Merge branch 'no_configs' into 'master'

Remove the need for config in extractor and algo

Closes #4

See merge request !5

bob/ip/pytorch_extractor/MLPAlgorithm.py

@@ -23,6 +23,8 @@ from bob.pad.base.utils import convert_list_of_frame_cont_to_array
 
 import bob.io.base
 
+import torch
+
 
 # =============================================================================
 # Main body :
@@ -34,36 +36,15 @@ class MLPAlgorithm(Algorithm):
 
     Attributes
     -----------
-    config_file: str
-        Relative name of the config file.
-        The path should be relative to ``config_group``,
-        for example: "test_data/mlp_algo_test_config.py".
-
-        This file **must** contain at least the following definitions:
-
-        Function namely ``transform``, which takes numpy.ndarray as input,
-        and returns a transformed Tensor. The dimensionality of the output
-        tensor must match the format expected by the MLP.
 
-        A ``Network`` class, defining your network architecture. Note, if your
-        class is named differently, import it as ``Network``, for example:
-        ``from bob.learn.pytorch.architectures import MyNetwork as Network``
-
-        Optional: ``network_kwargs`` to be used for ``Network`` initialization.
-        For example, if you don't want to use a sigmoid in the output of the
-        MLP, set the kwargs accodingly. Note: in current algorithm the
-        ``forward()`` method of the ``Network`` is used for feature extraction.
-
-    config_group: str
-        Group/package name containing the configuration file. Usually all
-        configs should be stored in this folder/place.
-        For example: "bob.ip.pytorch_extractor".
-        Both ``config_file`` and ``config_group`` are used to access the
-        configuration module.
+    network : object
+        An instance of an MLP Network to be used for score computation.
+        Note: in current algorith the ``forward()`` method of the Network
+        is used for score computation. For example, if you don't want to use
+        a sigmoid in the output of an MLP, set the kwargs accodingly.
 
     model_file : str
-        A paths to the model file to be used for network initialization.
-        The network structure is defined in the config file.
+        A paths to the model file to be used for ``network`` initialization.
 
     url : str
         URL to download the pretrained models from.
@@ -91,16 +72,14 @@ class MLPAlgorithm(Algorithm):
     """
 
     def __init__(self,
-                 config_file,
-                 config_group,
+                 network,
                  model_file = None,
                  url = None,
                  archive_extension = '.tar.gz',
                  frame_level_scores_flag = True,
                  mean_std_norm_flag = True):
 
-        super(MLPAlgorithm, self).__init__(config_file = config_file,
-                                           config_group = config_group,
+        super(MLPAlgorithm, self).__init__(network = network,
                                            model_file = model_file,
                                            url = url,
                                            archive_extension = archive_extension,
@@ -109,8 +88,8 @@ class MLPAlgorithm(Algorithm):
                                            performs_projection=True,
                                            requires_projector_training=True)
 
-        self.config_file = config_file
-        self.config_group = config_group
+        self.transform = lambda x : torch.Tensor(x).unsqueeze(0)
+        self.network = network
         self.model_file = model_file
         self.url = url
         self.archive_extension = archive_extension
@@ -221,7 +200,7 @@ class MLPAlgorithm(Algorithm):
         ----------
         feature : FrameContainer or :py:class:`numpy.ndarray`
             Two types of inputs are accepted.
-            A Frame Container conteining the features of an individual frmaes,
+            A Frame Container containing the features of an individual frmaes,
             see ``bob.bio.video.utils.FrameContainer``.
             Or a ND feature array of the size (n_samples x n_features).
 
@@ -250,15 +229,11 @@ class MLPAlgorithm(Algorithm):
                                                self.features_mean,
                                                self.features_std)
 
-        # kwargs for the transform_and_net_forward function:
-        function_kwargs = {}
-        function_kwargs["config_file"] = self.config_file
-        function_kwargs["config_group"] = self.config_group
-        function_kwargs["model_file"] = self.model_file
-        function_kwargs["color_input_flag"] = False
-
         scores = transform_and_net_forward(feature = feature,
-                                           **function_kwargs)
+                                           transform = self.transform,
+                                           network = self.network,
+                                           model_file = self.model_file,
+                                           color_input_flag = False)
 
         return scores
 

bob/ip/pytorch_extractor/MultiNetPatchExtractor.py

 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-Created on Thu Oct 11 15:32:02 2018
-
 @author: Olegs Nikisins
 """
 
@@ -13,7 +11,6 @@ from bob.bio.base.extractor import Extractor
 import numpy as np
 
 from bob.ip.pytorch_extractor.utils import reshape_flat_patches
-from bob.ip.pytorch_extractor.utils import combinations
 from bob.ip.pytorch_extractor.utils import transform_and_net_forward
 from bob.ip.pytorch_extractor.utils import load_pretrained_model
 
@@ -37,35 +34,21 @@ class MultiNetPatchExtractor(Extractor, object):
 
     Attributes
     -----------
-    config_file: str
-        Relative name of the config file.
-        The path should be relative to ``config_group``,
-        for example: "test_data/multi_net_patch_extractor_test_config.py".
-
-        This file **must** contain at least the following definitions:
 
+    transform : object
         Function namely ``transform``, which is a  Compose transformation of
         torchvision package, to be applied to the input samples.
 
-        A ``Network`` class, defining your network architecture. Note, if your
-        class is named differently, import it as ``Network``, for example:
-        ``from bob.learn.pytorch.architectures import MyNetwork as Network``
-
-        Optional: ``network_kwargs`` to be used for ``Network`` initialization.
-        For example, if you want to use the latent embeddings of the autoencoder
-        class, set the kwargs accodingly. Note: in current extractor the
-        ``forward()`` method of the ``Network`` is used for feature extraction.
-
-    config_group: str
-        Group/package name containing the configuration file. Usually all
-        configs should be stored in this folder/place.
-        For example: "bob.ip.pytorch_extractor".
-        Both ``config_file`` and ``config_group`` are used to access the
-        configuration module.
+    network : object
+        An instance of the Network to be used for feature extraction.
+        Note: in current extractor the ``forward()`` method of the Network
+        is used for feature extraction. For example, if you want to use the
+        latent embeddings of the autoencoder class, initialize the network
+        accordingly.
 
     model_file : [str]
-        A list of paths to the model files to be used for network initialization.
-        The network structure is defined in the config file.
+        A list of paths to the model files to be used for ``network``
+        initialization.
 
     patches_num : [int]
         A list of inices specifying which patches will be selected for
@@ -95,7 +78,7 @@ class MultiNetPatchExtractor(Extractor, object):
         ``urls``. The downloaded models **will be placed to the locations**
         specified in ``model_file`` list.
 
-        For example, the pretrained model for the autoencoder pre-trained on
+        For example, a model for an autoencoder pre-trained on
         RGB faces of the size (3(channels) x 128 x 128) and fine-tuned
         on the BW-NIR-D data can be found here:
         ["https://www.idiap.ch/software/bob/data/bob/bob.ip.pytorch_extractor/master/"
@@ -110,8 +93,9 @@ class MultiNetPatchExtractor(Extractor, object):
     """
 
     # =========================================================================
-    def __init__(self, config_file,
-                 config_group,
+    def __init__(self,
+                 transform,
+                 network,
                  model_file,
                  patches_num,
                  patch_reshape_parameters = None,
@@ -122,8 +106,8 @@ class MultiNetPatchExtractor(Extractor, object):
         Init method.
         """
 
-        super(MultiNetPatchExtractor, self).__init__(config_file = config_file,
-                                                     config_group = config_group,
+        super(MultiNetPatchExtractor, self).__init__(transform = transform,
+                                                     network = network,
                                                      model_file = model_file,
                                                      patches_num = patches_num,
                                                      patch_reshape_parameters = patch_reshape_parameters,
@@ -131,8 +115,8 @@ class MultiNetPatchExtractor(Extractor, object):
                                                      urls = urls,
                                                      archive_extension = archive_extension)
 
-        self.config_file = config_file
-        self.config_group = config_group
+        self.transform = transform
+        self.network = network
         self.model_file = model_file
         self.patches_num = patches_num
         self.patch_reshape_parameters = patch_reshape_parameters
@@ -151,8 +135,6 @@ class MultiNetPatchExtractor(Extractor, object):
         patches : 2D :py:class:`numpy.ndarray`
             An array containing flattened patches. The dimensions are:
             ``num_patches x len_of_flat_patch``
-            Optional: the last column of the array can also be a binary mask.
-            This case is also handled.
 
         Returns
         --------
@@ -160,19 +142,6 @@ class MultiNetPatchExtractor(Extractor, object):
             Feature vector.
         """
 
-        # kwargs for the transform_and_net_forward function:
-        function_kwargs = {}
-        function_kwargs["config_file"] = self.config_file
-        function_kwargs["config_group"] = self.config_group
-        function_kwargs["model_file"] = self.model_file
-        function_kwargs["color_input_flag"] = self.color_input_flag
-
-        # convert all values in the dictionary to the list if not a list already:
-        function_kwargs = {k:[v] if not isinstance(v, list) else v for (k,v) in function_kwargs.items()}
-
-        # compute all possible key-value combinations:
-        function_kwargs = combinations(function_kwargs) # function_kwargs is now a list with kwargs
-
         # select patches specified by indices:
         patches_selected = [patches[idx] for idx in self.patches_num] # convert to list to make it iterable
 
@@ -181,11 +150,11 @@ class MultiNetPatchExtractor(Extractor, object):
 
         features_all_patches = []
 
-        # make sure the model_file and urls are not None:
+        # make sure the model_file and urls are not None, but lists:
         if self.model_file is None:
-            self.model_file = [self.model_file]
+            self.model_file = [None] * len(self.patches_num)
         if self.urls is None:
-            self.urls = [self.urls]
+            self.urls = [None] * len(self.patches_num)
 
         for idx, patch in enumerate(patches_3d):
 
@@ -194,16 +163,24 @@ class MultiNetPatchExtractor(Extractor, object):
                                   url = self.urls[self.patches_num[idx]],
                                   archive_extension = self.archive_extension)
 
-            if len(function_kwargs) == 1:  # patches are passed through the same network:
+            if len(self.model_file) == 1:  # patches are passed through the same network:
 
-                features = transform_and_net_forward(feature = patch, **function_kwargs[0])
+                features = transform_and_net_forward(feature = patch,
+                                                     transform = self.transform,
+                                                     network = self.network,
+                                                     model_file = self.model_file[0],
+                                                     color_input_flag = self.color_input_flag)
 
             else:  # patches are passed through different networks:
 
-                features = transform_and_net_forward(feature = patch, **function_kwargs[self.patches_num[idx]])
+                features = transform_and_net_forward(feature = patch,
+                                     transform = self.transform,
+                                     network = self.network,
+                                     model_file = self.model_file[idx],
+                                     color_input_flag = self.color_input_flag)
 
-#                    print ("The model we use for patch {} is:".format(str(idx)))
-#                    print (function_kwargs[self.patches_num(idx)]["model_file"])
+#                print ("The model we use for patch {} is:".format(str(idx)))
+#                print (self.model_file[idx])
 
             features_all_patches.append(features)
 
@@ -211,4 +188,3 @@ class MultiNetPatchExtractor(Extractor, object):
 
         return features
 
-

bob/ip/pytorch_extractor/test.py

@@ -49,6 +49,8 @@ def test_multi_net_patch_extractor():
     """
 
     from bob.ip.pytorch_extractor import MultiNetPatchExtractor
+    from bob.learn.pytorch.architectures import ConvAutoencoder
+    from torchvision import transforms
 
     # =========================================================================
     # prepare the test data:
@@ -62,9 +64,13 @@ def test_multi_net_patch_extractor():
     # =========================================================================
     # test the extractor:
 
-    CONFIG_FILE = "test_data/multi_net_patch_extractor_test_config.py"  # config containing an instance of Composed Transform and a Network class to be used in feature extractor
-    CONFIG_GROUP = "bob.ip.pytorch_extractor"
-    # use specific/unique model for each patch. Models pre-trained on CelebA and fine-tuned (3 layers) on BATL:
+    # transform to be applied to input patches:
+    TRANSFORM = transforms.Compose([transforms.ToTensor(),
+                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+                                    ])
+
+    # use latent embeddings in the feature extractor:
+    NETWORK = ConvAutoencoder(return_latent_embedding = True)
 
     MODEL_FILE = None
 
@@ -72,16 +78,16 @@ def test_multi_net_patch_extractor():
     PATCH_RESHAPE_PARAMETERS = [3, 128, 128]  # reshape vectorized patches to this dimensions before passing to the Network
     COLOR_INPUT_FLAG = True
 
-    image_extractor = MultiNetPatchExtractor(config_file = CONFIG_FILE,
-                                             config_group = CONFIG_GROUP,
-                                             model_file = MODEL_FILE,
-                                             patches_num = PATCHES_NUM,
-                                             patch_reshape_parameters = PATCH_RESHAPE_PARAMETERS,
-                                             color_input_flag = COLOR_INPUT_FLAG,
-                                             urls = None)
+    extractor = MultiNetPatchExtractor(transform = TRANSFORM,
+                                       network = NETWORK,
+                                       model_file = MODEL_FILE,
+                                       patches_num = PATCHES_NUM,
+                                       patch_reshape_parameters = PATCH_RESHAPE_PARAMETERS,
+                                       color_input_flag = COLOR_INPUT_FLAG,
+                                       urls = None)
 
     # pass through encoder only, compute latent vector:
-    latent_vector = image_extractor(patch_flat)
+    latent_vector = extractor(patch_flat)
 
     # test:
     assert latent_vector.shape == (1296,)
@@ -101,6 +107,8 @@ def test_mlp_algorithm():
     """
 
     from bob.ip.pytorch_extractor import MLPAlgorithm
+    from bob.learn.pytorch.architectures import TwoLayerMLP
+
 
     # =========================================================================
     # prepare the test data / feature vector:
@@ -110,16 +118,19 @@ def test_mlp_algorithm():
     # =========================================================================
     # test the extractor:
 
-    CONFIG_FILE = "test_data/mlp_algo_test_config.py"  # config containing an instance of Composed Transform and a Network class to be used in feature extractor
-    CONFIG_GROUP = "bob.ip.pytorch_extractor"
+    # don't use the sigmoid in the output:
+    NETWORK = TwoLayerMLP(in_features = 1296,
+                          n_hidden_relu = 10,
+                          apply_sigmoid = False)
+
     MODEL_FILE = None
 
-    algorithm = MLPAlgorithm(config_file = CONFIG_FILE,
-                             config_group = CONFIG_GROUP,
+    algorithm = MLPAlgorithm(network = NETWORK,
                              model_file = MODEL_FILE,
                              url = None,
                              archive_extension = '.tar.gz',
-                             frame_level_scores_flag = True)
+                             frame_level_scores_flag = True,
+                             mean_std_norm_flag = True)
 
     # pass through encoder only, compute latent vector:
     score = algorithm.project(features)

bob/ip/pytorch_extractor/test_data/mlp_algo_test_config.py

-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-@author: Olegs Nikisins
-"""
-#==============================================================================
-# Import here:
-
-import torch
-
-
-#==============================================================================
-# Define parameters here:
-
-"""
-Transformations to be applied to the input 1D numpy arrays (feature vectors).
-Only conversion to Tensor and unsqueezing is needed to match the input of
-TwoLayerMLP network
-"""
-
-def transform(x):
-    """
-    Convert input to Tensor and unsqueeze to match the input of
-    TwoLayerMLP network.
-
-    Arguments
-    ---------
-    x : numpy array
-        1D numpy array / feature vector.
-
-    Return
-    ------
-    x_transform : Tensor
-        Torch tensor, transformed ``x`` to be used as MLP input.
-    """
-
-    return torch.Tensor(x).unsqueeze(0)
-
-
-"""
-Define the network to be trained as a class, named ``Network``.
-Note: Do not change the name of the below class, always import as ``Network``.
-"""
-
-from bob.learn.pytorch.architectures import TwoLayerMLP as Network
-
-
-"""
-kwargs to be used for ``Network`` initialization. The name must be ``network_kwargs``.
-"""
-network_kwargs = {}
-network_kwargs['in_features'] = 1296
-network_kwargs['n_hidden_relu'] = 10
-network_kwargs['apply_sigmoid'] = False # don't use sigmoid to make the scores more even
-
-

bob/ip/pytorch_extractor/test_data/multi_net_patch_extractor_test_config.py

-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-@author: Olegs Nikisins
-"""
-#==============================================================================
-# Import here:
-
-from torchvision import transforms
-
-
-#==============================================================================
-# Define parameters here:
-
-"""
-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))
-                                ])
-
-
-"""
-Define the network to be trained as a class, named ``Network``.
-Note: Do not change the name of the below class, always import as ``Network``.
-"""
-
-from bob.learn.pytorch.architectures import ConvAutoencoder as Network
-
-
-"""
-kwargs to be used for ``Network`` initialization. The name must be ``network_kwargs``.
-"""
-network_kwargs = {}
-network_kwargs['return_latent_embedding'] = True

bob/ip/pytorch_extractor/utils.py

 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-Created on Fri Jan 25 14:29:55 2019
-
 @author: Olegs Nikisins
 """
 
@@ -10,8 +8,6 @@ Created on Fri Jan 25 14:29:55 2019
 # Import what is needed here:
 import os
 
-import importlib
-
 from torchvision import transforms
 
 import numpy as np
@@ -143,52 +139,25 @@ def apply_transform_as_pil(img_array, transform):
     return pil_img
 
 
-def _init_the_network(config_module):
-    """
-    Initialize the network, given imported configuration module.
-
-    Parameters
-    ----------
-    config_module : object
-        Module containing Network configuration parameters.
-
-    Returns
-    -------
-    model : object
-        An instance of the Network class.
-    """
-
-    if "network_kwargs" in dir(config_module):
-        network_kwargs = config_module.network_kwargs
-        model = config_module.Network(**network_kwargs)
-
-    else:
-        model = config_module.Network()
-
-    return model
-
-
-def _transform(feature, config_module, color_input_flag):
+# =============================================================================
+def _transform(feature, color_input_flag, transform = None):
     """
-    Apply transformation defined in the configuration module to the input data.
+    Apply transformation to the input data.
 
     Currently two types of the transformation are supported:
 
     First, Compose transformation of torchvision package.
 
     Second, custom transformation functions, to be applied to each feature
-    vector. For example, mean-std normalization.
-
-    In both cases define transform() method in the ``config_module``.
+    vector. For example, mean-std normalization, or simple conversion of
+    input numpy vector to PyTorch tensors.
 
     Parameters
     ----------
+
     feature : :py:class:`numpy.ndarray`
         ND feature array of the size (N_samples x dim1 x dim2 x ...).
 
-    config_module : object
-        Module containing Network configuration parameters.
-
     color_input_flag : bool
         If set to ``True``, the input is considered to be a color image of the
         size ``(3, H, W)``. The tensor to be passed through the net will be
@@ -196,17 +165,21 @@ def _transform(feature, config_module, color_input_flag):
         If set to ``False``, the input is considered to be a set of BW images
         of the size ``(n_samples, H, W)``. The tensor to be passed through
         the net will be of the size ``(n_samples, 1, H, W)``.
+
+    transform : object
+        Function namely ``transform`` to be applied to the input samples.
+        Default: None .
     """
 
-    if "transform" in dir(config_module):
+    if transform is not None:
 
-        if isinstance(config_module.transform, transforms.Compose):
+        if isinstance(transform, transforms.Compose):
 
-            feature = apply_transform_as_pil(feature, config_module.transform)
+            feature = apply_transform_as_pil(feature, transform)
 
         else:
 
-            feature = np.stack([config_module.transform(item) for item in feature])
+            feature = np.stack([transform(item) for item in feature])
 
     if color_input_flag:
         # convert feature array to Tensor of size (1, 3, H, W)
@@ -221,20 +194,16 @@ def _transform(feature, config_module, color_input_flag):
 
 # =============================================================================
 def transform_and_net_forward(feature,
-                              config_file,
-                              config_group,
+                              transform,
+                              network,
                               model_file,
                               color_input_flag = False):
     """
     This function performs the following steps:
 
-    1. Import config module.
-
-    2. Applies transformation defined in the config file to the input data.
+    1. Applies transformation defined in the ``transform`` argument.
 
-    3. Initializes the Network class with optional kwargs.
-
-    4. Pass the transformed data through ``forward()`` method of the Network
+    2. Pass the transformed data through ``forward()`` method of the Network
     class.
 
     Parameters
@@ -243,17 +212,15 @@ def transform_and_net_forward(feature,
     feature : :py:class:`numpy.ndarray`
         ND feature array of the size (N_samples x dim1 x dim2 x ...).
 
-    config_file: str
-        Relative name of the config file defining the network, training data,
-        and training parameters. The path should be relative to
-        ``config_group``, for example: "autoencoder/netN.py".
+    transform : object
+        Function namely ``transform`` to be applied to the input samples.
 
-    config_group : str
-        Group/package name containing the configuration file. Usually all
-        configs should be stored in this folder/place.
-        For example: "bob.pad.face.config.pytorch".
-        Both ``config_file`` and ``config_group`` are used to access the
-        configuration module.
+    network : object
+        An instance of the Network to be used for feature extraction.
+        Note: in current extractor the ``forward()`` method of the Network
+        is used for feature extraction. For example, if you want to use the
+        latent embeddings of the autoencoder class, initialize the network
+        accordingly.
 
     model_file : str
         A path to the model file to be used for network initialization.
@@ -274,38 +241,25 @@ def transform_and_net_forward(feature,
         Output of the network per input sample/frame.
     """
 
-    # =========================================================================
-    # Create relative module name given path:
-    relative_mod_name = '.' + os.path.splitext(config_file)[0].replace(os.path.sep, '.')
-
-    # import configuration module:
-    config_module = importlib.import_module(relative_mod_name, config_group)
-
-
     # =========================================================================
     # transform the input image or feature vector:
-    feature_tensor = _transform(feature, config_module, color_input_flag)
-
+    feature_tensor = _transform(feature, color_input_flag, transform)
 
     # =========================================================================
-    # Initialize the model
-    local_model = _init_the_network(config_module)
-
     # Load the pre-trained model into the network
     if model_file is not None:
 
         model_state = torch.load(model_file, map_location=lambda storage,loc:storage)
 
         # Initialize the state of the model:
-        local_model.load_state_dict(model_state)
+        network.load_state_dict(model_state)
 
     # Model is used for evaluation only:
-    local_model.train(False)
-
+    network.train(False)
 
     # =========================================================================
     # Pass the transformed feature vector through the network:
-    output_tensor = local_model.forward(Variable(feature_tensor))
+    output_tensor = network.forward(Variable(feature_tensor))
 
     net_output = output_tensor.data.numpy().squeeze()
 
@@ -314,6 +268,7 @@ def transform_and_net_forward(feature,
     return net_output.astype(np.float)
 
 
+# =============================================================================
 def load_pretrained_model(model_path, url, archive_extension = '.tar.gz'):
     """
     Loads the model from the given ``url``, if the model specified in the
@@ -322,6 +277,7 @@ def load_pretrained_model(model_path, url, archive_extension = '.tar.gz'):
 
     Arguments
     ---------
+
     model_path : str
         Absolute file name pointing to the model.