Commit 922a147a authored by Guillaume HEUSCH's avatar Guillaume HEUSCH

[extractor] added LightCNN9 extractor

parent ccd49918
import numpy
import torch
from torch.autograd import Variable
import torchvision.transforms as transforms
from bob.learn.pytorch.architectures import LightCNN9
from import Extractor
class LightCNN9Extractor(Extractor):
""" The class implementing the feature extraction of LightCNN9 embeddings.
network: :py:class:`torch.nn.Module`
The network architecture
to_tensor: :py:mod:`torchvision.transforms`
The transform from numpy.array to torch.Tensor
norm: :py:mod:`torchvision.transforms`
The transform to normalize the input
def __init__(self, model_file=None, num_classes=79077):
""" Init method
model_file: str
The path of the trained network to load
num_classes: int
The number of classes.
Extractor.__init__(self, skip_extractor_training=True)
# model = LightCNN9(num_classes)
if model_file is None:
# do nothing (used mainly for unit testing)
cp = torch.load(model_file, map_location='cpu')
# checked if pre-trained model was saved using nn.DataParallel ...
saved_with_nnDataParallel = False:
for k, v in cp['state_dict'].items():
if 'module' in k:
saved_with_nnDataParallel = True
# it it was, you have to rename the keys of state_dict ... (i.e. remove 'module.')
if saved_with_nnDataParallel:
if 'state_dict' in cp:
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in cp['state_dict'].items():
name = k[7:]
new_state_dict[name] = v
# image pre-processing
self.to_tensor = transforms.ToTensor()
self.norm = transforms.Normalize((0.5,), (0.5,))
def __call__(self, image):
""" Extract features from an image
image : 2D :py:class:`numpy.ndarray` (floats)
The grayscale image to extract the features from. Its size must be 128x128
feature : :py:class:`numpy.ndarray` (floats)
The extracted features as a 1d array of size 320
# torchvision.transforms expect a numpy array of size HxWxC
input_image = numpy.expand_dims(image, axis=2)
input_image = self.to_tensor(input_image)
input_image = self.norm(input_image)
input_image = input_image.unsqueeze(0)
# to be compliant with the loaded model, where weight and biases are torch.FloatTensor
input_image = input_image.float()
_ , features =
features =
return features
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