diff --git a/bob/paper/mccnn/tifs2018/config/FASNet_config.py b/bob/paper/mccnn/tifs2018/config/FASNet_config.py index b0f805b2777787512f6323a5f492cb854fe9723e..2acbff15078af3897e73591aa11751607475da0d 100644 --- a/bob/paper/mccnn/tifs2018/config/FASNet_config.py +++ b/bob/paper/mccnn/tifs2018/config/FASNet_config.py @@ -96,8 +96,35 @@ from bob.learn.pytorch.extractor.image import FASNetExtractor from bob.bio.video.extractor import Wrapper -MODEL_FILE= None # Replace with '<PATH_TO_MODEL>' -#################################################################### +# MODEL_FILE= None # Replace with '<PATH_TO_MODEL>' +# #################################################################### + +# If you want to use the pretrained model + +import pkg_resources + +MODEL_FILE = pkg_resources.resource_filename( 'bob.paper.mccnn.tifs2018', 'models/fasnet.pth') + +URL='http://www.idiap.ch/~ageorge/model_100_0.pth' + +if not os.path.exists(MODEL_FILE): + + logger.info('Downloading the FASNet model') + + bob.extension.download.download_file(URL,MODEL_FILE) + + logger.info('Downloaded FASNet model to location: {}'.format(MODEL_FILE)) + + + + + +MODEL_FILE= + +/idiap/temp/ageorge/MCCNN_paperpackage/bob.paper.mccnn.tifs2018/src/bob.paper.mccnn.tifs2018/bob/paper/mccnn/tifs2018/models/mccnn_best_C1-B1-FFC.pth +/idiap/temp/ageorge/MCCNN_paperpackage/bob.paper.mccnn.tifs2018/src/bob.paper.mccnn.tifs2018/bob/paper/mccnn/tifs2018/models/fasnet.pth + + _img_transform = transforms.Compose([transforms.ToPILImage(),transforms.Resize(224, interpolation=2),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], diff --git a/bob/paper/mccnn/tifs2018/config/MCCNN_config.py b/bob/paper/mccnn/tifs2018/config/MCCNN_config.py index 71e02fc6258924abc20df2c16512162805bcae0e..d698bcccb793f88ecafb2807f6b2bfd42b90db38 100644 --- a/bob/paper/mccnn/tifs2018/config/MCCNN_config.py +++ b/bob/paper/mccnn/tifs2018/config/MCCNN_config.py @@ -92,10 +92,27 @@ from bob.learn.pytorch.extractor.image import MCCNNExtractor from bob.bio.video.extractor import Wrapper -MODEL_FILE= None # Replace with '<PATH_TO_MODEL>' -#################################################################### +# MODEL_FILE= None # Replace with '<PATH_TO_MODEL>' +# #################################################################### + +# If you want to use the pretrained model + +import pkg_resources + +MODEL_FILE = pkg_resources.resource_filename( 'bob.paper.mccnn.tifs2018', 'models/mccnn_best_C1-B1-FFC.pth') + +URL='http://www.idiap.ch/~ageorge/model_100_0.pth' + +if not os.path.exists(MODEL_FILE): + + logger.info('Downloading the MCCNN model') + + bob.extension.download.download_file(URL,MODEL_FILE) + + logger.info('Downloaded MCCNN model to location: {}'.format(MODEL_FILE)) + -ADAPTED_LAYERS= 'conv1-group1-block1-ffc' +ADAPTED_LAYERS= 'conv1-group1-ffc' #################################################################### SELECTED_CHANNELS= [0,1,2,3] diff --git a/bob/paper/mccnn/tifs2018/models/__init__.py b/bob/paper/mccnn/tifs2018/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391