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Commit 5615c74b authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
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Removed legacy components

parent 81e443ea
Pipeline #46558 failed with stage
in 14 minutes and 25 seconds
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bob/learn/pytorch/extractor/audio/DummyExtractor.py
View file @ 5615c74b
import numpy
from bob.bio.base_legacy.extractor import Extractor
from bob.bio.base.extractor import Extractor
class DummyExtractor(Extractor):
""" The Dummy class for passing the extracted embedding.
""" The Dummy class for passing the extracted embedding.
"""
def __init__(self):
""" Init function
def __init__(self):
""" Init function
"""
super(DummyExtractor, self).__init__()
def __call__(self, data):
"""__call__(data) -> feature
super(DummyExtractor, self).__init__()
def __call__(self, data):
"""__call__(data) -> feature
This function will actually perform the feature extraction.
It must be overwritten by derived classes.
......@@ -27,4 +29,5 @@ class DummyExtractor(Extractor):
feature : object (usually :py:class:`numpy.ndarray`)
The extracted feature.
"""
return data
\ No newline at end of file
return data
bob/learn/pytorch/extractor/image/DeepPixBiS.py
View file @ 5615c74b
import numpy as np
import numpy as np
import torch
from torch.autograd import Variable
import torchvision.transforms as transforms
from bob.learn.pytorch.architectures import DeepPixBiS
from bob.bio.base_legacy.extractor import Extractor
from bob.bio.base.extractor import Extractor
import logging
logger = logging.getLogger("bob.learn.pytorch")
class DeepPixBiSExtractor(Extractor):
""" The class implementing the DeepPixBiS score computation.
""" The class implementing the DeepPixBiS score computation.
Attributes
----------
......@@ -21,9 +23,21 @@ class DeepPixBiSExtractor(Extractor):
The transform from numpy.array to torch.Tensor
"""
def __init__(self, transforms = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]), model_file=None, scoring_method='pixel_mean'):
""" Init method
def __init__(
self,
transforms=transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
),
model_file=None,
scoring_method="pixel_mean",
):
""" Init method
Parameters
----------
......@@ -37,38 +51,44 @@ class DeepPixBiSExtractor(Extractor):
"""
Extractor.__init__(self, skip_extractor_training=True)
# model
self.transforms = transforms
self.network = DeepPixBiS(pretrained=False)
self.scoring_method = scoring_method
self.available_scoring_methods=['pixel_mean','binary','combined']
logger.debug('Scoring method is : {}'.format(self.scoring_method.upper()))
if model_file is None:
# do nothing (used mainly for unit testing)
logger.debug("No pretrained file provided")
pass
else:
logger.debug('Starting to load the pretrained PAD model')
try:
cp = torch.load(model_file)
except:
raise ValueError('Failed to load the model file : {}'.format(model_file))
if 'state_dict' in cp:
self.network.load_state_dict(cp['state_dict'])
else:
raise ValueError('Failed to load the state_dict for model file: {}'.format(model_file))
logger.debug('Loaded the pretrained PAD model')
self.network.eval()
Extractor.__init__(self, skip_extractor_training=True)
# model
self.transforms = transforms
self.network = DeepPixBiS(pretrained=False)
self.scoring_method = scoring_method
self.available_scoring_methods = ["pixel_mean", "binary", "combined"]
logger.debug("Scoring method is : {}".format(self.scoring_method.upper()))
if model_file is None:
# do nothing (used mainly for unit testing)
logger.debug("No pretrained file provided")
pass
else:
logger.debug("Starting to load the pretrained PAD model")
try:
cp = torch.load(model_file)
except:
raise ValueError(
"Failed to load the model file : {}".format(model_file)
)
def __call__(self, image):
""" Extract features from an image
if "state_dict" in cp:
self.network.load_state_dict(cp["state_dict"])
else:
raise ValueError(
"Failed to load the state_dict for model file: {}".format(
model_file
)
)
logger.debug("Loaded the pretrained PAD model")
self.network.eval()
def __call__(self, image):
""" Extract features from an image
Parameters
----------
......@@ -81,23 +101,27 @@ class DeepPixBiSExtractor(Extractor):
The extracted feature is a scalar values ~1 for bonafide and ~0 for PAs
"""
input_image = np.rollaxis(np.rollaxis(image, 2),2) # changes from CxHxW to HxWxC
input_image = self.transforms(input_image)
input_image = input_image.unsqueeze(0)
output = self.network.forward(Variable(input_image))
output_pixel = output[0].data.numpy().flatten()
output_binary = output[1].data.numpy().flatten()
if self.scoring_method=='pixel_mean':
score=np.mean(output_pixel)
elif self.scoring_method=='binary':
score=np.mean(output_binary)
elif self.scoring_method=='combined':
score= (np.mean(output_pixel)+np.mean(output_binary))/2.0
else:
raise ValueError('Scoring method {} is not implemented.'.format(self.scoring_method))
# output is a scalar score
return np.reshape(score,(1,-1))
input_image = np.rollaxis(
np.rollaxis(image, 2), 2
) # changes from CxHxW to HxWxC
input_image = self.transforms(input_image)
input_image = input_image.unsqueeze(0)
output = self.network.forward(Variable(input_image))
output_pixel = output[0].data.numpy().flatten()
output_binary = output[1].data.numpy().flatten()
if self.scoring_method == "pixel_mean":
score = np.mean(output_pixel)
elif self.scoring_method == "binary":
score = np.mean(output_binary)
elif self.scoring_method == "combined":
score = (np.mean(output_pixel) + np.mean(output_binary)) / 2.0
else:
raise ValueError(
"Scoring method {} is not implemented.".format(self.scoring_method)
)
# output is a scalar score
return np.reshape(score, (1, -1))
bob/learn/pytorch/extractor/image/FASNet.py
View file @ 5615c74b
import numpy as np
import numpy as np
import torch
from torch.autograd import Variable
import torchvision.transforms as transforms
from bob.learn.pytorch.architectures import FASNet
from bob.bio.base_legacy.extractor import Extractor
from bob.bio.base.extractor import Extractor
import logging
logger = logging.getLogger("bob.learn.pytorch")
class FASNetExtractor(Extractor):
""" The class implementing the FASNet score computation.
""" The class implementing the FASNet score computation.
Attributes
----------
......@@ -21,10 +23,22 @@ class FASNetExtractor(Extractor):
The transform from numpy.array to torch.Tensor
"""
def __init__(self, transforms = transforms.Compose([transforms.ToPILImage(),transforms.Resize(224, interpolation=2),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]), model_file=None):
""" Init method
def __init__(
self,
transforms=transforms.Compose(
[
transforms.ToPILImage(),
transforms.Resize(224, interpolation=2),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
),
model_file=None,
):
""" Init method
Parameters
----------
......@@ -40,34 +54,32 @@ class FASNetExtractor(Extractor):
"""
Extractor.__init__(self, skip_extractor_training=True)
# model
self.transforms = transforms
self.network = FASNet(pretrained=False)
Extractor.__init__(self, skip_extractor_training=True)
#self.network=self.network.to(device)
# model
self.transforms = transforms
self.network = FASNet(pretrained=False)
if model_file is None:
# do nothing (used mainly for unit testing)
logger.debug("No pretrained file provided")
pass
else:
# self.network=self.network.to(device)
if model_file is None:
# do nothing (used mainly for unit testing)
logger.debug("No pretrained file provided")
pass
else:
# With the new training
logger.debug('Starting to load the pretrained PAD model')
cp = torch.load(model_file)
if 'state_dict' in cp:
self.network.load_state_dict(cp['state_dict'])
# With the new training
logger.debug("Starting to load the pretrained PAD model")
cp = torch.load(model_file)
if "state_dict" in cp:
self.network.load_state_dict(cp["state_dict"])
logger.debug('Loaded the pretrained PAD model')
self.network.eval()
logger.debug("Loaded the pretrained PAD model")
def __call__(self, image):
""" Extract features from an image
self.network.eval()
def __call__(self, image):
""" Extract features from an image
Parameters
----------
......@@ -83,14 +95,16 @@ class FASNetExtractor(Extractor):
The extracted feature is a scalar values ~1 for bonafide and ~0 for PAs
"""
input_image = np.rollaxis(np.rollaxis(image, 2),2) # changes to 128x128xnum_channels
input_image = self.transforms(input_image)
input_image = input_image.unsqueeze(0)
output = self.network.forward(Variable(input_image))
output = output.data.numpy().flatten()
# output is a scalar score
input_image = np.rollaxis(
np.rollaxis(image, 2), 2
) # changes to 128x128xnum_channels
input_image = self.transforms(input_image)
input_image = input_image.unsqueeze(0)
output = self.network.forward(Variable(input_image))
output = output.data.numpy().flatten()
# output is a scalar score
return output
return output
bob/learn/pytorch/extractor/image/LightCNN29.py
View file @ 5615c74b
......@@ -6,10 +6,11 @@ from torch.autograd import Variable
import torchvision.transforms as transforms
from bob.learn.pytorch.architectures import LightCNN29
from bob.bio.base_legacy.extractor import Extractor
from bob.bio.base.extractor import Extractor
class LightCNN29Extractor(Extractor):
""" The class implementing the feature extraction of LightCNN29 embeddings.
""" The class implementing the feature extraction of LightCNN29 embeddings.
Attributes
----------
......@@ -21,9 +22,9 @@ class LightCNN29Extractor(Extractor):
The transform to normalize the input
"""
def __init__(self, model_file=None, num_classes=79077):
""" Init method
def __init__(self, model_file=None, num_classes=79077):
""" Init method
Parameters
----------
......@@ -33,44 +34,45 @@ class LightCNN29Extractor(Extractor):
The number of classes.
"""
Extractor.__init__(self, skip_extractor_training=True)
# model
self.network = LightCNN29(num_classes=num_classes)
if model_file is None:
# do nothing (used mainly for unit testing)
pass
else:
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
break
# if 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
self.network.load_state_dict(new_state_dict)
else:
self.network.load_state_dict(cp['state_dict'])
self.network.eval()
# 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
Extractor.__init__(self, skip_extractor_training=True)
# model
self.network = LightCNN29(num_classes=num_classes)
if model_file is None:
# do nothing (used mainly for unit testing)
pass
else:
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
break
# if 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
self.network.load_state_dict(new_state_dict)
else:
self.network.load_state_dict(cp["state_dict"])
self.network.eval()
# 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
Parameters
----------
......@@ -83,16 +85,16 @@ class LightCNN29Extractor(Extractor):
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 = self.network.forward(Variable(input_image))
features = features.data.numpy().flatten()
return features
# 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 = self.network.forward(Variable(input_image))
features = features.data.numpy().flatten()
return features
bob/learn/pytorch/extractor/image/LightCNN29v2.py
View file @ 5615c74b
......@@ -6,10 +6,11 @@ from torch.autograd import Variable
import torchvision.transforms as transforms
from bob.learn.pytorch.architectures import LightCNN29v2
from bob.bio.base_legacy.extractor import Extractor
from bob.bio.base.extractor import Extractor
class LightCNN29v2Extractor(Extractor):
""" The class implementing the feature extraction of LightCNN29v2 embeddings.
""" The class implementing the feature extraction of LightCNN29v2 embeddings.
Attributes
----------
......@@ -21,9 +22,9 @@ class LightCNN29v2Extractor(Extractor):
The transform to normalize the input
"""
def __init__(self, model_file=None, num_classes=79077):
""" Init method
def __init__(self, model_file=None, num_classes=79077):
""" Init method
Parameters
----------
......@@ -33,44 +34,45 @@ class LightCNN29v2Extractor(Extractor):
The number of classes.
"""
Extractor.__init__(self, skip_extractor_training=True)
# model
self.network = LightCNN29v2(num_classes=num_classes)
if model_file is None:
# do nothing (used mainly for unit testing)
pass
else:
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
break
# if 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
self.network.load_state_dict(new_state_dict)
else:
self.network.load_state_dict(cp['state_dict'])
self.network.eval()
# 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
Extractor.__init__(self, skip_extractor_training=True)
# model
self.network = LightCNN29v2(num_classes=num_classes)
if model_file is None:
# do nothing (used mainly for unit testing)
pass
else:
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
break
# if 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
self.network.load_state_dict(new_state_dict)
else:
self.network.load_state_dict(cp["state_dict"])
self.network.eval()
# 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
Parameters
----------
......@@ -83,16 +85,16 @@ class LightCNN29v2Extractor(Extractor):
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 = self.network.forward(Variable(input_image))
features = features.data.numpy().flatten()
return features
# 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)