# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # This work is licensed under the Creative Commons Attribution-NonCommercial # 4.0 International License. To view a copy of this license, visit # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. """Linear Separability (LS).""" from collections import defaultdict import numpy as np import sklearn.svm import tensorflow as tf import dnnlib.tflib as tflib from metrics import metric_base from training import misc #---------------------------------------------------------------------------- classifier_urls = [ 'https://drive.google.com/uc?id=1Q5-AI6TwWhCVM7Muu4tBM7rp5nG_gmCX', # celebahq-classifier-00-male.pkl 'https://drive.google.com/uc?id=1Q5c6HE__ReW2W8qYAXpao68V1ryuisGo', # celebahq-classifier-01-smiling.pkl 'https://drive.google.com/uc?id=1Q7738mgWTljPOJQrZtSMLxzShEhrvVsU', # celebahq-classifier-02-attractive.pkl 'https://drive.google.com/uc?id=1QBv2Mxe7ZLvOv1YBTLq-T4DS3HjmXV0o', # celebahq-classifier-03-wavy-hair.pkl 'https://drive.google.com/uc?id=1QIvKTrkYpUrdA45nf7pspwAqXDwWOLhV', # celebahq-classifier-04-young.pkl 'https://drive.google.com/uc?id=1QJPH5rW7MbIjFUdZT7vRYfyUjNYDl4_L', # celebahq-classifier-05-5-o-clock-shadow.pkl 'https://drive.google.com/uc?id=1QPZXSYf6cptQnApWS_T83sqFMun3rULY', # celebahq-classifier-06-arched-eyebrows.pkl 'https://drive.google.com/uc?id=1QPgoAZRqINXk_PFoQ6NwMmiJfxc5d2Pg', # celebahq-classifier-07-bags-under-eyes.pkl 'https://drive.google.com/uc?id=1QQPQgxgI6wrMWNyxFyTLSgMVZmRr1oO7', # celebahq-classifier-08-bald.pkl 'https://drive.google.com/uc?id=1QcSphAmV62UrCIqhMGgcIlZfoe8hfWaF', # celebahq-classifier-09-bangs.pkl 'https://drive.google.com/uc?id=1QdWTVwljClTFrrrcZnPuPOR4mEuz7jGh', # celebahq-classifier-10-big-lips.pkl 'https://drive.google.com/uc?id=1QgvEWEtr2mS4yj1b_Y3WKe6cLWL3LYmK', # celebahq-classifier-11-big-nose.pkl 'https://drive.google.com/uc?id=1QidfMk9FOKgmUUIziTCeo8t-kTGwcT18', # celebahq-classifier-12-black-hair.pkl 'https://drive.google.com/uc?id=1QthrJt-wY31GPtV8SbnZQZ0_UEdhasHO', # celebahq-classifier-13-blond-hair.pkl 'https://drive.google.com/uc?id=1QvCAkXxdYT4sIwCzYDnCL9Nb5TDYUxGW', # celebahq-classifier-14-blurry.pkl 'https://drive.google.com/uc?id=1QvLWuwSuWI9Ln8cpxSGHIciUsnmaw8L0', # celebahq-classifier-15-brown-hair.pkl 'https://drive.google.com/uc?id=1QxW6THPI2fqDoiFEMaV6pWWHhKI_OoA7', # celebahq-classifier-16-bushy-eyebrows.pkl 'https://drive.google.com/uc?id=1R71xKw8oTW2IHyqmRDChhTBkW9wq4N9v', # celebahq-classifier-17-chubby.pkl 'https://drive.google.com/uc?id=1RDn_fiLfEGbTc7JjazRXuAxJpr-4Pl67', # celebahq-classifier-18-double-chin.pkl 'https://drive.google.com/uc?id=1RGBuwXbaz5052bM4VFvaSJaqNvVM4_cI', # celebahq-classifier-19-eyeglasses.pkl 'https://drive.google.com/uc?id=1RIxOiWxDpUwhB-9HzDkbkLegkd7euRU9', # celebahq-classifier-20-goatee.pkl 'https://drive.google.com/uc?id=1RPaNiEnJODdr-fwXhUFdoSQLFFZC7rC-', # celebahq-classifier-21-gray-hair.pkl 'https://drive.google.com/uc?id=1RQH8lPSwOI2K_9XQCZ2Ktz7xm46o80ep', # celebahq-classifier-22-heavy-makeup.pkl 'https://drive.google.com/uc?id=1RXZM61xCzlwUZKq-X7QhxOg0D2telPow', # celebahq-classifier-23-high-cheekbones.pkl 'https://drive.google.com/uc?id=1RgASVHW8EWMyOCiRb5fsUijFu-HfxONM', # celebahq-classifier-24-mouth-slightly-open.pkl 'https://drive.google.com/uc?id=1RkC8JLqLosWMaRne3DARRgolhbtg_wnr', # celebahq-classifier-25-mustache.pkl 'https://drive.google.com/uc?id=1RqtbtFT2EuwpGTqsTYJDyXdnDsFCPtLO', # celebahq-classifier-26-narrow-eyes.pkl 'https://drive.google.com/uc?id=1Rs7hU-re8bBMeRHR-fKgMbjPh-RIbrsh', # celebahq-classifier-27-no-beard.pkl 'https://drive.google.com/uc?id=1RynDJQWdGOAGffmkPVCrLJqy_fciPF9E', # celebahq-classifier-28-oval-face.pkl 'https://drive.google.com/uc?id=1S0TZ_Hdv5cb06NDaCD8NqVfKy7MuXZsN', # celebahq-classifier-29-pale-skin.pkl 'https://drive.google.com/uc?id=1S3JPhZH2B4gVZZYCWkxoRP11q09PjCkA', # celebahq-classifier-30-pointy-nose.pkl 'https://drive.google.com/uc?id=1S3pQuUz-Jiywq_euhsfezWfGkfzLZ87W', # celebahq-classifier-31-receding-hairline.pkl 'https://drive.google.com/uc?id=1S6nyIl_SEI3M4l748xEdTV2vymB_-lrY', # celebahq-classifier-32-rosy-cheeks.pkl 'https://drive.google.com/uc?id=1S9P5WCi3GYIBPVYiPTWygrYIUSIKGxbU', # celebahq-classifier-33-sideburns.pkl 'https://drive.google.com/uc?id=1SANviG-pp08n7AFpE9wrARzozPIlbfCH', # celebahq-classifier-34-straight-hair.pkl 'https://drive.google.com/uc?id=1SArgyMl6_z7P7coAuArqUC2zbmckecEY', # celebahq-classifier-35-wearing-earrings.pkl 'https://drive.google.com/uc?id=1SC5JjS5J-J4zXFO9Vk2ZU2DT82TZUza_', # celebahq-classifier-36-wearing-hat.pkl 'https://drive.google.com/uc?id=1SDAQWz03HGiu0MSOKyn7gvrp3wdIGoj-', # celebahq-classifier-37-wearing-lipstick.pkl 'https://drive.google.com/uc?id=1SEtrVK-TQUC0XeGkBE9y7L8VXfbchyKX', # celebahq-classifier-38-wearing-necklace.pkl 'https://drive.google.com/uc?id=1SF_mJIdyGINXoV-I6IAxHB_k5dxiF6M-', # celebahq-classifier-39-wearing-necktie.pkl ] #---------------------------------------------------------------------------- def prob_normalize(p): p = np.asarray(p).astype(np.float32) assert len(p.shape) == 2 return p / np.sum(p) def mutual_information(p): p = prob_normalize(p) px = np.sum(p, axis=1) py = np.sum(p, axis=0) result = 0.0 for x in range(p.shape[0]): p_x = px[x] for y in range(p.shape[1]): p_xy = p[x][y] p_y = py[y] if p_xy > 0.0: result += p_xy * np.log2(p_xy / (p_x * p_y)) # get bits as output return result def entropy(p): p = prob_normalize(p) result = 0.0 for x in range(p.shape[0]): for y in range(p.shape[1]): p_xy = p[x][y] if p_xy > 0.0: result -= p_xy * np.log2(p_xy) return result def conditional_entropy(p): # H(Y|X) where X corresponds to axis 0, Y to axis 1 # i.e., How many bits of additional information are needed to where we are on axis 1 if we know where we are on axis 0? p = prob_normalize(p) y = np.sum(p, axis=0, keepdims=True) # marginalize to calculate H(Y) return max(0.0, entropy(y) - mutual_information(p)) # can slip just below 0 due to FP inaccuracies, clean those up. #---------------------------------------------------------------------------- class LS(metric_base.MetricBase): def __init__(self, num_samples, num_keep, attrib_indices, minibatch_per_gpu, **kwargs): assert num_keep <= num_samples super().__init__(**kwargs) self.num_samples = num_samples self.num_keep = num_keep self.attrib_indices = attrib_indices self.minibatch_per_gpu = minibatch_per_gpu def _evaluate(self, Gs, num_gpus): minibatch_size = num_gpus * self.minibatch_per_gpu # Construct TensorFlow graph for each GPU. result_expr = [] for gpu_idx in range(num_gpus): with tf.device('/gpu:%d' % gpu_idx): Gs_clone = Gs.clone() # Generate images. latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:]) dlatents = Gs_clone.components.mapping.get_output_for(latents, None, is_validation=True) images = Gs_clone.components.synthesis.get_output_for(dlatents, is_validation=True, randomize_noise=True) # Downsample to 256x256. The attribute classifiers were built for 256x256. if images.shape[2] > 256: factor = images.shape[2] // 256 images = tf.reshape(images, [-1, images.shape[1], images.shape[2] // factor, factor, images.shape[3] // factor, factor]) images = tf.reduce_mean(images, axis=[3, 5]) # Run classifier for each attribute. result_dict = dict(latents=latents, dlatents=dlatents[:,-1]) for attrib_idx in self.attrib_indices: classifier = misc.load_pkl(classifier_urls[attrib_idx]) logits = classifier.get_output_for(images, None) predictions = tf.nn.softmax(tf.concat([logits, -logits], axis=1)) result_dict[attrib_idx] = predictions result_expr.append(result_dict) # Sampling loop. results = [] for _ in range(0, self.num_samples, minibatch_size): results += tflib.run(result_expr) results = {key: np.concatenate([value[key] for value in results], axis=0) for key in results[0].keys()} # Calculate conditional entropy for each attribute. conditional_entropies = defaultdict(list) for attrib_idx in self.attrib_indices: # Prune the least confident samples. pruned_indices = list(range(self.num_samples)) pruned_indices = sorted(pruned_indices, key=lambda i: -np.max(results[attrib_idx][i])) pruned_indices = pruned_indices[:self.num_keep] # Fit SVM to the remaining samples. svm_targets = np.argmax(results[attrib_idx][pruned_indices], axis=1) for space in ['latents', 'dlatents']: svm_inputs = results[space][pruned_indices] try: svm = sklearn.svm.LinearSVC() svm.fit(svm_inputs, svm_targets) svm.score(svm_inputs, svm_targets) svm_outputs = svm.predict(svm_inputs) except: svm_outputs = svm_targets # assume perfect prediction # Calculate conditional entropy. p = [[np.mean([case == (row, col) for case in zip(svm_outputs, svm_targets)]) for col in (0, 1)] for row in (0, 1)] conditional_entropies[space].append(conditional_entropy(p)) # Calculate separability scores. scores = {key: 2**np.sum(values) for key, values in conditional_entropies.items()} self._report_result(scores['latents'], suffix='_z') self._report_result(scores['dlatents'], suffix='_w') #----------------------------------------------------------------------------