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import click
import os
from app.utils.cortex_utils import cortex_folder, download_cortex_files, find_unprocessed_files
from app.search.search_class import find_nearest_vector_for_images
from app.search.search_dense import find_dense_embedding_for_images
from app.search.json import params_dense_dict
from app.search.params import timestamp
@click.command('')
@click.option('-f', '--folder_id', 'opt_folder_id', type=int,
help='Folder ID to process')
@click.option('-ls', '--latent_steps', 'opt_latent_steps', default=2000, type=int,
help='Number of optimization iterations')
@click.option('-ds', '--dense_steps', 'opt_dense_steps', default=2000, type=int,
help='Number of optimization iterations')
@click.option('-v', '--video', 'opt_video', is_flag=True,
help='Export a video for each dataset')
@click.option('-rp', '--reprocess', 'opt_reprocess', is_flag=True,
help='Reprocess images')
@click.option('-sc', '--stochastic_clipping', 'opt_stochastic_clipping', is_flag=True,
help='Compute feature loss')
@click.option('-lc', '--label_clipping', 'opt_label_clipping', is_flag=True,
help='Normalize labels every N steps')
@click.option('-feat', '--use_feature_detector', 'opt_use_feature_detector', is_flag=True,
help='Compute feature loss')
@click.option('-ll', '--feature_layers', 'opt_feature_layers', default="1a,2a,3a,4a,7a",
help='Feature layers used for loss')
@click.option('-snap', '--snapshot_interval', 'opt_snapshot_interval', default=20,
help='Interval to store sample images')
@click.option('-clip', '--clip_interval', 'opt_clip_interval', default=300,
help='Interval to clip vectors')
@click.pass_context
def cli(ctx, opt_folder_id, opt_latent_steps, opt_dense_steps, opt_video, opt_reprocess,
opt_stochastic_clipping, opt_label_clipping, opt_use_feature_detector, opt_feature_layers, opt_snapshot_interval, opt_clip_interval):
"""
The full process:
- Fetch new images from the cortex
- Extract labels and base latents
- Extract dense embeddings
- Upload extract images to the cortex
"""
folder = cortex_folder(opt_folder_id)
files = download_cortex_files(opt_folder_id)
unprocessed_files = find_unprocessed_files(files, reprocess=opt_reprocess)
if len(unprocessed_files) == 0:
print("All files processed, nothing to do")
return
print("Processing folder {} ({}), {} new files".format(folder['name'], folder['id'], len(unprocessed_files)))
tag = "folder_{}_{}".format(folder['id'], timestamp())
paths = [file['path'] for file in unprocessed_files]
opt_feature_layers = opt_feature_layers.split(',')
find_nearest_vector_for_images(
paths=paths,
opt_dims=512,
opt_steps=opt_latent_steps,
opt_video=False,
opt_tag=tag,
opt_limit=-1,
opt_stochastic_clipping=opt_stochastic_clipping,
opt_label_clipping=opt_label_clipping,
opt_use_feature_detector=True,
opt_feature_layers=opt_feature_layers,
opt_snapshot_interval=opt_snapshot_interval,
opt_clip_interval=opt_clip_interval,
opt_folder_id=folder['id']
)
params = params_dense_dict(tag, folder_id=folder['id'])
find_dense_embedding_for_images(params,
opt_tag=tag,
opt_feature_layers=opt_feature_layers,
opt_save_progress=True)
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