lot of things

losses.py

@@ -6,6 +6,11 @@ import scipy.io as sio
 
 from scipy.spatial import cKDTree
 from tensorflow_graphics.geometry.representation.mesh.normals import vertex_normals
+from tensorflow_graphics.geometry.convolution.graph_convolution import edge_convolution_template
+
+from custom_utils.custom_utils import maskFacesBatch, _maskFaces
+from custom_utils.graph_utils import sparse_to_tensor
+
 
 def d_logistic(real_images, generator, discriminator, z_dim, conditions):
     batch_size = tf.shape(real_images)[0]
@@ -202,6 +207,57 @@ def uv_mask_loss(pred, uv_mask):
     return invalid_count / total_count
 
 
+
+
+def merge_verts(Vs, batchDim):
+    V = tf.concat(tf.unstack(Vs, num=batchDim), axis=0)
+    return V
+    
+
+def _bend_loss_single(x, F, L, mask):
+    verts = tf.boolean_mask(x, mask, axis=0)
+    VN = vertex_normals(verts, F)
+    bend = edge_convolution_template(
+        VN, L, sizes=None,
+        edge_function=lambda x,y: (x - y)**2,
+        reduction='weighted',
+        edge_function_kwargs={}
+    )
+    loss = tf.reduce_sum(bend)
+    return loss
+
+def bend_loss(x, F, L, mask):
+    # x: predicted outfits
+    # F: faces
+    # L: laplacian
+    loss = tf.stack([_bend_loss_single(x[i], F[i], L[i], mask[i]) for i in range(x.shape[0])])
+    return loss
+
+
+# def bend_loss(x, F, L, mask):
+#     # x: predicted outfits
+#     # F: faces
+#     # L: laplacian
+#     # x = x * mask[:,:,tf.newaxis]
+#     batch_size = x.shape[0]
+#     verts, faces = maskFacesBatch(x, F, mask)
+#     nr_valid_verts = [tf.reduce_sum(mask[i]) for i in range(batch_size)]
+#     x = tf.concat(verts, axis=0)
+#     F = tf.concat([faces[i] + tf.reduce_sum(nr_valid_verts[:i]) for i in range(1, batch_size)])
+#     F = tf.concat((faces[0], F), axis=0)
+#     VN = vertex_normals(x, F)
+#     bend = edge_convolution_template(
+#         VN, L, sizes=None,
+#         edge_function=lambda x,y: (x - y)**2,
+#         reduction='weighted',
+#         edge_function_kwargs={}
+#     )
+#     loss = tf.reduce_sum(bend, axis=[1])
+#     loss = tf.reshape(loss, (batch_size, 14475))
+#     loss = tf.reduce_mean(loss, axis=1)
+#     return loss
+
+
 if __name__ == '__main__':
     V = tf.ones((4, 14475, 3))
     B = tf.zeros((4, 14475, 3))