save

aaa_helpers.txt

@@ -1,3 +1,8 @@
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_10_10p_r2" > nohup/review/curv_1_orig_curv_10_10p_r2.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_25_10p_r2" > nohup/review/curv_1_orig_curv_25_10p_r2.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2" > nohup/review/curv_1_orig_curv_100_10p_r2.out 2>&1 &
+
 one final convolutional layer for smoothing, on the final coordinates
 
 skirts!!!!

configs/config_abl_pyr_32.py

@@ -1,7 +1,7 @@
 config = dict()
 
 config['gpu'] = '0'
-config['name'] = 'pyr_abl_32'
+config['name'] = 'pyr_abl_32_r1'
 config['notes'] = ''
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE

configs/config_abl_pyr_32_skirt.py

@@ -1,8 +1,8 @@
 config = dict()
 
 config['gpu'] = '3'
-config['name'] = 'pyr_abl_32_skirt_2'
-config['notes'] = 'continued from pyr_abl_32_skirt-180k'
+config['name'] = 'pyr_abl_32_skirt_r1'
+config['notes'] = ''
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING
@@ -24,7 +24,10 @@ config['pyramid']['resolutions'] = [32]
 config['pyramid']['pretrained_pyramid'] = ''
 config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_skirt'
 
-config['train_dir'] = '/home/workspace/local-data/train_skirt'
+config['pretrained_model'] = ''
+config['pretrained_model_iteration'] = ''
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
 config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
 config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
 config['batch_size'] = 4
@@ -35,7 +38,7 @@ config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
 config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
 config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
 
-config['dataset_in_memory'] = False
+config['dataset_in_memory'] = True
 config['resolution'] = None
 config['latent_dim'] = 128
 
@@ -65,6 +68,7 @@ config['signature_static'] = { 'uv_static': (512, 256, 3),
 config['pyramid']['is_label'] = False
 config['pyramid']['latent_dim'] = config['latent_dim']
 config['pyramid']['add_noise'] = True
+config['pyramid']['noise_amount'] = 0.001
 
 config['pyramid']['loss_weights'] = [1,1,1,1,1]
 

convert_to_uv_local_single.py

@@ -384,9 +384,9 @@ def test(verts, faces, values):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/local-data/valid')
+    parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/test_skirt_ready_2')
     parser.add_argument("-s", "--start", help = "start of range", default=0)
-    parser.add_argument("-e", "--end", help = "end of range", default=1500)
+    parser.add_argument("-e", "--end", help = "end of range", default=30000)
     parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
     parser.add_argument("-sk", "--skirt", help = "end of range", default=1)
     args = parser.parse_args()
@@ -408,7 +408,7 @@ if __name__ == '__main__':
     m2uv = np.load('custom_utils/m2uv' + ("_skirt" if args.skirt == 1 else "") + '.npy', allow_pickle=True).item()
     m2uv = m2uv_array(m2uv)
     uv_mask = np.load('custom_utils/mask_256x512' + ("_skirt" if args.skirt == 1 else "") + '.npy')
-    curvature_label_type = '6_25'
+    curvature_label_type = '6_25 '
 
     uv_res = 256
     uv_mask = (cv2.resize(uv_mask * 255.0, (uv_res, uv_res * 2)) / 255).astype(bool)[:, :, np.newaxis]
@@ -436,6 +436,23 @@ if __name__ == '__main__':
 
     # ids = os.listdir(os.path.join(local_folder, 'mesh_static'))[int(args.start):int(args.end)]
     # ids = [id.split('.')[0] for id in ids]
+
+    is_skirt = True if args.skirt == 1 else False
+    skirt_modifier = '_skirt' if is_skirt else ''
+
+    folder = args.folder
+    frames_file = 'frames' + skirt_modifier + '_10p.txt'
+    neighbourhood = 10
+
+    # frames = []
+    # with open(os.path.join(folder, frames_file), 'r') as f:
+    #     lines = f.readlines()
+    #     lines = [line.strip() for line in lines]
+    # frames = lines#[int(args.start):int(args.end)]
+    # ids = [frame.split('_')[0] for frame in frames]
+
+    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {args.skirt} -neigh {neighbourhood}')
+
     ids = os.listdir(os.path.join(local_folder, 'pose_shape'))
     ids = [id.split('_')[0] for id in ids]
     ids = sorted(list(set(ids)))[int(args.start):int(args.end)]
@@ -452,16 +469,16 @@ if __name__ == '__main__':
         # plt.show()
         # pass
 
-        frames = glob.glob(os.path.join(local_folder, 'pose_shape', id + '_*'))
-        frames = len(frames)
-        assert frames <= 300
+        frames_len = glob.glob(os.path.join(local_folder, 'pose_shape', id + '_*'))
+        frames_len = len(frames_len)
+        assert frames_len <= 300
 
         # offsets = sio.loadmat(os.path.join(folder, id, garment + '_offsets_static.mat'))['label'][:,:3]
         verts_static = np.load(os.path.join(local_folder, 'mesh_static', id + '.npz'))['mesh_static']
         mask = np.load(os.path.join(local_folder, 'mesh_mask', id + '.npy')).astype(bool)
         # body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'),  True)['V']
         # gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'),  True)['V']
-        # curvature = np.load(os.path.join(folder, id, 'curvature_label_' + curvature_label_type + '.npy'))
+        curvature = np.stack([np.load(os.path.join(local_folder, f'mesh_curvature_{neighbourhood}_2',  f'{id}_{i}.npz'))['mesh_curvature'] for i in range(frames_len)])
         # mesh_unposed = [np.load(os.path.join(folder, id, 'mesh_unposed',  id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
         # mesh_unposed = np.stack([mesh_unposed[i]['mesh_unposed'] for i in range(gt.shape[0])])
         # mesh_unposed_no_shape = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape',  id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
@@ -470,8 +487,8 @@ if __name__ == '__main__':
         # body_no_orient = np.stack([body_no_orient[i]['mesh_posed_body_no_orient'] for i in range(gt.shape[0])])
         # mesh_unposed_no_shape_one_hot = [np.load(os.path.join(local_folder, 'mesh_unposed_no_shape_one_hot',  id + '_' + str(i) + '.npz')) for i in range(frames)]
         # mesh_unposed_no_shape_one_hot = [mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(frames)]
-        mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient',  id + '_' + str(i) + '.npz')) for i in range(frames)]
-        mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
+        # mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient',  id + '_' + str(i) + '.npz')) for i in range(frames)]
+        # mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
 
 
         # verts_dynamic = gt
@@ -491,11 +508,11 @@ if __name__ == '__main__':
         # uv_body = convert_to_uv_batch(uv_res, m2uv, uv_map, body[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_posed_body_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, body_no_orient[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_gt = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
-        # uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+        uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_unposed_no_shape = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
-        uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
+        # uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
         # uv_shaped_body = convert_to_uv(uv_res, m2uv, uv_map, shaped_body, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
         print(time.perf_counter() - start)
 
@@ -516,9 +533,9 @@ if __name__ == '__main__':
         # for i in range(uv_gt.shape[0]):
         #     np.savez_compressed(os.path.join(folder, id, 'gt_uv', garment + '_' + str(i) + '.npz'), uv_gt=uv_gt[i, :, :, :])
 
-        # os.makedirs(os.path.join(folder, id, 'curvature_uv'), exist_ok=True)
-        # for i in range(uv_curvature.shape[0]):
-        #     np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
+        os.makedirs(os.path.join(folder, f'uv_curvature_{neighbourhood}_2'), exist_ok=True)
+        for i in range(uv_curvature.shape[0]):
+            np.savez_compressed(os.path.join(folder, f'uv_curvature_{neighbourhood}_2', id + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
 
         # os.makedirs(os.path.join(folder, id, 'uv_unposed'), exist_ok=True)
         # for i in range(uv_unposed.shape[0]):

curvature_analysis.py

@@ -0,0 +1,235 @@
+import os
+import sys
+import tqdm
+import argparse
+import random
+import string
+import time
+import numpy as np
+import trimesh as tm
+import cmapy as cm
+import scipy as sp
+import scipy.io as sio
+import plotly.graph_objects as go
+from pyntcloud import PyntCloud
+from scipy.sparse import csr_matrix, coo_matrix
+from scipy.sparse.csgraph import floyd_warshall, dijkstra
+from plotly.subplots import make_subplots
+from custom_utils.custom_utils import _maskFaces
+
+from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
+from custom_utils.custom_utils import _maskFaces
+
+def export_to_ply(mesh, file):
+    ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
+    output_file = open(file, "wb+")
+    output_file.write(ply)
+    output_file.close()
+
+
+def get_closest_n_points(verts, idx, n):
+    dist = sp.spatial.distance.cdist(verts[idx, :], verts)
+    dist[dist == 0] = 1e9
+    idx_sort = np.argsort(dist, axis=-1)
+    closest = idx_sort[:, :n]
+    return closest
+
+
+def calculate_dist(folder, neighbourhood):
+    out = dict()
+    frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
+
+    for frame in tqdm.tqdm(frames):
+        id = frame.split('_')[0]
+        if id in out.keys():
+            continue
+
+        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        out[id] = k_neighbors
+
+    return out
+
+
+def histedges_equalN(x, nbin):
+    npt = len(x)
+    return np.interp(np.linspace(0, npt, nbin + 1),
+                     np.arange(npt),
+                     np.sort(x))
+
+
+def get_bins(values, nr_of_bins):
+    flattened = []
+    for c in values:
+        flattened.extend(c.flatten())
+    flattened = np.array(flattened)
+    bins = histedges_equalN(flattened, nr_of_bins)
+    return bins
+
+
+def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=[6]):
+    id = frame.split('_')[0]
+    frame_nr = int(frame.split('_')[1].split('.')[0])
+
+    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+    v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+    
+    m = tm.Trimesh(v, faces, process=False, validate=False)
+
+    outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+    outliers = np.nonzero(outliers)[0]
+    outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
+    outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+    faces = np.concatenate((faces, outliers_faces), axis=0)
+
+    verts = v
+
+    m = tm.Trimesh(verts, faces, process=False, validate=False)
+    export_to_ply(m, temp_file)
+
+    cloud = PyntCloud.from_file(temp_file)
+
+    curvatures = []
+    for neighbourhood, nr_labels in zip(neighbourhoods, nr_of_labels):
+        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        #k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+        cloud.add_scalar_field("curvature", ev=ev)
+        curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+        
+        bins_local = bins[neighbourhood] if bins is not None else get_bins(curvature, nr_labels)
+        labels = np.linspace(0, bins_local.shape[0] - 2, bins_local.shape[0] - 1)
+
+        frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
+        for j in range(bins_local.shape[0] - 2):
+            frame_labels[(curvature >= bins_local[j]) * (curvature < bins_local[j+1])] = labels[j]
+        frame_labels[curvature >= bins_local[-2]] = labels[-1]
+
+        curvatures.append(frame_labels)
+        
+    return curvatures
+
+
+def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels):
+    id = frame.split('_')[0]
+    frame_nr = frame.split('_')[1].split('.')[0]
+
+    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+    verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+    
+    curvature_labels_all = generate_curvatures(folder, frame, bins, neighbourhoods, nr_of_labels)
+    colors_binned_all = []
+
+    for curvature_labels in curvature_labels_all:
+        colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
+        colors_binned = np.zeros((curvature_labels.shape[0], 3))
+        for i in range(int(curvature_labels.max())+1):
+            # print(np.count_nonzero(curvature_labels == i))
+            colors_binned[curvature_labels == i, :] = colors[i]
+        colors_binned_all.append(colors_binned)
+
+
+    fig = make_subplots(rows=1, cols=len(colors_binned_all)+1, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(len(colors_binned_all)+1)] ])
+
+    colors = np.ones((verts.shape[0],3))*np.array([255,200,200])[None,:]
+
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=verts[:,0],
+                y=verts[:,1],
+                z=verts[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=colors,
+                lighting={ 'ambient':0.9, 'diffuse':0.5, 'roughness':0.5 }
+            ),
+            row=1, col=1)
+
+    for i, colors_binned in enumerate(colors_binned_all):
+        fig.add_trace(go.Mesh3d(
+                    # 8 vertices of a cube
+                    x=verts[:,0],
+                    y=verts[:,1],
+                    z=verts[:,2],
+                    # i, j and k give the vertices of triangles
+                    i = faces[:,0],
+                    j = faces[:,1],
+                    k = faces[:,2],
+                    vertexcolor=colors_binned,
+                    # lighting={ 'ambient':0.8, 'diffuse':0.5, 'roughness':0.5 }
+                ),
+                row=1, col=i+2)
+    
+
+
+    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
+                    scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
+                    scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
+                    scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
+                    # scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
+                    )
+    helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+                yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+                zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
+        
+    fig.update_layout(scene = helper,
+                        scene2 = helper,
+                        scene3 = helper,
+                        scene4 = helper,
+                        # scene5 = helper,
+                        )
+
+
+    # meshes = [go.Mesh3d(
+    #             x=x[:,0],
+    #             y=x[:,1],
+    #             z=x[:,2],
+    #             i = faces[:,0],
+    #             j = faces[:,1],
+    #             k = faces[:,2],
+    #             vertexcolor=colors_binned
+    #             )]
+
+    # fig = go.Figure(data=meshes)
+    # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
+    # fig.update_layout(
+    #     scene = dict(
+    #         xaxis = dict(nticks=4, range=[-1,1],),
+    #                     yaxis = dict(nticks=4, range=[-1,1],),
+    #                     zaxis = dict(nticks=4, range=[-1,1],),)
+    #                     )
+
+    fig.show()
+
+    print()
+
+
+if __name__ == '__main__':
+
+    is_skirt = True
+    skirt_modifier = '_skirt' if is_skirt else ''
+    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
+    # k_neighbours(k=25)
+
+    folder = '/home/workspace/local-data/valid_skirt'
+    bins = dict()
+    bins[10] = np.load(f'custom_data/train_bins_skirt_10.npy')
+    bins[25] = np.load(f'custom_data/train_bins_skirt.npy')
+    bins[100] = np.load(f'custom_data/train_bins_skirt_100.npy')
+
+    bins = None
+
+
+    nr_of_labels = 6
+    neighbourhood = 50
+    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
+    if not is_skirt:
+        dist = np.load('custom_data/dijkstra.npy')
+    else:
+        dist = np.load('custom_data/dijkstra_skirt.npy')
+
+    visualize_curvature_labels(folder, '02572_1', bins, neighbourhoods=[25, 25, 25], nr_of_labels=[3,6,12])

curvature_label_parallel.py

@@ -4,15 +4,17 @@ import os
 
 if __name__ == '__main__':
 
-    folder = '/home/workspace/local-data/cape/train/male'
-    out_folder = '/home/workspace/local-data/cape_processed/train/male'
-    ids = os.listdir(folder)
+    # folder = '/home/workspace/data/dataset/test_skirt_ready_2'
+    # folder = '/home/workspace/data/dataset/local-cache/train_skirt'
+    folder = '/home/workspace/local-data/valid_skirt'
+    ids = os.listdir(folder + '/mesh_posed')
 
     count = 0
-    step = 1
+    step = 10000
 
-    while count < len(ids):
+    while count < 20*step:
+    # while count < 10000:
         # os.system('nohup python MeshUtils/curvature_labels.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_train_' + str(count) + '_' + str(count+step) + ' > curv_train_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
-        os.system('nohup python curvature_labels_cape.py -if ' + folder + ' -of ' + out_folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
+        os.system('nohup python curvature_labels.py -f ' + folder + ' -s ' + str(0) + ' -e ' + str(0 + step) + ' -n ' + 'speed_test_' + str(count) + '_' + str(count+step) + ' > nohup/speed/speed_test_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('python Data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + '')
         count += step

curvature_labels.py

@@ -4,6 +4,7 @@ import tqdm
 import argparse
 import random
 import string
+import time
 import numpy as np
 import trimesh as tm
 import cmapy as cm
@@ -40,6 +41,8 @@ def export_to_ply(mesh, file):
     output_file.close()
 
 
+
+
 def get_closest_n_points(verts, idx, n):
     dist = sp.spatial.distance.cdist(verts[idx, :], verts)
     dist[dist == 0] = 1e9
@@ -80,67 +83,65 @@ def k_neighbours(k):
     pass
 
 
-def generate_curvatures(folder, neighbourhood):
+def calculate_dist(folder, neighbourhood, frames):
+    out = dict()
+    # frames = frames[int(args.start):int(args.end)]
 
-    ids = os.listdir(folder)[int(args.start):int(args.end)]
-
-    curvatures_all = []
-
-    for id in tqdm.tqdm(ids):
-        garment_dir = os.path.join(folder, id)
-
-        if not os.path.isdir(garment_dir):
+    for frame in tqdm.tqdm(frames):
+        id = frame.split('_')[0]
+        if id in out.keys():
             continue
 
-        # if os.path.exists(garment_dir + '/curvature_25_raw.npy'):
-        #     continue
-
-        curvatures_garment = []
-
-        garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
-        # assert len (garments) == 1
-        if len (garments) != 1:
-            continue
-        garment = garments[0]
-    
-        bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
-        offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
-        mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
-        
-        _, faces = _maskFaces(bodies[0, :, :], F, mask)
-        v = bodies[0, mask, :] + offsetss[0, mask, :]
-        m = tm.Trimesh(v, faces, process=False, validate=False)
-
-        outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
-        outliers = np.nonzero(outliers)[0]
-        outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
-        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
-        faces = np.concatenate((faces, outliers_faces), axis=0)
-
-        step = 0
-        for body, offsets in zip(bodies, offsetss):
-            verts = body + offsets
-            verts = verts[mask, :]
-
-            m = tm.Trimesh(verts, faces, process=False, validate=False)
-            export_to_ply(m, temp_file)
-
-            cloud = PyntCloud.from_file(temp_file)
-            # cloud.plot(mesh=True)
-
+        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
         k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-            ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
-            cloud.add_scalar_field("curvature", ev=ev)
-            curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
-            curvatures_garment.append(curvature)
+        out[id] = k_neighbors
 
-            print(step)
-            step += 1
+    return out
 
-        # curvatures_all.append(np.array(curvatures_garment))
-        np.save(garment_dir + '/curvature_25_raw.npy', np.array(curvatures_garment))
+def generate_curvatures(folder, neighbourhood, frames):
 
-    return curvatures_all
+    # ids = os.listdir(folder)[int(args.start):int(args.end)]
+    # frames = frames[int(args.start):int(args.end)]
+    os.makedirs(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}'), exist_ok=True)
+
+    k_neighbors_cache = calculate_dist(folder, neighbourhood, frames)
+
+    # for frame in tqdm.tqdm(frames):
+
+    #     id = frame.split('_')[0]
+    #     frame_nr = frame.split('_')[1].split('.')[0]
+    
+    #     # bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
+    #     # offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
+    #     mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    #     _, faces = _maskFaces(np.zeros((14475,3)), F, mask) #np.load(os.path.join(folder, 'faces', f'{id}.npy'))
+    #     v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+        
+    #     m = tm.Trimesh(v, faces, process=False, validate=False)
+
+    #     outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+    #     outliers = np.nonzero(outliers)[0]
+    #     outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
+    #     outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+    #     faces = np.concatenate((faces, outliers_faces), axis=0)
+
+    #     verts = v
+
+    #     m = tm.Trimesh(verts, faces, process=False, validate=False)
+    #     export_to_ply(m, temp_file)
+
+    #     cloud = PyntCloud.from_file(temp_file)
+
+    #     k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+    #     ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+    #     cloud.add_scalar_field("curvature", ev=ev)
+    #     curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+    #     out = np.zeros((14475), dtype=np.float32)
+    #     out[mask] = curvature
+        
+    #     np.savez_compressed(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{id}_{frame_nr}.npz'), mesh_curvature_raw=out)
+
+    return None
 
 
 def get_bins(values, nr_of_bins):
@@ -152,41 +153,23 @@ def get_bins(values, nr_of_bins):
     return bins
 
 
-def generate_curvature_labels(folder, bins, curvatures):
+def generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood):
 
-    ids = os.listdir(folder)#[:10]
+    os.makedirs(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2'), exist_ok=True)
     labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
 
-    i = 0
-    for id in tqdm.tqdm(ids):
-        garment_dir = os.path.join(folder, id)
-
-        if not os.path.isdir(garment_dir):
-            continue
-
-        garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
-        # assert len (garments) == 1
-        if len (garments) != 1:
-            continue
-        garment = garments[0]
-        mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
-
+    for i, frame in enumerate(tqdm.tqdm(frames)):
+        # id = frame.split('_')[0]
+        # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy'))
+        # print(i) 
         curvature = curvatures[i]
-        i += 1
 
-        curvature_label = np.zeros((curvature.shape[0], curvature.shape[1]))
-        frame = 0
-        for frame in range(curvature.shape[0]):
-            frame_labels = np.zeros((curvature.shape[1]), dtype=np.float32)
+        frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
         for j in range(bins.shape[0] - 2):
-                frame_labels[(curvature[frame, :] >= bins[j]) * (curvature[frame, :] < bins[j+1])] = labels[j]
-            frame_labels[curvature[frame, :] >= bins[-2]] = labels[-1]
-            curvature_label[frame, :] = frame_labels
-            frame += 1
+            frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
+        frame_labels[curvature >= bins[-2]] = labels[-1]
 
-        unmasked_label = np.zeros((curvature.shape[0], 14475), dtype=np.float32)
-        unmasked_label[:, mask] = curvature_label
-        np.save(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".npy"), unmasked_label)
+        np.savez_compressed(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2', f'{frame}.npz'), mesh_curvature=frame_labels)
         # writePC2(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".pc16"), unmasked_label, True)
 
 
@@ -257,24 +240,10 @@ def visualize_curvature_labels(folder):
         print()
 
 
-def read_raw_curvatures(folder):
-
-    ids = os.listdir(folder)[int(args.start):int(args.end)]
-
+def read_raw_curvatures(folder, frames, neighbourhood):
     curvatures_all = []
-
-    for id in tqdm.tqdm(ids):
-        garment_dir = os.path.join(folder, id)
-
-        if not os.path.isdir(garment_dir):
-            continue
-
-        garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
-        # assert len (garments) == 1
-        if len (garments) != 1:
-            continue
-
-        curv = np.load(garment_dir + '/curvature_25_raw.npy')
+    for frame in tqdm.tqdm(frames):
+        curv = np.load(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{frame}.npz'))['mesh_curvature_raw']
         curvatures_all.append(curv)
     
     return curvatures_all
@@ -282,28 +251,46 @@ def read_raw_curvatures(folder):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/test')
+    parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/local-cache/train_skirt')
     parser.add_argument("-s", "--start", help = "start of range", default=0)
-    parser.add_argument("-e", "--end", help = "end of range", default=1500)
+    parser.add_argument("-e", "--end", help = "end of range", default=30000)
     parser.add_argument("-n", "--name", help = "end of range", default="lhunor")
     args = parser.parse_args()
 
-    F = np.load("FeatureLines/superSMPL/smpl_faces.npy")
+    is_skirt = True
+    skirt_modifier = '_skirt' if is_skirt else ''
+    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
     # k_neighbours(k=25)
 
     folder = args.folder
-    nr_of_labels = 6
-    neighbourhood = 25
-    temp_file = 'temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
-    dist = np.load('MeshUtils/dijsktra.npy')
-    # dist = np.load('MeshUtils/dijkstra_skirt.npy')
+    frames_file = 'frames' + skirt_modifier + '_10p.txt'
 
-    generate_curvatures(folder, neighbourhood)
-    # curvatures = read_raw_curvatures(folder)
+    nr_of_labels = 6
+    neighbourhood = 100
+    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
+    if not is_skirt:
+        dist = np.load('custom_data/dijkstra.npy')
+    else:
+        dist = np.load('custom_data/dijkstra_skirt.npy')
+
+    print('SKIRT' if is_skirt else 'NOT SKIRT')
+    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {is_skirt} -neigh {neighbourhood}')
+
+    frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
+    frames = [frame.split('.')[0] for frame in frames]
+
+    # frames = []
+    # with open(os.path.join(folder, frames_file), 'r') as f:
+    #     lines = f.readlines()
+    #     lines = [line.strip() for line in lines]
+    # frames = lines[int(args.start):int(args.end)]
+
+    generate_curvatures(folder, neighbourhood, frames)
+    # curvatures = read_raw_curvatures(folder, frames, neighbourhood)
     # bins = get_bins(curvatures, nr_of_labels)
-    # np.save('train_bins_skirt.npy', bins)
-    # bins = np.load('train_bins.npy')
-    # generate_curvature_labels(folder, bins, curvatures)
+    # np.save(f'custom_data/train_bins_skirt_{neighbourhood}.npy', bins)
+    # bins = np.load(f'custom_data/train_bins_skirt_{neighbourhood}.npy')
+    # generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
     # visualize_curvature_labels(folder)
 
     pass

cvae.py

@@ -45,7 +45,7 @@ from models.cvae_model import CVAENew as CVAE
 
 
 def init_wandb():
-    run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb')
+    run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('cvae.py')
     artifact.add_file('models/cvae_model.py')
@@ -58,7 +58,9 @@ def init_wandb():
     artifact.add_file('convnext/models/convnext_tf.py')
     config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
     for config_file_name in config_file_names:
-        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
+        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_adaptive=False, is_smpl_orig=config['is_cape'])

cvae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.post.config_abl_pyr_64_128_5 import config
+from configs.config_abl_pyr_32_skirt import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.pyramid_model_optimized import PyramidNew as Pyramid
+from models.pyramid_model_optimized import Pyramid
 from postprocessing import fix_collision
 
 
@@ -59,9 +59,11 @@ def init_wandb():
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
-    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.post.')]
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs')]
     for config_file_name in config_file_names:
-        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
+        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'])
@@ -208,7 +210,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
     return fig
 
 
-@tf.function
+# @tf.function
 def train_step(batch):
     levels_input, conds = group_inputs_for_levels(batch, config)
 
@@ -261,8 +263,8 @@ def train_step(batch):
         else:
             reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
 
-        if config['use_norm_loss'] and step > 2000:
-            reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+        # if config['use_norm_loss'] and step > 2000:
+        #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
         kl_total += kl
 
@@ -334,8 +336,8 @@ def valid_step(batch):
     else:
         reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
     
-    if config['use_norm_loss'] and step > 2000:
-        reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+    # if config['use_norm_loss'] and step > 2000:
+    #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
     kl_total += kl
 
@@ -731,7 +733,7 @@ def speed_step(levels_input, conds):
 
 
 if __name__ == '__main__':
-    init_wandb()
+    # init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str, default='lhunor')
     args = vars(parser.parse_args())
@@ -790,7 +792,7 @@ if __name__ == '__main__':
     # generate_results()
 
     ckpt = tf.train.Checkpoint(model=model)
-    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
+    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
 
     # load_model(model, 'checkpoints/pyr_abl_32_skirt', iteration='180000')
 
@@ -807,18 +809,18 @@ if __name__ == '__main__':
         model.static_enc.trainable = False
         model.body_enc.trainable = False
         model.curv_enc.trainable = False
-
+        model.cond_encoder.trainable = False
 
     # load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
     # model.levels[0].upscale_enhancer.trainable = False
 
 
-    for i in range(len(model.levels)):
+    for i in range(len(model.levels)-1):
         load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
         model.levels[i].upscale_enhancer.trainable = False
-    # if config['pyramid']['resolutions'][-1] != 512:
-    #     load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
-    #     model.upscale_enhancer.trainable = False
+    if config['pyramid']['resolutions'][-1] != 512:
+        load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
+        model.upscale_enhancer.trainable = False
 
     for i in range(len(model.levels)-1):
         model.levels[i].trainable = False

fix_curv_uv_loc.py

@@ -0,0 +1,23 @@
+import os
+import shutil
+import tqdm
+
+if __name__ == '__main__':
+    folder = '/home/workspace/data/dataset/local-cache/train_skirt'
+    is_skirt = True 
+    skirt_modifier = '_skirt' if is_skirt else ''
+    frames_file = 'frames' + skirt_modifier + '_10p.txt'
+    neighbourhood = 10
+
+    frames = []
+    with open(os.path.join(folder, frames_file), 'r') as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+    frames = lines
+
+    os.makedirs(os.path.join(folder, f'uv_curvature_{neighbourhood}'), exist_ok=True)
+    for frame in tqdm.tqdm(frames):
+        id = frame.split('_')[0]
+        src = os.path.join(folder, f'{id}', 'curvature_uv_10', f'{frame}.npz')
+        dst = os.path.join(folder, f'uv_curvature_{neighbourhood}', f'{frame}.npz')
+        shutil.move(src, dst)

models/cvae_model.py

@@ -43,7 +43,7 @@ class CVAE(tf.keras.Model):
         self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
                                                  tf.keras.layers.Dense(256, activation='relu'),
                                                  tf.keras.layers.Dense(128, activation='relu'),
-                                                 tf.keras.layers.Lambda(lambda x: x / 1000),
+                                                #  tf.keras.layers.Lambda(lambda x: x / 1000),
                                                  tf.keras.layers.Dense(config['latent_dim'], activation='tanh')])
 
         self.encoder = VariationalEncoder(config['cvae']['latent_dim'], config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])

run_parallel.py

@@ -4,24 +4,26 @@ import os
 
 if __name__ == '__main__':
 
+    # folder = '/home/workspace/data/dataset/local-cache/train_skirt'
     folder = '/home/workspace/data/dataset/test_skirt_ready_2'
+    # folder = '/home/workspace/local-data/valid_skirt'
     # 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:]
+    # ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
     skirt = 0
 
     count = 0
-    step = 2000
+    step = 20
 
     # !!!!!!! change skirt parameter in script
 
-    while count < len(ids):
+    while count < 150:
         # 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 convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_posed_no_orient_test_skirt_ready_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_posed_no_orient_test_skirt_ready_' + 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_curv_2_10_test_skirt_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_curv_2_10_test_skirt_' + 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_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 &')