convolutional weights model

configs/config_abl_all_post.py

@@ -20,8 +20,8 @@ ablation_count = [config['use_skinning'], config['remove_global_orientation'], c
 assert ablation_count == 1 or ablation_count == 0
 
 config['gpu'] = '5'
-config['name'] = 'iccv_weights_test_3'
+config['name'] = 'iccv_weights_test_5'
-config['notes'] = ''
+config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning'
 
 config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
 if config['use_skinning']:
@@ -93,7 +93,8 @@ config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
 config['cvae']['encoder_dims'] = [32, 64, 128, 256, 64]
 config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
 config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
-
+config['cvae']['decoder_weights_depths'] = [3, 3, 3, 5, 3]
+config['cvae']['decoder_weights_dims'] = [24, 32, 64, 128, 256]
 
 config['cond_ae_static'] = dict()
 config['cond_ae_static']['is_label'] = False

convnext/models/convnext_tf.py

@@ -222,7 +222,7 @@ class ConvNeXtReverse(tf.keras.Model):
 
     def __init__(self, 
                  depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], kernel_size=7,
-                 drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
+                 drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., learn_weights=False,
                  ):
         super().__init__()
         self.upsample_layers = []  # stem and 3 intermediate downsampling conv layers
@@ -255,27 +255,20 @@ class ConvNeXtReverse(tf.keras.Model):
             self.stages.append(stage)
             cur += depths[i]
 
+        if not learn_weights:
             self.last = Dense(3, activation='tanh')
+        else:
+            self.last = Activation('softmax')
+            # self.last = Dense(24, activation='softmax')
 
-        self.weights_model = tf.keras.Sequential([Block(dim=dims[-1]*2, kernel_size=kernel_size),
-                                                  Block(dim=dims[-1]*2, kernel_size=kernel_size),
-                                                  Block(dim=dims[-1]*2, kernel_size=kernel_size),
-                                                  Flatten(),
-                                                  Dense(128 * 24),
-                                                  Reshape((24, 128)),
-                                                  Dense(14475),
-                                                  Lambda(lambda x: tf.transpose(x, (0, 2, 1))),
-                                                  Lambda(lambda x: tf.nn.softmax(x, axis=-1))])
 
     def call(self, x, training):
-        weights = self.weights_model(x, training=training)
-        
         for i in reversed(range(len(self.upsample_layers))):
             x = self.upsample_layers[i](x, training=training)
             x = self.stages[i](x, training=training)
 
         x = self.last(x, training=training)
-        return x, weights
+        return x
 
 
 class ConvNeXtReverseWithSkip(tf.keras.Model):

cvae.py

@@ -38,7 +38,7 @@ else:
 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, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision, plot_weights
+from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, UV2MeshLabel, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision, plot_weights
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
 from models.cvae_model import CVAENew as CVAE
@@ -62,6 +62,7 @@ def init_wandb():
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
+uv2mesh_label = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
 
 
 def kl_loss(mean, logvar):
@@ -71,10 +72,10 @@ def kl_loss(mean, logvar):
     return kl_loss
 
 
-def blend_weights_reg(pred_w):
+def blend_weights_reg(pred_w, gender=None):
     orig = smpl.weights
     l1 = tf.reduce_mean(tf.abs(pred_w - orig[None, 0, :, :]))
-    return l1 * 1e-3
+    return l1
 
 
 # 3d loss on posed !!!!!!!!!!!!
@@ -91,6 +92,7 @@ def train_step(batch):
         pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
 
         mesh_pred = uv2mesh(pred)
+        mesh_weigths = uv2mesh_label(weights)
 
         if config['use_skinning']:
             shape = batch['pose_shape'][:, 72:]
@@ -100,7 +102,12 @@ def train_step(batch):
             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 = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights)
+            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_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths)
 
         # is the output already masked?
 
@@ -112,9 +119,9 @@ def train_step(batch):
         n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
         # conc_loss = concept_loss(pred, batch['uv_unposed'])
         kl = kl_loss(mean, logvar)
-        weights_l = blend_weights_reg(weights)
+        weights_l = blend_weights_reg(mesh_weigths)
 
-        reconstr_loss = l2_mesh_loss + weights_l
+        reconstr_loss = l2_mesh_loss + 1e-6 * weights_l
         if config['use_extra_losses']:
             if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True:
                 reconstr_loss = reconstr_loss + (e_loss) * config['reg_loss_coeff']
@@ -150,6 +157,7 @@ def valid_step(batch):
     pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
 
     mesh_pred = uv2mesh(pred)
+    mesh_weigths = uv2mesh_label(weights)
 
     if config['use_skinning']:
         shape = batch['pose_shape'][:, 72:]
@@ -159,7 +167,12 @@ def valid_step(batch):
         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 = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights)
+        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_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths)
 
     # is the output already masked?
 
@@ -171,9 +184,9 @@ def valid_step(batch):
     n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
     # conc_loss = concept_loss(pred, batch['uv_unposed'])
     kl = kl_loss(mean, logvar)
-    weights_l = blend_weights_reg(weights)
+    weights_l = blend_weights_reg(mesh_weigths)
 
-    reconstr_loss = l2_mesh_loss + weights_l
+    reconstr_loss = l2_mesh_loss + 1e-6 * weights_l
     if config['use_extra_losses']:
         reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
     if config['use_norm_loss']:
@@ -261,6 +274,7 @@ def plot_everything(batch: dict, type: str):
     pred, mean, logvar, weights = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
     
     mesh_pred = uv2mesh(pred).numpy()
+    mesh_weigths = uv2mesh_label(weights)
     
     orig_img = uv_orig
     uv_static_img = batch['uv_static']
@@ -285,7 +299,12 @@ def plot_everything(batch: dict, type: str):
             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 = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred, weights).numpy()
+        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_posed = smpl.run_garment_with_weights(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped, mesh_weigths).numpy()
         gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
         garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
 
@@ -305,9 +324,9 @@ def plot_everything(batch: dict, type: str):
     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], garment_posed[i], faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) 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 = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i], flip_xy=(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) }
+    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=(config['remove_global_orientation'])) for i in range(4) }
-    figs_weights = { type + '/weights_' + str(i): plot_weights(weights[0,:,i], body[0], F) for i in range(24) }
+    figs_weights = { type + '/weights_' + str(i): plot_weights(mesh_weigths[0,:,i], body[0], F) for i in range(24) }
     # figs_weights = { type + '/weights_' + str(i): plot_weights(smpl.weights[0,:,i], body[0], F) for i in range(24) }
 
     mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0))

models/base.py

@@ -171,13 +171,13 @@ class AEEncoderNew(tf.keras.Model):
 
 class DecoderNew(tf.keras.Model):
     
-    def __init__(self, depths, dims, kernel_size=7, is_label=False):
+    def __init__(self, depths, dims, kernel_size=7, is_label=False, learn_weights=False):
         super(DecoderNew, self).__init__()
 
         self.is_label = is_label
 
         if not is_label:
-            self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size)
+            self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size, learn_weights=learn_weights)
         else:
             self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7)
             

models/cvae_model.py

@@ -181,6 +181,7 @@ class CVAENew(tf.keras.Model):
 
         self.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
         self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
+        self.decoder_weights = DecoderNew(config['cvae']['decoder_weights_depths'], config['cvae']['decoder_weights_dims'], config['cvae']['kernel_size'], learn_weights=True)
 
         self.reshape = tf.keras.layers.Reshape((8, 4, 64))
 
@@ -213,7 +214,8 @@ class CVAENew(tf.keras.Model):
         
         z = tf.concat([eps, z_cond], axis=-1)
 
-        x, weights = self.decoder(z, training=False)
+        x = self.decoder(z, training=False)
+        weights = self.decoder_weights(z, training=False)
         
         if self.avg_garment is not None:
             x = x + self.avg_garment[tf.newaxis,:,:,:]
@@ -271,7 +273,8 @@ class CVAENew(tf.keras.Model):
 
 
         z = tf.concat([z, z_cond], axis=-1)
-        x, weights = self.decoder(z, training=training)
+        x = self.decoder(z, training=training)
+        weights = self.decoder_weights(z, training=training)
 
         if self.avg_garment is not None:
             x = x + self.avg_garment[tf.newaxis,:,:,:]