Have you support Conv1D operator? Could you share the code? Thx!!
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Hi,
I imported DeeplabV3+(xception) model named 'xception65_coco_voc_trainval'
downloaded from TF model zoo
(https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md)
It runs well on CPU but gets some error on GPU.
```
target = tvm.target.cuda()
ctx = tvm.gpu(0)
mo
I tried ```padlist = _infer_value(inputs[1], params, mod).asnumpy()```, and the
error is fixed. Thanks!
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You are receiving this b
Thanks for your quick reply.
```
WARNING:root:Attribute _node_name is ignored in relay.sym.take
WARNING:root:Attribute _target_layout is ignored in relay.sym.take
WARNING:root:Attribute Tdim is ignored in relay.sym.expand_dims
WARNING:root:Attribute T is ignored in relay.sym.expand_dims
WARNING:r
Hi,
When I import SSD-Resnet34 (it is downloaded from
https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection,
ssd-resnet34 1200x1200 Tensorflow model)
```
import tvm
from tvm import relay
import tensorflow as tf
from tvm.relay.frontend.tensorflow_parser import TFPars
Oh, I got it.
Just using _op.sigmoid() will solve this. I misunderstand backward and forward
functions.
```
def _mx_logistic_regression_output(inputs, attrs):
loss = _op.sigmoid(inputs[0])
return loss
```
Thank you again!~
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Thank you very much for your reply.
I also tried the way you suggested. But still got wrong result.
```
def _mx_logistic_regression_output(inputs, attrs):
label = inputs[1]
pred = _op.sigmoid(inputs[0])
# We use the stable formula: max(pred, 0) - pred * label + log(1 +
exp(-abs(pred
Hi,
I was trying to import NCF by MXNet relay. Check this: [MXNet
NCF](https://github.com/apache/incubator-mxnet/tree/master/example/neural_collaborative_filtering)
There is an unsupported operator: LogisticRegressionOutput
According to the SigmoidBinaryCrossEntropyLoss code:
https://github
Sorry I didn't make it clear.
The code pasted in the first issue works well after adopting your solution.
Then I tested 'gpt2' model, it reported an error:
```
TypeError: int() argument must be a string, a bytes-like object or a number,
not ‘Call’
```
The test code is shown below:
```
from tvm
Thanks!
I try to fix it by
```
def _tensortonum():
def _impl(inputs, input_types):
return inputs[0]
return _impl
```
Another error accurs:
```
TypeError: int() argument must be a string, a bytes-like object or a number,
not 'Call'
```
```
'58', %58 : Scalar = prim::ImplicitTe
Hi,
I was trying to import
[bert-base-uncased](https://pypi.org/project/pytorch-pretrained-bert/) by
PyTorch relay. It said that "NotImplementedError: The following operators are
not implemented: ['prim::ImplicitTensorToNum']"
I can't find any useful information about 'ImplicitTensorTo
I think I'm facing the same error.
First, I merged the code from [Dynamic NMS and
strided_slice](https://github.com/apache/incubator-tvm/pull/4312/) to support
importing model [TensorFlow model:
ssd_resnet_50_fpn_coco](https://github.com/tensorflow/models/blob/master/research/object_detection
Hello,
As you said 'For example, ‘IteratorV2’, ‘IteratorGetNext’, ‘SaveV2’,
‘RestoreV2’, 'Assign’, and ‘Assert’. I know that those operators can be
avoided my changing the model '
I am training a NCF model by using [TensorFlow model ncf
code](https://github.com/tensorflow/models/tree/r1.12.
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