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# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Debugging script to visualize the dataset video and frame sampling."""
import sys
from absl import app
from absl import flags
from absl import logging
from base_configs import validate_config
import matplotlib.pyplot as plt
from ml_collections import config_flags
import torchvision
from xirl.common import get_pretraining_dataloaders
# pylint: disable=logging-fstring-interpolation
FLAGS = flags.FLAGS
flags.DEFINE_boolean("debug", False, "Turn off shuffling and data aug.")
config_flags.DEFINE_config_file(
"config",
"base_configs/pretrain.py",
"File path to the training hyperparameter configuration.",
)
def main(_):
validate_config(FLAGS.config, mode="pretrain")
config = FLAGS.config
if FLAGS.debug:
config.data.pretraining_video_sampler = "same_class"
num_ctx_frames = config.frame_sampler.num_context_frames
num_frames = config.frame_sampler.num_frames_per_sequence
pretrain_loaders = get_pretraining_dataloaders(config, FLAGS.debug)
try:
loader = pretrain_loaders["train"]
logging.info("Total videos: %d", loader.dataset.total_vids)
for batch_idx, batch in enumerate(loader):
logging.info("Batch #%d", batch_idx)
frames = batch["frames"]
b, _, c, h, w = frames.shape
frames = frames.view(b, num_frames, num_ctx_frames, c, h, w)
for b in range(frames.shape[0]):
logging.info("\tBatch Item %s", str(b))
grid_img = torchvision.utils.make_grid(frames[b, :, -1], nrow=5)
plt.imshow(grid_img.permute(1, 2, 0))
plt.show()
except KeyboardInterrupt:
sys.exit()
if __name__ == "__main__":
app.run(main)
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