convolution based upscale enhancement
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+33
-3
@@ -99,11 +99,11 @@ class UpscalerBasicNew(tf.keras.Model):
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self.flatten = tf.keras.layers.Flatten(name='upscale_enhancer_flattent')
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self.seq = tf.keras.Sequential([
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tf.keras.layers.Dense(1024, activation='relu', name='upscale_enhancer_dense_1'),
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tf.keras.layers.Dense(4096, activation='relu', name='upscale_enhancer_dense_1'),
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tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_1'),
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tf.keras.layers.Dense(1024, activation='relu', name='upscale_enhancer_dense_2'),
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tf.keras.layers.Dense(2048, activation='relu', name='upscale_enhancer_dense_2'),
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tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_2'),
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tf.keras.layers.Dense(1024, activation='relu', name='upscale_enhancer_dense_3'),
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tf.keras.layers.Dense(2048, activation='relu', name='upscale_enhancer_dense_3'),
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tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_3'),
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tf.keras.layers.Dense(low_res * (low_res//2) * 3, activation='tanh', name='upscale_enhancer_dense_final'),
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])
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@@ -123,6 +123,36 @@ class UpscalerBasicNew(tf.keras.Model):
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return x
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class UpscalerBasicConv(tf.keras.Model):
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def __init__(self, low_res=32):
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super(UpscalerBasicConv, self).__init__()
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self.low_res = low_res
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self.seq = tf.keras.Sequential([
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tf.keras.layers.Conv2D(4096, kernel_size=3, padding='same', activation='relu', name='upscale_enhancer_conv_1'),
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tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_1'),
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tf.keras.layers.Conv2D(2048, kernel_size=3, padding='same', activation='relu', name='upscale_enhancer_conv_2'),
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tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_2'),
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tf.keras.layers.Conv2D(1024, kernel_size=3, padding='same', activation='relu', name='upscale_enhancer_conv_3'),
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tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_3'),
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tf.keras.layers.Conv2D(128, kernel_size=3, padding='same', activation='relu', name='upscale_enhancer_conv_4'),
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tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_4'),
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tf.keras.layers.Dense(3, activation='tanh', name='upscale_enhancer_dense_final'),
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])
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def call(self, inputs, training=None):
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img, mask = inputs
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x = self.seq(img, training=training)
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x = x * (1 - mask[:,:,:,tf.newaxis])
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x = x + img
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return x
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# class Upscaler(tf.keras.Model):
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# def __init__(self, bottleneck_size):
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+32
-30
@@ -1,5 +1,5 @@
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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os.environ["CUDA_VISIBLE_DEVICES"] = "3"
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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@@ -30,11 +30,14 @@ from dataset import DatasetFactory, StaticDataset
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from custom_utils.custom_utils import batch_to_dict
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# from custom_utils.smpl import SMPL
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# from models.upscaler import Upscaler
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from models.upscaler import UpscalerBasicNew as Upscaler
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from models.upscaler import UpscalerBasicConv as Upscaler
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name='up_3_d256_512_2'
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# !!!!!!!!!! CHANGE SSIM BASED ON RES
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# !!!!!!!!!! CHANGE SKIRT OR NOT
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name='32_512_3'
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def init_wandb():
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run = wandb.init(project='vae', name=name,
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run = wandb.init(project='upscale', name=name,
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notes='\
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changed ms-ssim scale weights | \
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inpainting style | \
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@@ -44,6 +47,8 @@ use mask for loss | \
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using ms ssim for higher res | \
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training from low res to low res | \
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named the layers, added tanh to final layer | \
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changed l1 loss reduce axis | \
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try convolutional architecture | \
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')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('upscale_test.py')
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@@ -53,7 +58,6 @@ named the layers, added tanh to final layer | \
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artifact.add_file('convnext/models/convnext_tf.py')
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run.log_artifact(artifact)
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# !!!!!!!!!! CHANGE SKIRT OR NOT
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uv2mesh = UV2Mesh(res=256, is_skirt=False)
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@@ -98,17 +102,15 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces):
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@tf.function
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def train_step(batch):
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with tf.GradientTape() as tape:
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pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
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pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=True)
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pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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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))
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# 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)))
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# 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))
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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)))
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# 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))
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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))
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# 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)))
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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))
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loss = 1e3 * (1 - ms_ssim)
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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'])
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@@ -118,17 +120,17 @@ def train_step(batch):
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# UPDATE!
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@tf.function
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def valid_step(batch):
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pred = model(batch['uv_unposed' + low_resolution_modifier], batch['uv_mask'])
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pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
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pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_unposed']
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l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_unposed'] - pred) )), axis=1))
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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))
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# 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))
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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))
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# 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)))
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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))
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loss = 1e3 * (1 - ms_ssim)
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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'])
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@@ -138,7 +140,7 @@ def valid_step(batch):
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def plot_everything(batch: dict, type: str):
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pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]])
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pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
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pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
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mesh_pred = uv2mesh(pred)
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@@ -162,20 +164,20 @@ def plot_everything(batch: dict, type: str):
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def train():
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# valid_iterator = iter(dataset_valid)
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valid_iterator = iter(dataset_valid)
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step = 0
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for batch in dataset_train:
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batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
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train_loss = train_step(batch)
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# data_valid = valid_iterator.get_next()
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# data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
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valid_loss = {}#valid_step(data_valid)
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data_valid = valid_iterator.get_next()
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data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
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valid_loss = valid_step(data_valid)
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if step % 10000 == 0:
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if step % 10000 == 0 or (step < 5000 and step % 500 == 0):
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train_figs = plot_everything(batch, 'train')
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valid_figs = {}#plot_everything(data_valid, 'valid')
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valid_figs = plot_everything(data_valid, 'valid')
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wandb.log({ **train_loss, **valid_loss, **train_figs, **valid_figs }, step=step)
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else:
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wandb.log({ **train_loss, **valid_loss }, step=step)
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@@ -187,8 +189,8 @@ def train():
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print(step)
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if step > 300000:
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break
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# if step > 900000:
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# break
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step += 1
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@@ -203,7 +205,7 @@ if __name__ == '__main__':
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# uv_mask_body = np.load('custom_utils/mask_256x512.npy')
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# 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)
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low_res = 256
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low_res = 32
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high_res = 512
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uv_mask_body = tf.ones((low_res, low_res//2))
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low_resolution_modifier = '_' + str(low_res)
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@@ -222,10 +224,10 @@ if __name__ == '__main__':
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'uv_mask' + low_resolution_modifier: (low_res, low_res//2,),
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}
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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])
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=False, resolutions=[high_res, low_res])
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dataset_valid = None #factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=32, signature=signature_dynamic)
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static_dataset_valid = None #StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
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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)
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train', signature=signature_static, use_filled=False, resolutions=[high_res, low_res], in_memory=True)
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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)
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static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid', signature=signature_static, use_filled=False, resolutions=[high_res, low_res], in_memory=True)
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model = Upscaler(low_res=low_res)
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