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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/03/12 15:17:36 UTC
[GitHub] [incubator-mxnet] manasi198 opened a new issue #14400: training own
model to own data in pascal voc format
manasi198 opened a new issue #14400: training own model to own data in pascal voc format
URL: https://github.com/apache/incubator-mxnet/issues/14400
Note: Providing complete information in the most concise form is the best way to get help. This issue template serves as the checklist for essential information to most of the technical issues and bug reports. For non-technical issues and feature requests, feel free to present the information in what you believe is the best form.
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## Description
i have create a sample model to my own dataset in pascal voc format in mxnet but while running the net i am getting erre
## Environment info (Required)
```
What to do:
1. Download the diagnosis script from https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
2. Run the script using `python diagnose.py` and paste its output here.
import mxnet as mx
from mxnet import nd, gluon, init, autograd
from mxnet import viz, sym
from mxnet.gluon import nn
from mxnet.gluon.data.vision import datasets, transforms
import numpy as np
import matplotlib.pyplot as plt
from time import time
classes= ['floor']
from gluoncv.data import VOCDetection
class VOCLike(VOCDetection):
CLASSES = ['floor']
def __init__(self, root, splits, transform=None, index_map=None, preload_label=True):
super(VOCLike, self).__init__(root, splits, transform, index_map, preload_label)
train = VOCLike(root='VOCtemplate', splits=((2018, 'train'),))
valid_dataset=VOCLike(root='VOCtemplate', splits=((2018, 'val'),))
print('length of dataset:', len(train))
print('len_val',len(valid_dataset))
print('label example:')
c=0;
batch_size = 4
train_data = gluon.data.DataLoader(
train,
batch_size=batch_size,
shuffle=True,
num_workers=0)
###print(len(train_data))
val_data = gluon.data.DataLoader(
valid_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
###print(len(val_data))
print(train_data)
for data,label in train_data:
print(data.shape)
net = nn.Sequential()
net.add(nn.Conv2D(channels=8, kernel_size=2, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=2, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Flatten(),
nn.Dense(120, activation="relu"),
nn.Dense(10))
try:
a = mx.nd.zeros((1,), ctx=mx.gpu(0))
ctx = [mx.gpu(0)]
except:
ctx = [mx.cpu()]
net.initialize(init=init.Xavier(),ctx=ctx)
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'AdaGrad', {'learning_rate': 0.01})
def acc(output, label):
correct_preds = output.argmax(axis=1) == label.astype('float32')
return correct_preds.mean().asscalar()
for epoch in range(10):
train_loss, train_acc, valid_acc = 0., 0., 0.
tic = time()
for data, label in train_data:
data = data.copyto(mx.cpu(0))
label = label.copyto(mx.cpu(0))
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
trainer.step(batch_size)
train_loss += loss.mean().asscalar()
train_acc += acc(output, label)
Package used (Python/R/Scala/Julia):
python 2.7
For Scala user, please provide:
1. Java version: (`java -version`)
2. Maven version: (`mvn -version`)
3. Scala runtime if applicable: (`scala -version`)
For R user, please provide R `sessionInfo()`:
## Build info (Required if built from source)
Compiler (gcc/clang/mingw/visual studio):
MXNet commit hash:
(Paste the output of `git rev-parse HEAD` here.)
Build config:
(Paste the content of config.mk, or the build command.)
## Error Message:
(Paste the complete error message, including stack trace.)
infer_shape error. Arguments:
data: (4L, 512L, 512L, 3L)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-9926ba7deb21> in <module>()
12 label = label.copyto(mx.cpu(0))
13 with autograd.record():
---> 14 output = net(data)
15 loss = softmax_cross_entropy(output, label)
16
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in __call__(self, *args)
539 hook(self, args)
540
--> 541 out = self.forward(*args)
542
543 for hook in self._forward_hooks.values():
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/nn/basic_layers.pyc in forward(self, x)
51 def forward(self, x):
52 for block in self._children.values():
---> 53 x = block(x)
54 return x
55
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in __call__(self, *args)
539 hook(self, args)
540
--> 541 out = self.forward(*args)
542
543 for hook in self._forward_hooks.values():
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in forward(self, x, *args)
911 params = {i: j.data(ctx) for i, j in self._reg_params.items()}
912 except DeferredInitializationError:
--> 913 self._deferred_infer_shape(x, *args)
914 for _, i in self.params.items():
915 i._finish_deferred_init()
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in _deferred_infer_shape(self, *args)
792 error_msg = "Deferred initialization failed because shape"\
793 " cannot be inferred. {}".format(e)
--> 794 raise ValueError(error_msg)
795
796 def _call_cached_op(self, *args):
ValueError: Deferred initialization failed because shape cannot be inferred. Error in operator conv2_fwd: [10:56:15] src/operator/nn/convolution.cc:196: Check failed: dilated_ksize_x <= AddPad(dshape[3], param_.pad[1]) (5 vs. 3) kernel size exceed input
Stack trace returned 10 entries:
[bt] (0) /home/manasi/.local/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x1d86a2) [0x7fc5da5556a2]
[bt] (1) /home/manasi/.local/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x1d8cb8) [0x7fc5da555cb8]
[bt] (2) /home/manasi/.local/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x49a408) [0x7fc5da817408]
[bt] (3) /home/manasi/.local/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2bfc3bf) [0x7fc5dcf793bf]
[bt] (4) /home/manasi/.local/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x2bfeed0) [0x7fc5dcf7bed0]
[bt] (5) /home/manasi/.local/lib/python2.7/site-packages/mxnet/libmxnet.so(MXSymbolInferShape+0x1539) [0x7fc5dcef6969]
[bt] (6) /home/manasi/anaconda2/lib/python2.7/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7fc615a80ec0]
[bt] (7) /home/manasi/anaconda2/lib/python2.7/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7fc615a8087d]
[bt] (8) /home/manasi/anaconda2/lib/python2.7/lib-dynload/_ctypes.so(_ctypes_callproc+0x4d6) [0x7fc615c968d6]
[bt] (9) /home/manasi/anaconda2/lib/python2.7/lib-dynload/_ctypes.so(+0x8b31) [0x7fc615c8cb31]
## Minimum reproducible example
(If you are using your own code, please provide a short script that reproduces the error. Otherwise, please provide link to the existing example.)
## Steps to reproduce
(Paste the commands you ran that produced the error.)
1.
2.
## What have you tried to solve it?
1.
2.
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