implement pytorch version

smpl_torch.py

@@ -0,0 +1,261 @@
+# based on https://github.com/CalciferZh/SMPL/blob/master/smpl_torch_batch.py
+
+import os
+import sys
+import pickle
+import torch
+import numpy as np
+import scipy.io as sio
+from time import time
+from torch.nn import Module
+from utils import plot_mesh
+
+class SMPLModel(Module):
+  def __init__(self, device=None, path='', orig_smpl=False, is_skirt=False):
+    
+    super(SMPLModel, self).__init__()
+    self.J_regressor = []
+    self.v_template = []
+    self.shapedirs = []
+    self.posedirs = []
+    self.weights = []
+    self.is_skirt = is_skirt
+    # Load SMPL models
+    # path = os.path.abspath(os.path.dirname(__file__))
+    with open(os.path.join(path, 'super_model_f.pkl'), 'rb') as f:
+        female = pickle.load(f, encoding="latin1")
+        self.J_regressor.append(torch.tensor(np.array(female['J_regressor'].todense(), dtype=np.float32)))
+        self.v_template.append(torch.tensor(female['v_template'], dtype=torch.float32))
+        self.posedirs.append(torch.tensor(female['posedirs'], dtype=torch.float32))
+        self.shapedirs.append(torch.tensor(female['shapedirs'], dtype=torch.float32))
+        self.weights.append(torch.tensor(female['weights'], dtype=torch.float32))
+    with open(os.path.join(path, 'super_model_m.pkl'), 'rb') as f:
+        male = pickle.load(f, encoding="latin1")
+        self.J_regressor.append(torch.tensor(np.array(male['J_regressor'].todense(), dtype=np.float32)))
+        self.v_template.append(torch.tensor(male['v_template'], dtype=torch.float32))
+        self.posedirs.append(torch.tensor(male['posedirs'], dtype=torch.float32))
+        self.shapedirs.append(torch.tensor(male['shapedirs'], dtype=torch.float32))
+        self.weights.append(torch.tensor(male['weights'], dtype=torch.float32))
+        kintree_table = male['kintree_table']
+        id_to_col = {kintree_table[1, i]: i for i in range(kintree_table.shape[1])}
+        self.parent = {i: id_to_col[kintree_table[0, i]] for i in range(1, kintree_table.shape[1])}
+    self.J_regressor = torch.stack(self.J_regressor, axis=0).to(device)
+    self.v_template = torch.stack(self.v_template, axis=0).to(device)
+    self.shapedirs = torch.stack(self.shapedirs, axis=0).to(device)
+    self.posedirs = torch.stack(self.posedirs, axis=0).to(device)
+    self.weights = torch.stack(self.weights, axis=0).to(device)
+
+    # Topology no head/hands/feet
+    F = sio.loadmat(os.path.join(path, 'ssmpl_faces.mat'))
+    self.f = np.array(F['faces_default'])
+    self.f_skirt = np.array(F['faces_skirt'])
+    self.nn = np.array(sio.loadmat(os.path.join(path, 'NN.mat')))
+    self.to_keep = sio.loadmat(os.path.join(path, 'keep_remove.mat'))['keep'].reshape(-1)
+
+    self.weights_all = self.weights
+    # self.weights = self.weights[:,self.to_keep,:]
+    
+    self.shape = (1,27578,3)
+    self.device = device
+
+    if orig_smpl:
+        self.to_keep = self.to_keep[self.to_keep < 6890]
+        self.J_regressor = self.J_regressor[:,:,:6890]
+        self.v_template = self.v_template[:,:6890,:]
+        self.shapedirs = self.shapedirs[:,:6890,:,:]
+        self.posedirs = self.posedirs[:,:6890,:,:]
+        self.weights_all = self.weights_all[:,:6890,:]
+        self.weights = self.weights_all[:,self.to_keep,:]
+        self.shape = (1, 6890, 3)
+
+  @staticmethod
+  def rodrigues(r):
+    """
+    Rodrigues' rotation formula that turns axis-angle tensor into rotation
+    matrix in a batch-ed manner.
+    Parameter:
+    ----------
+    r: Axis-angle rotation tensor of shape [batch_size * angle_num, 1, 3].
+    Return:
+    -------
+    Rotation matrix of shape [batch_size * angle_num, 3, 3].
+    """
+    eps = r.clone().normal_(std=1e-8)
+    theta = torch.norm(r + eps, dim=(1, 2), keepdim=True)  # dim cannot be tuple
+    theta_dim = theta.shape[0]
+    r_hat = r / theta
+    cos = torch.cos(theta)
+    z_stick = torch.zeros(theta_dim, dtype=torch.float32).to(r.device)
+    m = torch.stack(
+      (z_stick, -r_hat[:, 0, 2], r_hat[:, 0, 1], r_hat[:, 0, 2], z_stick,
+       -r_hat[:, 0, 0], -r_hat[:, 0, 1], r_hat[:, 0, 0], z_stick), dim=1)
+    m = torch.reshape(m, (-1, 3, 3))
+    i_cube = (torch.eye(3, dtype=torch.float32).unsqueeze(dim=0) \
+             + torch.zeros((theta_dim, 3, 3), dtype=torch.float32)).to(r.device)
+    A = r_hat.permute(0, 2, 1)
+    dot = torch.matmul(A, r_hat)
+    R = cos * i_cube + (1 - cos) * dot + torch.sin(theta) * m
+    return R
+
+  @staticmethod
+  def with_zeros(x):
+    """
+    Append a [0, 0, 0, 1] tensor to a [3, 4] tensor.
+    Parameter:
+    ---------
+    x: Tensor to be appended.
+    Return:
+    ------
+    Tensor after appending of shape [4,4]
+    """
+    ones = torch.tensor(
+      [[[0.0, 0.0, 0.0, 1.0]]], dtype=torch.float32
+    ).expand(x.shape[0],-1,-1).to(x.device)
+    ret = torch.cat((x, ones), dim=1)
+    return ret
+
+  @staticmethod
+  def pack(x):
+    """
+    Append zero tensors of shape [4, 3] to a batch of [4, 1] shape tensor.
+    Parameter:
+    ----------
+    x: A tensor of shape [batch_size, 4, 1]
+    Return:
+    ------
+    A tensor of shape [batch_size, 4, 4] after appending.
+    """
+    zeros43 = torch.zeros(
+      (x.shape[0], x.shape[1], 4, 3), dtype=torch.float32).to(x.device)
+    ret = torch.cat((zeros43, x), dim=3)
+    return ret
+
+  def write_obj(self, verts, file_name):
+    with open(file_name, 'w') as fp:
+      for v in verts:
+        fp.write('v %f %f %f\n' % (v[0], v[1], v[2]))
+
+      for f in self.faces + 1:
+        fp.write('f %d %d %d\n' % (f[0], f[1], f[2]))
+
+  def forward(self, betas, pose, trans, remove=True):
+    
+    """
+          Construct a compute graph that takes in parameters and outputs a tensor as
+          model vertices. Face indices are also returned as a numpy ndarray.
+          
+          20190128: Add batch support.
+          Parameters:
+          ---------
+          pose: Also known as 'theta', an [N, 24, 3] tensor indicating child joint rotation
+          relative to parent joint. For root joint it's global orientation.
+          Represented in a axis-angle format.
+          betas: Parameter for model shape. A tensor of shape [N, 10] as coefficients of
+          PCA components. Only 10 components were released by SMPL author.
+          trans: Global translation tensor of shape [N, 3].
+          Return:
+          ------
+          A 3-D tensor of [N * 6890 * 3] for vertices,
+          and the corresponding [N * 19 * 3] joint positions.
+    """
+    batch_num = betas.shape[0]
+
+    # v_shaped = torch.tensordot(betas, self.shapedirs, dims=([1], [2])) + self.v_template
+    v_shaped = []
+    v_shaped.append(torch.tensordot(betas[:,:10], self.shapedirs[0], dims=[[1], [2]]) + torch.reshape(self.v_template[0], shape=self.shape)) # female
+    v_shaped.append(torch.tensordot(betas[:,:10], self.shapedirs[1], dims=[[1], [2]]) + torch.reshape(self.v_template[1], shape=self.shape)) # male
+    v_shaped = torch.stack(v_shaped, axis=-1)
+    
+    # J = torch.matmul(self.J_regressor, v_shaped)
+    J = torch.stack([torch.matmul(self.J_regressor[g.to(torch.long)], v_shaped[i, :, :, g.to(torch.long)]) for i, g in enumerate(betas[:,-1])])
+    R_cube_big = self.rodrigues(pose.view(-1, 1, 3)).reshape(batch_num, -1, 3, 3)
+
+    R_cube = R_cube_big[:, 1:, :, :]
+    I_cube = (torch.eye(3, dtype=torch.float32).unsqueeze(dim=0) + \
+      torch.zeros((batch_num, R_cube.shape[1], 3, 3), dtype=torch.float32)).to(self.device)
+    lrotmin = (R_cube - I_cube).reshape(batch_num, -1, 1).squeeze(dim=2)
+    v_posed = torch.stack([v_shaped[i, :, :, g.to(torch.long)] + torch.tensordot(lrotmin[i], self.posedirs[g.to(torch.long)], dims=([0], [2])) for i, g in enumerate(betas[:,-1])])
+
+    results = []
+    results.append(
+      self.with_zeros(torch.cat((R_cube_big[:, 0], torch.reshape(J[:, 0, :], (-1, 3, 1))), dim=2))
+    )
+    for i in range(1, 24):
+      results.append(
+        torch.matmul(
+          results[self.parent[i]],
+          self.with_zeros(
+            torch.cat(
+              (R_cube_big[:, i], torch.reshape(J[:, i, :] - J[:, self.parent[i], :], (-1, 3, 1))),
+              dim=2
+            )
+          )
+        )
+      )
+    
+    stacked = torch.stack(results, dim=1)
+    results = stacked - \
+      self.pack(
+        torch.matmul(
+          stacked,
+          torch.reshape(
+            torch.cat((J, torch.zeros((batch_num, 24, 1), dtype=torch.float32).to(self.device)), dim=2),
+            (batch_num, 24, 4, 1)
+          )
+        )
+      )
+    # Restart from here
+    T = torch.stack([torch.tensordot(results[i], self.weights[g.to(torch.long)], dims=([0], [1])).permute(2, 0, 1) for i, g in enumerate(betas[:,-1])])
+    rest_shape_h = torch.cat(
+      (v_posed, torch.ones((batch_num, v_posed.shape[1], 1), dtype=torch.float32).to(self.device)), dim=2
+    )
+    v = torch.matmul(T, torch.reshape(rest_shape_h, (batch_num, -1, 4, 1)))
+    v = torch.reshape(v, (batch_num, -1, 4))[:, :, :3]
+    result = v + torch.reshape(trans, (batch_num, 1, 3))
+    # estimate 3D joint locations
+    # print(result.shape)
+    # print(self.joint_regressor.shape)
+    joints = torch.stack([torch.tensordot(result[i], self.J_regressor[g.to(torch.long)], dims=([0], [1])) for i, g in enumerate(betas[:,-1])]).transpose(1, 2)
+    
+    root_joint = torch.reshape(joints[:,0,:], (-1, 1, 3))
+    result = result - root_joint
+    joints = joints - root_joint
+
+    if remove:
+      result = result[:,self.to_keep,:]
+
+    return result, joints
+
+
+def test_gpu(gpu_id=[0], path=''):
+  if len(gpu_id) > 0 and torch.cuda.is_available():
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id[0])
+    device = torch.device('cuda')
+  else:
+    device = torch.device('cpu')
+  #print(device)
+
+  pose_size = 72
+  beta_size = 10
+
+  np.random.seed(9608)
+  model = SMPLModel(device=device, path=path)
+  for i in range(5):
+      pose = torch.from_numpy((np.random.rand(4, pose_size) - 0.5) * 0.4)\
+              .type(torch.float32).to(device)
+      betas = torch.from_numpy((np.random.rand(4, beta_size) - 0.5) * 0.06) \
+              .type(torch.float32).to(device)
+      betas = torch.concat((betas, torch.zeros((4,1), device=device)), axis=1)
+      s = time()
+      trans = torch.from_numpy(np.zeros((4, 3))).type(torch.float32).to(device)
+      result, joints = model(betas, pose, trans)
+      print(time() - s)
+      plot_mesh(result[0].cpu().numpy(), model.f).show()
+      
+  # outmesh_path = './smpl_torch_{}.obj'
+  # for i in range(result.shape[0]):
+      # model.write_obj(result[i], outmesh_path.format(i))
+  
+
+if __name__ == '__main__':
+  test_gpu([5], path='model_files')

utils.py

@@ -15,9 +15,12 @@ def plot_mesh(verts, faces):
         ),)
 
     fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
-    helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+    # helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
-                yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+    #             yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
-                zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
+    #             zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
+    helper = dict(xaxis = dict(range=[-1,1], ),
+                yaxis = dict(range=[-1,1], ),
+                zaxis = dict(range=[-1,1], ) )
     fig.update_layout(scene = helper)
     
     fig.update_layout(margin=dict(l=0, r=0, b=0, t=0, pad=0))