Merge branch 'main' into iccv_review

configs/ae_norm_1.py

@@ -0,0 +1,73 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '0'
+config['name'] = 'norm_torch_2'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'norm'] 
+config['script'] = 'scripts/ae_norm_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/train'
+config['valid_dir'] = '/data-local/127/valid'
+config['test_dir'] = '/data-net/dataset/test_ready'
+config['train_frames'] = None
+config['valid_frames'] = None
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 40 * config['bottleneck_scale']
+
+block_setting = [
+    CNBlockConfig(16, 32, 3),
+    CNBlockConfig(32, 64, 3),
+    CNBlockConfig(64, 128, 9),
+    CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_mask',
+                        'uv_normals',
+                        'mesh_mask',
+                        'mesh_normals',
+                        }

data/calculate_normals.py

@@ -0,0 +1,112 @@
+import os
+import sys
+import numpy as np
+import trimesh as tm
+import scipy.io as sio
+import plotly.graph_objects as go
+import plotly.express as px
+from tqdm import tqdm
+import torch
+from scipy.spatial.transform import Rotation as R
+import argparse
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+from data.cloth3d_io import readOBJ, readPC2
+from data.utils import Frame
+from smpl.smpl_torch import SMPL
+
+
+def calculate_normals(vertices, mesh_file, poses):
+    _, faces, _, _ = readOBJ(mesh_file)
+    rodr = smpl.rodrigues(torch.tensor(poses.T.reshape(-1, 1, 3), dtype=torch.float32)).numpy().reshape(vertices.shape[0], -1, 3, 3)
+
+    normals = []
+    for i in range(vertices.shape[0]):
+        r = R.from_matrix(rodr[i, 0, :, :])
+        # assert r.as_matrix() == rodr[frame, 0, :, :]
+        out = r.apply(vertices[i], inverse=True)
+
+        # create a plotly mesh plot of out and vertices [i]
+        # fig = go.Figure(data=[
+        #     go.Mesh3d(
+        #         x=out[:, 0], y=out[:, 2], z=out[:, 1],
+        #         # i, j and k give the vertices of triangles
+        #         i=faces[:, 0], j=faces[:, 1], k=faces[:, 2],
+        #         name='y',
+        #         showscale=False
+        #     ),
+        #     go.Mesh3d(
+        #         x=vertices[i][:, 0], y=vertices[i][:, 1], z=vertices[i][:, 2],
+        #         # i, j and k give the vertices of triangles
+        #         i=faces[:, 0], j=faces[:, 1], k=faces[:, 2],
+        #         name='x',
+        #         showscale=False
+        #     )
+        # ])
+        # fig.show()
+    
+        mesh = tm.Trimesh(vertices=out, faces=faces, process=False, validate=False)
+        normals.append(mesh.vertex_normals)
+    return np.stack(normals)
+
+
+def get_frames(folder):
+    uv_static_folder = os.path.join(folder, 'pose_shape') 
+    frames = sorted(os.listdir(uv_static_folder))
+
+    frames = [frame.split('.')[0] for frame in frames] # .split('_')[0]
+    frames = [Frame(frame.split('_')[0], frame.split('_')[1]) for frame in frames]
+    return frames
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f", "--folder", help = "end of range", default='')
+    parser.add_argument("-f_obj", "--folder_object", help = "end of range", default='/')
+    parser.add_argument("-s", "--start", help = "start of range", default=0)
+    parser.add_argument("-e", "--end", help = "end of range", default=10)
+    parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
+    args = parser.parse_args()
+
+    obj_folder = args.folder_object # '/data-net/dataset/cloth3d/train'
+    folder = args.folder #'/data-local/127/train'
+
+    os.makedirs(os.path.join(folder, 'mesh_normals'), exist_ok=True)
+    frames = get_frames(folder)
+    smpl = SMPL('cpu', 'smpl')
+
+    ids = {frame.id for frame in frames}
+    ids = sorted(list(ids))[int(args.start):int(args.end)]
+
+    for id in tqdm(ids):
+        # find obj files in the folder
+        garment_obj_folder = os.path.join(obj_folder, id[:5])
+        garments = [file.split('.')[0] for file in os.listdir(garment_obj_folder) if file.endswith('.obj')]
+
+        if len(id) == 5:
+            garment = garments[0]
+        elif len(id) == 6:
+            garment = garments[1]
+        else:
+            raise ValueError('Invalid frame id')
+
+        mesh_file = os.path.join(garment_obj_folder, f'{garment}.obj')
+        vertices =readPC2(os.path.join(garment_obj_folder, f'{garment}.pc16'), True)['V']
+        poses = sio.loadmat(os.path.join(garment_obj_folder, f'info.mat'))['poses']
+        normals = calculate_normals(vertices, mesh_file, poses)
+
+        map = sio.loadmat(os.path.join(garment_obj_folder, f'{garment}_map.mat'))['map'][0]
+        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy'))
+
+        mesh_normals = normals[:,map,:]
+        mesh_normals = mesh_normals * mask[None,:, None]
+
+        for i in range(vertices.shape[0]):
+            # np.savez(os.path.join(folder, 'mesh_normals', f'{id}_{i}.npz'), mesh_normals=mesh_normals)
+            np.savez_compressed(os.path.join(folder, 'mesh_normals', f'{id}_{i}.npz'), mesh_normals=mesh_normals[i])
+
+        # pass
+
+        # print(frame.id, frame.frame_nr)
+

data/dataset.py

@@ -175,6 +175,9 @@ class Dataset(Dataset):
         if 'mesh_curvature' in self.signature:
             mesh_curvature = np.load(os.path.join(self.folder, 'mesh_curvature', frame.id + separator + frame.frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
             item['mesh_curvature'] = mesh_curvature.astype(np.int32)
+        if 'mesh_normals' in self.signature:
+            mesh_normals = np.load(os.path.join(self.folder, 'mesh_normals', frame.id + separator + frame.frame_nr + '.npz')); mesh_normals = mesh_normals['mesh_normals']
+            item['mesh_normals'] = mesh_normals
 
         for res in self.resolutions:
             res_string = ''
@@ -203,6 +206,10 @@ class Dataset(Dataset):
                 uv_posed = np.load(os.path.join(self.folder, 'uv_posed_no_orient' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_posed = uv_posed['uv_posed_no_orient']
                 item['uv_posed' + res_string] = uv_posed
 
+            if 'uv_normals' + res_string in self.signature:
+                uv_normals = np.load(os.path.join(self.folder, 'uv_normals' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_normals = uv_normals['uv_normals']
+                item['uv_normals' + res_string] = uv_normals
+
 
         data = { key: (torch.from_numpy(value) if not (type(value) == str or type(value) == torch.Tensor) else value) for key, value in item.items()}
 

docker/docker-compose.yml

@@ -34,6 +34,7 @@ services:
       - '/data-net/guest/lhunor/data:/data-net'
       - '/data-fast/126-data1/lhunor/data:/data-local/126'
       - '/data-fast/127-data1/lhunor/data:/data-local/127'
+      - '/data-fast/127-data2/lhunor/data:/data-local/127_2'
       - '/data-fast/128-data1/lhunor/data:/data-local/128'
     deploy:
       resources:

run_parallel.py

@@ -4,24 +4,27 @@ import os
 
 if __name__ == '__main__':
 
-    folder = '/home/workspace/data/dataset/cloth3d/train'
+    # folder = '/home/workspace/data/dataset/cloth3d/train'
     # folder = '/home/workspace/local-data/cape_processed/train/male'
     # folder = '/home/workspace/local-data/valid_skirt'
     # folder = '/home/workspace/local-data/valid'
     # ids = os.listdir(folder)
     # ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
     skirt = 0
+    obj_folder = '/data-net/dataset/cloth3d/train'
+    folder = '/data-local/127/train'
 
-    count = int(500 * 1e3)
+    count = 0
-    step = 10000
+    step = 100
 
     # !!!!!!! change skirt parameter in script
 
-    while count < 510 * 1e3:
+    while count <1200:
         # os.system('nohup python convert_to_uv_cape.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
-        os.system('nohup python data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_2_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv/train_2_' + str(count) + '_' + str(count+step) + '_again.out' + ' 2>&1 &')
+        # os.system('nohup python data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_2_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv/train_2_' + str(count) + '_' + str(count+step) + '_again.out' + ' 2>&1 &')
+        os.system(f'nohup python data/calculate_normals.py -f {folder} -f_obj {obj_folder}  -s {count} -e {(count+step)} -n normals_train_calc_{count}_{(count+step)} > nohup/normals/normals_train_calc_{count}_{(count+step)}.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_valid_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_valid_skirt_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python downscale.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + ' > nohup/downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')

scripts/ae_norm_train.py

@@ -0,0 +1,218 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+import argparse
+import importlib
+
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--name', type=str)
+parser.add_argument('--config', type=str)
+parser.add_argument('--debug', action='store_true')
+args = vars(parser.parse_args())
+
+config_module = importlib.import_module(args['config'])
+config = config_module.config
+if args['debug']:
+    from configs.ae_norm_1 import config
+os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
+if args['debug']:
+    os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
+
+import time
+import wandb
+import random
+import numpy as np
+import torch
+from torch import nn
+# from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure as MSSSIM
+from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, get_summary_str
+import plotly.graph_objects as go
+import plotly.express as ex
+import torch.optim as optim
+
+if config['is_skirt']:
+    from losses.losses import edge_loss_skirt as edge_loss
+else:
+    from losses.losses import edge_loss
+
+from data.dataset import Dataset
+# from torch.utils.data import DataLoader
+from custom_utils.custom_utils import plot_3d
+from models.cond_encoder import AutoEncoder
+from utils.checkpoints import CheckpointSaver, load_model
+from custom_utils.custom_utils import Accumulator
+from utils.utils import InfiniteDataLoader as DataLoader
+
+from pytorch_msssim import MS_SSIM
+
+
+def init_wandb():
+    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/data-local/127_2/temp', tags=config['tags'])
+    artifact = wandb.Artifact('code', type='code')
+    artifact.add_file('scripts/ae_norm_train.py')
+    artifact.add_file('losses/losses.py')
+    artifact.add_file('models/base.py')
+    artifact.add_file('models/cond_encoder.py')
+    artifact.add_file('models/convnext.py')
+    artifact.add_file('data/dataset.py')
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
+    for config_file_name in config_file_names:
+        file_name = '/'.join(config_file_name.split('.')) + '.py'
+        if os.path.exists(file_name):
+            artifact.add_file(file_name)
+    run.log_artifact(artifact)
+
+uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
+
+
+
+def train_step(batch):
+    model.train()
+    pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.1
+
+    ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_normals'] + 1.1))
+    loss = 1e3 * (1 - ms_ssim)
+
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    model.eval()
+    with torch.inference_mode():
+        mesh_pred = uv2mesh(pred)
+        mesh_orig = batch['mesh_normals']
+        l1_loss = torch.mean(torch.sum((torch.abs(batch['uv_normals'] - pred)), axis=1))
+        metric = torch.mean(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1)))
+    
+    return { 'train/loss': loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }
+        
+
+def valid_step(batch):
+    model.eval()
+
+    with torch.inference_mode():
+        pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.1
+
+        mesh_pred = uv2mesh(pred)
+        mesh_orig = batch['mesh_normals']
+
+        l1_loss = torch.mean(torch.sum((torch.abs(batch['uv_normals'] - pred)), axis=1))
+        ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_normals'] + 1.1))
+        loss = 1e3 * (1 - ms_ssim)
+
+        metric = torch.mean(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1)))
+    
+    return { 'valid/loss': loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, }
+       
+
+def plot_everything(batch: dict, type: str):
+    model.eval()
+    pred = model([batch['uv_normals'], batch['uv_mask']]).detach() * 1.1
+    
+    # mesh_pred = uv2mesh(pred)
+    # mesh_orig = batch['mesh_normals']
+    
+    orig_img = torch.permute(batch['uv_normals'], [0,2,3,1])
+    pred_img = torch.permute(pred, [0,2,3,1])
+    img = torch.concat((orig_img, pred_img), axis=2)
+    imgs = [wandb.Image((img[i].cpu().numpy() + 1.1) / 2.2) for i in range(4)]
+
+    # F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
+    # garment_unposed, faces = mesh_pred.cpu().numpy(), F
+    # gt = [mesh_orig[i].cpu().numpy() for i in range(pred.shape[0])]
+    
+    # body = uv2mesh(batch['uv_body_posed']).cpu().numpy()
+    # garment_posed = garment_unposed
+    # collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))]
+    # collision_faces = [np.concatenate((F, faces + (3638 if config['is_cape'] else 14475)), axis=0) for i in range(len(garment_posed))]
+    # collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
+
+    # figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces, collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
+    figs = {}
+
+    return { **figs, **{ type + '/images': imgs } }
+
+
+def train():
+    valid_iterator = iter(valid_dataloader)
+
+    step = 0
+    for batch in train_dataloader:
+        train_loss = train_step(batch)
+        train_accumulator.update(train_loss)
+
+        if step % 25 == 0:
+            data_valid = next(valid_iterator)
+            valid_loss = valid_step(data_valid)
+            valid_accumulator.update(valid_loss)
+
+        if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
+            to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
+            if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
+                train_figs = plot_everything(batch, 'train')
+                valid_figs = plot_everything(data_valid, 'valid')
+                to_log = { **to_log, **train_figs, **valid_figs }
+            if step == 0:
+                to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
+
+            if not args['debug']:
+                wandb.log(to_log, step=step)
+            train_accumulator.reset()
+            valid_accumulator.reset()
+            print(step)
+
+        if step % 10000 == 0:
+            ckpt_saver_enc(model.encoder, step, optimizer, scaler=None)
+            ckpt_saver_dec(model.decoder, step, optimizer, scaler=None)
+
+        step += 1
+
+
+class Scale(torch.nn.Module):
+    def __init__(self, scale_factor):
+        super().__init__()
+        self.scale_factor = scale_factor
+
+    def forward(self, x):
+        return x * self.scale_factor
+
+
+if __name__ == '__main__':
+    if not args['debug']:
+        init_wandb()
+
+    skirt_modifier = '_skirt' if config['is_skirt'] else ''
+
+    dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape'])
+    dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape'])
+    train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True)
+    valid_dataloader = DataLoader(dataset_valid, batch_size=4, drop_last=True, shuffle=True)
+    train_accumulator = Accumulator()
+    valid_accumulator = Accumulator()
+
+    model : AutoEncoder = AutoEncoder(config['cond_ae'])
+    optimizer = optim.Adam(model.parameters(), lr=config['lr'])
+
+    # _MSSSIM = MSSSIM(data_range=2).to(config['device'])
+    _MSSSIM = MS_SSIM(data_range=2.2, size_average=True, channel=3)
+
+    if config['pretrained_model'] != '':
+        load_model(model.encoder, config['pretrained_model'] + '_enc')
+        load_model(model.decoder, config['pretrained_model'] + '_dec')
+
+    ckpt_saver_enc = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_enc', top_n=5)
+    ckpt_saver_dec = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_dec', top_n=5)
+
+    run_name = config['name']
+    print(f'started training: {run_name}')
+
+    if not args['debug']:
+        # model = torch.compile(model)
+        wandb.watch(model, log='all', log_freq=10000, log_graph=True)
+
+    train()
+
+
+