1
This commit is contained in:
102
configs/generation/slat_flow_img_dit_L_64l8p2_fp16.json
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102
configs/generation/slat_flow_img_dit_L_64l8p2_fp16.json
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{
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"models": {
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"denoiser": {
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"name": "ElasticSLatFlowModel",
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"args": {
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"resolution": 64,
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"in_channels": 8,
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"out_channels": 8,
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"model_channels": 1024,
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"cond_channels": 1024,
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"num_blocks": 24,
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"num_heads": 16,
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||||||
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"mlp_ratio": 4,
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||||||
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"patch_size": 2,
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"num_io_res_blocks": 2,
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"io_block_channels": [128],
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"pe_mode": "ape",
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"qk_rms_norm": true,
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"use_fp16": true
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}
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}
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},
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"dataset": {
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"name": "ImageConditionedSLat",
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"args": {
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"latent_model": "dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16",
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"min_aesthetic_score": 4.5,
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"max_num_voxels": 32768,
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"image_size": 518,
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"normalization": {
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||||||
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"mean": [
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||||||
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-2.1687545776367188,
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||||||
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-0.004347046371549368,
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||||||
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-0.13352349400520325,
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||||||
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-0.08418072760105133,
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||||||
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-0.5271206498146057,
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||||||
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0.7238689064979553,
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||||||
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-1.1414450407028198,
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||||||
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1.2039363384246826
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||||||
|
],
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||||||
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"std": [
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||||||
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2.377650737762451,
|
||||||
|
2.386378288269043,
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||||||
|
2.124418020248413,
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||||||
|
2.1748552322387695,
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||||||
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2.663944721221924,
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||||||
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2.371192216873169,
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||||||
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2.6217446327209473,
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||||||
|
2.684523105621338
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||||||
|
]
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},
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"pretrained_slat_dec": "microsoft/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16"
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}
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},
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"trainer": {
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"name": "ImageConditionedSparseFlowMatchingCFGTrainer",
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"args": {
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"max_steps": 1000000,
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"batch_size_per_gpu": 8,
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"batch_split": 4,
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"optimizer": {
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"name": "AdamW",
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"args": {
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"lr": 0.0001,
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"weight_decay": 0.0
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}
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},
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"ema_rate": [
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0.9999
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],
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"fp16_mode": "inflat_all",
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"fp16_scale_growth": 0.001,
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"elastic": {
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||||||
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"name": "LinearMemoryController",
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"args": {
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||||||
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"target_ratio": 0.75,
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"max_mem_ratio_start": 0.5
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}
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},
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"grad_clip": {
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"name": "AdaptiveGradClipper",
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"args": {
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"max_norm": 1.0,
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"clip_percentile": 95
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}
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},
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"i_log": 500,
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"i_sample": 10000,
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"i_save": 10000,
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"p_uncond": 0.1,
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"t_schedule": {
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"name": "logitNormal",
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"args": {
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"mean": 1.0,
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"std": 1.0
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}
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},
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"sigma_min": 1e-5,
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"image_cond_model": "dinov2_vitl14_reg"
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}
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}
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}
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101
configs/generation/slat_flow_txt_dit_B_64l8p2_fp16.json
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101
configs/generation/slat_flow_txt_dit_B_64l8p2_fp16.json
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@@ -0,0 +1,101 @@
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{
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"models": {
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"denoiser": {
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"name": "ElasticSLatFlowModel",
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"args": {
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"resolution": 64,
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"in_channels": 8,
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"out_channels": 8,
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"model_channels": 768,
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"cond_channels": 768,
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"num_blocks": 12,
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"num_heads": 12,
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"mlp_ratio": 4,
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"patch_size": 2,
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"num_io_res_blocks": 2,
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"io_block_channels": [128],
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"pe_mode": "ape",
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"qk_rms_norm": true,
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"use_fp16": true
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}
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}
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},
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"dataset": {
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||||||
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"name": "TextConditionedSLat",
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||||||
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"args": {
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||||||
|
"latent_model": "dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16",
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|
"min_aesthetic_score": 4.5,
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"max_num_voxels": 32768,
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|
"normalization": {
|
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|
"mean": [
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|
-2.1687545776367188,
|
||||||
|
-0.004347046371549368,
|
||||||
|
-0.13352349400520325,
|
||||||
|
-0.08418072760105133,
|
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|
-0.5271206498146057,
|
||||||
|
0.7238689064979553,
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|
-1.1414450407028198,
|
||||||
|
1.2039363384246826
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||||||
|
],
|
||||||
|
"std": [
|
||||||
|
2.377650737762451,
|
||||||
|
2.386378288269043,
|
||||||
|
2.124418020248413,
|
||||||
|
2.1748552322387695,
|
||||||
|
2.663944721221924,
|
||||||
|
2.371192216873169,
|
||||||
|
2.6217446327209473,
|
||||||
|
2.684523105621338
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"pretrained_slat_dec": "microsoft/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16"
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||||||
|
}
|
||||||
|
},
|
||||||
|
"trainer": {
|
||||||
|
"name": "TextConditionedSparseFlowMatchingCFGTrainer",
|
||||||
|
"args": {
|
||||||
|
"max_steps": 1000000,
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||||||
|
"batch_size_per_gpu": 16,
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||||||
|
"batch_split": 4,
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||||||
|
"optimizer": {
|
||||||
|
"name": "AdamW",
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||||||
|
"args": {
|
||||||
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"lr": 0.0001,
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||||||
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"weight_decay": 0.0
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||||||
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}
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||||||
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},
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||||||
|
"ema_rate": [
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|
0.9999
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||||||
|
],
|
||||||
|
"fp16_mode": "inflat_all",
|
||||||
|
"fp16_scale_growth": 0.001,
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||||||
|
"elastic": {
|
||||||
|
"name": "LinearMemoryController",
|
||||||
|
"args": {
|
||||||
|
"target_ratio": 0.75,
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||||||
|
"max_mem_ratio_start": 0.5
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||||||
|
}
|
||||||
|
},
|
||||||
|
"grad_clip": {
|
||||||
|
"name": "AdaptiveGradClipper",
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||||||
|
"args": {
|
||||||
|
"max_norm": 1.0,
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||||||
|
"clip_percentile": 95
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}
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},
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||||||
|
"i_log": 500,
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||||||
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"i_sample": 10000,
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||||||
|
"i_save": 10000,
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||||||
|
"p_uncond": 0.1,
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"t_schedule": {
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||||||
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"name": "logitNormal",
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||||||
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"args": {
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"mean": 1.0,
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||||||
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"std": 1.0
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}
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||||||
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},
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||||||
|
"sigma_min": 1e-5,
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"text_cond_model": "openai/clip-vit-large-patch14"
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||||||
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}
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||||||
|
}
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||||||
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}
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101
configs/generation/slat_flow_txt_dit_L_64l8p2_fp16.json
Normal file
101
configs/generation/slat_flow_txt_dit_L_64l8p2_fp16.json
Normal file
@@ -0,0 +1,101 @@
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|||||||
|
{
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"models": {
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|
"denoiser": {
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||||||
|
"name": "ElasticSLatFlowModel",
|
||||||
|
"args": {
|
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"resolution": 64,
|
||||||
|
"in_channels": 8,
|
||||||
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"out_channels": 8,
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"model_channels": 1024,
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"cond_channels": 768,
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"num_blocks": 24,
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||||||
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"num_heads": 16,
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||||||
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"mlp_ratio": 4,
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||||||
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"patch_size": 2,
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||||||
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"num_io_res_blocks": 2,
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||||||
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"io_block_channels": [128],
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||||||
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"pe_mode": "ape",
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||||||
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"qk_rms_norm": true,
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||||||
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"use_fp16": true
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||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"dataset": {
|
||||||
|
"name": "TextConditionedSLat",
|
||||||
|
"args": {
|
||||||
|
"latent_model": "dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16",
|
||||||
|
"min_aesthetic_score": 4.5,
|
||||||
|
"max_num_voxels": 32768,
|
||||||
|
"normalization": {
|
||||||
|
"mean": [
|
||||||
|
-2.1687545776367188,
|
||||||
|
-0.004347046371549368,
|
||||||
|
-0.13352349400520325,
|
||||||
|
-0.08418072760105133,
|
||||||
|
-0.5271206498146057,
|
||||||
|
0.7238689064979553,
|
||||||
|
-1.1414450407028198,
|
||||||
|
1.2039363384246826
|
||||||
|
],
|
||||||
|
"std": [
|
||||||
|
2.377650737762451,
|
||||||
|
2.386378288269043,
|
||||||
|
2.124418020248413,
|
||||||
|
2.1748552322387695,
|
||||||
|
2.663944721221924,
|
||||||
|
2.371192216873169,
|
||||||
|
2.6217446327209473,
|
||||||
|
2.684523105621338
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"pretrained_slat_dec": "microsoft/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"trainer": {
|
||||||
|
"name": "TextConditionedSparseFlowMatchingCFGTrainer",
|
||||||
|
"args": {
|
||||||
|
"max_steps": 1000000,
|
||||||
|
"batch_size_per_gpu": 8,
|
||||||
|
"batch_split": 4,
|
||||||
|
"optimizer": {
|
||||||
|
"name": "AdamW",
|
||||||
|
"args": {
|
||||||
|
"lr": 0.0001,
|
||||||
|
"weight_decay": 0.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"ema_rate": [
|
||||||
|
0.9999
|
||||||
|
],
|
||||||
|
"fp16_mode": "inflat_all",
|
||||||
|
"fp16_scale_growth": 0.001,
|
||||||
|
"elastic": {
|
||||||
|
"name": "LinearMemoryController",
|
||||||
|
"args": {
|
||||||
|
"target_ratio": 0.75,
|
||||||
|
"max_mem_ratio_start": 0.5
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"grad_clip": {
|
||||||
|
"name": "AdaptiveGradClipper",
|
||||||
|
"args": {
|
||||||
|
"max_norm": 1.0,
|
||||||
|
"clip_percentile": 95
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"i_log": 500,
|
||||||
|
"i_sample": 10000,
|
||||||
|
"i_save": 10000,
|
||||||
|
"p_uncond": 0.1,
|
||||||
|
"t_schedule": {
|
||||||
|
"name": "logitNormal",
|
||||||
|
"args": {
|
||||||
|
"mean": 1.0,
|
||||||
|
"std": 1.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"sigma_min": 1e-5,
|
||||||
|
"text_cond_model": "openai/clip-vit-large-patch14"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
101
configs/generation/slat_flow_txt_dit_XL_64l8p2_fp16.json
Normal file
101
configs/generation/slat_flow_txt_dit_XL_64l8p2_fp16.json
Normal file
@@ -0,0 +1,101 @@
|
|||||||
|
{
|
||||||
|
"models": {
|
||||||
|
"denoiser": {
|
||||||
|
"name": "ElasticSLatFlowModel",
|
||||||
|
"args": {
|
||||||
|
"resolution": 64,
|
||||||
|
"in_channels": 8,
|
||||||
|
"out_channels": 8,
|
||||||
|
"model_channels": 1280,
|
||||||
|
"cond_channels": 768,
|
||||||
|
"num_blocks": 28,
|
||||||
|
"num_heads": 16,
|
||||||
|
"mlp_ratio": 4,
|
||||||
|
"patch_size": 2,
|
||||||
|
"num_io_res_blocks": 3,
|
||||||
|
"io_block_channels": [256],
|
||||||
|
"pe_mode": "ape",
|
||||||
|
"qk_rms_norm": true,
|
||||||
|
"use_fp16": true
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"dataset": {
|
||||||
|
"name": "TextConditionedSLat",
|
||||||
|
"args": {
|
||||||
|
"latent_model": "dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16",
|
||||||
|
"min_aesthetic_score": 4.5,
|
||||||
|
"max_num_voxels": 32768,
|
||||||
|
"normalization": {
|
||||||
|
"mean": [
|
||||||
|
-2.1687545776367188,
|
||||||
|
-0.004347046371549368,
|
||||||
|
-0.13352349400520325,
|
||||||
|
-0.08418072760105133,
|
||||||
|
-0.5271206498146057,
|
||||||
|
0.7238689064979553,
|
||||||
|
-1.1414450407028198,
|
||||||
|
1.2039363384246826
|
||||||
|
],
|
||||||
|
"std": [
|
||||||
|
2.377650737762451,
|
||||||
|
2.386378288269043,
|
||||||
|
2.124418020248413,
|
||||||
|
2.1748552322387695,
|
||||||
|
2.663944721221924,
|
||||||
|
2.371192216873169,
|
||||||
|
2.6217446327209473,
|
||||||
|
2.684523105621338
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"pretrained_slat_dec": "microsoft/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"trainer": {
|
||||||
|
"name": "TextConditionedSparseFlowMatchingCFGTrainer",
|
||||||
|
"args": {
|
||||||
|
"max_steps": 1000000,
|
||||||
|
"batch_size_per_gpu": 4,
|
||||||
|
"batch_split": 4,
|
||||||
|
"optimizer": {
|
||||||
|
"name": "AdamW",
|
||||||
|
"args": {
|
||||||
|
"lr": 0.0001,
|
||||||
|
"weight_decay": 0.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"ema_rate": [
|
||||||
|
0.9999
|
||||||
|
],
|
||||||
|
"fp16_mode": "inflat_all",
|
||||||
|
"fp16_scale_growth": 0.001,
|
||||||
|
"elastic": {
|
||||||
|
"name": "LinearMemoryController",
|
||||||
|
"args": {
|
||||||
|
"target_ratio": 0.75,
|
||||||
|
"max_mem_ratio_start": 0.5
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"grad_clip": {
|
||||||
|
"name": "AdaptiveGradClipper",
|
||||||
|
"args": {
|
||||||
|
"max_norm": 1.0,
|
||||||
|
"clip_percentile": 95
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"i_log": 500,
|
||||||
|
"i_sample": 10000,
|
||||||
|
"i_save": 10000,
|
||||||
|
"p_uncond": 0.1,
|
||||||
|
"t_schedule": {
|
||||||
|
"name": "logitNormal",
|
||||||
|
"args": {
|
||||||
|
"mean": 1.0,
|
||||||
|
"std": 1.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"sigma_min": 1e-5,
|
||||||
|
"text_cond_model": "openai/clip-vit-large-patch14"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
71
configs/vae/slat_vae_dec_rf_swin8_B_64l8_fp16.json
Normal file
71
configs/vae/slat_vae_dec_rf_swin8_B_64l8_fp16.json
Normal file
@@ -0,0 +1,71 @@
|
|||||||
|
{
|
||||||
|
"models": {
|
||||||
|
"decoder": {
|
||||||
|
"name": "ElasticSLatRadianceFieldDecoder",
|
||||||
|
"args": {
|
||||||
|
"resolution": 64,
|
||||||
|
"model_channels": 768,
|
||||||
|
"latent_channels": 8,
|
||||||
|
"num_blocks": 12,
|
||||||
|
"num_heads": 12,
|
||||||
|
"mlp_ratio": 4,
|
||||||
|
"attn_mode": "swin",
|
||||||
|
"window_size": 8,
|
||||||
|
"use_fp16": true,
|
||||||
|
"representation_config": {
|
||||||
|
"rank": 16,
|
||||||
|
"dim": 8
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"dataset": {
|
||||||
|
"name": "SLat2Render",
|
||||||
|
"args": {
|
||||||
|
"image_size": 512,
|
||||||
|
"latent_model": "dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16",
|
||||||
|
"min_aesthetic_score": 4.5,
|
||||||
|
"max_num_voxels": 32768
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"trainer": {
|
||||||
|
"name": "SLatVaeRadianceFieldDecoderTrainer",
|
||||||
|
"args": {
|
||||||
|
"max_steps": 1000000,
|
||||||
|
"batch_size_per_gpu": 4,
|
||||||
|
"batch_split": 2,
|
||||||
|
"optimizer": {
|
||||||
|
"name": "AdamW",
|
||||||
|
"args": {
|
||||||
|
"lr": 1e-4,
|
||||||
|
"weight_decay": 0.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"ema_rate": [
|
||||||
|
0.9999
|
||||||
|
],
|
||||||
|
"fp16_mode": "inflat_all",
|
||||||
|
"fp16_scale_growth": 0.001,
|
||||||
|
"elastic": {
|
||||||
|
"name": "LinearMemoryController",
|
||||||
|
"args": {
|
||||||
|
"target_ratio": 0.75,
|
||||||
|
"max_mem_ratio_start": 0.5
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"grad_clip": {
|
||||||
|
"name": "AdaptiveGradClipper",
|
||||||
|
"args": {
|
||||||
|
"max_norm": 1.0,
|
||||||
|
"clip_percentile": 95
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"i_log": 500,
|
||||||
|
"i_sample": 10000,
|
||||||
|
"i_save": 10000,
|
||||||
|
"loss_type": "l1",
|
||||||
|
"lambda_ssim": 0.2,
|
||||||
|
"lambda_lpips": 0.2
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
105
configs/vae/slat_vae_enc_dec_gs_swin8_B_64l8_fp16.json
Normal file
105
configs/vae/slat_vae_enc_dec_gs_swin8_B_64l8_fp16.json
Normal file
@@ -0,0 +1,105 @@
|
|||||||
|
{
|
||||||
|
"models": {
|
||||||
|
"encoder": {
|
||||||
|
"name": "ElasticSLatEncoder",
|
||||||
|
"args": {
|
||||||
|
"resolution": 64,
|
||||||
|
"in_channels": 1024,
|
||||||
|
"model_channels": 768,
|
||||||
|
"latent_channels": 8,
|
||||||
|
"num_blocks": 12,
|
||||||
|
"num_heads": 12,
|
||||||
|
"mlp_ratio": 4,
|
||||||
|
"attn_mode": "swin",
|
||||||
|
"window_size": 8,
|
||||||
|
"use_fp16": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"decoder": {
|
||||||
|
"name": "ElasticSLatGaussianDecoder",
|
||||||
|
"args": {
|
||||||
|
"resolution": 64,
|
||||||
|
"model_channels": 768,
|
||||||
|
"latent_channels": 8,
|
||||||
|
"num_blocks": 12,
|
||||||
|
"num_heads": 12,
|
||||||
|
"mlp_ratio": 4,
|
||||||
|
"attn_mode": "swin",
|
||||||
|
"window_size": 8,
|
||||||
|
"use_fp16": true,
|
||||||
|
"representation_config": {
|
||||||
|
"lr": {
|
||||||
|
"_xyz": 1.0,
|
||||||
|
"_features_dc": 1.0,
|
||||||
|
"_opacity": 1.0,
|
||||||
|
"_scaling": 1.0,
|
||||||
|
"_rotation": 0.1
|
||||||
|
},
|
||||||
|
"perturb_offset": true,
|
||||||
|
"voxel_size": 1.5,
|
||||||
|
"num_gaussians": 32,
|
||||||
|
"2d_filter_kernel_size": 0.1,
|
||||||
|
"3d_filter_kernel_size": 9e-4,
|
||||||
|
"scaling_bias": 4e-3,
|
||||||
|
"opacity_bias": 0.1,
|
||||||
|
"scaling_activation": "softplus"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"dataset": {
|
||||||
|
"name": "SparseFeat2Render",
|
||||||
|
"args": {
|
||||||
|
"image_size": 512,
|
||||||
|
"model": "dinov2_vitl14_reg",
|
||||||
|
"resolution": 64,
|
||||||
|
"min_aesthetic_score": 4.5,
|
||||||
|
"max_num_voxels": 32768
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"trainer": {
|
||||||
|
"name": "SLatVaeGaussianTrainer",
|
||||||
|
"args": {
|
||||||
|
"max_steps": 1000000,
|
||||||
|
"batch_size_per_gpu": 4,
|
||||||
|
"batch_split": 2,
|
||||||
|
"optimizer": {
|
||||||
|
"name": "AdamW",
|
||||||
|
"args": {
|
||||||
|
"lr": 1e-4,
|
||||||
|
"weight_decay": 0.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"ema_rate": [
|
||||||
|
0.9999
|
||||||
|
],
|
||||||
|
"fp16_mode": "inflat_all",
|
||||||
|
"fp16_scale_growth": 0.001,
|
||||||
|
"elastic": {
|
||||||
|
"name": "LinearMemoryController",
|
||||||
|
"args": {
|
||||||
|
"target_ratio": 0.75,
|
||||||
|
"max_mem_ratio_start": 0.5
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"grad_clip": {
|
||||||
|
"name": "AdaptiveGradClipper",
|
||||||
|
"args": {
|
||||||
|
"max_norm": 1.0,
|
||||||
|
"clip_percentile": 95
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"i_log": 500,
|
||||||
|
"i_sample": 10000,
|
||||||
|
"i_save": 10000,
|
||||||
|
"loss_type": "l1",
|
||||||
|
"lambda_ssim": 0.2,
|
||||||
|
"lambda_lpips": 0.2,
|
||||||
|
"lambda_kl": 1e-06,
|
||||||
|
"regularizations": {
|
||||||
|
"lambda_vol": 10000.0,
|
||||||
|
"lambda_opacity": 0.001
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
92
dataset_toolkits/datasets/Toys4k.py
Normal file
92
dataset_toolkits/datasets/Toys4k.py
Normal file
@@ -0,0 +1,92 @@
|
|||||||
|
import os
|
||||||
|
import re
|
||||||
|
import argparse
|
||||||
|
import zipfile
|
||||||
|
from concurrent.futures import ThreadPoolExecutor
|
||||||
|
from tqdm import tqdm
|
||||||
|
import pandas as pd
|
||||||
|
from utils import get_file_hash
|
||||||
|
|
||||||
|
|
||||||
|
def add_args(parser: argparse.ArgumentParser):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
def get_metadata(**kwargs):
|
||||||
|
metadata = pd.read_csv("hf://datasets/JeffreyXiang/TRELLIS-500K/Toys4k.csv")
|
||||||
|
return metadata
|
||||||
|
|
||||||
|
|
||||||
|
def download(metadata, output_dir, **kwargs):
|
||||||
|
os.makedirs(output_dir, exist_ok=True)
|
||||||
|
|
||||||
|
if not os.path.exists(os.path.join(output_dir, 'raw', 'toys4k_blend_files.zip')):
|
||||||
|
print("\033[93m")
|
||||||
|
print("Toys4k have to be downloaded manually")
|
||||||
|
print(f"Please download the toys4k_blend_files.zip file and place it in the {output_dir}/raw directory")
|
||||||
|
print("Visit https://github.com/rehg-lab/lowshot-shapebias/tree/main/toys4k for more information")
|
||||||
|
print("\033[0m")
|
||||||
|
raise FileNotFoundError("toys4k_blend_files.zip not found")
|
||||||
|
|
||||||
|
downloaded = {}
|
||||||
|
metadata = metadata.set_index("file_identifier")
|
||||||
|
with zipfile.ZipFile(os.path.join(output_dir, 'raw', 'toys4k_blend_files.zip')) as zip_ref:
|
||||||
|
with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor, \
|
||||||
|
tqdm(total=len(metadata), desc="Extracting") as pbar:
|
||||||
|
def worker(instance: str) -> str:
|
||||||
|
try:
|
||||||
|
zip_ref.extract(os.path.join('toys4k_blend_files', instance), os.path.join(output_dir, 'raw'))
|
||||||
|
sha256 = get_file_hash(os.path.join(output_dir, 'raw/toys4k_blend_files', instance))
|
||||||
|
pbar.update()
|
||||||
|
return sha256
|
||||||
|
except Exception as e:
|
||||||
|
pbar.update()
|
||||||
|
print(f"Error extracting for {instance}: {e}")
|
||||||
|
return None
|
||||||
|
|
||||||
|
sha256s = executor.map(worker, metadata.index)
|
||||||
|
executor.shutdown(wait=True)
|
||||||
|
|
||||||
|
for k, sha256 in zip(metadata.index, sha256s):
|
||||||
|
if sha256 is not None:
|
||||||
|
if sha256 == metadata.loc[k, "sha256"]:
|
||||||
|
downloaded[sha256] = os.path.join("raw/toys4k_blend_files", k)
|
||||||
|
else:
|
||||||
|
print(f"Error downloading {k}: sha256s do not match")
|
||||||
|
|
||||||
|
return pd.DataFrame(downloaded.items(), columns=['sha256', 'local_path'])
|
||||||
|
|
||||||
|
|
||||||
|
def foreach_instance(metadata, output_dir, func, max_workers=None, desc='Processing objects') -> pd.DataFrame:
|
||||||
|
import os
|
||||||
|
from concurrent.futures import ThreadPoolExecutor
|
||||||
|
from tqdm import tqdm
|
||||||
|
|
||||||
|
# load metadata
|
||||||
|
metadata = metadata.to_dict('records')
|
||||||
|
|
||||||
|
# processing objects
|
||||||
|
records = []
|
||||||
|
max_workers = max_workers or os.cpu_count()
|
||||||
|
try:
|
||||||
|
with ThreadPoolExecutor(max_workers=max_workers) as executor, \
|
||||||
|
tqdm(total=len(metadata), desc=desc) as pbar:
|
||||||
|
def worker(metadatum):
|
||||||
|
try:
|
||||||
|
local_path = metadatum['local_path']
|
||||||
|
sha256 = metadatum['sha256']
|
||||||
|
file = os.path.join(output_dir, local_path)
|
||||||
|
record = func(file, sha256)
|
||||||
|
if record is not None:
|
||||||
|
records.append(record)
|
||||||
|
pbar.update()
|
||||||
|
except Exception as e:
|
||||||
|
print(f"Error processing object {sha256}: {e}")
|
||||||
|
pbar.update()
|
||||||
|
|
||||||
|
executor.map(worker, metadata)
|
||||||
|
executor.shutdown(wait=True)
|
||||||
|
except:
|
||||||
|
print("Error happened during processing.")
|
||||||
|
|
||||||
|
return pd.DataFrame.from_records(records)
|
||||||
1
dataset_toolkits/setup.sh
Normal file
1
dataset_toolkits/setup.sh
Normal file
@@ -0,0 +1 @@
|
|||||||
|
pip install pillow imageio imageio-ffmpeg tqdm easydict opencv-python-headless pandas open3d objaverse huggingface_hub open_clip_torch
|
||||||
43
dataset_toolkits/utils.py
Normal file
43
dataset_toolkits/utils.py
Normal file
@@ -0,0 +1,43 @@
|
|||||||
|
from typing import *
|
||||||
|
import hashlib
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
def get_file_hash(file: str) -> str:
|
||||||
|
sha256 = hashlib.sha256()
|
||||||
|
# Read the file from the path
|
||||||
|
with open(file, "rb") as f:
|
||||||
|
# Update the hash with the file content
|
||||||
|
for byte_block in iter(lambda: f.read(4096), b""):
|
||||||
|
sha256.update(byte_block)
|
||||||
|
return sha256.hexdigest()
|
||||||
|
|
||||||
|
# ===============LOW DISCREPANCY SEQUENCES================
|
||||||
|
|
||||||
|
PRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53]
|
||||||
|
|
||||||
|
def radical_inverse(base, n):
|
||||||
|
val = 0
|
||||||
|
inv_base = 1.0 / base
|
||||||
|
inv_base_n = inv_base
|
||||||
|
while n > 0:
|
||||||
|
digit = n % base
|
||||||
|
val += digit * inv_base_n
|
||||||
|
n //= base
|
||||||
|
inv_base_n *= inv_base
|
||||||
|
return val
|
||||||
|
|
||||||
|
def halton_sequence(dim, n):
|
||||||
|
return [radical_inverse(PRIMES[dim], n) for dim in range(dim)]
|
||||||
|
|
||||||
|
def hammersley_sequence(dim, n, num_samples):
|
||||||
|
return [n / num_samples] + halton_sequence(dim - 1, n)
|
||||||
|
|
||||||
|
def sphere_hammersley_sequence(n, num_samples, offset=(0, 0)):
|
||||||
|
u, v = hammersley_sequence(2, n, num_samples)
|
||||||
|
u += offset[0] / num_samples
|
||||||
|
v += offset[1]
|
||||||
|
u = 2 * u if u < 0.25 else 2 / 3 * u + 1 / 3
|
||||||
|
theta = np.arccos(1 - 2 * u) - np.pi / 2
|
||||||
|
phi = v * 2 * np.pi
|
||||||
|
return [phi, theta]
|
||||||
86
dataset_toolkits/voxelize.py
Normal file
86
dataset_toolkits/voxelize.py
Normal file
@@ -0,0 +1,86 @@
|
|||||||
|
import os
|
||||||
|
import copy
|
||||||
|
import sys
|
||||||
|
import importlib
|
||||||
|
import argparse
|
||||||
|
import pandas as pd
|
||||||
|
from easydict import EasyDict as edict
|
||||||
|
from functools import partial
|
||||||
|
import numpy as np
|
||||||
|
import open3d as o3d
|
||||||
|
import utils3d
|
||||||
|
|
||||||
|
|
||||||
|
def _voxelize(file, sha256, output_dir):
|
||||||
|
mesh = o3d.io.read_triangle_mesh(os.path.join(output_dir, 'renders', sha256, 'mesh.ply'))
|
||||||
|
# clamp vertices to the range [-0.5, 0.5]
|
||||||
|
vertices = np.clip(np.asarray(mesh.vertices), -0.5 + 1e-6, 0.5 - 1e-6)
|
||||||
|
mesh.vertices = o3d.utility.Vector3dVector(vertices)
|
||||||
|
voxel_grid = o3d.geometry.VoxelGrid.create_from_triangle_mesh_within_bounds(mesh, voxel_size=1/64, min_bound=(-0.5, -0.5, -0.5), max_bound=(0.5, 0.5, 0.5))
|
||||||
|
vertices = np.array([voxel.grid_index for voxel in voxel_grid.get_voxels()])
|
||||||
|
assert np.all(vertices >= 0) and np.all(vertices < 64), "Some vertices are out of bounds"
|
||||||
|
vertices = (vertices + 0.5) / 64 - 0.5
|
||||||
|
utils3d.io.write_ply(os.path.join(output_dir, 'voxels', f'{sha256}.ply'), vertices)
|
||||||
|
return {'sha256': sha256, 'voxelized': True, 'num_voxels': len(vertices)}
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
dataset_utils = importlib.import_module(f'datasets.{sys.argv[1]}')
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument('--output_dir', type=str, required=True,
|
||||||
|
help='Directory to save the metadata')
|
||||||
|
parser.add_argument('--filter_low_aesthetic_score', type=float, default=None,
|
||||||
|
help='Filter objects with aesthetic score lower than this value')
|
||||||
|
parser.add_argument('--instances', type=str, default=None,
|
||||||
|
help='Instances to process')
|
||||||
|
parser.add_argument('--num_views', type=int, default=150,
|
||||||
|
help='Number of views to render')
|
||||||
|
dataset_utils.add_args(parser)
|
||||||
|
parser.add_argument('--rank', type=int, default=0)
|
||||||
|
parser.add_argument('--world_size', type=int, default=1)
|
||||||
|
parser.add_argument('--max_workers', type=int, default=None)
|
||||||
|
opt = parser.parse_args(sys.argv[2:])
|
||||||
|
opt = edict(vars(opt))
|
||||||
|
|
||||||
|
os.makedirs(os.path.join(opt.output_dir, 'voxels'), exist_ok=True)
|
||||||
|
|
||||||
|
# get file list
|
||||||
|
if not os.path.exists(os.path.join(opt.output_dir, 'metadata.csv')):
|
||||||
|
raise ValueError('metadata.csv not found')
|
||||||
|
metadata = pd.read_csv(os.path.join(opt.output_dir, 'metadata.csv'))
|
||||||
|
if opt.instances is None:
|
||||||
|
if opt.filter_low_aesthetic_score is not None:
|
||||||
|
metadata = metadata[metadata['aesthetic_score'] >= opt.filter_low_aesthetic_score]
|
||||||
|
if 'rendered' not in metadata.columns:
|
||||||
|
raise ValueError('metadata.csv does not have "rendered" column, please run "build_metadata.py" first')
|
||||||
|
metadata = metadata[metadata['rendered'] == True]
|
||||||
|
if 'voxelized' in metadata.columns:
|
||||||
|
metadata = metadata[metadata['voxelized'] == False]
|
||||||
|
else:
|
||||||
|
if os.path.exists(opt.instances):
|
||||||
|
with open(opt.instances, 'r') as f:
|
||||||
|
instances = f.read().splitlines()
|
||||||
|
else:
|
||||||
|
instances = opt.instances.split(',')
|
||||||
|
metadata = metadata[metadata['sha256'].isin(instances)]
|
||||||
|
|
||||||
|
start = len(metadata) * opt.rank // opt.world_size
|
||||||
|
end = len(metadata) * (opt.rank + 1) // opt.world_size
|
||||||
|
metadata = metadata[start:end]
|
||||||
|
records = []
|
||||||
|
|
||||||
|
# filter out objects that are already processed
|
||||||
|
for sha256 in copy.copy(metadata['sha256'].values):
|
||||||
|
if os.path.exists(os.path.join(opt.output_dir, 'voxels', f'{sha256}.ply')):
|
||||||
|
pts = utils3d.io.read_ply(os.path.join(opt.output_dir, 'voxels', f'{sha256}.ply'))[0]
|
||||||
|
records.append({'sha256': sha256, 'voxelized': True, 'num_voxels': len(pts)})
|
||||||
|
metadata = metadata[metadata['sha256'] != sha256]
|
||||||
|
|
||||||
|
print(f'Processing {len(metadata)} objects...')
|
||||||
|
|
||||||
|
# process objects
|
||||||
|
func = partial(_voxelize, output_dir=opt.output_dir)
|
||||||
|
voxelized = dataset_utils.foreach_instance(metadata, opt.output_dir, func, max_workers=opt.max_workers, desc='Voxelizing')
|
||||||
|
voxelized = pd.concat([voxelized, pd.DataFrame.from_records(records)])
|
||||||
|
voxelized.to_csv(os.path.join(opt.output_dir, f'voxelized_{opt.rank}.csv'), index=False)
|
||||||
260
setup.sh
Executable file
260
setup.sh
Executable file
@@ -0,0 +1,260 @@
|
|||||||
|
# Read Arguments
|
||||||
|
TEMP=`getopt -o h --long help,new-env,basic,train,xformers,flash-attn,diffoctreerast,vox2seq,spconv,mipgaussian,kaolin,nvdiffrast,demo -n 'setup.sh' -- "$@"`
|
||||||
|
|
||||||
|
eval set -- "$TEMP"
|
||||||
|
|
||||||
|
HELP=false
|
||||||
|
NEW_ENV=false
|
||||||
|
BASIC=false
|
||||||
|
TRAIN=false
|
||||||
|
XFORMERS=false
|
||||||
|
FLASHATTN=false
|
||||||
|
DIFFOCTREERAST=false
|
||||||
|
VOX2SEQ=false
|
||||||
|
LINEAR_ASSIGNMENT=false
|
||||||
|
SPCONV=false
|
||||||
|
ERROR=false
|
||||||
|
MIPGAUSSIAN=false
|
||||||
|
KAOLIN=false
|
||||||
|
NVDIFFRAST=false
|
||||||
|
DEMO=false
|
||||||
|
|
||||||
|
if [ "$#" -eq 1 ] ; then
|
||||||
|
HELP=true
|
||||||
|
fi
|
||||||
|
|
||||||
|
while true ; do
|
||||||
|
case "$1" in
|
||||||
|
-h|--help) HELP=true ; shift ;;
|
||||||
|
--new-env) NEW_ENV=true ; shift ;;
|
||||||
|
--basic) BASIC=true ; shift ;;
|
||||||
|
--train) TRAIN=true ; shift ;;
|
||||||
|
--xformers) XFORMERS=true ; shift ;;
|
||||||
|
--flash-attn) FLASHATTN=true ; shift ;;
|
||||||
|
--diffoctreerast) DIFFOCTREERAST=true ; shift ;;
|
||||||
|
--vox2seq) VOX2SEQ=true ; shift ;;
|
||||||
|
--spconv) SPCONV=true ; shift ;;
|
||||||
|
--mipgaussian) MIPGAUSSIAN=true ; shift ;;
|
||||||
|
--kaolin) KAOLIN=true ; shift ;;
|
||||||
|
--nvdiffrast) NVDIFFRAST=true ; shift ;;
|
||||||
|
--demo) DEMO=true ; shift ;;
|
||||||
|
--) shift ; break ;;
|
||||||
|
*) ERROR=true ; break ;;
|
||||||
|
esac
|
||||||
|
done
|
||||||
|
|
||||||
|
if [ "$ERROR" = true ] ; then
|
||||||
|
echo "Error: Invalid argument"
|
||||||
|
HELP=true
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$HELP" = true ] ; then
|
||||||
|
echo "Usage: setup.sh [OPTIONS]"
|
||||||
|
echo "Options:"
|
||||||
|
echo " -h, --help Display this help message"
|
||||||
|
echo " --new-env Create a new conda environment"
|
||||||
|
echo " --basic Install basic dependencies"
|
||||||
|
echo " --train Install training dependencies"
|
||||||
|
echo " --xformers Install xformers"
|
||||||
|
echo " --flash-attn Install flash-attn"
|
||||||
|
echo " --diffoctreerast Install diffoctreerast"
|
||||||
|
echo " --vox2seq Install vox2seq"
|
||||||
|
echo " --spconv Install spconv"
|
||||||
|
echo " --mipgaussian Install mip-splatting"
|
||||||
|
echo " --kaolin Install kaolin"
|
||||||
|
echo " --nvdiffrast Install nvdiffrast"
|
||||||
|
echo " --demo Install all dependencies for demo"
|
||||||
|
return
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$NEW_ENV" = true ] ; then
|
||||||
|
conda create -n trellis python=3.10
|
||||||
|
conda activate trellis
|
||||||
|
conda install pytorch==2.4.0 torchvision==0.19.0 pytorch-cuda=11.8 -c pytorch -c nvidia
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Get system information
|
||||||
|
WORKDIR=$(pwd)
|
||||||
|
PYTORCH_VERSION=$(python -c "import torch; print(torch.__version__)")
|
||||||
|
PLATFORM=$(python -c "import torch; print(('cuda' if torch.version.cuda else ('hip' if torch.version.hip else 'unknown')) if torch.cuda.is_available() else 'cpu')")
|
||||||
|
case $PLATFORM in
|
||||||
|
cuda)
|
||||||
|
CUDA_VERSION=$(python -c "import torch; print(torch.version.cuda)")
|
||||||
|
CUDA_MAJOR_VERSION=$(echo $CUDA_VERSION | cut -d'.' -f1)
|
||||||
|
CUDA_MINOR_VERSION=$(echo $CUDA_VERSION | cut -d'.' -f2)
|
||||||
|
echo "[SYSTEM] PyTorch Version: $PYTORCH_VERSION, CUDA Version: $CUDA_VERSION"
|
||||||
|
;;
|
||||||
|
hip)
|
||||||
|
HIP_VERSION=$(python -c "import torch; print(torch.version.hip)")
|
||||||
|
HIP_MAJOR_VERSION=$(echo $HIP_VERSION | cut -d'.' -f1)
|
||||||
|
HIP_MINOR_VERSION=$(echo $HIP_VERSION | cut -d'.' -f2)
|
||||||
|
# Install pytorch 2.4.1 for hip
|
||||||
|
if [ "$PYTORCH_VERSION" != "2.4.1+rocm6.1" ] ; then
|
||||||
|
echo "[SYSTEM] Installing PyTorch 2.4.1 for HIP ($PYTORCH_VERSION -> 2.4.1+rocm6.1)"
|
||||||
|
pip install torch==2.4.1 torchvision==0.19.1 --index-url https://download.pytorch.org/whl/rocm6.1 --user
|
||||||
|
mkdir -p /tmp/extensions
|
||||||
|
sudo cp /opt/rocm/share/amd_smi /tmp/extensions/amd_smi -r
|
||||||
|
cd /tmp/extensions/amd_smi
|
||||||
|
sudo chmod -R 777 .
|
||||||
|
pip install .
|
||||||
|
cd $WORKDIR
|
||||||
|
PYTORCH_VERSION=$(python -c "import torch; print(torch.__version__)")
|
||||||
|
fi
|
||||||
|
echo "[SYSTEM] PyTorch Version: $PYTORCH_VERSION, HIP Version: $HIP_VERSION"
|
||||||
|
;;
|
||||||
|
*)
|
||||||
|
;;
|
||||||
|
esac
|
||||||
|
|
||||||
|
if [ "$BASIC" = true ] ; then
|
||||||
|
pip install pillow imageio imageio-ffmpeg tqdm easydict opencv-python-headless scipy ninja rembg onnxruntime trimesh open3d xatlas pyvista pymeshfix igraph transformers
|
||||||
|
pip install git+https://github.com/EasternJournalist/utils3d.git@9a4eb15e4021b67b12c460c7057d642626897ec8
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$TRAIN" = true ] ; then
|
||||||
|
pip install tensorboard pandas lpips
|
||||||
|
pip uninstall -y pillow
|
||||||
|
sudo apt install -y libjpeg-dev
|
||||||
|
pip install pillow-simd
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$XFORMERS" = true ] ; then
|
||||||
|
# install xformers
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
if [ "$CUDA_VERSION" = "11.8" ] ; then
|
||||||
|
case $PYTORCH_VERSION in
|
||||||
|
2.0.1) pip install https://files.pythonhosted.org/packages/52/ca/82aeee5dcc24a3429ff5de65cc58ae9695f90f49fbba71755e7fab69a706/xformers-0.0.22-cp310-cp310-manylinux2014_x86_64.whl ;;
|
||||||
|
2.1.0) pip install xformers==0.0.22.post7 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.1.1) pip install xformers==0.0.23 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.1.2) pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.2.0) pip install xformers==0.0.24 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.2.1) pip install xformers==0.0.25 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.2.2) pip install xformers==0.0.25.post1 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.3.0) pip install xformers==0.0.26.post1 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.4.0) pip install xformers==0.0.27.post2 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.4.1) pip install xformers==0.0.28 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
2.5.0) pip install xformers==0.0.28.post2 --index-url https://download.pytorch.org/whl/cu118 ;;
|
||||||
|
*) echo "[XFORMERS] Unsupported PyTorch & CUDA version: $PYTORCH_VERSION & $CUDA_VERSION" ;;
|
||||||
|
esac
|
||||||
|
elif [ "$CUDA_VERSION" = "12.1" ] ; then
|
||||||
|
case $PYTORCH_VERSION in
|
||||||
|
2.1.0) pip install xformers==0.0.22.post7 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.1.1) pip install xformers==0.0.23 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.1.2) pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.2.0) pip install xformers==0.0.24 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.2.1) pip install xformers==0.0.25 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.2.2) pip install xformers==0.0.25.post1 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.3.0) pip install xformers==0.0.26.post1 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.4.0) pip install xformers==0.0.27.post2 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.4.1) pip install xformers==0.0.28 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
2.5.0) pip install xformers==0.0.28.post2 --index-url https://download.pytorch.org/whl/cu121 ;;
|
||||||
|
*) echo "[XFORMERS] Unsupported PyTorch & CUDA version: $PYTORCH_VERSION & $CUDA_VERSION" ;;
|
||||||
|
esac
|
||||||
|
elif [ "$CUDA_VERSION" = "12.4" ] ; then
|
||||||
|
case $PYTORCH_VERSION in
|
||||||
|
2.5.0) pip install xformers==0.0.28.post2 --index-url https://download.pytorch.org/whl/cu124 ;;
|
||||||
|
*) echo "[XFORMERS] Unsupported PyTorch & CUDA version: $PYTORCH_VERSION & $CUDA_VERSION" ;;
|
||||||
|
esac
|
||||||
|
else
|
||||||
|
echo "[XFORMERS] Unsupported CUDA version: $CUDA_MAJOR_VERSION"
|
||||||
|
fi
|
||||||
|
elif [ "$PLATFORM" = "hip" ] ; then
|
||||||
|
case $PYTORCH_VERSION in
|
||||||
|
2.4.1\+rocm6.1) pip install xformers==0.0.28 --index-url https://download.pytorch.org/whl/rocm6.1 ;;
|
||||||
|
*) echo "[XFORMERS] Unsupported PyTorch version: $PYTORCH_VERSION" ;;
|
||||||
|
esac
|
||||||
|
else
|
||||||
|
echo "[XFORMERS] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$FLASHATTN" = true ] ; then
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
pip install flash-attn
|
||||||
|
elif [ "$PLATFORM" = "hip" ] ; then
|
||||||
|
echo "[FLASHATTN] Prebuilt binaries not found. Building from source..."
|
||||||
|
mkdir -p /tmp/extensions
|
||||||
|
git clone --recursive https://github.com/ROCm/flash-attention.git /tmp/extensions/flash-attention
|
||||||
|
cd /tmp/extensions/flash-attention
|
||||||
|
git checkout tags/v2.6.3-cktile
|
||||||
|
GPU_ARCHS=gfx942 python setup.py install #MI300 series
|
||||||
|
cd $WORKDIR
|
||||||
|
else
|
||||||
|
echo "[FLASHATTN] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$KAOLIN" = true ] ; then
|
||||||
|
# install kaolin
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
case $PYTORCH_VERSION in
|
||||||
|
2.0.1) pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.0.1_cu118.html;;
|
||||||
|
2.1.0) pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.1.0_cu118.html;;
|
||||||
|
2.1.1) pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.1.1_cu118.html;;
|
||||||
|
2.2.0) pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.2.0_cu118.html;;
|
||||||
|
2.2.1) pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.2.1_cu118.html;;
|
||||||
|
2.2.2) pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.2.2_cu118.html;;
|
||||||
|
2.4.0) pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.4.0_cu121.html;;
|
||||||
|
*) echo "[KAOLIN] Unsupported PyTorch version: $PYTORCH_VERSION" ;;
|
||||||
|
esac
|
||||||
|
else
|
||||||
|
echo "[KAOLIN] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$NVDIFFRAST" = true ] ; then
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
mkdir -p /tmp/extensions
|
||||||
|
git clone https://github.com/NVlabs/nvdiffrast.git /tmp/extensions/nvdiffrast
|
||||||
|
pip install /tmp/extensions/nvdiffrast
|
||||||
|
else
|
||||||
|
echo "[NVDIFFRAST] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$DIFFOCTREERAST" = true ] ; then
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
mkdir -p /tmp/extensions
|
||||||
|
git clone --recurse-submodules https://github.com/JeffreyXiang/diffoctreerast.git /tmp/extensions/diffoctreerast
|
||||||
|
pip install /tmp/extensions/diffoctreerast
|
||||||
|
else
|
||||||
|
echo "[DIFFOCTREERAST] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$MIPGAUSSIAN" = true ] ; then
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
mkdir -p /tmp/extensions
|
||||||
|
git clone https://github.com/autonomousvision/mip-splatting.git /tmp/extensions/mip-splatting
|
||||||
|
pip install /tmp/extensions/mip-splatting/submodules/diff-gaussian-rasterization/
|
||||||
|
else
|
||||||
|
echo "[MIPGAUSSIAN] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$VOX2SEQ" = true ] ; then
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
mkdir -p /tmp/extensions
|
||||||
|
cp -r extensions/vox2seq /tmp/extensions/vox2seq
|
||||||
|
pip install /tmp/extensions/vox2seq
|
||||||
|
else
|
||||||
|
echo "[VOX2SEQ] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$SPCONV" = true ] ; then
|
||||||
|
# install spconv
|
||||||
|
if [ "$PLATFORM" = "cuda" ] ; then
|
||||||
|
case $CUDA_MAJOR_VERSION in
|
||||||
|
11) pip install spconv-cu118 ;;
|
||||||
|
12) pip install spconv-cu120 ;;
|
||||||
|
*) echo "[SPCONV] Unsupported PyTorch CUDA version: $CUDA_MAJOR_VERSION" ;;
|
||||||
|
esac
|
||||||
|
else
|
||||||
|
echo "[SPCONV] Unsupported platform: $PLATFORM"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
if [ "$DEMO" = true ] ; then
|
||||||
|
pip install gradio==4.44.1 gradio_litmodel3d==0.0.1
|
||||||
|
fi
|
||||||
7
test.py
Normal file
7
test.py
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
object_path = "test/3d_result/glb/543570111d344552b080ff6f875e4e83.glb"
|
||||||
|
|
||||||
|
path_parts = object_path.split("/", 1)
|
||||||
|
bucket_name = path_parts[0]
|
||||||
|
object_name = path_parts[1]
|
||||||
|
print(object_name)
|
||||||
|
print(bucket_name)
|
||||||
32
test_render_glb_preview.py
Normal file
32
test_render_glb_preview.py
Normal file
@@ -0,0 +1,32 @@
|
|||||||
|
import argparse
|
||||||
|
import os
|
||||||
|
import subprocess
|
||||||
|
|
||||||
|
|
||||||
|
def render_glb_preview(glb_path, output_path):
|
||||||
|
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||||
|
|
||||||
|
cmd = [
|
||||||
|
"blender",
|
||||||
|
"--background",
|
||||||
|
"--python",
|
||||||
|
"render_model.py",
|
||||||
|
"--",
|
||||||
|
glb_path,
|
||||||
|
output_path
|
||||||
|
]
|
||||||
|
|
||||||
|
result = subprocess.run(cmd, capture_output=True, text=True)
|
||||||
|
print(result)
|
||||||
|
if result.returncode != 0:
|
||||||
|
raise RuntimeError(
|
||||||
|
f"Blender render failed\nstdout:\n{result.stdout}\nstderr:\n{result.stderr}"
|
||||||
|
)
|
||||||
|
|
||||||
|
return output_path
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
x = render_glb_preview(glb_path='glb_output/sample_20260316_113848_a956a189.glb',
|
||||||
|
output_path='glb_output/static_model_image_20260316_113856_57ad56d2.png')
|
||||||
|
print(x)
|
||||||
158
train.py
Normal file
158
train.py
Normal file
@@ -0,0 +1,158 @@
|
|||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import json
|
||||||
|
import glob
|
||||||
|
import argparse
|
||||||
|
from easydict import EasyDict as edict
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torch.multiprocessing as mp
|
||||||
|
import numpy as np
|
||||||
|
import random
|
||||||
|
|
||||||
|
from trellis import models, datasets, trainers
|
||||||
|
from trellis.utils.dist_utils import setup_dist
|
||||||
|
|
||||||
|
|
||||||
|
def find_ckpt(cfg):
|
||||||
|
# Load checkpoint
|
||||||
|
cfg['load_ckpt'] = None
|
||||||
|
if cfg.load_dir != '':
|
||||||
|
if cfg.ckpt == 'latest':
|
||||||
|
files = glob.glob(os.path.join(cfg.load_dir, 'ckpts', 'misc_*.pt'))
|
||||||
|
if len(files) != 0:
|
||||||
|
cfg.load_ckpt = max([
|
||||||
|
int(os.path.basename(f).split('step')[-1].split('.')[0])
|
||||||
|
for f in files
|
||||||
|
])
|
||||||
|
elif cfg.ckpt == 'none':
|
||||||
|
cfg.load_ckpt = None
|
||||||
|
else:
|
||||||
|
cfg.load_ckpt = int(cfg.ckpt)
|
||||||
|
return cfg
|
||||||
|
|
||||||
|
|
||||||
|
def setup_rng(rank):
|
||||||
|
torch.manual_seed(rank)
|
||||||
|
torch.cuda.manual_seed_all(rank)
|
||||||
|
np.random.seed(rank)
|
||||||
|
random.seed(rank)
|
||||||
|
|
||||||
|
|
||||||
|
def get_model_summary(model):
|
||||||
|
model_summary = 'Parameters:\n'
|
||||||
|
model_summary += '=' * 128 + '\n'
|
||||||
|
model_summary += f'{"Name":<{72}}{"Shape":<{32}}{"Type":<{16}}{"Grad"}\n'
|
||||||
|
num_params = 0
|
||||||
|
num_trainable_params = 0
|
||||||
|
for name, param in model.named_parameters():
|
||||||
|
model_summary += f'{name:<{72}}{str(param.shape):<{32}}{str(param.dtype):<{16}}{param.requires_grad}\n'
|
||||||
|
num_params += param.numel()
|
||||||
|
if param.requires_grad:
|
||||||
|
num_trainable_params += param.numel()
|
||||||
|
model_summary += '\n'
|
||||||
|
model_summary += f'Number of parameters: {num_params}\n'
|
||||||
|
model_summary += f'Number of trainable parameters: {num_trainable_params}\n'
|
||||||
|
return model_summary
|
||||||
|
|
||||||
|
|
||||||
|
def main(local_rank, cfg):
|
||||||
|
# Set up distributed training
|
||||||
|
rank = cfg.node_rank * cfg.num_gpus + local_rank
|
||||||
|
world_size = cfg.num_nodes * cfg.num_gpus
|
||||||
|
if world_size > 1:
|
||||||
|
setup_dist(rank, local_rank, world_size, cfg.master_addr, cfg.master_port)
|
||||||
|
|
||||||
|
# Seed rngs
|
||||||
|
setup_rng(rank)
|
||||||
|
|
||||||
|
# Load data
|
||||||
|
dataset = getattr(datasets, cfg.dataset.name)(cfg.data_dir, **cfg.dataset.args)
|
||||||
|
|
||||||
|
# Build model
|
||||||
|
model_dict = {
|
||||||
|
name: getattr(models, model.name)(**model.args).cuda()
|
||||||
|
for name, model in cfg.models.items()
|
||||||
|
}
|
||||||
|
|
||||||
|
# Model summary
|
||||||
|
if rank == 0:
|
||||||
|
for name, backbone in model_dict.items():
|
||||||
|
model_summary = get_model_summary(backbone)
|
||||||
|
print(f'\n\nBackbone: {name}\n' + model_summary)
|
||||||
|
with open(os.path.join(cfg.output_dir, f'{name}_model_summary.txt'), 'w') as fp:
|
||||||
|
print(model_summary, file=fp)
|
||||||
|
|
||||||
|
# Build trainer
|
||||||
|
trainer = getattr(trainers, cfg.trainer.name)(model_dict, dataset, **cfg.trainer.args, output_dir=cfg.output_dir, load_dir=cfg.load_dir, step=cfg.load_ckpt)
|
||||||
|
|
||||||
|
# Train
|
||||||
|
if not cfg.tryrun:
|
||||||
|
if cfg.profile:
|
||||||
|
trainer.profile()
|
||||||
|
else:
|
||||||
|
trainer.run()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
# Arguments and config
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
## config
|
||||||
|
parser.add_argument('--config', type=str, required=True, help='Experiment config file')
|
||||||
|
## io and resume
|
||||||
|
parser.add_argument('--output_dir', type=str, required=True, help='Output directory')
|
||||||
|
parser.add_argument('--load_dir', type=str, default='', help='Load directory, default to output_dir')
|
||||||
|
parser.add_argument('--ckpt', type=str, default='latest', help='Checkpoint step to resume training, default to latest')
|
||||||
|
parser.add_argument('--data_dir', type=str, default='./data/', help='Data directory')
|
||||||
|
parser.add_argument('--auto_retry', type=int, default=3, help='Number of retries on error')
|
||||||
|
## dubug
|
||||||
|
parser.add_argument('--tryrun', action='store_true', help='Try run without training')
|
||||||
|
parser.add_argument('--profile', action='store_true', help='Profile training')
|
||||||
|
## multi-node and multi-gpu
|
||||||
|
parser.add_argument('--num_nodes', type=int, default=1, help='Number of nodes')
|
||||||
|
parser.add_argument('--node_rank', type=int, default=0, help='Node rank')
|
||||||
|
parser.add_argument('--num_gpus', type=int, default=-1, help='Number of GPUs per node, default to all')
|
||||||
|
parser.add_argument('--master_addr', type=str, default='localhost', help='Master address for distributed training')
|
||||||
|
parser.add_argument('--master_port', type=str, default='12345', help='Port for distributed training')
|
||||||
|
opt = parser.parse_args()
|
||||||
|
opt.load_dir = opt.load_dir if opt.load_dir != '' else opt.output_dir
|
||||||
|
opt.num_gpus = torch.cuda.device_count() if opt.num_gpus == -1 else opt.num_gpus
|
||||||
|
## Load config
|
||||||
|
config = json.load(open(opt.config, 'r'))
|
||||||
|
## Combine arguments and config
|
||||||
|
cfg = edict()
|
||||||
|
cfg.update(opt.__dict__)
|
||||||
|
cfg.update(config)
|
||||||
|
print('\n\nConfig:')
|
||||||
|
print('=' * 80)
|
||||||
|
print(json.dumps(cfg.__dict__, indent=4))
|
||||||
|
|
||||||
|
# Prepare output directory
|
||||||
|
if cfg.node_rank == 0:
|
||||||
|
os.makedirs(cfg.output_dir, exist_ok=True)
|
||||||
|
## Save command and config
|
||||||
|
with open(os.path.join(cfg.output_dir, 'command.txt'), 'w') as fp:
|
||||||
|
print(' '.join(['python'] + sys.argv), file=fp)
|
||||||
|
with open(os.path.join(cfg.output_dir, 'config.json'), 'w') as fp:
|
||||||
|
json.dump(config, fp, indent=4)
|
||||||
|
|
||||||
|
# Run
|
||||||
|
if cfg.auto_retry == 0:
|
||||||
|
cfg = find_ckpt(cfg)
|
||||||
|
if cfg.num_gpus > 1:
|
||||||
|
mp.spawn(main, args=(cfg,), nprocs=cfg.num_gpus, join=True)
|
||||||
|
else:
|
||||||
|
main(0, cfg)
|
||||||
|
else:
|
||||||
|
for rty in range(cfg.auto_retry):
|
||||||
|
try:
|
||||||
|
cfg = find_ckpt(cfg)
|
||||||
|
if cfg.num_gpus > 1:
|
||||||
|
mp.spawn(main, args=(cfg,), nprocs=cfg.num_gpus, join=True)
|
||||||
|
else:
|
||||||
|
main(0, cfg)
|
||||||
|
break
|
||||||
|
except Exception as e:
|
||||||
|
print(f'Error: {e}')
|
||||||
|
print(f'Retrying ({rty + 1}/{cfg.auto_retry})...')
|
||||||
|
|
||||||
107
trellis/datasets/sparse_structure.py
Normal file
107
trellis/datasets/sparse_structure.py
Normal file
@@ -0,0 +1,107 @@
|
|||||||
|
import os
|
||||||
|
import json
|
||||||
|
from typing import Union
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import torch
|
||||||
|
from torch.utils.data import Dataset
|
||||||
|
import utils3d
|
||||||
|
from .components import StandardDatasetBase
|
||||||
|
from ..representations.octree import DfsOctree as Octree
|
||||||
|
from ..renderers import OctreeRenderer
|
||||||
|
|
||||||
|
|
||||||
|
class SparseStructure(StandardDatasetBase):
|
||||||
|
"""
|
||||||
|
Sparse structure dataset
|
||||||
|
|
||||||
|
Args:
|
||||||
|
roots (str): path to the dataset
|
||||||
|
resolution (int): resolution of the voxel grid
|
||||||
|
min_aesthetic_score (float): minimum aesthetic score of the instances to be included in the dataset
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self,
|
||||||
|
roots,
|
||||||
|
resolution: int = 64,
|
||||||
|
min_aesthetic_score: float = 5.0,
|
||||||
|
):
|
||||||
|
self.resolution = resolution
|
||||||
|
self.min_aesthetic_score = min_aesthetic_score
|
||||||
|
self.value_range = (0, 1)
|
||||||
|
|
||||||
|
super().__init__(roots)
|
||||||
|
|
||||||
|
def filter_metadata(self, metadata):
|
||||||
|
stats = {}
|
||||||
|
metadata = metadata[metadata[f'voxelized']]
|
||||||
|
stats['Voxelized'] = len(metadata)
|
||||||
|
metadata = metadata[metadata['aesthetic_score'] >= self.min_aesthetic_score]
|
||||||
|
stats[f'Aesthetic score >= {self.min_aesthetic_score}'] = len(metadata)
|
||||||
|
return metadata, stats
|
||||||
|
|
||||||
|
def get_instance(self, root, instance):
|
||||||
|
position = utils3d.io.read_ply(os.path.join(root, 'voxels', f'{instance}.ply'))[0]
|
||||||
|
coords = ((torch.tensor(position) + 0.5) * self.resolution).int().contiguous()
|
||||||
|
ss = torch.zeros(1, self.resolution, self.resolution, self.resolution, dtype=torch.long)
|
||||||
|
ss[:, coords[:, 0], coords[:, 1], coords[:, 2]] = 1
|
||||||
|
return {'ss': ss}
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def visualize_sample(self, ss: Union[torch.Tensor, dict]):
|
||||||
|
ss = ss if isinstance(ss, torch.Tensor) else ss['ss']
|
||||||
|
|
||||||
|
renderer = OctreeRenderer()
|
||||||
|
renderer.rendering_options.resolution = 512
|
||||||
|
renderer.rendering_options.near = 0.8
|
||||||
|
renderer.rendering_options.far = 1.6
|
||||||
|
renderer.rendering_options.bg_color = (0, 0, 0)
|
||||||
|
renderer.rendering_options.ssaa = 4
|
||||||
|
renderer.pipe.primitive = 'voxel'
|
||||||
|
|
||||||
|
# Build camera
|
||||||
|
yaws = [0, np.pi / 2, np.pi, 3 * np.pi / 2]
|
||||||
|
yaws_offset = np.random.uniform(-np.pi / 4, np.pi / 4)
|
||||||
|
yaws = [y + yaws_offset for y in yaws]
|
||||||
|
pitch = [np.random.uniform(-np.pi / 4, np.pi / 4) for _ in range(4)]
|
||||||
|
|
||||||
|
exts = []
|
||||||
|
ints = []
|
||||||
|
for yaw, pitch in zip(yaws, pitch):
|
||||||
|
orig = torch.tensor([
|
||||||
|
np.sin(yaw) * np.cos(pitch),
|
||||||
|
np.cos(yaw) * np.cos(pitch),
|
||||||
|
np.sin(pitch),
|
||||||
|
]).float().cuda() * 2
|
||||||
|
fov = torch.deg2rad(torch.tensor(30)).cuda()
|
||||||
|
extrinsics = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda())
|
||||||
|
intrinsics = utils3d.torch.intrinsics_from_fov_xy(fov, fov)
|
||||||
|
exts.append(extrinsics)
|
||||||
|
ints.append(intrinsics)
|
||||||
|
|
||||||
|
images = []
|
||||||
|
|
||||||
|
# Build each representation
|
||||||
|
ss = ss.cuda()
|
||||||
|
for i in range(ss.shape[0]):
|
||||||
|
representation = Octree(
|
||||||
|
depth=10,
|
||||||
|
aabb=[-0.5, -0.5, -0.5, 1, 1, 1],
|
||||||
|
device='cuda',
|
||||||
|
primitive='voxel',
|
||||||
|
sh_degree=0,
|
||||||
|
primitive_config={'solid': True},
|
||||||
|
)
|
||||||
|
coords = torch.nonzero(ss[i, 0], as_tuple=False)
|
||||||
|
representation.position = coords.float() / self.resolution
|
||||||
|
representation.depth = torch.full((representation.position.shape[0], 1), int(np.log2(self.resolution)), dtype=torch.uint8, device='cuda')
|
||||||
|
|
||||||
|
image = torch.zeros(3, 1024, 1024).cuda()
|
||||||
|
tile = [2, 2]
|
||||||
|
for j, (ext, intr) in enumerate(zip(exts, ints)):
|
||||||
|
res = renderer.render(representation, ext, intr, colors_overwrite=representation.position)
|
||||||
|
image[:, 512 * (j // tile[1]):512 * (j // tile[1] + 1), 512 * (j % tile[1]):512 * (j % tile[1] + 1)] = res['color']
|
||||||
|
images.append(image)
|
||||||
|
|
||||||
|
return torch.stack(images)
|
||||||
|
|
||||||
135
trellis/modules/sparse/attention/windowed_attn.py
Executable file
135
trellis/modules/sparse/attention/windowed_attn.py
Executable file
@@ -0,0 +1,135 @@
|
|||||||
|
from typing import *
|
||||||
|
import torch
|
||||||
|
import math
|
||||||
|
from .. import SparseTensor
|
||||||
|
from .. import DEBUG, ATTN
|
||||||
|
|
||||||
|
if ATTN == 'xformers':
|
||||||
|
import xformers.ops as xops
|
||||||
|
elif ATTN == 'flash_attn':
|
||||||
|
import flash_attn
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Unknown attention module: {ATTN}")
|
||||||
|
|
||||||
|
|
||||||
|
__all__ = [
|
||||||
|
'sparse_windowed_scaled_dot_product_self_attention',
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def calc_window_partition(
|
||||||
|
tensor: SparseTensor,
|
||||||
|
window_size: Union[int, Tuple[int, ...]],
|
||||||
|
shift_window: Union[int, Tuple[int, ...]] = 0
|
||||||
|
) -> Tuple[torch.Tensor, torch.Tensor, List[int], List[int]]:
|
||||||
|
"""
|
||||||
|
Calculate serialization and partitioning for a set of coordinates.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
tensor (SparseTensor): The input tensor.
|
||||||
|
window_size (int): The window size to use.
|
||||||
|
shift_window (Tuple[int, ...]): The shift of serialized coordinates.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
(torch.Tensor): Forwards indices.
|
||||||
|
(torch.Tensor): Backwards indices.
|
||||||
|
(List[int]): Sequence lengths.
|
||||||
|
(List[int]): Sequence batch indices.
|
||||||
|
"""
|
||||||
|
DIM = tensor.coords.shape[1] - 1
|
||||||
|
shift_window = (shift_window,) * DIM if isinstance(shift_window, int) else shift_window
|
||||||
|
window_size = (window_size,) * DIM if isinstance(window_size, int) else window_size
|
||||||
|
shifted_coords = tensor.coords.clone().detach()
|
||||||
|
shifted_coords[:, 1:] += torch.tensor(shift_window, device=tensor.device, dtype=torch.int32).unsqueeze(0)
|
||||||
|
|
||||||
|
MAX_COORDS = shifted_coords[:, 1:].max(dim=0).values.tolist()
|
||||||
|
NUM_WINDOWS = [math.ceil((mc + 1) / ws) for mc, ws in zip(MAX_COORDS, window_size)]
|
||||||
|
OFFSET = torch.cumprod(torch.tensor([1] + NUM_WINDOWS[::-1]), dim=0).tolist()[::-1]
|
||||||
|
|
||||||
|
shifted_coords[:, 1:] //= torch.tensor(window_size, device=tensor.device, dtype=torch.int32).unsqueeze(0)
|
||||||
|
shifted_indices = (shifted_coords * torch.tensor(OFFSET, device=tensor.device, dtype=torch.int32).unsqueeze(0)).sum(dim=1)
|
||||||
|
fwd_indices = torch.argsort(shifted_indices)
|
||||||
|
bwd_indices = torch.empty_like(fwd_indices)
|
||||||
|
bwd_indices[fwd_indices] = torch.arange(fwd_indices.shape[0], device=tensor.device)
|
||||||
|
seq_lens = torch.bincount(shifted_indices)
|
||||||
|
seq_batch_indices = torch.arange(seq_lens.shape[0], device=tensor.device, dtype=torch.int32) // OFFSET[0]
|
||||||
|
mask = seq_lens != 0
|
||||||
|
seq_lens = seq_lens[mask].tolist()
|
||||||
|
seq_batch_indices = seq_batch_indices[mask].tolist()
|
||||||
|
|
||||||
|
return fwd_indices, bwd_indices, seq_lens, seq_batch_indices
|
||||||
|
|
||||||
|
|
||||||
|
def sparse_windowed_scaled_dot_product_self_attention(
|
||||||
|
qkv: SparseTensor,
|
||||||
|
window_size: int,
|
||||||
|
shift_window: Tuple[int, int, int] = (0, 0, 0)
|
||||||
|
) -> SparseTensor:
|
||||||
|
"""
|
||||||
|
Apply windowed scaled dot product self attention to a sparse tensor.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs.
|
||||||
|
window_size (int): The window size to use.
|
||||||
|
shift_window (Tuple[int, int, int]): The shift of serialized coordinates.
|
||||||
|
shift (int): The shift to use.
|
||||||
|
"""
|
||||||
|
assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]"
|
||||||
|
|
||||||
|
serialization_spatial_cache_name = f'window_partition_{window_size}_{shift_window}'
|
||||||
|
serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name)
|
||||||
|
if serialization_spatial_cache is None:
|
||||||
|
fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_window_partition(qkv, window_size, shift_window)
|
||||||
|
qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices))
|
||||||
|
else:
|
||||||
|
fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache
|
||||||
|
|
||||||
|
M = fwd_indices.shape[0]
|
||||||
|
T = qkv.feats.shape[0]
|
||||||
|
H = qkv.feats.shape[2]
|
||||||
|
C = qkv.feats.shape[3]
|
||||||
|
|
||||||
|
qkv_feats = qkv.feats[fwd_indices] # [M, 3, H, C]
|
||||||
|
|
||||||
|
if DEBUG:
|
||||||
|
start = 0
|
||||||
|
qkv_coords = qkv.coords[fwd_indices]
|
||||||
|
for i in range(len(seq_lens)):
|
||||||
|
seq_coords = qkv_coords[start:start+seq_lens[i]]
|
||||||
|
assert (seq_coords[:, 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch"
|
||||||
|
assert (seq_coords[:, 1:].max(dim=0).values - seq_coords[:, 1:].min(dim=0).values < window_size).all(), \
|
||||||
|
f"SparseWindowedScaledDotProductSelfAttention: window size exceeded"
|
||||||
|
start += seq_lens[i]
|
||||||
|
|
||||||
|
if all([seq_len == window_size for seq_len in seq_lens]):
|
||||||
|
B = len(seq_lens)
|
||||||
|
N = window_size
|
||||||
|
qkv_feats = qkv_feats.reshape(B, N, 3, H, C)
|
||||||
|
if ATTN == 'xformers':
|
||||||
|
q, k, v = qkv_feats.unbind(dim=2) # [B, N, H, C]
|
||||||
|
out = xops.memory_efficient_attention(q, k, v) # [B, N, H, C]
|
||||||
|
elif ATTN == 'flash_attn':
|
||||||
|
out = flash_attn.flash_attn_qkvpacked_func(qkv_feats) # [B, N, H, C]
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Unknown attention module: {ATTN}")
|
||||||
|
out = out.reshape(B * N, H, C) # [M, H, C]
|
||||||
|
else:
|
||||||
|
if ATTN == 'xformers':
|
||||||
|
q, k, v = qkv_feats.unbind(dim=1) # [M, H, C]
|
||||||
|
q = q.unsqueeze(0) # [1, M, H, C]
|
||||||
|
k = k.unsqueeze(0) # [1, M, H, C]
|
||||||
|
v = v.unsqueeze(0) # [1, M, H, C]
|
||||||
|
mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
|
||||||
|
out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C]
|
||||||
|
elif ATTN == 'flash_attn':
|
||||||
|
cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \
|
||||||
|
.to(qkv.device).int()
|
||||||
|
out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C]
|
||||||
|
|
||||||
|
out = out[bwd_indices] # [T, H, C]
|
||||||
|
|
||||||
|
if DEBUG:
|
||||||
|
qkv_coords = qkv_coords[bwd_indices]
|
||||||
|
assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch"
|
||||||
|
|
||||||
|
return qkv.replace(out)
|
||||||
54
trellis/modules/utils.py
Executable file
54
trellis/modules/utils.py
Executable file
@@ -0,0 +1,54 @@
|
|||||||
|
import torch.nn as nn
|
||||||
|
from ..modules import sparse as sp
|
||||||
|
|
||||||
|
FP16_MODULES = (
|
||||||
|
nn.Conv1d,
|
||||||
|
nn.Conv2d,
|
||||||
|
nn.Conv3d,
|
||||||
|
nn.ConvTranspose1d,
|
||||||
|
nn.ConvTranspose2d,
|
||||||
|
nn.ConvTranspose3d,
|
||||||
|
nn.Linear,
|
||||||
|
sp.SparseConv3d,
|
||||||
|
sp.SparseInverseConv3d,
|
||||||
|
sp.SparseLinear,
|
||||||
|
)
|
||||||
|
|
||||||
|
def convert_module_to_f16(l):
|
||||||
|
"""
|
||||||
|
Convert primitive modules to float16.
|
||||||
|
"""
|
||||||
|
if isinstance(l, FP16_MODULES):
|
||||||
|
for p in l.parameters():
|
||||||
|
p.data = p.data.half()
|
||||||
|
|
||||||
|
|
||||||
|
def convert_module_to_f32(l):
|
||||||
|
"""
|
||||||
|
Convert primitive modules to float32, undoing convert_module_to_f16().
|
||||||
|
"""
|
||||||
|
if isinstance(l, FP16_MODULES):
|
||||||
|
for p in l.parameters():
|
||||||
|
p.data = p.data.float()
|
||||||
|
|
||||||
|
|
||||||
|
def zero_module(module):
|
||||||
|
"""
|
||||||
|
Zero out the parameters of a module and return it.
|
||||||
|
"""
|
||||||
|
for p in module.parameters():
|
||||||
|
p.detach().zero_()
|
||||||
|
return module
|
||||||
|
|
||||||
|
|
||||||
|
def scale_module(module, scale):
|
||||||
|
"""
|
||||||
|
Scale the parameters of a module and return it.
|
||||||
|
"""
|
||||||
|
for p in module.parameters():
|
||||||
|
p.detach().mul_(scale)
|
||||||
|
return module
|
||||||
|
|
||||||
|
|
||||||
|
def modulate(x, shift, scale):
|
||||||
|
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
||||||
375
trellis/pipelines/trellis_image_to_3d.py
Normal file
375
trellis/pipelines/trellis_image_to_3d.py
Normal file
@@ -0,0 +1,375 @@
|
|||||||
|
from typing import *
|
||||||
|
from contextlib import contextmanager
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
import numpy as np
|
||||||
|
from torchvision import transforms
|
||||||
|
from PIL import Image
|
||||||
|
import rembg
|
||||||
|
from .base import Pipeline
|
||||||
|
from . import samplers
|
||||||
|
from ..modules import sparse as sp
|
||||||
|
|
||||||
|
|
||||||
|
class TrellisImageTo3DPipeline(Pipeline):
|
||||||
|
"""
|
||||||
|
Pipeline for inferring Trellis image-to-3D models.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
models (dict[str, nn.Module]): The models to use in the pipeline.
|
||||||
|
sparse_structure_sampler (samplers.Sampler): The sampler for the sparse structure.
|
||||||
|
slat_sampler (samplers.Sampler): The sampler for the structured latent.
|
||||||
|
slat_normalization (dict): The normalization parameters for the structured latent.
|
||||||
|
image_cond_model (str): The name of the image conditioning model.
|
||||||
|
"""
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
models: dict[str, nn.Module] = None,
|
||||||
|
sparse_structure_sampler: samplers.Sampler = None,
|
||||||
|
slat_sampler: samplers.Sampler = None,
|
||||||
|
slat_normalization: dict = None,
|
||||||
|
image_cond_model: str = None,
|
||||||
|
):
|
||||||
|
if models is None:
|
||||||
|
return
|
||||||
|
super().__init__(models)
|
||||||
|
self.sparse_structure_sampler = sparse_structure_sampler
|
||||||
|
self.slat_sampler = slat_sampler
|
||||||
|
self.sparse_structure_sampler_params = {}
|
||||||
|
self.slat_sampler_params = {}
|
||||||
|
self.slat_normalization = slat_normalization
|
||||||
|
self.rembg_session = None
|
||||||
|
self._init_image_cond_model(image_cond_model)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def from_pretrained(path: str) -> "TrellisImageTo3DPipeline":
|
||||||
|
"""
|
||||||
|
Load a pretrained model.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
path (str): The path to the model. Can be either local path or a Hugging Face repository.
|
||||||
|
"""
|
||||||
|
pipeline = super(TrellisImageTo3DPipeline, TrellisImageTo3DPipeline).from_pretrained(path)
|
||||||
|
new_pipeline = TrellisImageTo3DPipeline()
|
||||||
|
new_pipeline.__dict__ = pipeline.__dict__
|
||||||
|
args = pipeline._pretrained_args
|
||||||
|
|
||||||
|
new_pipeline.sparse_structure_sampler = getattr(samplers, args['sparse_structure_sampler']['name'])(**args['sparse_structure_sampler']['args'])
|
||||||
|
new_pipeline.sparse_structure_sampler_params = args['sparse_structure_sampler']['params']
|
||||||
|
|
||||||
|
new_pipeline.slat_sampler = getattr(samplers, args['slat_sampler']['name'])(**args['slat_sampler']['args'])
|
||||||
|
new_pipeline.slat_sampler_params = args['slat_sampler']['params']
|
||||||
|
|
||||||
|
new_pipeline.slat_normalization = args['slat_normalization']
|
||||||
|
|
||||||
|
new_pipeline._init_image_cond_model(args['image_cond_model'])
|
||||||
|
|
||||||
|
return new_pipeline
|
||||||
|
|
||||||
|
def _init_image_cond_model(self, name: str):
|
||||||
|
"""
|
||||||
|
Initialize the image conditioning model.
|
||||||
|
"""
|
||||||
|
dinov2_model = torch.hub.load('facebookresearch/dinov2', name, pretrained=True)
|
||||||
|
dinov2_model.eval()
|
||||||
|
self.models['image_cond_model'] = dinov2_model
|
||||||
|
transform = transforms.Compose([
|
||||||
|
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
||||||
|
])
|
||||||
|
self.image_cond_model_transform = transform
|
||||||
|
|
||||||
|
def preprocess_image(self, input: Image.Image) -> Image.Image:
|
||||||
|
"""
|
||||||
|
Preprocess the input image.
|
||||||
|
"""
|
||||||
|
# if has alpha channel, use it directly; otherwise, remove background
|
||||||
|
has_alpha = False
|
||||||
|
if input.mode == 'RGBA':
|
||||||
|
alpha = np.array(input)[:, :, 3]
|
||||||
|
if not np.all(alpha == 255):
|
||||||
|
has_alpha = True
|
||||||
|
if has_alpha:
|
||||||
|
output = input
|
||||||
|
else:
|
||||||
|
input = input.convert('RGB')
|
||||||
|
max_size = max(input.size)
|
||||||
|
scale = min(1, 1024 / max_size)
|
||||||
|
if scale < 1:
|
||||||
|
input = input.resize((int(input.width * scale), int(input.height * scale)), Image.Resampling.LANCZOS)
|
||||||
|
if getattr(self, 'rembg_session', None) is None:
|
||||||
|
self.rembg_session = rembg.new_session('u2net')
|
||||||
|
output = rembg.remove(input, session=self.rembg_session)
|
||||||
|
output_np = np.array(output)
|
||||||
|
alpha = output_np[:, :, 3]
|
||||||
|
bbox = np.argwhere(alpha > 0.8 * 255)
|
||||||
|
bbox = np.min(bbox[:, 1]), np.min(bbox[:, 0]), np.max(bbox[:, 1]), np.max(bbox[:, 0])
|
||||||
|
center = (bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2
|
||||||
|
size = max(bbox[2] - bbox[0], bbox[3] - bbox[1])
|
||||||
|
size = int(size * 1.2)
|
||||||
|
bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2
|
||||||
|
output = output.crop(bbox) # type: ignore
|
||||||
|
output = output.resize((518, 518), Image.Resampling.LANCZOS)
|
||||||
|
output = np.array(output).astype(np.float32) / 255
|
||||||
|
output = output[:, :, :3] * output[:, :, 3:4]
|
||||||
|
output = Image.fromarray((output * 255).astype(np.uint8))
|
||||||
|
return output
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def encode_image(self, image: Union[torch.Tensor, list[Image.Image]]) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
Encode the image.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
image (Union[torch.Tensor, list[Image.Image]]): The image to encode
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
torch.Tensor: The encoded features.
|
||||||
|
"""
|
||||||
|
if isinstance(image, torch.Tensor):
|
||||||
|
assert image.ndim == 4, "Image tensor should be batched (B, C, H, W)"
|
||||||
|
elif isinstance(image, list):
|
||||||
|
assert all(isinstance(i, Image.Image) for i in image), "Image list should be list of PIL images"
|
||||||
|
image = [i.resize((518, 518), Image.LANCZOS) for i in image]
|
||||||
|
image = [np.array(i.convert('RGB')).astype(np.float32) / 255 for i in image]
|
||||||
|
image = [torch.from_numpy(i).permute(2, 0, 1).float() for i in image]
|
||||||
|
image = torch.stack(image).to(self.device)
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Unsupported type of image: {type(image)}")
|
||||||
|
|
||||||
|
image = self.image_cond_model_transform(image).to(self.device)
|
||||||
|
features = self.models['image_cond_model'](image, is_training=True)['x_prenorm']
|
||||||
|
patchtokens = F.layer_norm(features, features.shape[-1:])
|
||||||
|
return patchtokens
|
||||||
|
|
||||||
|
def get_cond(self, image: Union[torch.Tensor, list[Image.Image]]) -> dict:
|
||||||
|
"""
|
||||||
|
Get the conditioning information for the model.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
image (Union[torch.Tensor, list[Image.Image]]): The image prompts.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The conditioning information
|
||||||
|
"""
|
||||||
|
cond = self.encode_image(image)
|
||||||
|
neg_cond = torch.zeros_like(cond)
|
||||||
|
return {
|
||||||
|
'cond': cond,
|
||||||
|
'neg_cond': neg_cond,
|
||||||
|
}
|
||||||
|
|
||||||
|
def sample_sparse_structure(
|
||||||
|
self,
|
||||||
|
cond: dict,
|
||||||
|
num_samples: int = 1,
|
||||||
|
sampler_params: dict = {},
|
||||||
|
) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
Sample sparse structures with the given conditioning.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
cond (dict): The conditioning information.
|
||||||
|
num_samples (int): The number of samples to generate.
|
||||||
|
sampler_params (dict): Additional parameters for the sampler.
|
||||||
|
"""
|
||||||
|
# Sample occupancy latent
|
||||||
|
flow_model = self.models['sparse_structure_flow_model']
|
||||||
|
reso = flow_model.resolution
|
||||||
|
noise = torch.randn(num_samples, flow_model.in_channels, reso, reso, reso).to(self.device)
|
||||||
|
sampler_params = {**self.sparse_structure_sampler_params, **sampler_params}
|
||||||
|
z_s = self.sparse_structure_sampler.sample(
|
||||||
|
flow_model,
|
||||||
|
noise,
|
||||||
|
**cond,
|
||||||
|
**sampler_params,
|
||||||
|
verbose=True
|
||||||
|
).samples
|
||||||
|
|
||||||
|
# Decode occupancy latent
|
||||||
|
decoder = self.models['sparse_structure_decoder']
|
||||||
|
coords = torch.argwhere(decoder(z_s)>0)[:, [0, 2, 3, 4]].int()
|
||||||
|
|
||||||
|
return coords
|
||||||
|
|
||||||
|
def decode_slat(
|
||||||
|
self,
|
||||||
|
slat: sp.SparseTensor,
|
||||||
|
formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
|
||||||
|
) -> dict:
|
||||||
|
"""
|
||||||
|
Decode the structured latent.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
slat (sp.SparseTensor): The structured latent.
|
||||||
|
formats (List[str]): The formats to decode the structured latent to.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The decoded structured latent.
|
||||||
|
"""
|
||||||
|
ret = {}
|
||||||
|
if 'mesh' in formats:
|
||||||
|
ret['mesh'] = self.models['slat_decoder_mesh'](slat)
|
||||||
|
if 'gaussian' in formats:
|
||||||
|
ret['gaussian'] = self.models['slat_decoder_gs'](slat)
|
||||||
|
if 'radiance_field' in formats:
|
||||||
|
ret['radiance_field'] = self.models['slat_decoder_rf'](slat)
|
||||||
|
return ret
|
||||||
|
|
||||||
|
def sample_slat(
|
||||||
|
self,
|
||||||
|
cond: dict,
|
||||||
|
coords: torch.Tensor,
|
||||||
|
sampler_params: dict = {},
|
||||||
|
) -> sp.SparseTensor:
|
||||||
|
"""
|
||||||
|
Sample structured latent with the given conditioning.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
cond (dict): The conditioning information.
|
||||||
|
coords (torch.Tensor): The coordinates of the sparse structure.
|
||||||
|
sampler_params (dict): Additional parameters for the sampler.
|
||||||
|
"""
|
||||||
|
# Sample structured latent
|
||||||
|
flow_model = self.models['slat_flow_model']
|
||||||
|
noise = sp.SparseTensor(
|
||||||
|
feats=torch.randn(coords.shape[0], flow_model.in_channels).to(self.device),
|
||||||
|
coords=coords,
|
||||||
|
)
|
||||||
|
sampler_params = {**self.slat_sampler_params, **sampler_params}
|
||||||
|
slat = self.slat_sampler.sample(
|
||||||
|
flow_model,
|
||||||
|
noise,
|
||||||
|
**cond,
|
||||||
|
**sampler_params,
|
||||||
|
verbose=True
|
||||||
|
).samples
|
||||||
|
|
||||||
|
std = torch.tensor(self.slat_normalization['std'])[None].to(slat.device)
|
||||||
|
mean = torch.tensor(self.slat_normalization['mean'])[None].to(slat.device)
|
||||||
|
slat = slat * std + mean
|
||||||
|
|
||||||
|
return slat
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def run(
|
||||||
|
self,
|
||||||
|
image: Image.Image,
|
||||||
|
num_samples: int = 1,
|
||||||
|
seed: int = 42,
|
||||||
|
sparse_structure_sampler_params: dict = {},
|
||||||
|
slat_sampler_params: dict = {},
|
||||||
|
formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
|
||||||
|
preprocess_image: bool = True,
|
||||||
|
) -> dict:
|
||||||
|
"""
|
||||||
|
Run the pipeline.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
image (Image.Image): The image prompt.
|
||||||
|
num_samples (int): The number of samples to generate.
|
||||||
|
seed (int): The random seed.
|
||||||
|
sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler.
|
||||||
|
slat_sampler_params (dict): Additional parameters for the structured latent sampler.
|
||||||
|
formats (List[str]): The formats to decode the structured latent to.
|
||||||
|
preprocess_image (bool): Whether to preprocess the image.
|
||||||
|
"""
|
||||||
|
if preprocess_image:
|
||||||
|
image = self.preprocess_image(image)
|
||||||
|
cond = self.get_cond([image])
|
||||||
|
torch.manual_seed(seed)
|
||||||
|
coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params)
|
||||||
|
slat = self.sample_slat(cond, coords, slat_sampler_params)
|
||||||
|
return self.decode_slat(slat, formats)
|
||||||
|
|
||||||
|
@contextmanager
|
||||||
|
def inject_sampler_multi_image(
|
||||||
|
self,
|
||||||
|
sampler_name: str,
|
||||||
|
num_images: int,
|
||||||
|
num_steps: int,
|
||||||
|
mode: Literal['stochastic', 'multidiffusion'] = 'stochastic',
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
Inject a sampler with multiple images as condition.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
sampler_name (str): The name of the sampler to inject.
|
||||||
|
num_images (int): The number of images to condition on.
|
||||||
|
num_steps (int): The number of steps to run the sampler for.
|
||||||
|
"""
|
||||||
|
sampler = getattr(self, sampler_name)
|
||||||
|
setattr(sampler, f'_old_inference_model', sampler._inference_model)
|
||||||
|
|
||||||
|
if mode == 'stochastic':
|
||||||
|
if num_images > num_steps:
|
||||||
|
print(f"\033[93mWarning: number of conditioning images is greater than number of steps for {sampler_name}. "
|
||||||
|
"This may lead to performance degradation.\033[0m")
|
||||||
|
|
||||||
|
cond_indices = (np.arange(num_steps) % num_images).tolist()
|
||||||
|
def _new_inference_model(self, model, x_t, t, cond, **kwargs):
|
||||||
|
cond_idx = cond_indices.pop(0)
|
||||||
|
cond_i = cond[cond_idx:cond_idx+1]
|
||||||
|
return self._old_inference_model(model, x_t, t, cond=cond_i, **kwargs)
|
||||||
|
|
||||||
|
elif mode =='multidiffusion':
|
||||||
|
from .samplers import FlowEulerSampler
|
||||||
|
def _new_inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, cfg_interval, **kwargs):
|
||||||
|
if cfg_interval[0] <= t <= cfg_interval[1]:
|
||||||
|
preds = []
|
||||||
|
for i in range(len(cond)):
|
||||||
|
preds.append(FlowEulerSampler._inference_model(self, model, x_t, t, cond[i:i+1], **kwargs))
|
||||||
|
pred = sum(preds) / len(preds)
|
||||||
|
neg_pred = FlowEulerSampler._inference_model(self, model, x_t, t, neg_cond, **kwargs)
|
||||||
|
return (1 + cfg_strength) * pred - cfg_strength * neg_pred
|
||||||
|
else:
|
||||||
|
preds = []
|
||||||
|
for i in range(len(cond)):
|
||||||
|
preds.append(FlowEulerSampler._inference_model(self, model, x_t, t, cond[i:i+1], **kwargs))
|
||||||
|
pred = sum(preds) / len(preds)
|
||||||
|
return pred
|
||||||
|
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Unsupported mode: {mode}")
|
||||||
|
|
||||||
|
sampler._inference_model = _new_inference_model.__get__(sampler, type(sampler))
|
||||||
|
|
||||||
|
yield
|
||||||
|
|
||||||
|
sampler._inference_model = sampler._old_inference_model
|
||||||
|
delattr(sampler, f'_old_inference_model')
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def run_multi_image(
|
||||||
|
self,
|
||||||
|
images: List[Image.Image],
|
||||||
|
num_samples: int = 1,
|
||||||
|
seed: int = 42,
|
||||||
|
sparse_structure_sampler_params: dict = {},
|
||||||
|
slat_sampler_params: dict = {},
|
||||||
|
formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
|
||||||
|
preprocess_image: bool = True,
|
||||||
|
mode: Literal['stochastic', 'multidiffusion'] = 'stochastic',
|
||||||
|
) -> dict:
|
||||||
|
"""
|
||||||
|
Run the pipeline with multiple images as condition
|
||||||
|
|
||||||
|
Args:
|
||||||
|
images (List[Image.Image]): The multi-view images of the assets
|
||||||
|
num_samples (int): The number of samples to generate.
|
||||||
|
sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler.
|
||||||
|
slat_sampler_params (dict): Additional parameters for the structured latent sampler.
|
||||||
|
preprocess_image (bool): Whether to preprocess the image.
|
||||||
|
"""
|
||||||
|
if preprocess_image:
|
||||||
|
images = [self.preprocess_image(image) for image in images]
|
||||||
|
cond = self.get_cond(images)
|
||||||
|
cond['neg_cond'] = cond['neg_cond'][:1]
|
||||||
|
torch.manual_seed(seed)
|
||||||
|
ss_steps = {**self.sparse_structure_sampler_params, **sparse_structure_sampler_params}.get('steps')
|
||||||
|
with self.inject_sampler_multi_image('sparse_structure_sampler', len(images), ss_steps, mode=mode):
|
||||||
|
coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params)
|
||||||
|
slat_steps = {**self.slat_sampler_params, **slat_sampler_params}.get('steps')
|
||||||
|
with self.inject_sampler_multi_image('slat_sampler', len(images), slat_steps, mode=mode):
|
||||||
|
slat = self.sample_slat(cond, coords, slat_sampler_params)
|
||||||
|
return self.decode_slat(slat, formats)
|
||||||
278
trellis/pipelines/trellis_text_to_3d.py
Normal file
278
trellis/pipelines/trellis_text_to_3d.py
Normal file
@@ -0,0 +1,278 @@
|
|||||||
|
from typing import *
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
import numpy as np
|
||||||
|
from transformers import CLIPTextModel, AutoTokenizer
|
||||||
|
import open3d as o3d
|
||||||
|
from .base import Pipeline
|
||||||
|
from . import samplers
|
||||||
|
from ..modules import sparse as sp
|
||||||
|
|
||||||
|
|
||||||
|
class TrellisTextTo3DPipeline(Pipeline):
|
||||||
|
"""
|
||||||
|
Pipeline for inferring Trellis text-to-3D models.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
models (dict[str, nn.Module]): The models to use in the pipeline.
|
||||||
|
sparse_structure_sampler (samplers.Sampler): The sampler for the sparse structure.
|
||||||
|
slat_sampler (samplers.Sampler): The sampler for the structured latent.
|
||||||
|
slat_normalization (dict): The normalization parameters for the structured latent.
|
||||||
|
text_cond_model (str): The name of the text conditioning model.
|
||||||
|
"""
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
models: dict[str, nn.Module] = None,
|
||||||
|
sparse_structure_sampler: samplers.Sampler = None,
|
||||||
|
slat_sampler: samplers.Sampler = None,
|
||||||
|
slat_normalization: dict = None,
|
||||||
|
text_cond_model: str = None,
|
||||||
|
):
|
||||||
|
if models is None:
|
||||||
|
return
|
||||||
|
super().__init__(models)
|
||||||
|
self.sparse_structure_sampler = sparse_structure_sampler
|
||||||
|
self.slat_sampler = slat_sampler
|
||||||
|
self.sparse_structure_sampler_params = {}
|
||||||
|
self.slat_sampler_params = {}
|
||||||
|
self.slat_normalization = slat_normalization
|
||||||
|
self._init_text_cond_model(text_cond_model)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def from_pretrained(path: str) -> "TrellisTextTo3DPipeline":
|
||||||
|
"""
|
||||||
|
Load a pretrained model.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
path (str): The path to the model. Can be either local path or a Hugging Face repository.
|
||||||
|
"""
|
||||||
|
pipeline = super(TrellisTextTo3DPipeline, TrellisTextTo3DPipeline).from_pretrained(path)
|
||||||
|
new_pipeline = TrellisTextTo3DPipeline()
|
||||||
|
new_pipeline.__dict__ = pipeline.__dict__
|
||||||
|
args = pipeline._pretrained_args
|
||||||
|
|
||||||
|
new_pipeline.sparse_structure_sampler = getattr(samplers, args['sparse_structure_sampler']['name'])(**args['sparse_structure_sampler']['args'])
|
||||||
|
new_pipeline.sparse_structure_sampler_params = args['sparse_structure_sampler']['params']
|
||||||
|
|
||||||
|
new_pipeline.slat_sampler = getattr(samplers, args['slat_sampler']['name'])(**args['slat_sampler']['args'])
|
||||||
|
new_pipeline.slat_sampler_params = args['slat_sampler']['params']
|
||||||
|
|
||||||
|
new_pipeline.slat_normalization = args['slat_normalization']
|
||||||
|
|
||||||
|
new_pipeline._init_text_cond_model(args['text_cond_model'])
|
||||||
|
|
||||||
|
return new_pipeline
|
||||||
|
|
||||||
|
def _init_text_cond_model(self, name: str):
|
||||||
|
"""
|
||||||
|
Initialize the text conditioning model.
|
||||||
|
"""
|
||||||
|
# load model
|
||||||
|
model = CLIPTextModel.from_pretrained(name)
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(name)
|
||||||
|
model.eval()
|
||||||
|
model = model.cuda()
|
||||||
|
self.text_cond_model = {
|
||||||
|
'model': model,
|
||||||
|
'tokenizer': tokenizer,
|
||||||
|
}
|
||||||
|
self.text_cond_model['null_cond'] = self.encode_text([''])
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def encode_text(self, text: List[str]) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
Encode the text.
|
||||||
|
"""
|
||||||
|
assert isinstance(text, list) and all(isinstance(t, str) for t in text), "text must be a list of strings"
|
||||||
|
encoding = self.text_cond_model['tokenizer'](text, max_length=77, padding='max_length', truncation=True, return_tensors='pt')
|
||||||
|
tokens = encoding['input_ids'].cuda()
|
||||||
|
embeddings = self.text_cond_model['model'](input_ids=tokens).last_hidden_state
|
||||||
|
|
||||||
|
return embeddings
|
||||||
|
|
||||||
|
def get_cond(self, prompt: List[str]) -> dict:
|
||||||
|
"""
|
||||||
|
Get the conditioning information for the model.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
prompt (List[str]): The text prompt.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The conditioning information
|
||||||
|
"""
|
||||||
|
cond = self.encode_text(prompt)
|
||||||
|
neg_cond = self.text_cond_model['null_cond']
|
||||||
|
return {
|
||||||
|
'cond': cond,
|
||||||
|
'neg_cond': neg_cond,
|
||||||
|
}
|
||||||
|
|
||||||
|
def sample_sparse_structure(
|
||||||
|
self,
|
||||||
|
cond: dict,
|
||||||
|
num_samples: int = 1,
|
||||||
|
sampler_params: dict = {},
|
||||||
|
) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
Sample sparse structures with the given conditioning.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
cond (dict): The conditioning information.
|
||||||
|
num_samples (int): The number of samples to generate.
|
||||||
|
sampler_params (dict): Additional parameters for the sampler.
|
||||||
|
"""
|
||||||
|
# Sample occupancy latent
|
||||||
|
flow_model = self.models['sparse_structure_flow_model']
|
||||||
|
reso = flow_model.resolution
|
||||||
|
noise = torch.randn(num_samples, flow_model.in_channels, reso, reso, reso).to(self.device)
|
||||||
|
sampler_params = {**self.sparse_structure_sampler_params, **sampler_params}
|
||||||
|
z_s = self.sparse_structure_sampler.sample(
|
||||||
|
flow_model,
|
||||||
|
noise,
|
||||||
|
**cond,
|
||||||
|
**sampler_params,
|
||||||
|
verbose=True
|
||||||
|
).samples
|
||||||
|
|
||||||
|
# Decode occupancy latent
|
||||||
|
decoder = self.models['sparse_structure_decoder']
|
||||||
|
coords = torch.argwhere(decoder(z_s)>0)[:, [0, 2, 3, 4]].int()
|
||||||
|
|
||||||
|
return coords
|
||||||
|
|
||||||
|
def decode_slat(
|
||||||
|
self,
|
||||||
|
slat: sp.SparseTensor,
|
||||||
|
formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
|
||||||
|
) -> dict:
|
||||||
|
"""
|
||||||
|
Decode the structured latent.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
slat (sp.SparseTensor): The structured latent.
|
||||||
|
formats (List[str]): The formats to decode the structured latent to.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The decoded structured latent.
|
||||||
|
"""
|
||||||
|
ret = {}
|
||||||
|
if 'mesh' in formats:
|
||||||
|
ret['mesh'] = self.models['slat_decoder_mesh'](slat)
|
||||||
|
if 'gaussian' in formats:
|
||||||
|
ret['gaussian'] = self.models['slat_decoder_gs'](slat)
|
||||||
|
if 'radiance_field' in formats:
|
||||||
|
ret['radiance_field'] = self.models['slat_decoder_rf'](slat)
|
||||||
|
return ret
|
||||||
|
|
||||||
|
def sample_slat(
|
||||||
|
self,
|
||||||
|
cond: dict,
|
||||||
|
coords: torch.Tensor,
|
||||||
|
sampler_params: dict = {},
|
||||||
|
) -> sp.SparseTensor:
|
||||||
|
"""
|
||||||
|
Sample structured latent with the given conditioning.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
cond (dict): The conditioning information.
|
||||||
|
coords (torch.Tensor): The coordinates of the sparse structure.
|
||||||
|
sampler_params (dict): Additional parameters for the sampler.
|
||||||
|
"""
|
||||||
|
# Sample structured latent
|
||||||
|
flow_model = self.models['slat_flow_model']
|
||||||
|
noise = sp.SparseTensor(
|
||||||
|
feats=torch.randn(coords.shape[0], flow_model.in_channels).to(self.device),
|
||||||
|
coords=coords,
|
||||||
|
)
|
||||||
|
sampler_params = {**self.slat_sampler_params, **sampler_params}
|
||||||
|
slat = self.slat_sampler.sample(
|
||||||
|
flow_model,
|
||||||
|
noise,
|
||||||
|
**cond,
|
||||||
|
**sampler_params,
|
||||||
|
verbose=True
|
||||||
|
).samples
|
||||||
|
|
||||||
|
std = torch.tensor(self.slat_normalization['std'])[None].to(slat.device)
|
||||||
|
mean = torch.tensor(self.slat_normalization['mean'])[None].to(slat.device)
|
||||||
|
slat = slat * std + mean
|
||||||
|
|
||||||
|
return slat
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def run(
|
||||||
|
self,
|
||||||
|
prompt: str,
|
||||||
|
num_samples: int = 1,
|
||||||
|
seed: int = 42,
|
||||||
|
sparse_structure_sampler_params: dict = {},
|
||||||
|
slat_sampler_params: dict = {},
|
||||||
|
formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
|
||||||
|
) -> dict:
|
||||||
|
"""
|
||||||
|
Run the pipeline.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
prompt (str): The text prompt.
|
||||||
|
num_samples (int): The number of samples to generate.
|
||||||
|
seed (int): The random seed.
|
||||||
|
sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler.
|
||||||
|
slat_sampler_params (dict): Additional parameters for the structured latent sampler.
|
||||||
|
formats (List[str]): The formats to decode the structured latent to.
|
||||||
|
"""
|
||||||
|
cond = self.get_cond([prompt])
|
||||||
|
torch.manual_seed(seed)
|
||||||
|
coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params)
|
||||||
|
slat = self.sample_slat(cond, coords, slat_sampler_params)
|
||||||
|
return self.decode_slat(slat, formats)
|
||||||
|
|
||||||
|
def voxelize(self, mesh: o3d.geometry.TriangleMesh) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
Voxelize a mesh.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
mesh (o3d.geometry.TriangleMesh): The mesh to voxelize.
|
||||||
|
sha256 (str): The SHA256 hash of the mesh.
|
||||||
|
output_dir (str): The output directory.
|
||||||
|
"""
|
||||||
|
vertices = np.asarray(mesh.vertices)
|
||||||
|
aabb = np.stack([vertices.min(0), vertices.max(0)])
|
||||||
|
center = (aabb[0] + aabb[1]) / 2
|
||||||
|
scale = (aabb[1] - aabb[0]).max()
|
||||||
|
vertices = (vertices - center) / scale
|
||||||
|
vertices = np.clip(vertices, -0.5 + 1e-6, 0.5 - 1e-6)
|
||||||
|
mesh.vertices = o3d.utility.Vector3dVector(vertices)
|
||||||
|
voxel_grid = o3d.geometry.VoxelGrid.create_from_triangle_mesh_within_bounds(mesh, voxel_size=1/64, min_bound=(-0.5, -0.5, -0.5), max_bound=(0.5, 0.5, 0.5))
|
||||||
|
vertices = np.array([voxel.grid_index for voxel in voxel_grid.get_voxels()])
|
||||||
|
return torch.tensor(vertices).int().cuda()
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def run_variant(
|
||||||
|
self,
|
||||||
|
mesh: o3d.geometry.TriangleMesh,
|
||||||
|
prompt: str,
|
||||||
|
num_samples: int = 1,
|
||||||
|
seed: int = 42,
|
||||||
|
slat_sampler_params: dict = {},
|
||||||
|
formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
|
||||||
|
) -> dict:
|
||||||
|
"""
|
||||||
|
Run the pipeline for making variants of an asset.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
mesh (o3d.geometry.TriangleMesh): The base mesh.
|
||||||
|
prompt (str): The text prompt.
|
||||||
|
num_samples (int): The number of samples to generate.
|
||||||
|
seed (int): The random seed
|
||||||
|
slat_sampler_params (dict): Additional parameters for the structured latent sampler.
|
||||||
|
formats (List[str]): The formats to decode the structured latent to.
|
||||||
|
"""
|
||||||
|
cond = self.get_cond([prompt])
|
||||||
|
coords = self.voxelize(mesh)
|
||||||
|
coords = torch.cat([
|
||||||
|
torch.arange(num_samples).repeat_interleave(coords.shape[0], 0)[:, None].int().cuda(),
|
||||||
|
coords.repeat(num_samples, 1)
|
||||||
|
], 1)
|
||||||
|
torch.manual_seed(seed)
|
||||||
|
slat = self.sample_slat(cond, coords, slat_sampler_params)
|
||||||
|
return self.decode_slat(slat, formats)
|
||||||
114
trellis/representations/mesh/flexicubes/README.md
Normal file
114
trellis/representations/mesh/flexicubes/README.md
Normal file
@@ -0,0 +1,114 @@
|
|||||||
|
## Flexible Isosurface Extraction for Gradient-Based Mesh Optimization (FlexiCubes)<br><sub>Official PyTorch implementation </sub>
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
FlexiCubes is a high-quality isosurface representation specifically designed for gradient-based mesh optimization with respect to geometric, visual, or even physical objectives. For more details, please refer to our [paper](https://arxiv.org/abs/2308.05371) and [project page](https://research.nvidia.com/labs/toronto-ai/flexicubes/).
|
||||||
|
|
||||||
|
## Highlights
|
||||||
|
* [Getting started](https://github.com/nv-tlabs/FlexiCubes#getting-started)
|
||||||
|
* [Basic workflow](https://github.com/nv-tlabs/FlexiCubes#example-usage)
|
||||||
|
* [nvdiffrec: image-based reconstruction example](https://github.com/NVlabs/nvdiffrec#news)
|
||||||
|
* [GET3D: generative AI example](https://github.com/nv-tlabs/GET3D#employing-flexicubes)
|
||||||
|
* [Bibtex](https://github.com/nv-tlabs/FlexiCubes#citation)
|
||||||
|
|
||||||
|
## Getting Started
|
||||||
|
|
||||||
|
The core functions of FlexiCubes are now in [Kaolin](https://github.com/NVIDIAGameWorks/kaolin/) starting from v0.15.0. See installation instructions [here](https://kaolin.readthedocs.io/en/latest/notes/installation.html) and API documentations [here](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.conversions.html?highlight=flexicubes#kaolin.ops.conversions.FlexiCubes)
|
||||||
|
|
||||||
|
The original code of the paper is still visible in `flexicube.py`.
|
||||||
|
|
||||||
|
## Example Usage
|
||||||
|
|
||||||
|
### Gradient-Based Mesh Optimization
|
||||||
|
We provide examples demonstrating how to use FlexiCubes for reconstructing unknown meshes through gradient-based optimization. Specifically, starting from randomly initialized SDF, we optimize the shape towards the reference mesh by minimizing their geometric difference, measured by multiview mask and depth losses. This workflow is a simplified version of `nvdiffrec` with code largely borrowed from the [nvdiffrec GitHub](https://github.com/NVlabs/nvdiffrec). We use the same pipeline to conduct the analysis in Section 3 and the main experiments described in Section 5 of our paper. We provide a detailed tutorial in `examples/optimization.ipynb`, along with an optimization script in `examples/optimize.py` which accepts command-line arguments.
|
||||||
|
|
||||||
|
|
||||||
|
To run the examples, it is suggested to install the Conda environment as detailed below:
|
||||||
|
```sh
|
||||||
|
conda create -n flexicubes python=3.9
|
||||||
|
conda activate flexicubes
|
||||||
|
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
|
||||||
|
pip install imageio trimesh tqdm matplotlib torch_scatter ninja
|
||||||
|
pip install git+https://github.com/NVlabs/nvdiffrast/
|
||||||
|
pip install kaolin==0.15.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.12.0_cu113.html
|
||||||
|
```
|
||||||
|
|
||||||
|
Then download the dataset collected by [Myles et al.](https://vcg.isti.cnr.it/Publications/2014/MPZ14/) as follows. We include one shape in 'examples/data/inputmodels/block.obj' if you want to test without downloading the full dataset.
|
||||||
|
|
||||||
|
```sh
|
||||||
|
cd examples
|
||||||
|
python download_data.py
|
||||||
|
```
|
||||||
|
|
||||||
|
After downloading the data, run shape optimization with the following example command:
|
||||||
|
```sh
|
||||||
|
python optimize.py --ref_mesh data/inputmodels/block.obj --out_dir out/block
|
||||||
|
```
|
||||||
|
You can find visualization and output meshes in the `out/block`. Below, we show the initial and final shapes during optimization, with the reference shape on the right.
|
||||||
|
|
||||||
|
<img src="images/block_init.png" alt="block_init" width="80%" height="80%">
|
||||||
|
|
||||||
|
<img src="images/block_final.png" alt="block_final" width="80%" height="80%">
|
||||||
|
|
||||||
|
|
||||||
|
To further demonstrate the flexibility of our FlexiCubes representation, which can accommodates both reconstruction objectives and regularizers defined on the extracted mesh, you can add a developability regularizer (proposed by [Stein et al.](https://www.cs.cmu.edu/~kmcrane/Projects/DiscreteDevelopable/)) to the previous reconstruction pipeline to encourage fabricability from panels:
|
||||||
|
```sh
|
||||||
|
python optimize.py --ref_mesh data/inputmodels/david.obj --out_dir out/david_dev --develop_reg True --iter=1250
|
||||||
|
```
|
||||||
|
|
||||||
|
### Extract mesh from known signed distance field
|
||||||
|
While not its designated use case, our function can extract a mesh from a known Signed Distance Field (SDF) without optimization. Please refer to the tutorial found in `examples/extraction.ipynb` for details.
|
||||||
|
|
||||||
|
## Tips for using FlexiCubes
|
||||||
|
### Regularization losses:
|
||||||
|
We commonly use three regularizers in our mesh optimization pipelines, referenced in lines `L104-L106` in `examples/optimize.py`. The weights of these regularizers should be scaled according to the your application objectives. Initially, it is suggested to employ low weights because strong regularization can hinder convergence. You can incrementally increase the weights if you notice artifacts appearing in the optimized meshes. Specifically:
|
||||||
|
|
||||||
|
* The loss function at `L104` helps to remove floaters in areas of the shape that are not supervised by the application objective, such as internal faces when using image supervision only.
|
||||||
|
* The L_dev loss at `L105` can be increased if you observe artifacts in flat areas, as illustrated in the image below.
|
||||||
|
* Generally, the L1 regularizer on flexible weights at `L106` does not have a significant impact during the optimization of a single shape. However, we found it to be effective in stabilizing training in generative pipelines such as GET3D.
|
||||||
|
<img src="images/ablate_L_dev.jpg" alt="Ablating L_dev" width="80%" height="80%">
|
||||||
|
|
||||||
|
### Resolution of voxel grid vs. tetrahedral grid:
|
||||||
|
If you are switching from our previous work, DMTet, it's important to note the difference in grid resolution when compared to FlexiCubes. In both implementations, the resolution is defined by the edge length: a grid resolution of `n` means the grid edge length is 1/n for both the voxel and tetrahedral grids. However, a tetrahedral grid with a resolution of `n` contains only `(n/2+1)³` grid vertices, in contrast to the `(n+1)³` vertices in a voxel grid. Consequently, if you are switching from DMTet to FlexiCubes while maintaining the same resolution, you will notice not only a denser output mesh but also a substantial increase in computational cost. To align the triangle count in the output meshes more closely, we recommend adopting a 4:5 resolution ratio between the voxel grid and the tetrahedral grid. For instance, in our paper, `64³` FlexiCubes generate approximately the same number of triangles as `80³` DMTet.
|
||||||
|
|
||||||
|
## Applications
|
||||||
|
FlexiCubes is now integrated into NVIDIA applications as a drop-in replacement for DMTet. You can visit their GitHub pages to see how FlexiCubes is used in advanced photogrammetry and 3D generative pipelines.
|
||||||
|
|
||||||
|
[Extracting Triangular 3D Models, Materials, and Lighting From Images (nvdiffrec)](https://github.com/NVlabs/nvdiffrec#news)
|
||||||
|
|
||||||
|
[GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://github.com/nv-tlabs/GET3D#employing-flexicubes)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## License
|
||||||
|
Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES.
|
||||||
|
|
||||||
|
This work is made available under the [Apache License 2.0](LICENSE.txt).
|
||||||
|
|
||||||
|
## Contributing
|
||||||
|
|
||||||
|
This project uses the [Developer Certificate of Origin 1.1](https://developercertificate.org/) to manage contributions.
|
||||||
|
|
||||||
|
By submitting a pull request, you certify that you agree to the terms of the DCO. See [DCO.txt](./DCO.txt) for details.
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
```bibtex
|
||||||
|
@article{shen2023flexicubes,
|
||||||
|
author = {Shen, Tianchang and Munkberg, Jacob and Hasselgren, Jon and Yin, Kangxue and Wang, Zian
|
||||||
|
and Chen, Wenzheng and Gojcic, Zan and Fidler, Sanja and Sharp, Nicholas and Gao, Jun},
|
||||||
|
title = {Flexible Isosurface Extraction for Gradient-Based Mesh Optimization},
|
||||||
|
year = {2023},
|
||||||
|
issue_date = {August 2023},
|
||||||
|
publisher = {Association for Computing Machinery},
|
||||||
|
address = {New York, NY, USA},
|
||||||
|
volume = {42},
|
||||||
|
number = {4},
|
||||||
|
issn = {0730-0301},
|
||||||
|
url = {https://doi.org/10.1145/3592430},
|
||||||
|
doi = {10.1145/3592430},
|
||||||
|
journal = {ACM Trans. Graph.},
|
||||||
|
month = {jul},
|
||||||
|
articleno = {37},
|
||||||
|
numpages = {16}
|
||||||
|
}
|
||||||
|
```
|
||||||
129
trellis/representations/mesh/flexicubes/examples/util.py
Normal file
129
trellis/representations/mesh/flexicubes/examples/util.py
Normal file
@@ -0,0 +1,129 @@
|
|||||||
|
# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
import numpy as np
|
||||||
|
import torch
|
||||||
|
import trimesh
|
||||||
|
import kaolin
|
||||||
|
import nvdiffrast.torch as dr
|
||||||
|
|
||||||
|
###############################################################################
|
||||||
|
# Functions adapted from https://github.com/NVlabs/nvdiffrec
|
||||||
|
###############################################################################
|
||||||
|
|
||||||
|
def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
|
||||||
|
return torch.sum(x*y, -1, keepdim=True)
|
||||||
|
|
||||||
|
def length(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor:
|
||||||
|
return torch.sqrt(torch.clamp(dot(x,x), min=eps)) # Clamp to avoid nan gradients because grad(sqrt(0)) = NaN
|
||||||
|
|
||||||
|
def safe_normalize(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor:
|
||||||
|
return x / length(x, eps)
|
||||||
|
|
||||||
|
def perspective(fovy=0.7854, aspect=1.0, n=0.1, f=1000.0, device=None):
|
||||||
|
y = np.tan(fovy / 2)
|
||||||
|
return torch.tensor([[1/(y*aspect), 0, 0, 0],
|
||||||
|
[ 0, 1/-y, 0, 0],
|
||||||
|
[ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)],
|
||||||
|
[ 0, 0, -1, 0]], dtype=torch.float32, device=device)
|
||||||
|
|
||||||
|
def translate(x, y, z, device=None):
|
||||||
|
return torch.tensor([[1, 0, 0, x],
|
||||||
|
[0, 1, 0, y],
|
||||||
|
[0, 0, 1, z],
|
||||||
|
[0, 0, 0, 1]], dtype=torch.float32, device=device)
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def random_rotation_translation(t, device=None):
|
||||||
|
m = np.random.normal(size=[3, 3])
|
||||||
|
m[1] = np.cross(m[0], m[2])
|
||||||
|
m[2] = np.cross(m[0], m[1])
|
||||||
|
m = m / np.linalg.norm(m, axis=1, keepdims=True)
|
||||||
|
m = np.pad(m, [[0, 1], [0, 1]], mode='constant')
|
||||||
|
m[3, 3] = 1.0
|
||||||
|
m[:3, 3] = np.random.uniform(-t, t, size=[3])
|
||||||
|
return torch.tensor(m, dtype=torch.float32, device=device)
|
||||||
|
|
||||||
|
def rotate_x(a, device=None):
|
||||||
|
s, c = np.sin(a), np.cos(a)
|
||||||
|
return torch.tensor([[1, 0, 0, 0],
|
||||||
|
[0, c, s, 0],
|
||||||
|
[0, -s, c, 0],
|
||||||
|
[0, 0, 0, 1]], dtype=torch.float32, device=device)
|
||||||
|
|
||||||
|
def rotate_y(a, device=None):
|
||||||
|
s, c = np.sin(a), np.cos(a)
|
||||||
|
return torch.tensor([[ c, 0, s, 0],
|
||||||
|
[ 0, 1, 0, 0],
|
||||||
|
[-s, 0, c, 0],
|
||||||
|
[ 0, 0, 0, 1]], dtype=torch.float32, device=device)
|
||||||
|
|
||||||
|
class Mesh:
|
||||||
|
def __init__(self, vertices, faces):
|
||||||
|
self.vertices = vertices
|
||||||
|
self.faces = faces
|
||||||
|
|
||||||
|
def auto_normals(self):
|
||||||
|
v0 = self.vertices[self.faces[:, 0], :]
|
||||||
|
v1 = self.vertices[self.faces[:, 1], :]
|
||||||
|
v2 = self.vertices[self.faces[:, 2], :]
|
||||||
|
nrm = safe_normalize(torch.cross(v1 - v0, v2 - v0))
|
||||||
|
self.nrm = nrm
|
||||||
|
|
||||||
|
def load_mesh(path, device):
|
||||||
|
mesh_np = trimesh.load(path)
|
||||||
|
vertices = torch.tensor(mesh_np.vertices, device=device, dtype=torch.float)
|
||||||
|
faces = torch.tensor(mesh_np.faces, device=device, dtype=torch.long)
|
||||||
|
|
||||||
|
# Normalize
|
||||||
|
vmin, vmax = vertices.min(dim=0)[0], vertices.max(dim=0)[0]
|
||||||
|
scale = 1.8 / torch.max(vmax - vmin).item()
|
||||||
|
vertices = vertices - (vmax + vmin) / 2 # Center mesh on origin
|
||||||
|
vertices = vertices * scale # Rescale to [-0.9, 0.9]
|
||||||
|
return Mesh(vertices, faces)
|
||||||
|
|
||||||
|
def compute_sdf(points, vertices, faces):
|
||||||
|
face_vertices = kaolin.ops.mesh.index_vertices_by_faces(vertices.clone().unsqueeze(0), faces)
|
||||||
|
distance = kaolin.metrics.trianglemesh.point_to_mesh_distance(points.unsqueeze(0), face_vertices)[0]
|
||||||
|
with torch.no_grad():
|
||||||
|
sign = (kaolin.ops.mesh.check_sign(vertices.unsqueeze(0), faces, points.unsqueeze(0))<1).float() * 2 - 1
|
||||||
|
sdf = (sign*distance).squeeze(0)
|
||||||
|
return sdf
|
||||||
|
|
||||||
|
def sample_random_points(n, mesh):
|
||||||
|
pts_random = (torch.rand((n//2,3),device='cuda') - 0.5) * 2
|
||||||
|
pts_surface = kaolin.ops.mesh.sample_points(mesh.vertices.unsqueeze(0), mesh.faces, 500)[0].squeeze(0)
|
||||||
|
pts_surface += torch.randn_like(pts_surface) * 0.05
|
||||||
|
pts = torch.cat([pts_random, pts_surface])
|
||||||
|
return pts
|
||||||
|
|
||||||
|
def xfm_points(points, matrix):
|
||||||
|
'''Transform points.
|
||||||
|
Args:
|
||||||
|
points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3]
|
||||||
|
matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4]
|
||||||
|
use_python: Use PyTorch's torch.matmul (for validation)
|
||||||
|
Returns:
|
||||||
|
Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4].
|
||||||
|
'''
|
||||||
|
out = torch.matmul(
|
||||||
|
torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2))
|
||||||
|
if torch.is_anomaly_enabled():
|
||||||
|
assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN"
|
||||||
|
return out
|
||||||
|
|
||||||
|
def interpolate(attr, rast, attr_idx, rast_db=None):
|
||||||
|
return dr.interpolate(
|
||||||
|
attr, rast, attr_idx, rast_db=rast_db,
|
||||||
|
diff_attrs=None if rast_db is None else 'all')
|
||||||
798
trellis/representations/mesh/flexicubes/tables.py
Normal file
798
trellis/representations/mesh/flexicubes/tables.py
Normal file
@@ -0,0 +1,798 @@
|
|||||||
|
# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
dmc_table = [
|
||||||
|
[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 5, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 5, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 8, 11, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 5, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 5, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 7, 8, 9, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 5, 7, 8, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 5, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 7, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 9, 10, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 5, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 5, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 7, 8, 9, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 7, 9, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 5, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[8, 9, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 7, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 9, 10, 11, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 8, 10, 11, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 5, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 7, 8, 9, -1, -1, -1], [1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 5, 7, 9, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 7, 8, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 7, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 8, 9, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 6, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 6, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [4, 6, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 6, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 5, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 5, 8, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 6, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 6, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 5, 6, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 5, 6, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 6, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 6, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [2, 3, 6, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 6, 7, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 6, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 6, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [2, 3, 4, 6, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 6, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [2, 3, 6, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 6, 7, 8, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 5, -1, -1, -1], [2, 3, 6, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 5, 6, 7, 8], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 5, 6, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 6, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 2, 3, 5, 6, 8], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 5, 6, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 10, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 9, 10, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 8, 9, 10, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 6, 8, 11, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 6, 11, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 9, 10, -1, -1, -1], [4, 6, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 6, 9, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 5, 10, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 5, 8, 10, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 6, 8, 9, 11, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 6, 9, 11, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 6, 8, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 5, 6, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 6, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 6, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 6, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[6, 7, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 6, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 6, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 6, 8, 9, 10], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 6, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [1, 3, 6, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 6, 7, 8, 10, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 5, 6, 7, 10], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 6, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 5, 6, 8, 9, 10], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 5, 6, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 8, 9, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 7, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 7, 9, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 6, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [4, 6, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 6, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 6, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[6, 7, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 6, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 6, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 6, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 11, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 8, 11, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 8, 9, 11, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 7, 11, -1, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [5, 6, 10, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 7, 9, 11, -1], [5, 6, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 6, 9, 10, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 8, 11, -1, -1, -1], [4, 6, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 6, 10, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 6, 8, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[6, 7, 8, 9, 10, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 6, 7, 9, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 6, 7, 8, 10, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 6, 7, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 5, 6, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [1, 2, 5, 6, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 6, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 5, 6, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [1, 2, 5, 6, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 7, -1, -1, -1], [1, 2, 5, 6, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 6, 9, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 5, 6, 7, 9], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 6, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [1, 2, 4, 6, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 6, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 6, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 6, 7, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 2, 3, 6, 7, 9], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 6, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 6, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 5, 6, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 5, 6, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 6, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 6, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [1, 3, 5, 6, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 5, 6, 7, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 6, 9, 11, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 6, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 6, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 6, 8, 9, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 6, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 6, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 6, 7, 8, 9, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 6, 7, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 7, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [5, 7, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [5, 7, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 8, 9, -1, -1, -1], [5, 7, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 5, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [4, 5, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 5, 9, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [4, 7, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 7, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 7, 8, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[8, 9, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 5, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [2, 3, 5, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 5, 7, 8, 9, 10], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 5, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 5, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [2, 3, 4, 5, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 5, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 7, 8, 9, 10], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 2, 3, 4, 7, 10], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 2, 3, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 5, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [1, 2, 5, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 5, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 5, 7, 8, 9, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 5, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 2, 3, 4, 5, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 5, 8, 9, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 4, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [1, 2, 4, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 4, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 4, 7, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 2, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 2, 3, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 2, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 5, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 5, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 5, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 4, 7, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
||||||
|
[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]]
|
||||||
|
]
|
||||||
|
num_vd_table = [0, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 1, 2, 2,
|
||||||
|
2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2,
|
||||||
|
1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 3, 2, 2, 1, 1, 1, 1,
|
||||||
|
1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 3, 2, 2, 2, 2, 2, 1, 3, 4, 2,
|
||||||
|
2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2,
|
||||||
|
3, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 3, 2, 4, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1,
|
||||||
|
2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1,
|
||||||
|
1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,
|
||||||
|
1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||||
|
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
|
||||||
|
check_table = [
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 0, 0, 194],
|
||||||
|
[1, -1, 0, 0, 193],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 1, 0, 164],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, -1, 0, 161],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, 1, 152],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, 1, 145],
|
||||||
|
[1, 0, 0, 1, 144],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, -1, 137],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 1, 0, 133],
|
||||||
|
[1, 0, 1, 0, 132],
|
||||||
|
[1, 1, 0, 0, 131],
|
||||||
|
[1, 1, 0, 0, 130],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, 1, 100],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, 1, 98],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, 1, 96],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 1, 0, 88],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, -1, 0, 82],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 1, 0, 74],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 1, 0, 72],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, -1, 70],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, -1, 0, 0, 67],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, -1, 0, 0, 65],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 0, 0, 56],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, -1, 0, 0, 52],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 0, 0, 44],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 0, 0, 40],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, -1, 38],
|
||||||
|
[1, 0, -1, 0, 37],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, -1, 0, 33],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, -1, 0, 0, 28],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, -1, 0, 26],
|
||||||
|
[1, 0, 0, -1, 25],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, -1, 0, 0, 20],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, -1, 0, 18],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, -1, 9],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[1, 0, 0, -1, 6],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0]
|
||||||
|
]
|
||||||
|
tet_table = [
|
||||||
|
[-1, -1, -1, -1, -1, -1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 0, 0, 4, 4, -1],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[0, 4, 0, 4, 4, -1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 0, 2, -1, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, -1, 2, 4, 4, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 0, 2, 4, 4, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 4, 2, 4, 4, 2],
|
||||||
|
[0, 4, 0, 4, 4, 0],
|
||||||
|
[2, 0, 2, 0, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 5, 2, 5, 5, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 0, 2, 0, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 1, 1, -1, 0, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[4, 1, 1, 4, 4, 1],
|
||||||
|
[0, 1, 1, 0, 0, 1],
|
||||||
|
[4, 0, 0, 4, 4, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[-1, 1, 1, 4, 4, 1],
|
||||||
|
[0, 1, 1, 4, 4, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[5, 1, 1, 5, 5, 1],
|
||||||
|
[0, 1, 1, 0, 0, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[8, 8, 8, 8, 8, 8],
|
||||||
|
[1, 1, 1, 4, 4, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 0, 0, 4, 4, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 4, 4, 1],
|
||||||
|
[0, 4, 0, 4, 4, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 5, 5, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[6, 6, 6, 6, 6, 6],
|
||||||
|
[6, -1, 0, 6, 0, 6],
|
||||||
|
[6, 0, 0, 6, 0, 6],
|
||||||
|
[6, 1, 1, 6, 1, 6],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 0, 0, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[6, 4, -1, 6, 4, 6],
|
||||||
|
[6, 4, 0, 6, 4, 6],
|
||||||
|
[6, 0, 0, 6, 0, 6],
|
||||||
|
[6, 1, 1, 6, 1, 6],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 0, 2, 2, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 0, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 4, 2, 2, 4, 2],
|
||||||
|
[0, 4, 0, 4, 4, 0],
|
||||||
|
[2, 0, 2, 2, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[6, 1, 1, 6, -1, 6],
|
||||||
|
[6, 1, 1, 6, 0, 6],
|
||||||
|
[6, 0, 0, 6, 0, 6],
|
||||||
|
[6, 2, 2, 6, 2, 6],
|
||||||
|
[4, 1, 1, 4, 4, 1],
|
||||||
|
[0, 1, 1, 0, 0, 1],
|
||||||
|
[4, 0, 0, 4, 4, 4],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[6, 1, 1, 6, 4, 6],
|
||||||
|
[6, 1, 1, 6, 4, 6],
|
||||||
|
[6, 0, 0, 6, 0, 6],
|
||||||
|
[6, 2, 2, 6, 2, 6],
|
||||||
|
[5, 1, 1, 5, 5, 1],
|
||||||
|
[0, 1, 1, 0, 0, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[6, 6, 6, 6, 6, 6],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 4, 1],
|
||||||
|
[0, 4, 0, 4, 4, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 5, 0, 5, 0, 5],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[0, 5, 0, 5, 0, 5],
|
||||||
|
[-1, 5, 0, 5, 0, 5],
|
||||||
|
[1, 5, 1, 5, 1, 5],
|
||||||
|
[4, 5, -1, 5, 4, 5],
|
||||||
|
[0, 5, 0, 5, 0, 5],
|
||||||
|
[4, 5, 0, 5, 4, 5],
|
||||||
|
[1, 5, 1, 5, 1, 5],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[0, 4, 0, 4, 4, 4],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[6, 6, 6, 6, 6, 6],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 5, 2, 5, -1, 5],
|
||||||
|
[0, 5, 0, 5, 0, 5],
|
||||||
|
[2, 5, 2, 5, 0, 5],
|
||||||
|
[1, 5, 1, 5, 1, 5],
|
||||||
|
[2, 5, 2, 5, 4, 5],
|
||||||
|
[0, 5, 0, 5, 0, 5],
|
||||||
|
[2, 5, 2, 5, 4, 5],
|
||||||
|
[1, 5, 1, 5, 1, 5],
|
||||||
|
[2, 4, 2, 4, 4, 2],
|
||||||
|
[0, 4, 0, 4, 4, 4],
|
||||||
|
[2, 0, 2, 0, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 6, 2, 6, 6, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 0, 2, 0, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 1, 1, 1, 0, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[4, 1, 1, 1, 4, 1],
|
||||||
|
[0, 1, 1, 1, 0, 1],
|
||||||
|
[4, 0, 0, 4, 4, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[1, 1, 1, 1, 4, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 0, 0, 4, 4, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[6, 0, 0, 6, 0, 6],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[6, 6, 6, 6, 6, 6],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[5, 5, 0, 5, 0, 5],
|
||||||
|
[5, 5, 0, 5, 0, 5],
|
||||||
|
[5, 5, 1, 5, 1, 5],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 4, 0, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[4, 4, 0, 4, 4, 4],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[8, 8, 8, 8, 8, 8],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 0, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 1, 1, 4, 4, 1],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[1, 1, 1, 1, 0, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 4, 2, 4, 4, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[2, 2, 2, 2, 2, 2],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[5, 5, 5, 5, 5, 5],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[4, 4, 4, 4, 4, 4],
|
||||||
|
[1, 1, 1, 1, 1, 1],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[0, 0, 0, 0, 0, 0],
|
||||||
|
[12, 12, 12, 12, 12, 12]
|
||||||
|
]
|
||||||
61
trellis/representations/mesh/utils_cube.py
Normal file
61
trellis/representations/mesh/utils_cube.py
Normal file
@@ -0,0 +1,61 @@
|
|||||||
|
import torch
|
||||||
|
cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [
|
||||||
|
1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.int)
|
||||||
|
cube_neighbor = torch.tensor([[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]])
|
||||||
|
cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6,
|
||||||
|
2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, requires_grad=False)
|
||||||
|
|
||||||
|
def construct_dense_grid(res, device='cuda'):
|
||||||
|
'''construct a dense grid based on resolution'''
|
||||||
|
res_v = res + 1
|
||||||
|
vertsid = torch.arange(res_v ** 3, device=device)
|
||||||
|
coordsid = vertsid.reshape(res_v, res_v, res_v)[:res, :res, :res].flatten()
|
||||||
|
cube_corners_bias = (cube_corners[:, 0] * res_v + cube_corners[:, 1]) * res_v + cube_corners[:, 2]
|
||||||
|
cube_fx8 = (coordsid.unsqueeze(1) + cube_corners_bias.unsqueeze(0).to(device))
|
||||||
|
verts = torch.stack([vertsid // (res_v ** 2), (vertsid // res_v) % res_v, vertsid % res_v], dim=1)
|
||||||
|
return verts, cube_fx8
|
||||||
|
|
||||||
|
|
||||||
|
def construct_voxel_grid(coords):
|
||||||
|
verts = (cube_corners.unsqueeze(0).to(coords) + coords.unsqueeze(1)).reshape(-1, 3)
|
||||||
|
verts_unique, inverse_indices = torch.unique(verts, dim=0, return_inverse=True)
|
||||||
|
cubes = inverse_indices.reshape(-1, 8)
|
||||||
|
return verts_unique, cubes
|
||||||
|
|
||||||
|
|
||||||
|
def cubes_to_verts(num_verts, cubes, value, reduce='mean'):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
cubes [Vx8] verts index for each cube
|
||||||
|
value [Vx8xM] value to be scattered
|
||||||
|
Operation:
|
||||||
|
reduced[cubes[i][j]][k] += value[i][k]
|
||||||
|
"""
|
||||||
|
M = value.shape[2] # number of channels
|
||||||
|
reduced = torch.zeros(num_verts, M, device=cubes.device)
|
||||||
|
return torch.scatter_reduce(reduced, 0,
|
||||||
|
cubes.unsqueeze(-1).expand(-1, -1, M).flatten(0, 1),
|
||||||
|
value.flatten(0, 1), reduce=reduce, include_self=False)
|
||||||
|
|
||||||
|
def sparse_cube2verts(coords, feats, training=True):
|
||||||
|
new_coords, cubes = construct_voxel_grid(coords)
|
||||||
|
new_feats = cubes_to_verts(new_coords.shape[0], cubes, feats)
|
||||||
|
if training:
|
||||||
|
con_loss = torch.mean((feats - new_feats[cubes]) ** 2)
|
||||||
|
else:
|
||||||
|
con_loss = 0.0
|
||||||
|
return new_coords, new_feats, con_loss
|
||||||
|
|
||||||
|
|
||||||
|
def get_dense_attrs(coords : torch.Tensor, feats : torch.Tensor, res : int, sdf_init=True):
|
||||||
|
F = feats.shape[-1]
|
||||||
|
dense_attrs = torch.zeros([res] * 3 + [F], device=feats.device)
|
||||||
|
if sdf_init:
|
||||||
|
dense_attrs[..., 0] = 1 # initial outside sdf value
|
||||||
|
dense_attrs[coords[:, 0], coords[:, 1], coords[:, 2], :] = feats
|
||||||
|
return dense_attrs.reshape(-1, F)
|
||||||
|
|
||||||
|
|
||||||
|
def get_defomed_verts(v_pos : torch.Tensor, deform : torch.Tensor, res):
|
||||||
|
return v_pos / res - 0.5 + (1 - 1e-8) / (res * 2) * torch.tanh(deform)
|
||||||
|
|
||||||
68
trellis/trainers/flow_matching/mixins/text_conditioned.py
Normal file
68
trellis/trainers/flow_matching/mixins/text_conditioned.py
Normal file
@@ -0,0 +1,68 @@
|
|||||||
|
from typing import *
|
||||||
|
import os
|
||||||
|
os.environ['TOKENIZERS_PARALLELISM'] = 'true'
|
||||||
|
import torch
|
||||||
|
from transformers import AutoTokenizer, CLIPTextModel
|
||||||
|
|
||||||
|
from ....utils import dist_utils
|
||||||
|
|
||||||
|
|
||||||
|
class TextConditionedMixin:
|
||||||
|
"""
|
||||||
|
Mixin for text-conditioned models.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text_cond_model: The text conditioning model.
|
||||||
|
"""
|
||||||
|
def __init__(self, *args, text_cond_model: str = 'openai/clip-vit-large-patch14', **kwargs):
|
||||||
|
super().__init__(*args, **kwargs)
|
||||||
|
self.text_cond_model_name = text_cond_model
|
||||||
|
self.text_cond_model = None # the model is init lazily
|
||||||
|
|
||||||
|
def _init_text_cond_model(self):
|
||||||
|
"""
|
||||||
|
Initialize the text conditioning model.
|
||||||
|
"""
|
||||||
|
# load model
|
||||||
|
with dist_utils.local_master_first():
|
||||||
|
model = CLIPTextModel.from_pretrained(self.text_cond_model_name)
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(self.text_cond_model_name)
|
||||||
|
model.eval()
|
||||||
|
model = model.cuda()
|
||||||
|
self.text_cond_model = {
|
||||||
|
'model': model,
|
||||||
|
'tokenizer': tokenizer,
|
||||||
|
}
|
||||||
|
self.text_cond_model['null_cond'] = self.encode_text([''])
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def encode_text(self, text: List[str]) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
Encode the text.
|
||||||
|
"""
|
||||||
|
assert isinstance(text, list) and isinstance(text[0], str), "TextConditionedMixin only supports list of strings as cond"
|
||||||
|
if self.text_cond_model is None:
|
||||||
|
self._init_text_cond_model()
|
||||||
|
encoding = self.text_cond_model['tokenizer'](text, max_length=77, padding='max_length', truncation=True, return_tensors='pt')
|
||||||
|
tokens = encoding['input_ids'].cuda()
|
||||||
|
embeddings = self.text_cond_model['model'](input_ids=tokens).last_hidden_state
|
||||||
|
|
||||||
|
return embeddings
|
||||||
|
|
||||||
|
def get_cond(self, cond, **kwargs):
|
||||||
|
"""
|
||||||
|
Get the conditioning data.
|
||||||
|
"""
|
||||||
|
cond = self.encode_text(cond)
|
||||||
|
kwargs['neg_cond'] = self.text_cond_model['null_cond'].repeat(cond.shape[0], 1, 1)
|
||||||
|
cond = super().get_cond(cond, **kwargs)
|
||||||
|
return cond
|
||||||
|
|
||||||
|
def get_inference_cond(self, cond, **kwargs):
|
||||||
|
"""
|
||||||
|
Get the conditioning data for inference.
|
||||||
|
"""
|
||||||
|
cond = self.encode_text(cond)
|
||||||
|
kwargs['neg_cond'] = self.text_cond_model['null_cond'].repeat(cond.shape[0], 1, 1)
|
||||||
|
cond = super().get_inference_cond(cond, **kwargs)
|
||||||
|
return cond
|
||||||
286
trellis/trainers/flow_matching/sparse_flow_matching.py
Normal file
286
trellis/trainers/flow_matching/sparse_flow_matching.py
Normal file
@@ -0,0 +1,286 @@
|
|||||||
|
from typing import *
|
||||||
|
import os
|
||||||
|
import copy
|
||||||
|
import functools
|
||||||
|
import torch
|
||||||
|
import torch.nn.functional as F
|
||||||
|
from torch.utils.data import DataLoader
|
||||||
|
import numpy as np
|
||||||
|
from easydict import EasyDict as edict
|
||||||
|
|
||||||
|
from ...modules import sparse as sp
|
||||||
|
from ...utils.general_utils import dict_reduce
|
||||||
|
from ...utils.data_utils import cycle, BalancedResumableSampler
|
||||||
|
from .flow_matching import FlowMatchingTrainer
|
||||||
|
from .mixins.classifier_free_guidance import ClassifierFreeGuidanceMixin
|
||||||
|
from .mixins.text_conditioned import TextConditionedMixin
|
||||||
|
from .mixins.image_conditioned import ImageConditionedMixin
|
||||||
|
|
||||||
|
|
||||||
|
class SparseFlowMatchingTrainer(FlowMatchingTrainer):
|
||||||
|
"""
|
||||||
|
Trainer for sparse diffusion model with flow matching objective.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
models (dict[str, nn.Module]): Models to train.
|
||||||
|
dataset (torch.utils.data.Dataset): Dataset.
|
||||||
|
output_dir (str): Output directory.
|
||||||
|
load_dir (str): Load directory.
|
||||||
|
step (int): Step to load.
|
||||||
|
batch_size (int): Batch size.
|
||||||
|
batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
|
||||||
|
batch_split (int): Split batch with gradient accumulation.
|
||||||
|
max_steps (int): Max steps.
|
||||||
|
optimizer (dict): Optimizer config.
|
||||||
|
lr_scheduler (dict): Learning rate scheduler config.
|
||||||
|
elastic (dict): Elastic memory management config.
|
||||||
|
grad_clip (float or dict): Gradient clip config.
|
||||||
|
ema_rate (float or list): Exponential moving average rates.
|
||||||
|
fp16_mode (str): FP16 mode.
|
||||||
|
- None: No FP16.
|
||||||
|
- 'inflat_all': Hold a inflated fp32 master param for all params.
|
||||||
|
- 'amp': Automatic mixed precision.
|
||||||
|
fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
|
||||||
|
finetune_ckpt (dict): Finetune checkpoint.
|
||||||
|
log_param_stats (bool): Log parameter stats.
|
||||||
|
i_print (int): Print interval.
|
||||||
|
i_log (int): Log interval.
|
||||||
|
i_sample (int): Sample interval.
|
||||||
|
i_save (int): Save interval.
|
||||||
|
i_ddpcheck (int): DDP check interval.
|
||||||
|
|
||||||
|
t_schedule (dict): Time schedule for flow matching.
|
||||||
|
sigma_min (float): Minimum noise level.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def prepare_dataloader(self, **kwargs):
|
||||||
|
"""
|
||||||
|
Prepare dataloader.
|
||||||
|
"""
|
||||||
|
self.data_sampler = BalancedResumableSampler(
|
||||||
|
self.dataset,
|
||||||
|
shuffle=True,
|
||||||
|
batch_size=self.batch_size_per_gpu,
|
||||||
|
)
|
||||||
|
self.dataloader = DataLoader(
|
||||||
|
self.dataset,
|
||||||
|
batch_size=self.batch_size_per_gpu,
|
||||||
|
num_workers=int(np.ceil(os.cpu_count() / torch.cuda.device_count())),
|
||||||
|
pin_memory=True,
|
||||||
|
drop_last=True,
|
||||||
|
persistent_workers=True,
|
||||||
|
collate_fn=functools.partial(self.dataset.collate_fn, split_size=self.batch_split),
|
||||||
|
sampler=self.data_sampler,
|
||||||
|
)
|
||||||
|
self.data_iterator = cycle(self.dataloader)
|
||||||
|
|
||||||
|
def training_losses(
|
||||||
|
self,
|
||||||
|
x_0: sp.SparseTensor,
|
||||||
|
cond=None,
|
||||||
|
**kwargs
|
||||||
|
) -> Tuple[Dict, Dict]:
|
||||||
|
"""
|
||||||
|
Compute training losses for a single timestep.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
x_0: The [N x ... x C] sparse tensor of the inputs.
|
||||||
|
cond: The [N x ...] tensor of additional conditions.
|
||||||
|
kwargs: Additional arguments to pass to the backbone.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
a dict with the key "loss" containing a tensor of shape [N].
|
||||||
|
may also contain other keys for different terms.
|
||||||
|
"""
|
||||||
|
noise = x_0.replace(torch.randn_like(x_0.feats))
|
||||||
|
t = self.sample_t(x_0.shape[0]).to(x_0.device).float()
|
||||||
|
x_t = self.diffuse(x_0, t, noise=noise)
|
||||||
|
cond = self.get_cond(cond, **kwargs)
|
||||||
|
|
||||||
|
pred = self.training_models['denoiser'](x_t, t * 1000, cond, **kwargs)
|
||||||
|
assert pred.shape == noise.shape == x_0.shape
|
||||||
|
target = self.get_v(x_0, noise, t)
|
||||||
|
terms = edict()
|
||||||
|
terms["mse"] = F.mse_loss(pred.feats, target.feats)
|
||||||
|
terms["loss"] = terms["mse"]
|
||||||
|
|
||||||
|
# log loss with time bins
|
||||||
|
mse_per_instance = np.array([
|
||||||
|
F.mse_loss(pred.feats[x_0.layout[i]], target.feats[x_0.layout[i]]).item()
|
||||||
|
for i in range(x_0.shape[0])
|
||||||
|
])
|
||||||
|
time_bin = np.digitize(t.cpu().numpy(), np.linspace(0, 1, 11)) - 1
|
||||||
|
for i in range(10):
|
||||||
|
if (time_bin == i).sum() != 0:
|
||||||
|
terms[f"bin_{i}"] = {"mse": mse_per_instance[time_bin == i].mean()}
|
||||||
|
|
||||||
|
return terms, {}
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def run_snapshot(
|
||||||
|
self,
|
||||||
|
num_samples: int,
|
||||||
|
batch_size: int,
|
||||||
|
verbose: bool = False,
|
||||||
|
) -> Dict:
|
||||||
|
dataloader = DataLoader(
|
||||||
|
copy.deepcopy(self.dataset),
|
||||||
|
batch_size=batch_size,
|
||||||
|
shuffle=True,
|
||||||
|
num_workers=0,
|
||||||
|
collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
|
||||||
|
)
|
||||||
|
|
||||||
|
# inference
|
||||||
|
sampler = self.get_sampler()
|
||||||
|
sample_gt = []
|
||||||
|
sample = []
|
||||||
|
cond_vis = []
|
||||||
|
for i in range(0, num_samples, batch_size):
|
||||||
|
batch = min(batch_size, num_samples - i)
|
||||||
|
data = next(iter(dataloader))
|
||||||
|
data = {k: v[:batch].cuda() if not isinstance(v, list) else v[:batch] for k, v in data.items()}
|
||||||
|
noise = data['x_0'].replace(torch.randn_like(data['x_0'].feats))
|
||||||
|
sample_gt.append(data['x_0'])
|
||||||
|
cond_vis.append(self.vis_cond(**data))
|
||||||
|
del data['x_0']
|
||||||
|
args = self.get_inference_cond(**data)
|
||||||
|
res = sampler.sample(
|
||||||
|
self.models['denoiser'],
|
||||||
|
noise=noise,
|
||||||
|
**args,
|
||||||
|
steps=50, cfg_strength=3.0, verbose=verbose,
|
||||||
|
)
|
||||||
|
sample.append(res.samples)
|
||||||
|
|
||||||
|
sample_gt = sp.sparse_cat(sample_gt)
|
||||||
|
sample = sp.sparse_cat(sample)
|
||||||
|
sample_dict = {
|
||||||
|
'sample_gt': {'value': sample_gt, 'type': 'sample'},
|
||||||
|
'sample': {'value': sample, 'type': 'sample'},
|
||||||
|
}
|
||||||
|
sample_dict.update(dict_reduce(cond_vis, None, {
|
||||||
|
'value': lambda x: torch.cat(x, dim=0),
|
||||||
|
'type': lambda x: x[0],
|
||||||
|
}))
|
||||||
|
|
||||||
|
return sample_dict
|
||||||
|
|
||||||
|
|
||||||
|
class SparseFlowMatchingCFGTrainer(ClassifierFreeGuidanceMixin, SparseFlowMatchingTrainer):
|
||||||
|
"""
|
||||||
|
Trainer for sparse diffusion model with flow matching objective and classifier-free guidance.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
models (dict[str, nn.Module]): Models to train.
|
||||||
|
dataset (torch.utils.data.Dataset): Dataset.
|
||||||
|
output_dir (str): Output directory.
|
||||||
|
load_dir (str): Load directory.
|
||||||
|
step (int): Step to load.
|
||||||
|
batch_size (int): Batch size.
|
||||||
|
batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
|
||||||
|
batch_split (int): Split batch with gradient accumulation.
|
||||||
|
max_steps (int): Max steps.
|
||||||
|
optimizer (dict): Optimizer config.
|
||||||
|
lr_scheduler (dict): Learning rate scheduler config.
|
||||||
|
elastic (dict): Elastic memory management config.
|
||||||
|
grad_clip (float or dict): Gradient clip config.
|
||||||
|
ema_rate (float or list): Exponential moving average rates.
|
||||||
|
fp16_mode (str): FP16 mode.
|
||||||
|
- None: No FP16.
|
||||||
|
- 'inflat_all': Hold a inflated fp32 master param for all params.
|
||||||
|
- 'amp': Automatic mixed precision.
|
||||||
|
fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
|
||||||
|
finetune_ckpt (dict): Finetune checkpoint.
|
||||||
|
log_param_stats (bool): Log parameter stats.
|
||||||
|
i_print (int): Print interval.
|
||||||
|
i_log (int): Log interval.
|
||||||
|
i_sample (int): Sample interval.
|
||||||
|
i_save (int): Save interval.
|
||||||
|
i_ddpcheck (int): DDP check interval.
|
||||||
|
|
||||||
|
t_schedule (dict): Time schedule for flow matching.
|
||||||
|
sigma_min (float): Minimum noise level.
|
||||||
|
p_uncond (float): Probability of dropping conditions.
|
||||||
|
"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class TextConditionedSparseFlowMatchingCFGTrainer(TextConditionedMixin, SparseFlowMatchingCFGTrainer):
|
||||||
|
"""
|
||||||
|
Trainer for sparse text-conditioned diffusion model with flow matching objective and classifier-free guidance.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
models (dict[str, nn.Module]): Models to train.
|
||||||
|
dataset (torch.utils.data.Dataset): Dataset.
|
||||||
|
output_dir (str): Output directory.
|
||||||
|
load_dir (str): Load directory.
|
||||||
|
step (int): Step to load.
|
||||||
|
batch_size (int): Batch size.
|
||||||
|
batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
|
||||||
|
batch_split (int): Split batch with gradient accumulation.
|
||||||
|
max_steps (int): Max steps.
|
||||||
|
optimizer (dict): Optimizer config.
|
||||||
|
lr_scheduler (dict): Learning rate scheduler config.
|
||||||
|
elastic (dict): Elastic memory management config.
|
||||||
|
grad_clip (float or dict): Gradient clip config.
|
||||||
|
ema_rate (float or list): Exponential moving average rates.
|
||||||
|
fp16_mode (str): FP16 mode.
|
||||||
|
- None: No FP16.
|
||||||
|
- 'inflat_all': Hold a inflated fp32 master param for all params.
|
||||||
|
- 'amp': Automatic mixed precision.
|
||||||
|
fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
|
||||||
|
finetune_ckpt (dict): Finetune checkpoint.
|
||||||
|
log_param_stats (bool): Log parameter stats.
|
||||||
|
i_print (int): Print interval.
|
||||||
|
i_log (int): Log interval.
|
||||||
|
i_sample (int): Sample interval.
|
||||||
|
i_save (int): Save interval.
|
||||||
|
i_ddpcheck (int): DDP check interval.
|
||||||
|
|
||||||
|
t_schedule (dict): Time schedule for flow matching.
|
||||||
|
sigma_min (float): Minimum noise level.
|
||||||
|
p_uncond (float): Probability of dropping conditions.
|
||||||
|
text_cond_model(str): Text conditioning model.
|
||||||
|
"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class ImageConditionedSparseFlowMatchingCFGTrainer(ImageConditionedMixin, SparseFlowMatchingCFGTrainer):
|
||||||
|
"""
|
||||||
|
Trainer for sparse image-conditioned diffusion model with flow matching objective and classifier-free guidance.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
models (dict[str, nn.Module]): Models to train.
|
||||||
|
dataset (torch.utils.data.Dataset): Dataset.
|
||||||
|
output_dir (str): Output directory.
|
||||||
|
load_dir (str): Load directory.
|
||||||
|
step (int): Step to load.
|
||||||
|
batch_size (int): Batch size.
|
||||||
|
batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
|
||||||
|
batch_split (int): Split batch with gradient accumulation.
|
||||||
|
max_steps (int): Max steps.
|
||||||
|
optimizer (dict): Optimizer config.
|
||||||
|
lr_scheduler (dict): Learning rate scheduler config.
|
||||||
|
elastic (dict): Elastic memory management config.
|
||||||
|
grad_clip (float or dict): Gradient clip config.
|
||||||
|
ema_rate (float or list): Exponential moving average rates.
|
||||||
|
fp16_mode (str): FP16 mode.
|
||||||
|
- None: No FP16.
|
||||||
|
- 'inflat_all': Hold a inflated fp32 master param for all params.
|
||||||
|
- 'amp': Automatic mixed precision.
|
||||||
|
fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
|
||||||
|
finetune_ckpt (dict): Finetune checkpoint.
|
||||||
|
log_param_stats (bool): Log parameter stats.
|
||||||
|
i_print (int): Print interval.
|
||||||
|
i_log (int): Log interval.
|
||||||
|
i_sample (int): Sample interval.
|
||||||
|
i_save (int): Save interval.
|
||||||
|
i_ddpcheck (int): DDP check interval.
|
||||||
|
|
||||||
|
t_schedule (dict): Time schedule for flow matching.
|
||||||
|
sigma_min (float): Minimum noise level.
|
||||||
|
p_uncond (float): Probability of dropping conditions.
|
||||||
|
image_cond_model (str): Image conditioning model.
|
||||||
|
"""
|
||||||
|
pass
|
||||||
77
trellis/trainers/utils.py
Normal file
77
trellis/trainers/utils.py
Normal file
@@ -0,0 +1,77 @@
|
|||||||
|
import torch.nn as nn
|
||||||
|
|
||||||
|
|
||||||
|
# FP16 utils
|
||||||
|
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
|
||||||
|
|
||||||
|
def make_master_params(model_params):
|
||||||
|
"""
|
||||||
|
Copy model parameters into a inflated tensor of full-precision parameters.
|
||||||
|
"""
|
||||||
|
master_params = _flatten_dense_tensors(
|
||||||
|
[param.detach().float() for param in model_params]
|
||||||
|
)
|
||||||
|
master_params = nn.Parameter(master_params)
|
||||||
|
master_params.requires_grad = True
|
||||||
|
return [master_params]
|
||||||
|
|
||||||
|
|
||||||
|
def unflatten_master_params(model_params, master_params):
|
||||||
|
"""
|
||||||
|
Unflatten the master parameters to look like model_params.
|
||||||
|
"""
|
||||||
|
return _unflatten_dense_tensors(master_params[0].detach(), model_params)
|
||||||
|
|
||||||
|
|
||||||
|
def model_params_to_master_params(model_params, master_params):
|
||||||
|
"""
|
||||||
|
Copy the model parameter data into the master parameters.
|
||||||
|
"""
|
||||||
|
master_params[0].detach().copy_(
|
||||||
|
_flatten_dense_tensors([param.detach().float() for param in model_params])
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def master_params_to_model_params(model_params, master_params):
|
||||||
|
"""
|
||||||
|
Copy the master parameter data back into the model parameters.
|
||||||
|
"""
|
||||||
|
for param, master_param in zip(
|
||||||
|
model_params, _unflatten_dense_tensors(master_params[0].detach(), model_params)
|
||||||
|
):
|
||||||
|
param.detach().copy_(master_param)
|
||||||
|
|
||||||
|
|
||||||
|
def model_grads_to_master_grads(model_params, master_params):
|
||||||
|
"""
|
||||||
|
Copy the gradients from the model parameters into the master parameters
|
||||||
|
from make_master_params().
|
||||||
|
"""
|
||||||
|
master_params[0].grad = _flatten_dense_tensors(
|
||||||
|
[param.grad.data.detach().float() for param in model_params]
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def zero_grad(model_params):
|
||||||
|
for param in model_params:
|
||||||
|
if param.grad is not None:
|
||||||
|
if param.grad.grad_fn is not None:
|
||||||
|
param.grad.detach_()
|
||||||
|
else:
|
||||||
|
param.grad.requires_grad_(False)
|
||||||
|
param.grad.zero_()
|
||||||
|
|
||||||
|
|
||||||
|
# LR Schedulers
|
||||||
|
from torch.optim.lr_scheduler import LambdaLR
|
||||||
|
|
||||||
|
class LinearWarmupLRScheduler(LambdaLR):
|
||||||
|
def __init__(self, optimizer, warmup_steps, last_epoch=-1):
|
||||||
|
self.warmup_steps = warmup_steps
|
||||||
|
super(LinearWarmupLRScheduler, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)
|
||||||
|
|
||||||
|
def lr_lambda(self, current_step):
|
||||||
|
if current_step < self.warmup_steps:
|
||||||
|
return float(current_step + 1) / self.warmup_steps
|
||||||
|
return 1.0
|
||||||
|
|
||||||
Reference in New Issue
Block a user