Hi everyone, I have a tensorflow model that consists mainly of the following layers:

- tf.layers.conv2d
- tf.layers.batch_normalization
- tf.layers.dense

I was able to generate the UFF, and it’s seems valid, but when I try to generate the engine I encounter the following error:

```
[TensorRT] INFO: Detecting Framework
[TensorRT] INFO: Parsing Model from uff
[TensorRT] INFO: UFFParser: parsing input_image
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/conv2d/kernel
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/conv2d/Conv2D
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/conv2d/bias
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/conv2d/BiasAdd
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/batch_normalization/gamma
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/batch_normalization/beta
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/batch_normalization/moving_mean
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/batch_normalization/moving_variance
[TensorRT] INFO: UFFParser: parsing PilotNet/Conv2dBatchNorm/batch_normalization/FusedBatchNorm
python: Network.h:104: virtual nvinfer1::DimsHW nvinfer1::NetworkDefaultConvolutionFormula::compute(nvinfer1::DimsHW, nvinfer1::DimsHW, nvinfer1::DimsHW, nvinfer1::DimsHW, nvinfer1::DimsHW, const char*): Assertion `(input.w() + padding.w() * 2) >= dkw && "Image width with padding must always be at least the width of the dilated filter."' failed.
```

The following is my model

```
#######################
Model
#######################
Tensor("input_image:0", shape=(?, 66, 200, 3), dtype=float32)
Tensor("PilotNet/Conv2dBatchNorm/Relu:0", shape=(?, 31, 98, 24), dtype=float32)
Tensor("PilotNet/Conv2dBatchNorm_1/Relu:0", shape=(?, 14, 47, 36), dtype=float32)
Tensor("PilotNet/Conv2dBatchNorm_2/Relu:0", shape=(?, 5, 22, 48), dtype=float32)
Tensor("PilotNet/Conv2dBatchNorm_3/Relu:0", shape=(?, 3, 20, 64), dtype=float32)
Tensor("PilotNet/Conv2dBatchNorm_4/Relu:0", shape=(?, 1, 18, 64), dtype=float32)
Tensor("PilotNet/Flatten/flatten/Reshape:0", shape=(?, 1152), dtype=float32)
Tensor("PilotNet/DenseBatchNorm/Relu:0", shape=(?, 400), dtype=float32)
Tensor("PilotNet/DenseBatchNorm_1/Relu:0", shape=(?, 50), dtype=float32)
Tensor("PilotNet/DenseBatchNorm_2/Relu:0", shape=(?, 10), dtype=float32)
Tensor("PilotNet/dense/MatMul:0", shape=(?, 1), dtype=float32)
```