257 lines
12 KiB
Python
257 lines
12 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 cv2
|
|
import numpy as np
|
|
from custom_utils.custom_utils import UV2Mesh, get_summary_html, load_model, maskFacesBatch
|
|
import plotly.graph_objects as go
|
|
import plotly.express as ex
|
|
|
|
from tf_utils import check_tf_version, allow_memory_growth
|
|
# from losses import edge_loss, uv_mask_loss
|
|
from dataset import DatasetFactory, StaticDataset
|
|
from custom_utils.custom_utils import batch_to_dict
|
|
# from custom_utils.smpl import SMPL
|
|
# from models.upscaler import Upscaler
|
|
from models.upscaler import UpscalerBasicConv as Upscaler
|
|
|
|
# !!!!!!!!!! CHANGE SSIM BASED ON RES
|
|
# !!!!!!!!!! CHANGE SKIRT OR NOT
|
|
|
|
name='256_512_skirt'
|
|
def init_wandb():
|
|
run = wandb.init(project='upscale', name=name,
|
|
notes='\
|
|
changed ms-ssim scale weights | \
|
|
inpainting style | \
|
|
MLP based | \
|
|
smaller batch | \
|
|
use mask for loss | \
|
|
using ms ssim for higher res | \
|
|
training from low res to low res | \
|
|
named the layers, added tanh to final layer | \
|
|
changed l1 loss reduce axis | \
|
|
try convolutional architecture | \
|
|
smaller features | \
|
|
', dir='/home/workspace/local-data/temp')
|
|
artifact = wandb.Artifact('code', type='code')
|
|
artifact.add_file('upscale_test.py')
|
|
artifact.add_file('models/upscaler.py')
|
|
artifact.add_file('losses.py')
|
|
artifact.add_file('dataset.py')
|
|
artifact.add_file('convnext/models/convnext_tf.py')
|
|
run.log_artifact(artifact)
|
|
|
|
is_skirt = True
|
|
is_cape = False
|
|
skirt_modifier = '_skirt' if is_skirt else ''
|
|
smpl_res = 3638 if is_cape else 14475
|
|
|
|
uv2mesh = UV2Mesh(res=256, is_skirt=is_skirt, is_smpl_orig=is_cape)
|
|
|
|
|
|
|
|
def plot_3d(mesh_gt, mesh_pred_unposed, faces):
|
|
mesh_pred_unposed[:,2] += 1
|
|
fig = go.Figure(data=[
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=mesh_gt[:,0],
|
|
y=mesh_gt[:,2],
|
|
z=mesh_gt[:,1],
|
|
# 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_unposed[:,0],
|
|
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],
|
|
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
|
|
|
|
|
|
# @tf.function
|
|
def train_step(batch):
|
|
with tf.GradientTape() as tape:
|
|
pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=True)
|
|
pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
|
|
|
|
mesh_pred = uv2mesh(pred)
|
|
mesh_orig = batch['mesh_unposed']
|
|
|
|
l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_unposed' + high_resolution_modifier] - pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis]) )), axis=-1))
|
|
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2, power_factors=(0.5, 0.2, 0.2, 0.1)))
|
|
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2))
|
|
loss = 1e3 * (1 - ms_ssim)
|
|
|
|
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'])
|
|
gradients = tape.gradient(loss, model.trainable_variables)
|
|
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
|
|
return { 'train/loss': loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim }
|
|
|
|
|
|
|
|
@tf.function
|
|
def valid_step(batch):
|
|
pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
|
|
pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
|
|
|
|
mesh_pred = uv2mesh(pred)
|
|
mesh_orig = batch['mesh_unposed']
|
|
|
|
l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_unposed' + high_resolution_modifier] - pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis]) )), axis=-1))
|
|
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2, power_factors=(0.5, 0.2, 0.2, 0.1)))
|
|
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2))
|
|
loss = 1e3 * (1 - ms_ssim)
|
|
|
|
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'])
|
|
return { 'valid/loss': loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim }
|
|
|
|
|
|
|
|
def plot_everything(batch: dict, type: str):
|
|
|
|
pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
|
|
pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
|
|
|
|
mesh_pred = uv2mesh(pred)
|
|
mesh_orig = batch['mesh_unposed']
|
|
|
|
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if is_cape else "") + ".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])]
|
|
gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
|
|
|
|
figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i]) for i in range(4) }
|
|
# 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) }
|
|
|
|
orig_img = batch['uv_unposed' + high_resolution_modifier]
|
|
img = tf.concat((orig_img, pred), axis=2)
|
|
imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
|
|
|
|
return { **figs, **{ type + '/images': imgs} }
|
|
|
|
|
|
|
|
def train():
|
|
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)
|
|
|
|
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)
|
|
|
|
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
|
|
if step % 10000 == 0 or (step < 2000 and step % 500 == 0):
|
|
train_figs = plot_everything(batch, 'train')
|
|
valid_figs = plot_everything(data_valid, 'valid')
|
|
to_log = { **to_log, **train_figs, **valid_figs }
|
|
|
|
if step == 0:
|
|
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
|
|
|
if step % 5000 == 0:
|
|
manager.save(checkpoint_number=step)
|
|
|
|
|
|
wandb.log(to_log, step = step)
|
|
|
|
print(step)
|
|
|
|
if step > 100000:
|
|
break
|
|
|
|
step += 1
|
|
|
|
if __name__ == '__main__':
|
|
# init_wandb()
|
|
allow_memory_growth()
|
|
parser = argparse.ArgumentParser(description='')
|
|
parser.add_argument('--name', type=str)
|
|
args = vars(parser.parse_args())
|
|
|
|
# smpl = SMPL()
|
|
# uv_mask_body = np.load('custom_utils/mask_256x512.npy')
|
|
# uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
|
|
|
|
low_res = 32
|
|
high_res = 512
|
|
uv_mask_body = tf.ones((low_res, low_res//2))
|
|
low_resolution_modifier = '_' + str(low_res)
|
|
high_resolution_modifier = ('_' + str(high_res)) if high_res != 512 else ''
|
|
|
|
factory = DatasetFactory()
|
|
signature_dynamic = { 'id': (), 'frame_nr': (),
|
|
'pose_shape': (72 + 11,),
|
|
'uv_unposed' + low_resolution_modifier: (low_res, low_res//2, 3),
|
|
'uv_unposed' + high_resolution_modifier: (high_res, high_res//2, 3),
|
|
'mesh_unposed': (smpl_res, 3,),
|
|
'mesh_posed': (smpl_res, 3,),
|
|
}
|
|
signature_static = {
|
|
'mesh_mask': (smpl_res,),
|
|
'uv_mask' + high_resolution_modifier: (high_res, high_res//2,),
|
|
'uv_mask' + low_resolution_modifier: (low_res, low_res//2,),
|
|
}
|
|
|
|
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
|
|
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
|
|
dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/valid_skirt', batch_size=8, signature=signature_dynamic,resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
|
|
static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid_skirt', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
|
|
|
|
# dataset_train = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/train/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
|
|
# static_dataset_train = StaticDataset(folder='/home/workspace/local-data/cape_processed/train/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
|
|
# dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/test/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
|
|
# static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/cape_processed/test/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
|
|
|
|
|
|
model = Upscaler(low_res=low_res, kernel_size=3)
|
|
|
|
optimizer = tf.optimizers.Adam(learning_rate=0.0001)
|
|
folder = 'models/ae_logs/'
|
|
|
|
ckpt = tf.train.Checkpoint(model=model)
|
|
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=20)
|
|
load_model(model, 'checkpoints/32_512_4')
|
|
|
|
train() |