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cal-gan-metrics-fromnpy.py
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cal-gan-metrics-fromnpy.py
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import argparse
import data_with_matchingclassifier as origin_dataset
import data_for_augmentedimages_for_quality_evaluation as test_dataset
# from generation_builder import ExperimentBuilder
from GAN_Metrics_Tensorflow.frechet_kernel_Inception_distance import *
from GAN_Metrics_Tensorflow.inception_score import *
# from GAN_Metrics_Tensorflow.calculate_FID import *
import numpy as np
from glob import glob
import os
import cv2
parser = argparse.ArgumentParser(description='Welcome to GAN-Shot-Learning script')
parser.add_argument('--batch_size', nargs="?", type=int, default=20, help='batch_size for experiment')
parser.add_argument('--discriminator_inner_layers', nargs="?", type=int, default=1,
help='discr_number_of_conv_per_layer')
parser.add_argument('--generator_inner_layers', nargs="?", type=int, default=1, help='discr_number_of_conv_per_layer')
parser.add_argument('--experiment_title', nargs="?", type=str, default="densenet_generator_fc", help='Experiment name')
parser.add_argument('--continue_from_epoch', nargs="?", type=int, default=-1, help='continue from checkpoint of epoch')
parser.add_argument('--num_of_gpus', nargs="?", type=int, default=1, help='discr_number_of_conv_per_layer')
parser.add_argument('--z_dim', nargs="?", type=int, default=100, help='The dimensionality of the z input')
parser.add_argument('--dropout_rate_value', type=float, default=0.5, help='dropout_rate_value')
parser.add_argument('--num_generations', nargs="?", type=int, default=16, help='num_generations')
parser.add_argument('--support_number', nargs="?", type=int, default=3, help='num_support')
parser.add_argument('--use_wide_connections', nargs="?", type=str, default="False",
help='Whether to use wide connections in discriminator')
parser.add_argument('--matching', nargs="?", type=int, default=0)
parser.add_argument('--fce', nargs="?", type=int, default=0)
parser.add_argument('--full_context_unroll_k', nargs="?", type=int, default=4)
parser.add_argument('--average_per_class_embeddings', nargs="?", type=int, default=0)
parser.add_argument('--is_training', nargs="?", type=int, default=0)
parser.add_argument('--samples_each_category', type=int, default=20)
parser.add_argument('--classification_total_epoch', type=int, default=200)
parser.add_argument('--dataset', type=str, default='flowers')
parser.add_argument('--general_classification_samples', type=int, default=5)
parser.add_argument('--selected_classes', type=int, default=0)
parser.add_argument('--image_width', nargs="?", type=int, default=128)
parser.add_argument('--image_channel', nargs="?", type=int, default=3)
args = parser.parse_args()
batch_size = args.batch_size
num_gpus = args.num_of_gpus
support_num = args.support_number
# data = dataset.OmniglotDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
# num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training)
if args.dataset == 'omniglot':
print('omniglot')
origin_data = origin_dataset.OmniglotDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000,
support_number=support_num, is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
test_data = test_dataset.OmniglotDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'vggface':
print('vggface')
origin_data = origin_dataset.VGGFaceDAGANDataset(batch_size=batch_size, last_training_class_index=1600,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
test_data = test_dataset.VGGFaceDAGANDataset(batch_size=batch_size, last_training_class_index=1600,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'miniimagenet':
print('miniimagenet')
origin_data = origin_dataset.miniImagenetDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000,
support_number=support_num, is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
test_data = test_dataset.miniImagenetDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000,
support_number=support_num, is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
elif args.dataset == 'emnist':
print('emnist')
origin_data = origin_dataset.emnistDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
test_data = test_dataset.emnistDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'figr':
print('figr')
origin_data = origin_dataset.FIGRDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
test_data = test_dataset.FIGRDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'fc100':
origin_data = origin_dataset.FC100DAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
test_data = test_dataset.FC100DAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'animals':
origin_data = origin_dataset.animalsDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
test_data = test_dataset.animalsDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'flowers':
origin_data = origin_dataset.flowersDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
test_data = test_dataset.flowersDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'flowersselected':
origin_data = origin_dataset.flowersselectedDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000,
support_number=support_num, is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
test_data = test_dataset.flowersselectedDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000,
support_number=support_num, is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes,
image_size=args.image_width)
elif args.dataset == 'birds':
origin_data = origin_dataset.birdsDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
test_data = test_dataset.birdsDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'SelectMOREanimals':
origin_data = origin_dataset.SelectMOREanimalsDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
test_data = test_dataset.SelectMOREanimalsDAGANDataset(batch_size=batch_size, last_training_class_index=900,
reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
def resize_image(image):
image = cv2.resize(image, (299, 299), interpolation=cv2.INTER_LINEAR)
return image
def get_real_fake_images(fake_images_path):
real_images = origin_data.get_total_batch_images('test', args.samples_each_category)
print('real images shape', np.shape(real_images))
# print('hererere',np.shape(real_images))
real_images_after = np.zeros([np.shape(real_images)[0],np.shape(real_images)[1], 299, 299, np.shape(real_images)[4]])
### real and fake images [category, samples, width, height, channel]
for i in range(np.shape(real_images)[0]):
for j in range(np.shape(real_images)[1]):
resized_image = resize_image(real_images[i][j])
if len(np.shape(resized_image)) < 3:
resized_image = np.expand_dims(resized_image, axis=-1)
real_images_after[i][j] = resized_image
real_images_after = 255 * (real_images_after / np.max(real_images_after))
real_images_after = np.transpose(real_images_after, axes=[0, 1, 4, 2, 3])
fake_images = test_data.get_total_batch_images('test', args.samples_each_category)
print('fake images shape', np.shape(fake_images))
fake_images_after = np.zeros([np.shape(fake_images)[0],np.shape(fake_images)[1], 299, 299, np.shape(fake_images)[4]])
for i in range(np.shape(fake_images)[0]):
for j in range(np.shape(fake_images)[1]):
resized_image = resize_image(fake_images[i][j])
if len(np.shape(resized_image)) < 3:
resized_image = np.expand_dims(resized_image, axis=-1)
fake_images_after[i][j] = resized_image
fake_images_after = 255 * (fake_images_after / np.max(fake_images_after))
fake_images_after = np.transpose(fake_images_after, axes=[0, 1, 4, 2, 3])
##### extending to three channel images for evaluation metrics
if np.shape(real_images_after)[2] < 3:
three_channel_real_images = np.concatenate([real_images_after, real_images_after, real_images_after], axis=1)
three_channel_fake_images = np.concatenate([fake_images_after, fake_images_after, fake_images_after], axis=1)
else:
three_channel_real_images = real_images_after
three_channel_fake_images = fake_images_after
# print('fake', np.max(three_channel_fake_images), np.min(three_channel_fake_images))
# print('real', np.max(three_channel_real_images), np.min(three_channel_real_images))
# print('real images', np.shape(three_channel_real_images))
# print('fake images', np.shape(three_channel_fake_images))
return three_channel_real_images, three_channel_fake_images
# return three_channel_fake_images, three_channel_fake_images
def inception_score(fake_images_path):
####only for generated images
_, images = get_real_fake_images(fake_images_path)
BATCH_SIZE = 32
images = np.reshape(images,[np.shape(images)[0]* np.shape(images)[1], np.shape(images)[2], np.shape(images)[3], np.shape(images)[4]])
inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
logits = inception_logits(inception_images)
IS = get_inception_score(BATCH_SIZE, images, inception_images, logits, splits=10)
print("IS: ", IS)
# def mean_fid(fake_images_path):
# real_images, fake_images = get_real_fake_images(fake_images_path)
# real_images_total = np.reshape(real_images, [np.shape(real_images)[0]*np.shape(real_images)[1], np.shape(real_images)[2],np.shape(real_images)[3],np.shape(real_images)[4]])
# fake_images_total = np.reshape(fake_images, [np.shape(real_images)[0]*np.shape(real_images)[1], np.shape(real_images)[2],np.shape(real_images)[3],np.shape(real_images)[4]])
# print('maxmium',np.max(real_images_total),np.max(fake_images_total))
# fid_value_total = calculate_fid_given_paths(real_images_total, fake_images_total)
# print('funit total fid is', fid_value_total)
# mFID = 0
# i = 0
# for i in range(np.shape(real_images)[0]):
# # if i > 5:
# # break
# FID = calculate_fid_given_paths(real_images[i], fake_images[i])
# print('funit {}_category_fid'.format(i), FID)
# mFID+= FID
# i = i + 1
# mFID = mFID / i
# print("funit mean FID : ", mFID)
def frechet_inception_distance(fake_images_path):
real_images, fake_images = get_real_fake_images(fake_images_path)
BATCH_SIZE = 32
# Run images through Inception.
inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
real_activation = tf.placeholder(tf.float32, [None, None], name='activations1')
fake_activation = tf.placeholder(tf.float32, [None, None], name='activations2')
fcd = frechet_classifier_distance_from_activations(real_activation, fake_activation)
activations = inception_activations(inception_images)
##### total FID
real_images_total = np.reshape(real_images, [np.shape(real_images)[0]*np.shape(real_images)[1], np.shape(real_images)[2],np.shape(real_images)[3],np.shape(real_images)[4]])
fake_images_total = np.reshape(fake_images, [np.shape(real_images)[0]*np.shape(real_images)[1], np.shape(real_images)[2],np.shape(real_images)[3],np.shape(real_images)[4]])
total_FID = get_fid(fcd, BATCH_SIZE, real_images_total, fake_images_total, inception_images, real_activation, fake_activation,
activations)
print('total FID:', total_FID)
mFID = 0
i = 0
for i in range(np.shape(real_images)[0]):
if i > 5:
break
FID = get_fid(fcd, BATCH_SIZE, real_images[i], fake_images[i], inception_images, real_activation, fake_activation,
activations)
print('{}_category_fid'.format(i), FID)
mFID+= FID
i = i + 1
mFID = mFID / i
print("mean FID : ", mFID)
# def kernel_inception_distance(fake_images_path):
# real_images, fake_images = get_real_fake_images(fake_images_path)
# BATCH_SIZE = 32
# inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
# real_activation = tf.placeholder(tf.float32, [None, None], name='activations1')
# fake_activation = tf.placeholder(tf.float32, [None, None], name='activations2')
# kcd_mean, kcd_stddev = kernel_classifier_distance_and_std_from_activations(real_activation, fake_activation,
# max_block_size=10)
# activations = inception_activations(inception_images)
# KID_mean = get_kid(kcd_mean, BATCH_SIZE, real_images, fake_images, inception_images, real_activation,
# fake_activation, activations)
# KID_stddev = get_kid(kcd_stddev, BATCH_SIZE, real_images, fake_images, inception_images, real_activation,
# fake_activation, activations)
# print()
# print("KID_mean : ", KID_mean * 100)
# print("KID_stddev : ", KID_stddev * 100)
# def mean_kernel_inception_distance():
# filenames = glob(os.path.join('./real_source', '*.*'))
# real_source_images = [get_images(filename) for filename in filenames]
# real_source_images = np.transpose(real_source_images, axes=[0, 3, 1, 2])
# filenames = glob(os.path.join('./real_target', '*.*'))
# real_target_images = [get_images(filename) for filename in filenames]
# real_target_images = np.transpose(real_target_images, axes=[0, 3, 1, 2])
# filenames = glob(os.path.join('./fake', '*.*'))
# fake_images = [get_images(filename) for filename in filenames]
# fake_images = np.transpose(fake_images, axes=[0, 3, 1, 2])
# BATCH_SIZE = 32
# # Run images through Inception.
# inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
# real_activation = tf.placeholder(tf.float32, [None, None], name='activations1')
# fake_activation = tf.placeholder(tf.float32, [None, None], name='activations2')
# fcd = frechet_classifier_distance_from_activations(real_activation, fake_activation)
# kcd_mean, kcd_stddev = kernel_classifier_distance_and_std_from_activations(real_activation, fake_activation,
# max_block_size=10)
# activations = inception_activations(inception_images)
# FID = get_fid(fcd, BATCH_SIZE, real_target_images, fake_images, inception_images, real_activation, fake_activation,
# activations)
# KID_mean = get_kid(kcd_mean, BATCH_SIZE, real_target_images, fake_images, inception_images, real_activation,
# fake_activation, activations)
# KID_stddev = get_kid(kcd_stddev, BATCH_SIZE, real_target_images, fake_images, inception_images, real_activation,
# fake_activation, activations)
# mean_FID = get_fid(fcd, BATCH_SIZE, real_source_images, fake_images, inception_images, real_activation,
# fake_activation, activations)
# mean_KID_mean = get_kid(kcd_mean, BATCH_SIZE, real_source_images, fake_images, inception_images, real_activation,
# fake_activation, activations)
# mean_KID_stddev = get_kid(kcd_stddev, BATCH_SIZE, real_source_images, fake_images, inception_images,
# real_activation, fake_activation, activations)
# mean_FID = (FID + mean_FID) / 2.0
# mean_KID_mean = (KID_mean + mean_KID_mean) / 2.0
# mean_KID_stddev = (KID_stddev + mean_KID_stddev) / 2.0
# print()
# print("mean_FID : ", mean_FID)
# print("mean_KID_mean : ", mean_KID_mean * 100)
# print("mean_KID_stddev : ", mean_KID_stddev * 100)
fake_images_path = './vggface1way3shotNEW/test/'
# mean_fid(fake_images_path)
#### calculating the IS from whole dataset
inception_score(fake_images_path)
#### calculating the FIS from each category
frechet_inception_distance(fake_images_path)
# kernel_inception_distance(fake_images_path)
# mean_kernel_inception_distance()
# python cal-gan-metrics.py --batch_size 20 --support_number 3 --dataset vggface --image_channel 3 --image_width 128