luni/cvpr
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

save

Browse Source
This commit is contained in:
luni
2023-03-07 22:02:55 +00:00
parent cbecf668df
commit cb76d36c4f
20 changed files with 626 additions and 168 deletions
Download Patch File Download Diff File Expand all files Collapse all files
aaa_helpers.txt
+4
View File
@@ -104,6 +104,7 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_pre_cape_f" > nohup/iccv
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_skirt.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skin" > nohup/iccv/pyr/iccv_pyr_32_64_128_skin_2.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skin_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_skin_skirt.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_reg" > nohup/iccv/pyr/iccv_pyr_32_64_128_reg_2.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_curv.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_curv_skirt.out 2>&1 &
@@ -117,6 +118,7 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256" > nohup/iccv
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_skirt_2.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_skin" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_skin.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_curv.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_curv_skirt.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_reg" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_reg.out 2>&1 &
nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512.out 2>&1 &
@@ -143,6 +145,8 @@ nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_reg > nohu
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_skirt.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_curv > nohup/iccv/evaluate/iccv_pyr_32_64_curv.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_curv_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_curv_skirt.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_10p > nohup/iccv/evaluate/iccv_pyr_32_64_10p.out 2>&1 &
nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_25p > nohup/iccv/evaluate/iccv_pyr_32_64_25p.out 2>&1 &
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 &
calculate_error.py
+2 -2
View File
@@ -1,6 +1,6 @@
import os
import sys
from configs.iccv.config_abl_pyr_32_25p import config
from configs.iccv.config_abl_pyr_32_64_25p import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
# import tensorflow as tf
@@ -65,7 +65,7 @@ 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/results/iccv/' + checkpoint_name + '-'
output_folder = '/home/workspace/local-data_128/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)}'
configs/iccv/config_abl_pyr_32_64_10p.py
+2 -2
View File
@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '4'
config['gpu'] = '0'
config['name'] = 'iccv_pyr_32_64_10p'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data_127/train'
config['valid_dir'] = '/home/workspace/local-data_127/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['test_dir'] = '/home/workspace/local-data/test_ready'
config['frames_file'] = 'frames_10p.txt'
configs/iccv/config_abl_pyr_32_64_128_256_512.py
+6 -4
View File
@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '0'
config['gpu'] = '1'
config['name'] = 'iccv_pyr_32_64_128_256_512'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
@@ -22,7 +22,7 @@ config['use_offsets'] = False
config['use_norm_loss'] = True
config['use_curvature'] = True
config['pretrained_model'] = ''
config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_512'
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
@@ -32,13 +32,15 @@ config['pyramid']['pretrained_pyramid_iteration'] = '90000'
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['valid_dir'] = '/home/workspace/local-data/train'
config['test_dir'] = '/home/workspace/local-data/test_ready'
config['frames_file'] = 'frames_00761_0.txt'
config['frames_file_valid'] = 'frames_00761_0.txt'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['dataset_in_memory'] = True
config['resolution'] = None
config['smpl_res'] = 14475
configs/iccv/config_abl_pyr_32_64_128_256_curv_skirt.py
+187
View File
@@ -0,0 +1,187 @@
config = dict()
config['gpu'] = '1'
config['name'] = 'iccv_pyr_32_64_128_256_curv_skirt'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv', 'curv', 'skirt']
# 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'] = False
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32, 64, 128, 256]
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_curv_skirt'
config['pyramid']['pretrained_pyramid_iteration'] = ''
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
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'] = [64, 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_64_128_curv_skirt.py
+10 -12
View File
@@ -94,11 +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)
@@ -107,13 +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_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
@@ -122,7 +120,7 @@ for res in config['pyramid']['resolutions']:
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['encoder_dims'] = [64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
configs/iccv/config_abl_pyr_32_64_128_reg_skirt.py
+10 -12
View File
@@ -94,11 +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)
@@ -107,13 +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_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
@@ -122,7 +120,7 @@ for res in config['pyramid']['resolutions']:
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['encoder_dims'] = [64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
configs/iccv/config_abl_pyr_32_64_128_skin_skirt.py
+12 -14
View File
@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '5'
config['gpu'] = '0'
config['name'] = 'iccv_pyr_32_64_128_skin_skirt'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
@@ -31,7 +31,7 @@ config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_skin_
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
config['batch_size'] = 4
config['is_skirt'] = True
@@ -100,11 +100,10 @@ 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)
@@ -113,13 +112,12 @@ 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_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
@@ -128,7 +126,7 @@ for res in config['pyramid']['resolutions']:
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['encoder_dims'] = [64, 128, level['latent_dim']]
level['decoder_depths'] = level['encoder_depths']
level['decoder_dims'] = level['encoder_dims']
level['avg_garment_path'] = ''
configs/iccv/config_abl_pyr_32_64_25p.py
+2 -2
View File
@@ -1,6 +1,6 @@
config = dict()
config['gpu'] = '5'
config['gpu'] = '1'
config['name'] = 'iccv_pyr_32_64_25p'
config['notes'] = ''
config['tags'] = ['pyr', 'iccv']
@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data_127/train'
config['valid_dir'] = '/home/workspace/local-data_127/valid'
config['test_dir'] = '/home/workspace/local-data_127/test_ready'
config['test_dir'] = '/home/workspace/local-data/test_ready'
config['frames_file'] = 'frames_25p.txt'
configs/iccv/config_abl_pyr_32_64_curv_skirt.py
+14 -18
View File
@@ -86,7 +86,6 @@ 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:
@@ -94,11 +93,10 @@ 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)
@@ -107,13 +105,12 @@ 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_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
@@ -121,13 +118,12 @@ 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'] = [64, 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_skirt'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
configs/iccv/config_abl_pyr_32_64_reg_skirt.py
+14 -18
View File
@@ -86,7 +86,6 @@ 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:
@@ -94,11 +93,10 @@ 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)
@@ -107,13 +105,12 @@ 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_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
@@ -121,13 +118,12 @@ 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'] = [64, 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_skirt'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
configs/iccv/config_abl_pyr_32_64_skin_skirt copy.py
+193
View File
@@ -0,0 +1,193 @@
config = dict()
config['gpu'] = '2'
config['name'] = 'iccv_pyr_32_64_skin_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'] = False # 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, 64]
config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skin_skirt'
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data_128/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['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),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
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)
if config['use_skinning']:
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
else:
config['signature_dynamic']['uv_posed' + 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'] = [64, 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_64_skin_skirt.py
+14 -17
View File
@@ -100,11 +100,10 @@ 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)
@@ -113,13 +112,12 @@ 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_skirt'#'checkpoints/up_3_d256_512_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 128:
@@ -127,13 +125,12 @@ 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'] = [64, 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_skirt'#'checkpoints/up_3_d128_256_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
elif res == 64:
curvature_analysis.py
+3 -3
View File
@@ -338,7 +338,7 @@ def visualize_neighbourhoods(folder, frame):
write_obj(f'examples/curvature_analysis/{id}_{frame_nr}_25.obj', verts, faces.astype(np.int32), colors_binned_all[1].astype(np.int32))
write_obj(f'examples/curvature_analysis/{id}_{frame_nr}_100.obj', verts, faces.astype(np.int32), colors_binned_all[2].astype(np.int32))
write_obj(f'examples/curvature_analysis/{id}_{frame_nr}_mesh.obj', verts, faces.astype(np.int32), np.ones((verts.shape[0], 3), dtype=np.int32) * 200)
save_stl(f'examples/curvature_analysis/{id}_{frame_nr}_mesh.stl', verts, faces.astype(np.int32))
fig.show()
return fig
@@ -372,10 +372,10 @@ if __name__ == '__main__':
i = 0
while True:
frame = np.random.choice(frames)
print(frame)
# frame = '03265_193'
# frame = '03102_49'
# fig, dice = visualize_curvature_labels(folder, results_folder, frame)
# wandb.log({f'{frame}-dice:{dice}': fig}, step=i)
visualize_neighbourhoods(folder, frame)
i += 1
print(frame)
i += 1
cvae_pyramid.py
+39 -39
View File
@@ -1,6 +1,6 @@
import os
import sys
from configs.iccv.config_abl_pyr_32_64_25p import config
from configs.iccv.config_abl_pyr_32_64_128_256_512 import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -730,12 +730,12 @@ def generate_results():
@tf.function
def speed_step(levels_input, conds):
return model.sample([levels_input, conds], training=False)
return model.sample([levels_input, conds])
if __name__ == '__main__':
init_wandb()
# init_wandb()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str, default='lhunor')
args = vars(parser.parse_args())
@@ -846,52 +846,52 @@ if __name__ == '__main__':
for batch in dataset_train:
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
# levels_input, conds = group_inputs_for_levels(batch, config)
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)
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[1:]))
print('mean:')
print(np.mean(times[10:]))
evaluate_ae_pyramid.py
+1 -1
View File
@@ -1,6 +1,6 @@
import os
import sys
from configs.iccv.config_abl_pyr_32_64_128_256_512_curv import config
from configs.iccv.config_abl_pyr_32_64_10p import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
get_error.py
+2 -2
View File
@@ -7,9 +7,9 @@ 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_128_256_512'
checkpoint_name = 'iccv_pyr_32_64_25p'
checkpoint_name_skirt = 'iccv_pyr_32_64_128_256_512_skirt'
iteration = '120000'
iteration = '155000'
iteration_skirt : str = '115000'
# checkpoint_name = 'abl_skin'
models/pyramid_level.py
+3 -3
View File
@@ -101,6 +101,7 @@ class PyramidLevelNew(tf.keras.Model):
def __init__(self, config):
super(PyramidLevelNew, self).__init__()
self.size = config['resolution']
self.latent_dim=config['latent_dim']
self.encoder = VariationalEncoderNew(config['encoder_depths'], config['encoder_dims'], kernel_size=config['kernel_size'], latent_dim=config['latent_dim'])
self.decoder = DecoderNew(config['decoder_depths'], config['decoder_dims'], config['kernel_size'])
@@ -126,13 +127,12 @@ class PyramidLevelNew(tf.keras.Model):
def sample(self, z_cond, uv_mask, noise=None):
if noise is None:
eps = tf.random.normal(shape=(uv_mask.shape[0], self.latent_dim))
eps = tf.random.normal(shape=(uv_mask.shape[0], 8, 4, self.latent_dim))
else:
eps = noise
z = tf.concat([eps, z_cond], axis=-1)
x = self.decoder(z)
x = self.decoder(z, training=False)
if self.avg_garment is not None:
x = x + self.avg_garment[tf.newaxis,:,:,:]
models/pyramid_model_optimized.py
+81
View File
@@ -320,6 +320,87 @@ class PyramidNew(tf.keras.Model):
return z_cond
def sample(self, inputs):
levels_input, conds = inputs
uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
z_cond = self.encode_conds(uv_static, uv_body, uv_curv, training=False)
pred = [None, None, None, None, None]
for i in range(len(self.levels)):
pred[i] = self.levels[i].sample(z_cond, levels_input[i][-2])
pred_1 = pred[0]
pred_2 = pred[1]
pred_3 = pred[2]
pred_4 = pred[3]
pred_5 = pred[4]
pred_1 = self.levels[0].enhance(pred_1, levels_input[0][-2], training=False)
pred_1 = tf.image.resize(pred_1, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR)
pred_1 = pred_1 * levels_input[1][-2][:,:,:,tf.newaxis]
pred_2 = pred_1 + pred_2
pred_2 = self.levels[1].enhance(pred_2, levels_input[1][-2], training=False)
pred_2 = tf.image.resize(pred_2, (self.levels[2].size, self.levels[2].size//2), tf.image.ResizeMethod.BILINEAR)
pred_2 = pred_2 * levels_input[2][-2][:,:,:,tf.newaxis]
pred_3 = pred_2 + pred_3
pred_3 = self.levels[2].enhance(pred_3, levels_input[2][-2], training=False)
pred_3 = tf.image.resize(pred_3, (self.levels[3].size, self.levels[3].size//2), tf.image.ResizeMethod.BILINEAR)
pred_3 = pred_3 * levels_input[3][-2][:,:,:,tf.newaxis]
pred_4 = pred_3 + pred_4
pred_4 = self.levels[3].enhance(pred_4, levels_input[3][-2], training=False)
pred_4 = tf.image.resize(pred_4, (self.levels[4].size, self.levels[4].size//2), tf.image.ResizeMethod.BILINEAR)
pred_4 = pred_4 * levels_input[4][-2][:,:,:,tf.newaxis]
pred_5 = pred_4 + pred_5
pred_5 = pred_5 * levels_input[4][-2][:,:,:,tf.newaxis]
return pred_5
def sample_seq(self, inputs):
levels_input, conds = inputs
uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
z_cond = self.encode_conds(uv_static, uv_body, uv_curv, training=False)
# level 1
pred_1 = self.levels[0].sample(z_cond, levels_input[0][-2])
pred_1 = self.levels[0].enhance(pred_1, levels_input[0][-2], training=False)
pred_1 = tf.image.resize(pred_1, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR)
pred_1 = pred_1 * levels_input[1][-2][:,:,:,tf.newaxis]
# level 2
pred_2 = self.levels[1].sample(z_cond, levels_input[1][-2])
pred_2 = pred_1 + pred_2
pred_2 = self.levels[1].enhance(pred_2, levels_input[1][-2], training=False)
pred_2 = tf.image.resize(pred_2, (self.levels[2].size, self.levels[2].size//2), tf.image.ResizeMethod.BILINEAR)
pred_2 = pred_2 * levels_input[2][-2][:,:,:,tf.newaxis]
# level 3
pred_3 = self.levels[2].sample(z_cond, levels_input[2][-2])
pred_3 = pred_2 + pred_3
pred_3 = self.levels[2].enhance(pred_3, levels_input[2][-2], training=False)
pred_3 = tf.image.resize(pred_3, (self.levels[3].size, self.levels[3].size//2), tf.image.ResizeMethod.BILINEAR)
pred_3 = pred_3 * levels_input[3][-2][:,:,:,tf.newaxis]
# level 4
pred_4 = self.levels[3].sample(z_cond, levels_input[3][-2])
pred_4 = pred_3 + pred_4
pred_4 = self.levels[3].enhance(pred_4, levels_input[3][-2], training=False)
pred_4 = tf.image.resize(pred_4, (self.levels[4].size, self.levels[4].size//2), tf.image.ResizeMethod.BILINEAR)
pred_4 = pred_4 * levels_input[4][-2][:,:,:,tf.newaxis]
# level 5
pred_5 = self.levels[4].sample(z_cond, levels_input[4][-2])
pred_5 = pred_5 * levels_input[4][-2][:,:,:,tf.newaxis]
pred_5 = pred_4 + pred_5
return pred_5
def call(self, inputs, training):
levels_input, conds = inputs
uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
plot_ablation.py
+27 -19
View File
@@ -1,5 +1,5 @@
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import sys
import time
import cv2
@@ -128,7 +128,11 @@ def get_experiment_plots(folder, frames, experiments, skirt_mods, bodies, faces,
frame_out = []
id = frame.split('_')[0]
for exp in experiments:
garment = np.load(os.path.join(folder, exp[0] + skirt_mods[id] + '-' + exp[1], frame + '.npy'))
path = os.path.join(folder, exp[0] + skirt_mods[id] + '-' + exp[1], frame + '.npy')
if not os.path.exists(path):
path = os.path.join('/home/workspace/local-data_128/results/iccv', exp[0] + skirt_mods[id] + '-' + exp[1], frame + '.npy')
assert os.path.exists(path), path
garment = np.load(path)
if exp == 'abl_orient':
garment = deorient(garment, frame, skirt_mods[id] == '_skirt')
garment = postprocess(garment, bodies[frame], id, skirt_mods[id] == '_skirt')
@@ -190,11 +194,11 @@ if __name__ == '__main__':
faces = get_faces()
frames = ['00079_135']
frames = ['00079_135', '00505_23', '00688_25', '01693_128', '00408_34']
# frames = [ '00075_4', '00475_273', '00761_295', '00793_3', '00993_220', '01134_112', '01278_108', '00718_38', '00951_34', '00962_60','01065_38','01191_83','01240_168']
# rand_frames = [frame.split('.')[0] for frame in os.listdir('/home/workspace/data/dataset/test_ready/pose_shape')]
# random.shuffle(rand_frames)
# rand_frames = rand_frames[:25]
rand_frames = [frame.split('.')[0] for frame in os.listdir('/home/workspace/data/dataset/test_ready/pose_shape')]
random.shuffle(rand_frames)
rand_frames = rand_frames[:25]
# frames.extend(rand_frames)
# rand_frames = [frame.split('.')[0] for frame in os.listdir('/home/workspace/data/dataset/test_skirt_ready_2/pose_shape')]
# random.shuffle(rand_frames)
@@ -207,34 +211,38 @@ if __name__ == '__main__':
cameras = {frame: camera_1 for frame in frames}
# abl_all replace abl_norm maybe
experiments = [['iccv_pyr_32', '335000'], ['iccv_pyr_32_skin', '330000'], ['iccv_pyr_32_curv', '155000'], ['iccv_pyr_32_reg', '150000']] # , 'pyra_32_res'
# experiments = [['iccv_pyr_32_64_128_256_512', '120000'], ['iccv_pyr_32_64_128_256_512_curv', '80000'], ['iccv_pyr_32_64_128_256_512_skin', '115000'], ['iccv_pyr_32_64_128_256_512_reg', '75000']] # , 'pyra_32_res'
# experiments = [['iccv_pyr_32_64_128_256_512', '120000'], ['iccv_pyr_32_64_128_256_curv', '80000'], ['iccv_pyr_32_64_128_256_skin', '145000'], ['iccv_pyr_32_64_128_256_reg', '35000']] # , 'pyra_32_res'
# experiments = [['iccv_pyr_32_64_128_256_512', '120000'], ['iccv_pyr_32_64_128_curv', '20000'], ['iccv_pyr_32_64_128_skin_2', '20000'], ['iccv_pyr_32_64_128_reg_2', '80000']] # , 'pyra_32_res'
experiments = [['abl_all_3', '130000']] # , 'pyra_32_res'
skirt_mods = { frame.split('_')[0]: ('_skirt' if os.path.exists('/home/workspace/data/dataset/test_skirt_ready_2/pose_shape/' + frame + '.npy') else '') for frame in frames }
bodies = get_bodies(frames, skirt_mods)
# gts = get_gt_image(frames, bodies, faces, cameras, skirt_mods)
gts = get_gt_meshes(frames, bodies, faces, cameras, skirt_mods)
results_folder_base = '/home/workspace/data/results/iccv/'
output_folder = os.path.join(results_folder_base, 'ablation_plots')
output_folder = os.path.join('examples/ablation/img_3')
output_folder_stl = os.path.join('examples/ablation/stl_3')
os.makedirs(output_folder, exist_ok=True)
os.makedirs(output_folder_stl, exist_ok=True)
# gts_img = get_gt_image(frames, bodies, faces, cameras, skirt_mods)
# images = get_experiment_plots(results_folder_base, frames, experiments, skirt_mods, bodies, faces, cameras)
# rows = [np.concatenate((gt, exp), axis=1) for gt, exp in zip(gts_img, images)]
# for frame, row in zip(frames, rows):
# custom_imshow(row).write_image(os.path.join(output_folder, frame + '_ablation.png'))
gts = get_gt_meshes(frames, bodies, faces, cameras, skirt_mods)
meshes = get_experiment_meshes(results_folder_base, frames, experiments, skirt_mods, bodies, faces, cameras)
for mesh, frame in zip(meshes, frames):
for exp, (garment, body, faces) in zip(experiments, mesh):
save_stl(f'examples/{frame}_{exp[0]}_garment.stl', garment, faces)
save_stl(f'examples/{frame}_{exp[0]}_body.stl', body, F)
save_stl(f'{output_folder_stl}/{frame}_{exp[0]}_garment.stl', garment, faces)
save_stl(f'{output_folder_stl}/{frame}_{exp[0]}_body.stl', body, F)
for mesh, frame in zip(gts, frames):
garment, body, faces = mesh
save_stl(f'examples/{frame}_gt_garment.stl', garment, faces)
save_stl(f'examples/{frame}_gt_body.stl', body, F)
save_stl(f'{output_folder_stl}/{frame}_gt_garment.stl', garment, faces)
save_stl(f'{output_folder_stl}/{frame}_gt_body.stl', body, F)
# rows = [np.concatenate((gt, exp), axis=1) for gt, exp in zip(gts, images)]
# for frame, row in zip(frames, rows):
# custom_imshow(row).write_image(os.path.join(output_folder, frame + '_ablation.png'))
# final = np.concatenate(rows, axis=0)