MNISTiterator miss channel information while flatten causing conv1d layer differed initialization failed.

Description

(A clear and concise description of what the bug is.)

I am using Mxnet 1.6.0.

The problem shows up when I am trying to flatten the Mnist dataset from 2D(1,28,28) to (1,784). And feed the data to a Conv1d layer.

Here is the iterator I am using: """ Flatten the dataset from (1,28,28) to (1,784)

train = mx.io.MNISTIter( image = "data/train-images-idx3-ubyte", label = "data/train-labels-idx1-ubyte", batch_size = 20, flat=True, data_shape = (784, ))

val = mx.io.MNISTIter( image="data/train-images-idx3-ubyte", label="data/train-labels-idx1-ubyte", batch_size=20, flat=True, data_shape=(784, ))

"After that, I try to feed it a model with a Conv1d as the first layer"

"Also I run the forward pass"

net = nn.Sequential() net.add( nn.Conv1D(channels=1,kernel_size=1,in_channels=1), nn.Dense(units=10) ) net.initialize()

for i, batch in enumerate(train):

print(batch)
print(i)
data = gluon.utils.split_and_load(batch.data[0].astype("float16"), ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch.label[0].astype("float16"), ctx_list=ctx, batch_axis=0)

with ag.record():
    for x, y in zip(data, label):
        z = net(x)

Error Message

[13:35:18] ../src/io/iter_mnist.cc:110: MNISTIter: load 60000 images, shuffle=1, shape=(20,784) [13:35:20] ../src/io/iter_mnist.cc:110: MNISTIter: load 60000 images, shuffle=1, shape=(20,784) DataBatch: data shapes: [(20, 784)] label shapes: [(20,)] 0 Traceback (most recent call last): File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/souce_code/mxnet/example/gluon/imdb.py", line 54, in z = net(x) File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/pcap/conv2d/tmp/venv/lib/python3.9/site-packages/mxnet/gluon/block.py", line 682, in call out = self.forward(args) File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/pcap/conv2d/tmp/venv/lib/python3.9/site-packages/mxnet/gluon/nn/basic_layers.py", line 55, in forward x = block(x) File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/pcap/conv2d/tmp/venv/lib/python3.9/site-packages/mxnet/gluon/block.py", line 682, in call out = self.forward(args) File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/pcap/conv2d/tmp/venv/lib/python3.9/site-packages/mxnet/gluon/block.py", line 1258, in forward return self.hybrid_forward(ndarray, x, *args, params) File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/pcap/conv2d/tmp/venv/lib/python3.9/site-packages/mxnet/gluon/nn/conv_layers.py", line 147, in hybrid_forward act = getattr(F, self._op_name)(x, weight, bias, name='fwd', self._kwargs) File "", line 169, in Convolution File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/pcap/conv2d/tmp/venv/lib/python3.9/site-packages/mxnet/_ctypes/ndarray.py", line 82, in _imperative_invoke check_call(_LIB.MXImperativeInvokeEx( File "/Users/xiangrenbaibaoxiang/Desktop/johnny_source_code/pcap/conv2d/tmp/venv/lib/python3.9/site-packages/mxnet/base.py", line 246, in check_call raise get_last_ffi_error() mxnet.base.MXNetError: Traceback (most recent call last): File "../src/operator/nn/convolution.cc", line 103 MXNetError: Check failed: dshp.ndim() == 3U (2 vs. 3) : Input data should be 3D in batch-num_filter-x

To Reproduce

Here is the iterator I am using: """ Flatten the dataset from (1,28,28) to (1,784)

train = mx.io.MNISTIter( image = "data/train-images-idx3-ubyte", label = "data/train-labels-idx1-ubyte", batch_size = 20, flat=True, data_shape = (784, ))

val = mx.io.MNISTIter( image="data/train-images-idx3-ubyte", label="data/train-labels-idx1-ubyte", batch_size=20, flat=True, data_shape=(784, ))

"After that, I try to feed it a model with a Conv1d as the first layer"

"Also I run the forward pass"

net = nn.Sequential() net.add( nn.Conv1D(channels=1,kernel_size=1,in_channels=1), nn.Dense(units=10) ) net.initialize()

for i, batch in enumerate(train):

print(batch)
print(i)
data = gluon.utils.split_and_load(batch.data[0].astype("float16"), ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch.label[0].astype("float16"), ctx_list=ctx, batch_axis=0)

with ag.record():
    for x, y in zip(data, label):
        z = net(x)

Steps to reproduce

(Paste the commands you ran that produced the error.)

Just run it.

What have you tried to solve it?

Environment

Mnxet 1.6.0, Maxos, Ubuntu16,04 LTS

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Environment Information

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apache / mxnet