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save_processed_images.py
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save_processed_images.py
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import datetime
import os
from itertools import islice
import torch
from tqdm import tqdm
from models import get_model
from models.communication import calc_segments_per_sketch
from utils.arguments import parser
from utils.data import get_data
def split_by_lengths(seq, num):
it = iter(seq)
for x in num:
out = list(islice(it, x))
if out:
yield out
else:
return # StopIteration
remain = list(it)
if remain:
yield remain
if __name__ == "__main__":
args = parser.parse_args()
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Add timestamp to logdir
if args.log_name is None:
LOG_DIR = os.path.join(
args.log_dir, datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)
else:
LOG_DIR = os.path.join(args.log_dir, args.log_name)
dataloader_coords, n_classes, label_names = get_data(
args.data_dir,
args.dataset,
batch_size=args.batch_size,
train=True,
preprocessed_root=args.preprocessed_root,
load_fgsbir_photos=args.load_fgsbir_photos,
resample_strokes=not args.no_stroke_resampling,
max_stroke_length=args.max_stroke_length,
shuffle=False,
)
test_dataloader_coords, _, _ = get_data(
args.data_dir,
args.dataset,
batch_size=args.n_log_img,
train=False,
preprocessed_root=args.preprocessed_root,
load_fgsbir_photos=args.load_fgsbir_photos,
resample_strokes=not args.no_stroke_resampling,
max_stroke_length=args.max_stroke_length,
shuffle=False,
)
if args.model_type == "none":
model = None
else:
model = get_model(args, 9, DEVICE)
if args.test is not None and (
not args.model_type == "communication" or args.loss_model_type != "none"
):
model.load_state_dict(torch.load(args.test))
model.eval()
model_type = args.model_type
if args.add_comm_model:
args.model_type = "communication"
comm_model = get_model(args, 9, DEVICE)
n_iters = 2
else:
comm_model = None
n_iters = 1
loaders = [("test", test_dataloader_coords), ("train", dataloader_coords)]
# Preprocess strokes
with torch.no_grad():
for dset, dset_loader in loaders:
new_coords_list = []
for batch in tqdm(dset_loader, desc=f"Pre-processing data {dset}"):
for k in batch.keys():
if k in [
"x_raw",
"x_raw_aug",
"scale",
"scale_aug",
"translate",
"translate_aug",
"parameters_aug",
]:
if isinstance(batch[k], list):
batch[k] = batch[k][0]
if k == "n_strokes_aug":
batch[k] = torch.stack(batch[k], dim=1).view(-1)
if isinstance(batch[k], torch.Tensor):
batch[k] = batch[k].to("cuda")
orig_strokes = batch["n_strokes"]
for citer in range(n_iters):
strokes = batch["n_strokes"]
if model is None:
points = list(
batch["x_raw"].to("cpu").split(list(strokes), dim=0)
)
scale = list(
batch["scale"].to("cpu").split(list(strokes), dim=0)
)
translate = list(
batch["translate"].to("cpu").split(list(strokes), dim=0)
)
stroke_lens = batch["stroke_lens"].split(list(strokes), dim=0)
_, _, segments_per_sketch = calc_segments_per_sketch(
batch["segments"], batch["stroke_lens"], batch["n_strokes"]
)
segments = batch["segments"].split(
list(segments_per_sketch), dim=0
)
new_coords_list += [
{
"x_raw": x.detach().cpu(),
"label": l.item(),
"n_strokes": n.detach().cpu(),
"scale": s.detach().cpu(),
"translate": t.detach().cpu(),
"segments": se.detach().cpu(),
"stroke_lens": sl.detach().cpu(),
}
for x, l, n, s, t, se, sl in zip(
points,
batch["label"],
strokes,
scale,
translate,
segments,
stroke_lens,
)
]
else:
if comm_model is not None and citer == 1:
new_coords = comm_model(**batch)
else:
new_coords = model.draw(**batch)
if citer == 1 or model_type in ["pmn", "communication"]:
if comm_model is not None and citer == 0:
for k, v in new_coords.items():
if k == "pcoords_raw":
batch["x_raw"] = v
elif k.startswith("pcoords"):
batch[k.replace("pcoords_", "")] = v
batch["n_strokes_orig"] = orig_strokes
else:
strokes = new_coords["pcoords_n_strokes"]
if isinstance(new_coords["pcoords_raw"], list):
offset = 0
points = []
for n_s in strokes:
points.append(
[
p.detach().cpu()
for p in new_coords["pcoords_raw"]
][offset : offset + n_s]
)
offset += n_s
else:
points = list(
new_coords["pcoords_raw"]
.detach()
.to("cpu")
.split(list(strokes), dim=0)
)
scale = list(
new_coords["pcoords_scale"]
.to("cpu")
.split(list(strokes), dim=0)
)
translate = list(
new_coords["pcoords_translate"]
.to("cpu")
.split(list(strokes), dim=0)
)
stroke_lens = batch["stroke_lens"].split(
list(orig_strokes), dim=0
)
_, _, segments_per_sketch = calc_segments_per_sketch(
batch["segments"],
batch["stroke_lens"],
batch["n_strokes"],
)
segments = batch["segments"].split(
list(segments_per_sketch), dim=0
)
new_coords_list += [
{
"x_raw": x,
"label": l.item(),
"n_strokes": n.detach().cpu(),
"scale": s.detach().cpu(),
"translate": t.detach().cpu(),
"segments": se.detach().cpu(),
"stroke_lens": sl.detach().cpu(),
}
for x, l, n, s, t, se, sl in zip(
points,
batch["label"],
strokes,
scale,
translate,
segments,
stroke_lens,
)
]
if "pcoords_prim_id" in new_coords:
prim_ids = list(
new_coords["pcoords_prim_id"]
.to("cpu")
.split(list(strokes), dim=0)
)
for i, pi in enumerate(prim_ids):
new_coords_list[-len(prim_ids) :][i][
"prim_id"
] = pi.detach().cpu()
else:
sketches = list(split_by_lengths((new_coords), strokes))
split_scale = list(
batch["scale"].split(list(strokes), dim=0)
)
split_translate = list(
batch["translate"].split(list(strokes), dim=0)
)
new_strokes = torch.Tensor([]).to("cpu")
scales = torch.Tensor([]).to("cuda")
translates = torch.Tensor([]).to("cuda")
strokes = list(strokes)
for i in range(len(sketches)):
strokes[i] = 0
for j in range(len(sketches[i])):
scales = torch.cat(
[
scales,
split_scale[i][j].repeat(
len(sketches[i][j]), 1, 1
),
],
dim=0,
)
translates = torch.cat(
[
translates,
split_translate[i][j].repeat(
len(sketches[i][j]), 1, 1
),
],
dim=0,
)
new_strokes = torch.cat(
[new_strokes, sketches[i][j]], dim=0
)
strokes[i] += len(sketches[i][j])
if comm_model is not None:
batch["x_raw"] = new_strokes.to(batch["x_raw"])
batch["n_strokes"] = torch.tensor(strokes).to(
batch["n_strokes"]
)
batch["scale"] = scales.to(batch["scale"])
batch["translate"] = translates.to(batch["translate"])
batch["n_strokes_orig"] = orig_strokes
else:
scale = list(scales.split(strokes, dim=0))
translate = list(translates.split(strokes, dim=0))
points = list(new_strokes.split(strokes, dim=0))
strokes = torch.tensor(strokes)
stroke_lens = batch["stroke_lens"].split(
list(batch["n_strokes"]), dim=0
)
_, _, segments_per_sketch = calc_segments_per_sketch(
batch["segments"],
batch["stroke_lens"],
batch["n_strokes"],
)
segments = batch["segments"].split(
list(segments_per_sketch), dim=0
)
new_coords_list += [
{
"x_raw": x,
"label": l.item(),
"n_strokes": n,
"scale": s.detach().cpu(),
"translate": t.detach().cpu(),
"segments": se.detach().cpu(),
"stroke_lens": sl.detach().cpu(),
"n_strokes_orig": n_orig.detach().cpu(),
}
for x, l, n, s, t, se, sl, n_orig in zip(
points,
batch["label"],
strokes,
scale,
translate,
segments,
stroke_lens,
orig_strokes,
)
]
dset_loader.dataset.set_preprocessed(new_coords_list)
dset_loader.dataset.save_preprocessed(
os.path.join(LOG_DIR, "data"),
save_images=not args.only_save_coords,
suffix=".npy",
)