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updated body AE

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luni
2022-09-13 16:47:13 +00:00
parent 6ca873c76e
commit e35d7e4eb6
2 changed files with 100 additions and 199 deletions
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auto_encoder_body.py
+20 -199
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@@ -1,4 +1,6 @@
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import time
import argparse
import wandb
@@ -7,27 +9,19 @@ import cv2
import numpy as np
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
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, 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 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.ae_body_model import AutoEncoder
name='ae_body_7_2'
name='ae_body_8'
def init_wandb():
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
run = wandb.init(project='vae', 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. \
@@ -36,10 +30,12 @@ def init_wandb():
larger batch_size.\
flattened bottleneck with 128 latent_dim\ remove global orientation\
fixed gender issue with deoriented bodies, again, aaand again\
trained on 100 garments')
trained on 100 garments\
remove multiplications with garment uv mask')
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder_body.py')
artifact.add_file('losses.py')
artifact.add_file('models/ae_body_model.py')
artifact.add_file('dataset.py')
artifact.add_file('convnext/models/convnext_tf.py')
run.log_artifact(artifact)
@@ -47,42 +43,6 @@ def init_wandb():
uv2mesh = UV2Mesh(res=256)
class DatasetSimple(tf.data.Dataset):
def _generator(folder):
# Opening the file
folder = folder.decode("utf-8")
uv_folder = os.path.join(folder, 'uv_posed')
frames = os.listdir(uv_folder)
random.shuffle(frames)
for frame in frames:
id, frame_nr = frame.split('.')[0].split('_')
mesh_mask = np.load(os.path.join(folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32)
uv_mask = np.load(os.path.join(folder, 'uv_mask', id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32)
mesh_unposed = np.load(os.path.join(folder, 'mesh_unposed', id + '_' + frame_nr + '.npz')); mesh_unposed = mesh_unposed['mesh_unposed']
uv_unposed = np.load(os.path.join(folder, 'uv_unposed', id + '_' + frame_nr + '.npz')); uv_unposed = uv_unposed['uv_unposed']; uv_unposed = np.transpose(uv_unposed, [2, 0, 1])
yield uv_unposed, mesh_unposed, uv_mask, mesh_mask
def __new__(cls, folder, res=256):
return tf.data.Dataset.from_generator(
cls._generator,
output_signature = (tf.TensorSpec(shape = (3, res * 2, res,), dtype = tf.float32),
tf.TensorSpec(shape = (14475, 3,), dtype = tf.float32),
tf.TensorSpec(shape = (res * 2, res,), dtype = tf.float32),
tf.TensorSpec(shape = (14475,), dtype = tf.float32), ),
args=(folder, )
)
def filter_resolutions_featuremaps(resolutions, featuremaps, res):
index = resolutions.index(res)
filtered_resolutions = resolutions[:index + 1]
filtered_featuremaps = featuremaps[:index + 1]
return filtered_resolutions, filtered_featuremaps
def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color):
mesh_pred_unposed[:,1] += 1
@@ -133,149 +93,12 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
return fig
class Bottleneck(tf.keras.Model):
def __init__(self, dim, latent_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)]
)
self.flatten = tf.keras.layers.Flatten()
self.dense = tf.keras.layers.Dense(latent_dim)
def call(self, inputs, training=None):
x = inputs
x = self.bottleneck(x)
x = self.flatten(x)
x = self.dense(x)
return x
class Decoder(tf.keras.Model):
def __init__(self, bottleneck_size, latent_dim):
super(Decoder, self).__init__()
self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
self.reshape = tf.keras.layers.Reshape((4, 2, 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):
x = inputs
x = self.dense(x)
x = self.reshape(x)
x = self.decoder(x)
return x
class AutoEncoder(tf.keras.Model):
def __init__(self):
super(AutoEncoder, self).__init__()
bottleneck_size = 512
latent_dim = 128
self.encoder = ConvNeXt(depths=[3, 3, 9, 9, 3, 3], dims=[32, 64, 128, 256, 256, bottleneck_size], include_top=False)
self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
self.decoder = Decoder(bottleneck_size, latent_dim)
def call(self, inputs, training=None):
x, _ = inputs
x = tf.transpose(x, [0,2,3,1])
x = self.encoder(x, training=training)
# 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.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
x = tf.transpose(x, [0,3,1,2])
# x = x * uv_mask[:,:, tf.newaxis]
x = x * uv_mask_body[tf.newaxis,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):
with tf.GradientTape() as tape:
pred = model([batch['uv_body_posed'], batch['uv_mask']])
pred = model(batch['uv_body_posed'])
# pred = (pred + zero_body) * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred = uv2mesh(pred)
@@ -283,14 +106,12 @@ def train_step(batch):
mesh_orig = batch['mesh_body_posed']
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones_like(batch['mesh_mask'])))
c_loss = 0 # 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_body_posed'] - 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_body_posed'], [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]))
l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed'] - pred) * uv_mask_body[tf.newaxis,tf.newaxis,:,:])), axis=1))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(tf.transpose(pred * uv_mask_body[tf.newaxis,tf.newaxis,:,:], [0,2,3,1]) + 1, tf.transpose(batch['uv_body_posed'] * uv_mask_body[tf.newaxis,tf.newaxis,:,:], [0,2,3,1]) + 1, max_val=2))
fft_loss = 0 # 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_body_posed'], tf.zeros_like(batch['uv_body_posed'])))), 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)
@@ -304,11 +125,11 @@ def train_step(batch):
# gradients = [tf.clip_by_value(g, -1.0, 1.0) for g in gradients]
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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 }
'train/fft_loss': fft_loss, 'train/euclid_error_reconst': metric_2, 'train/euclid_error_posed': euclid_posed }
# UPDATE!
@tf.function
def valid_step(batch):
pred = model([batch['uv_body_posed'], batch['uv_mask']])
@@ -332,7 +153,7 @@ def valid_step(batch):
def plot_everything(batch: dict, type: str):
pred = model([batch['uv_body_posed'], batch['uv_mask']])
pred = model(batch['uv_body_posed'])
mesh_pred = uv2mesh(pred)
mesh_orig = batch['mesh_body_posed']
@@ -417,12 +238,12 @@ if __name__ == '__main__':
signature_static = { #'uv_static': (3, 512, 256,),
# 'mesh_static': (14475, 3,),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
# 'uv_mask': (512, 256,),
# 'uv_shaped_body': (3, 512, 256,),
}
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=32, signature=signature_dynamic, use_filled=False)
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=False)
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=32, signature=signature_dynamic, use_filled=True)
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=True)
dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=32, signature=signature_dynamic)
static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)