save
This commit is contained in:
@@ -104,6 +104,7 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_pre_cape_f" > nohup/iccv
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
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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 &
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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 &
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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 &
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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 &
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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 &
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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 &
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@@ -117,6 +118,7 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256" > nohup/iccv
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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 &
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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 &
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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 &
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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 &
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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 &
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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 &
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@@ -143,6 +145,8 @@ nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_reg > nohu
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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 &
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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 &
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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 &
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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 &
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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 &
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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 &
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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 &
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+2
-2
@@ -1,6 +1,6 @@
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import os
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import sys
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from configs.iccv.config_abl_pyr_32_25p import config
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from configs.iccv.config_abl_pyr_32_64_25p import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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# import tensorflow as tf
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@@ -65,7 +65,7 @@ if __name__ == '__main__':
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garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
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output_folder = '/home/workspace/local-data/results/iccv/' + checkpoint_name + '-'
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output_folder = '/home/workspace/local-data_128/results/iccv/' + checkpoint_name + '-'
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# check if there is a folder that starts with this name using glob
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folders = glob.glob(output_folder + '*')
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assert len(folders) == 1, f'There should be only one checkpoint that starts with this name, but found {len(folders)}'
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@@ -1,6 +1,6 @@
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config = dict()
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config['gpu'] = '4'
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config['gpu'] = '0'
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config['name'] = 'iccv_pyr_32_64_10p'
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config['notes'] = ''
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config['tags'] = ['pyr', 'iccv']
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@@ -32,7 +32,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
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config['train_dir'] = '/home/workspace/local-data_127/train'
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config['valid_dir'] = '/home/workspace/local-data_127/valid'
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config['test_dir'] = '/home/workspace/local-data_127/test_ready'
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config['test_dir'] = '/home/workspace/local-data/test_ready'
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config['frames_file'] = 'frames_10p.txt'
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@@ -1,6 +1,6 @@
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config = dict()
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config['gpu'] = '0'
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config['gpu'] = '1'
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config['name'] = 'iccv_pyr_32_64_128_256_512'
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config['notes'] = ''
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config['tags'] = ['pyr', 'iccv']
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@@ -22,7 +22,7 @@ config['use_offsets'] = False
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config['use_norm_loss'] = True
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config['use_curvature'] = True
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config['pretrained_model'] = ''
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config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_512'
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config['pretrained_model_iteration'] = ''
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config['pyramid'] = dict()
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@@ -32,13 +32,15 @@ config['pyramid']['pretrained_pyramid_iteration'] = '90000'
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config['pyramid']['final_upscale_enhancer'] = ''
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config['train_dir'] = '/home/workspace/local-data/train'
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config['valid_dir'] = '/home/workspace/local-data/valid'
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config['valid_dir'] = '/home/workspace/local-data/train'
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config['test_dir'] = '/home/workspace/local-data/test_ready'
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config['frames_file'] = 'frames_00761_0.txt'
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config['frames_file_valid'] = 'frames_00761_0.txt'
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config['batch_size'] = 4
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config['is_skirt'] = False
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config['is_cape'] = False
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config['dataset_in_memory'] = False
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config['dataset_in_memory'] = True
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config['resolution'] = None
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config['smpl_res'] = 14475
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@@ -0,0 +1,187 @@
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config = dict()
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config['gpu'] = '1'
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config['name'] = 'iccv_pyr_32_64_128_256_curv_skirt'
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config['notes'] = ''
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config['tags'] = ['pyr', 'iccv', 'curv', 'skirt']
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# DON'T FORGET TO RESET SSIM LOSS USAGE
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# DON'T FORGET TO CHECK MODEL LOADING
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# DON'T FORGET TO CHECK IF STORE IN MEMORY
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# DON'T FORGET TO CHECK REG LOSS COEFF
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# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
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# DON'T FORGET TO CHECK IF SKIRT
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# DON'T FORGET TO CHECK IF CAPE
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# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
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# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
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config['use_skinning'] = True # and deshaping
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config['remove_global_orientation'] = True
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config['use_extra_losses'] = True # edge, collision, 3d mesh
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config['use_offsets'] = False
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config['use_norm_loss'] = True
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config['use_curvature'] = False
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config['pretrained_model'] = ''
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config['pretrained_model_iteration'] = ''
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config['pyramid'] = dict()
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config['pyramid']['resolutions'] = [32, 64, 128, 256]
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config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_curv_skirt'
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config['pyramid']['pretrained_pyramid_iteration'] = ''
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config['pyramid']['final_upscale_enhancer'] = ''
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config['train_dir'] = '/home/workspace/local-data_128/train_skirt'
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config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
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config['test_dir'] = '/home/workspace/local-data_128/test_skirt_ready_2'
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config['batch_size'] = 4
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config['is_skirt'] = True
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config['is_cape'] = False
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config['dataset_in_memory'] = False
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config['resolution'] = None
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config['smpl_res'] = 14475
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if config['is_cape']:
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config['smpl_res'] = 3638
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config['lr'] = 0.0001
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config['starting_balancing_coeff'] = 0.001
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config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
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config['avg_garment_path'] = ''
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config['bottleneck_scale'] = 2
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config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
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'pose_shape': (72 + 11,),
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'uv_body_posed': (512, 256, 3),
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'mesh_body_posed': (config['smpl_res'], 3,),
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'mesh_posed': (config['smpl_res'], 3,),
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'mesh_unposed': (config['smpl_res'], 3,),
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'uv_unposed': (512, 256, 3),
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'uv_curvature': (512, 256, 7),
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}
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config['signature_static'] = { 'uv_static': (512, 256, 3),
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'mesh_mask': (config['smpl_res'],),
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'uv_mask': (512, 256,),
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}
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config['pyramid']['is_label'] = False
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config['pyramid']['add_noise'] = False
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config['pyramid']['noise_amount'] = 0.0
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config['pyramid']['loss_weights'] = [1,1,1,1,1]
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for res in config['pyramid']['resolutions']:
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if res != 512:
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res_modifier = '_' + str(res)
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config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
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config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
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config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
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config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
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config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
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config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_skirt'
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config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
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config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
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config['pyramid']['freeze_encoders'] = True
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config['pyramid']['levels'] = []
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for res in config['pyramid']['resolutions']:
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if res == 512:
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level = dict()
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level['resolution'] = 512
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 7
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level['encoder_depths'] = [3, 3, 3, 9, 3]
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level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = ''
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config['pyramid']['levels'].append(level)
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elif res == 256:
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level = dict()
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level['resolution'] = 256
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 5
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level['encoder_depths'] = [3, 3, 9, 3]
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level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'checkpoints/up_3_d256_512_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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elif res == 128:
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level = dict()
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level['resolution'] = 128
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 5
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level['encoder_depths'] = [9, 9, 9]
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level['encoder_dims'] = [64, 128, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'checkpoints/up_3_d128_256_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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elif res == 64:
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level = dict()
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level['resolution'] = 64
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 3
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level['encoder_depths'] = [9, 9]
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level['encoder_dims'] = [64, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#checkpoints/up_3_d64_128_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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elif res == 32:
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level = dict()
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level['resolution'] = 32
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 3
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level['encoder_depths'] = [16]
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level['encoder_dims'] = [level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'checkpoints/up_3_d32_64_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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else:
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raise NotImplementedError
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config['cond_ae_static'] = dict()
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config['cond_ae_static']['is_label'] = False
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config['cond_ae_static']['kernel_size'] = 7
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config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
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config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
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config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
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config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
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config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
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config['cond_ae_static']['activation'] = 'tanh'
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config['cond_ae_curv'] = dict()
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config['cond_ae_curv']['is_label'] = True
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config['cond_ae_curv']['kernel_size'] = 7
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config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
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config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
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config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
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config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
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config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
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config['cond_ae_curv']['activation'] = 'tanh'
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config['cond_ae_body'] = dict()
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config['cond_ae_body']['is_label'] = False
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config['cond_ae_body']['kernel_size'] = 7
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config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
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config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
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config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
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config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
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config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
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config['cond_ae_body']['activation'] = 'tanh'
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@@ -94,11 +94,10 @@ for res in config['pyramid']['resolutions']:
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level['resolution'] = 512
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 7
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level['encoder_depths'] = [3, 3, 9, 3]
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level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
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level['decoder_input'] = (16, 8, 80)
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level['decoder_depths'] = [3, 3, 9, 3]
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level['decoder_dims'] = [256, 128, 64, 16]
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level['encoder_depths'] = [3, 3, 3, 9, 3]
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level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = ''
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config['pyramid']['levels'].append(level)
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@@ -107,13 +106,12 @@ for res in config['pyramid']['resolutions']:
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level['resolution'] = 256
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 5
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level['encoder_depths'] = [9, 9, 9, 9]
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level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
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level['decoder_input'] = (8, 4, 128)
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level['decoder_depths'] = [9, 9, 9, 9]
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level['decoder_dims'] = [256, 128, 64, 16]
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level['encoder_depths'] = [3, 3, 9, 3]
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level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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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'] = ''
|
||||
|
||||
@@ -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'] = ''
|
||||
|
||||
@@ -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'] = ''
|
||||
|
||||
@@ -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'
|
||||
|
||||
|
||||
@@ -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:
|
||||
|
||||
@@ -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:
|
||||
|
||||
@@ -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'
|
||||
@@ -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:
|
||||
|
||||
@@ -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)
|
||||
+39
-39
@@ -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:]))
|
||||
@@ -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
|
||||
|
||||
+2
-2
@@ -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'
|
||||
|
||||
@@ -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,:,:,:]
|
||||
|
||||
@@ -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]
|
||||
|
||||
+27
-19
@@ -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)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user