pyramid available with 128 res
This commit is contained in:
+50
-51
@@ -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"] = "2"
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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@@ -37,7 +37,7 @@ from custom_utils.smpl import SMPL
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from models.cvae_pyramid_model import Pyramid
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name='cvae_pyr_1'
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name='cvae_pyr_1_d128'
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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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@@ -48,7 +48,7 @@ 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 with offsets over the average of these\
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l2 loss on both 32 and full res, bottom predicts base, high res predicts offsets over this')
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l2 loss on both 128 and full res, bottom predicts base, high res predicts offsets over this')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('cvae_pyramid.py')
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artifact.add_file('models/cvae_model.py')
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@@ -60,9 +60,9 @@ l2 loss on both 32 and full res, bottom predicts base, high res predicts offsets
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uv2mesh = UV2Mesh(res=256)
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def plot_3d(mesh_gt, mesh_pred_unposed, mesh_pred_unposed_32, faces, collision, collision_faces, collision_color):
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def plot_3d(mesh_gt, mesh_pred_unposed, mesh_pred_unposed_low, faces, collision, collision_faces, collision_color):
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mesh_pred_unposed[:,2] += 1
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mesh_pred_unposed_32[:,2] += 2
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mesh_pred_unposed_low[:,2] += 2
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collision[:,2] += 3
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fig = go.Figure(data=[
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go.Mesh3d(
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@@ -82,9 +82,9 @@ def plot_3d(mesh_gt, mesh_pred_unposed, mesh_pred_unposed_32, faces, collision,
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k = faces[:,2],
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),
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go.Mesh3d(
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x=mesh_pred_unposed_32[:,0],
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y=mesh_pred_unposed_32[:,2],
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z=mesh_pred_unposed_32[:,1],
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x=mesh_pred_unposed_low[:,0],
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y=mesh_pred_unposed_low[:,2],
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z=mesh_pred_unposed_low[:,1],
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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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@@ -132,22 +132,22 @@ def train_step(batch):
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with tf.GradientTape() as tape:
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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uv_curvature_32 = tf.one_hot(batch['uv_curvature_32'], 7)
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uv_curvature_low = tf.one_hot(batch['uv_curvature' + resolution_modifier], 7)
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pred_full, mean_full, logvar_full, pred_32, mean_32, logvar_32, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature,
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batch['uv_unposed_32'], batch['uv_static_32'], batch['uv_body_posed_32'], uv_curvature_32])
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pred_full, mean_full, logvar_full, pred_low, mean_low, logvar_low, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature,
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batch['uv_unposed' + resolution_modifier], batch['uv_static' + resolution_modifier], batch['uv_body_posed' + resolution_modifier], uv_curvature_low])
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mesh_pred_full = uv2mesh(pred_full)
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mesh_pred_posed_full = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_full)
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mesh_pred_32 = uv2mesh(pred_32)
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mesh_pred_posed_32 = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_32)
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mesh_pred_low = uv2mesh(pred_low)
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mesh_pred_posed_low = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_low)
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mesh_orig = batch['mesh_unposed']
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shape = batch['pose_shape'][:, 72:]
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gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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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])])
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mesh_pred_posed_full = mesh_pred_posed_full + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred_posed_32 = mesh_pred_posed_32 + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred_posed_low = mesh_pred_posed_low + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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e_loss = tf.reduce_mean(edge_loss(mesh_pred_full, mesh_orig, batch['mesh_mask']))
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c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed_full, batch['mesh_body_posed'], batch['mesh_mask']))
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@@ -156,16 +156,16 @@ def train_step(batch):
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ms_ssim = tf.image.ssim_multiscale(pred_full + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=3)
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kl = kl_loss(mean_full, logvar_full)
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e_loss_32 = tf.reduce_mean(edge_loss(mesh_pred_32, mesh_orig, batch['mesh_mask']))
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c_loss_32 = tf.reduce_mean(collision_penalty(mesh_pred_posed_32, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss_32 = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed_32'], [0,2,3,1]) - pred_32)), axis=-1), axis=[1, 2])
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l2_loss_32 = 1e3 * tf.reduce_mean(tf.reduce_sum(((tf.transpose(batch['uv_unposed_32'], [0,2,3,1]) - pred_32) ** 2), axis=-1), axis=[1, 2])
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ms_ssim_32 = tf.image.ssim(pred_32 + 1, tf.transpose(batch['uv_unposed_32'], [0,2,3,1]) + 1, max_val=3)
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kl_32 = kl_loss(mean_full, logvar_full)
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e_loss_low = tf.reduce_mean(edge_loss(mesh_pred_low, mesh_orig, batch['mesh_mask']))
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c_loss_low = tf.reduce_mean(collision_penalty(mesh_pred_posed_low, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss_low = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) - pred_low)), axis=-1), axis=[1, 2])
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l2_loss_low = 1e3 * tf.reduce_mean(tf.reduce_sum(((tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) - pred_low) ** 2), axis=-1), axis=[1, 2])
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ms_ssim_low = tf.image.ssim(pred_low + 1, tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) + 1, max_val=3)
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kl_low = kl_loss(mean_full, logvar_full)
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reconstr_loss = l2_loss + l2_loss_32
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reconstr_loss = l2_loss + l2_loss_low
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reconstr_loss = tf.reduce_mean(reconstr_loss)
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loss = reconstr_loss + balancing_coeff * (kl + kl_32)
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loss = reconstr_loss + balancing_coeff * (kl + kl_low)
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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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@@ -173,25 +173,24 @@ def train_step(batch):
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mean = tf.reduce_mean(mean_full)
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logvar = tf.reduce_mean(logvar_full)
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ms_ssim_32 = tf.reduce_mean(ms_ssim_32)
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l1_loss_32 = tf.reduce_mean(l1_loss_32)
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l2_loss_32 = tf.reduce_mean(l2_loss_32)
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mean_32 = tf.reduce_mean(mean_32)
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logvar_32 = tf.reduce_mean(logvar_32)
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ms_ssim_low = tf.reduce_mean(ms_ssim_low)
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l1_loss_low = tf.reduce_mean(l1_loss_low)
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l2_loss_low = tf.reduce_mean(l2_loss_low)
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mean_low = tf.reduce_mean(mean_low)
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logvar_low = tf.reduce_mean(logvar_low)
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mesh_orig_2 = uv2mesh(batch['uv_unposed'])
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_full) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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metric_32 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_32) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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metric_low = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_low) ** 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_full) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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gradients = tape.gradient(loss, model.trainable_variables)
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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, 'train/reconstr_loss': reconstr_loss, 'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl,
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'train/var': np.e ** logvar, 'train/reg_coeff': reg_coeff, 'train/l2_loss': l2_loss,
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'train/edge_loss_32': e_loss_32, 'train/collision_loss_32': c_loss_32, 'train/l1_loss_32': l1_loss_32, 'train/euclid_error_32': metric_32, 'train/ms_ssim_32': ms_ssim_32,
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'train/mean_32': mean_32, 'train/logvar_32': logvar_32, 'train/kl_loss_32': kl_32,
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'train/var_32': np.e ** logvar_32, 'train/l2_loss_32': l2_loss_32 }
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'train/edge_loss' + resolution_modifier: e_loss_low, 'train/collision_loss' + resolution_modifier: c_loss_low, 'train/l1_loss' + resolution_modifier: l1_loss_low, 'train/euclid_error' + resolution_modifier: metric_low, 'train/ms_ssim' + resolution_modifier: ms_ssim_low,
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'train/mean' + resolution_modifier: mean_low, 'train/logvar' + resolution_modifier: logvar_low, 'train/kl_loss' + resolution_modifier: kl_low,
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'train/var' + resolution_modifier: np.e ** logvar_low, 'train/l2_loss' + resolution_modifier: l2_loss_low }
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@tf.function
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@@ -235,26 +234,26 @@ 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_32 = tf.one_hot(batch['uv_curvature_32'], 7)
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pred_full, mean_full, logvar_full, pred_32, mean_32, logvar_32, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature,
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batch['uv_unposed_32'], batch['uv_static_32'], batch['uv_body_posed_32'], uv_curvature_32])
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uv_curvature_low = tf.one_hot(batch['uv_curvature' + resolution_modifier], 7)
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pred_full, mean_full, logvar_full, pred_low, mean_low, logvar_low, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature,
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batch['uv_unposed' + resolution_modifier], batch['uv_static' + resolution_modifier], batch['uv_body_posed' + resolution_modifier], uv_curvature_low])
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mesh_pred_full = uv2mesh(pred_full)
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mesh_pred_32 = uv2mesh(pred_32)
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mesh_pred_low = uv2mesh(pred_low)
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mesh_orig = batch['mesh_unposed']
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orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1])
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uv_static_img = tf.transpose(batch['uv_static'], [0,2,3,1])
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uv_body_posed_img = tf.transpose(batch['uv_body_posed'], [0,2,3,1])
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pred_img = pred_full
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pred_32_up = tf.image.resize(pred_32, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img, pred_32_up), axis=2)
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pred_low_up = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img, pred_low_up), axis=2)
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imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
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F = np.load("custom_utils/smpl_faces.npy")
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mask = batch['mesh_mask'].numpy().astype(bool)
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garment_unposed, faces = maskFacesBatch(mesh_pred_full.numpy(), F, mask)
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garment_unposed_32, faces = maskFacesBatch(mesh_pred_32.numpy(), F, mask)
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garment_unposed_low, faces = maskFacesBatch(mesh_pred_low.numpy(), F, mask)
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gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred_full.shape[0])]
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body = batch['mesh_body_posed'].numpy()
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@@ -269,7 +268,7 @@ def plot_everything(batch: dict, type: str):
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collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))]
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collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
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figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], garment_unposed_32[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
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figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], garment_unposed_low[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
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mean_hist = wandb.Histogram(sequence=mean_full.numpy().mean(axis=0))
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logvar_hist = wandb.Histogram(sequence=logvar_full.numpy().mean(axis=0))
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@@ -277,8 +276,6 @@ def plot_everything(batch: dict, type: str):
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return { **figs, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
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def sample(folder):
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# run = wandb.init(project='vae_plots', name=folder.split('/')[-1])
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load_model(model,folder)
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@@ -400,6 +397,7 @@ def sample(folder):
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print()
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if __name__ == '__main__':
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init_wandb()
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allow_memory_growth()
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@@ -408,6 +406,8 @@ if __name__ == '__main__':
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args = vars(parser.parse_args())
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batch_size = 4
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low_resolution = 128
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resolution_modifier = '_' + str(low_resolution)
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smpl = SMPL()
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@@ -416,31 +416,30 @@ if __name__ == '__main__':
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'pose_shape': (72 + 11,),
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# 'uv_posed': (3, 512, 256,),
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'uv_body_posed': (3, 512, 256,),
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'uv_body_posed_32': (3, 32, 16,),
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'uv_body_posed' + resolution_modifier: (3, low_resolution, low_resolution//2,),
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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': (3, 512, 256,),
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'uv_unposed_32': (3, 32, 16,),
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'uv_unposed' + resolution_modifier: (3, low_resolution, low_resolution//2,),
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'uv_curvature': (512, 256),
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'uv_curvature_32': (32, 16),
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'uv_curvature' + resolution_modifier: (low_resolution, low_resolution//2),
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}
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signature_static = { 'uv_static': (3, 512, 256,),
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'uv_static_32': (3, 32, 16,),
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'uv_static' + resolution_modifier: (3, low_resolution, low_resolution//2,),
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# 'mesh_static': (14475, 3,),
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'mesh_mask': (14475,),
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'uv_mask': (512, 256,),
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'uv_shaped_body': (3, 512, 256,),
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# 'uv_shaped_body': (3, 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=True, resolutions=[512, 32])
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=True, resolutions=[512, 32])
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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=True, resolutions=[512, low_resolution])
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static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=True, resolutions=[512, low_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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model = Pyramid(latent_dim=128)
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model = Pyramid(latent_dim=128, low_res=low_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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@@ -61,6 +61,8 @@ class StaticDataset():
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assert set(resolutions).issubset(set([32, 128, 512]))
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self.possible_keys = ['uv_static', 'uv_static_32', 'uv_static_128' 'mesh_static', 'mesh_mask', 'uv_mask', 'uv_shaped_body']
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self.keys = [value for value in signature.keys() if value in self.possible_keys]
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if 'uv_static_128' in signature.keys():
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self.keys.append('uv_static_128')
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self.resolutions = resolutions
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self.filled = ''
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if use_filled:
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@@ -128,3 +128,54 @@ class AutoEncoder32(tf.keras.Model):
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# x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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class Decoder128(tf.keras.Model):
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def __init__(self, bottleneck_size, latent_dim):
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super(Decoder128, self).__init__()
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self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
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self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
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self.decoder = ConvNeXtReverseLabels(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])), nr_of_classes=7)
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def call(self, inputs, training=None):
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x = inputs
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x = self.dense(x)
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x = self.reshape(x)
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x = self.decoder(x)
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return x
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class AutoEncoder128(tf.keras.Model):
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def __init__(self):
|
||||
super(AutoEncoder128, self).__init__()
|
||||
|
||||
bottleneck_size = 512
|
||||
latent_dim = 128
|
||||
|
||||
self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
|
||||
self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
|
||||
self.decoder = Decoder128(bottleneck_size, latent_dim)
|
||||
|
||||
|
||||
def call(self, inputs, training=None):
|
||||
x, uv_mask = inputs
|
||||
|
||||
# x = tf.transpose(x, [0,2,3,1])
|
||||
x = self.encoder(x, training=training)
|
||||
|
||||
x = self.bottleneck(x, training=training)
|
||||
|
||||
x = self.decoder(x, training=training)
|
||||
# x = tf.transpose(x, [0,3,1,2])
|
||||
|
||||
# x = x * uv_mask[:,:, tf.newaxis]
|
||||
# x = x * uv_mask[:,tf.newaxis,:,:]
|
||||
# x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
return x
|
||||
|
||||
@@ -121,3 +121,52 @@ class AutoEncoder32(tf.keras.Model):
|
||||
# x = x * uv_mask[:,tf.newaxis,:,:]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Decoder128(tf.keras.Model):
|
||||
|
||||
def __init__(self, bottleneck_size, latent_dim):
|
||||
super(Decoder128, 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([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])))
|
||||
|
||||
def call(self, inputs, training=None):
|
||||
x = inputs
|
||||
|
||||
x = self.dense(x)
|
||||
x = self.reshape(x)
|
||||
x = self.decoder(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class AutoEncoder128(tf.keras.Model):
|
||||
|
||||
def __init__(self):
|
||||
super(AutoEncoder128, self).__init__()
|
||||
|
||||
|
||||
bottleneck_size = 512
|
||||
latent_dim = 128
|
||||
|
||||
self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
|
||||
self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
|
||||
self.decoder = Decoder128(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.bottleneck(x, training=training)
|
||||
|
||||
x = self.decoder(x, training=training)
|
||||
x = tf.transpose(x, [0,3,1,2])
|
||||
|
||||
# x = x * uv_mask[:,:, tf.newaxis]
|
||||
# x = x * uv_mask[:,tf.newaxis,:,:]
|
||||
|
||||
return x
|
||||
@@ -15,6 +15,10 @@ from models.ae_static_model import AutoEncoder as StaticAE
|
||||
from models.ae_body_model import AutoEncoder32 as BodyAE32
|
||||
from models.ae_curv_model import AutoEncoder32 as CurvatureAE32
|
||||
from models.ae_static_model import AutoEncoder32 as StaticAE32
|
||||
from models.ae_body_model import AutoEncoder128 as BodyAE128
|
||||
from models.ae_curv_model import AutoEncoder128 as CurvatureAE128
|
||||
from models.ae_static_model import AutoEncoder128 as StaticAE128
|
||||
|
||||
|
||||
class Bottleneck(tf.keras.Model):
|
||||
|
||||
@@ -339,6 +343,177 @@ class CVAE32(tf.keras.Model):
|
||||
return logits
|
||||
|
||||
|
||||
def call(self, inputs, training=None, zero_latent=False):
|
||||
uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
|
||||
uv_unposed, uv_template, uv_body_posed = tf.transpose(uv_unposed, [0,2,3,1]), tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
|
||||
z_static = self.static_enc(uv_template)
|
||||
z_body = self.body_enc(uv_body_posed)
|
||||
z_curv = self.curv_enc(uv_curv)
|
||||
|
||||
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
|
||||
|
||||
mean, logvar = self.encoder(x)
|
||||
z = self.reparameterize(mean, logvar)
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
z_cond = self.cond_encoder(z_cond)
|
||||
|
||||
if zero_latent:
|
||||
z = tf.zeros_like(z)
|
||||
|
||||
z = tf.concat([z, z_cond], axis=-1)
|
||||
|
||||
# z = self.shuffle(z)
|
||||
|
||||
# z = tf.concat([z, z_curv], axis=-1)
|
||||
x = self.decoder(z)
|
||||
|
||||
|
||||
# x = tf.transpose(x, [0,3,1,2])
|
||||
# x = x * uv_mask[:,tf.newaxis,:,:]
|
||||
# x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
# x = x + tf.transpose(uv_shaped_body, [0,2,3,1])
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
# x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
return x, mean, logvar, z
|
||||
|
||||
|
||||
|
||||
|
||||
class Encoder128(tf.keras.Model):
|
||||
|
||||
def __init__(self, latent_dim):
|
||||
super(Encoder128, self).__init__()
|
||||
|
||||
self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[256, 256, 128, 64], include_top=False)
|
||||
self.flatten = tf.keras.layers.Flatten()
|
||||
self.mean = tf.keras.layers.Dense(latent_dim)
|
||||
self.log_var = tf.keras.layers.Dense(latent_dim)
|
||||
|
||||
def call(self, inputs, training=None):
|
||||
x = inputs
|
||||
|
||||
x = self.encoder(x)
|
||||
x = self.flatten(x)
|
||||
mean = self.mean(x)
|
||||
log_var = self.log_var(x)
|
||||
|
||||
return mean, log_var
|
||||
|
||||
|
||||
class Decoder128(tf.keras.Model):
|
||||
|
||||
def __init__(self):
|
||||
super(Decoder128, self).__init__()
|
||||
|
||||
self.dense = tf.keras.layers.Dense(4 * 2 * 128)
|
||||
self.reshape = tf.keras.layers.Reshape((4, 2, 128))
|
||||
self.decoder = ConvNeXtReverse(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([256, 256, 128, 64])))
|
||||
|
||||
|
||||
def call(self, inputs, training=None):
|
||||
x = inputs
|
||||
|
||||
x = self.dense(x)
|
||||
x = self.reshape(x)
|
||||
x = self.decoder(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class CVAE128(tf.keras.Model):
|
||||
|
||||
def __init__(self, latent_dim=128,
|
||||
avg_garment_path='',
|
||||
static_model='',
|
||||
body_model='',
|
||||
curvature_model='',
|
||||
freeze_encoders=False):
|
||||
super(CVAE128, self).__init__()
|
||||
self.latent_dim = latent_dim
|
||||
|
||||
model : StaticAE128 = StaticAE128()
|
||||
if static_model != '':
|
||||
load_model(model, static_model)
|
||||
self.static_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
|
||||
|
||||
model : BodyAE128 = BodyAE128()
|
||||
if body_model != '':
|
||||
load_model(model, body_model)
|
||||
self.body_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
|
||||
|
||||
model : CurvatureAE128 = CurvatureAE128()
|
||||
if curvature_model != '':
|
||||
load_model(model, curvature_model)
|
||||
self.curv_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
|
||||
|
||||
if freeze_encoders:
|
||||
self.static_enc.trainable = False
|
||||
self.body_enc.trainable = False
|
||||
self.curv_enc.trainable = False
|
||||
|
||||
self.encoder = Encoder128(latent_dim)
|
||||
self.decoder = Decoder128()
|
||||
|
||||
if avg_garment_path != '':
|
||||
self.avg_garment = np.load(avg_garment_path)
|
||||
else:
|
||||
self.avg_garment = None
|
||||
|
||||
self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
|
||||
tf.keras.layers.Dense(256, activation='relu'),
|
||||
tf.keras.layers.Dense(128, activation='relu'),
|
||||
tf.keras.layers.Dense(latent_dim),])
|
||||
|
||||
|
||||
# @tf.function
|
||||
def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, nr_of_samples=4, fix_noise=False, zero_latent=False):
|
||||
if not fix_noise:
|
||||
eps = tf.random.normal(shape=(nr_of_samples, self.latent_dim))
|
||||
else:
|
||||
eps = tf.random.normal(shape=(1, self.latent_dim))
|
||||
eps = tf.tile(eps, [nr_of_samples,1])
|
||||
|
||||
uv_template = tf.tile(tf.transpose(uv_template, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
|
||||
uv_body_posed = tf.tile(tf.transpose(uv_body_posed, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
|
||||
if len(uv_curv.shape) == 3:
|
||||
uv_curv = tf.tile(uv_curv[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
|
||||
uv_mask = tf.tile(uv_mask[tf.newaxis,:,:], [nr_of_samples,1,1])
|
||||
|
||||
# uv_template, uv_body_posed = tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
|
||||
z_static = self.static_enc(uv_template)
|
||||
z_body = self.body_enc(uv_body_posed)
|
||||
z_curv = self.curv_enc(uv_curv)
|
||||
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
z_cond = self.cond_encoder(z_cond)
|
||||
|
||||
if zero_latent:
|
||||
eps = tf.zeros_like(eps)
|
||||
|
||||
z = tf.concat([eps, z_cond], axis=-1)
|
||||
|
||||
x = self.decoder(z)
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def reparameterize(self, mean, logvar):
|
||||
eps = tf.random.normal(shape=mean.shape)
|
||||
return eps * tf.exp(logvar * .5) + mean
|
||||
|
||||
|
||||
def decode(self, z, apply_sigmoid=False):
|
||||
logits = self.decoder(z)
|
||||
if apply_sigmoid:
|
||||
probs = tf.sigmoid(logits)
|
||||
return probs
|
||||
return logits
|
||||
|
||||
|
||||
def call(self, inputs, training=None, zero_latent=False):
|
||||
uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
|
||||
uv_unposed, uv_template, uv_body_posed = tf.transpose(uv_unposed, [0,2,3,1]), tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
|
||||
|
||||
@@ -7,15 +7,18 @@ import numpy as np
|
||||
# import plotly.express as ex
|
||||
|
||||
from custom_utils.custom_utils import load_model
|
||||
from models.cvae_model import CVAE, CVAE32
|
||||
from models.cvae_model import CVAE, CVAE128, CVAE32
|
||||
|
||||
|
||||
class Pyramid(tf.keras.Model):
|
||||
|
||||
def __init__(self, latent_dim=128, avg_garment_path=''):
|
||||
def __init__(self, low_res=32, latent_dim=128, avg_garment_path=''):
|
||||
super(Pyramid, self).__init__()
|
||||
|
||||
self.model_32 = CVAE32(latent_dim=latent_dim)
|
||||
if low_res == 32:
|
||||
self.model_low = CVAE32(latent_dim=latent_dim)
|
||||
if low_res == 128:
|
||||
self.model_low = CVAE128(latent_dim=latent_dim)
|
||||
self.model_full = CVAE(latent_dim=latent_dim)
|
||||
|
||||
if avg_garment_path != '':
|
||||
@@ -24,16 +27,16 @@ class Pyramid(tf.keras.Model):
|
||||
self.avg_garment = None
|
||||
|
||||
def call(self, inputs, training=None):
|
||||
uv_unposed, uv_static, uv_body_posed, uv_curv, uv_unposed_32, uv_static_32, uv_body_posed_32, uv_curv_32 = inputs
|
||||
uv_unposed, uv_static, uv_body_posed, uv_curv, uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low = inputs
|
||||
|
||||
pred_32, mean_32, logvar_32, _ = self.model_32([uv_unposed_32, uv_static_32, uv_body_posed_32, uv_curv_32], training=training)
|
||||
pred_low, mean_low, logvar_low, _ = self.model_low([uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low], training=training)
|
||||
|
||||
x = tf.image.resize(pred_32, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
|
||||
pred_full, mean_full, logvar_full, _ = self.model_full([uv_unposed, uv_static, uv_body_posed, uv_curv], training=training)
|
||||
|
||||
x = x + pred_full
|
||||
|
||||
return x, mean_full, logvar_full, pred_32, mean_32, logvar_32,
|
||||
return x, mean_full, logvar_full, pred_low, mean_low, logvar_low,
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user