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luni
2023-03-10 17:25:44 +00:00
parent cb76d36c4f
commit 4b1cb0b06d
27 changed files with 1890 additions and 207 deletions
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aaa_helpers.txt
+8
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@@ -77,6 +77,13 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_reg" > nohup/iccv/pyr/ic
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_reg_skirt" > nohup/iccv/pyr/iccv_pyr_32_reg_skirt.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_10p" > nohup/iccv/pyr/iccv_pyr_32_10p.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_25p" > nohup/iccv/pyr/iccv_pyr_32_25p.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_jumpsuit" > nohup/iccv/pyr/iccv_pyr_32_jumpsuit.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_trousers" > nohup/iccv/pyr/iccv_pyr_32_trousers.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_top" > nohup/iccv/pyr/iccv_pyr_32_top.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_tshirt" > nohup/iccv/pyr/iccv_pyr_32_tshirt.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_skirt_skirt.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_skirt_dress.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_batch" > nohup/iccv/pyr/iccv_pyr_32_skirt_batch.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64" > nohup/iccv/pyr/iccv_pyr_32_64.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_skirt.out 2>&1 &
@@ -151,6 +158,7 @@ nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_25p > nohu
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128 > nohup/iccv/evaluate/iccv_pyr_32_64_128.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skirt.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skin > nohup/iccv/evaluate/iccv_pyr_32_64_128_skin.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skin_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skin_skirt.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_reg > nohup/iccv/evaluate/iccv_pyr_32_64_128_reg.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_curv > nohup/iccv/evaluate/iccv_pyr_32_64_128_curv.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_curv_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_curv_skirt.out 2>&1 &
calculate_error.py
+53 -23
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@@ -1,37 +1,37 @@
import os
import sys
from configs.iccv.config_abl_pyr_32_64_25p import config
from configs.iccv.config_abl_pyr_32_64_128_skin_skirt import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
# import tensorflow as tf
import tensorflow as tf
# gpus = tf.config.experimental.list_physical_devices('GPU')
# if gpus:
# try:
# # Currently, memory growth needs to be the same across GPUs
# for gpu in gpus:
# tf.config.experimental.set_memory_growth(gpu, True)
# logical_gpus = tf.config.experimental.list_logical_devices('GPU')
# print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
# except RuntimeError as e:
# # Memory growth must be set before GPUs have been initialized
# print(e)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
import csv
import glob
import time
import argparse
import wandb
import tqdm
from tqdm import tqdm
import random
import numpy as np
import plotly.graph_objects as go
import plotly.express as ex
# if config['is_skirt']:
# from losses import edge_loss_skirt as edge_loss
# else:
# from losses import edge_loss
from losses import collision_loss, normal_loss
if config['is_skirt']:
from losses import edge_loss_skirt as edge_loss
else:
from losses import edge_loss
@@ -53,9 +53,13 @@ if __name__ == '__main__':
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())
print(config['name'])
# smpl = SMPL(use_one_hot=config['use_one_hot'])
skirt_modifier = '_skirt' if config['is_skirt'] else ''
error_type = 'collision' # 'euclidean' 'collision' edge normal
folder = config['test_dir']
checkpoint_name = config['name']
@@ -65,7 +69,7 @@ if __name__ == '__main__':
garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
output_folder = '/home/workspace/local-data_128/results/iccv/' + checkpoint_name + '-'
output_folder = '/home/workspace/local-data/results/iccv/' + checkpoint_name + '-'
# check if there is a folder that starts with this name using glob
folders = glob.glob(output_folder + '*')
assert len(folders) == 1, f'There should be only one checkpoint that starts with this name, but found {len(folders)}'
@@ -78,6 +82,7 @@ if __name__ == '__main__':
# ids = [id.split('.')[0] for id in ids]
# faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
print('Reading masks...')
masks_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_mask'
ids = os.listdir(masks_folder)
ids = [id.split('.')[0] for id in ids]
@@ -85,6 +90,11 @@ if __name__ == '__main__':
frames = os.listdir(output_folder)
frames = [frame for frame in frames if not frame.endswith('_fixed_coll.npy')]
# frames = frames[:100]
print('Reading bodies...')
bodies_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_posed_body_no_orient'
bodies = {frame : np.load(bodies_folder + '/' + frame[:-1] + 'z')['mesh_posed_body_no_orient'] for frame in tqdm(frames) }
if config['remove_global_orientation']:
gt_name = 'mesh_posed_no_orient'
@@ -99,12 +109,32 @@ if __name__ == '__main__':
else:
per_garment_errors = { 'Tshirt': [], 'Jumpsuit': [], 'Trousers': [], 'Top': [] }
for frame in tqdm.tqdm(frames):
for frame in tqdm(frames):
id = frame.split('_')[0]
frame_nr = frame.split('.')[0].split('_')[1]
gt = np.load(os.path.join(orig_folder, id + '_' + frame_nr + '.npz'))[gt_name][masks[id], :].astype(np.float32)
pred = np.load(os.path.join(output_folder, id + '_' + frame_nr + '.npy'))
metric = np.mean(np.sqrt(np.sum((gt - pred) ** 2, axis=-1)))
if error_type == 'edge':
pred_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
pred_unmasked[0, masks[id], :] = pred
gt_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
gt_unmasked[0, masks[id], :] = gt
metric = edge_loss(gt_unmasked, pred_unmasked, masks[id][None,:].astype(np.float32)).numpy()[0]
elif error_type == 'euclidean':
metric = np.mean(np.sqrt(np.sum((gt - pred) ** 2, axis=-1)))
elif error_type == 'collision':
pred_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
pred_unmasked[0, masks[id], :] = pred
metric = collision_loss(pred_unmasked, bodies[frame][None,:,:], masks[id][None,:].astype(np.float32))[0].numpy()[0]
elif error_type == 'normal':
pred_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
pred_unmasked[0, masks[id], :] = pred
gt_unmasked = np.zeros((1, 14475, 3), dtype=np.float32)
gt_unmasked[0, masks[id], :] = gt
metric = normal_loss(gt_unmasked, pred_unmasked, masks[id][None,:].astype(np.float32)).numpy()
else:
raise ValueError('Error type not supported')
errors[id + '_' + frame_nr] = metric
per_garment_errors[garment_type_table[id]].append(metric)
@@ -112,7 +142,7 @@ if __name__ == '__main__':
for key in per_garment_errors.keys():
print(key + ': ' + str(np.mean(per_garment_errors[key])))
with open('/home/workspace/data/results/errors/iccv/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
with open(f'/home/workspace/data/results/errors/iccv/{checkpoint_name}-{iteration}-{error_type}.csv', mode='w') as f:
file_writer = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
for key in sorted(errors.keys()):
compare_sampling.py
+65
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@@ -0,0 +1,65 @@
import os
import sys
import numpy as np
import trimesh as tm
from stl import mesh
# os.environ["CUDA_VISIBLE_DEVICES"] = '1'
# import tensorflow as tf
# gpus = tf.config.experimental.list_physical_devices('GPU')
# if gpus:
# try:
# # Currently, memory growth needs to be the same across GPUs
# for gpu in gpus:
# tf.config.experimental.set_memory_growth(gpu, True)
# logical_gpus = tf.config.experimental.list_logical_devices('GPU')
# print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
# except RuntimeError as e:
# # Memory growth must be set before GPUs have been initialized
# print(e)
# from losses import collision_loss, normal_loss
# from losses import edge_loss_skirt as edge_loss
# from losses import edge_loss
if __name__ == '__main__':
frame = '04173_193' # '02276_232', '02978_242', '04173_193'
folder = 'examples/sampling_analysis'
# original_folder = '/home/workspace/data/dataset/local-cache/train_skirt'
original_folder = '/home/workspace/local-data/train'
# folder = 'examples/sampling'
id = frame.split('_')[0]
mask = np.load(os.path.join(original_folder, 'mesh_mask', f'{id}.npy'))
gt = np.load(os.path.join(folder, frame, f'{frame}_gt.npy'))
faces = np.load(os.path.join(folder, frame, f'{frame}_faces.npy'))
edges = tm.Trimesh(vertices=gt, faces=faces, validate=False, process=False).edges
edges_gt = np.linalg.norm(gt[edges[:, 0]] - gt[edges[:, 1]], axis=1)
area_gt = np.sum(np.linalg.norm(np.cross(gt[faces[:, 0]] - gt[faces[:, 1]], gt[faces[:, 0]] - gt[faces[:, 2]]), axis=1))
errors = []
vars = []
area_diffs = []
# for i in [1,2,4,10]:
for i in range(15):
sample = np.load(os.path.join(folder, frame, f'{frame}_{i}.npy'))
edges_sample = np.linalg.norm(sample[edges[:, 0]] - sample[edges[:, 1]], axis=1)
error = np.mean(np.abs(edges_sample - edges_gt))
var = np.var(np.abs(edges_sample - edges_gt))
errors.append(error)
vars.append(var)
area_sample = np.sum(np.linalg.norm(np.cross(sample[faces[:, 0]] - sample[faces[:, 1]], sample[faces[:, 0]] - sample[faces[:, 2]]), axis=1))
area_diff = np.abs(area_sample - area_gt)
area_diffs.append(area_diff)
print(np.array(errors).mean())
print(np.array(vars).mean())
print(np.array(area_diffs).mean())
print(area_gt)
configs/iccv/config_abl_all_skirt_var.py
+4 -4
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@@ -18,8 +18,8 @@ config['use_norm_loss'] = True
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '0'
config['name'] = 'abl_all_skirt_var'
config['gpu'] = '3'
config['name'] = 'abl_all_skirt_var_dasda'
config['notes'] = ''
config['tags'] = ['iccv', 'abl_all', 'skirt']
@@ -35,9 +35,9 @@ config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
config['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
config['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
configs/iccv/config_abl_pyr_32.py
+16 -18
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@@ -33,6 +33,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
@@ -93,11 +94,10 @@ for res in config['pyramid']['resolutions']:
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
@@ -106,13 +106,12 @@ for res in config['pyramid']['resolutions']:
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
@@ -120,13 +119,12 @@ for res in config['pyramid']['resolutions']:
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 64]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
@@ -139,7 +137,7 @@ for res in config['pyramid']['resolutions']:
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
configs/iccv/config_abl_pyr_32_64_128_256_512.py
+1 -1
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@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '1'
config['gpu'] = '4'
config['name'] = 'iccv_pyr_32_64_128_256_512'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
configs/iccv/config_abl_pyr_32_64_128_256_512_curv.py
+1 -1
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@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '2'
config['gpu'] = '4'
config['name'] = 'iccv_pyr_32_64_128_256_512_curv'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv', 'curv']
configs/iccv/config_abl_pyr_32_64_128_256_512_reg.py
+1 -1
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@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '0'
config['gpu'] = '4'
config['name'] = 'iccv_pyr_32_64_128_256_512_reg'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv', 'reg']
configs/iccv/config_abl_pyr_32_64_128_256_512_skin.py
+1 -1
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@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '1'
config['gpu'] = '4'
config['name'] = 'iccv_pyr_32_64_128_256_512_skin'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
configs/iccv/config_abl_pyr_32_64_128_256_512_skirt.py
+1 -1
View File
@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '5'
config['gpu'] = '4'
config['name'] = 'iccv_pyr_32_64_128_256_512_skirt'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
configs/iccv/config_abl_pyr_32_batch.py
+187
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@@ -0,0 +1,187 @@
config = dict()
config['gpu'] = '7'
config['name'] = 'iccv_pyr_32_batch'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['batch_size'] = 64
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
configs/iccv/config_abl_pyr_32_jumpsuit.py
+190
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config = dict()
config['gpu'] = '0'
config['name'] = 'iccv_pyr_32_jumpsuit'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['frames_file'] = 'frames_jumpsuit.txt'
config['frames_file_valid'] = 'frames_jumpsuit.txt'
config['frames_file_test'] = 'frames_jumpsuit.txt'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
configs/iccv/config_abl_pyr_32_skirt_batch.py
+193
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config = dict()
config['gpu'] = '7'
config['name'] = 'iccv_pyr_32_skirt_batch'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
config['frames_file'] = 'frames_skirt.txt'
config['frames_file_valid'] = 'frames_skirt.txt'
config['frames_file_test'] = 'frames_skirt.txt'
config['batch_size'] = 128
config['is_skirt'] = True
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 64]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, level['latent_dim']]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
configs/iccv/config_abl_pyr_32_skirt_dress.py
+190
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config = dict()
config['gpu'] = '3'
config['name'] = 'iccv_pyr_32_skirt_dress'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
config['frames_file'] = 'frames_dress.txt'
config['frames_file_valid'] = 'frames_dress.txt'
config['frames_file_test'] = 'frames_dress.txt'
config['batch_size'] = 4
config['is_skirt'] = True
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
configs/iccv/config_abl_pyr_32_skirt_skirt.py
+190
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@@ -0,0 +1,190 @@
config = dict()
config['gpu'] = '4'
config['name'] = 'iccv_pyr_32_skirt_skirt'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
config['frames_file'] = 'frames_skirt.txt'
config['frames_file_valid'] = 'frames_skirt.txt'
config['frames_file_test'] = 'frames_skirt.txt'
config['batch_size'] = 4
config['is_skirt'] = True
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
configs/iccv/config_abl_pyr_32_top.py
+193
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@@ -0,0 +1,193 @@
config = dict()
config['gpu'] = '2'
config['name'] = 'iccv_pyr_32_top'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
config['frames_file'] = 'frames_top.txt'
config['frames_file_valid'] = 'frames_top.txt'
config['frames_file_test'] = 'frames_top.txt'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
configs/iccv/config_abl_pyr_32_trousers.py
+190
View File
@@ -0,0 +1,190 @@
config = dict()
config['gpu'] = '1'
config['name'] = 'iccv_pyr_32_trousers'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['frames_file'] = 'frames_trousers.txt'
config['frames_file_valid'] = 'frames_trousers.txt'
config['frames_file_test'] = 'frames_trousers.txt'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
configs/iccv/config_abl_pyr_32_tshirt.py
+193
View File
@@ -0,0 +1,193 @@
config = dict()
config['gpu'] = '3'
config['name'] = 'iccv_pyr_32_tshirt'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
# DON'T FORGET TO CHECK IF STORE IN MEMORY
# DON'T FORGET TO CHECK REG LOSS COEFF
# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
# DON'T FORGET TO CHECK IF SKIRT
# DON'T FORGET TO CHECK IF CAPE
# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
config['frames_file'] = 'frames_tshirt.txt'
config['frames_file_valid'] = 'frames_tshirt.txt'
config['frames_file_test'] = 'frames_tshirt.txt'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
config['bottleneck_scale'] = 2
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (config['smpl_res'], 3,),
'mesh_posed': (config['smpl_res'], 3,),
'mesh_unposed': (config['smpl_res'], 3,),
'uv_unposed': (512, 256, 3),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['add_noise'] = False
config['pyramid']['noise_amount'] = 0.0
config['pyramid']['loss_weights'] = [1,1,1,1,1]
for res in config['pyramid']['resolutions']:
if res != 512:
res_modifier = '_' + str(res)
config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1'
config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
config['pyramid']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 3, 9, 3]
level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
level = dict()
level['resolution'] = 128
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [128, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
level = dict()
level['resolution'] = 64
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [64, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = 64 * config['bottleneck_scale']
level['kernel_size'] = 3
level['encoder_depths'] = [16]
level['encoder_dims'] = [level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'
config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'
config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'
cvae.py
+2 -1
View File
@@ -2,6 +2,7 @@ import os
import sys
from configs.iccv.config_abl_all_skirt_var import config
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -479,7 +480,7 @@ def test():
if __name__ == '__main__':
init_wandb()
# init_wandb()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())
cvae_pyramid.py
+78 -59
View File
@@ -1,7 +1,8 @@
import os
import sys
from configs.iccv.config_abl_pyr_32_64_128_256_512 import config
from configs.iccv.config_abl_pyr_32_tshirt import config
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -417,6 +418,7 @@ def test_step(batch):
def plot_everything(batch: dict, type: str):
nr_to_plot = 4
if config['use_skinning']:
mesh_orig, uv_orig = batch['mesh_unposed'].numpy(), batch['uv_unposed'].numpy()
else:
@@ -426,8 +428,8 @@ def plot_everything(batch: dict, type: str):
output_levels = model(levels_input, training=False)
mesh_pred_levels = [uv2mesh(output_levels[i][0]).numpy() for i in range(len(output_levels))]
mesh_orig_posed = batch['mesh_posed'].numpy()
body = batch['mesh_body_posed'].numpy()
mesh_orig_posed = batch['mesh_posed'].numpy()[:nr_to_plot]
body = batch['mesh_body_posed'].numpy()[:nr_to_plot]
orig_img = (uv_orig + 1) / 2
uv_static_img = (batch['uv_static'].numpy() + 1) / 2
@@ -436,10 +438,10 @@ def plot_everything(batch: dict, type: str):
pred_img = np.concatenate([tf.image.resize(output_levels[i][0], (512, 256), tf.image.ResizeMethod.LANCZOS5) for i in range(len(output_levels))], axis=2)
pred_img = (pred_img - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) / (pred_img.max(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis] - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis])
img = tf.concat((uv_static_img, uv_body_posed_img, uv_curvature, orig_img, pred_img), axis=2)
imgs = [wandb.Image(img[i]) for i in range(4)]
imgs = [wandb.Image(img[i]) for i in range(nr_to_plot)]
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
mask = batch['mesh_mask'].numpy().astype(bool)
mask = batch['mesh_mask'].numpy().astype(bool)[:nr_to_plot]
_, faces = maskFacesBatch(np.ones_like(mesh_orig), F, mask)
if config['use_skinning']:
@@ -448,30 +450,30 @@ def plot_everything(batch: dict, type: str):
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]).numpy()
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])]
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(nr_to_plot)]
mesh_unposed_shaped_levels = [[] for _ in range(mask.shape[0])]
mesh_unposed_shaped_levels = [[] for _ in range(nr_to_plot)]
for i in range(len(output_levels)):
mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(mask.shape[0])]
for k in range(mask.shape[0]):
mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(nr_to_plot)]
for k in range(nr_to_plot):
mesh_unposed_shaped_levels[k].append(mesh_unposed_shaped[k])
# full res version
mesh_unposed_shaped_full = np.zeros((mask.shape[0], config['smpl_res'], 3), dtype=np.float32)
for i in range(mask.shape[0]):
mesh_unposed_shaped_full = np.zeros((nr_to_plot, config['smpl_res'], 3), dtype=np.float32)
for i in range(nr_to_plot):
mesh_unposed_shaped_full[i, mask[i], :] = mesh_unposed_shaped[i]
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_unposed_shaped_full).numpy()
mesh_posed = smpl.run_garment(batch['pose_shape'][:nr_to_plot, :72], batch['pose_shape'][:nr_to_plot, 72:], mesh_unposed_shaped_full).numpy()
else:
mesh_posed = mesh_pred_levels[-1]
gt = [mesh_orig[i][mask[i], :] for i in range(mesh_posed.shape[0])]
mesh_unposed_shaped_levels = [[mesh_pred_levels[i][j, mask[j], :] for i in range(len(output_levels))] for j in range(mask.shape[0])]
gt = [mesh_orig[i][mask[i], :] for i in range(nr_to_plot)]
mesh_unposed_shaped_levels = [[mesh_pred_levels[i][j, mask[j], :] for i in range(len(output_levels))] for j in range(nr_to_plot)]
gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(mask.shape[0])]
gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(nr_to_plot)]
mesh_posed_fixed = fix_collision(mesh_posed, body, mask).numpy()
mesh_posed = [mesh_posed[j, mask[j], :] for j in range(mask.shape[0])]
mesh_posed = [mesh_posed[j, mask[j], :] for j in range(nr_to_plot)]
if config['is_cape']:
collision = mesh_posed
@@ -487,11 +489,11 @@ def plot_everything(batch: dict, type: str):
if config['is_cape']:
figs_coll = dict()
else:
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(body.shape[0])]
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(nr_to_plot)]
collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(mesh_posed))]
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(mask.shape[0])]
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(nr_to_plot)]
collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(gt_posed))]
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(4) }
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(nr_to_plot) }
return { **figs, **figs_coll, **{ type + '/images': imgs, } }
@@ -728,14 +730,23 @@ def generate_results():
pass
@tf.function
def speed_step(levels_input, conds):
def speed_step(levels_input, conds, pose_shape, mask):
shape = pose_shape[:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
return model.sample([levels_input, conds])
# pred = model.sample([levels_input, conds])
out_levels = model([levels_input, conds], training=False)
pred = out_levels[-1][0]
mesh_pred = uv2mesh(pred)
mesh_pred_shaped = mesh_pred + shape_offsets * mask[:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(pose_shape[:, :72], pose_shape[:, 72:], mesh_pred_shaped)
return mesh_pred_posed
if __name__ == '__main__':
# init_wandb()
init_wandb()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str, default='lhunor')
args = vars(parser.parse_args())
@@ -760,8 +771,12 @@ if __name__ == '__main__':
resolutions=dataset_resolutions,
in_memory=config['dataset_in_memory'],
is_cape=config['is_cape'],
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None)
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
frames_file_skirt=config['frames_file'] if 'frames_file' in config.keys() else '',
folder_skirt=config['train_dir_skirt'] if 'train_dir_skirt' in config.keys() else '',
)
static_dataset_train = StaticDataset(folder=config['train_dir'],
folder_skirt=config['train_dir_skirt'] if 'train_dir_skirt' in config.keys() else '',
signature=config['signature_static'],
resolutions=dataset_resolutions,
in_memory=True,
@@ -772,8 +787,12 @@ if __name__ == '__main__':
resolutions=dataset_resolutions,
in_memory=config['dataset_in_memory'],
is_cape=config['is_cape'],
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None)
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None,
frames_file_skirt=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else '',
folder_skirt=config['valid_dir_skirt'] if 'valid_dir_skirt' in config.keys() else '',
)
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
folder_skirt=config['valid_dir_skirt'] if 'valid_dir_skirt' in config.keys() else '',
signature=config['signature_static'],
resolutions=dataset_resolutions,
in_memory=True,
@@ -848,50 +867,50 @@ if __name__ == '__main__':
levels_input, conds = group_inputs_for_levels(batch, config)
start = time.perf_counter()
out = speed_step(levels_input, conds)
t = time.perf_counter() - start
print(t)
times.append(t)
# start = time.perf_counter()
# out = speed_step(levels_input, conds, batch['pose_shape'] ,batch['mesh_mask'])
# t = time.perf_counter() - start
# print(t)
# times.append(t)
if step > 200:
break
# if step > 200:
# break
# train_loss = train_step(batch)
# train_accumulator.update(train_loss)
train_loss = train_step(batch)
train_accumulator.update(train_loss)
# if step % 25 == 0:
# data_valid = valid_iterator.get_next()
# data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
# valid_loss = valid_step(data_valid)
# valid_accumulator.update(valid_loss)
if step % 25 == 0:
data_valid = valid_iterator.get_next()
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
valid_loss = valid_step(data_valid)
valid_accumulator.update(valid_loss)
# if train_loss['train/euclid_error'] < 0.015:
# if balancing_activated_step == 0:
# balancing_activated_step = step
# balancing_coeff = balancing_coeff + 1 / 20000
if train_loss['train/euclid_error'] < 0.015:
if balancing_activated_step == 0:
balancing_activated_step = step
balancing_coeff = balancing_coeff + 1 / 20000
# 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 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)) } }
# to_log = { **to_log, **{ 'train/balancing_coeff': balancing_coeff, 'train/gamma': gamma } }
to_log = { **to_log, **{ 'train/balancing_coeff': balancing_coeff, 'train/gamma': gamma } }
# wandb.log(to_log, step=step)
# train_accumulator.reset()
# valid_accumulator.reset()
wandb.log(to_log, step=step)
train_accumulator.reset()
valid_accumulator.reset()
# if step % 5000 == 0:
# manager.save(checkpoint_number=step)
if step % 5000 == 0:
manager.save(checkpoint_number=step)
# print(step)
print(step)
step += 1
print('mean:')
print(np.mean(times[10:]))
# print('mean:')
# print(np.mean(times[10:]))
dataset.py
+39 -22
View File
@@ -54,10 +54,11 @@ from custom_utils.custom_utils import nest_conditions, batch_to_dict
class StaticDataset():
def __init__(self, folder, signature, use_filled=False, resolutions=[512], in_memory=True, is_cape=False) -> None:
def __init__(self, folder, signature, folder_skirt='', use_filled=False, resolutions=[512], in_memory=True, is_cape=False) -> None:
self.store = dict()
self.signature = signature
self.folder = folder
self.folder_skirt = folder_skirt
self.in_memory = in_memory
assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
self.possible_keys = ['uv_static', 'uv_static_32', 'uv_static_64', 'uv_static_128', 'uv_static_256',
@@ -85,6 +86,9 @@ class StaticDataset():
def read(self, id):
data = dict()
temp_folder = self.folder
if not os.path.exists(os.path.join(temp_folder, 'uv_mask', id + '.npz')):
temp_folder = self.folder_skirt
for res in self.resolutions:
res_string = ''
@@ -92,23 +96,23 @@ class StaticDataset():
res_string = '_' + str(res)
if 'uv_static' + res_string in self.signature.keys():
uv_static = np.load(os.path.join(self.folder, 'uv_static' + self.filled + res_string, id + '.npz')); uv_static = uv_static['uv_static' + self.filled]; data['uv_static' + res_string] = uv_static
uv_static = np.load(os.path.join(temp_folder, 'uv_static' + self.filled + res_string, id + '.npz')); uv_static = uv_static['uv_static' + self.filled]; data['uv_static' + res_string] = uv_static
if 'uv_mask' + res_string in self.signature.keys():
if self.is_cape:
data['uv_mask' + res_string] = self.uv_masks[res]
else:
uv_mask = np.load(os.path.join(self.folder, 'uv_mask' + res_string, id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32); data['uv_mask' + res_string] = uv_mask
uv_mask = np.load(os.path.join(temp_folder, 'uv_mask' + res_string, id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32); data['uv_mask' + res_string] = uv_mask
if 'mesh_static' in self.signature.keys():
mesh_static = np.load(os.path.join(self.folder, 'mesh_static', id + '.npz')); mesh_static = mesh_static['mesh_static']; data['mesh_static'] = mesh_static.astype(np.float32)
mesh_static = np.load(os.path.join(temp_folder, 'mesh_static', id + '.npz')); mesh_static = mesh_static['mesh_static']; data['mesh_static'] = mesh_static.astype(np.float32)
if 'mesh_mask' in self.signature.keys():
if self.is_cape:
data['mesh_mask'] = np.ones((3638), np.float32)
else:
mesh_mask = np.load(os.path.join(self.folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32); data['mesh_mask'] = mesh_mask
mesh_mask = np.load(os.path.join(temp_folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32); data['mesh_mask'] = mesh_mask
if 'uv_shaped_body' in self.signature.keys():
uv_shaped_body = np.load(os.path.join(self.folder, 'uv_shaped_body', id + '.npz')); uv_shaped_body = uv_shaped_body['uv_shaped_body']; data['uv_shaped_body'] = uv_shaped_body
uv_shaped_body = np.load(os.path.join(temp_folder, 'uv_shaped_body', id + '.npz')); uv_shaped_body = uv_shaped_body['uv_shaped_body']; data['uv_shaped_body'] = uv_shaped_body
return data
@@ -135,7 +139,7 @@ class StaticDataset():
class DatasetIdx(tf.data.Dataset):
def __new__(cls, folder, shuffle=True, is_cape=False, testing=False, frames_file=None):
def __new__(cls, folder, shuffle=True, is_cape=False, testing=False, frames_file=None, folder_skirt=None, frames_file_skirt=''):
if frames_file is None:
uv_static_folder = os.path.join(folder, 'pose_shape')
frames = sorted(os.listdir(uv_static_folder))
@@ -156,6 +160,12 @@ class DatasetIdx(tf.data.Dataset):
lines = [line.strip() for line in lines]
frames = lines
if frames_file_skirt != '':
with open(os.path.join(folder_skirt, frames_file_skirt), 'r') as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
frames.extend(lines)
if shuffle:
random.shuffle(frames)
@@ -194,10 +204,12 @@ class DatasetFactory():
use_one_hot=False,
use_no_orient=True,
testing=False,
frames_file=None):
frames_file=None,
frames_file_skirt='',
folder_skirt='',):
assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
dataset_idx = DatasetIdx(folder, shuffle, is_cape=is_cape, testing=testing, frames_file=frames_file)
dataset_idx = DatasetIdx(folder, shuffle, is_cape=is_cape, testing=testing, folder_skirt=folder_skirt, frames_file=frames_file, frames_file_skirt=frames_file_skirt)
separator = '_'
if is_cape:
@@ -222,6 +234,11 @@ class DatasetFactory():
id, frame_nr = frame_split[0].numpy().decode("utf-8"), frame_split[1].numpy().decode("utf-8")
# id, frame_nr = frame_split[0].numpy(), frame_split[1].numpy()
# static_info = static_data[id]
temp_folder = folder
if not os.path.exists(os.path.join(temp_folder, 'pose_shape', id + separator + frame_nr + '.npy')):
temp_folder = folder_skirt
item = dict()
if 'id' in signature.keys():
@@ -229,23 +246,23 @@ class DatasetFactory():
if 'frame_nr' in signature.keys():
item['frame_nr'] = frame_nr
if 'pose_shape' in signature.keys():
pose_shape = np.load(os.path.join(folder, 'pose_shape', id + separator + frame_nr + '.npy'))
pose_shape = np.load(os.path.join(temp_folder, 'pose_shape', id + separator + frame_nr + '.npy'))
if use_no_orient:
pose_shape[:3] = 0
item['pose_shape'] = pose_shape
if 'mesh_body_posed' in signature.keys():
if use_no_orient:
mesh_posed_body_no_orient = np.load(os.path.join(folder, 'mesh_posed_body_no_orient', id + separator + frame_nr + '.npz')); mesh_posed_body_no_orient = mesh_posed_body_no_orient['mesh_posed_body_no_orient']
mesh_posed_body_no_orient = np.load(os.path.join(temp_folder, 'mesh_posed_body_no_orient', id + separator + frame_nr + '.npz')); mesh_posed_body_no_orient = mesh_posed_body_no_orient['mesh_posed_body_no_orient']
item['mesh_body_posed'] = mesh_posed_body_no_orient
else:
mesh_body_posed = np.load(os.path.join(folder, 'mesh_body_posed', id + '_' + frame_nr + '.npy'))
mesh_body_posed = np.load(os.path.join(temp_folder, 'mesh_body_posed', id + '_' + frame_nr + '.npy'))
item['mesh_body_posed'] = mesh_body_posed
if 'mesh_posed' in signature.keys():
if use_no_orient:
mesh_posed = np.load(os.path.join(folder, 'mesh_posed_no_orient', id + separator + frame_nr + '.npz')); mesh_posed = mesh_posed['mesh_posed_no_orient'].astype(np.float32)
mesh_posed = np.load(os.path.join(temp_folder, 'mesh_posed_no_orient', id + separator + frame_nr + '.npz')); mesh_posed = mesh_posed['mesh_posed_no_orient'].astype(np.float32)
item['mesh_posed'] = mesh_posed
else:
mesh_posed = np.load(os.path.join(folder, 'mesh_posed', id + separator + frame_nr + '.npz')); mesh_posed = mesh_posed['mesh_posed'].astype(np.float32)
mesh_posed = np.load(os.path.join(temp_folder, 'mesh_posed', id + separator + frame_nr + '.npz')); mesh_posed = mesh_posed['mesh_posed'].astype(np.float32)
item['mesh_posed'] = mesh_posed
# if 'mesh_unposed' in signature.keys():
# mesh_unposed = np.load(os.path.join(folder, 'mesh_unposed', id + '_' + frame_nr + '.npz')); mesh_unposed = mesh_unposed['mesh_unposed']
@@ -255,12 +272,12 @@ class DatasetFactory():
# item['uv_unposed'] = uv_unposed
if 'mesh_unposed' in signature.keys():
# print('one_hot')
mesh_unposed_no_shape = np.load(os.path.join(folder, 'mesh_unposed_no_shape' + one_hot, id + separator + frame_nr + '.npz')); mesh_unposed_no_shape = mesh_unposed_no_shape['mesh_unposed_no_shape']
mesh_unposed_no_shape = np.load(os.path.join(temp_folder, 'mesh_unposed_no_shape' + one_hot, id + separator + frame_nr + '.npz')); mesh_unposed_no_shape = mesh_unposed_no_shape['mesh_unposed_no_shape']
item['mesh_unposed'] = mesh_unposed_no_shape
if 'mesh_curvature' in signature.keys():
mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
mesh_curvature = np.load(os.path.join(temp_folder, 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
item['mesh_curvature'] = mesh_curvature.astype(np.int32)
@@ -270,7 +287,7 @@ class DatasetFactory():
res_string = '_' + str(res)
if 'uv_curvature' + res_string in signature.keys():
uv_curvature = np.load(os.path.join(folder, 'uv_curvature' + res_string, id + separator + frame_nr + '.npz'));
uv_curvature = np.load(os.path.join(temp_folder, 'uv_curvature' + res_string, id + separator + frame_nr + '.npz'));
if res_string != '':
uv_curvature = uv_curvature['uv_curvature']
else:
@@ -279,24 +296,24 @@ class DatasetFactory():
item['uv_curvature' + res_string] = uv_curvature
if 'uv_unposed' + res_string in signature.keys():
uv_unposed_no_shape = np.load(os.path.join(folder, 'uv_unposed_no_shape' + one_hot_2 + res_string, id + separator + frame_nr + '.npz')); uv_unposed_no_shape = uv_unposed_no_shape['uv_unposed_no_shape']
uv_unposed_no_shape = np.load(os.path.join(temp_folder, 'uv_unposed_no_shape' + one_hot_2 + res_string, id + separator + frame_nr + '.npz')); uv_unposed_no_shape = uv_unposed_no_shape['uv_unposed_no_shape']
item['uv_unposed' + res_string] = uv_unposed_no_shape
if 'uv_body_posed' + res_string in signature.keys():
if use_no_orient:
uv_posed_body_no_orient = np.load(os.path.join(folder, uv_posed_body_no_orient_folder_name + res_string, id + separator + frame_nr + '.npz'))
uv_posed_body_no_orient = np.load(os.path.join(temp_folder, uv_posed_body_no_orient_folder_name + res_string, id + separator + frame_nr + '.npz'))
uv_posed_body_no_orient = uv_posed_body_no_orient['uv_posed_body_no_orient']
item['uv_body_posed' + res_string] = uv_posed_body_no_orient
else:
uv_body_posed = np.load(os.path.join(folder, 'uv_body_posed', id + '_' + frame_nr + '.npz')); uv_body_posed = uv_body_posed['uv_body']
uv_body_posed = np.load(os.path.join(temp_folder, 'uv_body_posed', id + '_' + frame_nr + '.npz')); uv_body_posed = uv_body_posed['uv_body']
item['uv_body_posed' + res_string] = uv_body_posed
if 'uv_posed' + res_string in signature.keys():
if use_no_orient:
uv_posed = np.load(os.path.join(folder, 'uv_posed_no_orient' + res_string, id + separator + frame_nr + '.npz')); uv_posed = uv_posed['uv_posed_no_orient']
uv_posed = np.load(os.path.join(temp_folder, 'uv_posed_no_orient' + res_string, id + separator + frame_nr + '.npz')); uv_posed = uv_posed['uv_posed_no_orient']
item['uv_posed' + res_string] = uv_posed
else:
uv_posed = np.load(os.path.join(folder, 'uv_posed' + res_string, id + separator + frame_nr + '.npz')); uv_posed = uv_posed['uv_gt']
uv_posed = np.load(os.path.join(temp_folder, 'uv_posed' + res_string, id + separator + frame_nr + '.npz')); uv_posed = uv_posed['uv_gt']
item['uv_posed' + res_string] = uv_posed
# print('here')
evaluate_ae_pyramid.py
+1 -1
View File
@@ -1,6 +1,6 @@
import os
import sys
from configs.iccv.config_abl_pyr_32_64_10p import config
from configs.iccv.config_abl_pyr_32_64_128_skin_skirt import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
get_error.py
+33 -35
View File
@@ -1,5 +1,6 @@
import os
import csv
import glob
import numpy as np
@@ -7,15 +8,12 @@ if __name__ == '__main__':
garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
folder = '/home/workspace/data/results/errors/iccv'
checkpoint_name = 'iccv_pyr_32_64_25p'
checkpoint_name_skirt = 'iccv_pyr_32_64_128_256_512_skirt'
iteration = '155000'
iteration_skirt : str = '115000'
checkpoint_name = 'iccv_pyr_32_64_128_skin'
checkpoint_name_skirt = 'iccv_pyr_32_64_128_skin_skirt'
iteration = '75' + '000'
iteration_skirt : str = '155' + '000'
error_type = 'collision' # 'euclidean' 'collision' edge normal
# checkpoint_name = 'abl_skin'
# checkpoint_name_skirt = checkpoint_name + '_skirt'
# iteration = '70000'
# iteration_skirt : str = iteration
errors = []
errors_skirt = []
@@ -23,20 +21,20 @@ if __name__ == '__main__':
per_garment_errors_skirt = { 'Tshirt': [], 'Dress': [], 'Skirt': [], 'Top': [] }
with open(os.path.join(folder, checkpoint_name + '-' + iteration + '.csv'), 'r') as f:
csv_reader = csv.reader(f, delimiter=',')
for row in csv_reader:
per_garment_errors[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
# with open(os.path.join(folder, f'{checkpoint_name}-{iteration}-{error_type}.csv'), 'r') as f:
# csv_reader = csv.reader(f, delimiter=',')
# for row in csv_reader:
# per_garment_errors[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
print('\nNon-skirt:')
for key in per_garment_errors.keys():
avg = np.mean(per_garment_errors[key])
errors.append(avg)
print(key + ': ' + str(avg))
# print('\nNon-skirt:')
# for key in per_garment_errors.keys():
# avg = np.mean(per_garment_errors[key])
# errors.append(avg)
# print(key + ': ' + str(avg))
print('\nAvg non-skirt: ' + str(np.mean(errors)))
# print('\nAvg non-skirt: ' + str(np.mean(errors)))
with open(os.path.join(folder, checkpoint_name_skirt + '-' + iteration_skirt + '.csv'), 'r') as f:
with open(os.path.join(folder, f'{checkpoint_name_skirt}-{iteration_skirt}-{error_type}.csv'), 'r') as f:
csv_reader = csv.reader(f, delimiter=',')
for row in csv_reader:
per_garment_errors_skirt[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
@@ -49,24 +47,24 @@ if __name__ == '__main__':
print('Avg skirt: ' + str(np.mean(errors_skirt)))
cum_avg_shirt = np.mean(per_garment_errors['Tshirt'] + per_garment_errors_skirt['Tshirt'])
cum_avg_top = np.mean(per_garment_errors['Top'] + per_garment_errors_skirt['Top'])
# cum_avg_shirt = np.mean(per_garment_errors['Tshirt'] + per_garment_errors_skirt['Tshirt'])
# cum_avg_top = np.mean(per_garment_errors['Top'] + per_garment_errors_skirt['Top'])
print('Global tshirt avg: ' + str(cum_avg_shirt))
print('Global top avg: ' + str(cum_avg_top))
# print('Global tshirt avg: ' + str(cum_avg_shirt))
# print('Global top avg: ' + str(cum_avg_top))
print('\nGlobal per garment: ')
# print('\nGlobal per garment: ')
global_errors = []
per_garment_errors['Tshirt'].extend(per_garment_errors_skirt['Tshirt'])
per_garment_errors['Top'].extend(per_garment_errors_skirt['Top'])
per_garment_errors['Skirt'] = per_garment_errors_skirt['Skirt']
per_garment_errors['Dress'] = per_garment_errors_skirt['Dress']
for key in per_garment_errors.keys():
avg = np.mean(per_garment_errors[key])
global_errors.append(avg)
print(key + ': ' + str(avg), 'samples: ' + str(len(per_garment_errors[key])))
# global_errors = []
# per_garment_errors['Tshirt'].extend(per_garment_errors_skirt['Tshirt'])
# per_garment_errors['Top'].extend(per_garment_errors_skirt['Top'])
# per_garment_errors['Skirt'] = per_garment_errors_skirt['Skirt']
# per_garment_errors['Dress'] = per_garment_errors_skirt['Dress']
# for key in per_garment_errors.keys():
# avg = np.mean(per_garment_errors[key])
# global_errors.append(avg)
# print(key + ': ' + str(avg), 'samples: ' + str(len(per_garment_errors[key])))
print('Global avg: ' + str(np.mean(global_errors)))
print(len(global_errors))
# print('Global avg: ' + str(np.mean(global_errors)))
# print(len(global_errors))
losses.py
+5 -3
View File
@@ -44,7 +44,7 @@ def _nearest_neighbour(V, B):
def _tf_nn_parallel(V, B):
return tf.map_fn(fn=lambda elem: tf.py_function(func=_nearest_neighbour, inp=elem, Tout=tf.int32), elems=(V,B), fn_output_signature=tf.int32, parallel_iterations=V.shape[0])
# @tf.function
@tf.function
def collision_loss(V, B, mask, thr=.004):
B_vn = vertex_normals(B, tf.tile(F_coll[None], [B.shape[0], 1, 1]))
@@ -96,6 +96,7 @@ edges_skirt = np.stack(np.nonzero(laplacian_loss.todense())).transpose()
def get_edge_mask(masks):
return tf.reduce_min(tf.gather(masks, edges, axis=1), axis=-1)
@tf.function
def edge_loss(x, y, mask):
e_mask = get_edge_mask(mask)
x_e = tf.gather(x, edges[:,0], axis=1) - tf.gather(x, edges[:,1], axis=1)
@@ -237,13 +238,14 @@ def kl_loss(mean, logvar):
kl_loss *= -0.5
return kl_loss
@tf.function
def normal_loss(pred, gt, mask, is_skirt=False, is_smpl_orig=False):
f_norm = None
if not is_skirt and not is_smpl_orig:
f_norm = F_norm
elif is_skirt:
f_norm = F_norm_skirt
# f_norm = F_norm_skirt
f_norm = tf.tile(F_coll_skirt[None,:,:], [pred.shape[0],1,1])
elif is_smpl_orig:
f_norm = F_norm_orig
else:
plot_examples.py
+25 -22
View File
@@ -329,30 +329,33 @@ if __name__ == '__main__':
x = np.concatenate((body, posed), axis=0)
# calcualte the sum of face ares of posed mesh that has faces_garment faces
# face_areas = np.linalg.norm(np.cross((x[faces[:,0],:] - x[faces[:,1],:]), (x[faces[:,0],:] - x[faces[:,2],:])), axis=1)
# m = tm.Trimesh(x, faces, process=False, validate=False, vertex_colors=colors)
# x = posed
# x_1 = unposed.copy()
# x_1[:, 0] = x_1[:, 0] + 0.75
# x_2 = unposed_shaped.copy()
# x_2[:, 0] = x_2[:, 0] + 1.75
# garments = np.concatenate((x, x_1, x_2), axis=0)
# all_faces = np.concatenate((faces_garment, faces_garment + x.shape[0], faces_garment + 2*x.shape[0]), axis=0).astype(np.int32)
# mesh = garments
# faces = all_faces
# fig = go.Figure()
# fig.add_trace(go.Mesh3d(
# x=mesh[:,0],
# y=mesh[:,2],
# z=mesh[:,1],
# i = faces[:,0],
# j = faces[:,1],
# k = faces[:,2],
# text="",
# lighting={ 'ambient':0.8, 'diffuse':0.5, 'roughness':0.5 },
x = posed
x_1 = unposed.copy()
x_1[:, 0] = x_1[:, 0] + 0.75
x_2 = unposed_shaped.copy()
x_2[:, 0] = x_2[:, 0] + 1.75
garments = np.concatenate((x, x_1, x_2), axis=0)
all_faces = np.concatenate((faces_garment, faces_garment + x.shape[0], faces_garment + 2*x.shape[0]), axis=0).astype(np.int32)
mesh = garments
faces = all_faces
fig = go.Figure()
fig.add_trace(go.Mesh3d(
x=mesh[:,0],
y=mesh[:,2],
z=mesh[:,1],
i = faces[:,0],
j = faces[:,1],
k = faces[:,2],
text="",
lighting={ 'ambient':0.8, 'diffuse':0.5, 'roughness':0.5 },
# ),)
# fig.show()
),)
fig.show()
# fig = plot_fixed(x, faces, colors)
@@ -367,7 +370,7 @@ if __name__ == '__main__':
# fig.write_image('examples/' + type + '_' + id_str + '_' + frame_nr + '_unposed_shaped.svg')
# uv_mapping_example(body, F, colors[:14475, :], np.ones((14475), bool))
uv_mapping_example_skirt(body, F, colors[:14475, :], np.ones((14475), bool))
# uv_mapping_example_skirt(body, F, colors[:14475, :], np.ones((14475), bool))
# curvature_example(batch['mesh_curvature'][0].numpy(), uv_curvature, posed, faces_garment, mask)
# plot_upscaling(batch['uv_unposed_32'].numpy()[0], batch['uv_unposed'].numpy()[0], batch['uv_mask_32'].numpy()[0], batch['uv_mask'].numpy()[0], faces_garment, colors_garment, mask)
preprocessing.py
+17 -7
View File
@@ -216,7 +216,7 @@ def generate_faces():
np.save(os.path.join(output_folder, id + '.npy'), faces.astype(np.int32))
def generate_frames_file(folder, ratio=0.1):
def generate_frames_file(folder, ratio=0.1, garment='', file_name='frames.txt'):
all_frames = os.listdir(os.path.join(folder, 'pose_shape'))
ids = [frame.split('_')[0] for frame in all_frames]
all_frames = [frame.split('.')[0] for frame in all_frames]
@@ -232,17 +232,15 @@ def generate_frames_file(folder, ratio=0.1):
selected = []
for id in tqdm.tqdm(sub_ids):
if garment_type_table[id] != 'Dress':
if garment_type_table[id] != garment:
continue
if len(selected) > 30000:
break
# frames = glob.glob(os.path.join(folder, 'pose_shape', id + '_*'))
frames = [frame for frame in all_frames if frame.startswith(id + '_')]
frames = [frame.split('/')[-1].split('.')[0] for frame in frames]
selected.extend(frames)
with open(os.path.join(folder, 'frames_dress_30k.txt'), 'w') as f:
with open(os.path.join(folder, file_name), 'w') as f:
for i, frame in enumerate(selected):
f.write(frame + '\n')
@@ -293,7 +291,7 @@ def generate_frames_file_filtered_cape():
if __name__ == '__main__':
src_folder = '/home/workspace/local-data/train_skirt' #cloth3d_skirt
src_folder = '/home/workspace/local-data/train' #cloth3d_skirt
# src_folder = r'D:\Work\Dataset\validation_2'
dest_folder = '/home/workspace/data/dataset/validation'
@@ -306,5 +304,17 @@ if __name__ == '__main__':
# verify_poses(src_folder)
# create_garment_type_table()
# generate_faces()
generate_frames_file(src_folder, ratio=1)
# generate_frames_file(src_folder, ratio=1)
# generate_frames_file_filtered_cape()
# # folders = ['/home/workspace/local-data/train', '/home/workspace/local-data/test_ready', '/home/workspace/local-data/valid']
# folders = ['/home/workspace/local-data/valid_skirt']
# # garments = ['Jumpsuit', 'Trousers', 'Tshirt', 'Top']
# # filename = ['frames_jumpsuit.txt', 'frames_trousers.txt', 'frames_tshirt.txt', 'frames_top.txt']
# garments = ['Skirt', 'Dress', 'Tshirt', 'Top']
# filename = ['frames_skirt.txt', 'frames_dress.txt', 'frames_tshirt.txt', 'frames_top.txt']
# for folder in folders:
# for garment, file in zip(garments, filename):
# generate_frames_file(folder, garment=garment, file_name=file, ratio=1)
sample_pro_2.py
+13 -7
View File
@@ -46,7 +46,8 @@ from models.pyramid_model_optimized import Pyramid
# from configs.config_abl_overfit_dress_var_3 import config as config_main
# from configs.config_abl_overfit_jumpsuit_2 import config as config_main
# from configs.config_cvae_curv import config as config_curv
from configs.iccv.config_abl_all_skirt_var import config as config_main
# from configs.iccv.config_abl_all_skirt_var import config as config_main
from configs.iccv.config_abl_skin_var import config as config_main
from configs.iccv.config_cvae_curv import config as config_curv
# from models.cvae_model_overfit_jumpsuit import CVAE
from models.cvae_model import CVAENew as CVAE
@@ -331,11 +332,16 @@ def sampling_example(frame, camera, nr=15):
for i in range(nr):
plot_fixed(posed_mesh_fixed[i], body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_' + str(i) + '.png'))
save_stl(os.path.join(out_folder, frame, frame + '_body.stl'), body, F_body)
save_stl(os.path.join(out_folder, frame, frame + '_gt.stl'), gt_mesh, faces.astype(np.int32))
# save_stl(os.path.join(out_folder, frame, frame + '_body.stl'), body, F_body)
# save_stl(os.path.join(out_folder, frame, frame + '_gt.stl'), gt_mesh, faces.astype(np.int32))
# for i in range(nr):
# save_stl(os.path.join(out_folder, frame, frame + '_' + str(i) + '.stl'), posed_mesh_fixed[i], faces.astype(np.int32))
np.save(os.path.join(out_folder, frame, frame + '_gt.npy'), gt_mesh)
np.save(os.path.join(out_folder, frame, frame + '_body.npy'), body)
np.save(os.path.join(out_folder, frame, frame + '_faces.npy'), faces.astype(np.int32))
for i in range(nr):
save_stl(os.path.join(out_folder, frame, frame + '_' + str(i) + '.stl'), posed_mesh_fixed[i], faces.astype(np.int32))
np.save(os.path.join(out_folder, frame, frame + '_' + str(i) + '.npy'), posed_mesh_fixed[i])
# custom_imshow(np.concatenate(row_sampling, axis=1)).write_image(os.path.join(out_folder, frame + '_sampling.png'))
print()
@@ -470,9 +476,9 @@ if __name__ == '__main__':
# frame = '04925_272'
# frames = ['01070_13', '02276_232', '02978_242', '04008_295', '04173_193']
frames = ['00214_111', '01661_210', '02144_249', '02151_37', '02930_275', '03812_3']
frames = ['02276_232', '02978_242', '04173_193']
out_folder = 'examples/sampling_skirt'
out_folder = 'examples/sampling_analysis'
os.makedirs(out_folder, exist_ok=True)
for frame in tqdm.tqdm(frames):
sampling_example(frame, camera_1)