def compile_bot(self):
""" Compile source file to directory with source file """
from ai_arena.contests.compilation import compile
from django.core.files import File
src = settings.MEDIA_ROOT + self.bot_source_file.name
log_target = settings.COMPILATION_TEMP_PATH + self.name + '.log'
target = settings.COMPILATION_TEMP_PATH + self.name + '.bin'
lang = self.bot_lang
exit_status = compile(src, target, lang, log_target)
if exit_status != 0:
log_file = open(log_target, 'r')
logs = parse_logs(log_file.read())
return (exit_status, logs)
else:
# Use compiled file in object bot
self.bot_bin_file.save(self.name, File(open(target)))
# Save changes made to bot object
self.save()
# Remove compiled file from directory with source
system('rm ' + target)
return (exit_status, "")
def create():
# read in logfile
f = open(config.path_to_logfile, "r")
logs = utils.parse_logs(f)
# filter to this week
filter_this_week = utils.filter_by_date(config.beginning_of_week)
logs = filter_this_week(logs)
# get tags
tags = utils.extract_sorted_tags(logs)
# for each tag, grab matching logs
tags_to_logs = {}
for tag in tags:
tags_to_logs[tag] = utils.filter_by_tags(tag)(logs)
# write groups to email
msg = []
for tag in tags_to_logs.keys():
msg.append('Tag: ' + tag)
for log in tags_to_logs[tag]:
msg.append(log['log'])
send_mail.sendMail(msg)
def train(caffe_home, log_path, output_log_file, solver, input_weight_file, output_weight_file, note):
""" Trains Caffe finetuning the given model. """
print("Training using data")
_run_trainer(caffe_home, log_path, output_log_file, solver, input_weight_file, note)
_generate_parsed_logs(caffe_home, log_path, output_log_file)
(training_details, validation_details) = utils.parse_logs(log_path, output_log_file)
_move_trained_weight_file(log_path, output_log_file, output_weight_file)
print "Finished training!"
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 train(caffe_home, log_path, output_log_file, solver, input_weight_file,
output_weight_file, note):
""" Trains Caffe finetuning the given model. """
print("Training using data")
_run_trainer(caffe_home, log_path, output_log_file, solver,
input_weight_file, note)
_generate_parsed_logs(caffe_home, log_path, output_log_file)
(training_details,
validation_details) = utils.parse_logs(log_path, output_log_file)
_move_trained_weight_file(log_path, output_log_file, output_weight_file)
print "Finished training!"
def get_best_model(out_dir):
scores = []
model_paths = []
for path in Path(out_dir).glob("*/*"):
try:
_, rt = parse_logs(os.path.join(path, "log"))
if rt is not None:
scores.append(rt)
model_paths.append(os.path.join(path, WEIGHTS_NAME))
except (AttributeError, FileNotFoundError, NotADirectoryError):
continue
if len(model_paths) == 0:
raise FileNotFoundError(
f"Could not find any trained models in {out_dir}")
best_score = np.argmax(np.array(scores))
return model_paths[int(best_score)]
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)