def predict_command(model, training_cnf, features_file, images_dir, weights_from, tag, sync, ):
util.check_required_program_args([model, training_cnf, features_file, images_dir, weights_from])
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
weights_from = str(weights_from)
image_features = np.load(features_file)
images = data.get_image_files(images_dir)
predictions = predict_withf(model, cnf, weights_from, image_features)
predict_dir = os.path.dirname(features_file)
prediction_results_dir = os.path.abspath(os.path.join(predict_dir, 'predictions', tag))
if not os.path.exists(prediction_results_dir):
os.makedirs(prediction_results_dir)
names = data.get_names(images)
image_prediction_probs = np.column_stack([names, predictions])
headers = ['score%d' % (i + 1) for i in range(predictions.shape[1])]
title = np.array(['image'] + headers)
image_prediction_probs = np.vstack([title, image_prediction_probs])
prediction_probs_file = os.path.join(prediction_results_dir, 'predictions.csv')
np.savetxt(prediction_probs_file, image_prediction_probs, delimiter=",", fmt="%s")
print('Predictions saved to: %s' % prediction_probs_file)
if cnf['classification']:
class_predictions = np.argmax(predictions, axis=1)
image_class_predictions = np.column_stack([names, class_predictions])
title = np.array(['image', 'label'])
image_class_predictions = np.vstack([title, image_class_predictions])
prediction_class_file = os.path.join(prediction_results_dir, 'predictions_class.csv')
np.savetxt(prediction_class_file, image_class_predictions, delimiter=",", fmt="%s")
print('Class predictions saved to: %s' % prediction_class_file)
def predict(model, training_cnf, predict_dir, weights_from, predict_type):
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
weights_from = str(weights_from)
images = data.get_image_files(predict_dir)
preprocessor = None
prediction_iterator = create_prediction_iter(cnf, model_def.crop_size,
preprocessor, False)
if predict_type == 'quasi':
predictor = QuasiCropPredictor(model, cnf, weights_from,
prediction_iterator, 20)
elif predict_type == '1_crop':
predictor = OneCropPredictor(model, cnf, weights_from,
prediction_iterator)
elif predict_type == '10_crop':
predictor = TenCropPredictor(model, cnf, weights_from,
prediction_iterator,
model_def.crop_size[0],
model_def.image_size[0])
else:
raise ValueError('Unknown predict_type: %s' % predict_type)
predictions = predictor.predict(images)
predictions = predictions.reshape(-1, 1000)
names = data.get_names(images)
for i, name in enumerate(names):
print("---Predictions for %s:" % name)
preds = (np.argsort(predictions[i])[::-1])[0:5]
for p in preds:
print(class_names[p], predictions[i][p])
def main(directory, convert_directory, test, crop_size, extension):
try:
os.mkdir(convert_directory)
except OSError:
pass
supported_extensions = set(['jpg', 'png', 'tiff', 'jpeg', 'tif'])
# filenames = [os.path.join(dp, f) for dp, dn, fn in os.walk(directory)
# for f in fn if f.split('.')[-1].lower() in supported_extensions]
filenames = [
each for each in os.listdir(directory) if each.endswith('.jpg')
]
filenames = [
os.path.join(directory, filename.strip('\n')) for filename in filenames
]
# with open('/home/artelus_server/data/segment_artelus/train.txt', 'r') as f:
# filenames = f.readlines()
# filenames = [os.path.join(directory, filename.strip(
# '\n') + '.jpg') for filename in filenames]
filenames = sorted(filenames)
if test:
names = data.get_names(filenames)
y = data.get_labels(names)
for f, level in zip(filenames, y):
if level == 1:
try:
img = convert(f, crop_size)
img.show()
Image.open(f).show()
real_raw_input = vars(__builtins__).get('raw_input', input)
real_raw_input('enter for next')
except KeyboardInterrupt:
exit(0)
print("Resizing images in {} to {}, this takes a while."
"".format(directory, convert_directory))
n = len(filenames)
# process in batches, sometimes weird things happen with Pool on my machine
batchsize = 500
batches = n // batchsize + 1
pool = Pool(N_PROC)
args = []
for f in filenames:
label_f = f[:-4] + '_final_mask.png'
args.append((convert, (directory, convert_directory, f, label_f,
crop_size, extension)))
for i in range(batches):
print("batch {:>2} / {}".format(i + 1, batches))
pool.map(process, args[i * batchsize:(i + 1) * batchsize])
pool.close()
print('done')
def main(directory, convert_directory, test, crop_size, extension):
try:
os.mkdir(convert_directory)
except OSError:
pass
filenames = [
each for each in os.listdir(directory) if each.endswith('.jpg')
]
filenames = [
os.path.join(directory, filename.strip('\n')) for filename in filenames
]
filenames = sorted(filenames)
if test:
names = data.get_names(filenames)
y = data.get_labels(names)
for f, level in zip(filenames, y):
if level == 1:
try:
img = convert(f, crop_size)
img.show()
Image.open(f).show()
real_raw_input = vars(__builtins__).get('raw_input', input)
real_raw_input('enter for next')
except KeyboardInterrupt:
exit(0)
print("Resizing images in {} to {}, this takes a while."
"".format(directory, convert_directory))
n = len(filenames)
# process in batches, sometimes weird things happen with Pool on my machine
batchsize = 500
batches = n // batchsize + 1
pool = Pool(N_PROC)
args = []
for f in filenames:
label_f = f[:-4] + '_final_mask.png'
args.append((convert, (directory, convert_directory, f, label_f,
crop_size, extension)))
for i in range(batches):
print("batch {:>2} / {}".format(i + 1, batches))
pool.map(process, args[i * batchsize:(i + 1) * batchsize])
pool.close()
print('done')
def predict(model, training_cnf, predict_dir, weights_from, dataset_name,
convert, image_size, sync, test_type):
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
weights_from = str(weights_from)
images = data.get_image_files(predict_dir)
standardizer = cnf.get('standardizer', None)
preprocessor = convert_preprocessor(image_size) if convert else None
prediction_iterator = create_prediction_iter(cnf, standardizer,
model_def.crop_size,
preprocessor, sync)
if test_type == 'quasi':
predictor = QuasiCropPredictor(model, cnf, weights_from,
prediction_iterator, 20)
predictions = predictor.predict(images)
if not os.path.exists(os.path.join(predict_dir, '..', 'results')):
os.mkdir(os.path.join(predict_dir, '..', 'results'))
if not os.path.exists(
os.path.join(predict_dir, '..', 'results', dataset_name)):
os.mkdir(os.path.join(predict_dir, '..', 'results', dataset_name))
names = data.get_names(images)
image_prediction_prob = np.column_stack([names, predictions])
headers = ['score%d' % (i + 1) for i in range(predictions.shape[1])]
title = np.array(['image'] + headers)
image_prediction_prob = np.vstack([title, image_prediction_prob])
labels_file_prob = os.path.abspath(
os.path.join(predict_dir, '..', 'results', dataset_name,
'predictions.csv'))
np.savetxt(labels_file_prob,
image_prediction_prob,
delimiter=",",
fmt="%s")
def predict(model, training_cnf, predict_dir, weights_from, dataset_name,
convert, image_size, sync, predict_type):
images = data.get_image_files(predict_dir)
# Form now, hard coded models, cnfs, and weights
# Need to take these from program inputs or an ensembling config file
print('Creating predictor 1')
weights_from1 = 'weights.sa/model-epoch-97.ckpt'
model1 = 'examples/mnist_model_sa.py'
training_cnf1 = 'examples/mnist_cnf.py'
model_def1 = util.load_module(model1)
model1 = model_def1.model
cnf1 = util.load_module(training_cnf1).cnf
standardizer = cnf1.get('standardizer', NoOpStandardizer())
preprocessor = convert_preprocessor(
model_def1.image_size[0]) if convert else None
prediction_iterator1 = create_prediction_iter(cnf1, standardizer,
model_def1.crop_size,
preprocessor, sync)
predictor1 = QuasiCropPredictor(model1, cnf1, weights_from1,
prediction_iterator1, 20)
# predictor1 = OneCropPredictor(model1, cnf1, weights_from1, prediction_iterator1)
print('Creating predictor 2')
weights_from2 = 'weights.rv/model-epoch-31.ckpt'
model2 = 'examples/mnist_model.py'
training_cnf2 = 'examples/mnist_cnf.py'
model_def2 = util.load_module(model2)
model2 = model_def2.model
cnf2 = util.load_module(training_cnf2).cnf
standardizer = cnf2.get('standardizer', NoOpStandardizer())
preprocessor = convert_preprocessor(
model_def2.image_size[0]) if convert else None
prediction_iterator2 = create_prediction_iter(cnf2, standardizer,
model_def2.crop_size,
preprocessor, sync)
predictor2 = QuasiCropPredictor(model2, cnf2, weights_from2,
prediction_iterator2, 20)
# predictor2 = OneCropPredictor(model2, cnf2, weights_from2, prediction_iterator2)
predictor = EnsemblePredictor([predictor1, predictor2])
predictions = predictor.predict(images)
if not os.path.exists(os.path.join(predict_dir, '..', 'results')):
os.mkdir(os.path.join(predict_dir, '..', 'results'))
if not os.path.exists(
os.path.join(predict_dir, '..', 'results', dataset_name)):
os.mkdir(os.path.join(predict_dir, '..', 'results', dataset_name))
names = data.get_names(images)
image_prediction_probs = np.column_stack([names, predictions])
headers = ['score%d' % (i + 1) for i in range(predictions.shape[1])]
title = np.array(['image'] + headers)
image_prediction_probs = np.vstack([title, image_prediction_probs])
prediction_probs_file = os.path.abspath(
os.path.join(predict_dir, '..', 'results', dataset_name,
'predictions.csv'))
np.savetxt(prediction_probs_file,
image_prediction_probs,
delimiter=",",
fmt="%s")
print('Predictions saved to: %s' % prediction_probs_file)
if cnf1['classification']:
class_predictions = np.argmax(predictions, axis=1)
image_class_predictions = np.column_stack([names, class_predictions])
title = np.array(['image', 'label'])
image_class_predictions = np.vstack([title, image_class_predictions])
prediction_class_file = os.path.abspath(
os.path.join(predict_dir, '..', 'results', dataset_name,
'predictions_class.csv'))
np.savetxt(prediction_class_file,
image_class_predictions,
delimiter=",",
fmt="%s")
print('Class predictions saved to: %s' % prediction_class_file)
def predict(model, output_layer, training_cnf, predict_dir, weights_from, tag,
convert, image_size, sync, predict_type):
util.check_required_program_args(
[model, training_cnf, predict_dir, weights_from])
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
weights_from = str(weights_from)
images = data.get_image_files(predict_dir)
preprocessor = convert_preprocessor(image_size) if convert else None
prediction_iterator = create_prediction_iter(cnf, model_def.crop_size,
preprocessor, sync)
if predict_type == 'quasi':
predictor = QuasiCropPredictor(model, cnf, weights_from,
prediction_iterator, 20, output_layer)
elif predict_type == '1_crop':
predictor = OneCropPredictor(model, cnf, weights_from,
prediction_iterator, output_layer)
elif predict_type == '10_crop':
predictor = TenCropPredictor(model, cnf, weights_from,
prediction_iterator,
model_def.crop_size[0],
model_def.image_size[0], output_layer)
else:
raise ValueError('Unknown predict_type: %s' % predict_type)
predictions = predictor.predict(images)
prediction_results_dir = os.path.abspath(
os.path.join(predict_dir, '..', 'predictions', tag))
if not os.path.exists(prediction_results_dir):
os.makedirs(prediction_results_dir)
if output_layer == 'predictions':
names = data.get_names(images)
image_prediction_probs = np.column_stack([names, predictions])
headers = ['score%d' % (i + 1) for i in range(predictions.shape[1])]
title = np.array(['image'] + headers)
image_prediction_probs = np.vstack([title, image_prediction_probs])
prediction_probs_file = os.path.join(prediction_results_dir,
'predictions.csv')
np.savetxt(prediction_probs_file,
image_prediction_probs,
delimiter=",",
fmt="%s")
print('Predictions saved to: %s' % prediction_probs_file)
if cnf['classification']:
class_predictions = np.argmax(predictions, axis=1)
image_class_predictions = np.column_stack(
[names, class_predictions])
title = np.array(['image', 'label'])
image_class_predictions = np.vstack(
[title, image_class_predictions])
prediction_class_file = os.path.join(prediction_results_dir,
'predictions_class.csv')
np.savetxt(prediction_class_file,
image_class_predictions,
delimiter=",",
fmt="%s")
print('Class predictions saved to: %s' % prediction_class_file)
else:
# feature extraction
features_file = os.path.join(prediction_results_dir, 'features.npy')
np.save(features_file, predictions)
print('Features from layer: %s saved to: %s' %
(output_layer, features_file))
