885 lines
42 KiB
Python
885 lines
42 KiB
Python
import os
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import sys
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from configs.iccv.config_abl_pyr_32_res_skirt 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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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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# Currently, memory growth needs to be the same across GPUs
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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logical_gpus = tf.config.experimental.list_logical_devices('GPU')
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print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
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except RuntimeError as e:
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# Memory growth must be set before GPUs have been initialized
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print(e)
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import time
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import tqdm
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import argparse
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import wandb
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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, get_summary_str, plot_3d_collision, group_inputs_for_levels
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import plotly.graph_objects as go
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import plotly.express as ex
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from plotly.subplots import make_subplots
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from PIL import Image
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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 losses import uv_mask_loss, bend_loss, kl_loss, collision_ratio, normal_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
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.smpl import SMPL
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from models.pyramid_model_optimized import PyramidNew as Pyramid
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from postprocessing import fix_collision
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from custom_utils.custom_utils import Accumulator
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def init_wandb():
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run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('cvae_pyramid.py')
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artifact.add_file('models/cvae_model.py')
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artifact.add_file('models/pyramid_model_optimized.py')
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artifact.add_file('models/pyramid_level.py')
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artifact.add_file('models/upscaler.py')
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artifact.add_file('losses.py')
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artifact.add_file('models/base.py')
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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_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
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for config_file_name in config_file_names:
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file_name = '/'.join(config_file_name.split('.')) + '.py'
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if os.path.exists(file_name):
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artifact.add_file(file_name)
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run.log_artifact(artifact)
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uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, collision_faces, collision_color):
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fig = make_subplots(rows=1, cols=1+len(mesh_pred_unposed_levels)+1+1, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(1+len(mesh_pred_unposed_levels)+1+1)] ])
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fig.add_trace(go.Mesh3d(
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x=mesh_gt[:,0],
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y=mesh_gt[:,2],
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z=mesh_gt[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2],
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text="Ground Truth"
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),
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row=1, col=1)
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for i, mesh in enumerate(mesh_pred_unposed_levels):
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fig.add_trace(go.Mesh3d(
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x=mesh[:,0],
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y=mesh[:,2],
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z=mesh[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2],
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text='Unposed level_' + str(i)
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),
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row=1, col=i+2)
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fig.add_trace(go.Mesh3d(
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x=mesh_posed[:,0],
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y=mesh_posed[:,1],
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z=mesh_posed[:,2],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2],
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text='Posed GT'
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),
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row=1, col=1+len(mesh_pred_unposed_levels)+1)
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fig.add_trace(go.Mesh3d(
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x=collision[:,0],
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y=collision[:,2],
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z=collision[:,1],
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i = collision_faces[:,0],
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j = collision_faces[:,1],
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k = collision_faces[:,2],
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vertexcolor=collision_color,
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text='Posed output on body'
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),
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row=1, col=1+len(mesh_pred_unposed_levels)+1+1)
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if len(mesh_pred_unposed_levels) == 1:
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
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scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
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scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
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scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
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)
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helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,1],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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fig.update_layout(scene = helper,
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scene2 = helper,
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scene3 = helper,
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scene4 = helper,
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)
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if len(mesh_pred_unposed_levels) == 2:
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
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scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
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scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
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scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
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scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
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)
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helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,1],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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fig.update_layout(scene = helper,
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scene2 = helper,
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scene3 = helper,
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scene4 = helper,
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scene5 = helper,
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)
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if len(mesh_pred_unposed_levels) == 3:
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
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scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
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scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
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scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
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scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
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scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,)
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)
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helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,1],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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fig.update_layout(scene = helper,
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scene2 = helper,
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scene3 = helper,
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scene4 = helper,
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scene5 = helper,
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scene6 = helper,
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)
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if len(mesh_pred_unposed_levels) == 4:
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
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scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
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scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
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scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
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scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
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scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,),
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scene7_aspectmode='manual', scene7_aspectratio=dict(x=1, y=1, z=1,),
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)
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helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,1],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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fig.update_layout(scene = helper,
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scene2 = helper,
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scene3 = helper,
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scene4 = helper,
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scene5 = helper,
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scene6 = helper,
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scene7 = helper,
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)
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if len(mesh_pred_unposed_levels) == 5:
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
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scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
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scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
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scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
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scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
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scene6_aspectmode='manual', scene6_aspectratio=dict(x=1, y=1, z=1,),
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scene7_aspectmode='manual', scene7_aspectratio=dict(x=1, y=1, z=1,),
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scene8_aspectmode='manual', scene8_aspectratio=dict(x=1, y=1, z=1,),
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)
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helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,1],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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fig.update_layout(scene = helper,
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scene2 = helper,
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scene3 = helper,
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scene4 = helper,
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scene5 = helper,
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scene6 = helper,
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scene7 = helper,
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scene8 = helper,
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)
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return fig
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@tf.function
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def train_step(batch):
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levels_input, conds = group_inputs_for_levels(batch, config)
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with tf.GradientTape() as tape:
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out_levels = model([levels_input, conds], training=True)
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if config['use_skinning']:
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mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
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else:
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mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
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output = dict()
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reconstr_loss_total = 0
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kl_total = 0
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#for i, res in enumerate(config['pyramid']['resolutions']):
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i = -1
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res = config['pyramid']['resolutions'][-1]
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res_modifier = '_' + str(res) if res != 512 else ''
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pred, mean, logvar = out_levels[i]
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mesh_pred = uv2mesh(pred)
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if config['use_skinning']:
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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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else:
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mesh_pred_posed = mesh_pred
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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, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
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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(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
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kl = kl_loss(mean, logvar)
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if config['use_extra_losses']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
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if not config['is_cape']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
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else:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
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kl_total += kl
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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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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output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
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'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
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'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
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'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
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loss = reconstr_loss_total + balancing_coeff * kl_total
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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/reconstr_loss_total': reconstr_loss_total, 'train/kl_total': kl_total }, **output }
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@tf.function
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def valid_step(batch):
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levels_input, conds = group_inputs_for_levels(batch, config)
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out_levels = model([levels_input, conds], training=False)
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if config['use_skinning']:
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mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
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else:
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mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
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output = dict()
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reconstr_loss_total = 0
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kl_total = 0
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#for i, res in enumerate(config['pyramid']['resolutions']):
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i = -1
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res = config['pyramid']['resolutions'][-1]
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res_modifier = '_' + str(res) if res != 512 else ''
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pred, mean, logvar = out_levels[i]
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mesh_pred = uv2mesh(pred)
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if config['use_skinning']:
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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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else:
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mesh_pred_posed = mesh_pred
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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, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
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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(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
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kl = kl_loss(mean, logvar)
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if config['use_extra_losses']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
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if not config['is_cape']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
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else:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
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kl_total += kl
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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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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output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
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'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
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'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
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'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
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loss = reconstr_loss_total + balancing_coeff * kl_total
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return { **{ 'valid/loss': loss, 'valid/reconstr_loss_total': reconstr_loss_total, 'valid/kl_total': kl_total }, **output }
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@tf.function
|
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def test_step(batch):
|
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levels_input, conds = group_inputs_for_levels(batch, config)
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out_levels = model([levels_input, conds], training=False)
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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_orig = batch['mesh_unposed']
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output = dict()
|
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reconstr_loss_total = 0
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kl_total = 0
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|
|
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#for i, res in enumerate(config['pyramid']['resolutions']):
|
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i = -1
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res = config['pyramid']['resolutions'][-1]
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res_modifier = '_' + str(res) if res != 512 else ''
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pred, mean, logvar = out_levels[i]
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mesh_pred = uv2mesh(pred)
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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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# is the output already masked?
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|
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
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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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|
# 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(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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kl = kl_loss(mean, logvar)
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
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if not config['is_cape']:
|
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
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# reconstr_loss_total += config['pyramid']['loss_weights'][i] * (l2_loss + (e_loss + c_loss) * config['reg_loss_coeff'])
|
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kl_total += kl
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|
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mean = tf.reduce_mean(mean)
|
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logvar = tf.reduce_mean(logvar)
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|
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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'])
|
|
euclid_error_per_garment = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1)), axis=1) / tf.reduce_sum(batch['mesh_mask'], axis=1)
|
|
|
|
output = { **output, **{ 'test/edge_loss' + res_modifier: e_loss, 'test/collision_loss' + res_modifier: c_loss,'test/euclid_error' + res_modifier: metric,
|
|
'test/mean' + res_modifier: mean, 'test/logvar' + res_modifier: logvar, 'test/kl_loss' + res_modifier: kl,
|
|
'test/var' + res_modifier: np.e ** logvar, 'test/l2_loss' + res_modifier: l2_loss,
|
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'test/euclid_error': metric, 'test/collision_ratio' + res_modifier: c_ratio } }
|
|
|
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loss = reconstr_loss_total + balancing_coeff * kl_total
|
|
|
|
return { **{ 'test/loss': loss, 'test/reconstr_loss_total': reconstr_loss_total, 'test/kl_total': kl_total }, **output }, euclid_error_per_garment
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|
|
|
|
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def plot_everything(batch: dict, type: str):
|
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if config['use_skinning']:
|
|
mesh_orig, uv_orig = batch['mesh_unposed'].numpy(), batch['uv_unposed'].numpy()
|
|
else:
|
|
mesh_orig, uv_orig = batch['mesh_posed'].numpy(), batch['uv_posed'].numpy()
|
|
|
|
levels_input = group_inputs_for_levels(batch, config)
|
|
output_levels = model(levels_input, training=False)
|
|
|
|
mesh_pred_levels = [uv2mesh(output_levels[i][0]).numpy() for i in range(len(output_levels))]
|
|
mesh_orig_posed = batch['mesh_posed'].numpy()
|
|
body = batch['mesh_body_posed'].numpy()
|
|
|
|
orig_img = (uv_orig + 1) / 2
|
|
uv_static_img = (batch['uv_static'].numpy() + 1) / 2
|
|
uv_body_posed_img = (batch['uv_body_posed'].numpy() + 1) / 2
|
|
uv_curvature = (np.tile(batch['uv_curvature'].numpy().argmax(-1)[:,:,:,np.newaxis], [1,1,1,3]) / 6)
|
|
pred_img = np.concatenate([tf.image.resize(output_levels[i][0], (512, 256), tf.image.ResizeMethod.LANCZOS5) for i in range(len(output_levels))], axis=2)
|
|
pred_img = (pred_img - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) / (pred_img.max(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis] - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis])
|
|
img = tf.concat((uv_static_img, uv_body_posed_img, uv_curvature, orig_img, pred_img), axis=2)
|
|
imgs = [wandb.Image(img[i]) for i in range(4)]
|
|
|
|
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
|
mask = batch['mesh_mask'].numpy().astype(bool)
|
|
_, faces = maskFacesBatch(np.ones_like(mesh_orig), F, mask)
|
|
|
|
if config['use_skinning']:
|
|
shape = batch['pose_shape'][:, 72:]
|
|
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
|
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]).numpy()
|
|
|
|
|
|
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])]
|
|
|
|
mesh_unposed_shaped_levels = [[] for _ in range(mask.shape[0])]
|
|
for i in range(len(output_levels)):
|
|
mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(mask.shape[0])]
|
|
for k in range(mask.shape[0]):
|
|
mesh_unposed_shaped_levels[k].append(mesh_unposed_shaped[k])
|
|
|
|
# full res version
|
|
mesh_unposed_shaped_full = np.zeros((mask.shape[0], config['smpl_res'], 3), dtype=np.float32)
|
|
for i in range(mask.shape[0]):
|
|
mesh_unposed_shaped_full[i, mask[i], :] = mesh_unposed_shaped[i]
|
|
|
|
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_unposed_shaped_full).numpy()
|
|
else:
|
|
mesh_posed = mesh_pred_levels[-1]
|
|
gt = [mesh_orig[i][mask[i], :] for i in range(mesh_posed.shape[0])]
|
|
mesh_unposed_shaped_levels = [[mesh_pred_levels[i][j, mask[j], :] for i in range(len(output_levels))] for j in range(mask.shape[0])]
|
|
|
|
|
|
gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(mask.shape[0])]
|
|
mesh_posed_fixed = fix_collision(mesh_posed, body, mask).numpy()
|
|
|
|
mesh_posed = [mesh_posed[j, mask[j], :] for j in range(mask.shape[0])]
|
|
|
|
if config['is_cape']:
|
|
collision = mesh_posed
|
|
collision_faces = faces
|
|
collision_color = [np.ones_like(mesh_posed[i], dtype=np.int32)*230 for i in range(len(mesh_posed))]
|
|
else:
|
|
collision = [np.concatenate((body[i], mesh_posed[i]), axis=0) for i in range(len(mesh_posed))]
|
|
collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(mesh_posed))]
|
|
collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(mesh_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(mesh_posed))]
|
|
|
|
figs = { type + "/garment_" + str(i): plot_3d(gt[i], mesh_unposed_shaped_levels[i], faces[i], gt_posed[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
|
|
|
if config['is_cape']:
|
|
figs_coll = dict()
|
|
else:
|
|
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(mesh_posed))]
|
|
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
|
collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(gt_posed))]
|
|
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
|
|
|
return { **figs, **figs_coll, **{ type + '/images': imgs, } }
|
|
|
|
|
|
def sample(folder):
|
|
# run = wandb.init(project='vae_plots', name=folder.split('/')[-1])
|
|
load_model(model,folder)
|
|
count = 0
|
|
for batch in dataset_train:
|
|
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
|
uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
|
|
nr_of_samples=2
|
|
|
|
pred = model.sample(batch['uv_static'][0], batch['uv_body_posed'][0], uv_curvature[0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
|
|
pose, shape = batch['pose_shape'][0, :72], batch['pose_shape'][0, 72:]
|
|
pose, shape = tf.tile(pose[tf.newaxis,:], [nr_of_samples,1]), tf.tile(shape[tf.newaxis,:], [nr_of_samples,1])
|
|
|
|
mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2]))
|
|
mesh_posed = smpl.run_garment(pose, shape, mesh_pred).numpy()
|
|
mesh_orig = batch['mesh_posed'][0].numpy()
|
|
mesh_orig_reposed = smpl.run_garment(pose[:1], shape[:1], batch['mesh_unposed'][:1]).numpy()[0]
|
|
|
|
body = batch['mesh_body_posed'][0].numpy()
|
|
body_color = np.ones_like(body, dtype=np.int32)*100
|
|
|
|
F = np.load("custom_utils/smpl_faces.npy")
|
|
mask = batch['mesh_mask'].numpy().astype(bool)[0]
|
|
_, faces = _maskFaces(np.ones((14475, 3)), F, mask)
|
|
|
|
|
|
pred_zero = model.sample(batch['uv_static'][0], batch['uv_body_posed'][0], uv_curvature[0], batch['uv_mask'][0], nr_of_samples=nr_of_samples, zero_latent=True)
|
|
mesh_pred_zero = uv2mesh(tf.transpose(pred_zero, [0,3,1,2]))
|
|
mesh_posed_zero = smpl.run_garment(pose, shape, mesh_pred_zero).numpy()
|
|
|
|
|
|
|
|
|
|
meshes = []
|
|
meshes.append(go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=mesh_orig_reposed[mask,0],
|
|
y=mesh_orig_reposed[mask,2],
|
|
z=mesh_orig_reposed[mask,1],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
))
|
|
|
|
for i in range(nr_of_samples):
|
|
x = mesh_posed[i][mask, :].copy()
|
|
# x[:, 2] += 1 * (i + 1)
|
|
x[:, 2] += 1
|
|
meshes.append(go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=x[:,0],
|
|
y=x[:,2],
|
|
z=x[:,1],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
))
|
|
|
|
|
|
for i in range(nr_of_samples):
|
|
x = mesh_posed_zero[i][mask, :].copy()
|
|
# x[:, 2] += 1 * (i + 1)
|
|
x[:, 2] += 2
|
|
meshes.append(go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=x[:,0],
|
|
y=x[:,2],
|
|
z=x[:,1],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
))
|
|
|
|
# for i in range(5):
|
|
# x = body.copy()
|
|
# x[:, 2] += 1 * i
|
|
# meshes.append(go.Mesh3d(
|
|
# # 8 vertices of a cube
|
|
# x=x[:,0],
|
|
# y=x[:,2],
|
|
# z=x[:,1],
|
|
# # i, j and k give the vertices of triangles
|
|
# i = F[:,0],
|
|
# j = F[:,1],
|
|
# k = F[:,2],
|
|
# vertexcolor=body_color
|
|
# ))
|
|
|
|
|
|
fig = go.Figure(data=meshes)
|
|
|
|
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=2, z=1))
|
|
# fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=nr_of_samples-1, z=1))
|
|
fig.update_layout(
|
|
scene = dict(
|
|
xaxis = dict(nticks=4, range=[-1,1],),
|
|
# yaxis = dict(nticks=4, range=[-1,nr_of_samples+1],),
|
|
yaxis = dict(nticks=4, range=[-1,3],),
|
|
zaxis = dict(nticks=4, range=[-1,1],),)
|
|
)
|
|
|
|
# wandb.log({'plot_' + str(count): fig})
|
|
fig.show()
|
|
|
|
pred = pred.numpy()
|
|
frames = [Image.fromarray(((pred[i] + 1) * 128).astype(np.int8), mode='RGB') for i in range(pred.shape[0])]
|
|
frames[0].save("temp/garment_1.gif", format="GIF", append_images=frames[1:],
|
|
save_all=True, duration=500, loop=0)
|
|
|
|
|
|
if count > 10:
|
|
break
|
|
|
|
print(count)
|
|
count += 1
|
|
|
|
print()
|
|
|
|
|
|
def test():
|
|
garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
|
|
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=dataset_resolutions, in_memory=False, do_repeat=False)
|
|
static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=dataset_resolutions, in_memory=False)
|
|
|
|
step = 0
|
|
losses = []
|
|
|
|
if config['is_skirt']:
|
|
per_garment_errors = { 'Tshirt': [], 'Dress': [], 'Skirt': [], 'Top': [] }
|
|
else:
|
|
per_garment_errors = { 'Tshirt': [], 'Jumpsuit': [], 'Trousers': [], 'Top': [] }
|
|
|
|
for batch in tqdm.tqdm(dataset_test):
|
|
batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
|
|
|
test_loss, euclid_error_per_garment = test_step(batch)
|
|
losses.append(test_loss)
|
|
|
|
for i, id in enumerate(batch['id']):
|
|
id_str = id.numpy().decode("utf-8")
|
|
per_garment_errors[garment_type_table[id_str]].append(euclid_error_per_garment[i].numpy())
|
|
|
|
to_log = { **{ 'current_step': step }, **test_loss }
|
|
if step % 1000 == 0:
|
|
figs = plot_everything(batch, 'test')
|
|
to_log = { **to_log, **figs }
|
|
|
|
wandb.log(to_log, step = step)
|
|
|
|
# print(step)
|
|
step += 1
|
|
|
|
# averages = dict()
|
|
path_to_plotly_html = "temp/plotly_figure.html"
|
|
|
|
histograms = dict()
|
|
for key in test_loss.keys():
|
|
accumulated = [losses[i][key].numpy() for i in range(len(losses))]
|
|
fig = ex.histogram(accumulated, title='Average: ' + str(np.asarray(accumulated).mean()))
|
|
fig.write_html(path_to_plotly_html, auto_play = False)
|
|
|
|
histograms['all/' + key.split('/')[-1] + '_hist'] = wandb.Html(path_to_plotly_html)
|
|
# averages['all/' + key.split('/')[-1] + '_avg'] = np.asarray(accumulated).mean()
|
|
|
|
for key in per_garment_errors.keys():
|
|
fig = ex.histogram(per_garment_errors[key], title='Average: ' + str(np.asarray(per_garment_errors[key]).mean()))
|
|
fig.write_html(path_to_plotly_html, auto_play = False)
|
|
histograms['all/' + key + 'euclid_error_hist'] = wandb.Html(path_to_plotly_html)
|
|
|
|
|
|
wandb.log({ **histograms }, step = step)
|
|
wandb.finish()
|
|
pass
|
|
|
|
|
|
def generate_results():
|
|
load_model(model, 'checkpoints/' + config['name'])
|
|
|
|
dataset_test = factory.get_dataset(folder=config['test_dir'],
|
|
batch_size=config['batch_size'],
|
|
# batch_size=1,
|
|
signature=config['signature_dynamic'],
|
|
resolutions=dataset_resolutions,
|
|
in_memory=config['dataset_in_memory'],
|
|
is_cape=config['is_cape'],
|
|
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
|
|
do_repeat=False,
|
|
shuffle=False)
|
|
static_dataset_test = StaticDataset(folder=config['test_dir'],
|
|
signature=config['signature_static'],
|
|
resolutions=dataset_resolutions,
|
|
in_memory=True,
|
|
is_cape=config['is_cape'])
|
|
folder = os.path.join('/home/workspace/data/results/cape', config['name'])
|
|
os.makedirs(folder, exist_ok=True)
|
|
|
|
for batch in tqdm.tqdm(dataset_test):
|
|
batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
|
|
|
levels_input, conds = group_inputs_for_levels(batch, config)
|
|
out_levels = model([levels_input, conds], training=False)
|
|
|
|
shape = batch['pose_shape'][:, 72:]
|
|
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
|
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
|
|
|
i = -1
|
|
pred = out_levels[i][0]
|
|
|
|
mesh_pred = uv2mesh(pred)
|
|
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
|
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
|
|
|
for i in range(batch['id'].shape[0]):
|
|
mask = batch['mesh_mask'][i].numpy().astype(bool)
|
|
id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
|
|
unposed = mesh_pred[i].numpy()[mask, :]
|
|
posed = mesh_pred_posed[i].numpy()[mask, :]
|
|
np.save(os.path.join(folder, id + '_' + frame_nr + '_posed.npy'), posed)
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|
np.save(os.path.join(folder, id + '_' + frame_nr + '_unposed.npy'), unposed)
|
|
|
|
pass
|
|
|
|
@tf.function
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|
def speed_step(levels_input, conds):
|
|
|
|
return model.sample([levels_input, conds], training=False)
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
init_wandb()
|
|
parser = argparse.ArgumentParser(description='')
|
|
parser.add_argument('--name', type=str, default='lhunor')
|
|
args = vars(parser.parse_args())
|
|
|
|
# config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
|
|
# assert len(config_file_names) == 1
|
|
# config_file_name = config_file_names[0]
|
|
# # assert config_file_name.split('.')[1].endswith(args.name)
|
|
# assert config_file_name.split('.')[1].endswith(config['name'])
|
|
|
|
smpl = SMPL(orig_smpl=config['is_cape'])
|
|
skirt_modifier = '_skirt' if config['is_skirt'] else ''
|
|
dataset_resolutions = config['pyramid']['resolutions'].copy()
|
|
if 512 not in dataset_resolutions:
|
|
dataset_resolutions.append(512)
|
|
|
|
factory = DatasetFactory()
|
|
dataset_train = factory.get_dataset(folder=config['train_dir'],
|
|
batch_size=config['batch_size'],
|
|
# batch_size=1,
|
|
signature=config['signature_dynamic'],
|
|
resolutions=dataset_resolutions,
|
|
in_memory=config['dataset_in_memory'],
|
|
is_cape=config['is_cape'],
|
|
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None)
|
|
static_dataset_train = StaticDataset(folder=config['train_dir'],
|
|
signature=config['signature_static'],
|
|
resolutions=dataset_resolutions,
|
|
in_memory=True,
|
|
is_cape=config['is_cape'])
|
|
dataset_valid = factory.get_dataset(folder=config['valid_dir'],
|
|
batch_size=config['batch_size'],
|
|
signature=config['signature_dynamic'],
|
|
resolutions=dataset_resolutions,
|
|
in_memory=config['dataset_in_memory'],
|
|
is_cape=config['is_cape'],
|
|
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None)
|
|
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
|
|
signature=config['signature_static'],
|
|
resolutions=dataset_resolutions,
|
|
in_memory=True,
|
|
is_cape=config['is_cape'])
|
|
|
|
model: Pyramid = Pyramid(config)
|
|
|
|
optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
|
|
balancing_coeff = config['starting_balancing_coeff']
|
|
reg_coeff = 1
|
|
valid_iterator = iter(dataset_valid)
|
|
gamma = 1
|
|
balancing_activated_step = 0
|
|
|
|
# sample('checkpoints/cvae_13')
|
|
# test()
|
|
|
|
# generate_results()
|
|
|
|
ckpt = tf.train.Checkpoint(model=model)
|
|
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + config['name'], max_to_keep=20)
|
|
train_accumulator = Accumulator()
|
|
valid_accumulator = Accumulator()
|
|
|
|
# load_model(model, 'checkpoints/pyr_abl_32_skirt', iteration='180000')
|
|
|
|
if config['pretrained_model'] != '':
|
|
# assert len(config['pyramid']['resolutions']) == 1
|
|
load_model(model, config['pretrained_model'], iteration=config['pretrained_model_iteration'])
|
|
|
|
if len(config['pyramid']['resolutions']) == 1:
|
|
model.static_enc.trainable = False
|
|
model.body_enc.trainable = False
|
|
model.curv_enc.trainable = False
|
|
else:
|
|
load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
|
|
model.static_enc.trainable = False
|
|
model.body_enc.trainable = False
|
|
model.curv_enc.trainable = False
|
|
|
|
|
|
# load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
|
|
# model.levels[0].upscale_enhancer.trainable = False
|
|
|
|
|
|
for i in range(len(model.levels)):
|
|
load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
|
|
model.levels[i].upscale_enhancer.trainable = False
|
|
# if config['pyramid']['resolutions'][-1] != 512:
|
|
# load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
|
# model.upscale_enhancer.trainable = False
|
|
|
|
for i in range(len(model.levels)-1):
|
|
model.levels[i].trainable = False
|
|
|
|
# load_model(model.cond_encoder, config['pyramid']['pretrained_pyramid'])
|
|
# load_model(model.levels[0], config['pyramid']['pretrained_pyramid'])
|
|
# load_model(model.levels[-1], 'checkpoints/cvae_19')
|
|
|
|
|
|
step = 0
|
|
times = []
|
|
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)
|
|
|
|
# start = time.perf_counter()
|
|
# out = speed_step(levels_input, conds)
|
|
# t = time.perf_counter() - start
|
|
# print(t)
|
|
# times.append(t)
|
|
|
|
# if step > 200:
|
|
# break
|
|
|
|
|
|
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 % 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 } }
|
|
|
|
wandb.log(to_log, step=step)
|
|
train_accumulator.reset()
|
|
valid_accumulator.reset()
|
|
|
|
if step % 5000 == 0:
|
|
manager.save(checkpoint_number=step)
|
|
|
|
print(step)
|
|
|
|
step += 1
|
|
# print('mean:')
|
|
# print(np.mean(times[1:])) |