155 lines
6.2 KiB
Python
155 lines
6.2 KiB
Python
import os
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import sys
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = '1'
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import time
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import argparse
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import wandb
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from tqdm import tqdm
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import random
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as ex
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from PIL import Image
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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from torchvision.transforms import Resize
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from torchvision.transforms.functional import InterpolationMode
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from pathlib import Path
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from data.utils import Frame
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from data.dataset import read_frames_file
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from utils.utils import save_stl
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from utils.checkpoints import CheckpointSaver, load_model
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from utils.postprocess import fix_collision
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from losses.losses import collision_loss, normal_loss, kl_loss
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from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision, group_inputs_for_levels
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from smpl.smpl_torch import SMPL
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from plotly.subplots import make_subplots
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from models.pyramid_model import Pyramid
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from data.dataset import Dataset
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from utils.checkpoints import CheckpointSaver, load_model
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from torch.utils.data import DataLoader
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import trimesh as tm
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from zipfile import ZipFile
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from configs.iccvw.pyr_64_128_256_512_e2e import config as config_1
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from configs.iccvw.pyr_64_128_256_512_skirt_e2e import config as config_2
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uv2mesh = dict()
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uv2mesh['nonskirt'] = UV2Mesh(res=256, is_skirt=False, is_smpl_orig=config_1['is_cape'])
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uv2mesh['skirt'] = UV2Mesh(res=256, is_skirt=True, is_smpl_orig=config_1['is_cape'])
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def step_pyramid(model, batch, config):
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with torch.inference_mode():
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levels_input, conds = group_inputs_for_levels(batch, config)
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out = []
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out_levels = model([levels_input, conds])
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for i in range(len(out_levels)):
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pred, _, _ = out_levels[i]
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mesh_pred = uv2mesh[picker](pred)
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if config['use_skinning']:
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shape = batch['pose_shape'][:, 72:]
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gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
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shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
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if not config['is_cape']:
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mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
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else:
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mesh_pred_posed = mesh_pred_shaped
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else:
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mesh_pred_posed = mesh_pred
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out.append(mesh_pred_posed)
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return out
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def create_temp_frames(folder: str, frame: Frame):
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with open(Path(folder) / 'frames_temp.txt', 'w') as f:
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f.writelines([f'{frame}\n' for _ in range(4)])
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if __name__ == '__main__':
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results_folder = Path('/data-local/127_2/results')
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out_folder = Path('temp/pyramid_ablation')
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iteration = {'nonskirt': 190000, 'skirt': 200000 }
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smpl = SMPL(orig_smpl=config_1['is_cape'], path='smpl', device=config_1['device'])
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dataset_resolutions = []
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if 'pyramid' in config_1:
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dataset_resolutions = config_1['pyramid']['resolutions'].copy()
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if 512 not in dataset_resolutions:
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dataset_resolutions.append(512)
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model = { 'nonskirt': Pyramid(config_1), 'skirt': Pyramid(config_2) }
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load_model(model['nonskirt'], 'checkpoints/iccvw/' + config_1['name'], iteration=iteration['nonskirt'])
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load_model(model['skirt'], 'checkpoints/iccvw/' + config_2['name'], iteration=iteration['skirt'])
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model['nonskirt'].eval()
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model['skirt'].eval()
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frames = ['00054_1_Dress', '01070_13_Jumpsuit', '03849_27_Dress', '06057_152_Dress',
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'03179_274_Dress', '04247_102_Tshirt', '01852_70_Tshirt']
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frames_corrupt = ['00300_69_Jumpsuit', '07608_77_Jumpsuit', '06955_117_Dress']
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frames = [Frame(frame_str=frame) for frame in frames]
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body_faces = smpl.f
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config = { 'nonskirt': config_1, 'skirt': config_2 }
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for frame in tqdm(frames):
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create_temp_frames(config_1['train_dir'], frame)
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dataset = Dataset(config_1['train_dir'], config_1['signature'], device=config_1['device'], frames_file='frames_temp.txt', use_cache=True, resolutions=dataset_resolutions, is_cape=config_1['is_cape'])
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dataloader = DataLoader(dataset, batch_size=4, drop_last=True, shuffle=True, )
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picker = 'skirt' if frame.garment in ['Skirt', 'Dress'] else 'nonskirt'
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batch = next(iter(dataloader))
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pred = step_pyramid(model[picker], batch, config[picker])
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fix_data = np.load(Path(dataset.folder) / 'mesh_fixed' / f'{frame.id}_{frame.garment}.npz')
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mask = fix_data['mask']
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faces = fix_data['faces']
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body = batch['mesh_body_posed'][0].cpu().numpy()
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gt = batch['mesh_posed'][0].cpu().numpy()
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save_stl(out_folder / f'{frame}_gt.stl', gt[mask, :], faces)
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save_stl(out_folder / f'{frame}_gt_body.stl', body, body_faces)
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for i, _pred in enumerate(pred):
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mesh_posed_fixed = fix_collision(_pred, batch['mesh_body_posed'], batch['mesh_mask']).cpu().numpy()
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mesh_posed_fixed = mesh_posed_fixed[0, mask, :]
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m = tm.Trimesh(mesh_posed_fixed, faces, process=False, validate=False)
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tm.smoothing.filter_humphrey(m, iterations=2)
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verts = m.vertices
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save_stl(out_folder / f'{frame}_lvl{i}_body.stl', body, body_faces)
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save_stl(out_folder / f'{frame}_lvl{i}_mesh.stl', verts, faces)
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# fig = go.Figure(data=[go.Mesh3d(x=verts[:,0], y=verts[:,2], z=verts[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1),
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# go.Mesh3d(x=body[:,0], y=body[:,2], z=body[:,1], i=body_faces[:,0], j=body_faces[:,1], k=body_faces[:,2], color='gray', opacity=1)
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# ])
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# fig.update_layout(scene=dict(aspectmode='cube'))
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# fig.update_layout(scene=dict(xaxis=dict(range=[-1, 1], ), yaxis=dict(range=[-1, 1], ), zaxis=dict(range=[-1, 1], ), ), )
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# fig.show()
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pass
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pass
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