def predict(frozen_model, training_cnf, predict_dir, image_size, output_path,
num_classes, gpu_memory_fraction):
cnf = util.load_module(training_cnf).cnf
standardizer = cnf['standardizer']
graph = util.load_frozen_graph(frozen_model)
preprocessor = convert_preprocessor(image_size)
predictor = SegmentPredictor(graph, standardizer, preprocessor)
# images = data.get_image_files(predict_dir)
image_names = [
filename.strip() for filename in os.listdir(predict_dir)
if filename.endswith('.jpg')
]
hist = np.zeros((num_classes, num_classes))
for image_filename in image_names:
final_prediction_map = predictor.predict(
os.path.join(predict_dir, image_filename))
final_prediction_map = final_prediction_map.transpose(0, 2,
1).squeeze()
gt_name = os.path.join(predict_dir,
image_filename[:-4] + '_final_mask' + '.png')
gt = convert(gt_name, image_size)
gt = np.asarray(gt)
gt = convert_labels(gt, image_size, image_size)
hist += compute_hist(gt, final_prediction_map, num_classes=num_classes)
iou = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
meaniou = np.nanmean(iou)
print('Mean IOU %5.5f' % meaniou)
def predict(frozen_model, training_cnf, image_path, image_size, output_path, gpu_memory_fraction):
cnf = util.load_module(training_cnf).cnf
standardizer = cnf['standardizer']
graph = util.load_frozen_graph(frozen_model)
preprocessor = convert_preprocessor(448)
predictor = SegmentPredictor(graph, standardizer, preprocessor)
final_prediction_map = predictor.predict(image_path)
final_prediction_map = final_prediction_map.transpose(0, 2, 1).squeeze()
image = data.load_image(image_path, preprocessor=preprocessor)
img = image.transpose(2, 1, 0)
img = Image.fromarray(img.astype('uint8'), 'RGB')
img.save('/tmp/test.png')
image_filename = image_path.split('/')[-1]
plot_masks('/tmp/test.png', final_prediction_map, output_path)
"""
def predict(frozen_model, training_cnf, predict_dir, image_size, output_path,
num_classes, gpu_memory_fraction):
cnf = util.load_module(training_cnf).cnf
standardizer = cnf['standardizer']
graph = util.load_frozen_graph(frozen_model)
preprocessor = convert_preprocessor(image_size)
predictor = SegmentPredictor(graph, standardizer, preprocessor)
# images = data.get_image_files(predict_dir)
image_names = [
filename.strip() for filename in os.listdir(predict_dir)
if filename.endswith('.jpg')
]
iou = IOU()
per_class_iou = iou.per_class_iou(predictor, predict_dir, image_size)
meaniou = iou.meaniou(predictor, predict_dir, image_size)
print(per_class_iou)
print('Mean IOU %5.5f' % meaniou)
def predict(model, model_def, output_layer, cnf, weights_from, convert, images, image_size, sync, predict_type):
global predictors
p_key = "%s-%s" % (weights_from, output_layer)
predictor = predictors.get(p_key)
if predictor is None:
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)
else:
raise ValueError('Unknown predict_type: %s' % predict_type)
predictors[p_key] = predictor
predictions = predictor.predict(images)
return predictions
def predict(model, model_def, output_layer, cnf, weights_from, convert, images,
image_size, sync, predict_type):
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)
return predictions
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):
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', NoOpStandardizer())
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)
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)
# print(predictions)
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 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))