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  8. eval/DiffuScene/config/livingroom_threed_front_splits.csv +2926 -0
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  10. eval/DiffuScene/config/obj_autoencoder/bed_living_diningrooms_lat32.yaml +71 -0
  11. eval/DiffuScene/config/obj_autoencoder/bedrooms.yaml +71 -0
  12. eval/DiffuScene/config/obj_autoencoder/diningrooms.yaml +73 -0
  13. eval/DiffuScene/config/obj_autoencoder/livingrooms.yaml +73 -0
  14. eval/DiffuScene/config/rearrange/diffusion_bedrooms_instancond_lat32_v_rearrange.yaml +101 -0
  15. eval/DiffuScene/config/rearrange/diffusion_diningrooms_instancond_lat32_v_rearrange.yaml +107 -0
  16. eval/DiffuScene/config/rearrange/diffusion_livingrooms_instancond_lat32_v_rearrange.yaml +104 -0
  17. eval/DiffuScene/config/text/diffusion_bedrooms_instancond_lat32_v_bert.yaml +107 -0
  18. eval/DiffuScene/config/text/diffusion_diningrooms_instancond_lat32_v_bert.yaml +100 -0
  19. eval/DiffuScene/config/text/diffusion_livingrooms_instancond_lat32_v_bert.yaml +102 -0
  20. eval/DiffuScene/config/uncond/diffusion_bedrooms_instancond_lat32_eps.yaml +94 -0
  21. eval/DiffuScene/config/uncond/diffusion_bedrooms_instancond_lat32_v.yaml +94 -0
  22. eval/DiffuScene/config/uncond/diffusion_diningrooms_instancond_lat32_eps.yaml +97 -0
  23. eval/DiffuScene/config/uncond/diffusion_diningrooms_instancond_lat32_v.yaml +97 -0
  24. eval/DiffuScene/config/uncond/diffusion_livingrooms_instancond_lat32_eps.yaml +94 -0
  25. eval/DiffuScene/config/uncond/diffusion_livingrooms_instancond_lat32_v.yaml +94 -0
  26. eval/DiffuScene/demo/floor_plan_texture_images_references +7 -0
  27. eval/DiffuScene/run/evaluate_test_results.sh +92 -0
  28. eval/DiffuScene/run/generate.sh +34 -0
  29. eval/DiffuScene/run/generate_rearrange.sh +25 -0
  30. eval/DiffuScene/run/generate_text.sh +35 -0
  31. eval/DiffuScene/run/quick_test.sh +50 -0
  32. eval/DiffuScene/run/render_test_results.sh +101 -0
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  36. eval/DiffuScene/run/train_rearrange.sh +20 -0
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2181
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2182
+ LivingDiningRoom-10119,test
2183
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2184
+ LivingDiningRoom-71754,test
2185
+ LivingDiningRoom-78283,test
2186
+ LivingDiningRoom-71082,test
2187
+ LivingDiningRoom-15709,test
2188
+ DiningRoom-6919,test
2189
+ LivingDiningRoom-38408,test
2190
+ LivingDiningRoom-23236,test
2191
+ LivingDiningRoom-34404,test
2192
+ LivingDiningRoom-78747,test
2193
+ LivingDiningRoom-4261,test
2194
+ LivingDiningRoom-2079,test
2195
+ LivingDiningRoom-11226,test
2196
+ LivingDiningRoom-5343,test
2197
+ LivingDiningRoom-19784,test
2198
+ LivingDiningRoom-83039,test
2199
+ LivingDiningRoom-8495,test
2200
+ DiningRoom-149671,test
2201
+ LivingDiningRoom-28707,test
2202
+ LivingDiningRoom-85449,test
2203
+ LivingDiningRoom-1767,test
2204
+ LivingDiningRoom-758,test
2205
+ LivingDiningRoom-7023,test
2206
+ LivingDiningRoom-96248,test
2207
+ LivingDiningRoom-2831,test
2208
+ LivingDiningRoom-24764,test
2209
+ LivingDiningRoom-18047,test
2210
+ DiningRoom-22535,test
2211
+ LivingDiningRoom-1196,test
2212
+ LivingDiningRoom-32152,test
2213
+ LivingDiningRoom-10397,test
2214
+ LivingDiningRoom-52730,test
2215
+ LivingDiningRoom-4830,test
2216
+ LivingDiningRoom-19698,test
2217
+ LivingDiningRoom-1572,test
2218
+ LivingDiningRoom-30479,test
2219
+ LivingDiningRoom-1141,test
2220
+ LivingDiningRoom-31529,test
2221
+ LivingDiningRoom-1768,test
2222
+ LivingDiningRoom-5630,test
2223
+ LivingDiningRoom-1654,test
2224
+ LivingDiningRoom-1901,test
2225
+ LivingDiningRoom-563,test
2226
+ LivingDiningRoom-5086,test
2227
+ LivingDiningRoom-233,test
2228
+ LivingDiningRoom-37350,test
2229
+ LivingDiningRoom-14290,test
2230
+ LivingDiningRoom-25714,test
2231
+ DiningRoom-515,test
2232
+ LivingDiningRoom-18782,test
2233
+ DiningRoom-11336,test
2234
+ LivingDiningRoom-7555,test
2235
+ LivingDiningRoom-11012,test
2236
+ LivingDiningRoom-16163,test
2237
+ LivingDiningRoom-30837,test
2238
+ LivingDiningRoom-1524,test
2239
+ LivingDiningRoom-14228,test
2240
+ DiningRoom-12866,test
2241
+ LivingDiningRoom-2764,test
2242
+ LivingDiningRoom-119151,test
2243
+ LivingDiningRoom-27730,test
2244
+ LivingDiningRoom-569,test
2245
+ LivingDiningRoom-12225,test
2246
+ DiningRoom-217315,test
2247
+ LivingDiningRoom-28456,test
2248
+ LivingDiningRoom-1172,test
2249
+ LivingDiningRoom-44225,test
2250
+ DiningRoom-11464,test
2251
+ LivingDiningRoom-50953,test
2252
+ LivingDiningRoom-15706,test
2253
+ DiningRoom-55572,test
2254
+ LivingDiningRoom-2620,test
2255
+ LivingDiningRoom-2891,test
2256
+ LivingDiningRoom-13447,test
2257
+ DiningRoom-1106,test
2258
+ LivingDiningRoom-5970,test
2259
+ LivingDiningRoom-19979,test
2260
+ LivingDiningRoom-16465,test
2261
+ DiningRoom-69007,test
2262
+ LivingDiningRoom-13538,test
2263
+ DiningRoom-88173,test
2264
+ DiningRoom-5694,test
2265
+ LivingDiningRoom-4742,test
2266
+ LivingDiningRoom-121211,test
2267
+ DiningRoom-11628,test
2268
+ LivingDiningRoom-34816,test
2269
+ LivingDiningRoom-8272,test
2270
+ DiningRoom-27933,test
2271
+ LivingDiningRoom-4693,test
2272
+ LivingDiningRoom-9373,test
2273
+ LivingDiningRoom-2872,test
2274
+ DiningRoom-60525,test
2275
+ LivingDiningRoom-7360,test
2276
+ DiningRoom-5492,test
2277
+ LivingDiningRoom-32055,test
2278
+ DiningRoom-168152,test
2279
+ LivingDiningRoom-6131,test
2280
+ LivingDiningRoom-11795,test
2281
+ LivingDiningRoom-17476,test
2282
+ DiningRoom-85692,test
2283
+ DiningRoom-113004,test
2284
+ LivingDiningRoom-71961,test
2285
+ LivingDiningRoom-1315,test
2286
+ LivingDiningRoom-64631,test
2287
+ LivingDiningRoom-38006,test
2288
+ LivingDiningRoom-9744,test
2289
+ LivingDiningRoom-13258,test
2290
+ LivingDiningRoom-11917,test
2291
+ LivingDiningRoom-6534,test
2292
+ LivingDiningRoom-15982,test
2293
+ LivingDiningRoom-13781,test
2294
+ LivingDiningRoom-9939,test
2295
+ LivingDiningRoom-12169,test
2296
+ LivingDiningRoom-37867,test
2297
+ LivingDiningRoom-19900,test
2298
+ LivingDiningRoom-69704,test
2299
+ DiningRoom-52759,test
2300
+ LivingDiningRoom-20487,test
2301
+ LivingDiningRoom-1255,test
2302
+ LivingDiningRoom-9895,test
2303
+ LivingDiningRoom-10957,test
2304
+ LivingDiningRoom-148144,test
2305
+ LivingDiningRoom-9588,test
2306
+ LivingDiningRoom-16770,test
2307
+ LivingDiningRoom-28655,test
2308
+ DiningRoom-44078,test
2309
+ LivingDiningRoom-72103,test
2310
+ LivingDiningRoom-1130,test
2311
+ LivingDiningRoom-73311,test
2312
+ DiningRoom-164,test
2313
+ LivingDiningRoom-8388,test
2314
+ LivingDiningRoom-13493,test
2315
+ LivingDiningRoom-3558,test
2316
+ LivingDiningRoom-16155,test
2317
+ LivingDiningRoom-37641,test
2318
+ LivingDiningRoom-62089,test
2319
+ LivingDiningRoom-8452,test
2320
+ DiningRoom-12246,test
2321
+ LivingDiningRoom-55391,test
2322
+ DiningRoom-6259,test
2323
+ LivingDiningRoom-5562,test
2324
+ LivingDiningRoom-6953,test
2325
+ LivingDiningRoom-16509,test
2326
+ LivingDiningRoom-518,test
2327
+ LivingDiningRoom-11167,test
2328
+ LivingDiningRoom-7298,test
2329
+ LivingDiningRoom-799,test
2330
+ LivingDiningRoom-6461,test
2331
+ LivingDiningRoom-41517,test
2332
+ DiningRoom-8951,test
2333
+ LivingDiningRoom-77579,val
2334
+ LivingDiningRoom-59834,val
2335
+ LivingDiningRoom-6403,val
2336
+ LivingDiningRoom-26998,val
2337
+ LivingDiningRoom-8606,val
2338
+ LivingDiningRoom-33289,val
2339
+ DiningRoom-3929,val
2340
+ LivingDiningRoom-5402,val
2341
+ DiningRoom-43282,val
2342
+ LivingDiningRoom-14970,val
2343
+ DiningRoom-32563,val
2344
+ LivingDiningRoom-2676,val
2345
+ LivingDiningRoom-45270,val
2346
+ DiningRoom-16187,val
2347
+ LivingDiningRoom-10177,val
2348
+ DiningRoom-17200,val
2349
+ LivingDiningRoom-36481,val
2350
+ LivingDiningRoom-1895,val
2351
+ DiningRoom-69515,val
2352
+ DiningRoom-38135,val
2353
+ LivingDiningRoom-11398,val
2354
+ LivingDiningRoom-10290,val
2355
+ DiningRoom-4433,val
2356
+ LivingDiningRoom-95560,val
2357
+ LivingDiningRoom-10060,val
2358
+ LivingDiningRoom-68606,val
2359
+ LivingDiningRoom-38729,val
2360
+ LivingDiningRoom-579,val
2361
+ LivingDiningRoom-72789,val
2362
+ DiningRoom-68877,val
2363
+ LivingDiningRoom-57027,val
2364
+ LivingDiningRoom-57947,val
2365
+ DiningRoom-132563,val
2366
+ LivingDiningRoom-10172,val
2367
+ LivingDiningRoom-20509,val
2368
+ DiningRoom-4358,val
2369
+ LivingDiningRoom-641,val
2370
+ LivingDiningRoom-11362,val
2371
+ LivingDiningRoom-37746,val
2372
+ LivingDiningRoom-16547,val
2373
+ LivingDiningRoom-11143,val
2374
+ LivingDiningRoom-2020,val
2375
+ LivingDiningRoom-634,val
2376
+ DiningRoom-2029,val
2377
+ DiningRoom-11774,val
2378
+ LivingDiningRoom-4760,val
2379
+ LivingDiningRoom-119327,val
2380
+ LivingDiningRoom-17669,val
2381
+ LivingDiningRoom-14899,val
2382
+ LivingDiningRoom-77047,val
2383
+ LivingDiningRoom-3844,val
2384
+ LivingDiningRoom-3715,val
2385
+ LivingDiningRoom-14573,val
2386
+ LivingDiningRoom-2943,val
2387
+ LivingDiningRoom-16818,val
2388
+ DiningRoom-98809,val
2389
+ LivingDiningRoom-262051,val
2390
+ LivingDiningRoom-42284,val
2391
+ LivingDiningRoom-98328,val
2392
+ DiningRoom-91959,val
2393
+ LivingDiningRoom-4094,val
2394
+ LivingDiningRoom-5280,val
2395
+ LivingDiningRoom-272614,val
2396
+ LivingDiningRoom-27919,val
2397
+ LivingDiningRoom-24660,val
2398
+ LivingDiningRoom-554,val
2399
+ LivingDiningRoom-32725,val
2400
+ LivingDiningRoom-36803,val
2401
+ LivingDiningRoom-12476,val
2402
+ LivingDiningRoom-10046,val
2403
+ LivingDiningRoom-54572,val
2404
+ LivingDiningRoom-4056,val
2405
+ DiningRoom-6877,val
2406
+ LivingDiningRoom-1105,val
2407
+ LivingDiningRoom-77748,val
2408
+ LivingDiningRoom-130326,val
2409
+ LivingDiningRoom-8262,val
2410
+ LivingDiningRoom-10951,val
2411
+ LivingDiningRoom-42210,val
2412
+ LivingDiningRoom-74085,val
2413
+ LivingDiningRoom-197,val
2414
+ DiningRoom-41976,val
2415
+ LivingDiningRoom-57220,val
2416
+ LivingDiningRoom-21727,val
2417
+ LivingDiningRoom-5823,val
2418
+ LivingDiningRoom-2663,val
2419
+ DiningRoom-18924,val
2420
+ DiningRoom-96608,val
2421
+ LivingDiningRoom-3259,val
2422
+ LivingDiningRoom-13775,val
2423
+ LivingDiningRoom-4327,val
2424
+ LivingDiningRoom-6594,val
2425
+ DiningRoom-78525,val
2426
+ DiningRoom-690,val
2427
+ LivingDiningRoom-14579,val
2428
+ DiningRoom-9450,val
2429
+ LivingDiningRoom-5117,val
2430
+ LivingDiningRoom-5362,val
2431
+ LivingDiningRoom-6855,val
2432
+ LivingDiningRoom-18266,val
2433
+ LivingDiningRoom-4564,val
2434
+ LivingDiningRoom-3474,val
2435
+ LivingDiningRoom-118911,val
2436
+ LivingDiningRoom-3965,val
2437
+ LivingDiningRoom-22470,val
2438
+ DiningRoom-10540,val
2439
+ LivingDiningRoom-5264,val
2440
+ LivingDiningRoom-4207,val
2441
+ LivingDiningRoom-277579,val
2442
+ DiningRoom-46577,val
2443
+ LivingDiningRoom-29720,val
2444
+ DiningRoom-2775,val
2445
+ LivingDiningRoom-8728,val
2446
+ LivingDiningRoom-14232,val
2447
+ LivingDiningRoom-12188,val
2448
+ LivingDiningRoom-919,val
2449
+ LivingDiningRoom-20097,val
2450
+ LivingDiningRoom-27306,val
2451
+ LivingDiningRoom-25702,val
2452
+ DiningRoom-20750,val
2453
+ DiningRoom-33249,val
2454
+ LivingDiningRoom-76909,val
2455
+ LivingDiningRoom-961,val
2456
+ DiningRoom-38885,val
2457
+ LivingDiningRoom-1342,val
2458
+ DiningRoom-17747,val
2459
+ LivingDiningRoom-2715,val
2460
+ LivingDiningRoom-48379,val
2461
+ DiningRoom-21124,val
2462
+ LivingDiningRoom-7220,val
2463
+ LivingDiningRoom-8149,val
2464
+ LivingDiningRoom-6422,val
2465
+ LivingDiningRoom-11117,val
2466
+ LivingDiningRoom-9643,val
2467
+ LivingDiningRoom-49915,val
2468
+ LivingDiningRoom-194,val
2469
+ DiningRoom-19118,val
2470
+ LivingDiningRoom-27142,val
2471
+ LivingDiningRoom-75508,val
2472
+ DiningRoom-50256,val
2473
+ LivingDiningRoom-2456,val
2474
+ DiningRoom-13798,val
2475
+ DiningRoom-2655,val
2476
+ LivingDiningRoom-58935,val
2477
+ LivingDiningRoom-4823,val
2478
+ LivingDiningRoom-8271,val
2479
+ DiningRoom-12114,val
2480
+ LivingDiningRoom-13289,val
2481
+ LivingDiningRoom-11910,val
2482
+ LivingDiningRoom-50563,val
2483
+ LivingDiningRoom-21042,val
2484
+ LivingDiningRoom-502,val
2485
+ LivingDiningRoom-4010,val
2486
+ LivingDiningRoom-270,val
2487
+ LivingDiningRoom-3395,val
2488
+ LivingDiningRoom-21808,val
2489
+ LivingDiningRoom-20731,val
2490
+ LivingDiningRoom-45776,val
2491
+ LivingDiningRoom-4440,val
2492
+ LivingDiningRoom-5328,val
2493
+ LivingDiningRoom-13989,val
2494
+ LivingDiningRoom-94924,val
2495
+ LivingDiningRoom-13972,val
2496
+ LivingDiningRoom-12275,val
2497
+ LivingDiningRoom-29533,val
2498
+ LivingDiningRoom-22994,val
2499
+ DiningRoom-70428,val
2500
+ LivingDiningRoom-79770,val
2501
+ LivingDiningRoom-6296,val
2502
+ LivingDiningRoom-14976,val
2503
+ LivingDiningRoom-16828,val
2504
+ LivingDiningRoom-13872,val
2505
+ LivingDiningRoom-5388,val
2506
+ LivingDiningRoom-16342,val
2507
+ LivingDiningRoom-26392,val
2508
+ DiningRoom-45358,val
2509
+ LivingDiningRoom-71738,val
2510
+ LivingDiningRoom-166823,val
2511
+ LivingDiningRoom-6807,val
2512
+ LivingDiningRoom-849,val
2513
+ LivingDiningRoom-4007,val
2514
+ LivingDiningRoom-8358,val
2515
+ LivingDiningRoom-2547,val
2516
+ LivingDiningRoom-43974,val
2517
+ LivingDiningRoom-21234,val
2518
+ LivingDiningRoom-2189,val
2519
+ LivingDiningRoom-11249,val
2520
+ LivingDiningRoom-30130,val
2521
+ LivingDiningRoom-12385,val
2522
+ LivingDiningRoom-1223,val
2523
+ LivingDiningRoom-16739,val
2524
+ LivingDiningRoom-14337,val
2525
+ LivingDiningRoom-11919,val
2526
+ DiningRoom-10547,val
2527
+ LivingDiningRoom-2575,val
2528
+ LivingDiningRoom-142185,val
2529
+ LivingDiningRoom-8428,val
2530
+ LivingDiningRoom-61051,val
2531
+ LivingDiningRoom-26811,val
2532
+ LivingDiningRoom-5381,val
2533
+ LivingDiningRoom-13545,val
2534
+ LivingDiningRoom-513,val
2535
+ LivingDiningRoom-2152,val
2536
+ LivingDiningRoom-9063,val
2537
+ DiningRoom-178861,val
2538
+ DiningRoom-13915,val
2539
+ LivingDiningRoom-13074,val
2540
+ LivingDiningRoom-83131,val
2541
+ LivingDiningRoom-13912,val
2542
+ LivingDiningRoom-1050,val
2543
+ LivingDiningRoom-3510,val
2544
+ LivingDiningRoom-18335,val
2545
+ LivingDiningRoom-41202,val
2546
+ LivingDiningRoom-20428,val
2547
+ DiningRoom-3653,val
2548
+ LivingDiningRoom-53384,val
2549
+ LivingDiningRoom-20064,val
2550
+ DiningRoom-42151,val
2551
+ LivingDiningRoom-58100,val
2552
+ LivingDiningRoom-16801,val
2553
+ LivingDiningRoom-51736,val
2554
+ LivingDiningRoom-1762146,val
2555
+ LivingDiningRoom-1164,val
2556
+ DiningRoom-35384,val
2557
+ LivingDiningRoom-8389,val
2558
+ DiningRoom-67934,val
2559
+ LivingDiningRoom-12138,val
2560
+ LivingDiningRoom-20748,val
2561
+ DiningRoom-6376,val
2562
+ DiningRoom-37596,val
2563
+ LivingDiningRoom-18813,val
2564
+ LivingDiningRoom-1048,val
2565
+ LivingDiningRoom-16709,val
2566
+ LivingDiningRoom-74331,val
2567
+ LivingDiningRoom-15712,val
2568
+ LivingDiningRoom-84174,val
2569
+ LivingDiningRoom-25241,val
2570
+ LivingDiningRoom-109572,val
2571
+ LivingDiningRoom-46190,val
2572
+ LivingDiningRoom-15199,val
2573
+ LivingDiningRoom-6629,val
2574
+ LivingDiningRoom-25360,val
2575
+ LivingDiningRoom-13179,val
2576
+ LivingDiningRoom-211110,val
2577
+ LivingDiningRoom-22077,val
2578
+ DiningRoom-51741,val
2579
+ LivingDiningRoom-32710,val
2580
+ LivingDiningRoom-131278,val
2581
+ LivingDiningRoom-48663,val
2582
+ DiningRoom-15369,val
2583
+ LivingDiningRoom-142,val
2584
+ DiningRoom-2412,val
2585
+ LivingDiningRoom-74931,val
2586
+ LivingDiningRoom-64,val
2587
+ LivingDiningRoom-13451,val
eval/DiffuScene/config/invalid_threed_front_rooms.txt ADDED
@@ -0,0 +1,588 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ SecondBedroom-6080
2
+ MasterBedroom-7536
3
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4
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5
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6
+ Bedroom-40330
7
+ SecondBedroom-2113
8
+ SecondBedroom-1249
9
+ SecondBedroom-157677
10
+ MasterBedroom-84640
11
+ MasterBedroom-36768
12
+ Bedroom-257
13
+ MasterBedroom-21883
14
+ Bedroom-5983
15
+ Bedroom-71717
16
+ Bedroom-42996
17
+ Bedroom-12220
18
+ Bedroom-25231
19
+ SecondBedroom-38597
20
+ Bedroom-14229
21
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22
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23
+ Bedroom-1159
24
+ MasterBedroom-18413
25
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26
+ SecondBedroom-24706
27
+ MasterBedroom-6780
28
+ SecondBedroom-1712
29
+ Bedroom-151
30
+ SecondBedroom-49344
31
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32
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33
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34
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35
+ SecondBedroom-6138
36
+ MasterBedroom-74489
37
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38
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39
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40
+ Bedroom-66177
41
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42
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43
+ SecondBedroom-6484
44
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45
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46
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47
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48
+ SecondBedroom-278527
49
+ Bedroom-35328
50
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51
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52
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53
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54
+ Bedroom-28372
55
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56
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57
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58
+ MasterBedroom-5179
59
+ Bedroom-279829
60
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61
+ SecondBedroom-31632
62
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63
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64
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65
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66
+ SecondBedroom-11628
67
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68
+ SecondBedroom-24525
69
+ MasterBedroom-25039
70
+ SecondBedroom-96682
71
+ MasterBedroom-40879
72
+ SecondBedroom-13008
73
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74
+ Bedroom-10704
75
+ SecondBedroom-10877
76
+ SecondBedroom-11734
77
+ SecondBedroom-19
78
+ Bedroom-17277
79
+ Bedroom-163399
80
+ SecondBedroom-52196
81
+ Bedroom-3617
82
+ SecondBedroom-37579
83
+ Bedroom-760
84
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85
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86
+ SecondBedroom-230486
87
+ Bedroom-2194
88
+ SecondBedroom-18978
89
+ MasterBedroom-9164
90
+ Bedroom-463
91
+ SecondBedroom-12389
92
+ MasterBedroom-150123
93
+ SecondBedroom-14539
94
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2522
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2523
+ LivingRoom-63467,test
2524
+ LivingDiningRoom-79295,test
2525
+ LivingDiningRoom-2079,test
2526
+ LivingDiningRoom-88045,test
2527
+ LivingDiningRoom-3349,test
2528
+ LivingRoom-17396,test
2529
+ LivingRoom-19240,test
2530
+ LivingDiningRoom-3395,test
2531
+ LivingRoom-10686,test
2532
+ LivingDiningRoom-52564,test
2533
+ LivingRoom-4755,test
2534
+ LivingDiningRoom-48257,test
2535
+ LivingDiningRoom-1201,test
2536
+ LivingRoom-9184,test
2537
+ LivingDiningRoom-9530,test
2538
+ LivingDiningRoom-173587,test
2539
+ LivingRoom-2715,test
2540
+ LivingDiningRoom-34033,test
2541
+ LivingDiningRoom-51556,test
2542
+ LivingDiningRoom-5252,test
2543
+ LivingDiningRoom-26101,test
2544
+ LivingDiningRoom-758,test
2545
+ LivingDiningRoom-40503,test
2546
+ LivingDiningRoom-1446,test
2547
+ LivingRoom-41893,test
2548
+ LivingDiningRoom-9632,test
2549
+ LivingDiningRoom-64952,test
2550
+ LivingDiningRoom-941,test
2551
+ LivingRoom-15143,test
2552
+ LivingDiningRoom-8516,test
2553
+ LivingDiningRoom-8843,test
2554
+ LivingDiningRoom-76216,test
2555
+ LivingDiningRoom-16763,test
2556
+ LivingDiningRoom-2764,test
2557
+ LivingRoom-5361,test
2558
+ LivingDiningRoom-31506,test
2559
+ LivingDiningRoom-13632,test
2560
+ LivingDiningRoom-5082,test
2561
+ LivingDiningRoom-25231,test
2562
+ LivingDiningRoom-5087,test
2563
+ LivingDiningRoom-31273,test
2564
+ LivingRoom-25791,test
2565
+ LivingDiningRoom-6886,test
2566
+ LivingDiningRoom-65030,test
2567
+ LivingDiningRoom-48663,test
2568
+ LivingDiningRoom-7831,test
2569
+ LivingRoom-1183,test
2570
+ LivingDiningRoom-21234,test
2571
+ LivingRoom-33324,test
2572
+ LivingDiningRoom-12058,test
2573
+ LivingRoom-38844,test
2574
+ LivingRoom-34874,test
2575
+ LivingDiningRoom-634,test
2576
+ LivingRoom-31397,test
2577
+ LivingRoom-537,test
2578
+ LivingDiningRoom-14174,test
2579
+ LivingRoom-1800,test
2580
+ LivingRoom-151420,test
2581
+ LivingDiningRoom-3911,test
2582
+ LivingDiningRoom-2583,test
2583
+ LivingRoom-166911,test
2584
+ LivingDiningRoom-9517,test
2585
+ LivingDiningRoom-6789,test
2586
+ LivingDiningRoom-6966,test
2587
+ LivingDiningRoom-5222,test
2588
+ LivingDiningRoom-5856,test
2589
+ LivingDiningRoom-10383,test
2590
+ LivingRoom-1618,test
2591
+ LivingDiningRoom-29268,test
2592
+ LivingRoom-6045,test
2593
+ LivingDiningRoom-32393,test
2594
+ LivingDiningRoom-11771,test
2595
+ LivingDiningRoom-3700,test
2596
+ LivingDiningRoom-45776,test
2597
+ LivingDiningRoom-3844,test
2598
+ LivingDiningRoom-1744,test
2599
+ LivingDiningRoom-84752,test
2600
+ LivingRoom-24046,test
2601
+ LivingRoom-21245,test
2602
+ LivingDiningRoom-24240,test
2603
+ LivingDiningRoom-62277,test
2604
+ LivingDiningRoom-2065,test
2605
+ LivingDiningRoom-14405,test
2606
+ LivingDiningRoom-4471,test
2607
+ LivingDiningRoom-19931,test
2608
+ LivingDiningRoom-28686,test
2609
+ LivingRoom-25965,test
2610
+ LivingDiningRoom-10493,test
2611
+ LivingDiningRoom-15055,test
2612
+ LivingDiningRoom-60786,test
2613
+ LivingDiningRoom-28031,test
2614
+ LivingDiningRoom-24045,test
2615
+ LivingDiningRoom-1933,test
2616
+ LivingDiningRoom-5181,test
2617
+ LivingDiningRoom-21832,test
2618
+ LivingDiningRoom-69540,test
2619
+ LivingDiningRoom-24540,test
2620
+ LivingRoom-18176,test
2621
+ LivingDiningRoom-36786,test
2622
+ LivingRoom-15845,test
2623
+ LivingRoom-388162,test
2624
+ LivingDiningRoom-188065,test
2625
+ LivingDiningRoom-4906,test
2626
+ LivingRoom-31681,test
2627
+ LivingDiningRoom-22874,test
2628
+ LivingDiningRoom-19270,test
2629
+ LivingDiningRoom-3163,test
2630
+ LivingDiningRoom-166823,test
2631
+ LivingDiningRoom-1625,test
2632
+ LivingDiningRoom-128962,test
2633
+ LivingDiningRoom-334,test
2634
+ LivingRoom-21372,test
2635
+ LivingDiningRoom-20597,test
2636
+ LivingDiningRoom-30479,val
2637
+ LivingDiningRoom-8997,val
2638
+ LivingRoom-2690,val
2639
+ LivingRoom-35854,val
2640
+ LivingRoom-22322,val
2641
+ LivingDiningRoom-57414,val
2642
+ LivingRoom-59825,val
2643
+ LivingDiningRoom-54606,val
2644
+ LivingDiningRoom-10026,val
2645
+ LivingDiningRoom-13620,val
2646
+ LivingDiningRoom-18022,val
2647
+ LivingDiningRoom-11895,val
2648
+ LivingDiningRoom-39576,val
2649
+ LivingRoom-18,val
2650
+ LivingRoom-5208,val
2651
+ LivingDiningRoom-15484,val
2652
+ LivingDiningRoom-30387,val
2653
+ LivingDiningRoom-20720,val
2654
+ LivingDiningRoom-23360,val
2655
+ LivingDiningRoom-18273,val
2656
+ LivingRoom-21464,val
2657
+ LivingRoom-20122,val
2658
+ LivingDiningRoom-83247,val
2659
+ LivingRoom-23777,val
2660
+ LivingDiningRoom-38356,val
2661
+ LivingRoom-184499,val
2662
+ LivingDiningRoom-3620,val
2663
+ LivingDiningRoom-7386,val
2664
+ LivingDiningRoom-17826,val
2665
+ LivingDiningRoom-29822,val
2666
+ LivingDiningRoom-15277,val
2667
+ LivingDiningRoom-21946,val
2668
+ LivingDiningRoom-2109,val
2669
+ LivingDiningRoom-173006,val
2670
+ LivingDiningRoom-1524,val
2671
+ LivingDiningRoom-25947,val
2672
+ LivingDiningRoom-220,val
2673
+ LivingDiningRoom-16466,val
2674
+ LivingDiningRoom-119029,val
2675
+ LivingDiningRoom-2020,val
2676
+ LivingDiningRoom-16074,val
2677
+ LivingDiningRoom-28459,val
2678
+ LivingDiningRoom-3127,val
2679
+ LivingDiningRoom-6157,val
2680
+ LivingRoom-582,val
2681
+ LivingRoom-3695,val
2682
+ LivingRoom-9513,val
2683
+ LivingDiningRoom-22074,val
2684
+ LivingRoom-16017,val
2685
+ LivingDiningRoom-15471,val
2686
+ LivingDiningRoom-3674,val
2687
+ LivingRoom-1253,val
2688
+ LivingRoom-8603,val
2689
+ LivingRoom-11427,val
2690
+ LivingDiningRoom-1378,val
2691
+ LivingRoom-11758,val
2692
+ LivingRoom-24673,val
2693
+ LivingDiningRoom-12185,val
2694
+ LivingDiningRoom-180855,val
2695
+ LivingDiningRoom-46408,val
2696
+ LivingRoom-50363,val
2697
+ LivingRoom-45533,val
2698
+ LivingDiningRoom-22270,val
2699
+ LivingRoom-5033,val
2700
+ LivingDiningRoom-11295,val
2701
+ LivingDiningRoom-7013,val
2702
+ LivingRoom-12361,val
2703
+ LivingRoom-32727,val
2704
+ LivingRoom-472,val
2705
+ LivingRoom-23725,val
2706
+ LivingDiningRoom-4463,val
2707
+ LivingDiningRoom-17688,val
2708
+ LivingDiningRoom-77193,val
2709
+ LivingDiningRoom-60442,val
2710
+ LivingRoom-126,val
2711
+ LivingDiningRoom-3278,val
2712
+ LivingDiningRoom-56818,val
2713
+ LivingDiningRoom-5987,val
2714
+ LivingDiningRoom-118273,val
2715
+ LivingRoom-4576,val
2716
+ LivingDiningRoom-27589,val
2717
+ LivingDiningRoom-67456,val
2718
+ LivingDiningRoom-24924,val
2719
+ LivingDiningRoom-3416,val
2720
+ LivingDiningRoom-77748,val
2721
+ LivingDiningRoom-26356,val
2722
+ LivingRoom-7470,val
2723
+ LivingDiningRoom-20577,val
2724
+ LivingRoom-961,val
2725
+ LivingDiningRoom-11072,val
2726
+ LivingDiningRoom-10068,val
2727
+ LivingDiningRoom-119327,val
2728
+ LivingRoom-15009,val
2729
+ LivingDiningRoom-104650,val
2730
+ LivingDiningRoom-25783,val
2731
+ LivingDiningRoom-83039,val
2732
+ LivingRoom-22787,val
2733
+ LivingDiningRoom-35084,val
2734
+ LivingDiningRoom-15390,val
2735
+ LivingDiningRoom-1991,val
2736
+ LivingDiningRoom-6680,val
2737
+ LivingRoom-12311,val
2738
+ LivingDiningRoom-1149,val
2739
+ LivingDiningRoom-152,val
2740
+ LivingDiningRoom-69519,val
2741
+ LivingDiningRoom-62241,val
2742
+ LivingDiningRoom-1965,val
2743
+ LivingRoom-9590,val
2744
+ LivingDiningRoom-31715,val
2745
+ LivingDiningRoom-2523,val
2746
+ LivingRoom-4555,val
2747
+ LivingDiningRoom-6855,val
2748
+ LivingDiningRoom-1861,val
2749
+ LivingRoom-43189,val
2750
+ LivingDiningRoom-11117,val
2751
+ LivingDiningRoom-13555,val
2752
+ LivingRoom-19160,val
2753
+ LivingDiningRoom-15891,val
2754
+ LivingDiningRoom-11830,val
2755
+ LivingRoom-52940,val
2756
+ LivingRoom-6979,val
2757
+ LivingRoom-33315,val
2758
+ LivingDiningRoom-13900,val
2759
+ LivingRoom-3890,val
2760
+ LivingDiningRoom-13989,val
2761
+ LivingRoom-66039,val
2762
+ LivingRoom-3101,val
2763
+ LivingDiningRoom-13259,val
2764
+ LivingDiningRoom-13133,val
2765
+ LivingDiningRoom-12283,val
2766
+ LivingDiningRoom-16818,val
2767
+ LivingDiningRoom-22029,val
2768
+ LivingRoom-27729,val
2769
+ LivingDiningRoom-52497,val
2770
+ LivingDiningRoom-19900,val
2771
+ LivingDiningRoom-6226,val
2772
+ LivingRoom-76094,val
2773
+ LivingDiningRoom-12661,val
2774
+ LivingDiningRoom-513,val
2775
+ LivingDiningRoom-9081,val
2776
+ LivingRoom-4652,val
2777
+ LivingRoom-42021,val
2778
+ LivingDiningRoom-64,val
2779
+ LivingDiningRoom-22861,val
2780
+ LivingDiningRoom-79884,val
2781
+ LivingRoom-27836,val
2782
+ LivingRoom-20470,val
2783
+ LivingRoom-11034,val
2784
+ LivingDiningRoom-71961,val
2785
+ LivingRoom-22468,val
2786
+ LivingRoom-77421,val
2787
+ LivingRoom-1352,val
2788
+ LivingDiningRoom-563215,val
2789
+ LivingDiningRoom-8033,val
2790
+ LivingRoom-14784,val
2791
+ LivingRoom-82225,val
2792
+ LivingRoom-11489,val
2793
+ LivingDiningRoom-32725,val
2794
+ LivingDiningRoom-46222,val
2795
+ LivingDiningRoom-83166,val
2796
+ LivingDiningRoom-18796,val
2797
+ LivingRoom-45406,val
2798
+ LivingRoom-27552,val
2799
+ LivingRoom-10581,val
2800
+ LivingDiningRoom-145240,val
2801
+ LivingDiningRoom-5734,val
2802
+ LivingDiningRoom-1037,val
2803
+ LivingDiningRoom-33718,val
2804
+ LivingRoom-17644,val
2805
+ LivingRoom-1321,val
2806
+ LivingDiningRoom-514,val
2807
+ LivingDiningRoom-12811,val
2808
+ LivingDiningRoom-16338,val
2809
+ LivingDiningRoom-32976,val
2810
+ LivingDiningRoom-8272,val
2811
+ LivingDiningRoom-110465,val
2812
+ LivingDiningRoom-40034,val
2813
+ LivingDiningRoom-61857,val
2814
+ LivingDiningRoom-9678,val
2815
+ LivingRoom-12203,val
2816
+ LivingRoom-3973,val
2817
+ LivingDiningRoom-6807,val
2818
+ LivingDiningRoom-4850,val
2819
+ LivingDiningRoom-37511,val
2820
+ LivingRoom-950,val
2821
+ LivingDiningRoom-43900,val
2822
+ LivingRoom-4125,val
2823
+ LivingRoom-477,val
2824
+ LivingDiningRoom-35947,val
2825
+ LivingDiningRoom-2152,val
2826
+ LivingDiningRoom-21980,val
2827
+ LivingRoom-17377,val
2828
+ LivingDiningRoom-10705,val
2829
+ LivingDiningRoom-7722,val
2830
+ LivingDiningRoom-2610,val
2831
+ LivingDiningRoom-53390,val
2832
+ LivingDiningRoom-24764,val
2833
+ LivingDiningRoom-18037,val
2834
+ LivingRoom-2579,val
2835
+ LivingDiningRoom-114634,val
2836
+ LivingRoom-824,val
2837
+ LivingRoom-107144,val
2838
+ LivingRoom-4850,val
2839
+ LivingDiningRoom-64430,val
2840
+ LivingDiningRoom-11390,val
2841
+ LivingDiningRoom-5013,val
2842
+ LivingDiningRoom-94483,val
2843
+ LivingDiningRoom-3270,val
2844
+ LivingDiningRoom-26776,val
2845
+ LivingDiningRoom-22654,val
2846
+ LivingDiningRoom-31395,val
2847
+ LivingDiningRoom-56093,val
2848
+ LivingDiningRoom-22841,val
2849
+ LivingDiningRoom-9828,val
2850
+ LivingDiningRoom-73311,val
2851
+ LivingRoom-60575,val
2852
+ LivingDiningRoom-2478,val
2853
+ LivingRoom-25968,val
2854
+ LivingRoom-12266,val
2855
+ LivingDiningRoom-837,val
2856
+ LivingDiningRoom-13703,val
2857
+ LivingRoom-1645,val
2858
+ LivingDiningRoom-18266,val
2859
+ LivingDiningRoom-51298,val
2860
+ LivingDiningRoom-678,val
2861
+ LivingDiningRoom-159251,val
2862
+ LivingDiningRoom-57038,val
2863
+ LivingDiningRoom-33283,val
2864
+ LivingDiningRoom-3910,val
2865
+ LivingDiningRoom-5446,val
2866
+ LivingDiningRoom-20510,val
2867
+ LivingDiningRoom-2077,val
2868
+ LivingRoom-10766,val
2869
+ LivingDiningRoom-11033,val
2870
+ LivingDiningRoom-381,val
2871
+ LivingRoom-515,val
2872
+ LivingRoom-15957,val
2873
+ LivingDiningRoom-34353,val
2874
+ LivingDiningRoom-7940,val
2875
+ LivingDiningRoom-4879,val
2876
+ LivingRoom-3201,val
2877
+ LivingDiningRoom-1097,val
2878
+ LivingDiningRoom-13781,val
2879
+ LivingDiningRoom-24410,val
2880
+ LivingDiningRoom-13522,val
2881
+ LivingDiningRoom-12138,val
2882
+ LivingDiningRoom-1761262,val
2883
+ LivingDiningRoom-21000,val
2884
+ LivingDiningRoom-48237,val
2885
+ LivingDiningRoom-62183,val
2886
+ LivingDiningRoom-5280,val
2887
+ LivingDiningRoom-2209,val
2888
+ LivingDiningRoom-12602,val
2889
+ LivingDiningRoom-72695,val
2890
+ LivingRoom-135693,val
2891
+ LivingDiningRoom-3542,val
2892
+ LivingDiningRoom-44319,val
2893
+ LivingDiningRoom-35106,val
2894
+ LivingRoom-67028,val
2895
+ LivingDiningRoom-31936,val
2896
+ LivingRoom-78737,val
2897
+ LivingRoom-19435,val
2898
+ LivingDiningRoom-989,val
2899
+ LivingDiningRoom-1531,val
2900
+ LivingDiningRoom-212406,val
2901
+ LivingDiningRoom-6131,val
2902
+ LivingDiningRoom-98328,val
2903
+ LivingDiningRoom-45153,val
2904
+ LivingDiningRoom-11539,val
2905
+ LivingDiningRoom-10253,val
2906
+ LivingRoom-5549,val
2907
+ LivingDiningRoom-2349,val
2908
+ LivingDiningRoom-24350,val
2909
+ LivingDiningRoom-2522,val
2910
+ LivingRoom-9564,val
2911
+ LivingRoom-41466,val
2912
+ LivingDiningRoom-44955,val
2913
+ LivingRoom-38575,val
2914
+ LivingDiningRoom-15929,val
2915
+ LivingDiningRoom-2046,val
2916
+ LivingDiningRoom-7655,val
2917
+ LivingDiningRoom-1006,val
2918
+ LivingRoom-3225,val
2919
+ LivingDiningRoom-37551,val
2920
+ LivingRoom-37631,val
2921
+ LivingDiningRoom-98932,val
2922
+ LivingDiningRoom-34577,val
2923
+ LivingDiningRoom-173466,val
2924
+ LivingDiningRoom-103650,val
2925
+ LivingDiningRoom-1289,val
2926
+ LivingDiningRoom-1573,val
eval/DiffuScene/config/obj_autoencoder/bed_living_diningrooms.yaml ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_wocm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/bedrooms"
5
+ annotation_file: "../config/bedroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_bedroom"
10
+ #filter_fn: "no_filtering"
11
+ train_stats: "dataset_stats.txt"
12
+ room_layout_size: "64,64"
13
+ # for object autoencoder
14
+ path_to_3d_front_dataset_directory: "/cluster/balrog/jtang/3D-FRONT/"
15
+ path_to_3d_future_dataset_directory: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/"
16
+ path_to_model_info: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/model_info.json"
17
+ without_lamps: false
18
+
19
+ network:
20
+ type: "diffusion_scene_layout_ddpm"
21
+ # denoising network
22
+ net_type: "unet1d"
23
+
24
+ # concate squarewish layer
25
+ point_dim: 30 #29
26
+ latent_dim: 0
27
+ room_mask_condition: false # not use room_mask
28
+ sample_num_points: 12 # max_length
29
+
30
+ objectness_dim: 0
31
+ class_dim: 22
32
+ angle_dim: 2
33
+
34
+ # for object autoencoder
35
+ objfeat_dim: 64
36
+ kl_weight: 0.001
37
+
38
+ feature_extractor:
39
+ name: "resnet18"
40
+ feature_size: 64
41
+ freeze_bn: true
42
+ input_channels: 1
43
+
44
+ training:
45
+ splits: ["train", "val", "test"]
46
+ epochs: 1010
47
+ steps_per_epoch: 500
48
+ batch_size: 16
49
+ save_frequency: 100
50
+ max_grad_norm: 10
51
+ # optimizer
52
+ optimizer: Adam
53
+ weight_decay: 0.0
54
+ # schedule
55
+ schedule: 'step'
56
+ lr: 0.0001
57
+ lr_step: 400
58
+ lr_decay: 0.1
59
+
60
+ validation:
61
+ splits: ["val"]
62
+ frequency: 10
63
+ batch_size: 16
64
+ gen_traj: false
65
+ num_step: 100
66
+ gen_gt: false
67
+
68
+ logger:
69
+ type: "wandb"
70
+ project: "3D_FRONT_objautoencoder"
71
+
eval/DiffuScene/config/obj_autoencoder/bed_living_diningrooms_lat32.yaml ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_wocm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/bedrooms"
5
+ annotation_file: "../config/bedroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_bedroom"
10
+ #filter_fn: "no_filtering"
11
+ train_stats: "dataset_stats.txt"
12
+ room_layout_size: "64,64"
13
+ # for object autoencoder
14
+ path_to_3d_front_dataset_directory: "/cluster/balrog/jtang/3D-FRONT/"
15
+ path_to_3d_future_dataset_directory: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/"
16
+ path_to_model_info: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/model_info.json"
17
+ without_lamps: false
18
+
19
+ network:
20
+ type: "diffusion_scene_layout_ddpm"
21
+ # denoising network
22
+ net_type: "unet1d"
23
+
24
+ # concate squarewish layer
25
+ point_dim: 30 #29
26
+ latent_dim: 0
27
+ room_mask_condition: false # not use room_mask
28
+ sample_num_points: 12 # max_length
29
+
30
+ objectness_dim: 0
31
+ class_dim: 22
32
+ angle_dim: 2
33
+
34
+ # for object autoencoder
35
+ objfeat_dim: 32
36
+ kl_weight: 0.001
37
+
38
+ feature_extractor:
39
+ name: "resnet18"
40
+ feature_size: 64
41
+ freeze_bn: true
42
+ input_channels: 1
43
+
44
+ training:
45
+ splits: ["train", "val", "test"]
46
+ epochs: 1000
47
+ steps_per_epoch: 500
48
+ batch_size: 16
49
+ save_frequency: 100
50
+ max_grad_norm: 10
51
+ # optimizer
52
+ optimizer: Adam
53
+ weight_decay: 0.0
54
+ # schedule
55
+ schedule: 'step'
56
+ lr: 0.0001
57
+ lr_step: 400
58
+ lr_decay: 0.1
59
+
60
+ validation:
61
+ splits: ["val"]
62
+ frequency: 10
63
+ batch_size: 16
64
+ gen_traj: false
65
+ num_step: 100
66
+ gen_gt: false
67
+
68
+ logger:
69
+ type: "wandb"
70
+ project: "3D_FRONT_objautoencoder"
71
+
eval/DiffuScene/config/obj_autoencoder/bedrooms.yaml ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_wocm" #"cached_diffusion_wocm_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/bedrooms"
5
+ annotation_file: "../config/bedroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_bedroom"
10
+ #filter_fn: "no_filtering"
11
+ train_stats: "dataset_stats.txt"
12
+ room_layout_size: "64,64"
13
+ # for object autoencoder
14
+ path_to_3d_front_dataset_directory: "/cluster/balrog/jtang/3D-FRONT/"
15
+ path_to_3d_future_dataset_directory: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/"
16
+ path_to_model_info: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/model_info.json"
17
+ without_lamps: false
18
+
19
+ network:
20
+ type: "diffusion_scene_layout_ddpm"
21
+ # denoising network
22
+ net_type: "unet1d"
23
+
24
+ # concate squarewish layer
25
+ point_dim: 30 #29
26
+ latent_dim: 0
27
+ room_mask_condition: false # not use room_mask
28
+ sample_num_points: 12 # max_length
29
+
30
+ objectness_dim: 0
31
+ class_dim: 22
32
+ angle_dim: 2
33
+
34
+ # for object autoencoder
35
+ objfeat_dim: 32 #64
36
+ kl_weight: 0.001
37
+
38
+ feature_extractor:
39
+ name: "resnet18"
40
+ feature_size: 64
41
+ freeze_bn: true
42
+ input_channels: 1
43
+
44
+ training:
45
+ splits: ["train", "val", "test"]
46
+ epochs: 1010
47
+ steps_per_epoch: 500
48
+ batch_size: 16
49
+ save_frequency: 100
50
+ max_grad_norm: 10
51
+ # optimizer
52
+ optimizer: Adam
53
+ weight_decay: 0.0
54
+ # schedule
55
+ schedule: 'step'
56
+ lr: 0.0001
57
+ lr_step: 400
58
+ lr_decay: 0.1
59
+
60
+ validation:
61
+ splits: ["train", "val", "test"] #["val"]
62
+ frequency: 10
63
+ batch_size: 16
64
+ gen_traj: false
65
+ num_step: 100
66
+ gen_gt: false
67
+
68
+ logger:
69
+ type: "wandb"
70
+ project: "3D_FRONT_objautoencoder"
71
+
eval/DiffuScene/config/obj_autoencoder/diningrooms.yaml ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_wocm" #"cached_diffusion_wocm_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/diningrooms"
5
+ annotation_file: "../config/diningroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_diningroom"
10
+ #filter_fn: "no_filtering"
11
+ train_stats: "dataset_stats.txt"
12
+ room_layout_size: "64,64"
13
+ max_length: 21
14
+
15
+ # for object autoencoder
16
+ path_to_3d_front_dataset_directory: "/cluster/balrog/jtang/3D-FRONT/"
17
+ path_to_3d_future_dataset_directory: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/"
18
+ path_to_model_info: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/model_info.json"
19
+ without_lamps: false
20
+
21
+ network:
22
+ type: "diffusion_scene_layout_ddpm"
23
+ # denoising network
24
+ net_type: "unet1d"
25
+
26
+ # concate squarewish layer
27
+ point_dim: 33 #29
28
+ latent_dim: 0
29
+ room_mask_condition: false # not use room_mask
30
+ sample_num_points: 21 # max_length
31
+
32
+ objectness_dim: 0
33
+ class_dim: 25 #24+1
34
+ angle_dim: 2
35
+
36
+ # for object autoencoder
37
+ objfeat_dim: 32 #64
38
+ kl_weight: 0.001
39
+
40
+
41
+ feature_extractor:
42
+ name: "resnet18"
43
+ feature_size: 64
44
+ freeze_bn: true
45
+ input_channels: 1
46
+
47
+ training:
48
+ splits: ["train", "val", "test"]
49
+ epochs: 1010
50
+ steps_per_epoch: 500
51
+ batch_size: 16
52
+ save_frequency: 100
53
+ max_grad_norm: 10
54
+ # optimizer
55
+ optimizer: Adam
56
+ weight_decay: 0.0
57
+ # schedule
58
+ schedule: 'step'
59
+ lr: 0.0001
60
+ lr_step: 400
61
+ lr_decay: 0.1
62
+
63
+ validation:
64
+ splits: ["train", "val", "test"] #["val"]
65
+ frequency: 10
66
+ batch_size: 16
67
+ gen_traj: false
68
+ num_step: 100
69
+ gen_gt: false
70
+
71
+ logger:
72
+ type: "wandb"
73
+ project: "3D_FRONT_objautoencoder"
eval/DiffuScene/config/obj_autoencoder/livingrooms.yaml ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_wocm" #"cached_diffusion_wocm_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/livingrooms"
5
+ annotation_file: "../config/livingroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_livingroom"
10
+ #filter_fn: "no_filtering"
11
+ train_stats: "dataset_stats.txt"
12
+ room_layout_size: "64,64"
13
+ max_length: 21
14
+
15
+ # for object autoencoder
16
+ path_to_3d_front_dataset_directory: "/cluster/balrog/jtang/3D-FRONT/"
17
+ path_to_3d_future_dataset_directory: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/"
18
+ path_to_model_info: "/home/v-meiszhang/backup/3d-front/3D-FUTURE-model/model_info.json"
19
+ without_lamps: false
20
+
21
+ network:
22
+ type: "diffusion_scene_layout_ddpm"
23
+ # denoising network
24
+ net_type: "unet1d"
25
+
26
+ # concate squarewish layer
27
+ point_dim: 33 #29
28
+ latent_dim: 0
29
+ room_mask_condition: false # not use room_mask
30
+ sample_num_points: 21 # max_length
31
+
32
+ objectness_dim: 0
33
+ class_dim: 25 #24+1
34
+ angle_dim: 2
35
+
36
+ # for object autoencoder
37
+ objfeat_dim: 32 #64
38
+ kl_weight: 0.001
39
+
40
+
41
+ feature_extractor:
42
+ name: "resnet18"
43
+ feature_size: 64
44
+ freeze_bn: true
45
+ input_channels: 1
46
+
47
+ training:
48
+ splits: ["train", "val", "test"]
49
+ epochs: 1010
50
+ steps_per_epoch: 500
51
+ batch_size: 16
52
+ save_frequency: 100
53
+ max_grad_norm: 10
54
+ # optimizer
55
+ optimizer: Adam
56
+ weight_decay: 0.0
57
+ # schedule
58
+ schedule: 'step'
59
+ lr: 0.0001
60
+ lr_step: 400
61
+ lr_decay: 0.1
62
+
63
+ validation:
64
+ splits: ["train", "val", "test"] #["val"]
65
+ frequency: 10
66
+ batch_size: 16
67
+ gen_traj: false
68
+ num_step: 100
69
+ gen_gt: false
70
+
71
+ logger:
72
+ type: "wandb"
73
+ project: "3D_FRONT_objautoencoder"
eval/DiffuScene/config/rearrange/diffusion_bedrooms_instancond_lat32_v_rearrange.yaml ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_objfeatsnorm_lat32_wocm" #"_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/bedrooms_objfeats_32_64"
5
+ annotation_file: "../config/bedroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_bedroom"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+
13
+ network:
14
+ type: "diffusion_scene_layout_ddpm"
15
+ # denoising network
16
+ net_type: "unet1d"
17
+
18
+ # concate squarewish layer
19
+ point_dim: 62
20
+ latent_dim: 0
21
+ room_mask_condition: false # not use room_mask
22
+ sample_num_points: 12 # max_length
23
+
24
+ objectness_dim: 0
25
+ class_dim: 22
26
+ angle_dim: 2
27
+ objfeat_dim: 32
28
+
29
+ # class condition
30
+ learnable_embedding: true
31
+ instance_condition: true
32
+ instance_emb_dim: 128
33
+ class_condition: false
34
+ class_emb_dim: 128
35
+
36
+ # room arrange condition
37
+ room_arrange_condition: true
38
+ arrange_emb_dim: 384
39
+
40
+ # diffusion config
41
+ diffusion_kwargs:
42
+ schedule_type: 'linear'
43
+ beta_start: 0.0001
44
+ beta_end: 0.02
45
+ time_num: 1000
46
+ loss_type: 'mse'
47
+ model_mean_type: 'v' #'x0' #'eps'
48
+ model_var_type: 'fixedsmall'
49
+ loss_separate: true
50
+ # calculate iou loss
51
+ loss_iou: true
52
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/bedrooms_objfeats_32_64/dataset_stats.txt"
53
+
54
+ net_kwargs:
55
+ dim: 512
56
+ dim_mults: [1, 1, 1, 1]
57
+ channels: 5 #29 --30 --94
58
+ objectness_dim: 0
59
+ class_dim: 22
60
+ angle_dim: 2
61
+ objfeat_dim: 32
62
+ self_condition: true
63
+ context_dim: 0
64
+ instanclass_dim: 512 #128 + 384
65
+ modulate_time_context_instanclass: true
66
+
67
+ feature_extractor:
68
+ name: "resnet18"
69
+ feature_size: 64
70
+ freeze_bn: true
71
+ input_channels: 1
72
+
73
+ training:
74
+ splits: ["train", "val"]
75
+ epochs: 60000
76
+ steps_per_epoch: 500
77
+ batch_size: 128
78
+ save_frequency: 100
79
+ max_grad_norm: 10
80
+ # optimizer
81
+ optimizer: Adam
82
+ weight_decay: 0.0
83
+ # schedule
84
+ schedule: 'step'
85
+ lr: 0.0002
86
+ lr_step: 10000
87
+ lr_decay: 0.5
88
+
89
+ validation:
90
+ splits: ["test"]
91
+ frequency: 10
92
+ batch_size: 128
93
+ gen_traj: false
94
+ num_step: 100
95
+ gen_gt: false
96
+ gen_prob_map: false
97
+
98
+ logger:
99
+ type: "wandb"
100
+ project: "diffuscene"
101
+
eval/DiffuScene/config/rearrange/diffusion_diningrooms_instancond_lat32_v_rearrange.yaml ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_objfeatsnorm_lat32_wocm" #"_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/diningrooms_objfeats_32_64"
5
+ annotation_file: "../config/diningroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_diningroom"
10
+ #filter_fn: "no_filtering"
11
+ train_stats: "dataset_stats.txt"
12
+ room_layout_size: "64,64"
13
+ max_length: 21
14
+
15
+ network:
16
+ type: "diffusion_scene_layout_ddpm"
17
+ # denoising network
18
+ net_type: "unet1d"
19
+
20
+ # concate squarewish layer
21
+ point_dim: 65
22
+ latent_dim: 0
23
+ room_mask_condition: false # not use room_mask
24
+ sample_num_points: 21 # max_length
25
+
26
+ objectness_dim: 0
27
+ class_dim: 25 #24+1
28
+ angle_dim: 2
29
+ objfeat_dim: 32
30
+
31
+ # class condition
32
+ learnable_embedding: true
33
+ instance_condition: true
34
+ instance_emb_dim: 128
35
+ class_condition: false
36
+ class_emb_dim: 128
37
+
38
+ # room arrange condition
39
+ room_arrange_condition: true
40
+ arrange_emb_dim: 384
41
+
42
+ # diffusion config
43
+ diffusion_kwargs:
44
+ schedule_type: 'linear'
45
+ beta_start: 0.0001
46
+ beta_end: 0.02
47
+ time_num: 1000
48
+ loss_type: 'mse'
49
+ model_mean_type: 'v' #'eps'
50
+ model_var_type: 'fixedsmall'
51
+ loss_separate: true
52
+ # calculate iou loss
53
+ loss_iou: true
54
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/diningrooms_objfeats_32_64/dataset_stats.txt"
55
+
56
+ net_kwargs:
57
+ dim: 512
58
+ dim_mults: [1, 1, 1, 1]
59
+ channels: 5 # 30 --33 -- 97
60
+ objectness_dim: 0
61
+ class_dim: 25
62
+ angle_dim: 2
63
+ objfeat_dim: 32
64
+ self_condition: true
65
+ context_dim: 0
66
+ instanclass_dim: 512 # 128 + 384
67
+ # seperate_all: true # separate all
68
+ # merge_bbox: true
69
+ modulate_time_context_instanclass: true
70
+
71
+
72
+ feature_extractor:
73
+ name: "resnet18"
74
+ feature_size: 64
75
+ freeze_bn: true
76
+ input_channels: 1
77
+
78
+ training:
79
+ splits: ["train", "val"]
80
+ epochs: 150000
81
+ steps_per_epoch: 500
82
+ batch_size: 128
83
+ save_frequency: 1000
84
+ max_grad_norm: 10
85
+ # optimizer
86
+ optimizer: Adam
87
+ weight_decay: 0.0
88
+ # schedule
89
+ schedule: 'step'
90
+ lr: 0.0002
91
+ lr_step: 20000
92
+ lr_decay: 0.5
93
+
94
+
95
+ validation:
96
+ splits: ["test"]
97
+ frequency: 10
98
+ batch_size: 128
99
+ gen_traj: false
100
+ num_step: 100
101
+ gen_gt: false
102
+ gen_prob_map: false
103
+
104
+ logger:
105
+ type: "wandb"
106
+ project: "diffuscene"
107
+
eval/DiffuScene/config/rearrange/diffusion_livingrooms_instancond_lat32_v_rearrange.yaml ADDED
@@ -0,0 +1,104 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_objfeatsnorm_lat32_wocm" #"_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/livingrooms_objfeats_32_64"
5
+ annotation_file: "../config/livingroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_livingroom"
10
+ #filter_fn: "no_filtering"
11
+ train_stats: "dataset_stats.txt"
12
+ room_layout_size: "64,64"
13
+ max_length: 21
14
+
15
+ network:
16
+ type: "diffusion_scene_layout_ddpm"
17
+ # denoising network
18
+ net_type: "unet1d"
19
+
20
+ # concate squarewish layer
21
+ point_dim: 65 #33
22
+ latent_dim: 0
23
+ room_mask_condition: false # not use room_mask
24
+ sample_num_points: 21 # max_length
25
+
26
+ objectness_dim: 0
27
+ class_dim: 25
28
+ angle_dim: 2
29
+ objfeat_dim: 32
30
+
31
+ # class condition
32
+ learnable_embedding: true
33
+ instance_condition: true
34
+ instance_emb_dim: 128
35
+ class_condition: false
36
+ class_emb_dim: 128
37
+
38
+ # room arrange condition
39
+ room_arrange_condition: true
40
+ arrange_emb_dim: 384
41
+
42
+ # diffusion config
43
+ diffusion_kwargs:
44
+ schedule_type: 'linear'
45
+ beta_start: 0.0001
46
+ beta_end: 0.02
47
+ time_num: 1000
48
+ loss_type: 'mse'
49
+ model_mean_type: 'v' #'eps'
50
+ model_var_type: 'fixedsmall'
51
+ loss_separate: true
52
+ # calculate iou loss
53
+ loss_iou: true
54
+ train_stats_file: /cluster/balrog/jtang/3d_front_processed/livingrooms_objfeats_32_64/dataset_stats.txt
55
+
56
+ net_kwargs:
57
+ dim: 512
58
+ dim_mults: [1, 1, 1, 1]
59
+ channels: 5 # 30 --33 -- 97
60
+ objectness_dim: 0
61
+ class_dim: 25
62
+ angle_dim: 2
63
+ objfeat_dim: 32
64
+ self_condition: true
65
+ context_dim: 0
66
+ instanclass_dim: 512 # 128 + 384
67
+ modulate_time_context_instanclass: true
68
+
69
+
70
+ feature_extractor:
71
+ name: "resnet18"
72
+ feature_size: 64
73
+ freeze_bn: true
74
+ input_channels: 1
75
+
76
+ training:
77
+ splits: ["train", "val"]
78
+ epochs: 150000
79
+ steps_per_epoch: 500
80
+ batch_size: 128
81
+ save_frequency: 1000
82
+ max_grad_norm: 10
83
+ # optimizer
84
+ optimizer: Adam
85
+ weight_decay: 0.0
86
+ # schedule
87
+ schedule: 'step'
88
+ lr: 0.0002
89
+ lr_step: 15000
90
+ lr_decay: 0.5
91
+
92
+ validation:
93
+ splits: ["test"]
94
+ frequency: 10
95
+ batch_size: 128
96
+ gen_traj: false
97
+ num_step: 100
98
+ gen_gt: false
99
+ gen_prob_map: false
100
+
101
+ logger:
102
+ type: "wandb"
103
+ project: "diffuscene"
104
+
eval/DiffuScene/config/text/diffusion_bedrooms_instancond_lat32_v_bert.yaml ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_text_cosin_angle_objfeatsnorm_lat32_wocm" # textfix for eval "cached_diffusion_textfix_cosin_angle_objfeatsnorm_lat32_wocm"
4
+ dataset_directory: "/home/v-meiszhang/backup/diffuscene/3d_front_processed/bedrooms_objfeats_32_64"
5
+ annotation_file: "../config/bedroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_bedroom"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+
13
+
14
+ network:
15
+ type: "diffusion_scene_layout_ddpm"
16
+ # denoising network
17
+ net_type: "unet1d"
18
+
19
+ # concate squarewish layer
20
+ point_dim: 62
21
+ latent_dim: 0
22
+ room_mask_condition: false # not use room_mask
23
+ text_condition: True
24
+ text_embed_dim: 512
25
+ sample_num_points: 12 # max_length
26
+
27
+ objectness_dim: 0
28
+ class_dim: 22
29
+ angle_dim: 2
30
+ objfeat_dim: 32 #64
31
+
32
+ # class condition
33
+ learnable_embedding: true
34
+ instance_condition: true
35
+ instance_emb_dim: 128
36
+
37
+ # diffusion config
38
+ diffusion_kwargs:
39
+ schedule_type: 'linear'
40
+ beta_start: 0.0001
41
+ beta_end: 0.02
42
+ time_num: 1000
43
+ loss_type: 'mse'
44
+ model_mean_type: 'v' #'x0' #'eps'
45
+ model_var_type: 'fixedsmall'
46
+ loss_separate: true
47
+ # calculate iou loss
48
+ loss_iou: true
49
+ train_stats_file: "/home/v-meiszhang/backup/diffuscene/3d_front_processed/bedrooms_objfeats_32_64/dataset_stats.txt"
50
+
51
+ net_kwargs:
52
+ dim: 512
53
+ dim_mults: [1, 1, 1, 1]
54
+ channels: 62
55
+ objectness_dim: 0
56
+ class_dim: 22
57
+ angle_dim: 2
58
+ objfeat_dim: 32
59
+ self_condition: true
60
+ context_dim: 0
61
+ instanclass_dim: 128
62
+ seperate_all: true # separate all
63
+ merge_bbox: true
64
+ modulate_time_context_instanclass: true
65
+ text_condition: True
66
+ text_dim: 512
67
+
68
+ feature_extractor:
69
+ name: "resnet18"
70
+ feature_size: 64
71
+ freeze_bn: true
72
+ input_channels: 1
73
+
74
+ training:
75
+ splits: ["train", "val"]
76
+ epochs: 60000
77
+ steps_per_epoch: 500
78
+ batch_size: 128
79
+ save_frequency: 2000
80
+ max_grad_norm: 10
81
+ # optimizer
82
+ optimizer: Adam
83
+ weight_decay: 0.0
84
+ # schedule
85
+ schedule: 'step'
86
+ lr: 0.0002
87
+ lr_step: 10000
88
+ lr_decay: 0.5
89
+ # schedule: 'lambda'
90
+ # start_lr: 0.0002
91
+ # end_lr: 0.000001
92
+ # start_epoch: 1000 # 200k iter / 400 epochs -- 500 iter /epoch
93
+ # end_epoch: 4000 # 400k iter / 800 epochs
94
+
95
+ validation:
96
+ splits: ["test"]
97
+ frequency: 10
98
+ batch_size: 128
99
+ gen_traj: false
100
+ num_step: 100
101
+ gen_gt: true #false
102
+ gen_prob_map: false
103
+
104
+ logger:
105
+ type: "wandb"
106
+ project: "diffuscene"
107
+
eval/DiffuScene/config/text/diffusion_diningrooms_instancond_lat32_v_bert.yaml ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_text_cosin_angle_objfeatsnorm_lat32_wocm" # textfix for eval #"cached_diffusion_textfix_cosin_angle_objfeatsnorm_lat32_wocm"
4
+ dataset_directory: "/home/v-meiszhang/backup/diffuscene/3d_front_processed/diningrooms_objfeats_32_64"
5
+ annotation_file: "../config/diningroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_diningroom"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+ max_length: 21
13
+
14
+ network:
15
+ type: "diffusion_scene_layout_ddpm"
16
+ # denoising network
17
+ net_type: "unet1d"
18
+
19
+ # concate squarewish layer
20
+ point_dim: 65 #33
21
+ latent_dim: 0
22
+ room_mask_condition: false # not use room_mask
23
+ text_condition: True
24
+ text_embed_dim: 512
25
+ sample_num_points: 21 # max_length
26
+
27
+ objectness_dim: 0
28
+ class_dim: 25 #24+1
29
+ angle_dim: 2
30
+ objfeat_dim: 32 #64
31
+
32
+ # class condition
33
+ learnable_embedding: true
34
+ instance_condition: true
35
+ instance_emb_dim: 128
36
+
37
+ # diffusion config
38
+ diffusion_kwargs:
39
+ schedule_type: 'linear'
40
+ beta_start: 0.0001
41
+ beta_end: 0.02
42
+ time_num: 1000
43
+ loss_type: 'mse'
44
+ model_mean_type: 'v' #'eps'
45
+ model_var_type: 'fixedsmall'
46
+ loss_separate: true
47
+ # calculate iou loss
48
+ loss_iou: true
49
+ train_stats_file: /home/v-meiszhang/backup/diffuscene/3d_front_processed/diningrooms_objfeats_32_64/dataset_stats.txt
50
+
51
+ net_kwargs:
52
+ dim: 512
53
+ dim_mults: [1, 1, 1, 1]
54
+ channels: 65 # 33
55
+ objectness_dim: 0
56
+ class_dim: 25
57
+ angle_dim: 2
58
+ objfeat_dim: 32
59
+ self_condition: true
60
+ context_dim: 0
61
+ instanclass_dim: 128
62
+ seperate_all: true # separate all
63
+ text_condition: true
64
+ text_dim: 512
65
+
66
+ feature_extractor:
67
+ name: "resnet18"
68
+ feature_size: 64
69
+ freeze_bn: true
70
+ input_channels: 1
71
+
72
+ training:
73
+ splits: ["train", "val"]
74
+ epochs: 150000
75
+ steps_per_epoch: 500
76
+ batch_size: 128
77
+ save_frequency: 2000
78
+ max_grad_norm: 10
79
+ # optimizer
80
+ optimizer: Adam
81
+ weight_decay: 0.0
82
+ # schedule
83
+ schedule: 'step'
84
+ lr: 0.0002
85
+ lr_step: 20000
86
+ lr_decay: 0.5
87
+
88
+ validation:
89
+ splits: ["test"]
90
+ frequency: 10
91
+ batch_size: 128
92
+ gen_traj: false
93
+ num_step: 100
94
+ gen_gt: false
95
+ gen_prob_map: false
96
+
97
+ logger:
98
+ type: "wandb"
99
+ project: "diffuscene"
100
+
eval/DiffuScene/config/text/diffusion_livingrooms_instancond_lat32_v_bert.yaml ADDED
@@ -0,0 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_text_cosin_angle_objfeatsnorm_lat32_wocm" # textfix for eval #"cached_diffusion_textfix_cosin_angle_objfeatsnorm_lat32_wocm"
4
+ dataset_directory: "/home/v-meiszhang/backup/diffuscene/3d_front_processed/livingrooms_objfeats_32_64"
5
+ annotation_file: "../config/livingroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_livingroom"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+ max_length: 21
13
+
14
+ network:
15
+ type: "diffusion_scene_layout_ddpm"
16
+ # denoising network
17
+ net_type: "unet1d"
18
+
19
+ # concate squarewish layer
20
+ point_dim: 65 #33
21
+ latent_dim: 0
22
+ room_mask_condition: false # not use room_mask
23
+ text_condition: True
24
+ text_embed_dim: 512
25
+ sample_num_points: 21 # max_length
26
+
27
+ objectness_dim: 0
28
+ class_dim: 25 #24+1
29
+ angle_dim: 2
30
+ objfeat_dim: 32 #64
31
+
32
+ # class condition
33
+ learnable_embedding: true
34
+ instance_condition: true
35
+ instance_emb_dim: 128
36
+ class_condition: false
37
+ class_emb_dim: 128
38
+
39
+ # diffusion config
40
+ diffusion_kwargs:
41
+ schedule_type: 'linear'
42
+ beta_start: 0.0001
43
+ beta_end: 0.02
44
+ time_num: 1000
45
+ loss_type: 'mse'
46
+ model_mean_type: 'v' #'eps'
47
+ model_var_type: 'fixedsmall'
48
+ loss_separate: true
49
+ # calculate iou loss
50
+ loss_iou: true
51
+ train_stats_file: /home/v-meiszhang/backup/diffuscene/3d_front_processed/livingrooms_objfeats_32_64/dataset_stats.txt
52
+
53
+ net_kwargs:
54
+ dim: 512
55
+ dim_mults: [1, 1, 1, 1]
56
+ channels: 65 # 33
57
+ objectness_dim: 0
58
+ class_dim: 25
59
+ angle_dim: 2
60
+ objfeat_dim: 32
61
+ self_condition: true
62
+ context_dim: 0
63
+ instanclass_dim: 128
64
+ seperate_all: true # separate all
65
+ text_condition: true
66
+ text_dim: 512
67
+
68
+ feature_extractor:
69
+ name: "resnet18"
70
+ feature_size: 64
71
+ freeze_bn: true
72
+ input_channels: 1
73
+
74
+ training:
75
+ splits: ["train", "val"]
76
+ epochs: 120000 #60000
77
+ steps_per_epoch: 500
78
+ batch_size: 128
79
+ save_frequency: 2000
80
+ max_grad_norm: 10
81
+ # optimizer
82
+ optimizer: Adam
83
+ weight_decay: 0.0
84
+ # schedule
85
+ schedule: 'step'
86
+ lr: 0.0001
87
+ lr_step: 20000 #10000
88
+ lr_decay: 0.5
89
+
90
+ validation:
91
+ splits: ["test"]
92
+ frequency: 10
93
+ batch_size: 128
94
+ gen_traj: false
95
+ num_step: 100
96
+ gen_gt: true #false
97
+ gen_prob_map: false
98
+
99
+ logger:
100
+ type: "wandb"
101
+ project: "diffuscene"
102
+
eval/DiffuScene/config/uncond/diffusion_bedrooms_instancond_lat32_eps.yaml ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_objfeatsnorm_lat32_wocm" #"****_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/bedrooms_objfeats_32_64"
5
+ annotation_file: "../config/bedroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_bedroom" #"no_filtering"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+
13
+ network:
14
+ type: "diffusion_scene_layout_ddpm"
15
+ # denoising network
16
+ net_type: "unet1d"
17
+
18
+ # concate squarewish layer
19
+ point_dim: 62 #29
20
+ latent_dim: 0
21
+ room_mask_condition: false # not use room_mask
22
+ sample_num_points: 12 # max_length
23
+
24
+ objectness_dim: 0
25
+ class_dim: 22
26
+ angle_dim: 2
27
+ objfeat_dim: 32
28
+
29
+ # class condition
30
+ learnable_embedding: true
31
+ instance_condition: true
32
+ instance_emb_dim: 128
33
+
34
+ # diffusion config
35
+ diffusion_kwargs:
36
+ schedule_type: 'linear'
37
+ beta_start: 0.0001
38
+ beta_end: 0.02
39
+ time_num: 1000
40
+ loss_type: 'mse'
41
+ model_mean_type: 'eps'
42
+ model_var_type: 'fixedsmall'
43
+ loss_separate: true
44
+ # calculate iou loss
45
+ loss_iou: true
46
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/bedrooms_objfeats_32_64/dataset_stats.txt"
47
+
48
+ net_kwargs:
49
+ dim: 512
50
+ dim_mults: [1, 1, 1, 1]
51
+ channels: 62
52
+ objectness_dim: 0
53
+ class_dim: 22
54
+ angle_dim: 2
55
+ objfeat_dim: 32
56
+ context_dim: 0
57
+ instanclass_dim: 128
58
+ seperate_all: true
59
+
60
+ feature_extractor:
61
+ name: "resnet18"
62
+ feature_size: 64
63
+ freeze_bn: true
64
+ input_channels: 1
65
+
66
+ training:
67
+ splits: ["train", "val"]
68
+ epochs: 60000
69
+ steps_per_epoch: 500
70
+ batch_size: 128
71
+ save_frequency: 2000
72
+ max_grad_norm: 10
73
+ # optimizer
74
+ optimizer: Adam
75
+ weight_decay: 0.0
76
+ # schedule
77
+ schedule: 'step'
78
+ lr: 0.0002
79
+ lr_step: 10000
80
+ lr_decay: 0.5
81
+
82
+ validation:
83
+ splits: ["test"]
84
+ frequency: 10
85
+ batch_size: 128
86
+ gen_traj: false
87
+ num_step: 100
88
+ gen_gt: false
89
+ gen_prob_map: false
90
+
91
+ logger:
92
+ type: "wandb"
93
+ project: "diffuscene"
94
+
eval/DiffuScene/config/uncond/diffusion_bedrooms_instancond_lat32_v.yaml ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_objfeatsnorm_lat32_wocm" #"****_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/bedrooms_objfeats_32_64"
5
+ annotation_file: "../config/bedroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_bedroom" #"no_filtering"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+
13
+ network:
14
+ type: "diffusion_scene_layout_ddpm"
15
+ # denoising network
16
+ net_type: "unet1d"
17
+
18
+ # concate squarewish layer
19
+ point_dim: 62 #29
20
+ latent_dim: 0
21
+ room_mask_condition: false # not use room_mask
22
+ sample_num_points: 12 # max_length
23
+
24
+ objectness_dim: 0
25
+ class_dim: 22
26
+ angle_dim: 2
27
+ objfeat_dim: 32 #64
28
+
29
+ # class condition
30
+ learnable_embedding: true
31
+ instance_condition: true
32
+ instance_emb_dim: 128
33
+
34
+ # diffusion config
35
+ diffusion_kwargs:
36
+ schedule_type: 'linear'
37
+ beta_start: 0.0001
38
+ beta_end: 0.02
39
+ time_num: 1000
40
+ loss_type: 'mse'
41
+ model_mean_type: 'v' #'x0' #'eps'
42
+ model_var_type: 'fixedsmall'
43
+ loss_separate: true
44
+ # calculate iou loss
45
+ loss_iou: true
46
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/bedrooms_objfeats_32_64/dataset_stats.txt"
47
+
48
+ net_kwargs:
49
+ dim: 512
50
+ dim_mults: [1, 1, 1, 1]
51
+ channels: 62
52
+ objectness_dim: 0
53
+ class_dim: 22
54
+ angle_dim: 2
55
+ objfeat_dim: 32
56
+ context_dim: 0
57
+ instanclass_dim: 128
58
+ seperate_all: true
59
+
60
+ feature_extractor:
61
+ name: "resnet18"
62
+ feature_size: 64
63
+ freeze_bn: true
64
+ input_channels: 1
65
+
66
+ training:
67
+ splits: ["train", "val"]
68
+ epochs: 60000
69
+ steps_per_epoch: 500
70
+ batch_size: 128
71
+ save_frequency: 2000
72
+ max_grad_norm: 10
73
+ # optimizer
74
+ optimizer: Adam
75
+ weight_decay: 0.0
76
+ # schedule
77
+ schedule: 'step'
78
+ lr: 0.0002
79
+ lr_step: 10000
80
+ lr_decay: 0.5
81
+
82
+ validation:
83
+ splits: ["test"]
84
+ frequency: 10
85
+ batch_size: 128
86
+ gen_traj: false
87
+ num_step: 100
88
+ gen_gt: false
89
+ gen_prob_map: false
90
+
91
+ logger:
92
+ type: "wandb"
93
+ project: "diffuscene"
94
+
eval/DiffuScene/config/uncond/diffusion_diningrooms_instancond_lat32_eps.yaml ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_objfeatsnorm_lat32_wocm" #"****_wocm_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/diningrooms_objfeats_32_64"
5
+ annotation_file: "../config/diningroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_diningroom" # "no_filtering"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+ max_length: 21
13
+
14
+ network:
15
+ type: "diffusion_scene_layout_ddpm"
16
+ # denoising network
17
+ net_type: "unet1d"
18
+
19
+ # concate squarewish layer
20
+ point_dim: 65
21
+ latent_dim: 0
22
+ room_mask_condition: false # not use room_mask
23
+ sample_num_points: 21 # max_length
24
+
25
+ objectness_dim: 0
26
+ class_dim: 25 #24+1
27
+ angle_dim: 2
28
+ objfeat_dim: 32 #64
29
+
30
+ # class condition
31
+ learnable_embedding: true
32
+ instance_condition: true
33
+ instance_emb_dim: 128
34
+
35
+ # diffusion config
36
+ diffusion_kwargs:
37
+ schedule_type: 'linear'
38
+ beta_start: 0.0001
39
+ beta_end: 0.02
40
+ time_num: 1000
41
+ loss_type: 'mse'
42
+ model_mean_type: 'eps'
43
+ model_var_type: 'fixedsmall'
44
+ loss_separate: true
45
+ loss_iou: true
46
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/diningrooms_objfeats_32_64/dataset_stats.txt"
47
+
48
+
49
+ net_kwargs:
50
+ dim: 512
51
+ dim_mults: [1, 1, 1, 1]
52
+ channels: 65
53
+ objectness_dim: 0
54
+ class_dim: 25
55
+ angle_dim: 2
56
+ objfeat_dim: 32
57
+ context_dim: 0
58
+ instanclass_dim: 128
59
+ seperate_all: true
60
+
61
+
62
+ feature_extractor:
63
+ name: "resnet18"
64
+ feature_size: 64
65
+ freeze_bn: true
66
+ input_channels: 1
67
+
68
+ training:
69
+ splits: ["train", "val"]
70
+ epochs: 150000
71
+ steps_per_epoch: 500
72
+ batch_size: 128
73
+ save_frequency: 2000
74
+ max_grad_norm: 10
75
+ # optimizer
76
+ optimizer: Adam
77
+ weight_decay: 0.0
78
+ # schedule
79
+ schedule: 'step'
80
+ lr: 0.0002
81
+ lr_step: 20000
82
+ lr_decay: 0.5
83
+
84
+
85
+ validation:
86
+ splits: ["test"]
87
+ frequency: 10
88
+ batch_size: 128
89
+ gen_traj: false
90
+ num_step: 100
91
+ gen_gt: false
92
+ gen_prob_map: false
93
+
94
+ logger:
95
+ type: "wandb"
96
+ project: "diffuscene"
97
+
eval/DiffuScene/config/uncond/diffusion_diningrooms_instancond_lat32_v.yaml ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_objfeatsnorm_lat32_wocm" #"****_wocm_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/diningrooms_objfeats_32_64"
5
+ annotation_file: "../config/diningroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_diningroom" # "no_filtering"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+ max_length: 21
13
+
14
+ network:
15
+ type: "diffusion_scene_layout_ddpm"
16
+ # denoising network
17
+ net_type: "unet1d"
18
+
19
+ # concate squarewish layer
20
+ point_dim: 65
21
+ latent_dim: 0
22
+ room_mask_condition: false # not use room_mask
23
+ sample_num_points: 21 # max_length
24
+
25
+ objectness_dim: 0
26
+ class_dim: 25 #24+1
27
+ angle_dim: 2
28
+ objfeat_dim: 32 #64
29
+
30
+ # class condition
31
+ learnable_embedding: true
32
+ instance_condition: true
33
+ instance_emb_dim: 128
34
+
35
+ # diffusion config
36
+ diffusion_kwargs:
37
+ schedule_type: 'linear'
38
+ beta_start: 0.0001
39
+ beta_end: 0.02
40
+ time_num: 1000
41
+ loss_type: 'mse'
42
+ model_mean_type: 'v' #'eps'
43
+ model_var_type: 'fixedsmall'
44
+ loss_separate: true
45
+ loss_iou: true
46
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/diningrooms_objfeats_32_64/dataset_stats.txt"
47
+
48
+
49
+ net_kwargs:
50
+ dim: 512
51
+ dim_mults: [1, 1, 1, 1]
52
+ channels: 65
53
+ objectness_dim: 0
54
+ class_dim: 25
55
+ angle_dim: 2
56
+ objfeat_dim: 32
57
+ context_dim: 0
58
+ instanclass_dim: 128
59
+ seperate_all: true
60
+
61
+
62
+ feature_extractor:
63
+ name: "resnet18"
64
+ feature_size: 64
65
+ freeze_bn: true
66
+ input_channels: 1
67
+
68
+ training:
69
+ splits: ["train", "val"]
70
+ epochs: 150000
71
+ steps_per_epoch: 500
72
+ batch_size: 128
73
+ save_frequency: 2000
74
+ max_grad_norm: 10
75
+ # optimizer
76
+ optimizer: Adam
77
+ weight_decay: 0.0
78
+ # schedule
79
+ schedule: 'step'
80
+ lr: 0.0002
81
+ lr_step: 20000
82
+ lr_decay: 0.5
83
+
84
+
85
+ validation:
86
+ splits: ["test"]
87
+ frequency: 10
88
+ batch_size: 128
89
+ gen_traj: false
90
+ num_step: 100
91
+ gen_gt: false
92
+ gen_prob_map: false
93
+
94
+ logger:
95
+ type: "wandb"
96
+ project: "diffuscene"
97
+
eval/DiffuScene/config/uncond/diffusion_livingrooms_instancond_lat32_eps.yaml ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_wocm" #"****_wocm_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/livingrooms_objfeats_32_64"
5
+ annotation_file: "../config/livingroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_livingroom" # "no_filtering"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+ max_length: 21
13
+
14
+ network:
15
+ type: "diffusion_scene_layout_ddpm"
16
+ # denoising network
17
+ net_type: "unet1d"
18
+
19
+ # concate squarewish layer
20
+ point_dim: 65 #29
21
+ latent_dim: 0
22
+ room_mask_condition: false # not use room_mask
23
+ sample_num_points: 21 # max_length
24
+
25
+ objectness_dim: 0
26
+ objfeat_dim: 32
27
+ class_dim: 25 #24+1
28
+ angle_dim: 2
29
+
30
+ # class condition
31
+ learnable_embedding: true
32
+ instance_condition: true
33
+ instance_emb_dim: 128
34
+
35
+ # diffusion config
36
+ diffusion_kwargs:
37
+ schedule_type: 'linear'
38
+ beta_start: 0.0001
39
+ beta_end: 0.02
40
+ time_num: 1000
41
+ loss_type: 'mse'
42
+ model_mean_type: 'eps'
43
+ model_var_type: 'fixedsmall'
44
+ loss_separate: true
45
+ loss_iou: true
46
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/livingrooms_objfeats_32_64/dataset_stats.txt"
47
+
48
+ net_kwargs:
49
+ dim: 512
50
+ dim_mults: [1, 1, 1, 1]
51
+ channels: 65
52
+ objectness_dim: 0
53
+ objfeat_dim: 32
54
+ class_dim: 25
55
+ angle_dim: 2
56
+ context_dim: 0
57
+ instanclass_dim: 128
58
+ seperate_all: true
59
+
60
+ feature_extractor:
61
+ name: "resnet18"
62
+ feature_size: 64
63
+ freeze_bn: true
64
+ input_channels: 1
65
+
66
+ training:
67
+ splits: ["train", "val"]
68
+ epochs: 150000
69
+ steps_per_epoch: 500
70
+ batch_size: 128
71
+ save_frequency: 2000
72
+ max_grad_norm: 10
73
+ # optimizer
74
+ optimizer: Adam
75
+ weight_decay: 0.0
76
+ # schedule
77
+ schedule: 'step'
78
+ lr: 0.0002
79
+ lr_step: 20000
80
+ lr_decay: 0.5
81
+
82
+ validation:
83
+ splits: ["test"]
84
+ frequency: 10
85
+ batch_size: 128
86
+ gen_traj: false
87
+ num_step: 100
88
+ gen_gt: false
89
+ gen_prob_map: false
90
+
91
+ logger:
92
+ type: "wandb"
93
+ project: "diffuscene"
94
+
eval/DiffuScene/config/uncond/diffusion_livingrooms_instancond_lat32_v.yaml ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ data:
2
+ dataset_type: "cached_threedfront"
3
+ encoding_type: "cached_diffusion_cosin_angle_wocm" #"****_wocm_no_prm"
4
+ dataset_directory: "/cluster/balrog/jtang/3d_front_processed/livingrooms_objfeats_32_64"
5
+ annotation_file: "../config/livingroom_threed_front_splits.csv"
6
+ path_to_invalid_scene_ids: "../config/invalid_threed_front_rooms.txt"
7
+ path_to_invalid_bbox_jids: "../config/black_list.txt"
8
+ augmentations: ["fixed_rotations"]
9
+ filter_fn: "threed_front_livingroom" # "no_filtering"
10
+ train_stats: "dataset_stats.txt"
11
+ room_layout_size: "64,64"
12
+ max_length: 21
13
+
14
+ network:
15
+ type: "diffusion_scene_layout_ddpm"
16
+ # denoising network
17
+ net_type: "unet1d"
18
+
19
+ # concate squarewish layer
20
+ point_dim: 65 #29
21
+ latent_dim: 0
22
+ room_mask_condition: false # not use room_mask
23
+ sample_num_points: 21 # max_length
24
+
25
+ objectness_dim: 0
26
+ objfeat_dim: 32
27
+ class_dim: 25 #24+1
28
+ angle_dim: 2
29
+
30
+ # class condition
31
+ learnable_embedding: true
32
+ instance_condition: true
33
+ instance_emb_dim: 128
34
+
35
+ # diffusion config
36
+ diffusion_kwargs:
37
+ schedule_type: 'linear'
38
+ beta_start: 0.0001
39
+ beta_end: 0.02
40
+ time_num: 1000
41
+ loss_type: 'mse'
42
+ model_mean_type: 'v' #'eps'
43
+ model_var_type: 'fixedsmall'
44
+ loss_separate: true
45
+ loss_iou: true
46
+ train_stats_file: "/cluster/balrog/jtang/3d_front_processed/livingrooms_objfeats_32_64/dataset_stats.txt"
47
+
48
+ net_kwargs:
49
+ dim: 512
50
+ dim_mults: [1, 1, 1, 1]
51
+ channels: 65
52
+ objectness_dim: 0
53
+ objfeat_dim: 32
54
+ class_dim: 25
55
+ angle_dim: 2
56
+ context_dim: 0
57
+ instanclass_dim: 128
58
+ seperate_all: true
59
+
60
+ feature_extractor:
61
+ name: "resnet18"
62
+ feature_size: 64
63
+ freeze_bn: true
64
+ input_channels: 1
65
+
66
+ training:
67
+ splits: ["train", "val"]
68
+ epochs: 150000
69
+ steps_per_epoch: 500
70
+ batch_size: 128
71
+ save_frequency: 2000
72
+ max_grad_norm: 10
73
+ # optimizer
74
+ optimizer: Adam
75
+ weight_decay: 0.0
76
+ # schedule
77
+ schedule: 'step'
78
+ lr: 0.0002
79
+ lr_step: 20000
80
+ lr_decay: 0.5
81
+
82
+ validation:
83
+ splits: ["test"]
84
+ frequency: 10
85
+ batch_size: 128
86
+ gen_traj: false
87
+ num_step: 100
88
+ gen_gt: false
89
+ gen_prob_map: false
90
+
91
+ logger:
92
+ type: "wandb"
93
+ project: "diffuscene"
94
+
eval/DiffuScene/demo/floor_plan_texture_images_references ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - floor_00001.jpg https://www.flickr.com/photos/10815394@N03/5659658038/ Rick Johnson CC BY 2.0
2
+ - floor_00002.jpg https://www.flickr.com/photos/the_richards/28640262712/ Will Richards CC BY-NC 2.0
3
+ - floor_00003.jpg https://www.flickr.com/photos/filterforge/8508832931/ Filter Forge CC BY 2.0
4
+ - floor_00004.jpg https://www.flickr.com/photos/filterforge/8742921497/ Filter Forge CC BY 2.0
5
+ - floor_00005.jpg https://www.flickr.com/photos/filterforge/8946733689/ Filter Forge CC BY 2.0
6
+ - floor_00006.jpg https://www.flickr.com/photos/dhilowitz/6856348963/ David Hilowitz CC BY 2.0
7
+ - floor_00007.jpg https://www.flickr.com/photos/filterforge/31238918661/ Filter Forge CC BY 2.0
eval/DiffuScene/run/evaluate_test_results.sh ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Script to evaluate test results
3
+ # Usage: ./evaluate_test_results.sh [bedrooms|livingrooms|diningrooms] [optional_boundary_file]
4
+
5
+ # Get script directory and change to scripts folder
6
+ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
7
+ cd "$SCRIPT_DIR/../scripts"
8
+
9
+ # Set paths
10
+ exp_dir="../pretrained/pretrained_diffusion"
11
+ gt_json="/home/v-meiszhang/amlt-project/DiffuScene/training_data_with_content_v3.1.json"
12
+ config_dir="../config/text"
13
+
14
+ # Get room type from argument
15
+ room_type="${1:-bedrooms}"
16
+ # Optional override for scene boundary file
17
+ boundary_override="${2:-}"
18
+
19
+ echo "========================================"
20
+ echo "Evaluating Test Results"
21
+ echo "========================================"
22
+ echo "Room type: $room_type"
23
+ echo ""
24
+
25
+ # Set parameters based on room type
26
+ if [ "$room_type" == "bedrooms" ]; then
27
+ results_file="$exp_dir/bedrooms_bert/custom_text_test/test_results.json"
28
+ config_file="$config_dir/diffusion_bedrooms_instancond_lat32_v_bert.yaml"
29
+ num_classes=22
30
+ default_boundary="../scene_boundaries_bedrooms.json"
31
+ elif [ "$room_type" == "diningrooms" ]; then
32
+ results_file="$exp_dir/diningrooms_bert/custom_text_test/test_results.json"
33
+ config_file="$config_dir/diffusion_diningrooms_instancond_lat32_v_bert.yaml"
34
+ num_classes=19
35
+ default_boundary="../scene_boundaries_diningrooms.json"
36
+ elif [ "$room_type" == "livingrooms" ]; then
37
+ results_file="$exp_dir/livingrooms_bert/custom_text_test/test_results.json"
38
+ config_file="$config_dir/diffusion_livingrooms_instancond_lat32_v_bert.yaml"
39
+ num_classes=19
40
+ default_boundary="../scene_boundaries_livingrooms.json"
41
+ else
42
+ echo "Unknown room type: $room_type"
43
+ exit 1
44
+ fi
45
+
46
+ if [ -n "$boundary_override" ]; then
47
+ boundary_file="$boundary_override"
48
+ else
49
+ boundary_file="$default_boundary"
50
+ fi
51
+
52
+ # Check if results file exists
53
+ if [ ! -f "$results_file" ]; then
54
+ echo "Error: Results file not found: $results_file"
55
+ echo "Please run test_custom_text.sh first"
56
+ exit 1
57
+ fi
58
+
59
+ # Set output file
60
+ output_dir=$(dirname "$results_file")
61
+ output_file="$output_dir/evaluation_results.json"
62
+
63
+ echo "Results file: $results_file"
64
+ echo "Config file: $config_file"
65
+ echo "Output file: $output_file"
66
+ if [ -n "$boundary_file" ]; then
67
+ echo "Scene boundary file: $boundary_file"
68
+ fi
69
+ echo ""
70
+
71
+ # Run evaluation
72
+ eval_args=(
73
+ "$results_file"
74
+ "$gt_json"
75
+ --num_classes "$num_classes"
76
+ --room_type "$room_type"
77
+ --output "$output_file"
78
+ --config-file "$config_file"
79
+ )
80
+
81
+ if [ -n "$boundary_file" ] && [ -f "$boundary_file" ]; then
82
+ eval_args+=(--scene-boundaries "$boundary_file")
83
+ echo "Using pre-computed scene boundaries from: $boundary_file"
84
+ else
85
+ echo "No boundary file found. Will attempt to load from raw dataset using config."
86
+ fi
87
+
88
+ python evaluate_test_results.py "${eval_args[@]}"
89
+
90
+ echo ""
91
+ echo "✓ Evaluation complete!"
92
+ echo "Results saved to: $output_file"
eval/DiffuScene/run/generate.sh ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Please update the path to your own environment in config.yaml and following arguments befrore running the script
3
+ cd ./scripts
4
+
5
+ exp_dir="../pretrained"
6
+
7
+ ####'bedrooms'
8
+ config="../config/uncond/diffusion_bedrooms_instancond_lat32_v.yaml"
9
+ exp_name="bedrooms_uncond"
10
+ weight_file="$exp_dir/$exp_name/model_30000"
11
+ threed_future='/cluster/balrog/jtang/3d_front_processed/bedrooms/threed_future_model_bedroom.pkl'
12
+
13
+ python generate_diffusion.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
14
+ --without_screen --n_sequences 1000 --render_top2down --save_mesh --no_texture --without_floor --clip_denoised --retrive_objfeats
15
+
16
+
17
+ ###'diningrooms'
18
+ config="../config/uncond/diffusion_diningrooms_instancond_lat32_v.yaml"
19
+ exp_name="diningrooms_uncond"
20
+ weight_file="$exp_dir/$exp_name/model_82000"
21
+ threed_future='/cluster/balrog/jtang/3d_front_processed/diningrooms/threed_future_model_diningroom.pkl'
22
+
23
+ python generate_diffusion.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
24
+ --without_screen --n_sequences 1000 --render_top2down --save_mesh --no_texture --without_floor --clip_denoised --retrive_objfeats
25
+
26
+
27
+ ####'livingrooms'
28
+ config="../config/uncond/diffusion_livingrooms_instancond_lat32_v.yaml"
29
+ exp_name="livingrooms_uncond"
30
+ weight_file="$exp_dir/$exp_name/model_96000"
31
+ threed_future='/cluster/balrog/jtang/3d_front_processed/livingrooms/threed_future_model_livingroom.pkl'
32
+
33
+ python generate_diffusion.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
34
+ --without_screen --n_sequences 1000 --render_top2down --save_mesh --no_texture --without_floor --clip_denoised --retrive_objfeats
eval/DiffuScene/run/generate_rearrange.sh ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Please update the path to your own environment in config.yaml and following arguments befrore running the script
3
+
4
+ cd ./scripts
5
+
6
+ exp_dir="../pretrained"
7
+
8
+ ####'bedrooms'
9
+ config="../config/rearrange/diffusion_bedrooms_instancond_lat32_v_rearrange.yaml"
10
+ exp_name="bedrooms_rearrange"
11
+ weight_file=$exp_dir/$exp_name/model_17000
12
+ threed_future='/cluster/balrog/jtang/3d_front_processed/bedrooms/threed_future_model_bedroom.pkl'
13
+
14
+ python completion_rearrange.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
15
+ --without_screen --n_sequences 1000 --render_top2down --no_texture --without_floor --save_mesh --clip_denoised --retrive_objfeats --arrange_objects --compute_intersec
16
+
17
+
18
+ ####'livingrooms'
19
+ config="../config/rearrange/diffusion_livingrooms_instancond_lat32_v_rearrange.yaml"
20
+ exp_name="livingrooms_rearrange"
21
+ weight_file=$exp_dir/$exp_name/model_81000
22
+ threed_future='/cluster/balrog/jtang/3d_front_processed/livingrooms/threed_future_model_livingroom.pkl'
23
+
24
+ python completion_rearrange.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
25
+ --without_screen --n_sequences 1000 --render_top2down --no_texture --without_floor --save_mesh --clip_denoised --retrive_objfeats --arrange_objects --compute_intersec
eval/DiffuScene/run/generate_text.sh ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Please update the path to your own environment in config.yaml and following arguments befrore running the script
3
+ cd ./scripts
4
+
5
+
6
+ exp_dir="../pretrained"
7
+
8
+ ####'bedrooms'
9
+ config="../config/text/diffusion_bedrooms_instancond_lat32_v_bert.yaml"
10
+ exp_name="bedrooms_bert"
11
+ weight_file=$exp_dir/$exp_name/model_32000
12
+ threed_future='/cluster/balrog/jtang/3d_front_processed/bedrooms/threed_future_model_bedroom.pkl'
13
+
14
+ python generate_diffusion.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
15
+ --without_screen --n_sequences 1000 --render_top2down --save_mesh --no_texture --without_floor --clip_denoised --retrive_objfeats
16
+
17
+
18
+ ####'diningrooms'
19
+ config="../config/text/diffusion_diningrooms_instancond_lat32_v_bert.yaml"
20
+ exp_name="diningrooms_bert"
21
+ weight_file=$exp_dir/$exp_name/model_148000
22
+ threed_future='/cluster/balrog/jtang/3d_front_processed/diningrooms/threed_future_model_diningroom.pkl'
23
+
24
+ python generate_diffusion.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
25
+ --without_screen --n_sequences 1000 --render_top2down --save_mesh --no_texture --without_floor --clip_denoised --retrive_objfeats
26
+
27
+
28
+ ####'livingrooms'
29
+ config="../config/text/diffusion_livingrooms_instancond_lat32_v.yaml"
30
+ exp_name="livingrooms_bert"
31
+ weight_file=$exp_dir/$exp_name/model_118000
32
+ threed_future='/cluster/balrog/jtang/3d_front_processed/livingrooms/threed_future_model_livingroom.pkl'
33
+
34
+ python generate_diffusion.py $config $exp_dir/$exp_name/gen_top2down_notexture_nofloor $threed_future --weight_file $weight_file \
35
+ --without_screen --n_sequences 1000 --render_top2down --save_mesh --no_texture --without_floor --clip_denoised --retrive_objfeats
eval/DiffuScene/run/quick_test.sh ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Quick test with only 5 bedroom samples to verify the fix works
3
+
4
+ cd /home/v-meiszhang/amlt-project/DiffuScene/scripts
5
+
6
+ exp_dir="../pretrained/pretrained_diffusion"
7
+ json_file="/home/v-meiszhang/amlt-project/DiffuScene/training_data_with_content_v3.1.json"
8
+
9
+ config="../config/text/diffusion_bedrooms_instancond_lat32_v_bert.yaml"
10
+ exp_name="bedrooms_bert"
11
+ weight_file="$exp_dir/$exp_name/model_32000"
12
+ threed_future="/home/v-meiszhang/backup/diffuscene/3d_front_processed/threed_future_model_bedroom.pkl"
13
+ output_dir="$exp_dir/$exp_name/quick_test"
14
+
15
+ echo "========================================"
16
+ echo "Quick Test: 5 bedroom samples"
17
+ echo "========================================"
18
+ echo ""
19
+
20
+ python test_custom_text.py \
21
+ $config \
22
+ $output_dir \
23
+ $threed_future \
24
+ $json_file \
25
+ --weight_file $weight_file \
26
+ --n_sequences 5 \
27
+ --max_sentences 3 \
28
+ --clip_denoised \
29
+ --no_texture \
30
+ --without_floor
31
+
32
+ echo ""
33
+ echo "========================================"
34
+ echo "Test complete!"
35
+ echo "========================================"
36
+ echo ""
37
+
38
+ # Check if results file was created successfully
39
+ if [ -f "$output_dir/test_results.json" ]; then
40
+ echo "✓ Results file created: $output_dir/test_results.json"
41
+ echo ""
42
+ echo "Checking file integrity..."
43
+ python -c "import json; f=open('$output_dir/test_results.json'); data=json.load(f); print(f'✓ JSON valid! Found {len(data[\"results\"])} results'); f.close()"
44
+ echo ""
45
+ echo "First result:"
46
+ python -c "import json; f=open('$output_dir/test_results.json'); data=json.load(f); print(json.dumps(data['results'][0], indent=2)[:500]); f.close()"
47
+ else
48
+ echo "✗ Results file not found!"
49
+ exit 1
50
+ fi
eval/DiffuScene/run/render_test_results.sh ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Script to render test results
3
+ # Usage: ./render_test_results.sh [bedroom|livingroom|diningroom|all] [n_samples]
4
+
5
+ cd ./scripts
6
+
7
+ # Set paths
8
+ exp_dir="../pretrained/pretrained_diffusion"
9
+
10
+ # Get room type from argument
11
+ room_type="${1:-all}"
12
+ n_samples="${2:-10}"
13
+
14
+ # Function to render a specific room type
15
+ render_room_type() {
16
+ local room=$1
17
+
18
+ echo ""
19
+ echo "========================================"
20
+ echo "Rendering room type: $room"
21
+ echo "========================================"
22
+
23
+ if [ "$room" == "bedrooms" ]; then
24
+ config="../config/text/diffusion_bedrooms_instancond_lat32_v_bert.yaml"
25
+ results_file="$exp_dir/bedrooms_bert/custom_text_test/test_results.json"
26
+ threed_future="/home/v-meiszhang/backup/diffuscene/3d_front_processed/threed_future_model_bedroom.pkl"
27
+ elif [ "$room" == "diningrooms" ]; then
28
+ config="../config/text/diffusion_diningrooms_instancond_lat32_v_bert.yaml"
29
+ results_file="$exp_dir/diningrooms_bert/custom_text_test/test_results.json"
30
+ threed_future="/home/v-meiszhang/backup/diffuscene/3d_front_processed/threed_future_model_diningroom.pkl"
31
+ elif [ "$room" == "livingrooms" ]; then
32
+ config="../config/text/diffusion_livingrooms_instancond_lat32_v_bert.yaml"
33
+ results_file="$exp_dir/livingrooms_bert/custom_text_test/test_results.json"
34
+ threed_future="/home/v-meiszhang/backup/diffuscene/3d_front_processed/threed_future_model_livingroom.pkl"
35
+ else
36
+ echo "Unknown room type: $room"
37
+ return 1
38
+ fi
39
+
40
+ # Check if files exist
41
+ if [ ! -f "$results_file" ]; then
42
+ echo "Error: Results file not found: $results_file"
43
+ echo "Skipping $room..."
44
+ return 1
45
+ fi
46
+
47
+ if [ ! -f "$threed_future" ]; then
48
+ echo "Error: 3D-FUTURE file not found: $threed_future"
49
+ echo "Skipping $room..."
50
+ return 1
51
+ fi
52
+
53
+ output_dir=$(dirname "$results_file")
54
+
55
+ echo "Config: $config"
56
+ echo "Results file: $results_file"
57
+ echo "3D-FUTURE: $threed_future"
58
+ echo "Output: $output_dir"
59
+ echo "Number of samples: $n_samples"
60
+ echo ""
61
+
62
+ # Use xvfb for headless rendering
63
+ echo "Starting virtual display for headless rendering..."
64
+ xvfb-run -a -s "-screen 0 1024x768x24" python evaluate_and_render_test_results.py \
65
+ "$results_file" \
66
+ "$config" \
67
+ "$threed_future" \
68
+ --output_dir "$output_dir" \
69
+ --render_images \
70
+ --no_texture \
71
+ --without_floor \
72
+ --n_samples $n_samples
73
+
74
+ echo ""
75
+ echo "✓ Rendering complete for $room"
76
+ echo "Images saved to: $output_dir/rendered_scenes/"
77
+ }
78
+
79
+ # Main execution
80
+ echo "========================================"
81
+ echo "DiffuScene Test Results Rendering"
82
+ echo "========================================"
83
+ echo "Room type: $room_type"
84
+ echo "Number of samples to render: $n_samples"
85
+
86
+ if [ "$room_type" == "all" ]; then
87
+ echo "Rendering all room types..."
88
+
89
+ for room in bedrooms livingrooms diningrooms; do
90
+ render_room_type $room
91
+ done
92
+
93
+ echo ""
94
+ echo "========================================"
95
+ echo "ALL RENDERING COMPLETE"
96
+ echo "========================================"
97
+
98
+ else
99
+ # Render single room type
100
+ render_room_type "$room_type"
101
+ fi
eval/DiffuScene/run/run_benchmark.sh ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Benchmark evaluation script for DiffuScene
3
+ # Usage: ./run_benchmark.sh [layout_instr_bench|zones_data_mixed|both] [max_samples]
4
+
5
+ cd "$(dirname "$0")/../scripts"
6
+
7
+ # Activate conda environment
8
+ source ~/miniconda3/etc/profile.d/conda.sh
9
+ conda activate diffuscene
10
+
11
+ DATASET="${1:-both}"
12
+ MAX_SAMPLES="${2:-}"
13
+
14
+ echo "========================================"
15
+ echo "DiffuScene Benchmark Evaluation"
16
+ echo "========================================"
17
+ echo "Dataset: $DATASET"
18
+ echo "Max samples per type: ${MAX_SAMPLES:-all}"
19
+ echo ""
20
+
21
+ # Run evaluation
22
+ if [ -n "$MAX_SAMPLES" ]; then
23
+ python benchmark_evaluation.py --dataset $DATASET --output_dir ../benchmark_results --max_samples $MAX_SAMPLES
24
+ else
25
+ python benchmark_evaluation.py --dataset $DATASET --output_dir ../benchmark_results
26
+ fi
27
+
28
+ echo ""
29
+ echo "========================================"
30
+ echo "Benchmark evaluation complete!"
31
+ echo "Results saved to: ../benchmark_results/"
32
+ echo "========================================"
eval/DiffuScene/run/test_custom_text.sh ADDED
@@ -0,0 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Script to test DiffuScene with custom text inputs
3
+ # Usage: ./test_custom_text.sh [bedroom|livingroom|diningroom|all]
4
+
5
+ cd ./scripts
6
+
7
+ # Set your paths here
8
+ exp_dir="../pretrained/pretrained_diffusion"
9
+ json_file="/home/v-meiszhang/amlt-project/DiffuScene/training_data_with_content_v3.1.json"
10
+
11
+ # Get room type from argument or default
12
+ room_type="${1:-all}"
13
+
14
+ # Function to test a specific room type
15
+ test_room_type() {
16
+ local room=$1
17
+
18
+ echo ""
19
+ echo "========================================"
20
+ echo "Testing room type: $room"
21
+ echo "========================================"
22
+
23
+ if [ "$room" == "bedrooms" ]; then
24
+ config="../config/text/diffusion_bedrooms_instancond_lat32_v_bert.yaml"
25
+ exp_name="bedrooms_bert"
26
+ weight_file="$exp_dir/$exp_name/model_32000"
27
+ threed_future="/home/v-meiszhang/backup/diffuscene/3d_front_processed/threed_future_model_bedroom.pkl"
28
+ elif [ "$room" == "diningrooms" ]; then
29
+ config="../config/text/diffusion_diningrooms_instancond_lat32_v_bert.yaml"
30
+ exp_name="diningrooms_bert"
31
+ weight_file="$exp_dir/$exp_name/model_148000"
32
+ threed_future="/home/v-meiszhang/backup/diffuscene/3d_front_processed/threed_future_model_diningroom.pkl"
33
+ elif [ "$room" == "livingrooms" ]; then
34
+ config="../config/text/diffusion_livingrooms_instancond_lat32_v_bert.yaml"
35
+ exp_name="livingrooms_bert"
36
+ weight_file="$exp_dir/$exp_name/model_118000"
37
+ threed_future="/home/v-meiszhang/backup/diffuscene/3d_front_processed/threed_future_model_livingroom.pkl"
38
+ else
39
+ echo "Unknown room type: $room"
40
+ return 1
41
+ fi
42
+
43
+ # Check if files exist
44
+ if [ ! -f "$weight_file" ]; then
45
+ echo "Warning: Weight file not found: $weight_file"
46
+ echo "Skipping $room..."
47
+ return 1
48
+ fi
49
+
50
+ output_dir="$exp_dir/$exp_name/custom_text_test"
51
+
52
+ echo "Config: $config"
53
+ echo "Weight file: $weight_file"
54
+ echo "Output: $output_dir"
55
+ echo ""
56
+
57
+ python test_custom_text.py \
58
+ $config \
59
+ $output_dir \
60
+ $threed_future \
61
+ $json_file \
62
+ --room_type $room \
63
+ --weight_file $weight_file \
64
+ --n_sequences 500 \
65
+ --max_sentences 3 \
66
+ --clip_denoised \
67
+ --no_texture \
68
+ --without_floor
69
+
70
+ echo ""
71
+ echo "✓ Testing complete for $room"
72
+ echo "Results saved to: $output_dir/test_results.json"
73
+ }
74
+
75
+ # Main execution
76
+ echo "========================================"
77
+ echo "DiffuScene Custom Text Testing"
78
+ echo "========================================"
79
+ echo "JSON file: $json_file"
80
+
81
+ if [ "$room_type" == "all" ]; then
82
+ echo "Testing all room types..."
83
+
84
+ # First, classify the test data
85
+ echo ""
86
+ echo "Step 1: Classifying test samples by room type..."
87
+ python classify_room_types.py $json_file --output_dir ../classified_test_data
88
+
89
+ # Test each room type
90
+ for room in bedrooms livingrooms diningrooms; do
91
+ test_room_type $room
92
+ done
93
+
94
+ echo ""
95
+ echo "========================================"
96
+ echo "ALL TESTS COMPLETE"
97
+ echo "========================================"
98
+
99
+ else
100
+ # Test single room type
101
+ test_room_type "$room_type"
102
+ fi
eval/DiffuScene/run/train.sh ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Please update the path to your own environment in config.yaml and following arguments befrore running the script
3
+ cd ./scripts
4
+
5
+ exp_dir="your experiment directory"
6
+
7
+ #### bedrooms
8
+ config="../config/uncond/diffusion_bedrooms_instancond_lat32_v.yaml"
9
+ exp_name="diffusion_bedrooms_instancond_lat32_v"
10
+ python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
11
+
12
+ #### diningrooms
13
+ # config="../config/uncond/diffusion_diningrooms_instancond_lat32_v.yaml"
14
+ # exp_name="diffusion_diningrooms_instancond_lat32_v"
15
+ # python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
16
+
17
+ #### livingrooms
18
+ # config="../config/uncond/diffusion_livingrooms_instancond_lat32_v.yaml"
19
+ # exp_name="diffusion_livingrooms_instancond_lat32_v"
20
+ # python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
eval/DiffuScene/run/train_rearrange.sh ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Please update the path to your own environment in config.yaml and following arguments befrore running the script
3
+ cd ./scripts
4
+
5
+ exp_dir="your experiment directory"
6
+
7
+ #### bedrooms
8
+ config="../config/rearrange/diffusion_bedrooms_instancond_lat32_v_rearrange.yaml"
9
+ exp_name="diffusion_bedrooms_instancond_lat32_v_rearrange"
10
+ python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
11
+
12
+ #### diningrooms
13
+ config="../config/rearrange/diffusion_diningrooms_instancond_lat32_v_rearrange.yaml"
14
+ exp_name="diffusion_diningrooms_instancond_lat32_v_rearrange"
15
+ python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
16
+
17
+ #### livingrooms
18
+ config="../config/rearrange/diffusion_livingrooms_instancond_lat32_v_rearrange.yaml"
19
+ exp_name="diffusion_livingrooms_instancond_lat32_v_rearrange"
20
+ python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
eval/DiffuScene/run/train_text.sh ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # Please update the path to your own environment in config.yaml and following arguments befrore running the script
3
+ cd ./scripts
4
+
5
+ exp_dir="your experiment directory"
6
+
7
+ #### bedrooms
8
+ config="../config/text/diffusion_bedrooms_instancond_lat32_v_bert.yaml"
9
+ exp_name="diffusion_bedrooms_instancond_lat32_v_bert"
10
+ python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
11
+
12
+ #### diningrooms
13
+ config="../config/uncond/diffusion_diningrooms_instancond_lat32_v_bert.yaml"
14
+ exp_name="diffusion_diningrooms_instancond_lat32_v_bert"
15
+ python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
16
+
17
+ #### livingrooms
18
+ config="../config/uncond/diffusion_livingrooms_instancond_lat32_v_bert.yaml"
19
+ exp_name="diffusion_livingrooms_instancond_lat32_v_bert"
20
+ python train_diffusion.py $config $exp_dir --experiment_tag $exp_name --with_wandb_logger
eval/DiffuScene/scene_synthesis.egg-info/PKG-INFO ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Metadata-Version: 2.1
2
+ Name: scene-synthesis
3
+ Version: 0.0.0
4
+ Classifier: Intended Audience :: Science/Research
5
+ Classifier: Intended Audience :: Developers
6
+ Classifier: License :: Attribution-NonCommercial 4.0 International
7
+ Classifier: Topic :: Scientific/Engineering
8
+ Classifier: Programming Language :: Python
9
+ Classifier: Programming Language :: Python :: 3
10
+ Description-Content-Type: text/markdown
11
+ License-File: LICENSE
12
+
13
+ # DiffuScene
14
+
15
+ [**Paper**](https://arxiv.org/abs/2303.14207.pdf) | [**arXiv**](https://arxiv.org/abs/2303.14207.pdf) | [**Video**](https://www.youtube.com/embed/PFi-03T1lN0) | [**Project Page**](https://tangjiapeng.github.io/projects/DiffuScene/) <br>
16
+
17
+ This is the repository that contains source code for the paper:
18
+
19
+ **DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis**
20
+
21
+ - We present DiffuScene, a diffusion model for diverse and realistic indoor scene synthesis.
22
+ <div style="text-align: center">
23
+ <img src="media/pipeline.png" />
24
+ </div
25
+
26
+ - It can facilitate various down-stream applications: scene completion from partial scenes (left); scene arrangements of given objects (middle); scene generation from a text prompt describing partial scene configurations (right).
27
+ <div style="text-align: center">
28
+ <img src="media/teaser.png" />
29
+ </div>
30
+
31
+ ## Installation & Dependencies
32
+ You can create a conda environment called ```diffuscene``` using
33
+ ```
34
+ conda env create -f environment.yaml
35
+ conda activate diffuscene
36
+ ```
37
+
38
+ Next compile the extension modules. You can do this via
39
+ ```
40
+ python setup.py build_ext --inplace
41
+ pip install -e .
42
+ ```
43
+
44
+ Install ChamferDistancePytorch
45
+ ```
46
+ cd ChamferDistancePytorch/chamfer3D
47
+ python setup.py install
48
+ ```
49
+
50
+ ## Download
51
+
52
+ The pretrained model, results, and preprocess datasets are put into [GoogleDrive](https://drive.google.com/drive/folders/1EhvyNCAWWto6vMt0vXWMKBoSdYR_9pC2?usp=sharing)
53
+
54
+ ### Pretrained models and Results
55
+ The pretrained models of DiffuScene (unconditional, text, scene_rearrangement) can be downloaded from [here](https://drive.google.com/file/d/1pk9AzGcBz_kRfmRzvFNDW5byk4MwbXEm/view?usp=sharing).
56
+ The pretrained model of ShapeAutoencoder can be downloaded from [here](https://drive.google.com/file/d/1xuFa_Hh6BOZqfaAnlG-W_9mA1gilU6nI/view?usp=sharing).
57
+ The google drive also contains [the results of unconditional generation](https://drive.google.com/file/d/1EJ2VPZvbuKeR-ob1SsiwEC7cAEFDug3i/view?usp=sharing).
58
+
59
+ #### Preprocessed dataset
60
+ To directly train and evaluate DiffuScene without tedious data preprocessing, we also provide the preprocessed [3D-Front](https://drive.google.com/file/d/1UNSFN0kULyOzUErDPVvkKYbmfzA-4MsG/view?usp=sharing) and [3D-FUTURE](https://drive.google.com/file/d/16fz81Eh6B6pbZNGTMbe0FdfNwIuQicE1/view?usp=sharing)
61
+
62
+ ## Dataset
63
+
64
+ The training and evaluation are based on the [3D-FRONT](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset)
65
+ and the [3D-FUTURE](https://www.google.com/search?q=3d-future&oq=3d-fut&aqs=chrome.1.69i57j0j0i30l8.3909j0j7&sourceid=chrome&ie=UTF-8)
66
+ dataset. To download both datasets, please refer to the instructions provided in the dataset's
67
+ [webpage](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset).
68
+
69
+
70
+ ### Pickle the 3D-FUTURE dataset
71
+ To accelerate the preprocessing speed, we can sepcify the `PATH_TO_SCENES` environment variable for all scripts. This filepath contains the
72
+ parsed `ThreedFutureDataset` after being pickled. To pickle it, you can simply run this script as follows:
73
+ ```
74
+ python pickle_threed_future_dataset.py path_to_output_dir path_to_3d_front_dataset_dir path_to_3d_future_dataset_dir path_to_3d_future_model_info --dataset_filtering room_type
75
+ ```
76
+ Based on the pickled ThreedFutureDataset, we also provide a script to pickle the sampled point clouds of object CAD models,
77
+ which are used to shape autoencoder training and latent shape code extraction.
78
+ ```
79
+ python pickle_threed_future_pointcloud.py path_to_output_dir path_to_3d_front_dataset_dir path_to_3d_future_dataset_dir path_to_3d_future_model_info --dataset_filtering room_type
80
+ ```
81
+ For example,
82
+ ```
83
+ python pickle_threed_future_dataset.py /cluster/balrog/jtang/3d_front_processed/ /cluster/balrog/jtang/3D-FRONT/ /cluster/balrog/jtang/3D-FUTURE-model /cluster/balrog/jtang/3D-FUTURE-model/model_info.json --dataset_filtering threed_front_livingroom --annotation_file ../config/livingroom_threed_front_splits.csv
84
+
85
+ PATH_TO_SCENES="/cluster/balrog/jtang/3d_front_processed/threed_front.pkl" python pickle_threed_fucture_pointcloud.py /cluster/balrog/jtang/3d_front_processed/ /cluster/balrog/jtang/3D-FRONT/ /cluster/balrog/jtang/3D-FUTURE-model /cluster/balrog/jtang/3D-FUTURE-model/model_info.json --dataset_filtering threed_front_livingroom --annotation_file ../config/livingroom_threed_front_splits.csv
86
+ ```
87
+
88
+ Note that these two scripts should be separately executed for different room
89
+ types containing different objects. For the case of 3D-FRONT this is for the
90
+ bedrooms and the living/dining rooms, thus you have to run this script twice
91
+ with different `--dataset_filtering` and `--annotation_file`options. Please check the help menu for
92
+ additional details.
93
+
94
+ ### Train shape autoencoder
95
+ Then you can train the shape autoencoder using all models from bedrooms/diningrooms/livingrooms.
96
+ ```
97
+ cd ./scripts
98
+ PATH_TO_SCENES="/cluster/balrog/jtang/3d_front_processed/threed_front.pkl" python train_objautoencoder.py ../config/obj_autoencoder/bed_living_diningrooms_lat32.yaml your_objae_output_directory --experiment_tag "bed_living_diningrooms_lat32" --with_wandb_logger
99
+ ```
100
+
101
+ ### Pickle Latent Shape Code
102
+ Next, you can use the pre-train checkpoint of shape autoencoder to extract latent shape codes for each room type. Take the bedrooms for example:
103
+ ```
104
+ PATH_TO_SCENES="/cluster/balrog/jtang/3d_front_processed/threed_front.pkl" python generate_objautoencoder.py ../config/objautoencoder/bedrooms.yaml your_objae_output_directory --experiment_tag "bed_living_diningrooms_lat32"
105
+ ```
106
+
107
+ ### Preprocess 3D-Front dataset with latent shape codes
108
+ Finally, you can run `preprocessing_data.py` to read and pickle object properties (class label, location, orientation, size, and latent shape features) of each scene.
109
+ ```
110
+ PATH_TO_SCENES="/cluster/balrog/jtang/3d_front_processed/threed_front.pkl" python preprocess_data.py /cluster/balrog/jtang/3d_front_processed/livingrooms_objfeats_32_64 /cluster/balrog/jtang/3D-FRONT/ /cluster/balrog/jtang/3D-FUTURE-model /cluster/balrog/jtang/3D-FUTURE-model/model_info.json --dataset_filtering threed_front_livingroom --annotation_file ../config/livingroom_threed_front_splits.csv --add_objfeats
111
+ ```
112
+
113
+ ## Training & Evaluate Diffuscene
114
+ To train diffuscene on 3D Front-bedrooms, you can run
115
+ ```
116
+ ./run/train.sh
117
+ ./run/train_text.sh
118
+ ```
119
+
120
+ To generate the scene of unconditional and text-conditioned scene generation with our pretraiened models, you can run
121
+ ```
122
+ ./run/generate.sh
123
+ ./run/generate_text.sh
124
+ ```
125
+ If you want to calculate evaluation metrics of bbox IoU and average number of symmetric pairs, you can add the option```--compute_intersec```.
126
+ Please note that our current text-conditioned model is used to generate a full scene configuration from a text prompt of partial scene (2-3 sentences).
127
+ If you want to evaluate our method with text prompts of more sentences, you might need to re-train our method.
128
+
129
+ ## Evaluation Metrics
130
+ To evaluate FID and KID from rendered 2D images of generated and reference scenes, you can run:
131
+ ```
132
+ python compute_fid_scores.py $ground_truth_bedrooms_top2down_render_folder $generate_bedrooms_top2down_render_folder ../config/bedroom_threed_front_splits.csv
133
+ python compute_fid_scores.py $ground_truth_diningrooms_top2down_render_folder $generate_diningrooms_top2down_render_folder ../config/diningroom_threed_front_splits.csv
134
+ ```
135
+
136
+ To evaluate improved precision and recall, you can run:
137
+ ```
138
+ python improved_precision_recall.py $ground_truth_bedrooms_top2down_render_folder $generate_bedrooms_top2down_render_folder ../config/bedroom_threed_front_splits.csv
139
+ python improved_precision_recall.py $ground_truth_diningrooms_top2down_render_folder $generate_diningrooms_top2down_render_folder ../config/diningroom_threed_front_splits.csv
140
+ ```
141
+
142
+ ## Relevant Research
143
+
144
+ Please also check out the following papers that explore similar ideas:
145
+ - LEGO-Net: Learning Regular Rearrangements of Objects in Rooms.[[homepage]](https://ivl.cs.brown.edu/research/lego-net.html)
146
+ - Learning 3D Scene Priors with 2D Supervision. [[homepage]](https://yinyunie.github.io/sceneprior-page/)
147
+ - Sceneformer: Indoor Scene Generation with Transformers. [[homepage]](https://xinpeng-wang.github.io/sceneformer/)
148
+ - ATISS: Autoregressive Transformers for Indoor Scene Synthesis. [[homepage]](https://research.nvidia.com/labs/toronto-ai/ATISS/)
149
+ - Scene Synthesis via Uncertainty-Driven Attribute Synchronization [[pdf]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Scene_Synthesis_via_Uncertainty-Driven_Attribute_Synchronization_ICCV_2021_paper.pdf)
150
+ - Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images [[pdf]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Indoor_Scene_Generation_From_a_Collection_of_Semantic-Segmented_Depth_Images_ICCV_2021_paper.pdf)
151
+ - Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models [[pdf]](https://arxiv.org/pdf/1811.12463.pdf)
152
+
153
+ ## Citation
154
+
155
+ If you find DiffuScene useful for your work please cite:
156
+
157
+ ```
158
+ @inproceedings{tang2024diffuscene,
159
+ title={Diffuscene: Denoising diffusion models for generative indoor scene synthesis},
160
+ author={Tang, Jiapeng and Nie, Yinyu and Markhasin, Lev and Dai, Angela and Thies, Justus and Nie{\ss}ner, Matthias},
161
+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
162
+ year={2024}
163
+ }
164
+ ```
165
+
166
+ Contact [Jiapeng Tang](mailto:tangjiapengtjp@gmail.com) for questions, comments and reporting bugs.
167
+
168
+
169
+ ## Acknowledgements
170
+
171
+ Most of the code is borrowed from [ATISS](https://github.com/nv-tlabs/ATISS). We thank for Despoina Paschalidou her great works and repos.
172
+
173
+
eval/DiffuScene/scene_synthesis.egg-info/SOURCES.txt ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ LICENSE
2
+ README.md
3
+ setup.py
4
+ scene_synthesis/__init__.py
5
+ scene_synthesis/stats_logger.py
6
+ scene_synthesis/utils.py
7
+ scene_synthesis.egg-info/PKG-INFO
8
+ scene_synthesis.egg-info/SOURCES.txt
9
+ scene_synthesis.egg-info/dependency_links.txt
10
+ scene_synthesis.egg-info/top_level.txt
11
+ scene_synthesis/datasets/__init__.py
12
+ scene_synthesis/datasets/base.py
13
+ scene_synthesis/datasets/common.py
14
+ scene_synthesis/datasets/splits_builder.py
15
+ scene_synthesis/datasets/threed_front.py
16
+ scene_synthesis/datasets/threed_front_dataset.py
17
+ scene_synthesis/datasets/threed_front_scene.py
18
+ scene_synthesis/datasets/threed_future_dataset.py
19
+ scene_synthesis/datasets/utils.py
20
+ scene_synthesis/datasets/utils_io.py
21
+ scene_synthesis/datasets/utils_text.py
22
+ scene_synthesis/networks/__init__.py
23
+ scene_synthesis/networks/denoise_net.py
24
+ scene_synthesis/networks/diffusion_ddpm.py
25
+ scene_synthesis/networks/diffusion_scene_layout_ddpm.py
26
+ scene_synthesis/networks/feature_extractors.py
27
+ scene_synthesis/networks/foldingnet_autoencoder.py
28
+ scene_synthesis/networks/frozen_batchnorm.py
29
+ scene_synthesis/networks/loss.py
eval/DiffuScene/scene_synthesis.egg-info/dependency_links.txt ADDED
@@ -0,0 +1 @@
 
 
1
+
eval/DiffuScene/scene_synthesis.egg-info/top_level.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ scene_synthesis
eval/DiffuScene/scene_synthesis/__init__.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ # """Generative model for realistic syntheric scenes."""
2
+
3
+ __maintainer__ = "Jiapeng Tang"
4
+ __email__ = "jiapeng.tang@tum.de"
5
+ __url__ = "https://github.com/tangjiapeng/DiffuScene"
6
+ __description__ = "Library for plausible synthetic scene generation"
7
+ __keywords__ = "data-generation geometry 3D"
8
+ __license__ = "Attribution-NonCommercial 4.0 International"
9
+ __version__ = "0.1"
eval/DiffuScene/scene_synthesis/datasets/__init__.py ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from .base import THREED_FRONT_BEDROOM_FURNITURE, \
3
+ THREED_FRONT_LIVINGROOM_FURNITURE, THREED_FRONT_LIBRARY_FURNITURE
4
+ from .common import BaseDataset
5
+ from .threed_front import ThreedFront, CachedThreedFront
6
+ from .threed_front_dataset import dataset_encoding_factory
7
+
8
+ from .splits_builder import CSVSplitsBuilder
9
+
10
+
11
+ def get_raw_dataset(
12
+ config,
13
+ filter_fn=lambda s: s,
14
+ path_to_bounds=None,
15
+ split=["train", "val"]
16
+ ):
17
+ dataset_type = config["dataset_type"]
18
+ if "cached" in dataset_type:
19
+ # Make the train/test/validation splits
20
+ splits_builder = CSVSplitsBuilder(config["annotation_file"])
21
+ split_scene_ids = splits_builder.get_splits(split)
22
+
23
+ dataset = CachedThreedFront(
24
+ config["dataset_directory"],
25
+ config=config,
26
+ scene_ids=split_scene_ids
27
+ )
28
+ else:
29
+ dataset = ThreedFront.from_dataset_directory(
30
+ config["dataset_directory"],
31
+ config["path_to_model_info"],
32
+ config["path_to_models"],
33
+ config["path_to_room_masks_dir"],
34
+ path_to_bounds,
35
+ filter_fn
36
+ )
37
+ return dataset
38
+
39
+
40
+ def get_dataset_raw_and_encoded(
41
+ config,
42
+ filter_fn=lambda s: s,
43
+ path_to_bounds=None,
44
+ augmentations=None,
45
+ split=["train", "val"]
46
+ ):
47
+ dataset = get_raw_dataset(config, filter_fn, path_to_bounds, split=split)
48
+ encoding = dataset_encoding_factory(
49
+ config.get("encoding_type"),
50
+ dataset,
51
+ augmentations,
52
+ config.get("box_ordering", None)
53
+ )
54
+
55
+ return dataset, encoding
56
+
57
+
58
+ def get_encoded_dataset(
59
+ config,
60
+ filter_fn=lambda s: s,
61
+ path_to_bounds=None,
62
+ augmentations=None,
63
+ split=["train", "val"]
64
+ ):
65
+ _, encoding = get_dataset_raw_and_encoded(
66
+ config, filter_fn, path_to_bounds, augmentations, split
67
+ )
68
+ return encoding
69
+
70
+
71
+ def filter_function(config, split=["train", "val"], without_lamps=False):
72
+ print("Applying {} filtering".format(config["filter_fn"]))
73
+
74
+ if config["filter_fn"] == "no_filtering":
75
+ return lambda s: s
76
+
77
+ # Parse the list of the invalid scene ids
78
+ with open(config["path_to_invalid_scene_ids"], "r") as f:
79
+ invalid_scene_ids = set(l.strip() for l in f)
80
+
81
+ # Parse the list of the invalid bounding boxes
82
+ with open(config["path_to_invalid_bbox_jids"], "r") as f:
83
+ invalid_bbox_jids = set(l.strip() for l in f)
84
+
85
+ # Make the train/test/validation splits
86
+ splits_builder = CSVSplitsBuilder(config["annotation_file"])
87
+ split_scene_ids = splits_builder.get_splits(split)
88
+
89
+ if "threed_front_bedroom" in config["filter_fn"]:
90
+ return BaseDataset.filter_compose(
91
+ BaseDataset.with_room("bed"),
92
+ BaseDataset.at_least_boxes(3),
93
+ BaseDataset.at_most_boxes(13),
94
+ BaseDataset.with_object_types(
95
+ list(THREED_FRONT_BEDROOM_FURNITURE.keys())
96
+ ),
97
+ BaseDataset.with_generic_classes(THREED_FRONT_BEDROOM_FURNITURE),
98
+ BaseDataset.with_valid_scene_ids(invalid_scene_ids),
99
+ BaseDataset.with_valid_bbox_jids(invalid_bbox_jids),
100
+ BaseDataset.contains_object_types(
101
+ ["double_bed", "single_bed", "kids_bed"]
102
+ ),
103
+ BaseDataset.room_smaller_than_along_axis(4.0, axis=1),
104
+ BaseDataset.room_larger_than_along_axis(-0.005, axis=1),
105
+ BaseDataset.floor_plan_with_limits(6, 6, axis=[0, 2]),
106
+ BaseDataset.without_box_types(
107
+ ["ceiling_lamp", "pendant_lamp"]
108
+ if without_lamps else [""]
109
+ ),
110
+ BaseDataset.with_scene_ids(split_scene_ids)
111
+ )
112
+ elif "threed_front_livingroom" in config["filter_fn"]:
113
+ return BaseDataset.filter_compose(
114
+ BaseDataset.with_room("living"),
115
+ BaseDataset.at_least_boxes(3),
116
+ BaseDataset.at_most_boxes(21),
117
+ BaseDataset.with_object_types(
118
+ list(THREED_FRONT_LIVINGROOM_FURNITURE.keys())
119
+ ),
120
+ BaseDataset.with_generic_classes(
121
+ THREED_FRONT_LIVINGROOM_FURNITURE
122
+ ),
123
+ BaseDataset.with_valid_scene_ids(invalid_scene_ids),
124
+ BaseDataset.with_valid_bbox_jids(invalid_bbox_jids),
125
+ BaseDataset.room_smaller_than_along_axis(4.0, axis=1),
126
+ BaseDataset.room_larger_than_along_axis(-0.005, axis=1),
127
+ BaseDataset.floor_plan_with_limits(12, 12, axis=[0, 2]),
128
+ BaseDataset.without_box_types(
129
+ ["ceiling_lamp", "pendant_lamp"]
130
+ if without_lamps else [""]
131
+ ),
132
+ BaseDataset.with_scene_ids(split_scene_ids)
133
+ )
134
+ elif "threed_front_diningroom" in config["filter_fn"]:
135
+ return BaseDataset.filter_compose(
136
+ BaseDataset.with_room("dining"),
137
+ BaseDataset.at_least_boxes(3),
138
+ BaseDataset.at_most_boxes(21),
139
+ BaseDataset.with_object_types(
140
+ list(THREED_FRONT_LIVINGROOM_FURNITURE.keys())
141
+ ),
142
+ BaseDataset.with_generic_classes(
143
+ THREED_FRONT_LIVINGROOM_FURNITURE
144
+ ),
145
+ BaseDataset.with_valid_scene_ids(invalid_scene_ids),
146
+ BaseDataset.with_valid_bbox_jids(invalid_bbox_jids),
147
+ BaseDataset.room_smaller_than_along_axis(4.0, axis=1),
148
+ BaseDataset.room_larger_than_along_axis(-0.005, axis=1),
149
+ BaseDataset.floor_plan_with_limits(12, 12, axis=[0, 2]),
150
+ BaseDataset.without_box_types(
151
+ ["ceiling_lamp", "pendant_lamp"]
152
+ if without_lamps else [""]
153
+ ),
154
+ BaseDataset.with_scene_ids(split_scene_ids)
155
+ )
156
+ elif "threed_front_library" in config["filter_fn"]:
157
+ return BaseDataset.filter_compose(
158
+ BaseDataset.with_room("library"),
159
+ BaseDataset.at_least_boxes(3),
160
+ BaseDataset.with_object_types(
161
+ list(THREED_FRONT_LIBRARY_FURNITURE.keys())
162
+ ),
163
+ BaseDataset.with_generic_classes(THREED_FRONT_LIBRARY_FURNITURE),
164
+ BaseDataset.with_valid_scene_ids(invalid_scene_ids),
165
+ BaseDataset.with_valid_bbox_jids(invalid_bbox_jids),
166
+ BaseDataset.room_smaller_than_along_axis(4.0, axis=1),
167
+ BaseDataset.room_larger_than_along_axis(-0.005, axis=1),
168
+ BaseDataset.floor_plan_with_limits(6, 6, axis=[0, 2]),
169
+ BaseDataset.without_box_types(
170
+ ["ceiling_lamp", "pendant_lamp"]
171
+ if without_lamps else [""]
172
+ ),
173
+ BaseDataset.with_scene_ids(split_scene_ids)
174
+ )
175
+ elif config["filter_fn"] == "non_empty":
176
+ return lambda s: s if len(s.bboxes) > 0 else False
eval/DiffuScene/scene_synthesis/datasets/base.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ THREED_FRONT_BEDROOM_FURNITURE = {
3
+ "desk": "desk",
4
+ "nightstand": "nightstand",
5
+ "king-size bed": "double_bed",
6
+ "single bed": "single_bed",
7
+ "kids bed": "kids_bed",
8
+ "ceiling lamp": "ceiling_lamp",
9
+ "pendant lamp": "pendant_lamp",
10
+ "bookcase/jewelry armoire": "bookshelf",
11
+ "tv stand": "tv_stand",
12
+ "wardrobe": "wardrobe",
13
+ "lounge chair/cafe chair/office chair": "chair",
14
+ "dining chair": "chair",
15
+ "classic chinese chair": "chair",
16
+ "armchair": "armchair",
17
+ "dressing table": "dressing_table",
18
+ "dressing chair": "dressing_chair",
19
+ "corner/side table": "table",
20
+ "dining table": "table",
21
+ "round end table": "table",
22
+ "drawer chest/corner cabinet": "cabinet",
23
+ "sideboard/side cabinet/console table": "cabinet",
24
+ "children cabinet": "children_cabinet",
25
+ "shelf": "shelf",
26
+ "footstool/sofastool/bed end stool/stool": "stool",
27
+ "coffee table": "coffee_table",
28
+ "loveseat sofa": "sofa",
29
+ "three-seat/multi-seat sofa": "sofa",
30
+ "l-shaped sofa": "sofa",
31
+ "lazy sofa": "sofa",
32
+ "chaise longue sofa": "sofa",
33
+ }
34
+
35
+ THREED_FRONT_LIBRARY_FURNITURE = {
36
+ "bookcase/jewelry armoire": "bookshelf",
37
+ "desk": "desk",
38
+ "pendant lamp": "pendant_lamp",
39
+ "ceiling lamp": "ceiling_lamp",
40
+ "lounge chair/cafe chair/office chair": "lounge_chair",
41
+ "dining chair": "dining_chair",
42
+ "dining table": "dining_table",
43
+ "corner/side table": "corner_side_table",
44
+ "classic chinese chair": "chinese_chair",
45
+ "armchair": "armchair",
46
+ "shelf": "shelf",
47
+ "sideboard/side cabinet/console table": "console_table",
48
+ "footstool/sofastool/bed end stool/stool": "stool",
49
+ "barstool": "stool",
50
+ "round end table": "round_end_table",
51
+ "loveseat sofa": "loveseat_sofa",
52
+ "drawer chest/corner cabinet": "cabinet",
53
+ "wardrobe": "wardrobe",
54
+ "three-seat/multi-seat sofa": "multi_seat_sofa",
55
+ "wine cabinet": "wine_cabinet",
56
+ "coffee table": "coffee_table",
57
+ "lazy sofa": "lazy_sofa",
58
+ "children cabinet": "cabinet",
59
+ "chaise longue sofa": "chaise_longue_sofa",
60
+ "l-shaped sofa": "l_shaped_sofa",
61
+ "dressing table": "dressing_table",
62
+ "dressing chair": "dressing_chair",
63
+ }
64
+
65
+ THREED_FRONT_LIVINGROOM_FURNITURE = {
66
+ "bookcase/jewelry armoire": "bookshelf",
67
+ "desk": "desk",
68
+ "pendant lamp": "pendant_lamp",
69
+ "ceiling lamp": "ceiling_lamp",
70
+ "lounge chair/cafe chair/office chair": "lounge_chair",
71
+ "dining chair": "dining_chair",
72
+ "dining table": "dining_table",
73
+ "corner/side table": "corner_side_table",
74
+ "classic chinese chair": "chinese_chair",
75
+ "armchair": "armchair",
76
+ "shelf": "shelf",
77
+ "sideboard/side cabinet/console table": "console_table",
78
+ "footstool/sofastool/bed end stool/stool": "stool",
79
+ "barstool": "stool",
80
+ "round end table": "round_end_table",
81
+ "loveseat sofa": "loveseat_sofa",
82
+ "drawer chest/corner cabinet": "cabinet",
83
+ "wardrobe": "wardrobe",
84
+ "three-seat/multi-seat sofa": "multi_seat_sofa",
85
+ "wine cabinet": "wine_cabinet",
86
+ "coffee table": "coffee_table",
87
+ "lazy sofa": "lazy_sofa",
88
+ "children cabinet": "cabinet",
89
+ "chaise longue sofa": "chaise_longue_sofa",
90
+ "l-shaped sofa": "l_shaped_sofa",
91
+ "tv stand": "tv_stand"
92
+ }
eval/DiffuScene/scene_synthesis/datasets/common.py ADDED
@@ -0,0 +1,255 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from collections import Counter
3
+
4
+ import numpy as np
5
+ import torch
6
+ from torch.utils.data import IterableDataset, Dataset
7
+
8
+
9
+ class InfiniteDataset(IterableDataset):
10
+ """Decorate any Dataset instance to provide an infinite IterableDataset
11
+ version of it."""
12
+ def __init__(self, dataset, shuffle=True):
13
+ super().__init__()
14
+ self.dataset = dataset
15
+ self.shuffle = shuffle
16
+
17
+ def __iter__(self):
18
+ N = len(self.dataset)
19
+
20
+ # Split the work if we have multiple workers
21
+ # see https://pytorch.org/stable/data.html
22
+ worker_info = torch.utils.data.get_worker_info()
23
+ if worker_info is None:
24
+ start = 0
25
+ end = N
26
+ else:
27
+ num_workers = worker_info.num_workers
28
+ per_worker = (N + num_workers - 1) // num_workers
29
+ start = worker_info.id * per_worker
30
+ end = min(start + per_worker, N)
31
+
32
+ indices = np.arange(start, end)
33
+ while True:
34
+ if self.shuffle:
35
+ np.random.shuffle(indices)
36
+ for i in indices:
37
+ yield self.dataset[i]
38
+
39
+
40
+ class BaseDataset(Dataset):
41
+ """Implements the interface for all datasets that consist of scenes."""
42
+ def __init__(self, scenes):
43
+ assert len(scenes) > 0
44
+ print('contains {:d} scenes'.format( len(scenes) ) )
45
+ self.scenes = scenes
46
+
47
+ def __len__(self):
48
+ return len(self.scenes)
49
+
50
+ def __getitem__(self, idx):
51
+ return self.scenes[idx]
52
+
53
+ @property
54
+ def class_labels(self):
55
+ raise NotImplementedError()
56
+
57
+ @property
58
+ def n_classes(self):
59
+ return len(self.class_labels)
60
+
61
+ @property
62
+ def object_types(self):
63
+ raise NotImplementedError()
64
+
65
+ @property
66
+ def n_object_types(self):
67
+ """The number of distinct objects contained in the scenes."""
68
+ return len(self.object_types)
69
+
70
+ @property
71
+ def room_types(self):
72
+ return set([si.scene_type for si in self.scenes])
73
+
74
+ @property
75
+ def count_objects_in_rooms(self):
76
+ return Counter([len(si.bboxes) for si in self.scenes])
77
+
78
+ def post_process(self, s):
79
+ return s
80
+
81
+ @staticmethod
82
+ def with_valid_scene_ids(invalid_scene_ids):
83
+ def inner(scene):
84
+ return scene if scene.scene_id not in invalid_scene_ids else False
85
+ return inner
86
+
87
+ @staticmethod
88
+ def with_scene_ids(scene_ids):
89
+ def inner(scene):
90
+ return scene if scene.scene_id in scene_ids else False
91
+ return inner
92
+
93
+ @staticmethod
94
+ def with_room(scene_type):
95
+ def inner(scene):
96
+ return scene if scene_type in scene.scene_type else False
97
+ return inner
98
+
99
+ @staticmethod
100
+ def room_smaller_than_along_axis(max_size, axis=1):
101
+ def inner(scene):
102
+ return scene if scene.bbox[1][axis] <= max_size else False
103
+ return inner
104
+
105
+ @staticmethod
106
+ def room_larger_than_along_axis(min_size, axis=1):
107
+ def inner(scene):
108
+ return scene if scene.bbox[0][axis] >= min_size else False
109
+ return inner
110
+
111
+ @staticmethod
112
+ def floor_plan_with_limits(limit_x, limit_y, axis=[0, 2]):
113
+ def inner(scene):
114
+ min_bbox, max_bbox = scene.floor_plan_bbox
115
+ t_x = max_bbox[axis[0]] - min_bbox[axis[0]]
116
+ t_y = max_bbox[axis[1]] - min_bbox[axis[1]]
117
+ if t_x <= limit_x and t_y <= limit_y:
118
+ return scene
119
+ else:
120
+ False
121
+ return inner
122
+
123
+ @staticmethod
124
+ def with_valid_boxes(box_types):
125
+ def inner(scene):
126
+ for i in range(len(scene.bboxes)-1, -1, -1):
127
+ if scene.bboxes[i].label not in box_types:
128
+ scene.bboxes.pop(i)
129
+ return scene
130
+ return inner
131
+
132
+ @staticmethod
133
+ def without_box_types(box_types):
134
+ def inner(scene):
135
+ for i in range(len(scene.bboxes)-1, -1, -1):
136
+ if scene.bboxes[i].label in box_types:
137
+ scene.bboxes.pop(i)
138
+ return scene
139
+ return inner
140
+
141
+ @staticmethod
142
+ def with_generic_classes(box_types_map):
143
+ def inner(scene):
144
+ for box in scene.bboxes:
145
+ # Update the box label based on the box_types_map
146
+ box.label = box_types_map[box.label]
147
+ return scene
148
+ return inner
149
+
150
+ @staticmethod
151
+ def with_valid_bbox_jids(invalid_bbox_jds):
152
+ def inner(scene):
153
+ return (
154
+ False if any(b.model_jid in invalid_bbox_jds for b in scene.bboxes)
155
+ else scene
156
+ )
157
+ return inner
158
+
159
+ @staticmethod
160
+ def at_most_boxes(n):
161
+ def inner(scene):
162
+ return scene if len(scene.bboxes) <= n else False
163
+ return inner
164
+
165
+ @staticmethod
166
+ def at_least_boxes(n):
167
+ def inner(scene):
168
+ return scene if len(scene.bboxes) >= n else False
169
+ return inner
170
+
171
+ @staticmethod
172
+ def with_object_types(objects):
173
+ def inner(scene):
174
+ for b in scene.bboxes:
175
+ if b.label not in objects:
176
+ print(b.label)
177
+ return (
178
+ scene if all(b.label in objects for b in scene.bboxes)
179
+ else False
180
+ )
181
+ return inner
182
+
183
+ @staticmethod
184
+ def contains_object_types(objects):
185
+ def inner(scene):
186
+ return (
187
+ scene if any(b.label in objects for b in scene.bboxes)
188
+ else False
189
+ )
190
+ return inner
191
+
192
+ @staticmethod
193
+ def without_object_types(objects):
194
+ def inner(scene):
195
+ return (
196
+ False if any(b.label in objects for b in scene.bboxes)
197
+ else scene
198
+ )
199
+ return inner
200
+
201
+ @staticmethod
202
+ def filter_compose(*filters):
203
+ def inner(scene):
204
+ s = scene
205
+ fs = iter(filters)
206
+ try:
207
+ while s:
208
+ s = next(fs)(s)
209
+ except StopIteration:
210
+ pass
211
+ return s
212
+ return inner
213
+
214
+
215
+ class BaseScene(object):
216
+ """Contains all the information for a scene."""
217
+ def __init__(self, scene_id, scene_type, bboxes):
218
+ self.bboxes = bboxes
219
+ self.scene_id = scene_id
220
+ self.scene_type = scene_type
221
+
222
+ def __str__(self):
223
+ return "Scene: {} of type: {} contains {} bboxes".format(
224
+ self.scene_id, self.scene_type, self.nobjects
225
+ )
226
+
227
+ @property
228
+ def nobjects(self):
229
+ """Number of bounding boxes / objects in a Scene."""
230
+ return len(self.bboxes)
231
+
232
+ @property
233
+ def object_types(self):
234
+ """The set of object types in this scene."""
235
+ return sorted(set(b.label for b in self.bboxes))
236
+
237
+ @property
238
+ def n_object_types(self):
239
+ """Number of distinct objects in a Scene."""
240
+ return len(self.object_types)
241
+
242
+ def ordered_bboxes_with_centroid(self):
243
+ centroids = np.array([b.centroid for b in self.bboxes])
244
+ ordering = np.lexsort(centroids.T)
245
+ ordered_bboxes = [self.bboxes[i] for i in ordering]
246
+
247
+ return ordered_bboxes
248
+
249
+ def ordered_bboxes_with_class_labels(self, all_labels):
250
+ centroids = np.array([b.centroid for b in self.bboxes])
251
+ int_labels = np.array([[b.int_label(all_labels)] for b in self.bboxes])
252
+ ordering = np.lexsort(np.hstack([centroids, int_labels]).T)
253
+ ordered_bboxes = [self.bboxes[i] for i in ordering]
254
+
255
+ return ordered_bboxes
eval/DiffuScene/scene_synthesis/datasets/splits_builder.py ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import csv
3
+ import numpy as np
4
+
5
+ class SplitsBuilder(object):
6
+ def __init__(self, train_test_splits_file):
7
+ self._train_test_splits_file = train_test_splits_file
8
+ self._splits = {}
9
+
10
+ def train_split(self):
11
+ return self._splits["train"]
12
+
13
+ def test_split(self):
14
+ return self._splits["test"]
15
+
16
+ def val_split(self):
17
+ return self._splits["val"]
18
+
19
+ def _parse_train_test_splits_file(self):
20
+ with open(self._train_test_splits_file, "r") as f:
21
+ data = [row for row in csv.reader(f)]
22
+ return np.array(data)
23
+
24
+ def get_splits(self, keep_splits=["train, val"]):
25
+ if not isinstance(keep_splits , list):
26
+ keep_splits = [keep_splits]
27
+ # Return only the split
28
+ s = []
29
+ for ks in keep_splits:
30
+ s.extend(self._parse_split_file()[ks])
31
+ return s
32
+
33
+
34
+ class CSVSplitsBuilder(SplitsBuilder):
35
+ def _parse_split_file(self):
36
+ if not self._splits:
37
+ data = self._parse_train_test_splits_file()
38
+ for s in ["train", "test", "val", "overfit"]:
39
+ self._splits[s] = [r[0] for r in data if r[1] == s]
40
+ return self._splits
eval/DiffuScene/scene_synthesis/datasets/threed_front.py ADDED
@@ -0,0 +1,441 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from collections import Counter, OrderedDict
3
+ from functools import lru_cache
4
+ from tkinter import E
5
+ import numpy as np
6
+ import json
7
+ import os
8
+
9
+ from PIL import Image
10
+
11
+ from .common import BaseDataset
12
+ from .threed_front_scene import Room
13
+ from .utils import parse_threed_front_scenes
14
+
15
+
16
+ class ThreedFront(BaseDataset):
17
+ """Container for the scenes in the 3D-FRONT dataset.
18
+
19
+ Arguments
20
+ ---------
21
+ scenes: list of Room objects for all scenes in 3D-FRONT dataset
22
+ """
23
+ def __init__(self, scenes, bounds=None):
24
+ super().__init__(scenes)
25
+ assert isinstance(self.scenes[0], Room)
26
+ self._object_types = None
27
+ self._room_types = None
28
+ self._count_furniture = None
29
+ self._bbox = None
30
+
31
+ self._sizes = self._centroids = self._angles = self._objfeats = None
32
+ if bounds is not None:
33
+ self._sizes = bounds["sizes"]
34
+ self._centroids = bounds["translations"]
35
+ self._angles = bounds["angles"]
36
+ if "objfeats" in bounds.keys():
37
+ self._objfeats = bounds["objfeats"]
38
+ else:
39
+ self._objfeats = (np.array([1]), np.array([-1]), np.array([1])) # std, min, max
40
+
41
+ if "objfeats_32" in bounds.keys():
42
+ self._objfeats_32 = bounds["objfeats_32"]
43
+ else:
44
+ self._objfeats_32 = (np.array([1]), np.array([-1]), np.array([1])) # std, min, max
45
+
46
+ self._max_length = None
47
+
48
+ def __str__(self):
49
+ return "Dataset contains {} scenes with {} discrete types".format(
50
+ len(self.scenes), self.n_object_types
51
+ )
52
+
53
+ @property
54
+ def bbox(self):
55
+ """The bbox for the entire dataset is simply computed based on the
56
+ bounding boxes of all scenes in the dataset.
57
+ """
58
+ if self._bbox is None:
59
+ _bbox_min = np.array([1000, 1000, 1000])
60
+ _bbox_max = np.array([-1000, -1000, -1000])
61
+ for s in self.scenes:
62
+ bbox_min, bbox_max = s.bbox
63
+ _bbox_min = np.minimum(bbox_min, _bbox_min)
64
+ _bbox_max = np.maximum(bbox_max, _bbox_max)
65
+ self._bbox = (_bbox_min, _bbox_max)
66
+ return self._bbox
67
+
68
+ def _centroid(self, box, offset):
69
+ return box.centroid(offset)
70
+
71
+ def _size(self, box):
72
+ return box.size
73
+
74
+ def _compute_bounds(self):
75
+ _size_min = np.array([10000000]*3)
76
+ _size_max = np.array([-10000000]*3)
77
+ _centroid_min = np.array([10000000]*3)
78
+ _centroid_max = np.array([-10000000]*3)
79
+ _angle_min = np.array([10000000000])
80
+ _angle_max = np.array([-10000000000])
81
+ _objfeat_std = np.array([1])
82
+ _objfeat_min = np.array([10000000000])
83
+ _objfeat_max = np.array([-10000000000])
84
+ _objfeat_32_std = np.array([1])
85
+ _objfeat_32_min = np.array([10000000000])
86
+ _objfeat_32_max = np.array([-10000000000])
87
+ all_objfeats = []
88
+ all_objfeats_32 = []
89
+ for s in self.scenes:
90
+ for f in s.bboxes:
91
+ if np.any(f.size > 5):
92
+ print(s.scene_id, f.size, f.model_uid, f.scale)
93
+ centroid = self._centroid(f, -s.centroid)
94
+ _centroid_min = np.minimum(centroid, _centroid_min)
95
+ _centroid_max = np.maximum(centroid, _centroid_max)
96
+ _size_min = np.minimum(self._size(f), _size_min)
97
+ _size_max = np.maximum(self._size(f), _size_max)
98
+ _angle_min = np.minimum(f.z_angle, _angle_min)
99
+ _angle_max = np.maximum(f.z_angle, _angle_max)
100
+ all_objfeats.append(f.raw_model_norm_pc_lat())
101
+ all_objfeats_32.append(f.raw_model_norm_pc_lat32())
102
+
103
+ all_objfeats = np.stack(all_objfeats, axis=0)
104
+ all_objfeats_32 = np.stack(all_objfeats_32, axis=0)
105
+ _objfeat_std, _objfeat_min, _objfeat_max = np.array([all_objfeats.flatten().std()]), np.array([all_objfeats.min()]), np.array([all_objfeats.max()])
106
+ _objfeat_32_std, _objfeat_32_min, _objfeat_32_max = np.array([all_objfeats_32.flatten().std()]), np.array([all_objfeats_32.min()]), np.array([all_objfeats_32.max()])
107
+
108
+ self._sizes = (_size_min, _size_max)
109
+ self._centroids = (_centroid_min, _centroid_max)
110
+ self._angles = (_angle_min, _angle_max)
111
+ self._objfeats = (_objfeat_std, _objfeat_min, _objfeat_max)
112
+ self._objfeats_32 = ( _objfeat_32_std, _objfeat_32_min, _objfeat_32_max )
113
+
114
+ @property
115
+ def bounds(self):
116
+ return {
117
+ "translations": self.centroids,
118
+ "sizes": self.sizes,
119
+ "angles": self.angles,
120
+ "objfeats": self.objfeats,
121
+ "objfeats_32": self.objfeats_32,
122
+ }
123
+
124
+ @property
125
+ def sizes(self):
126
+ if self._sizes is None:
127
+ self._compute_bounds()
128
+ return self._sizes
129
+
130
+ @property
131
+ def centroids(self):
132
+ if self._centroids is None:
133
+ self._compute_bounds()
134
+ return self._centroids
135
+
136
+ @property
137
+ def angles(self):
138
+ if self._angles is None:
139
+ self._compute_bounds()
140
+ return self._angles
141
+
142
+ @property
143
+ def objfeats(self):
144
+ if self._objfeats is None:
145
+ self._compute_bounds()
146
+ return self._objfeats
147
+
148
+ @property
149
+ def objfeats_32(self):
150
+ if self._objfeats_32 is None:
151
+ self._compute_bounds()
152
+ return self._objfeats_32
153
+
154
+ @property
155
+ def count_furniture(self):
156
+ if self._count_furniture is None:
157
+ counts = []
158
+ for s in self.scenes:
159
+ counts.append(s.furniture_in_room)
160
+ counts = Counter(sum(counts, []))
161
+ counts = OrderedDict(sorted(counts.items(), key=lambda x: -x[1]))
162
+ self._count_furniture = counts
163
+ return self._count_furniture
164
+
165
+ @property
166
+ def class_order(self):
167
+ return dict(zip(
168
+ self.count_furniture.keys(),
169
+ range(len(self.count_furniture))
170
+ ))
171
+
172
+ @property
173
+ def class_frequencies(self):
174
+ object_counts = self.count_furniture
175
+ class_freq = {}
176
+ n_objects_in_dataset = sum(
177
+ [object_counts[k] for k, v in object_counts.items()]
178
+ )
179
+ for k, v in object_counts.items():
180
+ class_freq[k] = object_counts[k] / n_objects_in_dataset
181
+ return class_freq
182
+
183
+ @property
184
+ def object_types(self):
185
+ if self._object_types is None:
186
+ self._object_types = set()
187
+ for s in self.scenes:
188
+ self._object_types |= set(s.object_types)
189
+ self._object_types = sorted(self._object_types)
190
+ return self._object_types
191
+
192
+ @property
193
+ def room_types(self):
194
+ if self._room_types is None:
195
+ self._room_types = set([s.scene_type for s in self.scenes])
196
+ return self._room_types
197
+
198
+ @property
199
+ def class_labels(self):
200
+ return self.object_types + ["start", "end"]
201
+
202
+ # compute max_lenght for diffusion models
203
+ @property
204
+ def max_length(self):
205
+ if self._max_length is None:
206
+ _room_types = set([str(s.scene_type) for s in self.scenes])
207
+ if 'bed' in _room_types:
208
+ self._max_length = 12
209
+ elif 'living' in _room_types:
210
+ self._max_length = 21
211
+ elif 'dining' in _room_types:
212
+ self._max_length = 21
213
+ elif 'library' in _room_types:
214
+ self._max_length = 11
215
+
216
+ return self._max_length
217
+
218
+ @classmethod
219
+ def from_dataset_directory(cls, dataset_directory, path_to_model_info,
220
+ path_to_models, output_directory=None, path_to_room_masks_dir=None,
221
+ path_to_bounds=None, filter_fn=lambda s: s):
222
+ scenes = parse_threed_front_scenes(
223
+ dataset_directory,
224
+ path_to_model_info,
225
+ path_to_models,
226
+ output_directory,
227
+ path_to_room_masks_dir
228
+ )
229
+ bounds = None
230
+ if path_to_bounds:
231
+ bounds = np.load(path_to_bounds, allow_pickle=True)
232
+
233
+ return cls([s for s in map(filter_fn, scenes) if s], bounds)
234
+
235
+
236
+ class CachedRoom(object):
237
+ def __init__(
238
+ self,
239
+ scene_id,
240
+ room_layout,
241
+ floor_plan_vertices,
242
+ floor_plan_faces,
243
+ floor_plan_centroid,
244
+ class_labels,
245
+ translations,
246
+ sizes,
247
+ angles,
248
+ objfeats,
249
+ objfeats_32,
250
+ image_path
251
+ ):
252
+ self.scene_id = scene_id
253
+ self.room_layout = room_layout
254
+ self.floor_plan_faces = floor_plan_faces
255
+ self.floor_plan_vertices = floor_plan_vertices
256
+ self.floor_plan_centroid = floor_plan_centroid
257
+ self.class_labels = class_labels
258
+ self.translations = translations
259
+ self.sizes = sizes
260
+ self.angles = angles
261
+ self.objfeats = objfeats
262
+ self.objfeats_32 = objfeats_32
263
+ self.image_path = image_path
264
+
265
+ @property
266
+ def floor_plan(self):
267
+ return np.copy(self.floor_plan_vertices), \
268
+ np.copy(self.floor_plan_faces)
269
+
270
+ @property
271
+ def room_mask(self):
272
+ return self.room_layout[:, :, None]
273
+
274
+
275
+ class CachedThreedFront(ThreedFront):
276
+ def __init__(self, base_dir, config, scene_ids):
277
+ self._base_dir = base_dir
278
+ self.config = config
279
+
280
+ # add it for scene_type
281
+ self._parse_train_stats(config["train_stats"])
282
+
283
+ self._tags = sorted([
284
+ oi
285
+ for oi in os.listdir(self._base_dir)
286
+ if oi.split("_")[1] in scene_ids
287
+ ])
288
+ self._path_to_rooms = sorted([
289
+ os.path.join(self._base_dir, pi, "boxes.npz")
290
+ for pi in self._tags
291
+ ])
292
+
293
+ rendered_scene = "rendered_scene_256.png"
294
+ path_to_rendered_scene = os.path.join(
295
+ self._base_dir, self._tags[0], rendered_scene
296
+ )
297
+ if not os.path.isfile(path_to_rendered_scene):
298
+ rendered_scene = "rendered_scene_256_no_lamps.png"
299
+
300
+ path_to_rendered_scene = os.path.join(
301
+ self._base_dir, self._tags[0], rendered_scene
302
+ )
303
+ if not os.path.isfile(path_to_rendered_scene):
304
+ rendered_scene = "rendered_scene_notexture_256.png"
305
+ print('rendered_scene is :', rendered_scene)
306
+
307
+ self._path_to_renders = sorted([
308
+ os.path.join(self._base_dir, pi, rendered_scene)
309
+ for pi in self._tags
310
+ ])
311
+
312
+ def _get_room_layout(self, room_layout):
313
+ # Resize the room_layout if needed
314
+ img = Image.fromarray(room_layout[:, :, 0])
315
+ img = img.resize(
316
+ tuple(map(int, self.config["room_layout_size"].split(","))),
317
+ resample=Image.BILINEAR
318
+ )
319
+ D = np.asarray(img).astype(np.float32) / np.float32(255)
320
+ return D
321
+
322
+ def _get_room_rgb_2d(self, img_path):
323
+ # Resize the room_layout if needed
324
+ img = Image.open(img_path)
325
+ img = img.resize(
326
+ tuple(map(int, self.config["room_layout_size"].split(","))),
327
+ resample=Image.BILINEAR
328
+ )
329
+ D = np.asarray(img).astype(np.float32) / np.float32(255)
330
+ return D
331
+
332
+ @lru_cache(maxsize=32)
333
+ def __getitem__(self, i):
334
+ D = np.load(self._path_to_rooms[i])
335
+ return CachedRoom(
336
+ scene_id=D["scene_id"],
337
+ room_layout=self._get_room_layout(D["room_layout"]),
338
+ floor_plan_vertices=D["floor_plan_vertices"],
339
+ floor_plan_faces=D["floor_plan_faces"],
340
+ floor_plan_centroid=D["floor_plan_centroid"],
341
+ class_labels=D["class_labels"],
342
+ translations=D["translations"],
343
+ sizes=D["sizes"],
344
+ angles=D["angles"],
345
+ objfeats=D["objfeats"] if "objfeats" in D.keys() else None,
346
+ objfeats_32=D["objfeats_32"] if "objfeats_32" in D.keys() else None,
347
+ image_path=self._path_to_renders[i]
348
+ )
349
+
350
+ def get_room_params(self, i):
351
+ D = np.load(self._path_to_rooms[i])
352
+
353
+ room_rgb_2d = self.config.get('room_rgb_2d', False)
354
+ if room_rgb_2d:
355
+ room = self._get_room_rgb_2d(self._path_to_renders[i])
356
+ room = np.transpose(room[:, :, 0:3], (2, 0, 1))
357
+ else:
358
+ room = self._get_room_layout(D["room_layout"])
359
+ room = np.transpose(room[:, :, None], (2, 0, 1))
360
+
361
+ data_dict = {
362
+ "room_layout": room,
363
+ "class_labels": D["class_labels"],
364
+ "translations": D["translations"],
365
+ "sizes": D["sizes"],
366
+ "angles": D["angles"],
367
+
368
+ }
369
+ if "objfeats" in D.keys():
370
+ data_dict[ "objfeats" ] = D["objfeats"]
371
+ if "objfeats_32" in D.keys():
372
+ data_dict[ "objfeats_32" ] = D["objfeats_32"]
373
+
374
+ return data_dict
375
+
376
+ def __len__(self):
377
+ return len(self._path_to_rooms)
378
+
379
+ def __str__(self):
380
+ return "Dataset contains {} scenes with {} discrete types".format(
381
+ len(self), self.n_object_types
382
+ )
383
+
384
+ def _parse_train_stats(self, train_stats):
385
+ with open(os.path.join(self._base_dir, train_stats), "r") as f:
386
+ train_stats = json.load(f)
387
+ self._centroids = train_stats["bounds_translations"]
388
+ self._centroids = (
389
+ np.array(self._centroids[:3]),
390
+ np.array(self._centroids[3:])
391
+ )
392
+ self._sizes = train_stats["bounds_sizes"]
393
+ self._sizes = (np.array(self._sizes[:3]), np.array(self._sizes[3:]))
394
+ self._angles = train_stats["bounds_angles"]
395
+ self._angles = (np.array(self._angles[0]), np.array(self._angles[1]))
396
+ if "bounds_objfeats" in train_stats.keys():
397
+ self._objfeats = train_stats["bounds_objfeats"]
398
+ print("bounds_objfeats of dataset:", self._objfeats)
399
+ self._objfeats = ( np.array([self._objfeats[0]]), np.array([self._objfeats[1]]), np.array([self._objfeats[2]]) )
400
+ else:
401
+ self._objfeats = ( np.array([1]), np.array([-1]), np.array([1]) )
402
+
403
+ if "bounds_objfeats_32" in train_stats.keys():
404
+ self._objfeats_32 = train_stats["bounds_objfeats_32"]
405
+ print("bounds_objfeats_32 of dataset:", self._objfeats_32)
406
+ self._objfeats_32 = ( np.array([self._objfeats_32[0]]), np.array([self._objfeats_32[1]]), np.array([self._objfeats_32[2]]) )
407
+ else:
408
+ self._objfeats_32 = ( np.array([1]), np.array([-1]), np.array([1]) )
409
+
410
+
411
+ self._class_labels = train_stats["class_labels"]
412
+ self._object_types = train_stats["object_types"]
413
+ self._class_frequencies = train_stats["class_frequencies"]
414
+ self._class_order = train_stats["class_order"]
415
+ self._count_furniture = train_stats["count_furniture"]
416
+ self._max_length = self.config.get('max_length', 12)
417
+
418
+ @property
419
+ def class_labels(self):
420
+ return self._class_labels
421
+
422
+ @property
423
+ def object_types(self):
424
+ return self._object_types
425
+
426
+ @property
427
+ def class_frequencies(self):
428
+ return self._class_frequencies
429
+
430
+ @property
431
+ def class_order(self):
432
+ return self._class_order
433
+
434
+ @property
435
+ def count_furniture(self):
436
+ return self._count_furniture
437
+
438
+ #compute max_lenght for diffusion models
439
+ @property
440
+ def max_length(self):
441
+ return self._max_length
eval/DiffuScene/scene_synthesis/datasets/threed_front_dataset.py ADDED
@@ -0,0 +1,1084 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from math import ceil
3
+ import numpy as np
4
+
5
+ from functools import lru_cache
6
+ from scipy.ndimage import rotate
7
+
8
+ import torch
9
+ from torch.utils.data import Dataset, dataloader
10
+
11
+
12
+ class DatasetDecoratorBase(Dataset):
13
+ """A base class that helps us implement decorators for ThreeDFront-like
14
+ datasets."""
15
+ def __init__(self, dataset):
16
+ self._dataset = dataset
17
+
18
+ def __len__(self):
19
+ return len(self._dataset)
20
+
21
+ def __getitem__(self, idx):
22
+ return self._dataset[idx]
23
+
24
+ @property
25
+ def bounds(self):
26
+ return self._dataset.bounds
27
+
28
+ @property
29
+ def n_classes(self):
30
+ return self._dataset.n_classes
31
+
32
+ @property
33
+ def class_labels(self):
34
+ return self._dataset.class_labels
35
+
36
+ @property
37
+ def class_frequencies(self):
38
+ return self._dataset.class_frequencies
39
+
40
+ @property
41
+ def n_object_types(self):
42
+ return self._dataset.n_object_types
43
+
44
+ @property
45
+ def object_types(self):
46
+ return self._dataset.object_types
47
+
48
+ @property
49
+ def feature_size(self):
50
+ return self.bbox_dims + self.n_classes
51
+
52
+ @property
53
+ def bbox_dims(self):
54
+ raise NotImplementedError()
55
+
56
+ # compute max_length for diffusion models
57
+ @property
58
+ def max_length(self):
59
+ return self._dataset.max_length
60
+
61
+ def post_process(self, s):
62
+ return self._dataset.post_process(s)
63
+
64
+
65
+ class BoxOrderedDataset(DatasetDecoratorBase):
66
+ def __init__(self, dataset, box_ordering=None):
67
+ super().__init__(dataset)
68
+ self.box_ordering = box_ordering
69
+
70
+ @lru_cache(maxsize=16)
71
+ def _get_boxes(self, scene_idx):
72
+ scene = self._dataset[scene_idx]
73
+ if self.box_ordering is None:
74
+ return scene.bboxes
75
+ elif self.box_ordering == "class_frequencies":
76
+ return scene.ordered_bboxes_with_class_frequencies(
77
+ self.class_frequencies
78
+ )
79
+ else:
80
+ raise NotImplementedError()
81
+
82
+
83
+ class DataEncoder(BoxOrderedDataset):
84
+ """DataEncoder is a wrapper for all datasets we have
85
+ """
86
+ @property
87
+ def property_type(self):
88
+ raise NotImplementedError()
89
+
90
+
91
+ class RoomLayoutEncoder(DataEncoder):
92
+ @property
93
+ def property_type(self):
94
+ return "room_layout"
95
+
96
+ def __getitem__(self, idx):
97
+ """Implement the encoding for the room layout as images."""
98
+ img = self._dataset[idx].room_mask[:, :, 0:1]
99
+ return np.transpose(img, (2, 0, 1))
100
+
101
+ @property
102
+ def bbox_dims(self):
103
+ return 0
104
+
105
+
106
+ class ClassLabelsEncoder(DataEncoder):
107
+ """Implement the encoding for the class labels."""
108
+ @property
109
+ def property_type(self):
110
+ return "class_labels"
111
+
112
+ def __getitem__(self, idx):
113
+ # Make a local copy of the class labels
114
+ classes = self.class_labels
115
+
116
+ # Get the scene
117
+ boxes = self._get_boxes(idx)
118
+ L = len(boxes) # sequence length
119
+ C = len(classes) # number of classes
120
+ class_labels = np.zeros((L, C), dtype=np.float32)
121
+ for i, bs in enumerate(boxes):
122
+ class_labels[i] = bs.one_hot_label(classes)
123
+ return class_labels
124
+
125
+ @property
126
+ def bbox_dims(self):
127
+ return 0
128
+
129
+
130
+ class TranslationEncoder(DataEncoder):
131
+ @property
132
+ def property_type(self):
133
+ return "translations"
134
+
135
+ def __getitem__(self, idx):
136
+ # Get the scene
137
+ scene = self._dataset[idx]
138
+ boxes = self._get_boxes(idx)
139
+ L = len(boxes) # sequence length
140
+ translations = np.zeros((L, 3), dtype=np.float32)
141
+ for i, bs in enumerate(boxes):
142
+ translations[i] = bs.centroid(-scene.centroid)
143
+ return translations
144
+
145
+ @property
146
+ def bbox_dims(self):
147
+ return 3
148
+
149
+
150
+ class SizeEncoder(DataEncoder):
151
+ @property
152
+ def property_type(self):
153
+ return "sizes"
154
+
155
+ def __getitem__(self, idx):
156
+ # Get the scene
157
+ boxes = self._get_boxes(idx)
158
+ L = len(boxes) # sequence length
159
+ sizes = np.zeros((L, 3), dtype=np.float32)
160
+ for i, bs in enumerate(boxes):
161
+ sizes[i] = bs.size
162
+ return sizes
163
+
164
+ @property
165
+ def bbox_dims(self):
166
+ return 3
167
+
168
+
169
+ class AngleEncoder(DataEncoder):
170
+ @property
171
+ def property_type(self):
172
+ return "angles"
173
+
174
+ def __getitem__(self, idx):
175
+ # Get the scene
176
+ boxes = self._get_boxes(idx)
177
+ # Get the rotation matrix for the current scene
178
+ L = len(boxes) # sequence length
179
+ angles = np.zeros((L, 1), dtype=np.float32)
180
+ for i, bs in enumerate(boxes):
181
+ angles[i] = bs.z_angle
182
+ return angles
183
+
184
+ @property
185
+ def bbox_dims(self):
186
+ return 1
187
+
188
+ class ObjFeatEncoder(DataEncoder):
189
+ @property
190
+ def property_type(self):
191
+ return "objfeats"
192
+
193
+ def __getitem__(self, idx):
194
+ # Get the scene
195
+ boxes = self._get_boxes(idx)
196
+ # Get the rotation matrix for the current scene
197
+ L = len(boxes) # sequence length
198
+ C = len(boxes[0].raw_model_norm_pc_lat())
199
+ latents = np.zeros((L, C), dtype=np.float32)
200
+ for i, bs in enumerate(boxes):
201
+ latents[i, :] = bs.raw_model_norm_pc_lat()
202
+ return latents
203
+
204
+ @property
205
+ def bbox_dims(self):
206
+ return 64
207
+
208
+ class ObjFeat32Encoder(DataEncoder):
209
+ @property
210
+ def property_type(self):
211
+ return "objfeats_32"
212
+
213
+ def __getitem__(self, idx):
214
+ # Get the scene
215
+ boxes = self._get_boxes(idx)
216
+ # Get the rotation matrix for the current scene
217
+ L = len(boxes) # sequence length
218
+ C = len(boxes[0].raw_model_norm_pc_lat32())
219
+ latents = np.zeros((L, C), dtype=np.float32)
220
+ for i, bs in enumerate(boxes):
221
+ latents[i, :] = bs.raw_model_norm_pc_lat32()
222
+ return latents
223
+
224
+ @property
225
+ def bbox_dims(self):
226
+ return 32
227
+
228
+ class DatasetCollection(DatasetDecoratorBase):
229
+ def __init__(self, *datasets):
230
+ super().__init__(datasets[0])
231
+ self._datasets = datasets
232
+
233
+ @property
234
+ def bbox_dims(self):
235
+ return sum(d.bbox_dims for d in self._datasets)
236
+
237
+ def __getitem__(self, idx):
238
+ sample_params = {}
239
+ for di in self._datasets:
240
+ sample_params[di.property_type] = di[idx]
241
+ return sample_params
242
+
243
+ @staticmethod
244
+ def collate_fn(samples):
245
+ # We assume that all samples have the same set of keys
246
+ key_set = set(samples[0].keys()) - set(["length"])
247
+ # remove text _keys
248
+ text_keys = set( ["description", "desc_emb"] )
249
+ key_set = key_set - text_keys
250
+
251
+ # Compute the max length of the sequences in the batch
252
+ max_length = max(sample["length"] for sample in samples)
253
+
254
+ # Assume that all inputs that are 3D or 1D do not need padding.
255
+ # Otherwise, pad the first dimension.
256
+ padding_keys = set(k for k in key_set if len(samples[0][k].shape) == 2)
257
+ sample_params = {}
258
+ sample_params.update({
259
+ k: np.stack([sample[k] for sample in samples], axis=0)
260
+ for k in (key_set-padding_keys)
261
+ })
262
+
263
+ sample_params.update({
264
+ k: np.stack([
265
+ np.vstack([
266
+ sample[k],
267
+ np.zeros((max_length-len(sample[k]), sample[k].shape[1]))
268
+ ]) for sample in samples
269
+ ], axis=0)
270
+ for k in padding_keys
271
+ })
272
+ sample_params["lengths"] = np.array([
273
+ sample["length"] for sample in samples
274
+ ])
275
+
276
+ if "description" in samples[0].keys():
277
+ sample_params["description"] = [ sample["description"] for sample in samples]
278
+
279
+ if "desc_emb" in samples[0].keys():
280
+ sample_params["desc_emb"] = np.stack([sample["desc_emb"] for sample in samples], axis=0)
281
+
282
+ # Make torch tensors from the numpy tensors
283
+ torch_sample = {
284
+ k: torch.from_numpy(sample_params[k]).float()
285
+ for k in sample_params if k != "description"
286
+ }
287
+
288
+ torch_sample.update({
289
+ k: torch_sample[k][:, None]
290
+ for k in torch_sample.keys()
291
+ if "_tr" in k
292
+ })
293
+
294
+ if "description" in samples[0].keys():
295
+ torch_sample["description"] = sample_params["description"]
296
+
297
+ return torch_sample
298
+
299
+
300
+ class CachedDatasetCollection(DatasetCollection):
301
+ def __init__(self, dataset):
302
+ super().__init__(dataset)
303
+ self._dataset = dataset
304
+
305
+ def __getitem__(self, idx):
306
+ return self._dataset.get_room_params(idx)
307
+
308
+ @property
309
+ def bbox_dims(self):
310
+ return self._dataset.bbox_dims
311
+
312
+
313
+ class RotationAugmentation(DatasetDecoratorBase):
314
+ def __init__(self, dataset, min_rad=0.174533, max_rad=5.06145, fixed=False):
315
+ super().__init__(dataset)
316
+ self._min_rad = min_rad
317
+ self._max_rad = max_rad
318
+ self._fixed = fixed
319
+
320
+ @staticmethod
321
+ def rotation_matrix_around_y(theta):
322
+ R = np.zeros((3, 3))
323
+ R[0, 0] = np.cos(theta)
324
+ R[0, 2] = -np.sin(theta)
325
+ R[2, 0] = np.sin(theta)
326
+ R[2, 2] = np.cos(theta)
327
+ R[1, 1] = 1.
328
+ return R
329
+
330
+ @property
331
+ def rot_angle(self):
332
+ if np.random.rand() < 0.5:
333
+ return np.random.uniform(self._min_rad, self._max_rad)
334
+ else:
335
+ return 0.0
336
+
337
+ @property
338
+ def fixed_rot_angle(self):
339
+ if np.random.rand() < 0.25:
340
+ return np.pi * 1.5
341
+ elif np.random.rand() < 0.50:
342
+ return np.pi
343
+ elif np.random.rand() < 0.75:
344
+ return np.pi * 0.5
345
+ else:
346
+ return 0.0
347
+
348
+ def __getitem__(self, idx):
349
+ # Get the rotation matrix for the current scene
350
+ if self._fixed:
351
+ rot_angle = self.fixed_rot_angle
352
+ else:
353
+ rot_angle = self.rot_angle
354
+ R = RotationAugmentation.rotation_matrix_around_y(rot_angle)
355
+
356
+ sample_params = self._dataset[idx]
357
+ for k, v in sample_params.items():
358
+ if k == "translations":
359
+ sample_params[k] = v.dot(R)
360
+ elif k == "angles":
361
+ angle_min, angle_max = self.bounds["angles"]
362
+ sample_params[k] = \
363
+ (v + rot_angle - angle_min) % (2 * np.pi) + angle_min
364
+ elif k == "room_layout":
365
+ # Fix the ordering of the channels because it was previously
366
+ # changed
367
+ img = np.transpose(v, (1, 2, 0))
368
+ sample_params[k] = np.transpose(rotate(
369
+ img, rot_angle * 180 / np.pi, reshape=False
370
+ ), (2, 0, 1))
371
+ return sample_params
372
+
373
+
374
+
375
+ class Scale(DatasetDecoratorBase):
376
+ @staticmethod
377
+ def scale(x, minimum, maximum):
378
+ X = x.astype(np.float32)
379
+ X = np.clip(X, minimum, maximum)
380
+ X = ((X - minimum) / (maximum - minimum))
381
+ X = 2 * X - 1
382
+ return X
383
+
384
+ @staticmethod
385
+ def descale(x, minimum, maximum):
386
+ x = (x + 1) / 2
387
+ x = x * (maximum - minimum) + minimum
388
+ return x
389
+
390
+ def __getitem__(self, idx):
391
+ bounds = self.bounds
392
+ sample_params = self._dataset[idx]
393
+ for k, v in sample_params.items():
394
+ if k == "objfeats" or k == "objfeats_32":
395
+ continue
396
+
397
+ elif k in bounds:
398
+ sample_params[k] = Scale.scale(
399
+ v, bounds[k][0], bounds[k][1]
400
+ )
401
+ return sample_params
402
+
403
+ def post_process(self, s):
404
+ bounds = self.bounds
405
+ sample_params = {}
406
+ for k, v in s.items():
407
+ if k == "room_layout" or k == "class_labels" or k == "relations" or k == "description" or k == "desc_emb":
408
+ sample_params[k] = v
409
+
410
+ elif k == "objfeats" or k == "objfeats_32":
411
+ continue
412
+
413
+ else:
414
+ sample_params[k] = Scale.descale(
415
+ v, bounds[k][0], bounds[k][1]
416
+ )
417
+ return super().post_process(sample_params)
418
+
419
+ @property
420
+ def bbox_dims(self):
421
+ return 3 + 3 + 1
422
+
423
+
424
+ class Scale_CosinAngle(DatasetDecoratorBase):
425
+ @staticmethod
426
+ def scale(x, minimum, maximum):
427
+ X = x.astype(np.float32)
428
+ X = np.clip(X, minimum, maximum)
429
+ X = ((X - minimum) / (maximum - minimum))
430
+ X = 2 * X - 1
431
+ return X
432
+
433
+ @staticmethod
434
+ def descale(x, minimum, maximum):
435
+ x = (x + 1) / 2
436
+ x = x * (maximum - minimum) + minimum
437
+ return x
438
+
439
+ def __getitem__(self, idx):
440
+ bounds = self.bounds
441
+ sample_params = self._dataset[idx]
442
+ for k, v in sample_params.items():
443
+ if k == "angles":
444
+ # [cos, sin]
445
+ sample_params[k] = np.concatenate([np.cos(v), np.sin(v)], axis=-1)
446
+
447
+ elif k == "objfeats" or k == "objfeats_32":
448
+ continue
449
+
450
+ elif k in bounds:
451
+ sample_params[k] = Scale.scale(
452
+ v, bounds[k][0], bounds[k][1]
453
+ )
454
+ return sample_params
455
+
456
+ def post_process(self, s):
457
+ bounds = self.bounds
458
+ sample_params = {}
459
+ for k, v in s.items():
460
+ if k == "room_layout" or k == "class_labels" or k == "relations" or k == "description" or k == "desc_emb":
461
+ sample_params[k] = v
462
+
463
+ elif k == "angles":
464
+ # theta = arctan sin/cos y/x
465
+ sample_params[k] = np.arctan2(v[:, :, 1:2], v[:, :, 0:1])
466
+
467
+ elif k == "objfeats" or k == "objfeats_32":
468
+ continue
469
+
470
+ else:
471
+ sample_params[k] = Scale.descale(
472
+ v, bounds[k][0], bounds[k][1]
473
+ )
474
+ return super().post_process(sample_params)
475
+
476
+ @property
477
+ def bbox_dims(self):
478
+ return 3 + 3 + 2
479
+
480
+
481
+ class Scale_CosinAngle_ObjfeatsNorm(DatasetDecoratorBase):
482
+ @staticmethod
483
+ def scale(x, minimum, maximum):
484
+ X = x.astype(np.float32)
485
+ X = np.clip(X, minimum, maximum)
486
+ X = ((X - minimum) / (maximum - minimum))
487
+ X = 2 * X - 1
488
+ return X
489
+
490
+ @staticmethod
491
+ def descale(x, minimum, maximum):
492
+ x = (x + 1) / 2
493
+ x = x * (maximum - minimum) + minimum
494
+ return x
495
+
496
+ def __getitem__(self, idx):
497
+ bounds = self.bounds
498
+ sample_params = self._dataset[idx]
499
+ for k, v in sample_params.items():
500
+ if k == "angles":
501
+ # [cos, sin]
502
+ sample_params[k] = np.concatenate([np.cos(v), np.sin(v)], axis=-1)
503
+
504
+ elif k == "objfeats" or k == "objfeats_32":
505
+ sample_params[k] = Scale.scale(
506
+ v, bounds[k][1], bounds[k][2]
507
+ )
508
+
509
+ elif k in bounds:
510
+ sample_params[k] = Scale.scale(
511
+ v, bounds[k][0], bounds[k][1]
512
+ )
513
+ return sample_params
514
+
515
+ def post_process(self, s):
516
+ bounds = self.bounds
517
+ sample_params = {}
518
+ for k, v in s.items():
519
+ if k == "room_layout" or k == "class_labels" or k == "relations" or k == "description" or k == "desc_emb":
520
+ sample_params[k] = v
521
+
522
+ elif k == "angles":
523
+ # theta = arctan sin/cos y/x
524
+ sample_params[k] = np.arctan2(v[:, :, 1:2], v[:, :, 0:1])
525
+
526
+ elif k == "objfeats" or k == "objfeats_32":
527
+ sample_params[k] = Scale.descale(
528
+ v, bounds[k][1], bounds[k][2]
529
+ )
530
+
531
+ else:
532
+ sample_params[k] = Scale.descale(
533
+ v, bounds[k][0], bounds[k][1]
534
+ )
535
+ return super().post_process(sample_params)
536
+
537
+ @property
538
+ def bbox_dims(self):
539
+ return 3 + 3 + 2
540
+
541
+
542
+ class DisturbTransOrient(DatasetDecoratorBase):
543
+ def __getitem__(self, idx):
544
+ sample_params = self._dataset[idx]
545
+ for k, v in sample_params.items():
546
+ if k == "translations":
547
+ L, C = v.shape
548
+ np.random.seed(idx); noise = 0.1 * np.random.randn(L, C)
549
+ sample_params[k] = v + noise
550
+ elif k == "angles":
551
+ L, C = v.shape
552
+ np.random.seed(idx*2); noise = 0.1 * np.random.randn(L, C)
553
+ sample_params[k] = v + noise
554
+ else:
555
+ sample_params[k] = v
556
+ return sample_params
557
+
558
+
559
+ class Jitter(DatasetDecoratorBase):
560
+ def __getitem__(self, idx):
561
+ sample_params = self._dataset[idx]
562
+ for k, v in sample_params.items():
563
+ if k == "room_layout" or k == "class_labels" or k == "relations" or k == "description" or k == "desc_emb" or k == "objfeats" or k == "objfeats_32":
564
+ sample_params[k] = v
565
+ else:
566
+ sample_params[k] = v + np.random.normal(0, 0.01)
567
+ return sample_params
568
+
569
+
570
+ class Permutation(DatasetDecoratorBase):
571
+ def __init__(self, dataset, permutation_keys, permutation_axis=0):
572
+ super().__init__(dataset)
573
+ self._permutation_keys = permutation_keys
574
+ self._permutation_axis = permutation_axis
575
+
576
+ def __getitem__(self, idx):
577
+ sample_params = self._dataset[idx]
578
+
579
+ shapes = sample_params["class_labels"].shape
580
+ ordering = np.random.permutation(shapes[self._permutation_axis])
581
+
582
+ for k in self._permutation_keys:
583
+ sample_params[k] = sample_params[k][ordering]
584
+ return sample_params
585
+
586
+
587
+ class OrderedDataset(DatasetDecoratorBase):
588
+ def __init__(self, dataset, ordered_keys, box_ordering=None):
589
+ super().__init__(dataset)
590
+ self._ordered_keys = ordered_keys
591
+ self._box_ordering = box_ordering
592
+
593
+ def __getitem__(self, idx):
594
+ if self._box_ordering is None:
595
+ return self._dataset[idx]
596
+
597
+ if self._box_ordering != "class_frequencies":
598
+ raise NotImplementedError()
599
+
600
+ sample = self._dataset[idx]
601
+ order = self._get_class_frequency_order(sample)
602
+ for k in self._ordered_keys:
603
+ sample[k] = sample[k][order]
604
+ return sample
605
+
606
+ def _get_class_frequency_order(self, sample):
607
+ t = sample["translations"]
608
+ c = sample["class_labels"].argmax(-1)
609
+ class_frequencies = self.class_frequencies
610
+ class_labels = self.class_labels
611
+ f = np.array([
612
+ [class_frequencies[class_labels[ci]]]
613
+ for ci in c
614
+ ])
615
+
616
+ return np.lexsort(np.hstack([t, f]).T)[::-1]
617
+
618
+ ####
619
+ import random
620
+ try:
621
+ import torchtext
622
+ TORCHTEXT_AVAILABLE = True
623
+ except (ImportError, OSError) as e:
624
+ print(f"Warning: torchtext not available ({e}). GloVe embeddings will not work.")
625
+ TORCHTEXT_AVAILABLE = False
626
+ torchtext = None
627
+ from num2words import num2words
628
+ from nltk.tokenize import word_tokenize
629
+ from .utils_text import compute_rel, get_article
630
+ from collections import Counter, defaultdict
631
+
632
+ def dict_bbox_to_vec(dict_box):
633
+ '''
634
+ input: {'min': [1,2,3], 'max': [4,5,6]}
635
+ output: [1,2,3,4,5,6]
636
+ '''
637
+ return dict_box['min'] + dict_box['max']
638
+
639
+ def clean_obj_name(name):
640
+ return name.replace('_', ' ')
641
+
642
+
643
+ class Add_Text(DatasetDecoratorBase):
644
+ def __init__(self, dataset, eval=False, max_sentences=3, max_token_length=50): # 40
645
+ super().__init__(dataset)
646
+ self.eval = eval
647
+ self.max_sentences = max_sentences
648
+ if TORCHTEXT_AVAILABLE and torchtext is not None:
649
+ import os
650
+ cache_dir = os.path.expanduser('~/.vector_cache')
651
+ self.glove = torchtext.vocab.GloVe(name="6B", dim=50, cache=cache_dir)
652
+ else:
653
+ self.glove = None
654
+ print("Warning: GloVe embeddings not available. Text embeddings will not work.")
655
+ self.max_token_length = max_token_length
656
+
657
+ def __getitem__(self, idx):
658
+ sample = self._dataset[idx]
659
+
660
+
661
+ # Add relationship between objects
662
+ sample = self.add_relation(sample)
663
+
664
+ # Add description
665
+ sample = self.add_description(sample)
666
+
667
+ sample = self.add_glove_embeddings(sample)
668
+ return sample
669
+
670
+ def add_relation(self, sample):
671
+ '''
672
+ Add relations to sample['relations']
673
+ '''
674
+ relations = []
675
+ num_objs = len(sample['translations'])
676
+
677
+ for ndx in range(num_objs):
678
+ this_box_trans = sample['translations'][ndx, :]
679
+ this_box_sizes = sample['sizes'][ndx, :]
680
+ this_box = { 'min': list(this_box_trans-this_box_sizes), 'max': list(this_box_trans+this_box_sizes) }
681
+
682
+ # only backward relations
683
+ choices = [other for other in range(num_objs) if other < ndx]
684
+ for other_ndx in choices:
685
+ prev_box_trans = sample['translations'][other_ndx, :]
686
+ prev_box_sizes = sample['sizes'][other_ndx, :]
687
+ prev_box = { 'min': list(prev_box_trans-prev_box_sizes), 'max': list(prev_box_trans+prev_box_sizes) }
688
+ box1 = dict_bbox_to_vec(this_box)
689
+ box2 = dict_bbox_to_vec(prev_box)
690
+
691
+ relation_str, distance = compute_rel(box1, box2)
692
+ if relation_str is not None:
693
+ relation = (ndx, relation_str, other_ndx, distance)
694
+ relations.append(relation)
695
+
696
+ sample['relations'] = relations
697
+
698
+ return sample
699
+
700
+ def add_description(self, sample):
701
+ '''
702
+ Add text descriptions to each scene
703
+ sample['description'] = str is a sentence
704
+ eg: 'The room contains a bed, a table and a chair. The chair is next to the window'
705
+ '''
706
+ sentences = []
707
+ # clean object names once
708
+ classes = self.class_labels
709
+ class_index = sample['class_labels'].argmax(-1)
710
+ obj_names = list(map(clean_obj_name, [classes[ind] for ind in class_index ] ))
711
+ # objects that can be referred to
712
+ refs = []
713
+ # TODO: handle commas, use "and"
714
+ # TODO: don't repeat, get counts and pluralize
715
+ # describe the first 2 or 3 objects
716
+ if self.eval:
717
+ first_n = 3
718
+ else:
719
+ first_n = random.choice([2, 3])
720
+ # first_n = len(obj_names)
721
+ first_n_names = obj_names[:first_n]
722
+ first_n_counts = Counter(first_n_names)
723
+
724
+ s = 'The room has '
725
+ for ndx, name in enumerate(sorted(set(first_n_names), key=first_n_names.index)):
726
+ if ndx == len(set(first_n_names)) - 1 and len(set(first_n_names)) >= 2:
727
+ s += "and "
728
+ if first_n_counts[name] > 1:
729
+ s += f'{num2words(first_n_counts[name])} {name}s '
730
+ else:
731
+ s += f'{get_article(name)} {name} '
732
+ if ndx == len(set(first_n_names)) - 1:
733
+ s += ". "
734
+ if ndx < len(set(first_n_names)) - 2:
735
+ s += ', '
736
+ sentences.append(s)
737
+ refs = set(range(first_n))
738
+
739
+ # for each object, the "position" of that object within its class
740
+ # eg: sofa table table sofa
741
+ # -> 1 1 2 1
742
+ # use this to get "first", "second"
743
+
744
+ seen_counts = defaultdict(int)
745
+ in_cls_pos = [0 for _ in obj_names]
746
+ for ndx, name in enumerate(first_n_names):
747
+ seen_counts[name] += 1
748
+ in_cls_pos[ndx] = seen_counts[name]
749
+
750
+ for ndx in range(1, len(obj_names)):
751
+ # higher prob of describing the 2nd object
752
+ prob_thresh = 0.3
753
+
754
+ if self.eval:
755
+ random_num = 1.0
756
+ else:
757
+ random_num = random.random()
758
+ if random_num > prob_thresh:
759
+ # possible backward references for this object
760
+ possible_relations = [r for r in sample['relations'] \
761
+ if r[0] == ndx \
762
+ and r[2] in refs \
763
+ and r[3] < 1.5]
764
+ if len(possible_relations) == 0:
765
+ continue
766
+ # now future objects can refer to this object
767
+ refs.add(ndx)
768
+
769
+ # if we haven't seen this object already
770
+ if in_cls_pos[ndx] == 0:
771
+ # update the number of objects of this class which have been seen
772
+ seen_counts[obj_names[ndx]] += 1
773
+ # update the in class position of this object = first, second ..
774
+ in_cls_pos[ndx] = seen_counts[obj_names[ndx]]
775
+
776
+ # pick any one
777
+ if self.eval:
778
+ (n1, rel, n2, dist) = possible_relations[0]
779
+ else:
780
+ (n1, rel, n2, dist) = random.choice(possible_relations)
781
+ o1 = obj_names[n1]
782
+ o2 = obj_names[n2]
783
+
784
+ # prepend "second", "third" for repeated objects
785
+ if seen_counts[o1] > 1:
786
+ o1 = f'{num2words(in_cls_pos[n1], ordinal=True)} {o1}'
787
+ if seen_counts[o2] > 1:
788
+ o2 = f'{num2words(in_cls_pos[n2], ordinal=True)} {o2}'
789
+
790
+ # dont relate objects of the same kind
791
+ if o1 == o2:
792
+ continue
793
+
794
+ a1 = get_article(o1)
795
+
796
+ if 'touching' in rel:
797
+ if ndx in (1, 2):
798
+ s = F'The {o1} is next to the {o2}'
799
+ else:
800
+ s = F'There is {a1} {o1} next to the {o2}'
801
+ elif rel in ('left of', 'right of'):
802
+ if ndx in (1, 2):
803
+ s = f'The {o1} is to the {rel} the {o2}'
804
+ else:
805
+ s = f'There is {a1} {o1} to the {rel} the {o2}'
806
+ elif rel in ('surrounding', 'inside', 'behind', 'in front of', 'on', 'above'):
807
+ if ndx in (1, 2):
808
+ s = F'The {o1} is {rel} the {o2}'
809
+ else:
810
+ s = F'There is {a1} {o1} {rel} the {o2}'
811
+ s += ' . '
812
+ sentences.append(s)
813
+
814
+ # set back into the sample
815
+ sample['description'] = sentences
816
+
817
+ # delete sample['relations']
818
+ del sample['relations']
819
+ return sample
820
+
821
+ def add_glove_embeddings(self, sample):
822
+ sentence = ''.join(sample['description'][:self.max_sentences])
823
+ sample['description'] = sentence
824
+ tokens = list(word_tokenize(sentence))
825
+ # pad to maximum length
826
+ tokens += ['<pad>'] * (self.max_token_length - len(tokens))
827
+
828
+ # embed words
829
+ sample['desc_emb'] = torch.cat([self.glove[token].unsqueeze(0) for token in tokens]).numpy()
830
+
831
+ return sample
832
+
833
+
834
+ class Autoregressive(DatasetDecoratorBase):
835
+ def __getitem__(self, idx):
836
+ sample_params = self._dataset[idx]
837
+
838
+ sample_params_target = {}
839
+ # Compute the target from the input
840
+ for k, v in sample_params.items():
841
+ if k == "room_layout" or k == "length":
842
+ pass
843
+
844
+ elif k == "relations" or k == "description" or k == "desc_emb":
845
+ pass
846
+
847
+ elif k == "class_labels":
848
+ class_labels = np.copy(v)
849
+ L, C = class_labels.shape
850
+ # Add the end label the end of each sequence
851
+ end_label = np.eye(C)[-1]
852
+ sample_params_target[k+"_tr"] = np.vstack([
853
+ class_labels, end_label
854
+ ])
855
+ else:
856
+ p = np.copy(v)
857
+ # Set the attributes to for the end symbol
858
+ _, C = p.shape
859
+ sample_params_target[k+"_tr"] = np.vstack([p, np.zeros(C)])
860
+
861
+ sample_params.update(sample_params_target)
862
+
863
+ # Add the number of bounding boxes in the scene
864
+ sample_params["length"] = sample_params["class_labels"].shape[0]
865
+
866
+ return sample_params
867
+
868
+ def collate_fn(self, samples):
869
+ return DatasetCollection.collate_fn(samples)
870
+
871
+ @property
872
+ def bbox_dims(self):
873
+ return 7
874
+
875
+
876
+ class AutoregressiveWOCM(Autoregressive):
877
+ def __getitem__(self, idx):
878
+ sample_params = super().__getitem__(idx)
879
+
880
+ # Split the boxes and generate input sequences and target boxes
881
+ L, C = sample_params["class_labels"].shape
882
+ n_boxes = np.random.randint(0, L+1)
883
+
884
+ for k, v in sample_params.items():
885
+ if k == "room_layout" or k == "length":
886
+ pass
887
+
888
+ elif k == "relations" or k == "description" or k == "desc_emb":
889
+ pass
890
+
891
+ else:
892
+ if "_tr" in k:
893
+ sample_params[k] = v[n_boxes]
894
+ else:
895
+ sample_params[k] = v[:n_boxes]
896
+ sample_params["length"] = n_boxes
897
+
898
+ return sample_params
899
+
900
+ class Diffusion(DatasetDecoratorBase):
901
+ def __getitem__(self, idx):
902
+ sample_params = self._dataset[idx]
903
+ max_length = self._dataset.max_length
904
+
905
+ # Add the number of bounding boxes in the scene
906
+ sample_params["length"] = sample_params["class_labels"].shape[0]
907
+
908
+ sample_params_target = {}
909
+ # Compute the target from the input
910
+ for k, v in sample_params.items():
911
+ if k == "room_layout" or k == "length":
912
+ pass
913
+
914
+ elif k == "relations" or k == "description" or k == "desc_emb":
915
+ #print(k, len(v))
916
+ pass
917
+
918
+ elif k == "class_labels":
919
+ class_labels = np.copy(v)
920
+ # Delete the start label
921
+ new_class_labels = np.concatenate([class_labels[:, :-2], class_labels[:, -1:]], axis=-1) #hstack
922
+ L, C = new_class_labels.shape
923
+ # Pad the end label in the end of each sequence, and convert the class labels to -1, 1
924
+ end_label = np.eye(C)[-1]
925
+ sample_params_target[k] = np.vstack([
926
+ new_class_labels, np.tile(end_label[None, :], [max_length - L, 1])
927
+ ]).astype(np.float32) * 2.0 - 1.0
928
+
929
+ else:
930
+ p = np.copy(v)
931
+ # Set the attributes to for the end symbol
932
+ L, C = p.shape
933
+ sample_params_target[k] = np.vstack([p, np.tile(np.zeros(C)[None, :], [max_length - L, 1])]).astype(np.float32)
934
+
935
+ sample_params.update(sample_params_target)
936
+
937
+ return sample_params
938
+
939
+ def collate_fn(self, samples):
940
+ ''' Collater that puts each data field into a tensor with outer dimension
941
+ batch size.
942
+ Args:
943
+ samples: samples
944
+ '''
945
+
946
+ samples = list(filter(lambda x: x is not None, samples))
947
+ return dataloader.default_collate(samples)
948
+
949
+ @property
950
+ def bbox_dims(self):
951
+ return 7
952
+
953
+
954
+ def dataset_encoding_factory(
955
+ name,
956
+ dataset,
957
+ augmentations=None,
958
+ box_ordering=None
959
+ ):
960
+ # NOTE: The ordering might change after augmentations so really it should
961
+ # be done after the augmentations. For class frequencies it is fine
962
+ # though.
963
+ if "cached" in name:
964
+ if "objfeats" in name:
965
+ if "lat32" in name:
966
+ dataset_collection = OrderedDataset(
967
+ CachedDatasetCollection(dataset),
968
+ ["class_labels", "translations", "sizes", "angles", "objfeats_32"],
969
+ box_ordering=box_ordering
970
+ )
971
+ print("use lat32 as objfeats")
972
+ else:
973
+ dataset_collection = OrderedDataset(
974
+ CachedDatasetCollection(dataset),
975
+ ["class_labels", "translations", "sizes", "angles", "objfeats"],
976
+ box_ordering=box_ordering
977
+ )
978
+ print("use lat64 as objfeats")
979
+ else:
980
+ dataset_collection = OrderedDataset(
981
+ CachedDatasetCollection(dataset),
982
+ ["class_labels", "translations", "sizes", "angles"],
983
+ box_ordering=box_ordering
984
+ )
985
+ else:
986
+ box_ordered_dataset = BoxOrderedDataset(
987
+ dataset,
988
+ box_ordering
989
+ )
990
+ room_layout = RoomLayoutEncoder(box_ordered_dataset)
991
+ class_labels = ClassLabelsEncoder(box_ordered_dataset)
992
+ translations = TranslationEncoder(box_ordered_dataset)
993
+ sizes = SizeEncoder(box_ordered_dataset)
994
+ angles = AngleEncoder(box_ordered_dataset)
995
+ objfeats = ObjFeatEncoder(box_ordered_dataset)
996
+ objfeats_32 = ObjFeat32Encoder(box_ordered_dataset)
997
+
998
+ dataset_collection = DatasetCollection(
999
+ room_layout,
1000
+ class_labels,
1001
+ translations,
1002
+ sizes,
1003
+ angles,
1004
+ objfeats,
1005
+ objfeats_32,
1006
+ )
1007
+
1008
+ if name == "basic":
1009
+ return DatasetCollection(
1010
+ class_labels,
1011
+ translations,
1012
+ sizes,
1013
+ angles,
1014
+ objfeats,
1015
+ objfeats_32
1016
+ )
1017
+
1018
+ if isinstance(augmentations, list):
1019
+ for aug_type in augmentations:
1020
+ if aug_type == "rotations":
1021
+ print("Applying rotation augmentations")
1022
+ dataset_collection = RotationAugmentation(dataset_collection)
1023
+ elif aug_type == "fixed_rotations":
1024
+ print("Applying fixed rotation augmentations")
1025
+ dataset_collection = RotationAugmentation(dataset_collection, fixed=True)
1026
+ elif aug_type == "jitter":
1027
+ print("Applying jittering augmentations")
1028
+ dataset_collection = Jitter(dataset_collection)
1029
+
1030
+ if "textfix" in name:
1031
+ print("add text into input dict for evalation")
1032
+ dataset_collection = Add_Text(dataset_collection, eval=True)
1033
+ elif "text" in name:
1034
+ print("add text into input dict for training")
1035
+ dataset_collection = Add_Text(dataset_collection, eval=False)
1036
+
1037
+
1038
+ # Scale the input
1039
+ if "cosin_angle" in name or "objfeatsnorm" in name:
1040
+ print('use consin_angles instead of original angles, AND use normalized objfeats')
1041
+ dataset_collection = Scale_CosinAngle_ObjfeatsNorm(dataset_collection)
1042
+ elif "cosin_angle" in name:
1043
+ print('use consin_angles instead of original angles')
1044
+ dataset_collection = Scale_CosinAngle(dataset_collection)
1045
+ else:
1046
+ dataset_collection = Scale(dataset_collection)
1047
+
1048
+
1049
+ permute_keys = ["class_labels", "translations", "sizes", "angles"]
1050
+ if "objfeats" in name:
1051
+ if "lat32" in name:
1052
+ permute_keys.append("objfeats_32")
1053
+ else:
1054
+ permute_keys.append("objfeats")
1055
+ print("permute keys are:", permute_keys)
1056
+
1057
+
1058
+ # for diffusion (represent objectness as the last channel of class label)
1059
+ if "diffusion" in name:
1060
+ if "eval" in name:
1061
+ return dataset_collection
1062
+ elif "wocm_no_prm" in name:
1063
+ return Diffusion(dataset_collection)
1064
+ elif "wocm" in name:
1065
+ dataset_collection = Permutation(
1066
+ dataset_collection,
1067
+ permute_keys,
1068
+ )
1069
+ return Diffusion(dataset_collection)
1070
+
1071
+ # for autoregressive model
1072
+ elif "autoregressive" in name:
1073
+ if "eval" in name:
1074
+ return dataset_collection
1075
+ elif "wocm_no_prm" in name:
1076
+ return AutoregressiveWOCM(dataset_collection)
1077
+ elif "wocm" in name:
1078
+ dataset_collection = Permutation(
1079
+ dataset_collection,
1080
+ permute_keys,
1081
+ )
1082
+ return AutoregressiveWOCM(dataset_collection)
1083
+ else:
1084
+ raise NotImplementedError()
eval/DiffuScene/scene_synthesis/datasets/threed_front_scene.py ADDED
@@ -0,0 +1,664 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from collections import Counter
3
+ from dataclasses import dataclass
4
+ from functools import cached_property, reduce, lru_cache
5
+ import json
6
+ import os
7
+
8
+ import numpy as np
9
+ from PIL import Image
10
+
11
+ import trimesh
12
+
13
+ from .common import BaseScene
14
+
15
+ from simple_3dviz import Lines, Mesh, Spherecloud
16
+ from simple_3dviz.renderables.textured_mesh import Material, TexturedMesh
17
+ from simple_3dviz.behaviours.keyboard import SnapshotOnKey
18
+ from simple_3dviz.behaviours.misc import LightToCamera
19
+
20
+
21
+ def rotation_matrix(axis, theta):
22
+ """Axis-angle rotation matrix from 3D-Front-Toolbox."""
23
+ axis = np.asarray(axis)
24
+ axis = axis / np.sqrt(np.dot(axis, axis))
25
+ a = np.cos(theta / 2.0)
26
+ b, c, d = -axis * np.sin(theta / 2.0)
27
+ aa, bb, cc, dd = a * a, b * b, c * c, d * d
28
+ bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
29
+ return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
30
+ [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
31
+ [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
32
+
33
+
34
+ @dataclass
35
+ class Asset:
36
+ """Contains the information for each 3D-FUTURE model."""
37
+ super_category: str
38
+ category: str
39
+ style: str
40
+ theme: str
41
+ material: str
42
+
43
+ @property
44
+ def label(self):
45
+ return self.category
46
+
47
+
48
+ class ModelInfo(object):
49
+ """Contains all the information for all 3D-FUTURE models.
50
+
51
+ Arguments
52
+ ---------
53
+ model_info_data: list of dictionaries containing the information
54
+ regarding the 3D-FUTURE models.
55
+ """
56
+ def __init__(self, model_info_data):
57
+ self.model_info_data = model_info_data
58
+ self._model_info = None
59
+ # List to keep track of the different styles, themes
60
+ self._styles = []
61
+ self._themes = []
62
+ self._categories = []
63
+ self._super_categories = []
64
+ self._materials = []
65
+
66
+ @property
67
+ def model_info(self):
68
+ if self._model_info is None:
69
+ self._model_info = {}
70
+ # Create a dictionary of all models/assets in the dataset
71
+ for m in self.model_info_data:
72
+ # Keep track of the different styles
73
+ if m["style"] not in self._styles and m["style"] is not None:
74
+ self._styles.append(m["style"])
75
+ # Keep track of the different themes
76
+ if m["theme"] not in self._themes and m["theme"] is not None:
77
+ self._themes.append(m["theme"])
78
+ # Keep track of the different super-categories
79
+ if m["super-category"] not in self._super_categories and m["super-category"] is not None:
80
+ self._super_categories.append(m["super-category"])
81
+ # Keep track of the different categories
82
+ if m["category"] not in self._categories and m["category"] is not None:
83
+ self._categories.append(m["category"])
84
+ # Keep track of the different categories
85
+ if m["material"] not in self._materials and m["material"] is not None:
86
+ self._materials.append(m["material"])
87
+
88
+ super_cat = "unknown_super-category"
89
+ cat = "unknown_category"
90
+
91
+ if m["super-category"] is not None:
92
+ super_cat = m["super-category"].lower().replace(" / ", "/")
93
+
94
+ if m["category"] is not None:
95
+ cat = m["category"].lower().replace(" / ", "/")
96
+
97
+ self._model_info[m["model_id"]] = Asset(
98
+ super_cat,
99
+ cat,
100
+ m["style"],
101
+ m["theme"],
102
+ m["material"]
103
+ )
104
+
105
+ return self._model_info
106
+
107
+ @property
108
+ def styles(self):
109
+ return self._styles
110
+
111
+ @property
112
+ def themes(self):
113
+ return self._themes
114
+
115
+ @property
116
+ def materials(self):
117
+ return self._materials
118
+
119
+ @property
120
+ def categories(self):
121
+ return set([s.lower().replace(" / ", "/") for s in self._categories])
122
+
123
+ @property
124
+ def super_categories(self):
125
+ return set([
126
+ s.lower().replace(" / ", "/")
127
+ for s in self._super_categories
128
+ ])
129
+
130
+ @classmethod
131
+ def from_file(cls, path_to_model_info):
132
+ with open(path_to_model_info, "rb") as f:
133
+ model_info = json.load(f)
134
+
135
+ return cls(model_info)
136
+
137
+
138
+ class BaseThreedFutureModel(object):
139
+ def __init__(self, model_uid, model_jid, position, rotation, scale):
140
+ self.model_uid = model_uid
141
+ self.model_jid = model_jid
142
+ self.position = position
143
+ self.rotation = rotation
144
+ self.scale = scale
145
+
146
+ def _transform(self, vertices):
147
+ # the following code is adapted and slightly simplified from the
148
+ # 3D-Front toolbox (json2obj.py). It basically scales, rotates and
149
+ # translates the model based on the model info.
150
+ ref = [0, 0, 1]
151
+ axis = np.cross(ref, self.rotation[1:])
152
+ theta = np.arccos(np.dot(ref, self.rotation[1:]))*2
153
+ vertices = vertices * self.scale
154
+ if np.sum(axis) != 0 and not np.isnan(theta):
155
+ R = rotation_matrix(axis, theta)
156
+ vertices = vertices.dot(R.T)
157
+ vertices += self.position
158
+
159
+ return vertices
160
+
161
+ def mesh_renderable(
162
+ self,
163
+ colors=(0.5, 0.5, 0.5, 1.0),
164
+ offset=[[0, 0, 0]],
165
+ with_texture=False
166
+ ):
167
+ if not with_texture:
168
+ m = self.raw_model_transformed(offset)
169
+ return Mesh.from_faces(m.vertices, m.faces, colors=colors)
170
+ else:
171
+ m = TexturedMesh.from_file(self.raw_model_path)
172
+ m.scale(self.scale)
173
+ # Extract the predicted affine transformation to position the
174
+ # mesh
175
+ theta = self.z_angle
176
+ R = np.zeros((3, 3))
177
+ R[0, 0] = np.cos(theta)
178
+ R[0, 2] = -np.sin(theta)
179
+ R[2, 0] = np.sin(theta)
180
+ R[2, 2] = np.cos(theta)
181
+ R[1, 1] = 1.
182
+
183
+ # Apply the transformations in order to correctly position the mesh
184
+ m.affine_transform(R=R, t=self.position)
185
+ m.affine_transform(t=offset)
186
+ return m
187
+
188
+
189
+ class ThreedFutureModel(BaseThreedFutureModel):
190
+ def __init__(
191
+ self,
192
+ model_uid,
193
+ model_jid,
194
+ model_info,
195
+ position,
196
+ rotation,
197
+ scale,
198
+ path_to_models
199
+ ):
200
+ super().__init__(model_uid, model_jid, position, rotation, scale)
201
+ self.model_info = model_info
202
+ self.path_to_models = path_to_models
203
+ self._label = None
204
+
205
+ @property
206
+ def raw_model_path(self):
207
+ return os.path.join(
208
+ self.path_to_models,
209
+ self.model_jid,
210
+ "raw_model.obj"
211
+ )
212
+
213
+ # add normalized point cloud of raw_model
214
+ @property
215
+ def raw_model_norm_pc_path(self):
216
+ return os.path.join(
217
+ self.path_to_models,
218
+ self.model_jid,
219
+ "raw_model_norm_pc.npz"
220
+ )
221
+
222
+ @property
223
+ def raw_model_norm_pc_lat_path(self):
224
+ return os.path.join(
225
+ self.path_to_models,
226
+ self.model_jid,
227
+ "raw_model_norm_pc_lat.npz"
228
+ )
229
+
230
+ @property
231
+ def raw_model_norm_pc_lat32_path(self):
232
+ return os.path.join(
233
+ self.path_to_models,
234
+ self.model_jid,
235
+ "raw_model_norm_pc_lat32.npz"
236
+ )
237
+
238
+
239
+
240
+ @property
241
+ def texture_image_path(self):
242
+ return os.path.join(
243
+ self.path_to_models,
244
+ self.model_jid,
245
+ "texture.png"
246
+ )
247
+
248
+ @property
249
+ def path_to_bbox_vertices(self):
250
+ return os.path.join(
251
+ self.path_to_models,
252
+ self.model_jid,
253
+ "bbox_vertices.npy"
254
+ )
255
+
256
+ # add normalized point cloud of raw_model
257
+ def raw_model_norm_pc(self):
258
+ points = np.load(self.raw_model_norm_pc_path)["points"].astype(np.float32)
259
+ return points
260
+
261
+ def raw_model_norm_pc_lat(self):
262
+ latent = np.load(self.raw_model_norm_pc_lat_path)["latent"].astype(np.float32)
263
+ return latent
264
+
265
+ def raw_model_norm_pc_lat32(self):
266
+ latent = np.load(self.raw_model_norm_pc_lat32_path)["latent"].astype(np.float32)
267
+ return latent
268
+
269
+ def raw_model(self):
270
+ try:
271
+ return trimesh.load(
272
+ self.raw_model_path,
273
+ process=False,
274
+ force="mesh",
275
+ skip_materials=True,
276
+ skip_texture=True
277
+ )
278
+ except Exception as e:
279
+ print(f"Loading model failed: {self.raw_model_path}", flush=True)
280
+ print(f"Error: {e}", flush=True)
281
+ raise
282
+
283
+ def raw_model_transformed(self, offset=[[0, 0, 0]]):
284
+ model = self.raw_model()
285
+ faces = np.array(model.faces)
286
+ vertices = self._transform(np.array(model.vertices)) + offset
287
+
288
+ return trimesh.Trimesh(vertices, faces)
289
+
290
+ def centroid(self, offset=[[0, 0, 0]]):
291
+ return self.corners(offset).mean(axis=0)
292
+
293
+ @cached_property
294
+ def size(self):
295
+ corners = self.corners()
296
+ return np.array([
297
+ np.sqrt(np.sum((corners[4]-corners[0])**2))/2,
298
+ np.sqrt(np.sum((corners[2]-corners[0])**2))/2,
299
+ np.sqrt(np.sum((corners[1]-corners[0])**2))/2
300
+ ])
301
+
302
+ def bottom_center(self, offset=[[0, 0, 0]]):
303
+ centroid = self.centroid(offset)
304
+ size = self.size
305
+ return np.array([centroid[0], centroid[1]-size[1], centroid[2]])
306
+
307
+ @cached_property
308
+ def bottom_size(self):
309
+ return self.size * [1, 2, 1]
310
+
311
+ @cached_property
312
+ def z_angle(self):
313
+ # See BaseThreedFutureModel._transform for the origin of the following
314
+ # code.
315
+ ref = [0, 0, 1]
316
+ axis = np.cross(ref, self.rotation[1:])
317
+ theta = np.arccos(np.dot(ref, self.rotation[1:]))*2
318
+
319
+ if np.sum(axis) == 0 or np.isnan(theta):
320
+ return 0
321
+
322
+ assert np.dot(axis, [1, 0, 1]) == 0
323
+ assert 0 <= theta <= 2*np.pi
324
+
325
+ if theta >= np.pi:
326
+ theta = theta - 2*np.pi
327
+
328
+ return np.sign(axis[1]) * theta
329
+
330
+ @property
331
+ def label(self):
332
+ if self._label is None:
333
+ self._label = self.model_info.label
334
+ return self._label
335
+
336
+ @label.setter
337
+ def label(self, _label):
338
+ self._label = _label
339
+
340
+ def corners(self, offset=[[0, 0, 0]]):
341
+ try:
342
+ bbox_vertices = np.load(self.path_to_bbox_vertices, mmap_mode="r")
343
+ except:
344
+ bbox_vertices = np.array(self.raw_model().bounding_box.vertices)
345
+ np.save(self.path_to_bbox_vertices, bbox_vertices)
346
+ c = self._transform(bbox_vertices)
347
+ return c + offset
348
+
349
+ def origin_renderable(self, offset=[[0, 0, 0]]):
350
+ corners = self.corners(offset)
351
+ return Lines(
352
+ [
353
+ corners[0], corners[4],
354
+ corners[0], corners[2],
355
+ corners[0], corners[1]
356
+ ],
357
+ colors=np.array([
358
+ [1.0, 0.0, 0.0, 1.0],
359
+ [1.0, 0.0, 0.0, 1.0],
360
+ [0.0, 1.0, 0.0, 1.0],
361
+ [0.0, 1.0, 0.0, 1.0],
362
+ [0.0, 0.0, 1.0, 1.0],
363
+ [0.0, 0.0, 1.0, 1.0]
364
+ ]),
365
+ width=0.02
366
+ )
367
+
368
+ def bbox_corners_renderable(
369
+ self, sizes=0.1, colors=(1, 0, 0), offset=[[0, 0, 0]]
370
+ ):
371
+ return Spherecloud(self.corners(offset), sizes=sizes, colors=colors)
372
+
373
+ def bbox_renderable(
374
+ self, colors=(0.00392157, 0., 0.40392157, 1.), offset=[[0, 0, 0]]
375
+ ):
376
+ alpha = np.array(self.size)[None]
377
+ epsilon = np.ones((1, 2)) * 0.1
378
+ translation = np.array(self.centroid(offset))[None]
379
+ R = np.zeros((1, 3, 3))
380
+ theta = np.array(self.z_angle)
381
+ R[:, 0, 0] = np.cos(theta)
382
+ R[:, 0, 2] = -np.sin(theta)
383
+ R[:, 2, 0] = np.sin(theta)
384
+ R[:, 2, 2] = np.cos(theta)
385
+ R[:, 1, 1] = 1.
386
+
387
+ return Mesh.from_superquadrics(alpha, epsilon, translation, R, colors)
388
+
389
+ def show(
390
+ self,
391
+ behaviours=[LightToCamera()],
392
+ with_bbox_corners=False,
393
+ offset=[[0, 0, 0]]
394
+ ):
395
+ renderables = self.mesh_renderable(offset=offset)
396
+ if with_bbox_corners:
397
+ renderables += [self.bbox_corners_renderable(offset=offset)]
398
+ show(renderables, behaviours=behaviours)
399
+
400
+ def one_hot_label(self, all_labels):
401
+ return np.eye(len(all_labels))[self.int_label(all_labels)]
402
+
403
+ def int_label(self, all_labels):
404
+ return all_labels.index(self.label)
405
+
406
+ def copy_from_other_model(self, other_model):
407
+ model = ThreedFutureModel(
408
+ model_uid=other_model.model_uid,
409
+ model_jid=other_model.model_jid,
410
+ model_info=other_model.model_info,
411
+ position=self.position,
412
+ rotation=self.rotation,
413
+ scale=other_model.scale,
414
+ path_to_models=self.path_to_models
415
+ )
416
+ model.label = self.label
417
+ return model
418
+
419
+
420
+ class ThreedFutureExtra(BaseThreedFutureModel):
421
+ def __init__(
422
+ self,
423
+ model_uid,
424
+ model_jid,
425
+ xyz,
426
+ faces,
427
+ model_type,
428
+ position,
429
+ rotation,
430
+ scale
431
+ ):
432
+ super().__init__(model_uid, model_jid, position, rotation, scale)
433
+ self.xyz = xyz
434
+ self.faces = faces
435
+ self.model_type = model_type
436
+
437
+ def raw_model_transformed(self, offset=[[0, 0, 0]]):
438
+ vertices = self._transform(np.array(self.xyz)) + offset
439
+ faces = np.array(self.faces)
440
+ return trimesh.Trimesh(vertices, faces)
441
+
442
+ def show(
443
+ self, behaviours=[LightToCamera(), SnapshotOnKey()], offset=[[0, 0, 0]]
444
+ ):
445
+ renderables = self.mesh_renderable(offset=offset)
446
+ show(renderables, behaviours)
447
+
448
+
449
+ class Room(BaseScene):
450
+ def __init__(
451
+ self, scene_id, scene_type, bboxes, extras, json_path,
452
+ path_to_room_masks_dir=None
453
+ ):
454
+ super().__init__(scene_id, scene_type, bboxes)
455
+ self.json_path = json_path
456
+ self.extras = extras
457
+
458
+ self.uid = "_".join([self.json_path, scene_id])
459
+ self.path_to_room_masks_dir = path_to_room_masks_dir
460
+ if path_to_room_masks_dir is not None:
461
+ self.path_to_room_mask = os.path.join(
462
+ self.path_to_room_masks_dir, self.uid, "room_mask.png"
463
+ )
464
+ else:
465
+ self.path_to_room_mask = None
466
+
467
+ @property
468
+ def floor(self):
469
+ return [ei for ei in self.extras if ei.model_type == "Floor"][0]
470
+
471
+ @property
472
+ @lru_cache(maxsize=512)
473
+ def bbox(self):
474
+ corners = np.empty((0, 3))
475
+ for f in self.bboxes:
476
+ corners = np.vstack([corners, f.corners()])
477
+ return np.min(corners, axis=0), np.max(corners, axis=0)
478
+
479
+ @cached_property
480
+ def bboxes_centroid(self):
481
+ a, b = self.bbox
482
+ return (a+b)/2
483
+
484
+ @property
485
+ def furniture_in_room(self):
486
+ return [f.label for f in self.bboxes]
487
+
488
+ @property
489
+ def floor_plan(self):
490
+ def cat_mesh(m1, m2):
491
+ v1, f1 = m1
492
+ v2, f2 = m2
493
+ v = np.vstack([v1, v2])
494
+ f = np.vstack([f1, f2 + len(v1)])
495
+ return v, f
496
+
497
+ # Compute the full floor plan
498
+ vertices, faces = reduce(
499
+ cat_mesh,
500
+ ((ei.xyz, ei.faces) for ei in self.extras if ei.model_type == "Floor")
501
+ )
502
+ return np.copy(vertices), np.copy(faces)
503
+
504
+ @cached_property
505
+ def floor_plan_bbox(self):
506
+ vertices, faces = self.floor_plan
507
+ return np.min(vertices, axis=0), np.max(vertices, axis=0)
508
+
509
+ @cached_property
510
+ def floor_plan_centroid(self):
511
+ a, b = self.floor_plan_bbox
512
+ return (a+b)/2
513
+
514
+ @cached_property
515
+ def centroid(self):
516
+ return self.floor_plan_centroid
517
+
518
+ @property
519
+ def count_furniture_in_room(self):
520
+ return Counter(self.furniture_in_room)
521
+
522
+ @property
523
+ def room_mask(self):
524
+ return self.room_mask_rotated(0)
525
+
526
+ def room_mask_rotated(self, angle=0):
527
+ # The angle is in rad
528
+ im = Image.open(self.path_to_room_mask).convert("RGB")
529
+ # Downsample the room_mask image by applying bilinear interpolation
530
+ im = im.rotate(angle * 180 / np.pi, resample=Image.BICUBIC)
531
+
532
+ return np.asarray(im).astype(np.float32) / np.float32(255)
533
+
534
+ def category_counts(self, class_labels):
535
+ """List of category counts in the room
536
+ """
537
+ print(class_labels)
538
+ if "start" in class_labels and "end" in class_labels:
539
+ class_labels = class_labels[:-2]
540
+ category_counts = [0]*len(class_labels)
541
+
542
+ for di in self.furniture_in_room:
543
+ category_counts[class_labels.index(di)] += 1
544
+ return category_counts
545
+
546
+ def ordered_bboxes_with_centroid(self):
547
+ centroids = np.array([f.centroid(-self.centroid) for f in self.bboxes])
548
+ ordering = np.lexsort(centroids.T)
549
+ ordered_bboxes = [self.bboxes[i] for i in ordering]
550
+
551
+ return ordered_bboxes
552
+
553
+ def ordered_bboxes_with_class_labels(self, all_labels):
554
+ centroids = np.array([f.centroid(-self.centroid) for f in self.bboxes])
555
+ int_labels = np.array(
556
+ [[f.int_label(all_labels)] for f in self.bboxes]
557
+ )
558
+ ordering = np.lexsort(np.hstack([centroids, int_labels]).T)
559
+ ordered_bboxes = [self.bboxes[i] for i in ordering]
560
+
561
+ return ordered_bboxes
562
+
563
+ def ordered_bboxes_with_class_frequencies(self, class_order):
564
+ centroids = np.array([f.centroid(-self.centroid) for f in self.bboxes])
565
+ label_order = np.array([
566
+ [class_order[f.label]] for f in self.bboxes
567
+ ])
568
+ ordering = np.lexsort(np.hstack([centroids, label_order]).T)
569
+ ordered_bboxes = [self.bboxes[i] for i in ordering[::-1]]
570
+
571
+ return ordered_bboxes
572
+
573
+ def furniture_renderables(
574
+ self,
575
+ colors=(0.5, 0.5, 0.5),
576
+ with_bbox_corners=False,
577
+ with_origin=False,
578
+ with_bboxes=False,
579
+ with_objects_offset=False,
580
+ with_floor_plan_offset=False,
581
+ with_floor_plan=False,
582
+ with_texture=False
583
+ ):
584
+ if with_objects_offset:
585
+ offset = -self.bboxes_centroid
586
+ elif with_floor_plan_offset:
587
+ offset = -self.floor_plan_centroid
588
+ else:
589
+ offset = [[0, 0, 0]]
590
+
591
+ renderables = [
592
+ f.mesh_renderable(
593
+ colors=colors, offset=offset, with_texture=with_texture
594
+ )
595
+ for f in self.bboxes
596
+ ]
597
+ if with_origin:
598
+ renderables += [f.origin_renderable(offset) for f in self.bboxes]
599
+ if with_bbox_corners:
600
+ for f in self.bboxes:
601
+ renderables += [f.bbox_corners_renderable(offset=offset)]
602
+ if with_bboxes:
603
+ for f in self.bboxes:
604
+ renderables += [f.bbox_renderable(offset=offset)]
605
+ if with_floor_plan:
606
+ vertices, faces = self.floor_plan
607
+ vertices = vertices + offset
608
+ renderables += [
609
+ Mesh.from_faces(vertices, faces, colors=(0.8, 0.8, 0.8, 0.6))
610
+ ]
611
+ return renderables
612
+
613
+ def show(
614
+ self,
615
+ behaviours=[LightToCamera(), SnapshotOnKey()],
616
+ with_bbox_corners=False,
617
+ with_bboxes=False,
618
+ with_objects_offset=False,
619
+ with_floor_plan_offset=False,
620
+ with_floor_plan=False,
621
+ background=(1.0, 1.0, 1.0, 1.0),
622
+ camera_target=(0, 0, 0),
623
+ camera_position=(-2, -2, -2),
624
+ up_vector=(0, 0, 1),
625
+ window_size=(512, 512)
626
+ ):
627
+ renderables = self.furniture_renderables(
628
+ with_bbox_corners=with_bbox_corners,
629
+ with_bboxes=with_bboxes,
630
+ with_objects_offset=with_objects_offset,
631
+ with_floor_plan_offset=with_floor_plan_offset,
632
+ with_floor_plan=with_floor_plan
633
+ )
634
+ show(
635
+ renderables, behaviours=behaviours,
636
+ size=window_size, camera_position=camera_position,
637
+ camera_target=camera_target, up_vector=up_vector,
638
+ background=background
639
+ )
640
+
641
+ def augment_room(self, objects_dataset):
642
+ bboxes = self.bboxes
643
+ # Randomly pick an asset to be augmented
644
+ bi = np.random.choice(self.bboxes)
645
+ query_label = bi.label
646
+ query_size = bi.size + np.random.normal(0, 0.02)
647
+ # Retrieve the new asset based on the size of the picked asset
648
+ furniture = objects_dataset.get_closest_furniture_to_box(
649
+ query_label, query_size
650
+ )
651
+ bi_retrieved = bi.copy_from_other_model(furniture)
652
+
653
+ new_bboxes = [
654
+ box for box in bboxes if not box == bi
655
+ ] + [bi_retrieved]
656
+
657
+ return Room(
658
+ scene_id=self.scene_id + "_augm",
659
+ scene_type=self.scene_type,
660
+ bboxes=new_bboxes,
661
+ extras=self.extras,
662
+ json_path=self.json_path,
663
+ path_to_room_masks_dir=self.path_to_room_masks_dir
664
+ )
eval/DiffuScene/scene_synthesis/datasets/threed_future_dataset.py ADDED
@@ -0,0 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import numpy as np
3
+ import pickle
4
+
5
+ from .utils import parse_threed_future_models
6
+ import torch
7
+ from torch.utils.data import Dataset, dataloader
8
+
9
+ class ThreedFutureDataset(object):
10
+ def __init__(self, objects):
11
+ assert len(objects) > 0
12
+ self.objects = objects
13
+
14
+ def __len__(self):
15
+ return len(self.objects)
16
+
17
+ def __str__(self):
18
+ return "Dataset contains {} objects with {} discrete types".format(
19
+ len(self)
20
+ )
21
+
22
+ def __getitem__(self, idx):
23
+ return self.objects[idx]
24
+
25
+ def _filter_objects_by_label(self, label):
26
+ return [oi for oi in self.objects if oi.label == label]
27
+
28
+ def get_closest_furniture_to_box(self, query_label, query_size):
29
+ objects = self._filter_objects_by_label(query_label)
30
+
31
+ mses = {}
32
+ for i, oi in enumerate(objects):
33
+ mses[oi] = np.sum((oi.size - query_size)**2, axis=-1)
34
+ sorted_mses = [k for k, v in sorted(mses.items(), key=lambda x:x[1])]
35
+ return sorted_mses[0]
36
+
37
+ def get_closest_furniture_to_2dbox(self, query_label, query_size):
38
+ objects = self._filter_objects_by_label(query_label)
39
+
40
+ mses = {}
41
+ for i, oi in enumerate(objects):
42
+ mses[oi] = (
43
+ (oi.size[0] - query_size[0])**2 +
44
+ (oi.size[2] - query_size[1])**2
45
+ )
46
+ sorted_mses = [k for k, v in sorted(mses.items(), key=lambda x: x[1])]
47
+ return sorted_mses[0]
48
+
49
+ def get_closest_furniture_to_objfeats(self, query_label, query_objfeat):
50
+ objects = self._filter_objects_by_label(query_label)
51
+
52
+ mses = {}
53
+ for i, oi in enumerate(objects):
54
+ if query_objfeat.shape[0] == 32:
55
+ mses[oi] = np.sum((oi.raw_model_norm_pc_lat32() - query_objfeat)**2, axis=-1)
56
+ else:
57
+ mses[oi] = np.sum((oi.raw_model_norm_pc_lat() - query_objfeat)**2, axis=-1)
58
+ sorted_mses = [k for k, v in sorted(mses.items(), key=lambda x:x[1])]
59
+ return sorted_mses[0]
60
+
61
+ def get_closest_furniture_to_objfeats_and_size(self, query_label, query_objfeat, query_size):
62
+ objects = self._filter_objects_by_label(query_label)
63
+
64
+ objs = []
65
+ mses_feat = []
66
+ mses_size = []
67
+ for i, oi in enumerate(objects):
68
+ if query_objfeat.shape[0] == 32:
69
+ mses_feat.append( np.sum((oi.raw_model_norm_pc_lat32() - query_objfeat)**2, axis=-1) )
70
+ else:
71
+ mses_feat.append( np.sum((oi.raw_model_norm_pc_lat() - query_objfeat)**2, axis=-1) )
72
+ mses_size.append( np.sum((oi.size - query_size)**2, axis=-1) )
73
+ objs.append(oi)
74
+
75
+ ind = np.lexsort( (mses_feat, mses_size) )
76
+ return objs[ ind[0] ]
77
+
78
+ @classmethod
79
+ def from_dataset_directory(
80
+ cls, dataset_directory, path_to_model_info, path_to_models, output_directory=None,
81
+ ):
82
+ objects = parse_threed_future_models(
83
+ dataset_directory, path_to_models, path_to_model_info, output_directory
84
+ )
85
+ return cls(objects)
86
+
87
+ @classmethod
88
+ def from_pickled_dataset(cls, path_to_pickled_dataset):
89
+ with open(path_to_pickled_dataset, "rb") as f:
90
+ dataset = pickle.load(f)
91
+ return dataset
92
+
93
+
94
+ class ThreedFutureNormPCDataset(ThreedFutureDataset):
95
+ def __init__(self, objects, num_samples=2048):
96
+ super().__init__(objects)
97
+
98
+ self.num_samples = num_samples
99
+
100
+ def __len__(self):
101
+ return len(self.objects)
102
+
103
+ def __str__(self):
104
+ return "Dataset contains {} objects with {} discrete types".format(
105
+ len(self)
106
+ )
107
+
108
+ def __getitem__(self, idx):
109
+ obj = self.objects[idx]
110
+ model_uid = obj.model_uid
111
+ model_jid = obj.model_jid
112
+ raw_model_path = obj.raw_model_path
113
+ raw_model_norm_pc_path = obj.raw_model_norm_pc_path
114
+ points = obj.raw_model_norm_pc()
115
+
116
+ points_subsample = points[np.random.choice(points.shape[0], self.num_samples), :]
117
+
118
+ points_torch = torch.from_numpy(points_subsample).float()
119
+ data_dict = {"points": points_torch, "idx": idx}
120
+ return data_dict
121
+
122
+ def get_model_jid(self, idx):
123
+ obj = self.objects[idx]
124
+ model_uid = obj.model_uid
125
+ model_jid = obj.model_jid
126
+ data_dict = {"model_jid": model_jid}
127
+ return data_dict
128
+
129
+ def collate_fn(self, samples):
130
+ ''' Collater that puts each data field into a tensor with outer dimension
131
+ batch size.
132
+ Args:
133
+ samples: samples
134
+ '''
135
+
136
+ samples = list(filter(lambda x: x is not None, samples))
137
+ return dataloader.default_collate(samples)
138
+
139
+