from src.preprocessing.preprocessing import compute_embedding from src.postprocessing.postprocessing import animate_logo from AnimationTransformer import AnimationTransformer from AnimationTransformer import predict import torch.nn as torch import torch import pandas as pd import shutil def animateLogo(path : str, targetPath : str): try: # Copy the original file to the new location with the new filename shutil.copyfile(path, targetPath) print(f"File copied and renamed to {targetPath}") except Exception as e: print(f"An error occurred: {e}") #transformer NUM_HEADS = 47 # Dividers of 282: {1, 2, 3, 6, 47, 94, 141, 282} NUM_ENCODER_LAYERS = 6 NUM_DECODER_LAYERS = 4 DROPOUT=0.21 # CONSTANTS FEATURE_DIM = 282 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device) model = AnimationTransformer( dim_model=FEATURE_DIM, num_heads=NUM_HEADS, num_encoder_layers=NUM_ENCODER_LAYERS, num_decoder_layers=NUM_DECODER_LAYERS, dropout_p=DROPOUT, use_positional_encoder=True ).to(device) model.load_state_dict(torch.load("models/animation_transformer2.pth", map_location=torch.device('cpu')), strict=False) try: df = compute_embedding(path, "models/deepSVG_hierarchical_ordered.pth.tar") except: return False df = df.drop("animation_id", axis=1) df = pd.concat([df, pd.DataFrame(0, index=df.index, columns=range(df.shape[1], df.shape[1] + 26))], axis=1, ignore_index=True).astype(float) inp = torch.tensor(df.values) print(inp, inp.shape) sos_token = torch.zeros(282) sos_token[256] = 1 result = predict(model, inp, sos_token=sos_token, device=device, max_length=inp.shape[0], eos_scaling=0.5, temperature=100) result = pd.DataFrame(result[1:, -26:].cpu().detach().numpy()) result = pd.DataFrame({"model_output" : [row.tolist() for index, row in result.iterrows()]}) result["animation_id"] = range(len(result)) print(result, path) animate_logo(result, targetPath) return True #logo = "data/examples/logo_181.svg" #animateLogo(logo)