245 lines
8.3 KiB
Python
245 lines
8.3 KiB
Python
import torch
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import numpy as np
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# import tensorflow_graphics as tfg
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import scipy as sp
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import scipy.io as sio
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from pytorch3d.structures import Meshes
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from scipy.spatial import cKDTree
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# from tensorflow_graphics.geometry.representation.mesh.normals import vertex_normals
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# from tensorflow_graphics.geometry.convolution.graph_convolution import edge_convolution_template
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# from hbertiche/PBNS
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""" COLLISION FUNCTIONS START """
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F_coll = np.load("smpl/smpl_faces.npy")
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F_coll_skirt = np.load("smpl/smpl_faces_skirt.npy")
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F_coll_orig = np.load("smpl/smpl_faces_orig.npy")
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F_norm = np.tile(F_coll[np.newaxis,:,:], [4,1,1])
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F_norm_skirt = np.tile(F_coll_skirt[np.newaxis,:,:], [4,1,1])
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F_norm_orig = np.tile(F_coll_orig[np.newaxis,:,:], [4,1,1])
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def _nearest_neighbour(V, B):
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tree = cKDTree(B.numpy())
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return tree.query(V.numpy(), workers=-1, distance_upper_bound=0.2)[1]
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# @tf.function
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# def _tf_nn_parallel(V, B):
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# return tf.map_fn(fn=lambda elem: tf.py_function(func=_nearest_neighbour, inp=elem, Tout=tf.int32), elems=(V,B), fn_output_signature=tf.int32, parallel_iterations=V.shape[0])
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# @tf.function
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def collision_loss(V, B, mask, thr=.004):
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mesh = Meshes(B, torch.tile(F_coll[None], [B.shape[0], 1, 1]))
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B_vn = torch.stack(mesh.verts_normals_list())
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# compute correspondences
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idx = torch.stack([_nearest_neighbour(V[i], B[i]) for i in range(V.shape[0])])
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N, M = V.shape[:2]
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# _idx = tf.tile(np.array(list(range(N)), np.int32)[:,None], [1, M])
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_idx = torch.ones((N, M), dtype=torch.int32, device=V.get_device()) * np.array(list(range(N)), np.int32)[:,None]
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idx = torch.stack((_idx, idx), axis=-1)
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# compute loss
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D = V - B[idx]
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b_vn = B_vn[idx]
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dot = torch.einsum('abc,abc->ab', D, b_vn)
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thresholded = torch.minimum(dot - thr, 0) * mask
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loss = torch.sum(thresholded ** 2, axis=-1)
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ratio = torch.sum((thresholded < 0).to(torch.float32)) / torch.sum(mask)
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return loss, ratio
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# def collision_ratio(V, B, mask, thr=.004):
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# B_vn = vertex_normals(B, tf.tile(F[None], [B.shape[0], 1, 1]))
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# # compute correspondences
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# idx = _tf_nn_parallel(V, B)
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# N, M = V.shape[:2]
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# # _idx = tf.tile(np.array(list(range(N)), np.int32)[:,None], [1, M])
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# _idx = tf.ones((N, M), dtype=tf.int32) * np.array(list(range(N)), np.int32)[:,None]
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# idx = tf.stack((_idx, idx), axis=-1)
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# # compute loss
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# D = V - tf.gather_nd(B, idx)
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# b_vn = tf.gather_nd(B_vn, idx)
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# dot = tf.einsum('abc,abc->ab', D, b_vn)
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# loss = tf.minimum(dot - thr, 0) * mask # / tf.reduce_sum(mask, axis=-1)
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# ratio = tf.reduce_sum(tf.cast(loss < 0, dtype=tf.float32)) / tf.reduce_sum(mask)
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# return ratio
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""" COLLISION FUNCTIONS END """
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laplacian_loss = sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo()
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edges: torch.Tensor = torch.tensor(np.stack(np.nonzero(laplacian_loss.todense())).transpose(), device='cuda')
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laplacian_loss_skirt = sio.loadmat("custom_utils/ssmpl_skirt0.mat")['L'].tocoo()
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edges_skirt: torch.Tensor = torch.tensor(np.stack(np.nonzero(laplacian_loss.todense())).transpose(), device='cuda')
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def get_edge_mask(masks: torch.Tensor):
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if edges.get_device() != masks.get_device():
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edges.to(masks.get_device())
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return torch.min(masks[:,edges], dim=-1)[0]
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def edge_loss(x, y, mask):
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e_mask = get_edge_mask(mask)
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x_e = x[:,edges[:,0],:] - x[:,edges[:,1],:]
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x_e = torch.sum(x_e ** 2, -1)
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x_e = torch.sqrt(x_e + 1e-9)
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y_e = y[:,edges[:,0],:] - y[:,edges[:,1],:]
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y_e = torch.sum(y_e ** 2, -1)
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y_e = torch.sqrt(y_e + 1e-9)
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d_e = y_e - x_e
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d_e = d_e * e_mask
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loss = torch.sum(d_e ** 2, dim=-1)# / tf.reduce_sum(e_mask, axis=-1)
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return loss
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def get_edge_mask_skirt(masks: torch.Tensor):
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if edges.get_device() != masks.get_device():
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edges.to(masks.get_device())
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return torch.min(masks[:,edges_skirt], dim=-1)[0]
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def edge_loss_skirt(x, y, mask):
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e_mask = get_edge_mask_skirt(mask)
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x_e = x[:,edges_skirt[:,0],:] - x[:,edges_skirt[:,1],:]
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x_e = torch.sum(x_e ** 2, -1)
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x_e = torch.sqrt(x_e + 1e-9)
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y_e = y[:,edges_skirt[:,0],:] - y[:,edges_skirt[:,1],:]
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y_e = torch.sum(y_e ** 2, -1)
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y_e = torch.sqrt(y_e + 1e-9)
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d_e = y_e - x_e
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d_e = d_e * e_mask
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loss = torch.sum(d_e ** 2, dim=-1)# / tf.reduce_sum(e_mask, axis=-1)
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return loss
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# class PairwiseDistance(tf.keras.layers.Layer):
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# def __init__(self, **kwargs):
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# super(PairwiseDistance, self).__init__(**kwargs)
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# def call(self, A, B):
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# rA = tf.reduce_sum(tf.square(A), axis=-1)
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# rB = tf.reduce_sum(tf.square(B), axis=-1)
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# transpose_axes = [0, 2, 1]
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# distances = - 2*tf.matmul(A, tf.transpose(B, transpose_axes)) + rA[:, :, tf.newaxis] + rB[:, tf.newaxis, :]
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# return distances
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# class NearestNeighbour(tf.keras.layers.Layer):
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# def __init__(self, **kwargs):
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# super(NearestNeighbour, self).__init__(**kwargs)
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# def call(self, A, B):
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# distances = PairwiseDistance(dtype=self.dtype)(A, B)
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# nearest_neighbour = tf.argmin(distances, axis=-1)
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# return tf.cast(nearest_neighbour, dtype=tf.int32)
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#
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# @tf.function
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def collision_penalty(va, vb, mask, eps=1e-3):
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nb = vertex_normals(vb, tf.tile(F[None], [vb.shape[0], 1, 1]))
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batch_size = tf.cast(tf.shape(va)[0], va.dtype)
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closest_vertices = NearestNeighbour(dtype=va.dtype)(va, vb)
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vb = tf.gather(vb, closest_vertices, batch_dims=1)
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nb = tf.gather(nb, closest_vertices, batch_dims=1)
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distance = tf.reduce_sum(nb*(va - vb), axis=-1)
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interpenetration = tf.maximum(eps - distance * mask, 0)
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return tf.reduce_sum(interpenetration**3) / batch_size
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whole_garment_uv_mask = np.load('custom_utils/mask_256x512.npy')
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def uv_mask_loss(pred, uv_mask):
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inv_mask = (1 - uv_mask) * whole_garment_uv_mask
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pred = tf.reduce_sum(pred, axis=1)
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invalid = pred * inv_mask
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invalid = invalid > 0.01
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invalid_count = tf.math.count_nonzero(invalid, dtype=tf.float32)
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total_count = tf.math.count_nonzero(inv_mask, dtype=tf.float32)
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return invalid_count / total_count
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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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def _bend_loss_single(x, F, L, mask):
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verts = tf.boolean_mask(x, mask, axis=0)
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VN = vertex_normals(verts, F)
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bend = edge_convolution_template(
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VN, L, sizes=None,
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edge_function=lambda x,y: (x - y)**2,
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reduction='weighted',
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edge_function_kwargs={}
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)
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loss = tf.reduce_sum(bend)
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return loss
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def bend_loss(x, F, L, mask):
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# x: predicted outfits
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# F: faces
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# L: laplacian
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loss = tf.stack([_bend_loss_single(x[i], F[i], L[i], mask[i]) for i in range(x.shape[0])])
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return loss
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# def bend_loss(x, F, L, mask):
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# # x: predicted outfits
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# # F: faces
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# # L: laplacian
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# # x = x * mask[:,:,tf.newaxis]
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# batch_size = x.shape[0]
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# verts, faces = maskFacesBatch(x, F, mask)
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# nr_valid_verts = [tf.reduce_sum(mask[i]) for i in range(batch_size)]
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# x = tf.concat(verts, axis=0)
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# F = tf.concat([faces[i] + tf.reduce_sum(nr_valid_verts[:i]) for i in range(1, batch_size)])
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# F = tf.concat((faces[0], F), axis=0)
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# VN = vertex_normals(x, F)
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# bend = edge_convolution_template(
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# VN, L, sizes=None,
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# edge_function=lambda x,y: (x - y)**2,
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# reduction='weighted',
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# edge_function_kwargs={}
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# )
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# loss = tf.reduce_sum(bend, axis=[1])
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# loss = tf.reshape(loss, (batch_size, 14475))
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# loss = tf.reduce_mean(loss, axis=1)
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# return loss
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def log_normal_pdf(sample, mean, logvar, raxis=1):
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log2pi = tf.math.log(2. * np.pi)
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res = tf.reduce_sum(-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi), axis=raxis)
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return res
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def kl_loss(mean, logvar):
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kl_loss = 1 + logvar - tf.square(mean) - tf.exp(logvar)
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kl_loss = tf.reduce_mean(kl_loss)
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kl_loss *= -0.5
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return kl_loss
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def normal_loss(pred, gt, mask, is_skirt=False, is_smpl_orig=False):
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f_norm = None
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if not is_skirt and not is_smpl_orig:
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f_norm = F_norm
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elif is_skirt:
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f_norm = F_norm_skirt
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elif is_smpl_orig:
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f_norm = F_norm_orig
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else:
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raise NotImplementedError
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pred_norm = vertex_normals(pred, f_norm)
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gt_norm = vertex_normals(gt, f_norm)
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return tf.reduce_mean(tf.reduce_mean((tf.reduce_sum(tf.math.abs(pred_norm - gt_norm), axis=-1) * mask), axis=-1))
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