def parallel_testing(test_image, test_label, svm, scaler, gmm):
gray = io.load_grayscale_image(test_image)
kpt, des = feature_extraction.sift(gray)
labels = np.array([test_label] * des.shape[0])
ind = np.array([0] * des.shape[0])
fisher, _ = bovw.fisher_vectors(des, labels, ind, gmm)
prediction_prob = classification.predict_svm(fisher, svm, std_scaler=scaler)
predicted_class = svm.classes_[np.argmax(prediction_prob)]
return predicted_class == test_label, predicted_class, np.ravel(prediction_prob)
def parallel_testing(test_image, test_label, svm, scaler, gmm, model, pca):
D, _, _ = feature_extraction.compute_CNN_features(None, test_image, None,
model)
D_pca = pca.transform(D)
D_pca = np.float32(D_pca)
labels = np.array([test_label] * D.shape[0])
ind = np.array([0] * D.shape[0])
fisher, _ = bovw.fisher_vectors(D_pca,
labels,
ind,
gmm,
normalization='l2')
prediction_prob = classification.predict_svm(fisher,
svm,
std_scaler=scaler)
# print prediction_prob
# print prediction_prob.shape
predicted_class = svm.classes_[np.argmax(prediction_prob)]
return predicted_class == test_label, predicted_class, np.ravel(
prediction_prob)
io.save_object(I, 'train_dense_indices', ignore=True)
print('Elapsed time: {:.2f} s'.format(time.time() - start))
temp = time.time()
print('Applying PCA...')
pca, D = feature_extraction.pca(D)
print('Elapsed time: {:.2f} s'.format(time.time() - temp))
temp = time.time()
print('Creating GMM model with {} Gaussians'.format(settings.codebook_size))
gmm = bovw.create_gmm(D, codebook_name='gmm_{}_dense_pca_{}'.format(settings.codebook_size, settings.pca_reduction))
print('Elapsed time: {:.2f} s'.format(time.time() - temp))
temp = time.time()
print('Getting Fisher vectors from training set...')
fisher, labels = bovw.fisher_vectors(D, L, I, gmm)
print('Elapsed time: {:.2f} s'.format(time.time() - temp))
temp = time.time()
# Train Linear SVM classifier
print('Training the SVM classifier...')
lin_svm, std_scaler, _ = classification.train_linear_svm(fisher, train_labels, C=1, dim_reduction=None)
print('Elapsed time: {:.2f} s'.format(time.time() - temp))
temp = time.time()
# Read the test set
test_images_filenames, test_labels = io.load_test_set()
print('Loaded {} test images.'.format(len(test_images_filenames)))
# Feature extraction with sift, prediction with SVM and aggregation to obtain final class
print('Predicting test data...')
def train():
best_accuracy = 0
best_params = {}
cv_results = {}
""" SETTINGS """
settings.n_jobs = 1
# Read the training set
train_images_filenames, train_labels = io.load_training_set()
io.log('Loaded {} train images.'.format(len(train_images_filenames)))
k = 64
io.log('Obtaining dense CNN features...')
start_feature = time.time()
try:
D, L, I = io.load_object('train_CNN_descriptors', ignore=True), \
io.load_object('train_CNN_labels', ignore=True), \
io.load_object('train_CNN_indices', ignore=True)
except IOError:
# load VGG model
base_model = VGG16(weights='imagenet')
# io.save_object(base_model, 'base_model', ignore=True)
# visualize topology in an image
plot(base_model,
to_file='modelVGG16.png',
show_shapes=True,
show_layer_names=True)
# crop the model up to a certain layer
model = Model(input=base_model.input,
output=base_model.get_layer('block5_conv2').output)
D, L, I = feature_extraction.parallel_CNN_features(
train_images_filenames,
train_labels,
model,
num_samples_class=-1,
n_jobs=settings.n_jobs)
io.save_object(D, 'train_CNN_descriptors', ignore=True)
io.save_object(L, 'train_CNN_labels', ignore=True)
io.save_object(I, 'train_CNN_indices', ignore=True)
feature_time = time.time() - start_feature
io.log('Elapsed time: {:.2f} s'.format(feature_time))
for dim_red in pca_reduction:
io.log('Applying PCA ... ')
start_pca = time.time()
settings.pca_reduction = D.shape[1] * dim_red
pca, D_pca = feature_extraction.pca(D)
pca_time = time.time() - start_pca
io.log('Elapsed time: {:.2f} s'.format(pca_time))
for k in codebook_size:
io.log('Creating GMM model (k = {})'.format(k))
start_gmm = time.time()
settings.codebook_size = k
gmm = bovw.create_gmm(
D_pca,
'gmm_{}_pca_{}_CNNfeature'.format(k, settings.pca_reduction))
gmm_time = time.time() - start_gmm
io.log('Elapsed time: {:.2f} s'.format(gmm_time))
io.log('Getting Fisher vectors from training set...')
start_fisher = time.time()
fisher, labels = bovw.fisher_vectors(D_pca,
L,
I,
gmm,
normalization='l2')
fisher_time = time.time() - start_fisher
io.log('Elapsed time: {:.2f} s'.format(fisher_time))
io.log('Scaling features...')
start_scaler = time.time()
std_scaler = StandardScaler().fit(fisher)
vis_words = std_scaler.transform(fisher)
scaler_time = time.time() - start_scaler
io.log('Elapsed time: {:.2f} s'.format(scaler_time))
io.log('Optimizing SVM hyperparameters...')
start_crossvalidation = time.time()
svm = SVC(kernel='precomputed')
random_search = RandomizedSearchCV(svm,
params_distribution,
n_iter=n_iter,
scoring='accuracy',
n_jobs=settings.n_jobs,
refit=False,
cv=3,
verbose=1)
# Precompute Gram matrix
gram = kernels.intersection_kernel(vis_words, vis_words)
random_search.fit(gram, labels)
crossvalidation_time = time.time() - start_crossvalidation
io.log('Elapsed time: {:.2f} s'.format(crossvalidation_time))
# Convert MaskedArrays to ndarrays to avoid unpickling bugs
results = random_search.cv_results_
results['param_C'] = results['param_C'].data
# Appending all parameter-scores combinations
cv_results.update({
(k): {
'cv_results':
results,
'feature_time':
feature_time,
'pca_time':
pca_time,
'gmm_time':
gmm_time,
'fisher_time':
fisher_time,
'scaler_time':
scaler_time,
'crossvalidation_time':
crossvalidation_time,
'total_time':
feature_time + pca_time + gmm_time + fisher_time +
scaler_time + crossvalidation_time
}
})
io.save_object(cv_results,
'intersection_svm_CNNfeatures',
ignore=True)
# Obtaining the parameters which yielded the best accuracy
if random_search.best_score_ > best_accuracy:
best_accuracy = random_search.best_score_
best_params = random_search.best_params_
best_params.update({'k': k, 'pca': dim_red})
io.log('-------------------------------\n')
io.log('\nSaving best parameters...')
io.save_object(best_params,
'best_params_intersection_svm_CNNfeatures',
ignore=True)
best_params_file = os.path.abspath(
'./ignore/best_params_intersection_svm_CNNfeatures.pickle')
io.log('Saved at {}'.format(best_params_file))
io.log('\nSaving all cross-validation values...')
io.save_object(cv_results, 'intersection_svm_CNNfeatures', ignore=True)
cv_results_file = os.path.abspath(
'./ignore/intersection_svm_CNNfeatures.pickle')
io.log('Saved at {}'.format(cv_results_file))
io.log('\nBEST PARAMS')
io.log('k={}, dim_red={}, C={} --> accuracy: {:.3f}'.format(
best_params['k'], best_params['pca'], best_params['C'], best_accuracy))
def train():
best_accuracy = 0
best_params = {}
cv_results = {}
""" SETTINGS """
settings.n_jobs = 1
# Read the training set
train_images_filenames, train_labels = io.load_training_set()
io.log('Loaded {} train images.'.format(len(train_images_filenames)))
# Parameter sweep for dense SIFT
for ds in dense_sampling_density:
io.log('Obtaining dense features with sampling parameter {}...'.format(
ds))
start_sift = time.time()
settings.dense_sampling_density = ds
try:
D, L, I = io.load_object('train_dense_descriptors_{}'.format(settings.dense_sampling_density), ignore=True), \
io.load_object('train_dense_labels_{}'.format(settings.dense_sampling_density), ignore=True), \
io.load_object('train_dense_indices_{}'.format(settings.dense_sampling_density), ignore=True)
except IOError:
D, L, I, _ = feature_extraction.parallel_dense(
train_images_filenames,
train_labels,
num_samples_class=-1,
n_jobs=settings.n_jobs)
io.save_object(D,
'train_dense_descriptors_{}'.format(
settings.dense_sampling_density),
ignore=True)
io.save_object(L,
'train_dense_labels_{}'.format(
settings.dense_sampling_density),
ignore=True)
io.save_object(I,
'train_dense_indices_{}'.format(
settings.dense_sampling_density),
ignore=True)
sift_time = time.time() - start_sift
io.log('Elapsed time: {:.2f} s'.format(sift_time))
# Parameter sweep for PCA
for dim_red in pca_reduction:
io.log('Applying PCA (dim = {})...'.format(dim_red))
start_pca = time.time()
settings.pca_reduction = dim_red
pca, D_pca = feature_extraction.pca(D)
pca_time = time.time() - start_pca
io.log('Elapsed time: {:.2f} s'.format(pca_time))
# Parameter sweep for codebook size
for k in codebook_size:
io.log('Creating GMM model (k = {})'.format(k))
start_gmm = time.time()
settings.codebook_size = k
gmm = bovw.create_gmm(
D_pca, 'gmm_{}_dense_{}_pca_{}'.format(k, ds, dim_red))
gmm_time = time.time() - start_gmm
io.log('Elapsed time: {:.2f} s'.format(gmm_time))
io.log('Getting Fisher vectors from training set...')
start_fisher = time.time()
fisher, labels = bovw.fisher_vectors(D_pca,
L,
I,
gmm,
normalization='l2')
fisher_time = time.time() - start_fisher
io.log('Elapsed time: {:.2f} s'.format(fisher_time))
io.log('Scaling features...')
start_scaler = time.time()
std_scaler = StandardScaler().fit(fisher)
vis_words = std_scaler.transform(fisher)
scaler_time = time.time() - start_scaler
io.log('Elapsed time: {:.2f} s'.format(scaler_time))
io.log('Optimizing SVM hyperparameters...')
start_crossvalidation = time.time()
svm = SVC(kernel='precomputed')
random_search = RandomizedSearchCV(svm,
params_distribution,
n_iter=n_iter,
scoring='accuracy',
n_jobs=settings.n_jobs,
refit=False,
cv=3,
verbose=1)
# Precompute Gram matrix
gram = kernels.intersection_kernel(vis_words, vis_words)
random_search.fit(gram, labels)
crossvalidation_time = time.time() - start_crossvalidation
io.log('Elapsed time: {:.2f} s'.format(crossvalidation_time))
# Convert MaskedArrays to ndarrays to avoid unpickling bugs
results = random_search.cv_results_
results['param_C'] = results['param_C'].data
# Appending all parameter-scores combinations
cv_results.update({
(k, dim_red, ds): {
'cv_results':
results,
'sift_time':
sift_time,
'pca_time':
pca_time,
'gmm_time':
gmm_time,
'fisher_time':
fisher_time,
'scaler_time':
scaler_time,
'crossvalidation_time':
crossvalidation_time,
'total_time':
sift_time + pca_time + gmm_time + fisher_time +
scaler_time + crossvalidation_time
}
})
io.save_object(
cv_results,
'intersection_svm_optimization_fisher_vectors_l2',
ignore=True)
# Obtaining the parameters which yielded the best accuracy
if random_search.best_score_ > best_accuracy:
best_accuracy = random_search.best_score_
best_params = random_search.best_params_
best_params.update({'k': k, 'pca': dim_red, 'ds': ds})
io.log('-------------------------------\n')
io.log('\nSaving best parameters...')
io.save_object(
best_params,
'best_params_intersection_svm_optimization_fisher_vectors_l2',
ignore=True)
best_params_file = os.path.abspath(
'./ignore/best_params_intersection_svm_optimization_fisher_vectors_l2.pickle'
)
io.log('Saved at {}'.format(best_params_file))
io.log('\nSaving all cross-validation values...')
io.save_object(cv_results,
'intersection_svm_optimization_fisher_vectors_l2',
ignore=True)
cv_results_file = os.path.abspath(
'./ignore/intersection_svm_optimization_fisher_vectors_l2.pickle')
io.log('Saved at {}'.format(cv_results_file))
io.log('\nBEST PARAMS')
io.log('k={}, C={}, dim_red={}, dense_grid={} --> accuracy: {:.3f}'.format(
best_params['k'], best_params['C'], best_params['pca'],
best_params['ds'], best_accuracy))
n_jobs=settings.n_jobs)
io.save_object(D, 'train_CNN_descriptors', ignore=True)
io.save_object(L, 'train_CNN_labels', ignore=True)
io.save_object(I, 'train_CNN_indices', ignore=True)
# get the features from images
settings.pca_reduction = D.shape[1] / 2
pca, D_pca = feature_extraction.pca(D)
k = settings.codebook_size
gmm = bovw.create_gmm(
D_pca, 'gmm_{}_pca_{}_CNNfeature'.format(k, settings.pca_reduction))
fisher, labels = bovw.fisher_vectors(D_pca,
L,
I,
gmm,
normalization='l2',
spatial_pyramid=False)
# std_scaler = StandardScaler().fit(fisher)
# vis_words = std_scaler.transform(fisher)
print('Training the SVM classifier...')
lin_svm, std_scaler, _ = classification.train_intersection_svm(
fisher, train_labels, C=0.0268101613883, dim_reduction=None)
# Read the test set
test_images_filenames, test_labels = io.load_test_set()
print('Loaded {} test images.'.format(len(test_images_filenames)))
# Feature extraction with sift, prediction with SVM and aggregation to obtain final class
print('Predicting test data...')
