added deepsad base code

Deep-SAD-PyTorch/src/utils/__init__.py

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+from .config import Config
+from .visualization.plot_images_grid import plot_images_grid
+from .misc import enumerate_discrete, log_sum_exp, binary_cross_entropy

Deep-SAD-PyTorch/src/utils/config.py

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+import json
+
+
+class Config(object):
+    """Base class for experimental setting/configuration."""
+
+    def __init__(self, settings):
+        self.settings = settings
+
+    def load_config(self, import_json):
+        """Load settings dict from import_json (path/filename.json) JSON-file."""
+
+        with open(import_json, 'r') as fp:
+            settings = json.load(fp)
+
+        for key, value in settings.items():
+            self.settings[key] = value
+
+    def save_config(self, export_json):
+        """Save settings dict to export_json (path/filename.json) JSON-file."""
+
+        with open(export_json, 'w') as fp:
+            json.dump(self.settings, fp)

Deep-SAD-PyTorch/src/utils/misc.py

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+import torch
+
+from torch.autograd import Variable
+
+
+# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
+def enumerate_discrete(x, y_dim):
+    """
+    Generates a 'torch.Tensor' of size batch_size x n_labels of the given label.
+
+    :param x: tensor with batch size to mimic
+    :param y_dim: number of total labels
+    :return variable
+    """
+
+    def batch(batch_size, label):
+        labels = (torch.ones(batch_size, 1) * label).type(torch.LongTensor)
+        y = torch.zeros((batch_size, y_dim))
+        y.scatter_(1, labels, 1)
+        return y.type(torch.LongTensor)
+
+    batch_size = x.size(0)
+    generated = torch.cat([batch(batch_size, i) for i in range(y_dim)])
+
+    if x.is_cuda:
+        generated = generated.to(x.device)
+
+    return Variable(generated.float())
+
+
+def log_sum_exp(tensor, dim=-1, sum_op=torch.sum):
+    """
+    Uses the LogSumExp (LSE) as an approximation for the sum in a log-domain.
+
+    :param tensor: Tensor to compute LSE over
+    :param dim: dimension to perform operation over
+    :param sum_op: reductive operation to be applied, e.g. torch.sum or torch.mean
+    :return: LSE
+    """
+    max, _ = torch.max(tensor, dim=dim, keepdim=True)
+    return torch.log(sum_op(torch.exp(tensor - max), dim=dim, keepdim=True) + 1e-8) + max
+
+
+def binary_cross_entropy(x, y):
+    eps = 1e-8
+    return -torch.sum(y * torch.log(x + eps) + (1 - y) * torch.log(1 - x + eps), dim=-1)

Deep-SAD-PyTorch/src/utils/visualization/plot_images_grid.py

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+import torch
+import matplotlib
+matplotlib.use('Agg')  # or 'PS', 'PDF', 'SVG'
+
+import matplotlib.pyplot as plt
+import numpy as np
+from torchvision.utils import make_grid
+
+
+def plot_images_grid(x: torch.tensor, export_img, title: str = '', nrow=8, padding=2, normalize=False, pad_value=0):
+    """Plot 4D Tensor of images of shape (B x C x H x W) as a grid."""
+
+    grid = make_grid(x, nrow=nrow, padding=padding, normalize=normalize, pad_value=pad_value)
+    npgrid = grid.cpu().numpy()
+
+    plt.imshow(np.transpose(npgrid, (1, 2, 0)), interpolation='nearest')
+
+    ax = plt.gca()
+    ax.xaxis.set_visible(False)
+    ax.yaxis.set_visible(False)
+
+    if not (title == ''):
+        plt.title(title)
+
+    plt.savefig(export_img, bbox_inches='tight', pad_inches=0.1)
+    plt.clf()