Tao unet input size error

Please provide the following information when requesting support.

• Hardware RTX3090
• Network Type unet / vgg16
• TLT Version

dockers: [‘nvidia/tao/tao-too lkit-tf’, ‘nvidia/tao/tao-toolkit-pyt’, ‘nvidia/tao/tao-toolkit-lm’]
format_version: 2.0
toolkit_version: 3.22.02
published_date: 02/28/2022
• Training spec file(If have, please share here)
unet_train_vgg_6S.txt (1.5 KB)

• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)

After training an initial model at 512X512 images, there are precision issues for the actual location of specific classified pixels.

The actual camera frame is 1280X720, so we created a new data set using the actual camera. All images and masks are png, 1280X720.

As per documentation on tao unet https://docs.nvidia.com/tao/tao-toolkit/text/semantic_segmentation/unet.html#creating-a-configuration-file,

model_input_height int The model input height dimension of the model, which should be a multiple of 16. >100
model_input_width int The model input width dimension of the model, which should be a multiple of 16. >100

and my spec file has:

model_config {
model_input_width: 1280
model_input_height: 720
model_input_channels: 3
num_layers: 16
all_projections: true
arch: “vgg”
use_batch_norm: False
training_precision {
backend_floatx: FLOAT32

However training with

!tao unet train --gpus=1 --gpu_index=$GPU_INDEX
-e $SPECS_DIR/unet_train_vgg_6S.txt
-m $USER_EXPERIMENT_DIR/pretrained_vgg16/vgg_16.hdf5
-n model
-k $KEY

Results in error:

ValueError: A Concatenate layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 512, 46, 80), (None, 512, 45, 80)]
2022-05-24 21:20:21,149 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.

The complete training log attached
tao unet train log 2022 05 24.txt (33.5 KB)

Could you set below and retry?
all_projections: False

Exactly the same result! Please bear in mind we are using vgg16

Could you please use a new result folder and retry?

Could you also try another kind of pretrained model?

There is no update from you for a period, assuming this is not an issue anymore.
Hence we are closing this topic. If need further support, please open a new one.

This topic was automatically closed 14 days after the last reply. New replies are no longer allowed.