def parse_command_line():
parser = argparse.ArgumentParser(description="Trains Caffe model against prepared data")
parser.add_argument("--log_path", help="The path to where to place log files",
type=str, default="logs")
parser.add_argument("--log_num", help="""Number that will be appended to log files; this will
be automatically padded and added with zeros, such as output00001.log""", type=int,
default=1)
parser.add_argument("--note", help="Adds extra note into training logs.", type=str,
default=None)
parser.add_argument("--solver", help="The path to our Caffe solver prototxt file",
type=str, default="src/caffe_model/bvlc_alexnet/solver.prototxt")
parser.add_argument("--input_weight_file", help="""A pre-trained Caffe model that we will use
to start training with in order to fine-tune from""", type=str,
default="src/caffe_model/bvlc_alexnet/bvlc_alexnet.caffemodel")
parser.add_argument("--output_weight_file", help="""Where to place the final, trained Caffe
model""", type=str, default="logs/latest_bvlc_alexnet_finetuned.caffemodel")
args = vars(parser.parse_args())
caffe_home = utils.assert_caffe_setup()
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
log_path = os.path.abspath(args["log_path"])
log_num = args["log_num"]
(output_ending, output_log_prefix, output_log_file) = utils.get_log_path_details(log_path, log_num)
solver = os.path.abspath(args["solver"])
input_weight_file = os.path.abspath(args["input_weight_file"])
output_weight_file = os.path.abspath(args["output_weight_file"])
train(caffe_home, log_path, output_log_file, solver, input_weight_file, output_weight_file,
args["note"])
def parse_command_line():
parser = argparse.ArgumentParser(description="""Tests a trained Caffe model to see how well
it does, generating quality graphs and statistics""")
parser.add_argument("--log_path", help="The path to where to place log files and graphs",
type=str, default="logs")
parser.add_argument("--log_num", help="""Number that will be appended to log files; this will
be automatically padded and added with zeros, such as output00001.log""", type=int,
default=1)
parser.add_argument("--input_weight_file", help="""The trained and fine-tuned Caffe model that
we will be testing; defaults to the last trained model from train.py""", type=str,
default="logs/latest_bvlc_alexnet_finetuned.caffemodel")
parser.add_argument("--note", help="Adds extra note onto generated quality graphs.", type=str,
default="")
parser.add_argument("--solver", help="The path to our Caffe solver prototxt file",
type=str, default="src/caffe_model/bvlc_alexnet/solver.prototxt")
parser.add_argument("--deploy", help="""Path to our Caffe deploy/inference time prototxt file""",
type=str, default="src/caffe_model/bvlc_alexnet/deploy.prototxt")
parser.add_argument("--threshold", help="""The percentage threshold over which we assume
something is a cloud. Note that this value is from 0.0 to 100.0""", type=float, default=0.1)
parser.add_argument("--validation_leveldb", help="""Path to where the validation leveldb file is""",
type=str, default="data/leveldb/validation_leveldb")
parser.add_argument("--width", help="Width of image during training", type=int, default=256)
parser.add_argument("--height", help="Height of image during training", type=int, default=256)
parser.add_argument("--inference_width", help="Width of image during training", type=int,
default=227)
parser.add_argument("--inference_height", help="Height of image during training", type=int,
default=227)
parser.add_argument("--training_mean_pickle", help="Path to pickled mean values", type=str,
default="data/imagenet/imagenet_mean.npy")
args = vars(parser.parse_args())
print "Testing trained model..."
caffe_home = utils.assert_caffe_setup()
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
log_path = os.path.abspath(args["log_path"])
log_num = args["log_num"]
(output_ending, output_log_prefix, output_log_file) = utils.get_log_path_details(log_path, log_num)
output_graph_path = output_log_prefix
(training_details, validation_details) = utils.parse_logs(log_path, output_log_file)
plot_results(training_details, validation_details, args["note"], output_graph_path, args["solver"])
validation_leveldb = os.path.abspath(args["validation_leveldb"])
deploy = os.path.abspath(args["deploy"])
input_weight_file = os.path.abspath(args["input_weight_file"])
training_mean_pickle = os.path.abspath(args["training_mean_pickle"])
predict.test_validation(args["threshold"], output_log_prefix, validation_leveldb,
deploy, args["width"], args["height"], args["inference_width"],
args["inference_height"], input_weight_file, training_mean_pickle)
def parse_command_line():
parser = argparse.ArgumentParser(description="""Prepares data for training via Caffe""")
parser.add_argument("--input_metadata", help="Path to where our bounding box metadata is",
type=str, default="data/planetlab/metadata/annotated.json")
parser.add_argument("--input_images", help="Path to where our unbounded images are",
type=str, default="data/planetlab/metadata")
parser.add_argument("--output_images", help="Path to place our cropped, bounded images",
type=str, default="data/planetlab/images/bounded")
parser.add_argument("--output_leveldb", help="Path to place our prepared leveldb directories",
type=str, default="data/leveldb")
parser.add_argument("--width", help="Width of image at training time (it will be scaled to this)",
type=int, default=256)
parser.add_argument("--height", help="Height of image at training time (it will be scaled to this)",
type=int, default=256)
parser.add_argument("--log_path", help="The path to where to place log files",
type=str, default="logs")
parser.add_argument("--log_num", help="""Number that will be appended to log files; this will
be automatically padded and added with zeros, such as output00001.log""", type=int,
default=1)
parser.add_argument("--do_augmentation", help="Whether to do data augmentation",
dest="do_augmentation", action="store_true")
parser.set_defaults(do_augmentation=False)
args = vars(parser.parse_args())
utils.assert_caffe_setup()
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
log_path = os.path.abspath(args["log_path"])
log_num = args["log_num"]
(output_ending, output_log_prefix, output_log_file) = utils.get_log_path_details(log_path, log_num)
input_metadata = os.path.abspath(args["input_metadata"])
input_images = os.path.abspath(args["input_images"])
output_images = os.path.abspath(args["output_images"])
output_leveldb = os.path.abspath(args["output_leveldb"])
prepare_data(input_metadata, input_images, output_images, output_leveldb, args["width"],
args["height"], args["do_augmentation"], output_log_prefix)
def parse_command_line():
parser = argparse.ArgumentParser(
description="Trains Caffe model against prepared data")
parser.add_argument("--log_path",
help="The path to where to place log files",
type=str,
default="logs")
parser.add_argument(
"--log_num",
help="""Number that will be appended to log files; this will
be automatically padded and added with zeros, such as output00001.log""",
type=int,
default=1)
parser.add_argument("--note",
help="Adds extra note into training logs.",
type=str,
default=None)
parser.add_argument("--solver",
help="The path to our Caffe solver prototxt file",
type=str,
default="src/caffe_model/bvlc_alexnet/solver.prototxt")
parser.add_argument(
"--input_weight_file",
help="""A pre-trained Caffe model that we will use
to start training with in order to fine-tune from""",
type=str,
default="src/caffe_model/bvlc_alexnet/bvlc_alexnet.caffemodel")
parser.add_argument(
"--output_weight_file",
help="""Where to place the final, trained Caffe
model""",
type=str,
default="logs/latest_bvlc_alexnet_finetuned.caffemodel")
args = vars(parser.parse_args())
caffe_home = utils.assert_caffe_setup()
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
log_path = os.path.abspath(args["log_path"])
log_num = args["log_num"]
(output_ending, output_log_prefix,
output_log_file) = utils.get_log_path_details(log_path, log_num)
solver = os.path.abspath(args["solver"])
input_weight_file = os.path.abspath(args["input_weight_file"])
output_weight_file = os.path.abspath(args["output_weight_file"])
train(caffe_home, log_path, output_log_file, solver, input_weight_file,
output_weight_file, args["note"])
def parse_command_line():
parser = argparse.ArgumentParser(
description="""Prepares data for training via Caffe""")
parser.add_argument("--input_metadata",
help="Path to where our bounding box metadata is",
type=str,
default="data/planetlab/metadata/annotated.json")
parser.add_argument("--input_images",
help="Path to where our unbounded images are",
type=str,
default="data/planetlab/metadata")
parser.add_argument("--output_images",
help="Path to place our cropped, bounded images",
type=str,
default="data/planetlab/images/bounded")
parser.add_argument("--output_leveldb",
help="Path to place our prepared leveldb directories",
type=str,
default="data/leveldb")
parser.add_argument(
"--width",
help="Width of image at training time (it will be scaled to this)",
type=int,
default=256)
parser.add_argument(
"--height",
help="Height of image at training time (it will be scaled to this)",
type=int,
default=256)
parser.add_argument("--log_path",
help="The path to where to place log files",
type=str,
default="logs")
parser.add_argument(
"--log_num",
help="""Number that will be appended to log files; this will
be automatically padded and added with zeros, such as output00001.log""",
type=int,
default=1)
parser.add_argument("--do_augmentation",
help="Whether to do data augmentation",
dest="do_augmentation",
action="store_true")
parser.set_defaults(do_augmentation=False)
args = vars(parser.parse_args())
utils.assert_caffe_setup()
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
log_path = os.path.abspath(args["log_path"])
log_num = args["log_num"]
(output_ending, output_log_prefix,
output_log_file) = utils.get_log_path_details(log_path, log_num)
input_metadata = os.path.abspath(args["input_metadata"])
input_images = os.path.abspath(args["input_images"])
output_images = os.path.abspath(args["output_images"])
output_leveldb = os.path.abspath(args["output_leveldb"])
prepare_data(input_metadata, input_images, output_images, output_leveldb,
args["width"], args["height"], args["do_augmentation"],
output_log_prefix)
def parse_command_line():
parser = argparse.ArgumentParser(
description="""Tests a trained Caffe model to see how well
it does, generating quality graphs and statistics""")
parser.add_argument("--log_path",
help="The path to where to place log files and graphs",
type=str,
default="logs")
parser.add_argument(
"--log_num",
help="""Number that will be appended to log files; this will
be automatically padded and added with zeros, such as output00001.log""",
type=int,
default=1)
parser.add_argument(
"--input_weight_file",
help="""The trained and fine-tuned Caffe model that
we will be testing; defaults to the last trained model from train.py""",
type=str,
default="logs/latest_bvlc_alexnet_finetuned.caffemodel")
parser.add_argument("--note",
help="Adds extra note onto generated quality graphs.",
type=str,
default="")
parser.add_argument("--solver",
help="The path to our Caffe solver prototxt file",
type=str,
default="src/caffe_model/bvlc_alexnet/solver.prototxt")
parser.add_argument(
"--deploy",
help="""Path to our Caffe deploy/inference time prototxt file""",
type=str,
default="src/caffe_model/bvlc_alexnet/deploy.prototxt")
parser.add_argument("--threshold",
help="""The percentage threshold over which we assume
something is a cloud. Note that this value is from 0.0 to 100.0""",
type=float,
default=0.1)
parser.add_argument(
"--validation_leveldb",
help="""Path to where the validation leveldb file is""",
type=str,
default="data/leveldb/validation_leveldb")
parser.add_argument("--width",
help="Width of image during training",
type=int,
default=256)
parser.add_argument("--height",
help="Height of image during training",
type=int,
default=256)
parser.add_argument("--inference_width",
help="Width of image during training",
type=int,
default=227)
parser.add_argument("--inference_height",
help="Height of image during training",
type=int,
default=227)
parser.add_argument("--training_mean_pickle",
help="Path to pickled mean values",
type=str,
default="data/imagenet/imagenet_mean.npy")
args = vars(parser.parse_args())
print "Testing trained model..."
caffe_home = utils.assert_caffe_setup()
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
log_path = os.path.abspath(args["log_path"])
log_num = args["log_num"]
(output_ending, output_log_prefix,
output_log_file) = utils.get_log_path_details(log_path, log_num)
output_graph_path = output_log_prefix
(training_details,
validation_details) = utils.parse_logs(log_path, output_log_file)
plot_results(training_details, validation_details, args["note"],
output_graph_path, args["solver"])
validation_leveldb = os.path.abspath(args["validation_leveldb"])
deploy = os.path.abspath(args["deploy"])
input_weight_file = os.path.abspath(args["input_weight_file"])
training_mean_pickle = os.path.abspath(args["training_mean_pickle"])
predict.test_validation(args["threshold"], output_log_prefix,
validation_leveldb, deploy, args["width"],
args["height"], args["inference_width"],
args["inference_height"], input_weight_file,
training_mean_pickle)