working weight learning
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@@ -38,7 +38,7 @@ else:
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from losses import collision_ratio, kl_loss, normal_loss #, concept_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, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
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from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision, plot_weights
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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 CVAENew as CVAE
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@@ -71,9 +71,15 @@ def kl_loss(mean, logvar):
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return kl_loss
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def blend_weights_reg(pred_w):
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orig = smpl.weights
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l1 = tf.reduce_mean(tf.abs(pred_w - orig[None, 0, :, :]))
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return l1 * 1e-3
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# 3d loss on posed !!!!!!!!!!!!
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# @tf.function
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@tf.function
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def train_step(batch):
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with tf.GradientTape() as tape:
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@@ -82,7 +88,7 @@ def train_step(batch):
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else:
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mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
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pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
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pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
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mesh_pred = uv2mesh(pred)
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@@ -94,25 +100,26 @@ 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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else:
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mesh_pred_posed = mesh_pred
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mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights)
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# is the output already masked?
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
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# l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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# conc_loss = concept_loss(pred, batch['uv_unposed'])
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kl = kl_loss(mean, logvar)
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weights_l = blend_weights_reg(weights)
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reconstr_loss = l2_loss
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reconstr_loss = l2_mesh_loss + weights_l
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if config['use_extra_losses']:
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if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True:
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reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss) * config['reg_loss_coeff']
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reconstr_loss = reconstr_loss + (e_loss) * config['reg_loss_coeff']
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else:
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reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
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reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
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@@ -128,8 +135,8 @@ def train_step(batch):
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return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
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'train/reconstr_loss': reconstr_loss, 'train/collision_ratio': c_ratio,
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'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
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'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
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# 'train/ms_ssim': ms_ssim,
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'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss, 'train/weights_loss': weights_l,
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# 'train/ms_ssim': ms_ssim, 'train/l2_loss': l2_loss,
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}
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@@ -140,7 +147,7 @@ def valid_step(batch):
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else:
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mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
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pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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mesh_pred = uv2mesh(pred)
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@@ -152,22 +159,23 @@ def valid_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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else:
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mesh_pred_posed = mesh_pred
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mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights)
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# is the output already masked?
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
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# l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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# conc_loss = concept_loss(pred, batch['uv_unposed'])
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kl = kl_loss(mean, logvar)
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weights_l = blend_weights_reg(weights)
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reconstr_loss = l2_loss
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reconstr_loss = l2_mesh_loss + weights_l
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if config['use_extra_losses']:
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reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
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reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
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if config['use_norm_loss']:
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reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
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@@ -182,7 +190,7 @@ def valid_step(batch):
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return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric,
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'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss, 'valid/collision_ratio': c_ratio,
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'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar,
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'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar, 'valid/weights_loss': weights_l,
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'valid/normal_loss': n_loss,# 'valid/ms_ssim': ms_ssim,
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}
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@@ -194,7 +202,7 @@ def test_step(batch):
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else:
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mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
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pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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mesh_pred = uv2mesh(pred)
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@@ -208,7 +216,7 @@ def test_step(batch):
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mesh_orig_shaped = mesh_orig + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_orig_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_orig_shaped)
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else:
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mesh_pred_posed = mesh_pred
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mesh_pred_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights)
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mesh_orig_posed = mesh_orig
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mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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@@ -250,7 +258,7 @@ def plot_everything(batch: dict, type: str):
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else:
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mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
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pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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mesh_pred = uv2mesh(pred).numpy()
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@@ -277,7 +285,7 @@ def plot_everything(batch: dict, type: str):
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mesh_pred[i, mask[i], :] += shape_offsets.numpy()[i, mask[i], :]
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mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy()
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else:
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mesh_posed = mesh_pred
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mesh_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights).numpy()
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gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
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garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
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@@ -296,13 +304,16 @@ def plot_everything(batch: dict, type: str):
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collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))]
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collision_gt = [np.concatenate((body[i], gt_posed[i]), 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], faces[i], garment_posed[i], faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
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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) }
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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) }
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figs_weights = { type + '/weights_' + str(i): plot_weights(weights[0,:,i], body[0], F) for i in range(24) }
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# figs_weights = { type + '/weights_' + str(i): plot_weights(smpl.weights[0,:,i], body[0], F) for i in range(24) }
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mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0))
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logvar_hist = wandb.Histogram(sequence=logvar.numpy().mean(axis=0))
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return { **figs, **figs_coll, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
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return { **figs, **figs_coll, **figs_weights, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
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def sample(folder):
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@@ -476,7 +487,7 @@ def test():
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if __name__ == '__main__':
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# init_wandb()
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init_wandb()
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--name', type=str)
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args = vars(parser.parse_args())
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