import argparse import copy import os import random from pathlib import Path import numpy as np import torch from PIL import Image from torch.utils.data import DataLoader from tqdm import trange from datasets import build_dataset from engine import evaluate_floor, generate from models import build_model def get_args_parser(): parser = argparse.ArgumentParser("Raster2Seq evaluation script", add_help=False) parser.add_argument("--batch_size", default=10, type=int) parser.add_argument("--debug", action="store_true") parser.add_argument("--input_channels", default=1, type=int) parser.add_argument("--image_norm", action="store_true") parser.add_argument("--eval_every_epoch", type=int, default=20) parser.add_argument("--ckpt_every_epoch", type=int, default=20) parser.add_argument("--label_smoothing", type=float, default=0.0) parser.add_argument("--ignore_index", type=int, default=-1) parser.add_argument("--image_size", type=int, default=256) parser.add_argument("--ema4eval", action="store_true") parser.add_argument("--measure_time", action="store_true") parser.add_argument("--disable_sampling_cache", action="store_true") parser.add_argument("--use_anchor", action="store_true") parser.add_argument("--drop_wd", action="store_true") parser.add_argument("--iou_thres", type=float, default=0.5) parser.add_argument("--disable_sem_rich", action="store_true") parser.add_argument("--wd_only", action="store_true") parser.add_argument("--disable_image_transform", action="store_true") parser.add_argument("--num_subset_images", type=int, default=-1) parser.add_argument("--converter_version", type=str, default="v1") parser.add_argument("--inject_cls_embed", action="store_true") # raster2seq parser.add_argument("--poly2seq", action="store_true") parser.add_argument("--seq_len", type=int, default=1024) parser.add_argument("--num_bins", type=int, default=64) parser.add_argument("--pre_decoder_pos_embed", action="store_true") parser.add_argument("--learnable_dec_pe", action="store_true") parser.add_argument("--dec_qkv_proj", action="store_true") parser.add_argument("--dec_attn_concat_src", action="store_true") parser.add_argument("--per_token_sem_loss", action="store_true") parser.add_argument("--add_cls_token", action="store_true") # backbone parser.add_argument("--backbone", default="resnet50", type=str, help="Name of the convolutional backbone to use") parser.add_argument("--lr_backbone", default=0, type=float) parser.add_argument( "--dilation", action="store_true", help="If true, we replace stride with dilation in the last convolutional block (DC5)", ) parser.add_argument( "--position_embedding", default="sine", type=str, choices=("sine", "learned"), help="Type of positional embedding to use on top of the image features", ) parser.add_argument("--position_embedding_scale", default=2 * np.pi, type=float, help="position / size * scale") parser.add_argument("--num_feature_levels", default=4, type=int, help="number of feature levels") # Transformer parser.add_argument("--enc_layers", default=6, type=int, help="Number of encoding layers in the transformer") parser.add_argument("--dec_layers", default=6, type=int, help="Number of decoding layers in the transformer") parser.add_argument( "--dim_feedforward", default=1024, type=int, help="Intermediate size of the feedforward layers in the transformer blocks", ) parser.add_argument( "--hidden_dim", default=256, type=int, help="Size of the embeddings (dimension of the transformer)" ) parser.add_argument("--dropout", default=0.1, type=float, help="Dropout applied in the transformer") parser.add_argument( "--nheads", default=8, type=int, help="Number of attention heads inside the transformer's attentions" ) parser.add_argument( "--num_queries", default=800, type=int, help="Number of query slots (num_polys * max. number of corner per poly)", ) parser.add_argument("--num_polys", default=20, type=int, help="Number of maximum number of room polygons") parser.add_argument("--dec_n_points", default=4, type=int) parser.add_argument("--enc_n_points", default=4, type=int) parser.add_argument( "--query_pos_type", default="sine", type=str, choices=("static", "sine", "none"), help="Type of query pos in decoder - \ 1. static: same setting with DETR and Deformable-DETR, the query_pos is the same for all layers \ 2. sine: since embedding from reference points (so if references points update, query_pos also \ 3. none: remove query_pos", ) parser.add_argument( "--with_poly_refine", default=True, action="store_true", help="iteratively refine reference points (i.e. positional part of polygon queries)", ) parser.add_argument( "--masked_attn", default=False, action="store_true", help="if true, the query in one room will not be allowed to attend other room", ) parser.add_argument( "--semantic_classes", default=-1, type=int, help="Number of classes for semantically-rich floorplan: \ 1. default -1 means non-semantic floorplan \ 2. 19 for Structured3D: 16 room types + 1 door + 1 window + 1 empty", ) parser.add_argument( "--disable_poly_refine", action="store_true", help="iteratively refine reference points (i.e. positional part of polygon queries)", ) # aux parser.add_argument( "--no_aux_loss", dest="aux_loss", action="store_true", help="Disables auxiliary decoding losses (loss at each layer)", ) # dataset parameters parser.add_argument("--dataset_name", default="stru3d") parser.add_argument("--dataset_root", default="data/stru3d", type=str) parser.add_argument("--eval_set", default="test", type=str) parser.add_argument("--device", default="cuda", help="device to use for training / testing") parser.add_argument("--num_workers", default=2, type=int) parser.add_argument("--seed", default=42, type=int) parser.add_argument("--checkpoint", default="checkpoints/roomformer_scenecad.pth", help="resume from checkpoint") parser.add_argument("--output_dir", default="eval_stru3d", help="path where to save result") # visualization options parser.add_argument("--plot_pred", default=True, type=bool, help="plot predicted floorplan") parser.add_argument( "--plot_density", default=True, type=bool, help="plot predicited room polygons overlaid on the density map" ) parser.add_argument("--plot_gt", default=True, type=bool, help="plot ground truth floorplan") parser.add_argument("--save_pred", action="store_true", help="save_pred") return parser def main(args): device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) # build dataset and dataloader dataset_eval = build_dataset(image_set=args.eval_set, args=args) tokenizer = None if args.poly2seq: args.vocab_size = dataset_eval.get_vocab_size() tokenizer = dataset_eval.get_tokenizer() # overfit one sample if args.debug: dataset_eval = torch.utils.data.Subset(dataset_eval, [2]) dataset_eval[0] if args.num_subset_images > 0 and args.num_subset_images < len(dataset_eval): dataset_eval = torch.utils.data.Subset(dataset_eval, range(args.num_subset_images)) sampler_eval = torch.utils.data.SequentialSampler(dataset_eval) def trivial_batch_collator(batch): """ A batch collator that does nothing. """ return batch, None data_loader_eval = DataLoader( dataset_eval, args.batch_size, sampler=sampler_eval, drop_last=False, collate_fn=trivial_batch_collator, num_workers=args.num_workers, pin_memory=True, ) # build model model = build_model(args, train=False, tokenizer=tokenizer) model.to(device) n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) print("number of params:", n_parameters) for n, p in model.named_parameters(): print(n) output_dir = Path(args.output_dir) checkpoint = torch.load(args.checkpoint, map_location="cpu") if args.ema4eval: ckpt_state_dict = copy.deepcopy(checkpoint["ema"]) else: ckpt_state_dict = copy.deepcopy(checkpoint["model"]) for key, value in checkpoint["model"].items(): if key.startswith("module."): ckpt_state_dict[key[7:]] = checkpoint["model"][key] del ckpt_state_dict[key] missing_keys, unexpected_keys = model.load_state_dict(ckpt_state_dict, strict=False) unexpected_keys = [k for k in unexpected_keys if not (k.endswith("total_params") or k.endswith("total_ops"))] if len(missing_keys) > 0: print("Missing Keys: {}".format(missing_keys)) if len(unexpected_keys) > 0: print("Unexpected Keys: {}".format(unexpected_keys)) # disable grad for param in model.parameters(): param.requires_grad = False if args.measure_time: # images = torch.rand(args.batch_size, 3, args.image_size, args.image_size).to(device) images = ( torch.from_numpy(np.array(Image.open("data/coco_s3d_bw/val/03006.png").convert("RGB"))) .permute(2, 0, 1) .unsqueeze(0) .to(device) / 255.0 ) # INIT LOGGERS starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) repetitions = 50 timings = np.zeros((repetitions, 1)) if args.poly2seq: model = torch.compile(model) # compile model is not compatible with RoomFormer # GPU-WARM-UP for _ in trange(10, desc="GPU-WARM-UP"): if not args.poly2seq: _ = model(images) else: _ = model.forward_inference(images) # MEASURE PERFORMANCE with torch.no_grad(): for rep in trange(repetitions): starter.record() _ = generate( model, images, semantic_rich=args.semantic_classes > 0, use_cache=True, per_token_sem_loss=args.per_token_sem_loss, drop_wd=args.drop_wd, poly2seq=args.poly2seq, ) ender.record() # WAIT FOR GPU SYNC torch.cuda.synchronize() curr_time = starter.elapsed_time(ender) timings[rep] = curr_time mean_syn = np.sum(timings) / repetitions std_syn = np.std(timings) print("Inference time: {:.2f}+/-{:.2f}ms".format(mean_syn, std_syn)) exit(0) # save_dir = os.path.join(os.path.dirname(args.checkpoint), output_dir) # save_dir = os.path.join(output_dir, os.path.dirname(args.checkpoint).split('/')[-1]) save_dir = output_dir os.makedirs(save_dir, exist_ok=True) evaluate_floor( model, args.dataset_name, data_loader_eval, device, save_dir, plot_pred=args.plot_pred, plot_density=args.plot_density, plot_gt=args.plot_gt, semantic_rich=(args.semantic_classes > 0 and not args.disable_sem_rich), save_pred=args.save_pred, per_token_sem_loss=args.per_token_sem_loss, iou_thres=args.iou_thres, poly2seq=args.poly2seq, ) if __name__ == "__main__": parser = argparse.ArgumentParser("Raster2Seq evaluation script", parents=[get_args_parser()]) args = parser.parse_args() if args.debug: args.batch_size = 1 if args.disable_poly_refine: args.with_poly_refine = False main(args)