus = 0,1,2,3
args.gpus = 1
args.data_nthreads = 128
args.batch_size = 96
args.num_classes = 200
args.num_examples = 5994
# args = "0,1,2,3,4,5,6,7"
# batch_size_per_gpu = np.int(args.batch_size/len(args.gpus.replace(',','')))
batch_size_per_gpu = np.int(args.batch_size / 1)
# load pretrained model
dir_path = os.path.dirname(os.path.realpath(__file__))

a=mx.symbol.infer_shape
print(a.shape)

prefix = 'model/resnet-50'
epoch = 0
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)


(new_sym, new_args, new_auxs) = model.tasn(
    sym, arg_params, aux_params, args.num_classes, batch_size_per_gpu)     

# train
fit.fit(args        = args,
        network     = new_sym,
        data_loader = data.get_custom_iter,
        arg_params  = new_args,
        aux_params  = new_auxs,
        label_names = (['att_net_label', 'part_net_label', 'master_net_label', 'part_net_aux_label', 'master_net_aux_label']),
        eval_metric = evaluate.Multi_Accuracy(num=6))

[liuhangfan]

hi, did you fix it?