diff --git a/bob/learn/pytorch/architectures/ConvAutoencoder.py b/bob/learn/pytorch/architectures/ConvAutoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..741c1b6be89acea366d53bbf018925b698dcdb90
--- /dev/null
+++ b/bob/learn/pytorch/architectures/ConvAutoencoder.py
@@ -0,0 +1,46 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: Olegs Nikisins
+"""
+#==============================================================================
+# Import here:
+
+from torch import nn
+
+#==============================================================================
+# Define the network:
+
+class ConvAutoencoder(nn.Module):
+
+ def __init__(self):
+ super(ConvAutoencoder, self).__init__()
+ self.encoder = nn.Sequential(nn.Conv2d(3, 16, 5, padding=2),
+ nn.ReLU(True),
+ nn.MaxPool2d(2),
+ nn.Conv2d(16, 16, 5, padding=2),
+ nn.ReLU(True),
+ nn.MaxPool2d(2),
+ nn.Conv2d(16, 16, 3, padding=2),
+ nn.ReLU(True),
+ nn.MaxPool2d(2),
+ nn.Conv2d(16, 16, 3, padding=2),
+ nn.ReLU(True),
+ nn.MaxPool2d(2))
+
+ self.decoder = nn.Sequential(nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1),
+ nn.ReLU(True),
+ nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1),
+ nn.ReLU(True),
+ nn.ConvTranspose2d(16, 16, 5, stride=2, padding=2),
+ nn.ReLU(True),
+ nn.ConvTranspose2d(16, 3, 5, stride=2, padding=2),
+ nn.ReLU(True),
+ nn.ConvTranspose2d(3, 3, 2, stride=1, padding=1),
+ nn.Tanh())
+
+ def forward(self, x):
+ x = self.encoder(x)
+ x = self.decoder(x)
+ return x
+
diff --git a/bob/learn/pytorch/architectures/__init__.py b/bob/learn/pytorch/architectures/__init__.py
index 4d3cf39a0a91879f7623f1dfa65678e421d10ebb..d842c6bc4d5116a32eeb47e750e0541358fae9a1 100644
--- a/bob/learn/pytorch/architectures/__init__.py
+++ b/bob/learn/pytorch/architectures/__init__.py
@@ -7,6 +7,7 @@ from .DCGAN import DCGAN_discriminator
from .ConditionalGAN import ConditionalGAN_generator
from .ConditionalGAN import ConditionalGAN_discriminator
+from .ConvAutoencoder import ConvAutoencoder
from .utils import weights_init
diff --git a/bob/learn/pytorch/config/__init__.py b/bob/learn/pytorch/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/bob/learn/pytorch/config/autoencoder/__init__.py b/bob/learn/pytorch/config/autoencoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/bob/learn/pytorch/config/autoencoder/net1_celeba.py b/bob/learn/pytorch/config/autoencoder/net1_celeba.py
new file mode 100644
index 0000000000000000000000000000000000000000..73249afa83d9cb4723ec9856ad102ff0cdca8345
--- /dev/null
+++ b/bob/learn/pytorch/config/autoencoder/net1_celeba.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: Olegs Nikisins
+"""
+#==============================================================================
+# Import here:
+
+from torchvision import transforms
+
+from bob.pad.face.database import CELEBAPadDatabase
+
+from torch import nn
+
+
+#==============================================================================
+# Define parameters here:
+
+"""
+Note: do not change names of the below constants.
+"""
+NUM_EPOCHS = 70 # Maximum number of epochs
+BATCH_SIZE = 32 # Size of the batch
+LEARNING_RATE = 1e-3 # Learning rate
+NUM_WORKERS = 8 # The number of workers for the DataLoader
+
+
+"""
+Transformations to be applied sequentially to the input PIL image.
+Note: the variable name ``transform`` must be the same in all configuration files.
+"""
+
+transform = transforms.Compose([transforms.ToTensor(),
+ transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+ ])
+
+
+"""
+Set the parameters of the DataFolder dataset class.
+Note: do not change the name ``kwargs``.
+"""
+bob_hldi_instance = CELEBAPadDatabase(original_directory = "", original_extension = "")
+
+kwargs = {}
+kwargs["data_folder"] = "NO NEED TO SET HERE, WILL BE SET IN THE TRAINING SCRIPT"
+kwargs["transform"] = transform
+kwargs["extension"] = '.hdf5'
+kwargs["bob_hldi_instance"] = bob_hldi_instance
+kwargs["hldi_type"] = "pad"
+kwargs["groups"] = ['train']
+kwargs["protocol"] = 'grandtest'
+kwargs["purposes"] = ['real']
+kwargs["allow_missing_files"] = True
+
+
+"""
+Define the network to be trained as a class, named ``Network``.
+Note: Do not change the name of the below class.
+"""
+
+from bob.learn.pytorch.architectures import ConvAutoencoder as Network
+
+
+"""
+Define the loss to be used for training.
+Note: do not change the name of the below variable.
+"""
+loss_type = nn.MSELoss()
+
+
+"""
+OPTIONAL: if not defined loss will be computed in the training script.
+See training script for details
+
+Define the function to compute the loss. Don't change the signature of this
+function.
+"""
+# we don't define the loss_function for this configuration
+#def loss_function(output, img, target):
+
+
diff --git a/bob/learn/pytorch/datasets/__init__.py b/bob/learn/pytorch/datasets/__init__.py
index 4b4e9deca8f1d753771594fe1aa72926f9878f3b..3319fc7412d04edf153e3b8c6a13deb778268610 100644
--- a/bob/learn/pytorch/datasets/__init__.py
+++ b/bob/learn/pytorch/datasets/__init__.py
@@ -1,5 +1,6 @@
from .casia_webface import CasiaDataset
from .casia_webface import CasiaWebFaceDataset
+from .data_folder import DataFolder
# transforms
from .utils import FaceCropper
diff --git a/bob/learn/pytorch/datasets/data_folder.py b/bob/learn/pytorch/datasets/data_folder.py
new file mode 100644
index 0000000000000000000000000000000000000000..db21a477ef17230694ebee0c53d1ca497dd268a9
--- /dev/null
+++ b/bob/learn/pytorch/datasets/data_folder.py
@@ -0,0 +1,247 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: Olegs Nikisins
+"""
+
+#==============================================================================
+# Import what is needed here:
+
+import torch.utils.data as data
+
+import os
+
+import random
+random.seed( a = 7 )
+
+import PIL
+
+import numpy as np
+
+from torchvision import transforms
+
+import torch
+
+import h5py
+
+
+#==============================================================================
+def get_file_names_and_labels(files, data_folder, extension = ".hdf5", hldi_type = "pad"):
+ """
+ Get absolute names of the corresponding file objects and their class labels,
+ as well as keys defining name of the frame to load the data from.
+
+ **Parameters:**
+
+ ``files`` : [File]
+ A list of files objects defined in the High Level Database Interface
+ of the particular datbase.
+
+ ``data_folder`` : str
+ A directory containing the training data.
+
+ ``extension`` : str
+ Extension of the data files. Default: ".hdf5" .
+
+ ``hldi_type`` : str
+ Type of the high level database interface. Default: "pad".
+ Note: this is the only type supported at the moment.
+
+ **Returns:**
+
+ ``file_names_labels_keys`` : [(str, int, str)]
+ A list of tuples, where each tuple contain an absolute filename,
+ a corresponding label of the class, and a key defining the name of the
+ frame to extract the data from.
+ """
+
+ file_names_labels_keys = []
+
+ if hldi_type == "pad":
+
+ for f in files:
+
+ if f.attack_type is None:
+
+ label = 0
+
+ else:
+
+ label = 1
+
+ file_name = os.path.join(data_folder, f.path + extension)
+
+ if os.path.isfile(file_name): # if file is available:
+
+ with h5py.File(file_name, "r") as f_h5py:
+
+ file_keys = list(f_h5py.keys())
+
+ # elements of tuples in the below list are as follows:
+ # a filename a key is extracted from,
+ # a label corresponding to the file,
+ # a key defining a frame from the file.
+ file_names_labels_keys = file_names_labels_keys + [(file_name, label, key) for file_name, label, key in
+ zip([file_name]*len(file_keys), [label]*len(file_keys), file_keys)]
+
+ return file_names_labels_keys
+
+
+#==============================================================================
+class DataFolder(data.Dataset):
+ """
+ A generic data loader compatible with Bob High Level Database Interfaces
+ (HLDI). Only HLDI's of bob.pad.face are currently supported.
+ """
+
+ def __init__(self, data_folder,
+ transform = None,
+ extension = '.hdf5',
+ bob_hldi_instance = None,
+ hldi_type = "pad",
+ groups = ['train', 'dev', 'eval'],
+ protocol = 'grandtest',
+ purposes=['real', 'attack'],
+ allow_missing_files = True,
+ **kwargs):
+ """
+ **Parameters:**
+
+ ``data_folder`` : str
+ A directory containing the training data.
+
+ ``transform`` : callable
+ A function/transform that takes in a PIL image, and returns a
+ transformed version. E.g, ``transforms.RandomCrop``. Default: None.
+
+ ``extension`` : str
+ Extension of the data files. Default: ".hdf5".
+ Note: this is the only extension supported at the moment.
+
+ ``bob_hldi_instance`` : object
+ An instance of the HLDI interface. Only HLDI's of bob.pad.face
+ are currently supported.
+
+ ``hldi_type`` : str
+ String defining the type of the HLDI. Default: "pad".
+ Note: this is the only option currently supported.
+
+ ``groups`` : str or [str]
+ The groups for which the clients should be returned.
+ Usually, groups are one or more elements of ['train', 'dev', 'eval'].
+ Default: ['train', 'dev', 'eval'].
+
+ ``protocol`` : str
+ The protocol for which the clients should be retrieved.
+ Default: 'grandtest'.
+
+ ``purposes`` : str or [str]
+ The purposes for which File objects should be retrieved.
+ Usually it is either 'real' or 'attack'.
+ Default: ['real', 'attack'].
+
+ ``allow_missing_files`` : str or [str]
+ The missing files in the ``data_folder`` will not break the
+ execution if set to True.
+ Default: True.
+ """
+
+ self.data_folder = data_folder
+ self.transform = transform
+ self.extension = extension
+ self.bob_hldi_instance = bob_hldi_instance
+ self.hldi_type = hldi_type
+ self.groups = groups
+ self.protocol = protocol
+ self.purposes = purposes
+ self.allow_missing_files = allow_missing_files
+
+ if bob_hldi_instance is not None:
+
+ files = bob_hldi_instance.objects(groups = self.groups,
+ protocol = self.protocol,
+ purposes = self.purposes,
+ **kwargs)
+
+ file_names_labels_keys = get_file_names_and_labels(files = files,
+ data_folder = self.data_folder,
+ extension = self.extension,
+ hldi_type = self.hldi_type)
+
+ if self.allow_missing_files: # return only existing files
+
+ file_names_labels_keys = [f for f in file_names_labels_keys if os.path.isfile(f[0])]
+
+ else:
+
+ # TODO - add behaviour similar to image folder
+ file_names_labels_keys = []
+
+ self.file_names_labels_keys = file_names_labels_keys
+
+
+ #==========================================================================
+ def __getitem__(self, index):
+ """
+ Returns an image, possibly transformed, and a target class given index.
+
+ **Parameters:**
+
+ ``index`` : int.
+ An index of the sample to return.
+
+ **Returns:**
+
+ ``pil_img`` : Tensor or PIL Image
+ If ``self.transform`` is defined the output is the torch.Tensor,
+ otherwise the output is an instance of the PIL.Image.Image class.
+
+ ``target`` : int
+ Index of the class.
+ """
+
+ path, target, key = self.file_names_labels_keys[index]
+
+ with h5py.File(path, "r") as f_h5py:
+
+ img_array = np.array(f_h5py.get(key+'/array')) # The size now is (3 x W x H)
+
+ if isinstance(self.transform, transforms.Compose): # if an instance of torchvision composed transformation
+
+ if len(img_array.shape) == 3: # for color images
+
+ img_array_tr = np.swapaxes(img_array, 1, 2)
+ img_array_tr = np.swapaxes(img_array_tr, 0, 2)
+
+ pil_img = PIL.Image.fromarray( img_array_tr ) # convert to PIL from array of size (H x W x 3)
+
+ else: # for gray-scale images
+
+ pil_img = PIL.Image.fromarray( img_array, 'L' ) # convert to PIL from array of size (H x W)
+
+ if self.transform is not None:
+
+ pil_img = self.transform(pil_img)
+
+ else: # if custom transformation function is given
+
+ img_array_transformed = self.transform(img_array)
+
+ return torch.Tensor(img_array_transformed).unsqueeze(0), target # convert array to Tensor, also return target
+
+ return pil_img, target
+
+
+ #==========================================================================
+ def __len__(self):
+ """
+ **Returns:**
+
+ ``len`` : int
+ The length of the file list.
+ """
+ return len(self.file_names_labels_keys)
+
+
+
+
diff --git a/bob/learn/pytorch/scripts/pytorch_train.py b/bob/learn/pytorch/scripts/pytorch_train.py
index b5899b43bbec2378796eea592c096e3b7eccbcd9..588610dac672ac3e6ddb91050d1155f488a2c041 100644
--- a/bob/learn/pytorch/scripts/pytorch_train.py
+++ b/bob/learn/pytorch/scripts/pytorch_train.py
@@ -31,7 +31,7 @@ import argparse
import importlib
import os
-from bob.pad.face.database.pytorch import DataFolder
+from bob.learn.pytorch.datasets import DataFolder
import torch
from torch.utils.data import DataLoader
@@ -91,10 +91,10 @@ def parse_arguments(cmd_params=None):
parser.add_argument("-c", "--config-file", type=str, help="Relative name of the config file defining "
"the network, training data, and training parameters.",
- default = "autoencoder/autoencoder_config.py")
+ default = "autoencoder/net1_celeba.py")
parser.add_argument("-cg", "--config-group", type=str, help="Name of the group, where config file is stored.",
- default = "bob.pad.face.config.pytorch")
+ default = "bob.learn.pytorch.config")
parser.add_argument("-p", "--pretrained-model-path", type=str, help="Absolute name of the file, containing pre-trained Network "
"model, to de used for Network initialization before training.",
diff --git a/setup.py b/setup.py
index 56fad4be48222ae335ae179e3ea617a7d08598fc..5669786f5eb4d7e8fb7126528e7e1b5c54ba276f 100644
--- a/setup.py
+++ b/setup.py
@@ -72,7 +72,7 @@ setup(
'train_cnn.py = bob.learn.pytorch.scripts.train_cnn:main',
'train_dcgan.py = bob.learn.pytorch.scripts.train_dcgan:main',
'train_conditionalgan.py = bob.learn.pytorch.scripts.train_conditionalgan:main',
- 'pytorch-train-autoencoder-pad.py = bob.pad.face.script.pytorch.pytorch_train:main',
+ 'pytorch-train-autoencoder-pad.py = bob.learn.pytorch.scripts.pytorch_train:main',
],
},