MLPAlgorithm PAD algorithm V1 version

This is an MLP based PAD algorithm.

bob/ip/pytorch_extractor/test_data/mlp_algo_test_config.py

@@ -6,8 +6,6 @@
 #==============================================================================
 # Import here:
 
-import numpy as np
-
 import torch
 
 
@@ -16,17 +14,14 @@ import torch
 
 """
 Transformations to be applied to the input 1D numpy arrays (feature vectors).
-Here, for demonstrative purposes, the transformation is mean std-normalization,
-where mean and std values are just numpy generated vectors. In real applications,
-normalizers must be computed in the meaningfull way. This config is just for
-test purposes.
+Only conversion to Tensor and unsqueezing is needed to match the input of
+TwoLayerMLP network
 """
 
 def transform(x):
     """
-    Transformation function applying dummy mean-std normalization and converting
-    input numpy feature vectors to PyTorch tensors, making them compatible with
-    MLP.
+    Convert input to Tensor and unsqueeze to match the input of
+    TwoLayerMLP network.
 
     Arguments
     ---------
@@ -39,19 +34,7 @@ def transform(x):
         Torch tensor, transformed ``x`` to be used as MLP input.
     """
 
-    features_mean = np.zeros(x.shape)
-
-    features_std = np.ones(x.shape)
-
-    row_norm_list = []
-    for row in x:  # row is a sample
-        row_norm = (row - features_mean) / features_std
-        row_norm_list.append(row_norm)
-    x_norm = np.vstack(row_norm_list)
-
-    x_norm.squeeze()
-
-    return torch.Tensor(x_norm).unsqueeze(0)
+    return torch.Tensor(x).unsqueeze(0)
 
 
 """
@@ -66,7 +49,7 @@ 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'] = 100
+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