Sample scripts for training the Mask R-CNN model in the Penn-Fudan Database for Pedestrian Detection and Segmentation using PyTorch on DirectML
These scripts are collected from the tutorial here
Install the following prerequisites:
pip install -r pytorch\objectDetection\maskrcnn\requirements.txt
After installing the PyTorch on DirectML package (see GPU accelerated ML training), open a console to the root directory and run the setup script to download and convert data:
python pytorch\data\dataset.py
Running dataset.py should take at least a minute or so, since it downloads the CIFAR-10 dataset. The output of running it should look similar to the following:
>python pytorch\data\dataset.py
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to E:\work\dml\PyTorch\data\cifar-10-python\cifar-10-python.tar.gz
Failed download. Trying https -> http instead. Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to E:\work\dml\PyTorch\data\cifar-10-python\cifar-10-python.tar.gz
170499072it [00:32, 5250164.09it/s]
Extracting E:\work\dml\PyTorch\data\cifar-10-python\cifar-10-python.tar.gz to E:\work\dml\PyTorch\data\cifar-10-python
A helper script exists to train Mask R-CNN with PennFudanPed data:
cd pytorch\objectdetection\maskrcnn
python .\maskrcnn.py
The first few lines of output should look similar to the following (exact numbers may change):
>python .\maskrcnn.py
python .\maskrcnn.py
Epoch: [0] [ 0/60] eta: 0:38:26 lr: 0.000090 loss: 2.9777 (2.9777) loss_classifier: 0.7217 (0.7217) loss_box_reg: 0.0754 (0.0754) loss_mask: 1.6228 (1.6228) loss_objectness: 0.4175 (0.4175) loss_rpn_box_reg: 0.1404 (0.1404) time: 38.4439 data: 1.0955
Epoch: [0] [10/60] eta: 0:29:44 lr: 0.000936 loss: 2.4268 (2.4919) loss_classifier: 0.4056 (0.4158) loss_box_reg: 0.1691 (0.3631) loss_mask: 1.1679 (1.1600) loss_objectness: 0.1162 (0.3120) loss_rpn_box_reg: 0.1257 (0.2410) time: 35.6972 data: 0.1034
Epoch: [0] [20/60] eta: 0:23:14 lr: 0.001783 loss: 1.2172 (1.6717) loss_classifier: 0.0669 (0.2410) loss_box_reg: 0.1331 (0.2466) loss_mask: 0.5935 (0.8376) loss_objectness: 0.0565 (0.1873) loss_rpn_box_reg: 0.0574 (0.1593) time: 34.6860 data: 0.0042