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