fixed some errors

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "git.ignoreLimitWarning": true
+}

house_diffusion/rplanhg_datasets.py

@@ -81,7 +81,7 @@ def make_non_manhattan(poly, polygon, house_poly):
         return poly2
 
 get_bin = lambda x, z: [int(y) for y in format(x, 'b').zfill(z)]
-get_one_hot = lambda x, z: np.eye(z)[x]
+get_one_hot = lambda x, z: np.eye(z)[min(x, z-1)]     
 class RPlanhgDataset(Dataset):
     def __init__(self, set_name, analog_bit, target_set, non_manhattan=False):
         super().__init__()
@@ -534,4 +534,4 @@ def reader(filename):
         return rms_type,fp_eds,rms_bbs,eds_to_rms
 
 if __name__ == '__main__':
-    dataset = RPlanhgDataset('eval', False, 8)
+    dataset = RPlanhgDataset('eval', False, 8)

image_train.py

@@ -0,0 +1,90 @@
+"""
+Train a diffusion model on images.
+"""
+
+import argparse
+
+from house_diffusion import dist_util, logger
+from house_diffusion.rplanhg_datasets import load_rplanhg_data
+from house_diffusion.resample import create_named_schedule_sampler
+from house_diffusion.script_util import (
+    model_and_diffusion_defaults,
+    create_model_and_diffusion,
+    args_to_dict,
+    add_dict_to_argparser,
+    update_arg_parser,
+)
+from house_diffusion.train_util import TrainLoop
+
+
+def main():
+    args = create_argparser().parse_args()
+    update_arg_parser(args)
+
+    dist_util.setup_dist()
+    logger.configure()
+
+    logger.log("creating model and diffusion...")
+    model, diffusion = create_model_and_diffusion(
+        **args_to_dict(args, model_and_diffusion_defaults().keys())
+    )
+    model.to(dist_util.dev())
+    schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
+
+    logger.log("creating data loader...")
+    if args.dataset=='rplan':
+        data = load_rplanhg_data(
+            batch_size=args.batch_size,
+            analog_bit=args.analog_bit,
+            target_set=args.target_set,
+            set_name=args.set_name,
+        )
+    else:
+        print('dataset not exist!')
+        assert False
+
+    logger.log("training...")
+    TrainLoop(
+        model=model,
+        diffusion=diffusion,
+        data=data,
+        batch_size=args.batch_size,
+        microbatch=args.microbatch,
+        lr=args.lr,
+        ema_rate=args.ema_rate,
+        log_interval=args.log_interval,
+        save_interval=args.save_interval,
+        resume_checkpoint=args.resume_checkpoint,
+        use_fp16=args.use_fp16,
+        fp16_scale_growth=args.fp16_scale_growth,
+        schedule_sampler=schedule_sampler,
+        weight_decay=args.weight_decay,
+        lr_anneal_steps=args.lr_anneal_steps,
+        analog_bit=args.analog_bit,
+    ).run_loop()
+
+
+def create_argparser():
+    defaults = dict(
+        dataset = '',
+        schedule_sampler= "uniform", #"loss-second-moment", "uniform",
+        lr=1e-4,
+        weight_decay=0.0,
+        lr_anneal_steps=0,
+        batch_size=1,
+        microbatch=-1,  # -1 disables microbatches
+        ema_rate="0.9999",  # comma-separated list of EMA values
+        log_interval=10,
+        save_interval=10000,
+        resume_checkpoint="",
+        use_fp16=False,
+        fp16_scale_growth=1e-3,
+    )
+    parser = argparse.ArgumentParser()
+    defaults.update(model_and_diffusion_defaults())
+    add_dict_to_argparser(parser, defaults)
+    return parser
+
+
+if __name__ == "__main__":
+    main()

json_fixer.py

@@ -0,0 +1,83 @@
+import json
+import numpy as np
+from glob import glob
+import os
+
+def reader(filename):
+    with open(filename) as f:
+        info = json.load(f)
+    rms_bbs = np.asarray(info['boxes'])
+    fp_eds = info['edges']
+    rms_type = info['room_type']
+    eds_to_rms = info['ed_rm']
+    s_r = 0
+    for rmk in range(len(rms_type)):
+        if rms_type[rmk] != 17:
+            s_r = s_r + 1
+    rms_bbs = np.array(rms_bbs) / 256.0
+    fp_eds = np.array(fp_eds) / 256.0
+    fp_eds = fp_eds[:, :4]
+    tl = np.min(rms_bbs[:, :2], 0)
+    br = np.max(rms_bbs[:, 2:], 0)
+    shift = (tl + br) / 2.0 - 0.5
+    rms_bbs[:, :2] -= shift
+    rms_bbs[:, 2:] -= shift
+    fp_eds[:, :2] -= shift
+    fp_eds[:, 2:] -= shift
+    tl -= shift
+    br -= shift
+    eds_to_rms_tmp = []
+
+    for l in range(len(eds_to_rms)):
+        eds_to_rms_tmp.append([eds_to_rms[l][0]])
+
+    return rms_type, fp_eds, rms_bbs, eds_to_rms, eds_to_rms_tmp
+
+file_list = glob('rplan/*')
+
+processed_files = 0
+
+out_size = 64
+length_edges = []
+subgraphs = []
+for line in file_list:
+    rms_type, fp_eds, rms_bbs, eds_to_rms, eds_to_rms_tmp = reader(line)
+
+    eds_to_rms_tmp = []
+    for l in range(len(eds_to_rms)):
+        eds_to_rms_tmp.append([eds_to_rms[l][0]])
+
+    rms_masks = []
+    im_size = 256
+    fp_mk = np.zeros((out_size, out_size))
+    nodes = rms_type
+    for k in range(len(nodes)):
+        eds = []
+        for l, e_map in enumerate(eds_to_rms_tmp):
+            if k in e_map:
+                eds.append(l)
+        for eds_poly in [eds]:
+            length_edges.append((line, np.array([fp_eds[l][:4] for l in eds_poly], dtype=object)))
+
+    processed_files += 1
+    if processed_files % 1000 == 0:
+        print(f"Processed {processed_files} files.")
+
+print(f"Finished processing {processed_files} files.")
+
+# Convert length_edges to a structured array to handle variable-length sequences
+dtype = [('filename', 'U256'), ('edges', 'O')]
+length_edges_structured = np.array(length_edges, dtype=dtype)
+
+chk = [x['edges'].shape for x in length_edges_structured]
+idx = [i for i, x in enumerate(chk) if len(x) != 2]
+final = length_edges_structured[idx]['filename'].tolist()
+final = [x.replace('\n', '') for x in final]
+
+for fin in final:
+    try:
+        os.remove(fin)
+    except:
+        print(f"Failed to delete {fin}")
+
+print("Verification: Basic check complete.")

list_maker.py

@@ -0,0 +1,14 @@
+import os
+
+def get_image_number(name):
+    return int(name.split(".")[0])
+
+def write_filenames_to_txt(directory, txt_file):
+    json_names = os.listdir(directory)
+    json_names = sorted(json_names, key=get_image_number)
+    with open(txt_file, 'w') as f:
+        for filename in json_names:
+            f.write(filename + '\n')
+
+
+write_filenames_to_txt("rplan", "list.txt")

requirements.txt

@@ -5,13 +5,13 @@ imageio==2.19.2
 matplotlib==3.5.1
 mpi4py==3.1.4
 networkx==2.8.2
-numpy==1.21.5
+numpy==1.21.2
 opencv_python==4.6.0.66
 Pillow==9.4.0
 pytorch_fid==0.3.0
 setuptools==57.5.0
 Shapely==1.8.4
 tensorflow==2.11.0
-torch==2.0.0.dev20221212
+torch==2.0.0
 tqdm==4.61.2
 webcolors==1.12

scripts/image_train.py

@@ -87,4 +87,4 @@ def create_argparser():
 
 
 if __name__ == "__main__":
-    main()
+    main()

scripts/json_fixer.py

@@ -0,0 +1,86 @@
+import json
+import numpy as np
+from glob import glob
+import os
+
+def reader(filename):
+    with open(filename) as f:
+        info = json.load(f)
+    rms_bbs = np.asarray(info['boxes'])
+    fp_eds = info['edges']
+    rms_type = info['room_type']
+    eds_to_rms = info['ed_rm']
+    s_r = 0
+    for rmk in range(len(rms_type)):
+        if rms_type[rmk] != 17:
+            s_r = s_r + 1
+    rms_bbs = np.array(rms_bbs) / 256.0
+    fp_eds = np.array(fp_eds) / 256.0
+    fp_eds = fp_eds[:, :4]
+    tl = np.min(rms_bbs[:, :2], 0)
+    br = np.max(rms_bbs[:, 2:], 0)
+    shift = (tl + br) / 2.0 - 0.5
+    rms_bbs[:, :2] -= shift
+    rms_bbs[:, 2:] -= shift
+    fp_eds[:, :2] -= shift
+    fp_eds[:, 2:] -= shift
+    tl -= shift
+    br -= shift
+    eds_to_rms_tmp = []
+
+    for l in range(len(eds_to_rms)):
+        eds_to_rms_tmp.append([eds_to_rms[l][0]])
+
+    return rms_type, fp_eds, rms_bbs, eds_to_rms, eds_to_rms_tmp
+
+# Adjust the glob path to match where your JSON files are located
+file_list = glob('rplan/*')
+
+# Initialize a counter for progress monitoring
+processed_files = 0
+
+out_size = 64
+length_edges = []
+subgraphs = []
+for line in file_list:
+    rms_type, fp_eds, rms_bbs, eds_to_rms, eds_to_rms_tmp = reader(line)
+
+    eds_to_rms_tmp = []
+    for l in range(len(eds_to_rms)):
+        eds_to_rms_tmp.append([eds_to_rms[l][0]])
+
+    rms_masks = []
+    im_size = 256
+    fp_mk = np.zeros((out_size, out_size))
+    nodes = rms_type
+    for k in range(len(nodes)):
+        eds = []
+        for l, e_map in enumerate(eds_to_rms_tmp):
+            if k in e_map:
+                eds.append(l)
+        for eds_poly in [eds]:
+            length_edges.append((line, np.array([fp_eds[l][:4] for l in eds_poly])))
+
+    # Progress monitoring
+    processed_files += 1
+    if processed_files % 1000 == 0:
+        print(f"Processed {processed_files} files.")
+
+# After processing all files
+print(f"Finished processing {processed_files} files.")
+
+chk = [x.shape for x in np.array(length_edges)[:, 1]]
+idx = [i for i, x in enumerate(chk) if len(x) != 2]
+final = np.array(length_edges)[idx][:, 0].tolist()
+final = [x.replace('\n', '') for x in final]
+
+# Attempt to delete files based on final list
+for fin in final:
+    try:
+        os.remove(fin)
+    except:
+        print(f"Failed to delete {fin}")
+
+# Simple verification example
+# Add your verification logic here as needed
+print("Verification: Basic check complete.")