update main script
This commit is contained in:
+19
-4
@@ -1,7 +1,7 @@
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from configs.config_base import config
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config['gpu'] = '4'
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config['name'] = 'v2_4'
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config['gpu'] = '0'
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config['name'] = 'v2_7_4_3'
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config['notes'] = '\
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offsets over average unposed garment. remove global orientation (in posed body input and in pose_shape parameters. | \
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retrained body encoder with deoriented bodies | \
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@@ -12,6 +12,13 @@ training on 100 garments with offsets over the average of these | \
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fixed autoencoders!!!!!!!!! | \
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cvae only loads encoders instead AEs | \
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increase balancing coeff sooner | \
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use 3d mesh loss | \
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add final gc layer | \
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starting from v2_5_2, add tanh to cond_enc last layer | \
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NOT using one-hot skinning | \
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using new average garment with one-hot versions | \
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fix collision for plotting | \
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use 3d mesh loss | \
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'
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config['dataset_in_memory'] = False
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@@ -21,18 +28,23 @@ config['dataset_in_memory'] = False
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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 AVG GARMENT USED
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config['pretrained_model'] = 'checkpoints/v2_5_2'
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config['resolution'] = 512
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config['is_skirt'] = False
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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['l2_coeff'] = 1
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if config['resolution'] != 512:
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config['notes'] += '\
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using custom enhancer for upscaling, using not filled versions\
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'
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config['lr'] = 0.0001
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###############
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config['lr'] = 0.0001# 0.0001
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###############
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config['starting_balancing_coeff'] = 0.0001
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config['signature_dynamic'] = { 'id': (),
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@@ -58,7 +70,7 @@ if config['resolution'] != 512:
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config['signature_static']['uv_mask' + res_modifier] = (config['resolution'], config['resolution']//2)
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if config['resolution'] == 512:
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config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy'
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config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
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else:
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config['avg_garment_path'] = ''
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@@ -67,10 +79,13 @@ config['body_model'] = 'checkpoints/body_1'
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config['curvature_model'] = 'checkpoints/curv_1'
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config['upscale_enhancer'] = ''
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config['freeze_encoders'] = True
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config['use_one_hot'] = False
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config['cvae'] = dict()
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config['cvae']['is_label'] = False
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config['cvae']['latent_dim'] = config['latent_dim']
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config['cvae']['add_noise'] = True
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config['cvae']['noise_amount'] = 0.025
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if config['resolution'] == 512:
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config['cvae']['kernel_size'] = 7
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@@ -0,0 +1,101 @@
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config = dict()
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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 AVG GARMENT USED
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config['batch_size'] = 4
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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['test_dir'] = '/home/workspace/data/dataset/test_ready'
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config['use_filled'] = False
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config['resolution'] = 512
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config['is_skirt'] = False
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config['dataset_in_memory'] = False
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config['is_cape'] = False
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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['latent_dim'] = 128
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config['gpu'] = '1'
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config['name'] = 'ab_1'
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config['notes'] = ''
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config['pretrained_model'] = 'checkpoints/v2_5_2'
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config['resolution'] = 512
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config['is_skirt'] = False
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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['l2_coeff'] = 1
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###############
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config['lr'] = 0.0001# 0.0001
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###############
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config['starting_balancing_coeff'] = 0.0001
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config['signature_dynamic'] = { 'id': (),
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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': (14475, 3,),
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'mesh_posed': (14475, 3,),
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'mesh_unposed': (14475, 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': (14475,),
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'uv_mask': (512, 256,),
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}
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config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
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config['static_model'] = 'checkpoints/static_1'
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config['body_model'] = 'checkpoints/body_1'
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config['curvature_model'] = 'checkpoints/curv_1'
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config['upscale_enhancer'] = ''
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config['freeze_encoders'] = True
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config['use_one_hot'] = False
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config['cvae'] = dict()
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config['cvae']['is_label'] = False
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config['cvae']['latent_dim'] = config['latent_dim']
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config['cvae']['add_noise'] = True
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config['cvae']['noise_amount'] = 0.025
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config['cvae']['kernel_size'] = 7
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config['cvae']['encoder_depths'] = [3, 3, 9, 3]
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config['cvae']['encoder_dims'] = [32, 64, 128, 256]
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config['cvae']['decoder_input'] = (16, 8, 80)
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config['cvae']['decoder_depths'] = [3, 3, 9, 3]
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config['cvae']['decoder_dims'] = [256, 128, 64, 16]
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config['cond_ae'] = dict()
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config['cond_ae']['is_label'] = False
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config['cond_ae']['upscale_output'] = False
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config['cond_ae']['latent_dim'] = config['latent_dim']
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config['cond_ae']['kernel_size'] = 7
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config['cond_ae']['bottleneck_size'] = 64
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config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
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config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
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config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
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config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
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config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
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config['cond_ae_body'] = dict()
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config['cond_ae_body']['latent_dim'] = config['latent_dim']
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config['cond_ae_body']['kernel_size'] = 7
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config['cond_ae_body']['upscale_output'] = True
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config['cond_ae_body']['bottleneck_size'] = 512
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config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
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config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
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config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
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config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
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config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
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@@ -1,5 +1,7 @@
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import os
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from configs.config_cvae import config
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import sys
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from configs.config_cvae_test 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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@@ -18,23 +20,23 @@ if gpus:
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import time
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import argparse
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import wandb
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import tqdm
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import random
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import numpy as np
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import scipy as sp
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from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces
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import plotly.graph_objects as go
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import plotly.express as ex
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from PIL import Image
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from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
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from postprocessing import fix_collision
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if config['is_skirt']:
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from losses import edge_loss_skirt as edge_loss
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else:
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from losses import edge_loss
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from utils import str_to_bool
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from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
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from load_models import load_generator, load_discriminator
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from losses import uv_mask_loss, bend_loss, kl_loss
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from losses import collision_ratio, kl_loss #, concept_loss
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# from losses import collision_penalty as collision_penalty
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from losses import collision_loss as collision_penalty
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from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d
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@@ -44,7 +46,7 @@ from models.cvae_model import CVAE
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def init_wandb():
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run = wandb.init(project='cvae', name=config['name'], notes=config['notes'])
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run = wandb.init(project='cvae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('cvae.py')
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artifact.add_file('models/cvae_model.py')
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@@ -55,15 +57,12 @@ def init_wandb():
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artifact.add_file('models/cond_encoder.py')
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artifact.add_file('dataset.py')
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artifact.add_file('convnext/models/convnext_tf.py')
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config_file_name = [key for key in sys.modules.keys() if key.startswith('configs.')]
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assert len(config_file_name) == 1
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artifact.add_file('/'.join(config_file_name[0].split('.')) + '.py')
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run.log_artifact(artifact)
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uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'])
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def log_normal_pdf(sample, mean, logvar, raxis=1):
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log2pi = tf.math.log(2. * np.pi)
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res = tf.reduce_sum(-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi), axis=raxis)
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return res
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uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False)
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def kl_loss(mean, logvar):
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@@ -94,23 +93,27 @@ def train_step(batch):
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
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# conc_loss = concept_loss(pred, batch['uv_unposed'])
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kl = kl_loss(mean, logvar)
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reconstr_loss = l2_loss + (e_loss + c_loss) * config['reg_loss_coeff']
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reconstr_loss = (l2_loss + l2_mesh_loss) * config['l2_coeff'] + (e_loss + c_loss) * config['reg_loss_coeff']
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loss = reconstr_loss + g_balancing_coeff * kl
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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# c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim,
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'train/reconstr_loss': reconstr_loss,
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return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
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'train/reconstr_loss': reconstr_loss,
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'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
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'train/l2_loss': l2_loss }
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'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss
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# 'train/ms_ssim': ms_ssim, 'train/collision_ratio': c_ratio,
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}
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@tf.function
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@@ -132,9 +135,54 @@ def valid_step(batch):
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
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# conc_loss = concept_loss(pred, batch['uv_unposed'])
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kl = kl_loss(mean, logvar)
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reconstr_loss = l2_loss + (e_loss + c_loss) * config['reg_loss_coeff']
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reconstr_loss = (l2_loss + l2_mesh_loss) * config['l2_coeff'] + (e_loss + c_loss) * config['reg_loss_coeff']
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loss = reconstr_loss + g_balancing_coeff * kl
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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# c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric,
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'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss,
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'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar,
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# 'valid/ms_ssim': ms_ssim, 'valid/collision_ratio': c_ratio,
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}
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@tf.function
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def test_step(batch):
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pred, mean, logvar = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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shape = batch['pose_shape'][:, 72:]
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gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
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mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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mesh_orig_shaped = mesh_orig + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_orig_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_orig_shaped)
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# is the output already masked?
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
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# conc_loss = concept_loss(pred, batch['uv_unposed'])
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kl = kl_loss(mean, logvar)
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reconstr_loss = (l2_loss + l2_mesh_loss) * config['l2_coeff'] + (e_loss + c_loss) * config['reg_loss_coeff']
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loss = reconstr_loss + g_balancing_coeff * kl
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mean = tf.reduce_mean(mean)
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@@ -142,10 +190,14 @@ def valid_step(batch):
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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euclid_posed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
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euclid_posed_fixed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed_fixed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
|
||||
|
||||
return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim,
|
||||
'train/reconstr_loss': reconstr_loss,
|
||||
'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar }
|
||||
return { 'test/loss': loss, 'test/edge_loss': e_loss, 'test/collision_loss': c_loss, 'test/euclid_error': metric, 'test/ms_ssim': ms_ssim,
|
||||
'test/reconstr_loss': reconstr_loss, 'test/collision_ratio': c_ratio, 'test/l2_mesh_loss': l2_mesh_loss,
|
||||
'test/mean': mean, 'test/logvar': logvar, 'test/kl_loss': kl, 'test/var': np.e ** logvar,
|
||||
'test/euclid_posed': euclid_posed, 'test/euclid_posed_fixed': euclid_posed_fixed }
|
||||
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
@@ -175,18 +227,23 @@ def plot_everything(batch: dict, type: str):
|
||||
for i in range(body.shape[0]):
|
||||
mesh_pred[i, mask[i], :] += shape_offsets.numpy()[i, mask[i], :]
|
||||
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy()
|
||||
mesh_posed_fixed = fix_collision(mesh_posed, body, mask).numpy()
|
||||
|
||||
garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])]
|
||||
collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))]
|
||||
collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))]
|
||||
collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
|
||||
|
||||
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(body.shape[0])]
|
||||
collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))]
|
||||
|
||||
figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
|
||||
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
|
||||
mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0), num_bins=model.latent_dim)
|
||||
logvar_hist = wandb.Histogram(sequence=logvar.numpy().mean(axis=0), num_bins=model.latent_dim)
|
||||
|
||||
return { **figs, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
|
||||
return { **figs, **figs_coll, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
|
||||
|
||||
|
||||
def sample(folder):
|
||||
@@ -219,8 +276,6 @@ def sample(folder):
|
||||
mesh_posed_zero = smpl.run_garment(pose, shape, mesh_pred_zero).numpy()
|
||||
|
||||
|
||||
|
||||
|
||||
meshes = []
|
||||
meshes.append(go.Mesh3d(
|
||||
# 8 vertices of a cube
|
||||
@@ -314,7 +369,10 @@ def sample(folder):
|
||||
|
||||
|
||||
def test():
|
||||
load_model(model, 'checkpoints/' + config['name'].split('_')[0])
|
||||
str_split = config['name'].split('_')
|
||||
test_idx = str_split.index('test')
|
||||
checkpoint_name = '_'.join(str_split[:test_idx])
|
||||
load_model(model, 'checkpoints/' + checkpoint_name)
|
||||
|
||||
dataset_test = factory.get_dataset(folder=config['test_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False, do_repeat=False)
|
||||
static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False)
|
||||
@@ -322,23 +380,20 @@ def test():
|
||||
step = 0
|
||||
losses = []
|
||||
|
||||
for batch in dataset_test:
|
||||
for batch in tqdm.tqdm(dataset_test):
|
||||
batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
test_loss = valid_step(batch)
|
||||
test_loss = test_step(batch)
|
||||
losses.append(test_loss)
|
||||
|
||||
to_log = { **{ 'current_step': step }, **test_loss }
|
||||
if step % 100 == 0:
|
||||
if step % 1000 == 0:
|
||||
figs = plot_everything(batch, 'test')
|
||||
to_log = { **to_log, **figs }
|
||||
|
||||
wandb.log(to_log, step = step)
|
||||
|
||||
if step > 30:
|
||||
break
|
||||
|
||||
print(step)
|
||||
# print(step)
|
||||
step += 1
|
||||
|
||||
# averages = dict()
|
||||
@@ -362,20 +417,19 @@ def test():
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
init_wandb()
|
||||
allow_memory_growth()
|
||||
# init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
smpl = SMPL()
|
||||
smpl = SMPL(use_one_hot=config['use_one_hot'])
|
||||
skirt_modifier = '_skirt' if config['is_skirt'] else ''
|
||||
|
||||
factory = DatasetFactory()
|
||||
|
||||
dataset_train = factory.get_dataset(folder=config['train_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'])
|
||||
dataset_train = factory.get_dataset(folder=config['train_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'], use_one_hot=config['use_one_hot'])
|
||||
static_dataset_train = StaticDataset(folder=config['train_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=True)
|
||||
dataset_valid = factory.get_dataset(folder=config['valid_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'])
|
||||
dataset_valid = factory.get_dataset(folder=config['valid_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'], use_one_hot=config['use_one_hot'])
|
||||
static_dataset_valid = StaticDataset(folder=config['valid_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=True)
|
||||
|
||||
model = CVAE(config)
|
||||
@@ -385,43 +439,46 @@ if __name__ == '__main__':
|
||||
g_balancing_coeff = config['starting_balancing_coeff']
|
||||
|
||||
# sample('checkpoints/cvae_13')
|
||||
test()
|
||||
# test()
|
||||
|
||||
# ckpt = tf.train.Checkpoint(model=model)
|
||||
# manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
|
||||
ckpt = tf.train.Checkpoint(model=model)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
|
||||
|
||||
# valid_iterator = iter(dataset_valid)
|
||||
# step = 0
|
||||
# for batch in dataset_train:
|
||||
# batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
if config['pretrained_model'] != '':
|
||||
load_model(model, config['pretrained_model'])
|
||||
|
||||
# train_loss = train_step(batch)
|
||||
valid_iterator = iter(dataset_valid)
|
||||
step = 0
|
||||
for batch in dataset_train:
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
# 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)
|
||||
train_loss = train_step(batch)
|
||||
|
||||
# to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
|
||||
# if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
|
||||
# train_figs = plot_everything(batch, 'train')
|
||||
# valid_figs = plot_everything(data_valid, 'valid')
|
||||
# to_log = { **to_log, **train_figs, **valid_figs }
|
||||
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)
|
||||
|
||||
# if step % 10000 == 0:
|
||||
# manager.save(checkpoint_number=step)
|
||||
# if step == 0:
|
||||
# to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
|
||||
if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
valid_figs = plot_everything(data_valid, 'valid')
|
||||
to_log = { **to_log, **train_figs, **valid_figs }
|
||||
|
||||
# if train_loss['train/euclid_error'] < 0.01:
|
||||
# if balancing_activated_step == 0:
|
||||
# balancing_activated_step = step
|
||||
# g_balancing_coeff = g_balancing_coeff + 1 / 15000
|
||||
if step % 10000 == 0:
|
||||
manager.save(checkpoint_number=step)
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
# to_log = { **to_log, **{ 'train/balancing_coeff': g_balancing_coeff } }
|
||||
if train_loss['train/euclid_error'] < 0.019:
|
||||
if balancing_activated_step == 0:
|
||||
balancing_activated_step = step
|
||||
g_balancing_coeff = g_balancing_coeff + 1 / 10000
|
||||
|
||||
to_log = { **to_log, **{ 'train/balancing_coeff': g_balancing_coeff } }
|
||||
|
||||
# wandb.log(to_log, step = step)
|
||||
wandb.log(to_log, step = step)
|
||||
|
||||
# print(step)
|
||||
print(step)
|
||||
|
||||
# step += 1
|
||||
step += 1
|
||||
|
||||
Reference in New Issue
Block a user