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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/03/28 14:26:39 UTC
[GitHub] [incubator-mxnet] saptarshim-dal opened a new issue #14551: Simple
bind error while inferencing from an onnx model using mxnet on AWS EI
saptarshim-dal opened a new issue #14551: Simple bind error while inferencing from an onnx model using mxnet on AWS EI
URL: https://github.com/apache/incubator-mxnet/issues/14551
## Description
I am trying to infer from an onnx model using MXNet on AWS EI and encountering the following error. The code is working as expected while using MXNet 1.2.1 and onnx 1.1.2. I am a noob in ML and have no clue about the error I am getting.
## Environment info (Required)
AWS c5.xlarge EC2 instance
AWS eia1.large EI accelerator
Python 2.7
amazonei-mxnet 1.3.0
onnx 1.2.1
## Code
import mxnet as mx
from mxnet.contrib import onnx as onnx_mxnet
import numpy as np
from collections import namedtuple
from PIL import Image
image = Image.open('./Test_Images/Sadness/image0001582.jpgff.jpg')
image = image.resize((227, 227))
image_data = np.asarray(image, dtype=np.float32) - 128
image_data = np.ascontiguousarray(np.rollaxis(image_data, 2))
image_data = image_data[np.newaxis, :, :, :].astype(np.float32)
print(image_data.shape)
data_names = ['data']
Batch = namedtuple('Batch', data_names)
ctx = mx.eia()
sym, arg, aux = onnx_mxnet.import_model('./Models/facenet1001.onnx')
mod = mx.mod.Module(symbol=sym, data_names=data_names, context=ctx, label_names=None)
mod.bind(for_training=False, data_shapes=[(data_names[0],image_data.shape)], label_shapes=None)
mod.set_params(arg_params=arg, aux_params=aux, allow_missing=True, allow_extra=True)
mod.forward(Batch([mx.nd.array(image_data)]))
predictions = mod.get_outputs()[0].asnumpy()
predictions = predictions[0].tolist()
zipb_object = zip(['Anger', 'Confusion', 'Contempt', 'Disgust', 'Fear', 'Happiness', 'Neutral', 'NoFace', 'Sadness', 'Surprise'], predictions)
prediction_dictionary = dict(zipb_object)
print(prediction_dictionary)
## Error Message:
Using Amazon Elastic Inference Client Library Version: 1.2.12
Number of Elastic Inference Accelerators Available: 1
Elastic Inference Accelerator ID: eia-cb0a16c893874b43855dc0e0d2aacab7
Elastic Inference Accelerator Type: eia1.large
Traceback (most recent call last):
File "Inferrer_GPU_Only.py", line 58, in <module>
mod.bind(for_training=False, data_shapes=[(data_names[0],image_data.shape)], label_shapes=None)
File "/home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/module/module.py", line 434, in bind
state_names=self._state_names)
File "/home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/module/executor_group.py", line 279, in __init__
self.bind_exec(data_shapes, label_shapes, shared_group)
File "/home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/module/executor_group.py", line 382, in bind_exec
shared_group))
File "/home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/module/executor_group.py", line 669, in _bind_ith_exec
shared_buffer=shared_data_arrays, **input_shapes)
File "/home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/symbol/symbol.py", line 1529, in simple_bind
raise RuntimeError(error_msg)
RuntimeError: simple_bind error. Arguments:
data: (1, 3, 227, 227)
Error in operator broadcast_sub0: [14:16:11] src/operator/tensor/./elemwise_binary_broadcast_op.h:68: Check failed: l == 1 || r == 1 operands could not be broadcast together with shapes [1,3,227,227] [1,154587,1,1]
Stack trace returned 10 entries:
[bt] (0) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(dmlc::StackTrace()+0x44) [0x7ff5f791a394]
[bt] (1) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x21) [0x7ff5f791a771]
[bt] (2) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::op::BinaryBroadcastShape(nnvm::NodeAttrs const&, std::vector<nnvm::TShape, std::allocator<nnvm::TShape> >*, std::vector<nnvm::TShape, std::allocator<nnvm::TShape> >*)+0xbf7) [0x7ff5f855d4b7]
[bt] (3) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2ff9b4a) [0x7ff5fa300b4a]
[bt] (4) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::exec::InferShape(nnvm::Graph&&, std::vector<nnvm::TShape, std::allocator<nnvm::TShape> >&&, std::string const&)+0x150d) [0x7ff5fa30349d]
[bt] (5) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::exec::GraphExecutor::Init(nnvm::Symbol, mxnet::Context const&, std::map<std::string, mxnet::Context, std::less<std::string>, std::allocator<std::pair<std::string const, mxnet::Context> > > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::unordered_map<std::string, nnvm::TShape, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, nnvm::TShape> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::unordered_set<std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::string> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::unordered_map<std::string, mxnet::NDArray, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, mxnet::NDArray> > >*, mxnet::Executor*, std::unordered_map<nnvm::NodeEntry, mxnet::NDArray, nnvm::NodeEntryHash, nnvm::NodeEntryEqual, std::allocator<std::pair<nnvm::NodeEntry const, mxnet::NDArray> > > const&)+0x3fd) [0x7ff5fa2dff2d]
[bt] (6) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(mxnet::Executor::SimpleBind(nnvm::Symbol, mxnet::Context const&, std::map<std::string, mxnet::Context, std::less<std::string>, std::allocator<std::pair<std::string const, mxnet::Context> > > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::vector<mxnet::Context, std::allocator<mxnet::Context> > const&, std::unordered_map<std::string, nnvm::TShape, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, nnvm::TShape> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, int> > > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::unordered_set<std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::string> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> >*, std::unordered_map<std::string, mxnet::NDArray, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, mxnet::NDArray> > >*, mxnet::Executor*)+0x2a5) [0x7ff5fa2e2045]
[bt] (7) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/site-packages/mxnet/libmxnet.so(MXExecutorSimpleBind+0x23b4) [0x7ff5fa20c814]
[bt] (8) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7ff602024ec0]
[bt] (9) /home/ubuntu/anaconda3/envs/amazonei_mxnet_p27/lib/python2.7/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7ff60202487d]
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