update cvae_low version
This commit is contained in:
+23
-3
@@ -1,7 +1,7 @@
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from configs.config_base import config
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config['gpu'] = '3'
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config['name'] = 'cvae_19'
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config['gpu'] = '0'
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config['name'] = 'cvae_19_2'
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config['notes'] = '\
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offsets over average unposed garment. remove global orientation (in posed body input and in pose_shape parameters. \
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retrained body encoder with deoriented bodies \
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@@ -11,9 +11,17 @@ remove shape from unposed garment\
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training on 100 garments with offsets over the average of these\
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fixed autoencoders!!!!!!!!!\
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cvae only loads encoders instead AEs\
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with edge and collision loss\
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'
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# config['resolution'] = 32
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if config['resolution'] != 512:
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config['notes'] += '\
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using custom enhancer for upscaling, using not filled versions\
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'
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config['lr'] = 0.0001
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config['starting_balancing_coeff'] = 0.0001
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@@ -31,7 +39,19 @@ config['signature_static'] = { 'uv_static': (512, 256, 3),
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'uv_mask': (512, 256,),
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}
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config['avg_garment_path'] = 'custom_data/train_100_no_shape_avg_uv.npy'
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if config['resolution'] != 512:
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res_modifier = '_' + str(config['resolution'])
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config['signature_dynamic']['uv_body_posed' + res_modifier] = (config['resolution'], config['resolution']//2, 3)
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config['signature_dynamic']['uv_unposed' + res_modifier] = (config['resolution'], config['resolution']//2, 3)
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config['signature_dynamic']['uv_curvature' + res_modifier] = (config['resolution'], config['resolution']//2)
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config['signature_static']['uv_static' + res_modifier] = (config['resolution'], config['resolution']//2, 3)
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config['signature_static']['uv_mask' + res_modifier] = (config['resolution'], config['resolution']//2)
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if config['resolution'] == 512:
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config['avg_garment_path'] = 'custom_data/train_100_no_shape_avg_uv.npy'
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else:
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config['avg_garment_path'] = ''
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config['static_model'] = 'checkpoints/ae_static_7_2_x'
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config['body_model'] = 'checkpoints/ae_body_9_x'
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config['curvature_model'] = 'checkpoints/ae_curv_9_x'
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@@ -87,21 +87,23 @@ def train_step(batch):
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
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e_loss = edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])
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c_loss = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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l1_loss = tf.reduce_sum(tf.reduce_sum((tf.math.abs(batch['uv_unposed'] - pred)), axis=-1) * batch['uv_mask'], axis=[1, 2])
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l2_loss = 1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1) * batch['uv_mask'], axis=[1, 2])
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# ms_ssim = 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.image.ssim_multiscale(pred + 1, batch['uv_unposed'] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))
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# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale((tf.transpose(pred, [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], (tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1) * batch['uv_mask'][:,:,:,tf.newaxis], max_val=2))
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reconstr_loss = l2_loss
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reconstr_loss = l2_loss + e_loss + c_loss
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reconstr_loss = tf.reduce_mean(reconstr_loss)
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kl = kl_loss(mean, logvar)
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loss = reconstr_loss + balancing_coeff * kl
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ms_ssim = tf.reduce_mean(ms_ssim)
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l1_loss = tf.reduce_mean(l1_loss)
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l2_loss = tf.reduce_mean(l2_loss)
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e_loss = tf.reduce_mean(e_loss)
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c_loss = tf.reduce_mean(c_loss)
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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+25
-123
@@ -1,5 +1,6 @@
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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from configs.config_cvae import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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@@ -29,86 +30,29 @@ from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testin
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from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss, bend_loss
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# from losses import collision_penalty as collision_penalty
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from losses import collision_loss as collision_penalty
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from custom_utils.custom_utils import batch_to_dict, load_model
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from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.smpl import SMPL
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from models.cvae_model import CVAE#, CVAE128, CVAE32
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from models.cvae_model import CVAE
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from models.upscaler import UpscalerBasic as Upscaler
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name='cvae_low_4_2_d32'
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def init_wandb():
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run = wandb.init(project='vae', name=name,
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notes='learn offsets. smaller flattened latent vectors. with 25-85k pretraining. smaller batchsize. fixed balancing of reconst and kl loss for baseline. \
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new implementation for kl loss. no lr schedule. 25 jumpsuits, average reconst_loss before calculation, linearly increasing balancing, up to 1 from 0.1. \
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offsets over average unposed garment. remove global orientation (in posed body input and in pose_shape parameters. \
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retrained body encoder with deoriented bodies \
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use mean l2 loss. small batch. l2 * 1000, balancing to 1 from 0.01\
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fixed gender issue and retrained body encoder\
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remove shape from unposed garment\
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training on 100 garments , absolute, not offsets\
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using custom enhancer for upscaling, using not filled versions\
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calculating loss at full res')
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run = wandb.init(project='vae', name=config['name'], notes=config['notes'])
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('cvae.py')
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artifact.add_file('cvae_low.py')
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artifact.add_file('models/cvae_model.py')
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artifact.add_file('dataset.py')
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artifact.add_file('losses.py')
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artifact.add_file('configs/config_base.py')
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artifact.add_file('configs/config_cvae.py')
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artifact.add_file('models/base.py')
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artifact.add_file('models/cond_encoder.py')
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artifact.add_file('dataset.py')
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artifact.add_file('convnext/models/convnext_tf.py')
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run.log_artifact(artifact)
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uv2mesh = UV2Mesh(res=256)
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def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color):
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mesh_pred_unposed[:,2] += 1
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collision[:,2] += 2
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fig = go.Figure(data=[
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go.Mesh3d(
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# 8 vertices of a cube
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x=mesh_gt[:,0],
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y=mesh_gt[:,2],
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z=mesh_gt[:,1],
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# i, j and k give the vertices of triangles
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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),
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go.Mesh3d(
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# 8 vertices of a cube
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x=mesh_pred_unposed[:,0],
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y=mesh_pred_unposed[:,2],
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z=mesh_pred_unposed[:,1],
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# i, j and k give the vertices of triangles
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2],
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),
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go.Mesh3d(
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# 8 vertices of a cube
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x=collision[:,0],
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y=collision[:,2],
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z=collision[:,1],
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# i, j and k give the vertices of triangles
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i = collision_faces[:,0],
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j = collision_faces[:,1],
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k = collision_faces[:,2],
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vertexcolor=collision_color
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)
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])
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fig.update_layout(scene_aspectmode='manual',
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scene_aspectratio=dict(x=1, y=2, z=1))
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fig.update_layout(
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scene = dict(
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xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,3],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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return fig
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def log_normal_pdf(sample, mean, logvar, raxis=1):
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log2pi = tf.math.log(2. * np.pi)
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@@ -131,11 +75,10 @@ def train_step(batch):
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uv_curvature_low = tf.one_hot(batch['uv_curvature' + res_modifier], 7)
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pred, mean, logvar, z = model([batch['uv_unposed' + res_modifier], batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], uv_curvature_low,
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batch['uv_mask' + res_modifier], batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask']], use_mask=True)
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# pred = pred * batch['uv_mask' + res_modifier][:,:,:,tf.newaxis]
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pred_enhanced = enhance(upscale_enhancer, pred, batch['uv_mask' + res_modifier], training=False)
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pred_full_res = tf.image.resize(pred_enhanced, size=(512, 256), method=tf.image.ResizeMethod.LANCZOS5)
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mesh_pred = uv2mesh(pred_enhanced)
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mesh_pred = uv2mesh(pred_full_res)
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mesh_orig = batch['mesh_unposed']
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shape = batch['pose_shape'][:, 72:]
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@@ -153,7 +96,7 @@ def train_step(batch):
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ms_ssim = tf.image.ssim(pred + 1, batch['uv_unposed' + res_modifier] + 1, max_val=3)
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ms_ssim_full = tf.image.ssim(pred_full_res + 1, batch['uv_unposed'] + 1, max_val=3)
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else:
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ms_ssim = tf.image.ssim_multiscale(pred + 1, batch['uv_unposed' + res_modifier] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))
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ms_ssim = tf.image.ssim_multiscale(pred + 1, batch['uv_unposed' + res_modifier] + 1, max_val=3, power_factors=(0.5, 0.3, 0.1, 0.1))
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reconstr_loss = l2_loss_full
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reconstr_loss = tf.reduce_mean(reconstr_loss)
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@@ -167,18 +110,12 @@ def train_step(batch):
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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# mesh_orig_2 = uv2mesh(batch['uv_unposed' + res_modifier])
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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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# metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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euclid_posed = 0 # tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_skinned - batch['mesh_posed']) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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gradients = tape.gradient(loss, model.trainable_variables)
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# gradients = [tf.clip_by_norm(g, 1.0) for g in gradients]
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# gradients = [tf.clip_by_value(g, -1.0, 1.0) for g in gradients]
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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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,
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#'train/euclid_error_reconst': metric_2,
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'train/l2_loss_full_res': l2_loss_full, 'train/ms_ssim_full_res': ms_ssim_full,
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'train/euclid_error_posed': euclid_posed, 'train/reconstr_loss': reconstr_loss,
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'train/reconstr_loss': reconstr_loss,
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'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
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'train/reg_coeff': reg_coeff, 'train/l2_loss': l2_loss }
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@@ -226,8 +163,8 @@ def valid_step(batch):
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def plot_everything(batch: dict, type: str):
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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uv_curvature_low = tf.one_hot(batch['uv_curvature' + res_modifier], 7)
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pred, mean, logvar, z = model([batch['uv_unposed' + res_modifier], batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], uv_curvature_low, batch['uv_static'], batch['uv_body_posed'], uv_curvature])
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# pred = pred * batch['uv_mask' + res_modifier][:,:,:,tf.newaxis]
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pred, mean, logvar, z = model([batch['uv_unposed' + res_modifier], batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], uv_curvature_low,
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batch['uv_mask' + res_modifier], batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask']], use_mask=True)
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pred = enhance(upscale_enhancer, pred, batch['uv_mask' + res_modifier], training=False)
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mesh_pred = uv2mesh(pred).numpy()
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@@ -433,64 +370,31 @@ if __name__ == '__main__':
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res_modifier = '_' + str(resolution)
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factory = DatasetFactory()
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signature_dynamic = { 'id': (),
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'pose_shape': (72 + 11,),
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# 'uv_posed': (512, 256, 3),
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'uv_body_posed': (512, 256, 3),
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'uv_body_posed' + res_modifier: (resolution, resolution//2, 3),
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'mesh_body_posed': (14475, 3,),
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'mesh_posed': (14475, 3,),
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'mesh_unposed': (14475, 3,),
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'uv_unposed': (512, 256, 3),
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'uv_unposed' + res_modifier: (resolution, resolution//2, 3),
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'uv_curvature': (512, 256,),
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'uv_curvature' + res_modifier: (resolution, resolution//2),
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}
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signature_static = { 'uv_static': (512, 256, 3),
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'uv_static' + res_modifier: (resolution, resolution//2, 3),
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# 'mesh_static': (14475, 3,),
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'mesh_mask': (14475,),
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'uv_mask' + res_modifier: (resolution, resolution//2,),
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'uv_mask': (512, 256,),
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}
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dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=batch_size, signature=signature_dynamic, use_filled=False, resolutions=[512, resolution])
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=False, resolutions=[512, resolution])
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dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=batch_size, signature=signature_dynamic)
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static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
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dataset_train = factory.get_dataset(folder=config['train_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[512, config['resolution']])
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static_dataset_train = StaticDataset(folder=config['train_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[512, config['resolution']])
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dataset_valid = factory.get_dataset(folder=config['valid_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'])
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static_dataset_valid = StaticDataset(folder=config['valid_dir'], signature=config['signature_static'])
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model = CVAE(#avg_garment_path='custom_data/train_100_no_shape_avg_uv.npy',
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size=resolution,
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latent_dim=128, static_model='checkpoints/ae_static_6_copy',
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body_model='checkpoints/ae_body_8_copy',
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curvature_model='checkpoints/ae_curvature_7_copy',
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freeze_encoders=True,
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encoder_res=512)
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model = CVAE(config)
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upscale_enhancer = get_upscale_enhancer('checkpoints/up_2_3', res=resolution)
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# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0001, decay_steps=30000, decay_rate=0.1, staircase=False)
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optimizer = tf.optimizers.Adam(learning_rate=0.0001)
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balancing_coeff = 0.0001
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optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
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balancing_coeff = config['starting_balancing_coeff']
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reg_coeff = 1
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folder = 'models/ae_logs/'
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valid_iterator = iter(dataset_valid)
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gamma = 1
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balancing_activated_step = 0
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# avg_jumpsuit = np.load('custom_data/jumpsuit_20_avg_uv.npy')
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# sample('checkpoints/cvae_13')
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ckpt = tf.train.Checkpoint(model=model)
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manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=3)
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manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=3)
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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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batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
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train_loss = train_step(batch)
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@@ -514,8 +418,6 @@ if __name__ == '__main__':
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balancing_activated_step = step
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balancing_coeff = balancing_coeff + 1 / 15000
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# gamma = 0.99 * gamma + 0.01 * train_loss['train/reconstr_loss']
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to_log = { **to_log, **{ 'train/balancing_coeff': balancing_coeff, 'train/gamma': gamma } }
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wandb.log(to_log, step = step)
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