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lot of things

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luni
2022-08-06 12:27:24 +00:00
parent cd498e79f3
commit ee8f1cb68c
28 changed files with 4595 additions and 175 deletions
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auto_encoder.py
+280 -86
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@@ -1,7 +1,6 @@
import os
import time
import argparse
from cv2 import polarToCart
import wandb
import random
import numpy as np
@@ -9,6 +8,7 @@ import tensorflow as tf
from custom_utils.custom_utils import UV2Mesh, maskFacesBatch
from tensorboardX import SummaryWriter
import plotly.graph_objects as go
import plotly.express as ex
from utils import str_to_bool
from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
@@ -16,16 +16,25 @@ 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
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from convnext.models.convnext_tf import ConvNeXt
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
from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor
from dataset import DatasetFactory, StaticDataset
from custom_utils.custom_utils import batch_to_dict
from custom_utils.smpl import SMPL
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='ae_unposed_1', notes='convnext + stylegan generator')
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder.py')
artifact.add_file('stylegan2_ae/layers/mapping_block.py')
run.log_artifact(artifact)
name='ae_body_1'
# os.environ["CUDA_VISIBLE_DEVICES"] = "6"
# run = wandb.init(project='stylegan', name=name,
# notes='convnext + convnext, add dense with tanh at the end of reverse convnext, larger features. add lr scheduler. fft3d instead of rfft3d. \
# fft2d per channel and separate for top and bottom. fft_loss, all data. added convnext block as bottleneck, same as before, added extra metric. \
# . lowered learning rate because it goes to nan. shallow third stage. slower lr decay. with validation. higher lr rate.')
# artifact = wandb.Artifact('code', type='code')
# artifact.add_file('auto_encoder.py')
# artifact.add_file('stylegan2_ae/layers/mapping_block.py')
# artifact.add_file('convnext/models/convnext_tf.py')
# run.log_artifact(artifact)
uv2mesh = UV2Mesh(res=256)
@@ -67,8 +76,46 @@ def filter_resolutions_featuremaps(resolutions, featuremaps, res):
return filtered_resolutions, filtered_featuremaps
def plot_3d(mesh_gt, mesh_pred, faces):
mesh_pred[:,1] += 2
# def plot_3d(mesh_gt, mesh_pred, faces):
# mesh_pred[:,1] += 2
# fig = go.Figure(data=[
# go.Mesh3d(
# # 8 vertices of a cube
# x=mesh_gt[:,0],
# y=mesh_gt[:,1],
# z=mesh_gt[:,2],
# # i, j and k give the vertices of triangles
# i = faces[:,0],
# j = faces[:,1],
# k = faces[:,2]
# ),
# go.Mesh3d(
# # 8 vertices of a cube
# x=mesh_pred[:,0],
# y=mesh_pred[:,1],
# z=mesh_pred[:,2],
# # i, j and k give the vertices of triangles
# i = faces[:,0],
# j = faces[:,1],
# k = faces[:,2],
# )
# ])
# fig.update_layout(scene_aspectmode='manual',
# scene_aspectratio=dict(x=1, y=2, z=1))
# fig.update_layout(
# scene = dict(
# xaxis = dict(nticks=4, range=[-1,1],),
# yaxis = dict(nticks=4, range=[-1,3],),
# zaxis = dict(nticks=4, range=[-1,1],),)
# )
# return fig
def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color):
mesh_pred_unposed[:,1] += 1
collision[:,1] += 2
fig = go.Figure(data=[
go.Mesh3d(
# 8 vertices of a cube
@@ -82,13 +129,24 @@ def plot_3d(mesh_gt, mesh_pred, faces):
),
go.Mesh3d(
# 8 vertices of a cube
x=mesh_pred[:,0],
y=mesh_pred[:,1],
z=mesh_pred[:,2],
x=mesh_pred_unposed[:,0],
y=mesh_pred_unposed[:,1],
z=mesh_pred_unposed[:,2],
# i, j and k give the vertices of triangles
i = faces[:,0],
j = faces[:,1],
k = faces[:,2],
),
go.Mesh3d(
# 8 vertices of a cube
x=collision[:,0],
y=collision[:,1],
z=collision[:,2],
# i, j and k give the vertices of triangles
i = collision_faces[:,0],
j = collision_faces[:,1],
k = collision_faces[:,2],
vertexcolor=collision_color
)
])
@@ -103,31 +161,35 @@ def plot_3d(mesh_gt, mesh_pred, faces):
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 AutoEncoder(tf.keras.Model):
def __init__(self):
super(AutoEncoder, self).__init__()
resolutions = [4, 8, 16, 32, 64, 128, 256, 512, 1024]
g_featuremaps = [256, 256, 256, 256, 256, 128, 64, 32, 16]
train_resolutions, train_g_featuremaps = filter_resolutions_featuremaps(resolutions, g_featuremaps, 512)
g_params = {
'z_dim': 256,
'w_dim': 256,
'labels_dim': -1,
'n_mapping': 8,
'resolutions': train_resolutions,
'featuremaps': train_g_featuremaps,
}
# test_latent = tf.ones((1, g_params['z_dim']), dtype=tf.float32)
self.decoder = Generator(g_params)
# _ = generator(test_latent)
bottleneck_size = 32
self.encoder = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 256, g_params['z_dim']], include_top=False)
self.pooling = tf.keras.layers.GlobalAveragePooling2D()
self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, bottleneck_size], include_top=False)
self.bottleneck = Bottleneck(bottleneck_size)
self.decoder = ConvNeXtReverse(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])))
def call(self, inputs, training=None):
@@ -135,105 +197,237 @@ class AutoEncoder(tf.keras.Model):
x = tf.transpose(x, [0,2,3,1])
x = self.encoder(x, training=training)
x = self.pooling(x)
# x = self.pooling(x)
# x = self.reshape(x)
# x = self.flatten(x)
x = self.bottleneck(x, training=training)
x = self.decoder(x, training=training)
x = tf.transpose(x, [0,3,1,2])
# x = x * uv_mask[:,:, tf.newaxis]
x = x * uv_mask[:,tf.newaxis,:,:]
return x
def draw_cicle(shape,diamiter):
'''
Input:
shape : tuple (height, width)
diameter : scalar
Output:
np.array of shape that says True within a circle with diamiter = around center
'''
assert len(shape) == 2
TF = np.zeros(shape,dtype=bool)
center = np.array(TF.shape)/2.0
for iy in range(shape[0]):
for ix in range(shape[1]):
TF[iy,ix] = (iy- center[0])**2 + (ix - center[1])**2 < diamiter **2
return (TF)
TFcircleIN = draw_cicle(shape=(256,256), diamiter=15)
TFcircleIN = np.ones((512,256,3), dtype=bool) * np.concatenate((TFcircleIN, TFcircleIN), axis=0)[:,:,np.newaxis]
TFcircleOUT = ~TFcircleIN
def fft_loss_per_channel(pred, gt):
pred = tf.complex(pred, tf.zeros_like(pred))
gt = tf.complex(gt, tf.zeros_like(gt))
fft_pred_top = tf.stack([tf.signal.fftshift(tf.signal.fft2d(pred[:,i,:256,:])) for i in range(3)], axis=-1)
fft_gt_top = tf.stack([tf.signal.fftshift(tf.signal.fft2d(gt[:,i,:256,:])) for i in range(3)], axis=-1)
fft_pred_bottom = tf.stack([tf.signal.fftshift(tf.signal.fft2d(pred[:,i,256:,:])) for i in range(3)], axis=-1)
fft_gt_bottom = tf.stack([tf.signal.fftshift(tf.signal.fft2d(gt[:,i,256:,:])) for i in range(3)], axis=-1)
fft_pred = tf.concat((fft_pred_top, fft_pred_bottom), axis=1)
fft_gt = tf.concat((fft_gt_top, fft_gt_bottom), axis=1)
fft_pred_low = tf.where(TFcircleIN, fft_pred, tf.zeros_like(fft_pred))
fft_gt_low = tf.where(TFcircleIN, fft_gt, tf.zeros_like(fft_gt))
fft_pred_high = tf.where(TFcircleOUT, fft_pred, tf.zeros_like(fft_pred))
fft_gt_high = tf.where(TFcircleOUT, fft_gt, tf.zeros_like(fft_gt))
low_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred_low - fft_gt_low), axis=[1,2,3]))
high_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred_high - fft_gt_high), axis=[1,2,3]))
total = tf.reduce_mean(tf.reduce_sum(tf.math.abs(fft_pred - fft_gt), axis=[1,2,3]))
return total, low_loss, high_loss
# ex.imshow(tf.math.real(gt[0,0,:256,:])).show()
# img = tf.signal.fftshift(fft_gt[0][0])
# ex.imshow(tf.math.abs(tf.math.log(img))).show()
# low = tf.where(TFcircleIN, img, tf.zeros_like(img))
# ex.imshow(tf.math.real(tf.signal.ifft2d(tf.signal.ifftshift(low)))).show()
# high = tf.where(TFcircleOUT, img, tf.zeros_like(img))
# # ex.imshow(tf.math.abs(tf.math.log(high))).show()
# ex.imshow(tf.math.real(tf.signal.ifft2d(tf.signal.ifftshift(high)))).show()
print()
@tf.function
def train_step(batch):
uv_unposed, mesh_unposed, uv_mask, mesh_mask = batch
with tf.GradientTape() as tape:
pred = model([uv_unposed, uv_mask])
pred = model([batch['uv_unposed'], batch['uv_mask']])
# pred = (pred + zero_body) * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred = uv2mesh(pred)
mesh_orig = mesh_unposed
# mesh_pred = pred
mesh_orig = batch['mesh_unposed']
e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask)
c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(uv_unposed - pred)), axis=1) * uv_mask)
loss = l1_loss + e_loss + c_loss
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask)
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss = 1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask']))
# l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['mesh_unposed'] - pred)), axis=-1) * batch['mesh_mask'])
l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_unposed'] - pred)), axis=1) * batch['uv_mask'])
ms_ssim = tf.reduce_mean(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))
rfft_loss = 0 #tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.rfft3d(pred) - tf.signal.rfft3d(batch['uv_unposed'])), axis=[1,2,3]))
fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3]))
fft_loss_pc, fft_loss_pc_low, fft_loss_pc_high = 0,0,0 #fft_loss_per_channel(pred, batch['uv_unposed'])
# loss = fft_loss_pc # + e_loss + c_loss
loss = 1e3 * (1 - ms_ssim)
mesh_orig_2 = uv2mesh(batch['uv_unposed'])
# mesh_skinned = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred)
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'])
metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
euclid_posed = 0 # tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_skinned - batch['mesh_posed']) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
gradients = tape.gradient(loss, model.trainable_variables)
# gradients = [tf.clip_by_norm(g, 1.0) for g in gradients]
# gradients = [tf.clip_by_value(g, -1.0, 1.0) for g in gradients]
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric }
return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim,
'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 }
@tf.function
def valid_step(batch):
uv_unposed, mesh_unposed, uv_mask, mesh_mask = batch
pred = model([batch['uv_unposed'], batch['uv_mask']])
pred = model([uv_unposed, uv_mask])
mesh_pred = uv2mesh(pred)
mesh_orig = mesh_unposed
mesh_orig = batch['mesh_unposed']
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss = 1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask']))
l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_unposed'] - pred)), axis=1) * batch['uv_mask'])
ms_ssim = tf.reduce_mean(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))
fft_loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_unposed'], tf.zeros_like(batch['uv_unposed'])))), axis=[1,2,3]))
loss = 1e3 * (1 - ms_ssim)
mesh_orig_2 = uv2mesh(batch['uv_unposed'])
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'])
metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim,
'valid/fft_loss': fft_loss, 'valid/euclid_error_reconst': metric_2
}
e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask)
c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(uv_unposed - pred)), axis=1) * uv_mask)
loss = l1_loss + e_loss + c_loss
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask)
return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric }
def plot_everything(batch: dict, type: str):
pred = model([batch['uv_unposed'], batch['uv_mask']])
mesh_pred = uv2mesh(pred)
mesh_orig = batch['mesh_unposed']
orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1])
pred_img = tf.transpose(pred, [0,2,3,1])
img = tf.concat((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) }
return { **figs, **{ type + '/images': imgs } }
if __name__ == '__main__':
allow_memory_growth()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())
name = 'ae_unposed_1'
dataset_train = DatasetSimple('/home/workspace/data/dataset/stylegan_new/train').cache().batch(8).repeat()
dataset_valid = DatasetSimple('/home/workspace/data/dataset/stylegan_new/valid').cache().batch(8).repeat()
smpl = SMPL()
# R = smpl.run_rodrigues((np.zeros((8,72), dtype=np.float32)))
# zero_body = smpl.run(R, np.zeros((8, 11), dtype=np.float32))[0]
factory = DatasetFactory()
signature_dynamic = { 'id': (),
'pose_shape': (72 + 11,),
# 'uv_posed': (3, 512, 256,),
'uv_body_posed': (3, 512, 256,),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'mesh_unposed': (14475, 3,),
# 'uv_unposed': (3, 512, 256,)
}
signature_static = { #'uv_static': (3, 512, 256,),
# 'mesh_static': (14475, 3,),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
# 'uv_shaped_body': (3, 512, 256,),
}
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/train',
batch_size=8,
signature=signature_dynamic)
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/train',
signature=signature_static)
dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid',
batch_size=8,
signature=signature_dynamic)
static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid',
signature=signature_static)
model = AutoEncoder()
# optimizer = tf.optimizers.Adam(learning_rate=0.002, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
optimizer = tf.optimizers.Adam()
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0005, decay_steps=30000, decay_rate=0.1, staircase=False)
optimizer = tf.optimizers.Adam(learning_rate=lr_schedule)
folder = 'models/ae_logs/'
train_summary_writer = tf.summary.create_file_writer(folder + name + '/train')
# train_summary_writer = tf.summary.create_file_writer(folder + name + '/train')
# valid_summary_writer = tf.summary.create_file_writer(folder + name + '/valid')
valid_iterator = iter(dataset_valid)
step = 0
for batch in dataset_train:
batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
train_loss = train_step(batch)
for key in train_loss.keys():
with train_summary_writer.as_default():
tf.summary.scalar(key, train_loss[key], step=step)
data_valid = valid_iterator.get_next()
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
valid_loss = valid_step(data_valid)
data_valid = batch
# data_valid = iter(dataset_valid).get_next()
# valid_loss = valid_step(data_valid)
# for key in valid_loss.keys():
# with valid_summary_writer.as_default():
# tf.summary.scalar(key, valid_loss[key], step=step)
if step % 250 == 0:
pred = model([data_valid[0], data_valid[2]])
if step % 1000 == 0:
train_figs = plot_everything(batch, 'train')
valid_figs = plot_everything(data_valid, 'valid')
wandb.log({ **train_loss, **valid_loss, **train_figs, **valid_figs }, step=step)
else:
wandb.log({ **train_loss, **valid_loss }, step=step)
mesh_pred = uv2mesh(pred)
mesh_orig = data_valid[1]
orig_img = tf.transpose(data_valid[0], [0,2,3,1])
pred_img = tf.transpose(pred, [0,2,3,1])
img = tf.concat((orig_img, pred_img), axis=2)
with train_summary_writer.as_default():
tf.summary.image('images', img, step=step)
F = np.load("custom_utils/smpl_faces.npy")
garment, faces = maskFacesBatch(mesh_pred.numpy(), F, data_valid[3].numpy().astype(bool))
gt = [mesh_orig[i].numpy()[data_valid[3][i].numpy().astype(bool), :] for i in range(pred.shape[0])]
for i in range(pred.shape[0]):
fig = plot_3d(gt[i], garment[i], faces[i])
wandb.log({"garment_" + str(i): fig})
print(step)