348 lines
15 KiB
Python
348 lines
15 KiB
Python
import os
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
|
|
import tensorflow as tf
|
|
|
|
gpus = tf.config.experimental.list_physical_devices('GPU')
|
|
if gpus:
|
|
try:
|
|
# Currently, memory growth needs to be the same across GPUs
|
|
for gpu in gpus:
|
|
tf.config.experimental.set_memory_growth(gpu, True)
|
|
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
|
|
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
|
|
except RuntimeError as e:
|
|
# Memory growth must be set before GPUs have been initialized
|
|
print(e)
|
|
|
|
import time
|
|
import argparse
|
|
import wandb
|
|
import random
|
|
import numpy as np
|
|
import scipy as sp
|
|
from custom_utils.custom_utils import UV2Mesh, maskFacesBatch, _maskFaces
|
|
import plotly.graph_objects as go
|
|
import plotly.express as ex
|
|
from PIL import Image
|
|
from plotly.subplots import make_subplots
|
|
|
|
from custom_utils.smpl import SMPL
|
|
from custom_utils.custom_utils import load_model
|
|
|
|
from configs.config_abl_skin_var_3 import config as config_main
|
|
from configs.config_cvae_curv import config as config_curv
|
|
from models.cvae_model import CVAE
|
|
|
|
uv2mesh = UV2Mesh(res=256)
|
|
|
|
@tf.function
|
|
def train_step(batch):
|
|
|
|
pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask'], batch['uv_shaped_body']])
|
|
|
|
|
|
|
|
def plot_everything(batch: dict, type: str):
|
|
pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask'], batch['uv_shaped_body']])
|
|
|
|
mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2]))
|
|
mesh_orig = batch['mesh_unposed']
|
|
|
|
orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1])
|
|
uv_static_img = tf.transpose(batch['uv_static'], [0,2,3,1])
|
|
uv_body_posed_img = tf.transpose(batch['uv_body_posed'], [0,2,3,1])
|
|
pred_img = pred
|
|
img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2)
|
|
imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
|
|
|
|
F = np.load("custom_utils/smpl_faces.npy")
|
|
mask = batch['mesh_mask'].numpy().astype(bool)
|
|
garment_unposed, faces = maskFacesBatch(mesh_pred.numpy(), F, mask)
|
|
gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
|
|
|
|
body = batch['mesh_body_posed'].numpy()
|
|
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).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))]
|
|
|
|
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) }
|
|
|
|
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 } }
|
|
|
|
|
|
|
|
def get_bodies(folder, frames, random=True):
|
|
bodies = []
|
|
poses_shapes = []
|
|
uv_posed_body_no_orient = np.load(os.path.join(folder, 'uv_posed_body_no_orient', frames[0] + '.npz')); uv_posed_body_no_orient = uv_posed_body_no_orient['uv_posed_body_no_orient']; bodies.append(uv_posed_body_no_orient)
|
|
pose_shape = np.load(os.path.join(folder, 'pose_shape', frames[0] + '.npy')); poses_shapes.append(pose_shape)
|
|
if not random:
|
|
for _ in range(len(frames)-1):
|
|
bodies.append(uv_posed_body_no_orient)
|
|
poses_shapes.append(pose_shape)
|
|
else:
|
|
for i in range(1, len(frames)):
|
|
uv_posed_body_no_orient = np.load(os.path.join(folder, 'uv_posed_body_no_orient', frames[i] + '.npz')); uv_posed_body_no_orient = uv_posed_body_no_orient['uv_posed_body_no_orient']; bodies.append(uv_posed_body_no_orient)
|
|
pose_shape = np.load(os.path.join(folder, 'pose_shape', frames[i] + '.npy')); poses_shapes.append(pose_shape)
|
|
bodies = np.stack(bodies)
|
|
poses_shapes = np.stack(poses_shapes).astype(np.float32)
|
|
return bodies, poses_shapes
|
|
|
|
|
|
def get_curvs(folder, body, static, mask, frames, noise, random=True, model: CVAECurv=None, use_original=True):
|
|
assert (random and model is not None) or (not random)
|
|
uv_curvature_orig = np.load(os.path.join(folder, 'uv_curvature', frames[0] + '.npz')); uv_curvature_orig = uv_curvature_orig['curvature_uv']
|
|
|
|
noise = get_noise(not random, body.shape[0], latent_dim=128)
|
|
curv = model.sample(static, body, mask, noise)
|
|
|
|
uv_curvature = tf.math.argmax(curv, axis=-1).numpy()
|
|
if use_original:
|
|
uv_curvature[0,:,:] = uv_curvature_orig
|
|
out = tf.one_hot(uv_curvature, 7).numpy()
|
|
|
|
return out
|
|
|
|
def get_statics(folder, frames, random=True):
|
|
statics = []
|
|
uv_masks = []
|
|
mesh_masks = []
|
|
uv_static = np.load(os.path.join(folder, 'uv_static', frames[0].split('_')[0] + '.npz')); uv_static = uv_static['uv_static']; statics.append(uv_static)
|
|
uv_mask = np.load(os.path.join(folder, 'uv_mask', frames[0].split('_')[0] + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32); uv_masks.append(uv_mask)
|
|
mesh_mask = np.load(os.path.join(folder, 'mesh_mask', frames[0].split('_')[0] + '.npy')); mesh_masks.append(mesh_mask)
|
|
|
|
if not random:
|
|
for _ in range(len(frames)-1):
|
|
statics.append(uv_static)
|
|
uv_masks.append(uv_mask)
|
|
mesh_masks.append(mesh_mask)
|
|
else:
|
|
for i in range(1, len(frames)):
|
|
uv_static = np.load(os.path.join(folder, 'uv_static', frames[i].split('_')[0] + '.npz')); uv_static = uv_static['uv_static']; statics.append(uv_static)
|
|
uv_mask = np.load(os.path.join(folder, 'uv_mask', frames[i].split('_')[0] + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32); uv_masks.append(uv_mask)
|
|
mesh_mask = np.load(os.path.join(folder, 'mesh_mask', frames[i].split('_')[0] + '.npy')); mesh_masks.append(mesh_mask)
|
|
|
|
statics = np.stack(statics)
|
|
uv_masks = np.stack(uv_masks)
|
|
mesh_masks = np.stack(mesh_masks)
|
|
return statics, uv_masks, mesh_masks
|
|
|
|
|
|
def get_unposed(folder, frames, random=True):
|
|
uv_unposeds = []
|
|
mesh_unposeds = []
|
|
uv_unposed = np.load(os.path.join(folder, 'uv_posed', frames[0] + '.npz')); uv_unposed = uv_unposed['uv_gt']; uv_unposeds.append(uv_unposed)
|
|
mesh_unposed = np.load(os.path.join(folder, 'mesh_unposed_no_shape', frames[0] + '.npz')); mesh_unposed = mesh_unposed['mesh_unposed_no_shape']; mesh_unposeds.append(mesh_unposed)
|
|
if not random:
|
|
for _ in range(len(frames)-1):
|
|
uv_unposeds.append(uv_unposed)
|
|
mesh_unposeds.append(mesh_unposed)
|
|
else:
|
|
for i in range(1, len(frames)):
|
|
uv_static = np.load(os.path.join(folder, 'uv_static', frames[i].split('_')[0] + '.npz')); uv_static = uv_static['uv_static']; uv_unposeds.append(uv_static)
|
|
mesh_unposed = np.load(os.path.join(folder, 'mesh_unposed_no_shape', frames[i] + '.npz')); mesh_unposed = mesh_unposed['mesh_unposed_no_shape']; mesh_unposeds.append(mesh_unposed)
|
|
uv_unposeds = np.stack(uv_unposeds)
|
|
mesh_unposeds = np.stack(mesh_unposeds)
|
|
return uv_unposeds, mesh_unposeds
|
|
|
|
|
|
def make_square_2(figure):
|
|
figure.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
|
|
scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1))
|
|
helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
|
|
yaxis = dict(nticks=4, range=[-1,1],),
|
|
zaxis = dict(nticks=4, range=[-1,1],),)
|
|
figure.update_layout(scene = helper,
|
|
scene2 = helper)
|
|
return figure
|
|
|
|
def make_square_4(figure):
|
|
figure.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
|
|
scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
|
|
scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
|
|
scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1))
|
|
helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
|
|
yaxis = dict(nticks=4, range=[-1,1],),
|
|
zaxis = dict(nticks=4, range=[-1,1],),)
|
|
figure.update_layout(scene = helper,
|
|
scene2 = helper,
|
|
scene3 = helper,
|
|
scene4 = helper,)
|
|
return figure
|
|
|
|
|
|
def plot_inputs(unposed, static, body, curv, mesh_mask):
|
|
curv = np.tile(curv.argmax(-1)[:,:,np.newaxis], [1,1,3])/6
|
|
img = np.concatenate([unposed, static, body, curv], axis=-2)
|
|
ex.imshow((img + 1) / 2).show()
|
|
|
|
mesh_static = uv2mesh(static[np.newaxis,:,:,:]).numpy()[0]
|
|
mesh_body = uv2mesh(body[np.newaxis,:,:,:]).numpy()[0]
|
|
fig = make_subplots(rows=1, cols=2, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(2)] ])
|
|
F = np.load("custom_utils/smpl_faces.npy")
|
|
_, faces = _maskFaces(np.ones((14475, 3)), F, mesh_mask)
|
|
|
|
x = mesh_static[mesh_mask, :]
|
|
y = mesh_body
|
|
fig.add_trace(go.Mesh3d(
|
|
x=x[:,0],
|
|
y=x[:,1],
|
|
z=x[:,2],
|
|
i=faces[:,0],
|
|
j=faces[:,1],
|
|
k=faces[:,2]
|
|
),
|
|
row=1, col=1)
|
|
fig.add_trace(go.Mesh3d(
|
|
x=y[:,0],
|
|
y=y[:,2],
|
|
z=y[:,1],
|
|
i=F[:,0],
|
|
j=F[:,1],
|
|
k=F[:,2]
|
|
),
|
|
row=1, col=2)
|
|
make_square_2(fig)
|
|
fig.show()
|
|
|
|
|
|
def plot_variable_condition(condition, mesh_mask, is_curv=False, is_body=False):
|
|
if is_curv:
|
|
img = np.concatenate(condition.argmax(-1), axis=-1)
|
|
ex.imshow(img / img.max()).show()
|
|
else:
|
|
mesh = uv2mesh(condition).numpy()
|
|
fig = make_subplots(rows=1, cols=condition.shape[0], start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(condition.shape[0])] ])
|
|
F = np.load("custom_utils/smpl_faces.npy")
|
|
if is_body:
|
|
faces = [F for _ in range(condition.shape[0])]
|
|
else:
|
|
_, faces = maskFacesBatch(np.ones((condition.shape[0], 14475, 3)), F, mesh_mask)
|
|
for i, m in enumerate(mesh):
|
|
if not is_body:
|
|
m = m[mesh_mask[i], :]
|
|
if is_body:
|
|
x = m.copy()
|
|
m[:,1] = x[:,2]
|
|
m[:,2] = x[:,1]
|
|
fig.add_trace(go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=m[:,0],
|
|
y=m[:,1],
|
|
z=m[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[i][:,0],
|
|
j = faces[i][:,1],
|
|
k = faces[i][:,2]
|
|
),
|
|
row=1, col=i+1)
|
|
make_square_4(fig)
|
|
fig.show()
|
|
|
|
|
|
def plot_results(unposed, posed, body, mesh_mask):
|
|
fig = make_subplots(rows=1, cols=unposed.shape[0], start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(unposed.shape[0])] ])
|
|
F = np.load("custom_utils/smpl_faces.npy")
|
|
_, faces = maskFacesBatch(np.ones((unposed.shape[0], 14475, 3)), F, mesh_mask)
|
|
body_mesh = uv2mesh(body).numpy()
|
|
|
|
collision = [np.concatenate((body_mesh[i], posed[i][mesh_mask[i], :]), axis=0) for i in range(unposed.shape[0])]
|
|
collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(unposed.shape[0])]
|
|
collision_color = [np.concatenate((np.ones_like(body_mesh[i], dtype=np.int32)*100, np.ones_like(unposed[i], dtype=np.int32)*230), axis=0) for i in range(unposed.shape[0])]
|
|
|
|
for i, c in enumerate(collision):
|
|
fig.add_trace(go.Mesh3d(
|
|
x=c[:,0],
|
|
y=c[:,2],
|
|
z=c[:,1],
|
|
i=collision_faces[i][:,0],
|
|
j=collision_faces[i][:,1],
|
|
k=collision_faces[i][:,2],
|
|
vertexcolor=collision_color[i]
|
|
),
|
|
row=1, col=i+1)
|
|
make_square_4(fig)
|
|
fig.show()
|
|
|
|
|
|
|
|
def get_noise(fix_noise, nr, latent_dim=128):
|
|
eps = tf.random.normal(shape=(1 if fix_noise else nr, latent_dim))
|
|
if fix_noise:
|
|
eps = tf.tile(eps, [nr,1])
|
|
return eps
|
|
|
|
|
|
|
|
def sample(folder, frames, variable_condition, fix_noise, nr=4):
|
|
random_static, random_body, random_curv, random_unposed = variable_condition == 'static', variable_condition == 'body', variable_condition == 'curv', variable_condition == 'unposed'
|
|
|
|
noise = get_noise(fix_noise, nr)
|
|
noise_curv = get_noise(fix_noise, nr)
|
|
body, pose_shape = get_bodies(folder, frames, random_body)
|
|
static, uv_mask, mesh_mask = get_statics(folder, frames, random_static)
|
|
unposed, mesh_unposed = get_unposed(folder, frames, random_unposed)
|
|
curv = get_curvs(folder, body, static, uv_mask, frames, noise_curv, random_curv, curv_model, use_original=True)
|
|
|
|
pred = model.sample(static, body, curv, uv_mask, noise=noise)
|
|
mesh_pred = uv2mesh(pred)
|
|
|
|
gender = tf.cast(tf.greater_equal(pose_shape[:, -1] , 0.5), tf.int32)
|
|
shape_offsets = tf.stack([tf.gather(tf.tensordot(pose_shape[i,72:-1][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(len(frames))])
|
|
mesh_pred_shaped = (mesh_pred + shape_offsets).numpy()
|
|
pose_shape[:, :3] = 0
|
|
mesh_posed = smpl.run_garment(pose_shape[:, :72], pose_shape[:, 72:], mesh_pred_shaped).numpy()
|
|
|
|
plot_inputs(unposed[0], static[0], body[0], curv[0], mesh_mask[0])
|
|
|
|
variable_cond_data = None
|
|
if random_static:
|
|
variable_cond_data = static
|
|
elif random_body:
|
|
variable_cond_data = body
|
|
elif random_curv:
|
|
variable_cond_data = curv
|
|
elif random_unposed:
|
|
variable_cond_data = unposed
|
|
else:
|
|
assert False
|
|
|
|
plot_variable_condition(variable_cond_data, mesh_mask, is_curv=random_curv, is_body=random_body)
|
|
plot_variable_condition(curv, mesh_mask, is_curv=True, is_body=False)
|
|
|
|
plot_results(mesh_pred_shaped, mesh_posed, body, mesh_mask)
|
|
|
|
|
|
print()
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='')
|
|
parser.add_argument('--name', type=str)
|
|
args = vars(parser.parse_args())
|
|
|
|
folder = '/home/workspace/local-data/train'
|
|
|
|
variable_condition = 'static'
|
|
batch_size = 4
|
|
|
|
smpl = SMPL()
|
|
|
|
model = CVAE(config_main)
|
|
curv_model = CVAE(config_curv)
|
|
|
|
load_model(model, 'checkpoints/' + config_main['name'])
|
|
load_model(curv_model, 'checkpoints/' + config_curv['name'])
|
|
|
|
frames = ['01763_259', '007292_282', '00539_296', '00761_15']
|
|
|
|
sample(folder, frames, variable_condition, fix_noise=True)
|
|
|
|
print()
|