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luni
2022-11-04 16:17:58 +00:00
parent ebe8522905
commit 1c6b805519
18 changed files with 1413 additions and 486 deletions
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cvae.py
+123 -69
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@@ -1,6 +1,6 @@
import os
import sys
from configs.config_cvae_test import config
from configs.config_abl_norm import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -26,7 +26,6 @@ import numpy as np
import plotly.graph_objects as go
import plotly.express as ex
from PIL import Image
from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from postprocessing import fix_collision
if config['is_skirt']:
@@ -36,30 +35,30 @@ else:
from losses import collision_ratio, kl_loss #, concept_loss
from losses import collision_ratio, kl_loss, normal_loss #, concept_loss
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from dataset import DatasetFactory, StaticDataset
from custom_utils.smpl import SMPL
from models.cvae_model import CVAE
def init_wandb():
run = wandb.init(project='cvae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
artifact = wandb.Artifact('code', type='code')
artifact.add_file('cvae.py')
artifact.add_file('models/cvae_model.py')
artifact.add_file('losses.py')
artifact.add_file('configs/config_base.py')
artifact.add_file('configs/config_cvae.py')
# artifact.add_file('configs/config_base.py')
# artifact.add_file('configs/config_cvae.py')
artifact.add_file('models/base.py')
artifact.add_file('models/cond_encoder.py')
artifact.add_file('dataset.py')
artifact.add_file('convnext/models/convnext_tf.py')
config_file_name = [key for key in sys.modules.keys() if key.startswith('configs.')]
assert len(config_file_name) == 1
artifact.add_file('/'.join(config_file_name[0].split('.')) + '.py')
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
for config_file_name in config_file_names:
artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
run.log_artifact(artifact)
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False)
@@ -76,28 +75,42 @@ def kl_loss(mean, logvar):
def train_step(batch):
with tf.GradientTape() as tape:
pred, mean, logvar = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
if config['use_skinning']:
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
else:
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
mesh_pred = uv2mesh(pred)
mesh_orig = batch['mesh_unposed']
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
else:
mesh_pred_posed = mesh_pred
# is the output already masked?
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
n_loss = normal_loss(mesh_pred, mesh_orig, batch['mesh_mask'], is_skirt=config['is_skirt'])
# conc_loss = concept_loss(pred, batch['uv_unposed'])
kl = kl_loss(mean, logvar)
reconstr_loss = (l2_loss + l2_mesh_loss) * config['l2_coeff'] + (e_loss + c_loss) * config['reg_loss_coeff']
reconstr_loss = l2_loss
if config['use_extra_losses']:
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
if config['use_norm_loss'] and step > 2000:
reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
loss = reconstr_loss + g_balancing_coeff * kl
mean = tf.reduce_mean(mean)
@@ -105,41 +118,54 @@ def train_step(batch):
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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'])
# c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
'train/reconstr_loss': reconstr_loss,
'train/reconstr_loss': reconstr_loss, 'train/collision_ratio': c_ratio,
'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss
# 'train/ms_ssim': ms_ssim, 'train/collision_ratio': c_ratio,
'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
# 'train/ms_ssim': ms_ssim,
}
@tf.function
def valid_step(batch):
pred, mean, logvar = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
if config['use_skinning']:
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
else:
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
mesh_pred = uv2mesh(pred)
mesh_orig = batch['mesh_unposed']
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
else:
mesh_pred_posed = mesh_pred
# is the output already masked?
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
n_loss = normal_loss(mesh_pred, mesh_orig, batch['mesh_mask'], is_skirt=config['is_skirt'])
# conc_loss = concept_loss(pred, batch['uv_unposed'])
kl = kl_loss(mean, logvar)
reconstr_loss = (l2_loss + l2_mesh_loss) * config['l2_coeff'] + (e_loss + c_loss) * config['reg_loss_coeff']
reconstr_loss = l2_loss
if config['use_extra_losses']:
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
if config['use_norm_loss']:
reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
loss = reconstr_loss + g_balancing_coeff * kl
mean = tf.reduce_mean(mean)
@@ -150,49 +176,62 @@ def valid_step(batch):
# c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric,
'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss,
'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss, 'valid/collision_ratio': c_ratio,
'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar,
# 'valid/ms_ssim': ms_ssim, 'valid/collision_ratio': c_ratio,
'valid/normal_loss': n_loss,# 'valid/ms_ssim': ms_ssim,
}
@tf.function
def test_step(batch):
pred, mean, logvar = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
if config['use_skinning']:
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
else:
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
mesh_pred = uv2mesh(pred)
mesh_orig = batch['mesh_unposed']
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
mesh_orig_shaped = mesh_orig + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_orig_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_orig_shaped)
else:
mesh_pred_posed = mesh_pred
mesh_orig_posed = mesh_orig
mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
mesh_orig_shaped = mesh_orig + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_orig_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_orig_shaped)
# is the output already masked?
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
# conc_loss = concept_loss(pred, batch['uv_unposed'])
kl = kl_loss(mean, logvar)
reconstr_loss = (l2_loss + l2_mesh_loss) * config['l2_coeff'] + (e_loss + c_loss) * config['reg_loss_coeff']
reconstr_loss = l2_loss
if config['use_extra_losses']:
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
loss = reconstr_loss + g_balancing_coeff * kl
mean = tf.reduce_mean(mean)
logvar = tf.reduce_mean(logvar)
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, uv_orig + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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'])
euclid_posed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
euclid_posed_fixed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed_fixed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
return { 'test/loss': loss, 'test/edge_loss': e_loss, 'test/collision_loss': c_loss, 'test/euclid_error': metric, 'test/ms_ssim': ms_ssim,
'test/reconstr_loss': reconstr_loss, 'test/collision_ratio': c_ratio, 'test/l2_mesh_loss': l2_mesh_loss,
@@ -201,32 +240,46 @@ def test_step(batch):
def plot_everything(batch: dict, type: str):
pred, mean, logvar = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
if config['use_skinning']:
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
else:
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
mesh_pred = uv2mesh(pred).numpy()
mesh_orig = batch['mesh_unposed']
orig_img = batch['uv_unposed']
orig_img = uv_orig
uv_static_img = batch['uv_static']
uv_body_posed_img = batch['uv_body_posed']
pred_img = pred
img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2)
imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy")
mask = batch['mesh_mask'].numpy().astype(bool)
garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
garment_unposed = [garment_unposed[i] + shape_offsets.numpy()[i, mask[i], :] for i in range(mask.shape[0])]
gt = [mesh_orig[i].numpy()[mask[i], :] + shape_offsets.numpy()[i, mask[i], :] for i in range(pred.shape[0])]
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
garment_unposed = [garment_unposed[i] + shape_offsets.numpy()[i, mask[i], :] for i in range(mask.shape[0])]
gt = [mesh_orig[i].numpy()[mask[i], :] + shape_offsets.numpy()[i, mask[i], :] for i in range(pred.shape[0])]
for i in range(mesh_orig.shape[0]):
mesh_pred[i, mask[i], :] += shape_offsets.numpy()[i, mask[i], :]
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy()
else:
mesh_posed = mesh_pred
gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(mask.shape[0])]
body = batch['mesh_body_posed'].numpy()
for i in range(body.shape[0]):
mesh_pred[i, mask[i], :] += shape_offsets.numpy()[i, mask[i], :]
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy()
mesh_posed_fixed = fix_collision(mesh_posed, body, mask).numpy()
garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])]
@@ -236,9 +289,10 @@ def plot_everything(batch: dict, type: str):
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(body.shape[0])]
collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))]
collision_gt = [np.concatenate((body[i], gt_posed[i]), 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) }
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision[i], collision_fixed[i], collision_faces[i], collision_color[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], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0), num_bins=model.latent_dim)
logvar_hist = wandb.Histogram(sequence=logvar.numpy().mean(axis=0), num_bins=model.latent_dim)
@@ -246,7 +300,7 @@ def plot_everything(batch: dict, type: str):
return { **figs, **figs_coll, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
def sample(folder):
def sample(folder):
# run = wandb.init(project='vae_plots', name=folder.split('/')[-1])
load_model(model,folder)
count = 0
@@ -417,7 +471,7 @@ def test():
if __name__ == '__main__':
# init_wandb()
init_wandb()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
args = vars(parser.parse_args())
@@ -488,10 +542,10 @@ if __name__ == '__main__':
if step == 0:
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
if train_loss['train/euclid_error'] < 0.019:
if train_loss['train/euclid_error'] < 0.017:
if balancing_activated_step == 0:
balancing_activated_step = step
g_balancing_coeff = g_balancing_coeff + 1 / 10000
g_balancing_coeff = g_balancing_coeff + 1 / 20000
to_log = { **to_log, **{ 'train/balancing_coeff': g_balancing_coeff } }