convolution based upscale enhancement
This commit is contained in:
+32
-30
@@ -1,5 +1,5 @@
|
||||
import os
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
|
||||
import tensorflow as tf
|
||||
|
||||
gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
@@ -30,11 +30,14 @@ 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 UpscalerBasicNew as Upscaler
|
||||
from models.upscaler import UpscalerBasicConv as Upscaler
|
||||
|
||||
name='up_3_d256_512_2'
|
||||
# !!!!!!!!!! CHANGE SSIM BASED ON RES
|
||||
# !!!!!!!!!! CHANGE SKIRT OR NOT
|
||||
|
||||
name='32_512_3'
|
||||
def init_wandb():
|
||||
run = wandb.init(project='vae', name=name,
|
||||
run = wandb.init(project='upscale', name=name,
|
||||
notes='\
|
||||
changed ms-ssim scale weights | \
|
||||
inpainting style | \
|
||||
@@ -44,6 +47,8 @@ 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 | \
|
||||
')
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('upscale_test.py')
|
||||
@@ -53,7 +58,6 @@ named the layers, added tanh to final layer | \
|
||||
artifact.add_file('convnext/models/convnext_tf.py')
|
||||
run.log_artifact(artifact)
|
||||
|
||||
# !!!!!!!!!! CHANGE SKIRT OR NOT
|
||||
uv2mesh = UV2Mesh(res=256, is_skirt=False)
|
||||
|
||||
|
||||
@@ -98,17 +102,15 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces):
|
||||
@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]])
|
||||
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'][:,:,:,tf.newaxis] + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 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'][:,:,:,tf.newaxis] + 1, batch['uv_unposed'] * batch['uv_mask'][:,:,:,tf.newaxis] + 1, max_val=2))
|
||||
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))
|
||||
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'])
|
||||
@@ -118,17 +120,17 @@ def train_step(batch):
|
||||
|
||||
|
||||
|
||||
# UPDATE!
|
||||
@tf.function
|
||||
def valid_step(batch):
|
||||
pred = model(batch['uv_unposed' + low_resolution_modifier], batch['uv_mask'])
|
||||
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'] - pred) )), axis=1))
|
||||
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))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(tf.transpose(pred * uv_mask_body[tf.newaxis,tf.newaxis,:,:], [0,2,3,1]) + 1, tf.transpose(batch['uv_body_posed' + resolution_modifier] * uv_mask_body[tf.newaxis,tf.newaxis,:,:], [0,2,3,1]) + 1, max_val=2))
|
||||
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'])
|
||||
@@ -138,7 +140,7 @@ def valid_step(batch):
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
|
||||
pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
|
||||
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)
|
||||
@@ -162,20 +164,20 @@ def plot_everything(batch: dict, type: str):
|
||||
|
||||
|
||||
def train():
|
||||
# valid_iterator = iter(dataset_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)
|
||||
|
||||
# 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 = valid_iterator.get_next()
|
||||
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
|
||||
valid_loss = valid_step(data_valid)
|
||||
|
||||
if step % 10000 == 0:
|
||||
if step % 10000 == 0 or (step < 5000 and step % 500 == 0):
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
valid_figs = {}#plot_everything(data_valid, 'valid')
|
||||
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)
|
||||
@@ -187,8 +189,8 @@ def train():
|
||||
|
||||
print(step)
|
||||
|
||||
if step > 300000:
|
||||
break
|
||||
# if step > 900000:
|
||||
# break
|
||||
|
||||
step += 1
|
||||
|
||||
@@ -203,7 +205,7 @@ if __name__ == '__main__':
|
||||
# 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 = 256
|
||||
low_res = 32
|
||||
high_res = 512
|
||||
uv_mask_body = tf.ones((low_res, low_res//2))
|
||||
low_resolution_modifier = '_' + str(low_res)
|
||||
@@ -222,10 +224,10 @@ if __name__ == '__main__':
|
||||
'uv_mask' + low_resolution_modifier: (low_res, low_res//2,),
|
||||
}
|
||||
|
||||
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=8, signature=signature_dynamic, use_filled=False, resolutions=[high_res, low_res])
|
||||
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=False, resolutions=[high_res, low_res])
|
||||
dataset_valid = None #factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=32, signature=signature_dynamic)
|
||||
static_dataset_valid = None #StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
|
||||
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train', batch_size=8, signature=signature_dynamic, use_filled=False, resolutions=[high_res, low_res], in_memory=False)
|
||||
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train', signature=signature_static, use_filled=False, resolutions=[high_res, low_res], in_memory=True)
|
||||
dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/valid', batch_size=8, signature=signature_dynamic, use_filled=False, resolutions=[high_res, low_res], in_memory=False)
|
||||
static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid', signature=signature_static, use_filled=False, resolutions=[high_res, low_res], in_memory=True)
|
||||
|
||||
model = Upscaler(low_res=low_res)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user