Closed muhammad-maaz-confiz closed 3 years ago
@AlexKoff88 @vshampor @ljaljushkin
@vshampor, can you please provide a right command-line that we use in our validation?
As for the memory problem, I suggest using a smaller batch size but the accuracy metric (mAP) can be also smaller in this case.
Greetings, @muhammad-maaz-confiz !
In the examples/object_detection/configs/ssd_mobilenet_voc.json
, replace:
"input_info": {
"sample_size": [1, 3, 300, 300]
},
with
"input_info": {
"sample_size": [2, 3, 300, 300]
},
This shape specification is for the initial dry-run of the model's forward
function, which we require in order to build the internal graph representation of the PyTorch model. Since the model has batch-norm layers, even this dry-run has to occur with input tensors having batch dimension larger than 1 - will fix the config in the next release.
You can train the model with a smaller batch size by providing a -b <batch_size>
option to the example command line runs, or by specifying a "batch_size": <batch_size>
option in the config file - this should enable you to train the model if you have GPU memory constraints.
We only currently provide pre-trained quantized models for SSD-VGG, and these are the only models that we validate, despite that the configs for SSD-MobileNet are also part of the repository. @AlexKoff88, is this something that we should be fixing in the next release?
@vshampor, at least it is worth to validate that this config works, without getting the final mAP numbers.
Greetings, @muhammad-maaz-confiz !
In the
examples/object_detection/configs/ssd_mobilenet_voc.json
, replace:"input_info": { "sample_size": [1, 3, 300, 300] },
with
"input_info": { "sample_size": [2, 3, 300, 300] },
This shape specification is for the initial dry-run of the model's
forward
function, which we require in order to build the internal graph representation of the PyTorch model. Since the model has batch-norm layers, even this dry-run has to occur with input tensors having batch dimension larger than 1 - will fix the config in the next release.You can train the model with a smaller batch size by providing a
-b <batch_size>
option to the example command line runs, or by specifying a"batch_size": <batch_size>
option in the config file - this should enable you to train the model if you have GPU memory constraints.We only currently provide pre-trained quantized models for SSD-VGG, and these are the only models that we validate, despite that the configs for SSD-MobileNet are also part of the repository. @AlexKoff88, is this something that we should be fixing in the next release?
Thank you, the model is now training
Hi @vshampor,
I just wanted to confirm that the ssd_mobilenet_voc.json is using MobileNetV2 as the backbone with SSD head. Thanks
Maaz
Dear Team,
I am trying to train Mobilenet-SSD from
openvino_training_extensions/pytorch_toolkit/nncf/examples/object_detection/configs/ssd_mobilenet_voc.json
. I am getting the below error,The training command is (from directory
examples/object_detection
)Environment Details:
Also in case I have a small GPU like (RTX 2080 Super with 8 GB Memory), it will probably throw a out of memory error (it did when I tried to train ssd300_int8). Is there anyway that I would be able to train on 8 GB memory GPU? Furthermore, is there any pre-trained quantize models available to test the speed on a certain hardware? Thanks
Stay Safe, Maaz