From 8a158686576d437c5ae3abca63082c4c1333aeae Mon Sep 17 00:00:00 2001
From: ageorge <anjith.george@idiap.ch>
Date: Mon, 21 Jan 2019 18:13:36 +0100
Subject: [PATCH] WIP, MCCNN extractor
---
bob/ip/pytorch_extractor/MCCNN.py | 102 +++++++++++++++++++++++++++
bob/ip/pytorch_extractor/__init__.py | 2 +
bob/ip/pytorch_extractor/test.py | 14 ++++
3 files changed, 118 insertions(+)
create mode 100644 bob/ip/pytorch_extractor/MCCNN.py
diff --git a/bob/ip/pytorch_extractor/MCCNN.py b/bob/ip/pytorch_extractor/MCCNN.py
new file mode 100644
index 0000000..aa9bc5e
--- /dev/null
+++ b/bob/ip/pytorch_extractor/MCCNN.py
@@ -0,0 +1,102 @@
+import numpy as np
+
+import torch
+from torch.autograd import Variable
+
+import torchvision.transforms as transforms
+
+from bob.learn.pytorch.architectures import MCCNN
+from bob.bio.base.extractor import Extractor
+
+
+#TODO: Clean up
+
+
+class MCCNNExtractor(Extractor):
+ """ The class implementing the MC-CNN score computation.
+
+ Attributes
+ ----------
+ network: :py:class:`torch.nn.Module`
+ The network architecture
+ transforms: :py:mod:`torchvision.transforms`
+ The transform from numpy.array to torch.Tensor
+
+ """
+
+ def __init__(self, considered_modalities=['C','D','I','T'], model_file=None):
+ """ Init method
+
+ Parameters
+ ----------
+ considered_modalities: list
+ The list of modalities used C,D,I,T represents color, depth , infrared and thermal respectively
+ pretrained_lightCNN_modelpath: str
+ Path to the Pretrained LightCNN model
+ NB: There are two model files here; one is the pretrained Light CNN model which is used as the base network; then there is another model file
+ specifically trained for PAD (model_file) which contains the new adapted layers and the fully connected layers.
+ model_file: str
+ The path of the trained PAD network to load
+
+ """
+
+ Extractor.__init__(self, skip_extractor_training=True)
+
+ # model
+ self.network = MCCNN(considered_modalities=considered_modalities)#.net
+
+ #self.network=self.network.to(device)
+
+ if model_file is None:
+ # do nothing (used mainly for unit testing)
+
+ print("No pretrained file")
+ pass
+ else:
+
+ # Old approach
+
+ # state_dict = torch.load(model_file,map_location=lambda storage,loc:storage)
+
+ # self.network.load_state_dict(state_dict)
+
+ # With the new training
+ cp = torch.load(model_file)
+ if 'state_dict' in cp:
+ self.network.load_state_dict(cp['state_dict'])
+
+ print('Loaded the pretrained PAD model')
+
+ self.network.net.eval()
+
+ # image pre-processing
+ self.transforms= transforms.Compose([transforms.ToTensor()])
+
+ def __call__(self, image):
+ """ Extract features from an image
+
+ Parameters
+ ----------
+ image : 3D :py:class:`numpy.ndarray` (floats)
+ The multi-channel image to extract the score from. Its size must be 4x128x128;
+ The channels should be ordered in C D I T order (color, depth, infrared and thermal respectively)
+
+ Returns
+ -------
+ output : float
+ The extracted feature is a scalar values ~0 for bonafide and ~1 for PAs
+
+ """
+
+ input_image = np.rollaxis(np.rollaxis(image, 2),2)
+ input_image = self.transforms(input_image)
+ input_image = input_image.unsqueeze(0)
+
+ #print("input_image",input_image.shape)
+
+ output = self.network.forward(Variable(input_image))
+ output = output.data.numpy().flatten()
+
+ # output is a scalar score
+
+ return output
diff --git a/bob/ip/pytorch_extractor/__init__.py b/bob/ip/pytorch_extractor/__init__.py
index d3b6b9c..3bbb43c 100755
--- a/bob/ip/pytorch_extractor/__init__.py
+++ b/bob/ip/pytorch_extractor/__init__.py
@@ -1,5 +1,6 @@
from .CNN8 import CNN8Extractor
from .CasiaNet import CasiaNetExtractor
+from .MCCNN import MCCNNExtractor
# gets sphinx autodoc done right - don't remove it
def __appropriate__(*args):
@@ -19,6 +20,7 @@ def __appropriate__(*args):
__appropriate__(
CNN8Extractor,
CasiaNetExtractor,
+ MCCNNExtractor,
)
# gets sphinx autodoc done right - don't remove it
diff --git a/bob/ip/pytorch_extractor/test.py b/bob/ip/pytorch_extractor/test.py
index 6411890..95a6823 100644
--- a/bob/ip/pytorch_extractor/test.py
+++ b/bob/ip/pytorch_extractor/test.py
@@ -32,3 +32,17 @@ def test_casianet():
data = numpy.random.rand(3, 128, 128).astype("float32")
output = extractor(data)
assert output.shape[0] == 320
+
+
+def test_mccnn():
+ """ test for the MCCNN architecture
+
+ this architecture takes 4x128x128 images as input
+ output a single score
+ """
+ from . import MCCNNExtractor
+ extractor = MCCNNExtractor()
+ # this architecture expects 4x128x128 Multi channel images
+ data = numpy.random.rand(4, 128, 128).astype("float32")
+ output = extractor(data)
+ assert output.shape[0] == 1
--
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