def lambda_dump_input(obj):
current_iter = obj['current_iter']
input_data = obj['input_data']
if current_iter % 25 == 0 and args.print_inter_img == True:
save_image(output_dir + '/' + filename_array[0] + suffix +
+ '_' + str(current_iter.zfill(5)) + '.' +
filename_array[1],
input_data[0],
deprocess_type='vgg19')
lc_weight = 1.
ls_weight = 1.
print("lc_weight: %f, ls_weight: %f" % (lc_weight, ls_weight))
print('Compiling VGG headless 5 for content ' + lc_name +
' and style ' + ls_name)
ls = alpha * loss_style / lc_weight
lc = loss_content / ls_weight
loss = ls + lc
grads = K.gradients(loss, input_layer)
if optimizer == 'adam':
grads = norm_l2(grads)
iterate = K.function([input_layer], [loss, grads, lc, ls])
print('Training the image')
config = {'learning_rate': 5e-1}
best_input_data, losses = train_input(input_data,
iterate,
optimizer,
config,
max_iter=args.max_iter)
prefix = str(current_iter).zfill(4)
suffix = "_lc_weight%f_ls_weight%f" % (lc_weight, ls_weight)
fullOutPath = resultsDir + '/' + prefix + '_style' + ls_name + '_content' + lc_name + suffix + ".png"
# dump_as_hdf5(resultsDir + '/' + prefix + '_style' + ls_name + '_content' + lc_name + suffix + ".hdf5", best_input_data[0])
save_image(fullOutPath, best_input_data[0], deprocess_type='vgg19')
plot_losses(losses, resultsDir, prefix, suffix)
current_iter += 1
should_convert=False, custom_objects=custom_objects)
print('Predicting')
start = time.clock()
results = st_model.predict(X_test) # Equivalent to predict([X_test, False])
results_overfit = st_model.predict(X_overfit) # Equivalent to predict([X_test, False])
end = time.clock()
duration_batch = (end-start)/(X_test.shape[0] * X_overfit.shape[0])
print('Dumping results')
tmp_output_dir = output_dir + '/' + absolute_model_dir.split('/')[-2] + '/' + absolute_model_dir.split('/')[-1]
if not os.path.isdir(tmp_output_dir):
os.makedirs(tmp_output_dir)
for idx, im in enumerate(results):
prefix = str(current_iter).zfill(4)
fullOutPath = tmp_output_dir + '/' + prefix + "_" + str(idx) + ".png"
save_image(fullOutPath, im, deprocess_type='st')
fullOriPath = tmp_output_dir + '/' + prefix + "_" + str(idx) + "_ori.png"
save_image(fullOriPath, X_test[idx], deprocess_type='st')
current_iter += 1
for idx, im in enumerate(results_overfit):
prefix = str(current_iter).zfill(4)
fullOutPath = tmp_output_dir + '/' + prefix + str(idx) + "_overfit.png"
save_image(fullOutPath, im, deprocess_type='st')
fullOriPath = tmp_output_dir + '/' + prefix + str(idx) + "_overfit_ori.png"
save_image(fullOriPath, X_overfit[idx], deprocess_type='st')
current_iter += 1
suffix = "_content%s_alpha%f_beta%f_gamma%f" % (lc_name, alpha,
beta, gamma)
out_filename = filename_array[0] + suffix + '.' + filename_array[1]
def lambda_dump_input(obj):
current_iter = obj['current_iter']
input_data = obj['input_data']
if current_iter % 25 == 0 and args.print_inter_img == True:
save_image(output_dir + '/' + filename_array[0] + suffix +
+ '_' + str(current_iter.zfill(5)) + '.' +
filename_array[1],
input_data[0],
deprocess_type='vgg19')
best_input_data, losses = train_input(
input_data,
train_iteratee,
optimizer, {'learning_rate': config['learning_rate']},
max_iter=args.max_iter,
callbacks=[lambda_dump_input])
print('Dumping data')
save_image(output_dir + '/' + out_filename,
best_input_data[0],
deprocess_type='vgg19')
if args.no_dump_losses == False:
plot_losses(losses, output_dir, filename_array[0], suffix)
current_iter += 1