def main(DeepForest_config, model=None, args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
#Add seperate dir
#save time for logging
dirname = datetime.now().strftime("%Y%m%d_%H%M%S")
save_path = DeepForest_config["save_image_path"] + dirname
os.makedirs(save_path)
print("save path is {}".format(save_path))
#Evaluation metrics
site = DeepForest_config["evaluation_site"]
#create the NEON mAP generator
NEON_generator = create_NEON_generator(args.batch_size, DeepForest_config)
#NEON plot mAP
recall, precision = evaluate_pr(NEON_generator,
model,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold,
max_detections=args.max_detections,
save_path=save_path,
experiment=None)
return [recall, precision]
def main(DeepForest_config, model=None, args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
#Add seperate dir
#save time for logging
dirname = datetime.now().strftime("%Y%m%d_%H%M%S")
if args.save_path:
save_path = args.save_path + dirname
# make save path if it doesn't exist
if not os.path.exists(save_path):
os.makedirs(save_path)
else:
save_path = None
#Evaluation metrics
site = DeepForest_config["evaluation_site"]
#create the NEON mAP generator
NEON_generator = create_NEON_generator(args.batch_size, DeepForest_config)
#NEON plot mAP
recall, precision = evaluate_pr(NEON_generator,
model,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold,
max_detections=args.max_detections,
save_path=save_path,
experiment=None)
return [recall, precision]
def test_callback(model, experiment):
#create the NEON generator
NEON_generator = create_NEON_generator(DeepForest_config["batch_size"], DeepForest_config)
average_precisions = evalmAP.evaluate(
NEON_generator,
model,
iou_threshold=0.5,
score_threshold=0.15,
max_detections=300,
save_path="../snapshots/",
experiment=experiment
)
def main(DeepForest_config, args=None, experiment=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
#Add seperate dir
#save time for logging
dirname = datetime.now().strftime("%Y%m%d_%H%M%S")
# make save path if it doesn't exist
if args.save_path is not None and not os.path.exists(args.save_path +
dirname):
os.makedirs(args.save_path + dirname)
#Evaluation metrics
site = DeepForest_config["evaluation_site"]
#create the NEON mAP generator
NEON_generator = create_NEON_generator(args.batch_size, DeepForest_config)
# load the model
print('Loading model, this may take a second...')
model = models.load_model(args.model,
backbone_name=args.backbone,
convert=args.convert_model,
nms_threshold=DeepForest_config["nms_threshold"])
#print(model.summary())
#NEON plot mAP
average_precisions = evaluate(NEON_generator,
model,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold,
max_detections=args.max_detections,
save_path=args.save_path + dirname,
experiment=experiment)
return average_precisions
"/orange/ewhite/b.weinstein/retinanet/20190713_230957/resnet50_40.h5",
"NIWO":
"/orange/ewhite/b.weinstein/retinanet/20190712_055958/resnet50_40.h5",
"MLBS":
"/orange/ewhite/b.weinstein/retinanet/20190712_035528/resnet50_40.h5",
"All":
"/orange/ewhite/b.weinstein/retinanet/20190715_123358/resnet50_40.h5"
}
for trained_model in trained_models:
# load retinanet model
model_path = trained_models[trained_model]
model = models.load_model(model_path,
backbone_name='resnet50',
convert=True,
nms_threshold=DeepForest_config["nms_threshold"])
#Make a new dir to hold images
dirname = datetime.now().strftime("%Y%m%d_%H%M%S") + trained_model
save_image_path = os.path.join("..", "snapshots", dirname)
os.mkdir(save_image_path)
NEON_generator = generators.create_NEON_generator(
DeepForest_config["batch_size"], DeepForest_config, name="evaluation")
all_detections = _get_detections(
NEON_generator,
model,
score_threshold=DeepForest_config["score_threshold"],
max_detections=300,
save_path=save_image_path,
experiment=None)
def main(data, DeepForest_config, experiment, args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
#Add seperate dir
#save time for logging
dirname=datetime.now().strftime("%Y%m%d_%H%M%S")
experiment.log_parameter("Start Time", dirname)
# make save path if it doesn't exist
if args.save_path is not None and not os.path.exists(args.save_path + dirname):
os.makedirs(args.save_path + dirname)
# load the model
print('Loading model, this may take a second...')
model = models.load_model(args.model, backbone_name=args.backbone, convert=args.convert_model, input_channels=DeepForest_config["input_channels"], nms_threshold=DeepForest_config["nms_threshold"])
#print(model.summary())
## create the testing generators
#if DeepForest_config["evaluation_images"] > 0:
#generator = create_generator(args, data, DeepForest_config)
#average_precisions = evaluate(
#generator,
#model,
#iou_threshold=args.iou_threshold,
#score_threshold=args.score_threshold,
#max_detections=args.max_detections,
#save_path=args.save_path + dirname
#)
### print evaluation
#present_classes = 0
#precision = 0
#for label, (average_precision, num_annotations) in average_precisions.items():
#print('{:.0f} instances of class'.format(num_annotations),
#generator.label_to_name(label), 'with average precision: {:.3f}'.format(average_precision))
#if num_annotations > 0:
#present_classes += 1
#precision += average_precision
#print('mAP: {:.3f}'.format(precision / present_classes))
#experiment.log_metric("mAP", precision / present_classes)
##Evaluation metrics
#sites = DeepForest_config["evaluation_site"]
##create the NEON mAP generator
NEON_generator = create_NEON_generator(args.batch_size, DeepForest_config)
#NEON_recall_generator = create_NEON_generator(args.batch_size, DeepForest_config)
#recall = neonRecall(
#sites,
#NEON_recall_generator,
#model,
#score_threshold=args.score_threshold,
#save_path=args.save_path + dirname,
#max_detections=args.max_detections
#)
#print("Recall is {:0.3f}".format(recall))
#experiment.log_metric("Recall", recall)
#NEON plot mAP
average_precisions = evaluate(
NEON_generator,
model,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold,
max_detections=args.max_detections,
save_path=args.save_path + dirname,
experiment=experiment
)
# print evaluation
## print evaluation
present_classes = 0
precision = 0
for label, (average_precision, num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations),
generator.label_to_name(label), 'with average precision: {:.3f}'.format(average_precision))
if num_annotations > 0:
present_classes += 1
precision += average_precision
NEON_map = round(precision / present_classes,3)
print('Neon mAP: {:.3f}'.format(NEON_map))
experiment.log_metric("Neon mAP", NEON_map)
return [recall, NEON_map]
def create_callbacks(model, training_model, prediction_model, train_generator, validation_generator, args, experiment, DeepForest_config):
""" Creates the callbacks to use during training.
Args
model: The base model.
training_model: The model that is used for training.
prediction_model: The model that should be used for validation.
validation_generator: The generator for creating validation data.
args: parseargs args object.
Returns:
A list of callbacks used for training.
"""
callbacks = []
if args.evaluation and validation_generator:
evaluation = Evaluate(validation_generator,
experiment=experiment,
save_path=args.save_path,
score_threshold=args.score_threshold,
DeepForest_config=DeepForest_config,
)
evaluation = RedirectModel(evaluation, prediction_model)
callbacks.append(evaluation)
# save the model
if args.snapshots:
# ensure directory created first; otherwise h5py will error after epoch.
makedirs(args.snapshot_path)
checkpoint = keras.callbacks.ModelCheckpoint(
os.path.join(
args.snapshot_path,
'{backbone}_{{epoch:02d}}.h5'.format(backbone=args.backbone)
),
verbose=1
#,
#save_best_only=True,
#monitor="mAP",
#mode='max'
)
checkpoint = RedirectModel(checkpoint, model)
callbacks.append(checkpoint)
callbacks.append(keras.callbacks.ReduceLROnPlateau(
monitor = 'loss',
factor = 0.1,
patience = 2,
verbose = 1,
mode = 'auto',
epsilon = 0.0001,
cooldown = 0,
min_lr = 0
))
#Neon Callbacks
NEON_recall_generator = create_NEON_generator(args.batch_size, DeepForest_config)
recall = recallCallback(
generator=NEON_recall_generator,
save_path=args.save_path,
score_threshold=args.score_threshold,
experiment=experiment,
sites=DeepForest_config["evaluation_site"] )
recall = RedirectModel(recall, prediction_model)
callbacks.append(recall)
#create the NEON generator
NEON_generator = create_NEON_generator(args.batch_size, DeepForest_config)
neon_evaluation = NEONmAP(NEON_generator,
experiment=experiment,
save_path=args.save_path,
score_threshold=args.score_threshold,
DeepForest_config=DeepForest_config)
neon_evaluation = RedirectModel(neon_evaluation, prediction_model)
callbacks.append(neon_evaluation)
return callbacks
from DeepForest import evalmAP
from DeepForest.utils.generators import create_NEON_generator
parser = argparse.ArgumentParser(description='Prediction of a new image')
parser.add_argument('--model', help='path to training model')
parser.add_argument('--batch_size',
help='batch size for prediction',
default=1)
args = parser.parse_args()
#Config files
DeepForest_config = load_config(dir="..")
#Load hand annotations
neon_generator = create_NEON_generator(args.batch_size, DeepForest_config)
#Get detections and annotations
model = models.load_model(args.model,
backbone_name='resnet50',
convert=True,
nms_threshold=DeepForest_config["nms_threshold"])
labels_to_names = {0: 'Tree'}
all_detections = evalmAP._get_detections(
neon_generator,
model,
score_threshold=DeepForest_config["score_threshold"],
save_path="/Users/Ben/Downloads/")
all_annotations = evalmAP._get_annotations(neon_generator)
#Loop through images and match boxes.