remove unused parts

custom_utils/custom_utils.py

@@ -9,9 +9,6 @@ from plotly.subplots import make_subplots
 from torchvision.transforms import Resize
 from torchvision.transforms.functional import InterpolationMode
 
-# from tensorflow_graphics.geometry.convolution.graph_pooling import pool, unpool
-# from tensorflow_graphics.geometry.convolution.graph_convolution import edge_convolution_template
-
 
 def m2uv_array(m2uv, is_smpl_orig=False):
     result = np.zeros((14475 if not is_smpl_orig else 3638, 2), dtype=np.int32)
@@ -74,35 +71,6 @@ class UV2Mesh(nn.Module):
         return x
 
 
-
-class UV2MeshLabel():
-    def __init__(self, res=256, is_skirt=False, is_smpl_orig=False):
-        super(UV2MeshLabel, self).__init__()
-        self.res = res
-        uv_map = np.load('custom_utils/stretched_uv_map_' + ('skirt' if is_skirt else '3') + ('_orig' if is_smpl_orig else '') + '.npy')
-        self.uv_map_discreet = np.floor(uv_map * self.res).astype(np.int32)
-        m2uv = np.load('custom_utils/m2uv' + ('_skirt' if is_skirt else '') + ('_orig' if is_smpl_orig else '') + '.npy', allow_pickle=True).item()
-        m2uv = m2uv_array(m2uv, is_smpl_orig=is_smpl_orig)
-        self.uv_pixels = self.uv_map_discreet[m2uv]
-
-    # input = channels_first
-    # @tf.function
-    def uv_to_mesh(self, uv):
-        batch_size = uv.shape[0]
-
-        x1_ind = tf.tile(self.uv_pixels[:, 0, :][tf.newaxis, :, :], [batch_size, 1, 1])
-        x2_ind = tf.tile(self.uv_pixels[:, 1, :][tf.newaxis, :, :], [batch_size, 1, 1])
-
-        x1 = tf.gather_nd(uv, x1_ind, batch_dims=1)
-        x2 = tf.gather_nd(uv, x2_ind, batch_dims=1)
-
-        mesh = (x1 + x2) / 2
-        return mesh
-
-    def __call__(self, uv):
-        return self.uv_to_mesh(uv)
-
-
 def _maskFaces(V, F, mask, return_unfixed=False):
     V_mask = mask
     F_mask = np.take(V_mask, F.reshape(-1), axis=0)
@@ -119,20 +87,6 @@ def _maskFaces(V, F, mask, return_unfixed=False):
         return V_new, F_new
 
 
-# def _maskFaces_tf(V, F, mask):
-#     V_mask = mask
-#     F_mask = tf.gather(V_mask, F.reshape(-1), axis=0)
-#     F_mask = F_mask.reshape((-1, 3)).sum(axis=1) == 3
-#     F_mask = tf.reduce_sum(tf.reshape(F_mask, (-1, 3)), axis=1) == 3
-
-#     V_ind = tf.zeros(V_mask.shape)
-#     V_ind[V_mask] = tf.range(V_mask.sum())
-#     F_new = tf.gather(V_ind, F[F_mask, :].reshape(-1), axis=0).reshape((-1, 3))
-#     V_new = V[V_mask, :]
-
-#     return V_new, F_new
-
-
 def maskFacesBatch(V, F, mask):
     Vs = []
     Fs = []
@@ -272,25 +226,6 @@ def plot_3d_collision(gt, collision_1, collision_2, collision_faces, collision_c
     return fig
 
 
-def batch_to_dict(batch, signature):
-    result = dict()
-    for i, key in enumerate(signature.keys()):
-            result[key] = batch[i]
-    return result
-
-conditions = ['pose_shape', 'uv_static', 'uv_mask', 'uv_shaped_body']
-def nest_conditions(batch):
-    batch['conditions'] = {d: batch.pop(d) for d in conditions}
-    return batch
-
-
-def sparse_to_tensor(L):
-    idx = np.mat([L.row, L.col]).transpose().astype(np.float32)
-    data = L.data.astype(dtype=np.float32)
-    shape = L.shape
-    return tf.SparseTensor(idx, data, shape)
-
-
 
 def get_summary_str(model):
     # from torchview import draw_graph
@@ -304,6 +239,7 @@ def get_summary_str(model):
 
     return [f'all_params: {total_params}, trainable: {trainable_params}']
 
+
 def get_summary_html(model):
     lines = get_summary_str(model)
     html = ''
@@ -312,88 +248,6 @@ def get_summary_html(model):
     return html
 
 
-def repeat_laplacian(L, times):
-    idx = np.repeat(np.mat([L.row, L.col]), repeats=times, axis=1)
-    idx_offset = np.linspace(0, L.shape[0]  * (times-1), times).astype(np.int32)
-    idx_offset = np.repeat(idx_offset, L.row.shape[0], axis=0)
-    idx += idx_offset[np.newaxis,:]
-    data = np.repeat(L.data, repeats=times, axis=0)
-    shape = (L.shape[0] * times, L.shape[1] * times)
-    return sp.sparse.coo_matrix((data, (np.squeeze(np.asarray(idx[0,:])), np.squeeze(np.asarray(idx[1,:])))), shape)
-
-
-def merge_verts(Vs, batchDim):
-    V = tf.concat(tf.unstack(Vs, num=batchDim), axis=0)
-    return V
-
-
-class GraphConvolution(nn.Module):
-    def __init__(self, shape, activation=None, name='gcn', L=None, batchSize=None, normalization=None):
-        super(GraphConvolution, self).__init__()
-        self.shape = shape
-        self.w = self.add_weight(name=name + '/weight', shape=(2, *shape), initializer=tf.initializers.glorot_normal, trainable=True)
-        self.b = self.add_weight(name=name + '/bias', shape=shape[-1], initializer=tf.initializers.zeros, trainable=True)
-        self.activation = activation or (lambda x: x)
-        self.normalization = normalization
-        self.batchSize = batchSize
-        self.indices = tf.cast(tf.linspace(0, self.batchSize * L.shape[1], self.batchSize+1), dtype=tf.int32)
-        self.L = repeat_laplacian(L, self.batchSize)
-
-    def gather(self):
-        return [self.w, self.b]
-
-    def _single(self, X):
-        X0 = tf.einsum('ab,bc->ac', X, self.w[0]) # Node
-        X1 = tf.einsum('ab,bc->ac', X, self.w[1]) # Neigh
-        # Neigh conv
-        X1 = edge_convolution_template(
-            X1, sparse_to_tensor(self.L), sizes=None,
-            edge_function=lambda x, y: y, 
-            reduction='weighted', 
-            edge_function_kwargs={}
-        ) 
-        X = X0 + X1 + self.b
-        return X
-
-    def call(self, X, training):
-        X = merge_verts(X, self.batchSize)
-        X = self._single(X)
-        X = tf.stack([X[self.indices[i-1]:self.indices[i]] for i in range(1, len(self.indices))])
-        X = self.activation(X)
-        if self.normalization:
-            X = self.normalization(X, training=training)
-        return X
-
-
-def replace_arrays(string):
-    out = string[:1]
-    i = 0
-    while i < len(string) and string[i+1:].find('[') != -1:
-        start = string[i+1:].find('[')
-        end = string[i+1+start:].find(']')
-        out += string[i+1:i+1+start]
-        array = string[i+1+start:i+1+start+end+1].replace('\n', '')
-        out += array
-        i = i + 1 + start + end
-
-    out += string[i+1:]
-
-    return out
-
-
-def group_inputs_for_levels(batch, config):
-    levels_input = []
-    for res in config['pyramid']['resolutions']:
-        res_modifier = '_' + str(res) if res != 512 else ''
-        if config['use_skinning']:
-            uv_orig = batch['uv_unposed' + res_modifier]
-        else:
-            uv_orig = batch['uv_posed' + res_modifier]
-        levels_input.append([uv_orig, batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], batch['uv_curvature' + res_modifier], batch['uv_mask' + res_modifier], batch['uv_mask']])
-    conds = [batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature']]
-    return levels_input, conds
-
-
 class Accumulator():
     def __init__(self):
         self.dict = dict()