add face tasks

.gitignore

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+*.pyc

FACE_UTILS/LFWDataset.py

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+# -*- coding: utf-8 -*-
+import torchvision.datasets as datasets
+import os
+import numpy as np
+
+class LFWDataset(datasets.ImageFolder):
+    '''
+    '''
+    def __init__(self, dir, pairs_path, file_ext='jpg', transform=None):
+
+        super(LFWDataset, self).__init__(dir,transform)
+        self.pairs_path = pairs_path
+        # LFW dir contains 2 folders: faces and lists
+        self.validation_images = self.get_lfw_paths(dir,file_ext=file_ext)
+
+    def read_lfw_pairs(self,pairs_filename):
+        pairs = []
+        with open(pairs_filename, 'r') as f:
+            for line in f.readlines()[1:]:
+                pair = line.strip().split()
+                pairs.append(pair)
+        return np.array(pairs)
+    # !!!!!!!!!!!!!!!!!!!!!!!!!!!NOTICE YOUR FILE_EXTENSION!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+    def get_lfw_paths(self,lfw_dir,file_ext="jpg"):
+        pairs = self.read_lfw_pairs(self.pairs_path)
+        nrof_skipped_pairs = 0
+        path_list = []
+        issame_list = []
+        for i in range(len(pairs)):
+            pair = pairs[i]
+            if len(pair) == 3:
+                path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext)
+                path1 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])+'.'+file_ext)
+                issame = True
+            elif len(pair) == 4:
+                path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext)
+                path1 = os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])+'.'+file_ext)
+                issame = False
+            if os.path.exists(path0) and os.path.exists(path1):    # Only add the pair if both paths exist
+                path_list.append((path0,path1,issame))
+                issame_list.append(issame)
+            else:
+                nrof_skipped_pairs += 1
+        if nrof_skipped_pairs>0:
+            print('Skipped %d image pairs' % nrof_skipped_pairs)
+        return path_list
+
+    def __getitem__(self, index):
+        '''
+
+        Args:
+            index: Index of the triplet or the matches - not of a single image
+
+        Returns:
+
+        '''
+        def transform(img_path):
+            """Convert image into numpy array and apply transformation
+               Doing this so that it is consistent with all other datasets
+               to return a PIL Image.
+            """
+            img = self.loader(img_path)
+            return self.transform(img)
+        (path_1,path_2,issame) = self.validation_images[index]
+        img1, img2 = transform(path_1), transform(path_2)
+        return img1, img2, issame
+
+    def __len__(self):
+        return len(self.validation_images)

FACE_UTILS/__init__.py

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+"""Contains a bunch of utility functions."""
+import numpy as np
+import pdb
+
+
+def set_dataset_paths(args):
+    """Set default train and test path if not provided as input."""
+    args.train_path = 'data/%s/train' % (args.dataset)
+    args.val_path   = 'data/%s/val'   % (args.dataset)

FACE_UTILS/dataset.py

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+import collections
+import glob
+import os
+
+import numpy as np
+from PIL import Image
+
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.datasets as datasets
+import torchvision.models as models
+import torchvision.transforms as transforms
+import pdb
+
+VGGFACE_MEAN = [0.5, 0.5, 0.5]
+VGGFACE_STD  = [0.5, 0.5, 0.5]
+
+
+def train_loader(path, train_batch_size, num_workers=4, pin_memory=False, normalize=None):
+    if normalize is None:
+        normalize = transforms.Normalize(
+            mean=VGGFACE_MEAN, std=VGGFACE_STD)
+
+    train_transform = transforms.Compose([
+        transforms.Resize(112),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        normalize,
+    ])
+
+    train_dataset = datasets.ImageFolder(path, train_transform)
+
+    return torch.utils.data.DataLoader(train_dataset,
+        batch_size=train_batch_size, shuffle=True, sampler=None,
+        num_workers=num_workers, pin_memory=pin_memory)
+
+
+def val_loader(path, val_batch_size, num_workers=4, pin_memory=False, normalize=None):
+    if normalize is None:
+        normalize = transforms.Normalize(
+            mean=VGGFACE_MEAN, std=VGGFACE_STD)
+
+    val_transform = transforms.Compose([
+        transforms.Resize(112),
+        transforms.ToTensor(),
+        normalize,
+    ])
+
+    val_dataset = datasets.ImageFolder(path, val_transform)
+
+    return torch.utils.data.DataLoader(val_dataset,
+        batch_size=val_batch_size, shuffle=False, sampler=None,
+        num_workers=num_workers, pin_memory=pin_memory)