"avg_steps": total_steps_ran / steps,
}
sum_loss = 0.0
steps = 0.0
lol.eval()
# Save epoch snapshot using some validation image
model_path = os.path.join(args.output, args.name, 'last.pt')
screenshot_path = os.path.join(args.output, args.name, "screenshots",
str(epoch) + ".png")
create_folders(screenshot_path)
torch.save(lol.state_dict(), model_path)
time.sleep(1)
paint_model_run(model_path, validation_loader, destination=screenshot_path)
for index, x in enumerate(test_dataloader):
x = x[0]
img = Variable(x['img'].type(dtype), requires_grad=False)[None, ...]
ground_truth = x["steps"]
# Iterates over the line until the end
sol = ground_truth[0].cuda()
predicted_steps, length, _ = lol(img,
sol,
ground_truth,
max_steps=len(ground_truth),
disturb_sol=False)
parser.add_argument("--output", default="scripts/original/snapshots/training")
parser.add_argument("--model",
default="scripts/new/snapshots/training2/lol-last.pt")
args = parser.parse_args()
data_folder = os.getenv("DATA_FOLDER") if os.getenv("DATA_FOLDER") else "data"
target_folder = os.path.join(data_folder, "sfrs", args.dataset)
pages_folder = os.path.join(target_folder, "pages")
char_set_path = os.path.join(pages_folder, "character_set.json")
test_set_list_path = os.path.join(pages_folder, "validation.json")
test_set_list = load_file_list_direct(test_set_list_path)
test_dataset = LolDataset(test_set_list[0:1])
test_dataloader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=0,
collate_fn=lol_dataset.collate)
count = 0
while True:
for t in ["training", "training2"]:
model_path = "scripts/new/snapshots/" + t + "/last.pt"
paint_model_run(model_path,
test_dataloader,
destination=os.path.join("screenshots", t,
str(count) + ".png"))
time.sleep(30)
count += 1