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Ensured summaries are written

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-04-10 15:19:44 +02:00
parent a7f9d586f4
commit b173746784
1 changed files with 12 additions and 3 deletions
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11
src/twomartens/masterthesis/main.py
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@ -68,6 +68,7 @@ def _build_train(parser: argparse.ArgumentParser) -> None:
def _build_auto_encoder(parser: argparse.ArgumentParser) -> None: def _build_auto_encoder(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--coco_path", type=str, help="the path to the COCO data set") parser.add_argument("--coco_path", type=str, help="the path to the COCO data set")
parser.add_argument("--weights_path", type=str, help="path to the weights directory") parser.add_argument("--weights_path", type=str, help="path to the weights directory")
parser.add_argument("--summary_path", type=str, help="path to the summaries directory")
parser.add_argument("category", type=int, help="the COCO category to use") parser.add_argument("category", type=int, help="the COCO category to use")
parser.add_argument("num_epochs", type=int, help="the number of epochs to train", default=80) parser.add_argument("num_epochs", type=int, help="the number of epochs to train", default=80)
parser.add_argument("iteration", type=int, help="the training iteration") parser.add_argument("iteration", type=int, help="the training iteration")
@ -95,12 +96,20 @@ def _use(args: argparse.Namespace) -> None:
def _auto_encoder_train(args: argparse.Namespace) -> None: def _auto_encoder_train(args: argparse.Namespace) -> None:
from twomartens.masterthesis import data from twomartens.masterthesis import data
from twomartens.masterthesis.aae import train from twomartens.masterthesis.aae import train
import tensorflow as tf
from tensorflow.python.ops import summary_ops_v2
tf.enable_eager_execution()
coco_path = args.coco_path coco_path = args.coco_path
category = args.category category = args.category
batch_size = 32 batch_size = 32
coco_data = data.load_coco(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size) coco_data = data.load_coco(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size)
train.train_simple(coco_data, iteration=args.iteration, weights_prefix=args.weights_path, train_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/train/category-{category}/{args.iteration}"
)
with train_summary_writer.as_default():
train.train_simple(coco_data, iteration=args.iteration,
weights_prefix=f"{args.weights_path}/category-{category}",
channels=3, train_epoch=args.num_epochs, batch_size=batch_size) channels=3, train_epoch=args.num_epochs, batch_size=batch_size)