def extract_features():
if not args.list and len(args.files) == 0:
logger.error("No input file specified.")
return
import tensorflow as tf
import inspect
import feature_extractor as feature_extractor
# Add before any TF calls (https://github.com/tensorflow/tensorflow/issues/29931#issuecomment-504217770)
# Initialize the keras global outside of any tf.functions
temp = tf.zeros([4, 32, 32, 3])
tf.keras.applications.vgg16.preprocess_input(temp)
# Get all the available feature extractor names
extractor_names = list([
e[0] for e in inspect.getmembers(feature_extractor, inspect.isclass)
if e[0] != "FeatureExtractorBase"
])
module = __import__("feature_extractor")
if args.list:
print("%-30s | %-15s | %-4s | %-8s | %-5s" %
("NAME", "OUTPUT SHAPE", "RF", "IMG SIZE", "RF / IMG"))
print("-" * 80)
for e in list(map(lambda e: getattr(module, e), extractor_names)):
factor = e.RECEPTIVE_FIELD["size"][0] / float(e.IMG_SIZE)
print("%-30s | %-15s | %-4s | %-8s | %.3f %s" %
(e.__name__.replace("FeatureExtractor", ""), e.OUTPUT_SHAPE,
e.RECEPTIVE_FIELD["size"][0], e.IMG_SIZE, factor,
"!" if factor >= 2 else ""))
return
if args.extractor is None:
args.extractor = extractor_names
# args.extractor = filter(lambda f: "EfficientNet" in f, args.extractor)
if isinstance(args.files, basestring):
args.files = [args.files]
patches = PatchArray(args.files)
## WZL:
patches = patches.training_and_validation
# For the benchmark subset:
# patches = patches.training_and_validation[0:10]
## FieldSAFE:
# p = patches[:, 0, 0]
# f = p.round_numbers == 1
# patches = patches[f]
# vis = Visualize(patches)
# vis.show()
dataset = patches.to_dataset()
dataset_3D = patches.to_temporal_dataset(16)
total = patches.shape[0]
# Add progress bar if multiple extractors
if len(args.extractor) > 1:
args.extractor = tqdm(args.extractor,
desc="Extractors",
file=sys.stderr)
for extractor_name in args.extractor:
try:
bs = getattr(module, extractor_name).TEMPORAL_BATCH_SIZE
# shape = getattr(module, extractor_name).OUTPUT_SHAPE
# if np.prod(shape) > 300000:
# logger.warning("Skipping %s (output too big)" % extractor_name)
# continue
logger.info("Instantiating %s" % extractor_name)
extractor = getattr(module, extractor_name)()
# Get an instance
if bs > 1:
extractor.extract_dataset(dataset_3D, total)
else:
extractor.extract_dataset(dataset, total)
except KeyboardInterrupt:
logger.info("Terminated by CTRL-C")
return
except:
logger.error("%s: %s" % (extractor_name, traceback.format_exc()))
RECEPTIVE_FIELD = {'stride': (4.0, 4.0), 'size': (23, 23)}
# Only for tests
if __name__ == "__main__":
from common import PatchArray
extractor = FeatureExtractorC3D()
# extractor.plot_model(extractor.model)
patches = PatchArray()
p = patches[:, 0, 0]
f = np.zeros(p.shape, dtype=np.bool)
f[:] = np.logical_and(
p.directions == 1, # CCW and
np.logical_or(
p.labels == 2, # Anomaly or
np.logical_and(
p.round_numbers >= 7, # Round between 2 and 5
p.round_numbers <= 9)))
# Let's make contiguous blocks of at least 10, so
# we can do some meaningful temporal smoothing afterwards
for i, b in enumerate(f):
if b and i - 10 >= 0:
f[i - 10:i] = True
patches = patches[f]
extractor.extract_dataset(patches.to_temporal_dataset(), patches.shape[0])