demo.py

"""
    Small script for testing on few generic images given the model weights.
    In order to minimize the requirements, it runs only on CPU and images are
    processed one by one.
"""

import torch
import argparse
import pickle
from argparse import Namespace

from models.End_ExpansionNet_v2 import End_ExpansionNet_v2
from utils.image_utils import preprocess_image
from utils.language_utils import tokens2description

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Demo")
    parser.add_argument("--model_dim", type=int, default=512)
    parser.add_argument("--N_enc", type=int, default=3)
    parser.add_argument("--N_dec", type=int, default=3)
    parser.add_argument("--max_seq_len", type=int, default=74)
    parser.add_argument(
        "--load_path",
        type=str,
        default="/home/arpitsah/Desktop/Fall-2023/odml/On_Device_Image_Captioning/pretrained_weights/rf_model.pth",
    )
    parser.add_argument(
        "--load_path_vizWiz",
        type=str,
        default="/home/arpitsah/Desktop/Fall-2023/odml/On_Device_Image_Captioning/vizWiz_Weights/pretrained_weightscheckpoint_2023-10-10-16:26:11_epoch4it1968bs8_xe_.pth",
    )
    parser.add_argument(
        "--image_paths",
        type=str,
        default=[
            "On_Device_Image_Captioning/demo_material/tatin.jpg",
            "On_Device_Image_Captioning/demo_material/micheal.jpg",
            "On_Device_Image_Captioning/demo_material/napoleon.jpg",
            "On_Device_Image_Captioning/demo_material/cat_girl.jpg",
        ],
        nargs="+",
    )
    parser.add_argument("--beam_size", type=int, default=5)
    parser.add_argument("--Vizwiz", type=bool, default=False)

    args = parser.parse_args()

    drop_args = Namespace(enc=0.0, dec=0.0, enc_input=0.0, dec_input=0.0, other=0.0)
    model_args = Namespace(
        model_dim=args.model_dim,
        N_enc=args.N_enc,
        N_dec=args.N_dec,
        dropout=0.0,
        drop_args=drop_args,
    )

    with open(
        "/home/arpitsah/Desktop/Fall-2023/odml/On_Device_Image_Captioning/demo_material/demo_coco_tokens.pickle",
        "rb",
    ) as f:
        coco_tokens = pickle.load(f)
        sos_idx = coco_tokens["word2idx_dict"][coco_tokens["sos_str"]]
        eos_idx = coco_tokens["word2idx_dict"][coco_tokens["eos_str"]]

    print("Dictionary loaded ...")

    img_size = 384
    model = End_ExpansionNet_v2(
        swin_img_size=img_size,
        swin_patch_size=4,
        swin_in_chans=3,
        swin_embed_dim=192,
        swin_depths=[2, 2, 18, 2],
        swin_num_heads=[6, 12, 24, 48],
        swin_window_size=12,
        swin_mlp_ratio=4.0,
        swin_qkv_bias=True,
        swin_qk_scale=None,
        swin_drop_rate=0.0,
        swin_attn_drop_rate=0.0,
        swin_drop_path_rate=0.0,
        swin_norm_layer=torch.nn.LayerNorm,
        swin_ape=False,
        swin_patch_norm=True,
        swin_use_checkpoint=False,
        final_swin_dim=1536,
        d_model=model_args.model_dim,
        N_enc=model_args.N_enc,
        N_dec=model_args.N_dec,
        num_heads=8,
        ff=2048,
        num_exp_enc_list=[32, 64, 128, 256, 512],
        num_exp_dec=16,
        output_word2idx=coco_tokens["word2idx_dict"],
        output_idx2word=coco_tokens["idx2word_list"],
        max_seq_len=args.max_seq_len,
        drop_args=model_args.drop_args,
        rank="cpu",
    )
    if not args.Vizwiz:
        checkpoint = torch.load(args.load_path)
    else:
        checkpoint = torch.load(args.load_path_vizWiz)
    model.load_state_dict(checkpoint["model_state_dict"])
    print("Model loaded ...")

    input_images = []
    for path in args.image_paths:
        input_images.append(preprocess_image(path, img_size))

    print("Generating captions ...\n")
    for i in range(len(input_images)):
        path = args.image_paths[i]
        image = input_images[i]
        beam_search_kwargs = {
            "beam_size": args.beam_size,
            "beam_max_seq_len": args.max_seq_len,
            "sample_or_max": "max",
            "how_many_outputs": 1,
            "sos_idx": sos_idx,
            "eos_idx": eos_idx,
        }
        with torch.no_grad():
            pred, _ = model(
                enc_x=image,
                enc_x_num_pads=[0],
                mode="beam_search",
                **beam_search_kwargs
            )
        pred = tokens2description(
            pred[0][0], coco_tokens["idx2word_list"], sos_idx, eos_idx
        )
        print(path + " \n\tDescription: " + pred + "\n")

    print("Closed.")