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luni
2022-09-02 14:15:54 +00:00
parent ee8f1cb68c
commit 977e49900d
16 changed files with 2241 additions and 272 deletions
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cvae.py
+114 -209
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@@ -1,5 +1,5 @@
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
@@ -24,6 +24,7 @@ from custom_utils.custom_utils import UV2Mesh, maskFacesBatch, _maskFaces
from tensorboardX import SummaryWriter
import plotly.graph_objects as go
import plotly.express as ex
from PIL import Image
from utils import str_to_bool
from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
@@ -31,26 +32,22 @@ from load_models import load_generator, load_discriminator
from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss, bend_loss
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block
from stylegan2_ae.generator import Generator
from custom_utils.graph_utils import Reconstructor_V4_Decoder as GNNDecoder, sparse_to_tensor
from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor
from custom_utils.custom_utils import batch_to_dict, load_model
from dataset import DatasetFactory, StaticDataset
from custom_utils.custom_utils import batch_to_dict
from custom_utils.smpl import SMPL
from auto_encoder_body import AutoEncoder as BodyAE
from auto_encoder_curv import AutoEncoder as CurvatureAE
from auto_encoder_static import AutoEncoder as StaticAE
from models.cvae import CVAE
name='cvae_10_2'
name='cvae_15'
def init_wandb():
run = wandb.init(project='vae', name=name,
notes='learn offsets. smaller flattened latent vectors. with 25-85k pretraining. smaller batchsize. fixed balancing of reconst and kl loss for baseline.\
new implementation for kl loss. no lr schedule. 25 jumpsuits, average reconst_loss before calculation, linearly increasing balancing, up to 1000 from 250.\
offsets over average unposed garment. gamma balancing. remove global orientation (in posed body input and in pose_shape parameters. larger batchsize\
add edge and collision (fixed) loss. rebalanced l1 loss. increasing edge and coll coeff. retrained body encoder with deoriented bodies \
don\'t freeze encoders, lower lr')
notes='learn offsets. smaller flattened latent vectors. with 25-85k pretraining. smaller batchsize. fixed balancing of reconst and kl loss for baseline. \
new implementation for kl loss. no lr schedule. 25 jumpsuits, average reconst_loss before calculation, linearly increasing balancing, up to 1 from 0.1. \
offsets over average unposed garment. remove global orientation (in posed body input and in pose_shape parameters. \
retrained body encoder with deoriented bodies \
use mean l2 loss. small batch. l2 * 1000, balancing to 1 from 0.01\
fixed gender issue and retrained body encoder\
remove shape from unposed garment')
artifact = wandb.Artifact('code', type='code')
artifact.add_file('cvae.py')
artifact.add_file('dataset.py')
@@ -61,14 +58,14 @@ don\'t freeze encoders, lower lr')
uv2mesh = UV2Mesh(res=256)
def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color):
mesh_pred_unposed[:,1] += 1
collision[:,1] += 2
mesh_pred_unposed[:,2] += 1
collision[:,2] += 2
fig = go.Figure(data=[
go.Mesh3d(
# 8 vertices of a cube
x=mesh_gt[:,0],
y=mesh_gt[:,1],
z=mesh_gt[:,2],
y=mesh_gt[:,2],
z=mesh_gt[:,1],
# i, j and k give the vertices of triangles
i = faces[:,0],
j = faces[:,1],
@@ -77,8 +74,8 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
go.Mesh3d(
# 8 vertices of a cube
x=mesh_pred_unposed[:,0],
y=mesh_pred_unposed[:,1],
z=mesh_pred_unposed[:,2],
y=mesh_pred_unposed[:,2],
z=mesh_pred_unposed[:,1],
# i, j and k give the vertices of triangles
i = faces[:,0],
j = faces[:,1],
@@ -87,8 +84,8 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
go.Mesh3d(
# 8 vertices of a cube
x=collision[:,0],
y=collision[:,1],
z=collision[:,2],
y=collision[:,2],
z=collision[:,1],
# i, j and k give the vertices of triangles
i = collision_faces[:,0],
j = collision_faces[:,1],
@@ -108,151 +105,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
return fig
class Bottleneck(tf.keras.Model):
def __init__(self, dim):
super(Bottleneck, self).__init__()
self.bottleneck = tf.keras.Sequential(
[Block(dim=dim, drop_path=0,
layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
)
def call(self, inputs, training=None):
x = inputs
x = self.bottleneck(x, training=training)
return x
class Encoder(tf.keras.Model):
def __init__(self, latent_dim):
super(Encoder, self).__init__()
self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, 256], include_top=False)
self.flatten = tf.keras.layers.Flatten()
self.mean = tf.keras.layers.Dense(latent_dim)
self.log_var = tf.keras.layers.Dense(latent_dim)
def call(self, inputs, training=None):
x = inputs
x = self.encoder(x)
x = self.flatten(x)
mean = self.mean(x)
log_var = self.log_var(x)
return mean, log_var
class Decoder(tf.keras.Model):
def __init__(self):
super(Decoder, self).__init__()
self.dense = tf.keras.layers.Dense(16 * 8 * 80)
self.reshape = tf.keras.layers.Reshape((16, 8, 80))
self.decoder = ConvNeXtReverse(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([256, 128, 64, 16])))
def call(self, inputs, training=None):
x = inputs
x = self.dense(x)
x = self.reshape(x)
x = self.decoder(x)
return x
class CVAE(tf.keras.Model):
def __init__(self, latent_dim=128):
super(CVAE, self).__init__()
self.latent_dim = latent_dim
model : StaticAE = StaticAE()
load_model(model, 'checkpoints/ae_static_3')
self.static_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
# self.static_enc.trainable = False
model : BodyAE = BodyAE()
load_model(model, 'checpoints/ae_body_5')
self.body_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
# self.body_enc.trainable = False
model : CurvatureAE = CurvatureAE()
load_model(model, 'checpoints/ae_curvature_6')
self.curv_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
# self.curv_enc.trainable = False
self.encoder = Encoder(latent_dim)
self.decoder = Decoder()
# @tf.function
def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, eps=None):
if eps is None:
eps = tf.random.normal(shape=(4, self.latent_dim))
uv_template = tf.tile(tf.transpose(uv_template, [1,2,0])[tf.newaxis,:,:,:], [4,1,1,1])
uv_body_posed = tf.tile(tf.transpose(uv_body_posed, [1,2,0])[tf.newaxis,:,:,:], [4,1,1,1])
uv_curv = tf.tile(uv_curv[tf.newaxis,:,:,:], [4,1,1,1])
uv_mask = tf.tile(uv_mask[tf.newaxis,:,:], [4,1,1])
# uv_template, uv_body_posed = tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
z_static = self.static_enc(uv_template)
z_body = self.body_enc(uv_body_posed)
z_curv = self.curv_enc(uv_curv)
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
z = tf.concat([eps, z_cond], axis=-1)
x = self.decoder(z)
x = x + avg_jumpsuit[tf.newaxis,:,:,:]
x = x * uv_mask[:,:,:,tf.newaxis]
return x
def reparameterize(self, mean, logvar):
eps = tf.random.normal(shape=mean.shape)
return eps * tf.exp(logvar * .5) + mean
def decode(self, z, apply_sigmoid=False):
logits = self.decoder(z)
if apply_sigmoid:
probs = tf.sigmoid(logits)
return probs
return logits
def call(self, inputs, training=None):
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_shaped_body = inputs
uv_unposed, uv_template, uv_body_posed = tf.transpose(uv_unposed, [0,2,3,1]), tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
z_static = self.static_enc(uv_template)
z_body = self.body_enc(uv_body_posed)
z_curv = self.curv_enc(uv_curv)
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
mean, logvar = self.encoder(x)
z = self.reparameterize(mean, logvar)
z = tf.concat([z, z_static, z_body, z_curv], axis=-1)
x = self.decoder(z)
# x = tf.transpose(x, [0,3,1,2])
# x = x * uv_mask[:,tf.newaxis,:,:]
# x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
# x = x + tf.transpose(uv_shaped_body, [0,2,3,1])
x = x + avg_jumpsuit[tf.newaxis,:,:,:]
x = x * uv_mask[:,:,:,tf.newaxis]
return x, mean, logvar, z
def log_normal_pdf(sample, mean, logvar, raxis=1):
@@ -268,7 +121,7 @@ def kl_loss(mean, logvar):
return kl_loss
@tf.function
# @tf.function
def train_step(batch):
with tf.GradientTape() as tape:
@@ -280,10 +133,15 @@ def train_step(batch):
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred)
mesh_orig = batch['mesh_unposed']
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])])
mesh_pred_posed = mesh_pred_posed + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
l1_loss = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed'], [0,2,3,1]) - pred)), axis=-1) * batch['uv_mask'], axis=[1, 2])
l2_loss = 1e3 * tf.reduce_mean(tf.reduce_sum(((tf.transpose(batch['uv_unposed'], [0,2,3,1]) - pred) ** 2), axis=-1) * batch['uv_mask'], axis=[1, 2])
# ms_ssim = tf.image.ssim_multiscale(tf.transpose(pred, [0,2,3,1]) + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2)
ms_ssim = tf.image.ssim_multiscale(pred + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=3)
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale((tf.transpose(pred, [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], (tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], max_val=2))
@@ -292,17 +150,18 @@ def train_step(batch):
fft_loss_pc, fft_loss_pc_low, fft_loss_pc_high = 0,0,0 #fft_loss_per_channel(pred, batch['uv_unposed'])
tv_loss = tf.image.total_variation(pred)
reconstr_loss = l1_loss / 4
reconstr_loss = tf.reduce_mean(reconstr_loss) + reg_coeff *(e_loss + c_loss)
reconstr_loss = l2_loss
reconstr_loss = tf.reduce_mean(reconstr_loss)
# logpx_z = -reconstr_loss
# logpz = log_normal_pdf(z[:,:model.latent_dim], 0., 0.)
# logqz_x = log_normal_pdf(z[:,:model.latent_dim], mean, logvar)
# loss = -tf.reduce_mean(logpx_z + balancing_coeff * (logpz - logqz_x))
kl = kl_loss(mean, logvar)
loss = reconstr_loss / (2 * (gamma ** 2)) + balancing_coeff * kl
loss = reconstr_loss + balancing_coeff * kl
# loss = reconstr_loss + balancing_coeff * kl
ms_ssim = tf.reduce_mean(ms_ssim)
l1_loss = tf.reduce_mean(l1_loss)
l2_loss = tf.reduce_mean(l2_loss)
fft_loss = tf.reduce_mean(fft_loss)
tv_loss = tf.reduce_mean(tv_loss)
mean = tf.reduce_mean(mean)
@@ -320,7 +179,7 @@ def train_step(batch):
'train/fft_loss': fft_loss, 'train/euclid_error_reconst': metric_2, 'train/rfft_loss': rfft_loss, 'train/fft_oc_low': fft_loss_pc_low, 'train/fft_pc_high': fft_loss_pc_high,
'train/fft_loss_per_channel': fft_loss_pc, 'train/euclid_error_posed': euclid_posed, 'train/reconstr_loss': reconstr_loss, 'train/tv_loss': tv_loss,
'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/reconst_loss_norm': reconstr_loss / (2 * (gamma ** 2)), 'train/var': np.e ** logvar,
'train/reg_coeff': reg_coeff }
'train/reg_coeff': reg_coeff, 'train/l2_loss': l2_loss }
@@ -384,7 +243,12 @@ def plot_everything(batch: dict, type: str):
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])]
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])])
garment_posed = [mesh_posed[i, mask[i], :] + shape_offsets.numpy()[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))]
@@ -397,25 +261,25 @@ def plot_everything(batch: dict, type: str):
return { **figs, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
def load_model(model, checkpoint):
ckpt = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(ckpt, checkpoint, max_to_keep=1)
ckpt.restore(manager.latest_checkpoint)
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, 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])
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,:], [4,1]), tf.tile(shape[tf.newaxis,:], [4,1])
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
@@ -424,72 +288,110 @@ def sample(folder):
mask = batch['mesh_mask'].numpy().astype(bool)[0]
_, faces = _maskFaces(np.ones((14475, 3)), F, mask)
batch = 0
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[mask,0],
y=mesh_orig[mask,1],
z=mesh_orig[mask,2],
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(4):
for i in range(nr_of_samples):
x = mesh_posed[i][mask, :].copy()
x[:, 1] += 1 * (i + 1)
# x[:, 2] += 1 * (i + 1)
x[:, 2] += 1
meshes.append(go.Mesh3d(
# 8 vertices of a cube
x=x[:,0],
y=x[:,1],
z=x[:,2],
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[:, 1] += 1 * i
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[:,1],
z=x[:,2],
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
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=3, z=1))
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=[-1,5],),
# 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()
if __name__ == '__main__':
# init_wandb()
# allow_memory_growth()
allow_memory_growth()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())
batch_size = 12
batch_size = 4
smpl = SMPL()
@@ -511,27 +413,30 @@ if __name__ == '__main__':
'uv_shaped_body': (3, 512, 256,),
}
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train', batch_size=batch_size, signature=signature_dynamic)
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train', signature=signature_static)
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=batch_size, signature=signature_dynamic)
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static)
dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=batch_size, signature=signature_dynamic)
static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
model = CVAE()
model = CVAE(avg_garment_path='custom_data/jumpsuit_20_avg_uv.npy',
latent_dim=128, static_model='checkpoints/ae_static_3',
body_model='checpoints/ae_body_6',
curvature_model='checpoints/ae_curvature_6')
# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0001, decay_steps=30000, decay_rate=0.1, staircase=False)
optimizer = tf.optimizers.Adam(learning_rate=0.0001)
balancing_coeff = 250
balancing_coeff = 0.0001
reg_coeff = 1
folder = 'models/ae_logs/'
valid_iterator = iter(dataset_valid)
gamma = 1
balancing_activated_step = 0
avg_jumpsuit = np.load('custom_data/jumpsuit_20_avg_uv.npy')
# avg_jumpsuit = np.load('custom_data/jumpsuit_20_avg_uv.npy')
sample('checkpoints/cvae_10')
# sample('checkpoints/cvae_13')
ckpt = tf.train.Checkpoint(model=model)
@@ -561,9 +466,9 @@ if __name__ == '__main__':
if train_loss['train/euclid_error'] < 0.01:
if balancing_activated_step == 0:
balancing_activated_step = step
balancing_coeff = balancing_coeff + 1000 / 30000
if train_loss['train/euclid_error'] < 0.015:
reg_coeff = reg_coeff + 100 / 15000
balancing_coeff = balancing_coeff + 1 / 15000
# if train_loss['train/euclid_error'] < 0.015:
# reg_coeff = reg_coeff + 100 / 15000
# if step > 15000 and balancing_coeff <= 1e-3:
# balancing_coeff = 1e-2
# if step > 20000 and balancing_coeff <= 1e-3: