cleanup'

'

Author: aamini

mitdeeplearning/data/test_faces.h5py

@@ -105,118 +105,3 @@ def get_test_faces():
             images[key].append(image)
 
     return images["LF"], images["LM"], images["DF"], images["DM"]
-
-
-class PPBFaceEvaluator:
-    ''' Evaluate on the PPB dataset'''
-    def __init__(self, skip=4):
-
-        path_to_faces = tf.keras.utils.get_file('ppb', 'https://www.dropbox.com/s/l0lp6qxeplumouf/PPB.tar?dl=1', extract=True)
-        self.ppb_root = os.path.join(os.path.split(path_to_faces)[0], 'PPB-2017')
-
-        ppb_anno = os.path.join(self.ppb_root,'PPB-2017-metadata.csv')
-
-        self.anno_dict = {}
-        with open(ppb_anno) as f:
-            for line in f.read().split('\r'):
-                ind, name, gender, numeric, skin, country = line.split(',')
-                self.anno_dict[name] = (gender.lower(),skin.lower())
-
-        image_dir = os.path.join(self.ppb_root, "imgs")
-        image_files = sorted(os.listdir(image_dir))[::skip] #sample every 4 images for computation time in the lab
-
-        self.raw_images = {
-            'male_darker':[],
-            'male_lighter':[],
-            'female_darker':[],
-            'female_lighter':[],
-        }
-
-        for filename in image_files:
-            if not filename.endswith(".jpg"):
-                continue
-            image = cv2.imread(os.path.join(image_dir,filename))[:,:,::-1]
-            gender, skin = self.anno_dict[filename]
-            self.raw_images[gender+'_'+skin].append(image)
-
-
-    def get_sample_faces_from_demographic(self, gender, skin_color):
-        key = self.__get_key(gender, skin_color)
-        data = self.raw_images[key][50]/255.
-        return data
-
-
-    def evaluate(self, models_to_test, gender, skin_color, output_idx=None, from_logit=False, patch_stride=0.2, patch_depth=5):
-        correct_predictions = [0.0]*len(models_to_test)
-
-        key = self.__get_key(gender, skin_color)
-        num_faces = len(self.raw_images[key])
-
-        import progressbar
-        bar = progressbar.ProgressBar()
-        for face_idx in bar(range(num_faces)):
-
-            image = self.raw_images[key][face_idx]
-            height, width, _ = image.shape
-
-            patches, bboxes = slide_square(image, patch_stride, width/2, width, patch_depth)
-            patches = tf.cast(tf.constant(patches, dtype=tf.uint8), tf.float32)/255.
-
-            for model_idx, model in enumerate(models_to_test):
-                out = model(patches)
-                y = out if output_idx is None else out[output_idx]
-                y = y.numpy()
-                y_inds = np.argsort(y.flatten())
-                most_likely_prob = y[y_inds[-1]]
-                if (from_logit and most_likely_prob >= 0.0) or \
-                   (not from_logit and most_likely_prob >= 0.5):
-                        correct_predictions[model_idx] += 1
-
-        accuracy = [correct_predictions[i]/num_faces for i,_ in enumerate(models_to_test)]
-        return accuracy
-
-
-    def __get_key(self, gender, skin_color):
-        gender = gender.lower()
-        skin_color = skin_color.lower()
-        assert gender in ['male', 'female']
-        assert skin_color in ['lighter', 'darker']
-        return '{}_{}'.format(gender, skin_color)
-
-
-
-''' function to slide a square across image and extract square regions
-img = the image
-stride = (0,1], provides the fraction of the dimension for which to slide to generate a crop
-max_size = maximum square size
-min_size = minimum square size
-n = number of different sizes including min_size, max_size '''
-def slide_square(img, stride, min_size, max_size, n):
-    img_h, img_w = img.shape[:2]
-
-    square_sizes = np.linspace(min_size, max_size, n, dtype=np.int32)
-    square_images = []
-    square_bbox = [] # list of list of tuples: [(i1,j1), (i2,j2)] where i1,j1 is the top left corner; i2,j2 is bottom right corner
-    # for each of the square_sizes
-    for level, sq_dim in enumerate(square_sizes):
-
-        stride_length = int(stride*sq_dim)
-        stride_start_i = xrange(0,int(img_h-sq_dim+1),stride_length)
-        stride_start_j = xrange(0,int(img_w-sq_dim+1),stride_length)
-        for i in stride_start_i:
-            for j in stride_start_j:
-                square_top_left = (i,j)
-
-                square_bottom_right = (i+sq_dim,j+sq_dim)
-
-                square_corners = (square_top_left,square_bottom_right)
-                square_corners_show = ((j,i),(j+sq_dim,i+sq_dim))
-                square_image = img[i:i+sq_dim,j:j+sq_dim]
-
-                square_resize = cv2.resize(square_image, IM_SHAPE[:2], interpolation=cv2.INTER_NEAREST)
-                # append to list of images and bounding boxes
-
-                square_images.append(square_resize)
-                square_bbox.append(square_corners)
-
-    return square_images, square_bbox

mitdeeplearning/lab2.py

@@ -1,7 +1,6 @@
 import matplotlib.pyplot as plt
 import tensorflow as tf
 import time
-import progressbar
 import numpy as np
 
 from IPython import display as ipythondisplay

mitdeeplearning/util.py

@@ -22,13 +22,13 @@ def get_dist(pkgname):
 setup(
   name = 'mitdeeplearning',         # How you named your package folder (MyLib)
   packages = ['mitdeeplearning'],   # Chose the same as "name"
-  version = '0.5.4',      # Start with a small number and increase it with every change you make
+  version = '0.5.5',      # Start with a small number and increase it with every change you make
   license='MIT',        # Chose a license from here: https://help.github.com/articles/licensing-a-repository
   description = 'Official software labs for MIT Introduction to Deep Learning (http://introtodeeplearning.com)',   # Give a short description about your library
   author = 'Alexander Amini',                   # Type in your name
   author_email = '[email protected]',      # Type in your E-Mail
   url = 'http://introtodeeplearning.com',   # Provide either the link to your github or to your website
-  download_url = 'https://github.com/aamini/introtodeeplearning_labs/archive/v0.5.4.tar.gz',    # I explain this later on
+  download_url = 'https://github.com/aamini/introtodeeplearning_labs/archive/v0.5.5.tar.gz',    # I explain this later on
   keywords = ['deep learning', 'neural networks', 'tensorflow', 'introduction'],   # Keywords that define your package best
   install_requires=install_deps,
   classifiers=[