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multiclass_experiment.py
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multiclass_experiment.py
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from glob import glob
import os
import numpy as np
import matplotlib.pyplot as plt
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import LearningRateScheduler
from keras.layers import Activation
from keras import backend as K
from keras.models import Model
from models.wide_residual_network import create_wide_residual_network, SGDTorch, dense
from utils import load_cifar10, normalize_minus1_1, save_roc_pr_curve_data
from transformations import Transformer
def load_tinyimagenet(tinyimagenet_path='./'):
images = [plt.imread(fp) for fp in glob(os.path.join(tinyimagenet_path, '*.jpg'))]
for i in range(len(images)):
if len(images[i].shape) != 3:
images[i] = np.stack([images[i], images[i], images[i]], axis=-1)
images = np.stack(images)
images = normalize_minus1_1(K.cast_to_floatx(images))
return images
def train_cifar10():
(x_train, y_train), (x_test, y_test) = load_cifar10()
idg = ImageDataGenerator(
horizontal_flip=True,
height_shift_range=4,
width_shift_range=4,
fill_mode='reflect'
)
idg.fit(x_train)
n = 16
k = 8
mdl = create_wide_residual_network(x_train.shape[1:], 10, n, k)
mdl.compile(SGDTorch(lr=.1, momentum=0.9, nesterov=True), 'categorical_crossentropy', ['acc'])
lr_cb = LearningRateScheduler(lambda e: 0.1 * (0.2 ** (e >= 160 and 3 or e >= 120 and 2 or e >= 60 and 1 or 0)))
batch_size = 128
mdl.fit_generator(
generator=idg.flow(x_train, to_categorical(y_train), batch_size=batch_size),
epochs=200,
validation_data=(idg.standardize(x_test), to_categorical(y_test)),
callbacks=[lr_cb]
)
mdl.save_weights('cifar10_WRN_{}-{}.h5'.format(n, k))
def train_cifar10_transformations():
(x_train, y_train), _ = load_cifar10()
transformer = Transformer(8, 8)
def data_gen(x, y, batch_size):
while True:
ind_permutation = np.random.permutation(len(x))
for b_start_ind in range(0, len(x), batch_size):
batch_inds = ind_permutation[b_start_ind:b_start_ind + batch_size]
x_batch = x[batch_inds]
y_batch = y[batch_inds].flatten()
if K.image_data_format() == 'channels_first':
x_batch = np.transpose(x_batch, (0, 2, 3, 1))
y_t_batch = np.random.randint(0, transformer.n_transforms, size=len(x_batch))
x_batch = transformer.transform_batch(x_batch, y_t_batch)
if K.image_data_format() == 'channels_first':
x_batch = np.transpose(x_batch, (0, 3, 1, 2))
yield (x_batch, [to_categorical(y_batch, num_classes=10), to_categorical(y_t_batch, num_classes=transformer.n_transforms)])
n = 16
k = 8
base_mdl = create_wide_residual_network(x_train.shape[1:], 10, n, k)
transformations_cls_out = Activation('softmax')(dense(transformer.n_transforms)(base_mdl.get_layer(index=-3).output))
mdl = Model(base_mdl.input, [base_mdl.output, transformations_cls_out])
mdl.compile(SGDTorch(lr=.1, momentum=0.9, nesterov=True), 'categorical_crossentropy', ['acc'])
lr_cb = LearningRateScheduler(lambda e: 0.1 * (0.2 ** (e >= 160 and 3 or e >= 120 and 2 or e >= 60 and 1 or 0)))
batch_size = 128
mdl.fit_generator(
generator=data_gen(x_train, y_train, batch_size=batch_size),
steps_per_epoch=len(x_train) // batch_size,
epochs=200,
callbacks=[lr_cb]
)
mdl.save_weights('cifar10_WRN_doublehead-transformations_{}-{}.h5'.format(n, k))
def transformation_cifar10_vs_tinyimagenet():
_, (x_test, y_test) = load_cifar10()
x_test_out = load_tinyimagenet('/home/izikgo/Imagenet_resize/Imagenet_resize/')
transformer = Transformer(8, 8)
n = 16
k = 8
base_mdl = create_wide_residual_network(x_test.shape[1:], 10, n, k)
transformations_cls_out = Activation('softmax')(dense(transformer.n_transforms)(base_mdl.get_layer(index=-3).output))
mdl = Model(base_mdl.input, [base_mdl.output, transformations_cls_out])
mdl.load_weights('cifar10_WRN_doublehead-transformations_{}-{}.h5'.format(n, k))
scores_mdl = Model(mdl.input, mdl.output[1])
x_test_all = np.concatenate((x_test, x_test_out))
preds = np.zeros((len(x_test_all), transformer.n_transforms))
for t in range(transformer.n_transforms):
preds[:, t] = scores_mdl.predict(transformer.transform_batch(x_test_all, [t] * len(x_test_all)),
batch_size=128)[:, t]
labels = np.concatenate((np.ones(len(x_test)), np.zeros(len(x_test_out))))
scores = preds.mean(axis=-1)
save_roc_pr_curve_data(scores, labels, 'cifar10-vs-tinyimagenet_transformations.npz')
if __name__ == '__main__':
train_cifar10_transformations()
transformation_cifar10_vs_tinyimagenet()