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import pytorch_lightning as pl
import torch.optim as optim
from lib.model.v2a import V2A
from lib.model.body_model_params import BodyModelParams
from lib.model.deformer import SMPLDeformer
import cv2
import torch
from lib.model.loss import Loss
import hydra
import os
import numpy as np
from lib.utils.meshing import generate_mesh
from kaolin.ops.mesh import index_vertices_by_faces
import trimesh
from lib.model.deformer import skinning
from lib.utils import utils
class V2AModel(pl.LightningModule):
def __init__(self, opt) -> None:
super().__init__()
self.opt = opt
num_training_frames = opt.dataset.metainfo.end_frame - opt.dataset.metainfo.start_frame
self.betas_path = os.path.join(hydra.utils.to_absolute_path('..'), 'data', opt.dataset.metainfo.data_dir, 'mean_shape.npy')
self.gender = opt.dataset.metainfo.gender
self.model = V2A(opt.model, self.betas_path, self.gender, num_training_frames)
self.start_frame = opt.dataset.metainfo.start_frame
self.end_frame = opt.dataset.metainfo.end_frame
self.training_modules = ["model"]
self.training_indices = list(range(self.start_frame, self.end_frame))
self.body_model_params = BodyModelParams(num_training_frames, model_type='smpl')
self.load_body_model_params()
optim_params = self.body_model_params.param_names
for param_name in optim_params:
self.body_model_params.set_requires_grad(param_name, requires_grad=True)
self.training_modules += ['body_model_params']
self.loss = Loss(opt.model.loss)
def load_body_model_params(self):
body_model_params = {param_name: [] for param_name in self.body_model_params.param_names}
data_root = os.path.join('../data', self.opt.dataset.metainfo.data_dir)
data_root = hydra.utils.to_absolute_path(data_root)
body_model_params['betas'] = torch.tensor(np.load(os.path.join(data_root, 'mean_shape.npy'))[None], dtype=torch.float32)
body_model_params['global_orient'] = torch.tensor(np.load(os.path.join(data_root, 'poses.npy'))[self.training_indices][:, :3], dtype=torch.float32)
body_model_params['body_pose'] = torch.tensor(np.load(os.path.join(data_root, 'poses.npy'))[self.training_indices] [:, 3:], dtype=torch.float32)
body_model_params['transl'] = torch.tensor(np.load(os.path.join(data_root, 'normalize_trans.npy'))[self.training_indices], dtype=torch.float32)
for param_name in body_model_params.keys():
self.body_model_params.init_parameters(param_name, body_model_params[param_name], requires_grad=False)
def configure_optimizers(self):
params = [{'params': self.model.parameters(), 'lr':self.opt.model.learning_rate}]
params.append({'params': self.body_model_params.parameters(), 'lr':self.opt.model.learning_rate*0.1})
self.optimizer = optim.Adam(params, lr=self.opt.model.learning_rate, eps=1e-8)
self.scheduler = optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=self.opt.model.sched_milestones, gamma=self.opt.model.sched_factor)
return [self.optimizer], [self.scheduler]
def training_step(self, batch):
inputs, targets = batch
batch_idx = inputs["idx"]
body_model_params = self.body_model_params(batch_idx)
inputs['smpl_pose'] = torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1)
inputs['smpl_shape'] = body_model_params['betas']
inputs['smpl_trans'] = body_model_params['transl']
inputs['current_epoch'] = self.current_epoch
model_outputs = self.model(inputs)
loss_output = self.loss(model_outputs, targets)
for k, v in loss_output.items():
if k in ["loss"]:
self.log(k, v.item(), prog_bar=True, on_step=True)
else:
self.log(k, v.item(), prog_bar=True, on_step=True)
return loss_output["loss"]
def training_epoch_end(self, outputs) -> None:
# Canonical mesh update every 20 epochs
if self.current_epoch != 0 and self.current_epoch % 20 == 0:
cond = {'smpl': torch.zeros(1, 69).float().cuda()}
mesh_canonical = generate_mesh(lambda x: self.query_oc(x, cond), self.model.smpl_server.verts_c[0], point_batch=10000, res_up=2)
self.model.mesh_v_cano = torch.tensor(mesh_canonical.vertices[None], device = self.model.smpl_v_cano.device).float()
self.model.mesh_f_cano = torch.tensor(mesh_canonical.faces.astype(np.int64), device=self.model.smpl_v_cano.device)
self.model.mesh_face_vertices = index_vertices_by_faces(self.model.mesh_v_cano, self.model.mesh_f_cano)
return super().training_epoch_end(outputs)
def query_oc(self, x, cond):
x = x.reshape(-1, 3)
mnfld_pred = self.model.implicit_network(x, cond)[:,:,0].reshape(-1,1)
return {'sdf':mnfld_pred}
def query_wc(self, x):
x = x.reshape(-1, 3)
w = self.model.deformer.query_weights(x)
return w
def query_od(self, x, cond, smpl_tfs, smpl_verts):
x = x.reshape(-1, 3)
x_c, _ = self.model.deformer.forward(x, smpl_tfs, return_weights=False, inverse=True, smpl_verts=smpl_verts)
output = self.model.implicit_network(x_c, cond)[0]
sdf = output[:, 0:1]
return {'sdf': sdf}
def get_deformed_mesh_fast_mode(self, verts, smpl_tfs):
verts = torch.tensor(verts).cuda().float()
weights = self.model.deformer.query_weights(verts)
verts_deformed = skinning(verts.unsqueeze(0), weights, smpl_tfs).data.cpu().numpy()[0]
return verts_deformed
def validation_step(self, batch, *args, **kwargs):
output = {}
inputs, targets = batch
inputs['current_epoch'] = self.current_epoch
self.model.eval()
body_model_params = self.body_model_params(inputs['image_id'])
inputs['smpl_pose'] = torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1)
inputs['smpl_shape'] = body_model_params['betas']
inputs['smpl_trans'] = body_model_params['transl']
cond = {'smpl': inputs["smpl_pose"][:, 3:]/np.pi}
mesh_canonical = generate_mesh(lambda x: self.query_oc(x, cond), self.model.smpl_server.verts_c[0], point_batch=10000, res_up=3)
mesh_canonical = trimesh.Trimesh(mesh_canonical.vertices, mesh_canonical.faces)
output.update({
'canonical_mesh':mesh_canonical
})
split = utils.split_input(inputs, targets["total_pixels"][0], n_pixels=min(targets['pixel_per_batch'], targets["img_size"][0] * targets["img_size"][1]))
res = []
for s in split:
out = self.model(s)
for k, v in out.items():
try:
out[k] = v.detach()
except:
out[k] = v
res.append({
'rgb_values': out['rgb_values'].detach(),
'normal_values': out['normal_values'].detach(),
'fg_rgb_values': out['fg_rgb_values'].detach(),
})
batch_size = targets['rgb'].shape[0]
model_outputs = utils.merge_output(res, targets["total_pixels"][0], batch_size)
output.update({
"rgb_values": model_outputs["rgb_values"].detach().clone(),
"normal_values": model_outputs["normal_values"].detach().clone(),
"fg_rgb_values": model_outputs["fg_rgb_values"].detach().clone(),
**targets,
})
return output
def validation_step_end(self, batch_parts):
return batch_parts
def validation_epoch_end(self, outputs) -> None:
img_size = outputs[0]["img_size"]
rgb_pred = torch.cat([output["rgb_values"] for output in outputs], dim=0)
rgb_pred = rgb_pred.reshape(*img_size, -1)
fg_rgb_pred = torch.cat([output["fg_rgb_values"] for output in outputs], dim=0)
fg_rgb_pred = fg_rgb_pred.reshape(*img_size, -1)
normal_pred = torch.cat([output["normal_values"] for output in outputs], dim=0)
normal_pred = (normal_pred.reshape(*img_size, -1) + 1) / 2
rgb_gt = torch.cat([output["rgb"] for output in outputs], dim=1).squeeze(0)
rgb_gt = rgb_gt.reshape(*img_size, -1)
if 'normal' in outputs[0].keys():
normal_gt = torch.cat([output["normal"] for output in outputs], dim=1).squeeze(0)
normal_gt = (normal_gt.reshape(*img_size, -1) + 1) / 2
normal = torch.cat([normal_gt, normal_pred], dim=0).cpu().numpy()
else:
normal = torch.cat([normal_pred], dim=0).cpu().numpy()
rgb = torch.cat([rgb_gt, rgb_pred], dim=0).cpu().numpy()
rgb = (rgb * 255).astype(np.uint8)
fg_rgb = torch.cat([fg_rgb_pred], dim=0).cpu().numpy()
fg_rgb = (fg_rgb * 255).astype(np.uint8)
normal = (normal * 255).astype(np.uint8)
os.makedirs("rendering", exist_ok=True)
os.makedirs("normal", exist_ok=True)
os.makedirs('fg_rendering', exist_ok=True)
canonical_mesh = outputs[0]['canonical_mesh']
canonical_mesh.export(f"rendering/{self.current_epoch}.ply")
cv2.imwrite(f"rendering/{self.current_epoch}.png", rgb[:, :, ::-1])
cv2.imwrite(f"normal/{self.current_epoch}.png", normal[:, :, ::-1])
cv2.imwrite(f"fg_rendering/{self.current_epoch}.png", fg_rgb[:, :, ::-1])
def test_step(self, batch, *args, **kwargs):
inputs, targets, pixel_per_batch, total_pixels, idx = batch
num_splits = (total_pixels + pixel_per_batch -
1) // pixel_per_batch
results = []
scale, smpl_trans, smpl_pose, smpl_shape = torch.split(inputs["smpl_params"], [1, 3, 72, 10], dim=1)
body_model_params = self.body_model_params(inputs['idx'])
smpl_shape = body_model_params['betas'] if body_model_params['betas'].dim() == 2 else body_model_params['betas'].unsqueeze(0)
smpl_trans = body_model_params['transl']
smpl_pose = torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1)
smpl_outputs = self.model.smpl_server(scale, smpl_trans, smpl_pose, smpl_shape)
smpl_tfs = smpl_outputs['smpl_tfs']
cond = {'smpl': smpl_pose[:, 3:]/np.pi}
mesh_canonical = generate_mesh(lambda x: self.query_oc(x, cond), self.model.smpl_server.verts_c[0], point_batch=10000, res_up=4)
self.model.deformer = SMPLDeformer(betas=np.load(self.betas_path), gender=self.gender, K=7)
verts_deformed = self.get_deformed_mesh_fast_mode(mesh_canonical.vertices, smpl_tfs)
mesh_deformed = trimesh.Trimesh(vertices=verts_deformed, faces=mesh_canonical.faces, process=False)
os.makedirs("test_mask", exist_ok=True)
os.makedirs("test_rendering", exist_ok=True)
os.makedirs("test_fg_rendering", exist_ok=True)
os.makedirs("test_normal", exist_ok=True)
os.makedirs("test_mesh", exist_ok=True)
mesh_canonical.export(f"test_mesh/{int(idx.cpu().numpy()):04d}_canonical.ply")
mesh_deformed.export(f"test_mesh/{int(idx.cpu().numpy()):04d}_deformed.ply")
self.model.deformer = SMPLDeformer(betas=np.load(self.betas_path), gender=self.gender)
for i in range(num_splits):
indices = list(range(i * pixel_per_batch,
min((i + 1) * pixel_per_batch, total_pixels)))
batch_inputs = {"uv": inputs["uv"][:, indices],
"intrinsics": inputs['intrinsics'],
"pose": inputs['pose'],
"smpl_params": inputs["smpl_params"],
"smpl_pose": inputs["smpl_params"][:, 4:76],
"smpl_shape": inputs["smpl_params"][:, 76:],
"smpl_trans": inputs["smpl_params"][:, 1:4],
"idx": inputs["idx"] if 'idx' in inputs.keys() else None}
body_model_params = self.body_model_params(inputs['idx'])
batch_inputs.update({'smpl_pose': torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1)})
batch_inputs.update({'smpl_shape': body_model_params['betas']})
batch_inputs.update({'smpl_trans': body_model_params['transl']})
batch_targets = {"rgb": targets["rgb"][:, indices].detach().clone() if 'rgb' in targets.keys() else None,
"img_size": targets["img_size"]}
with torch.no_grad():
model_outputs = self.model(batch_inputs)
results.append({"rgb_values":model_outputs["rgb_values"].detach().clone(),
"fg_rgb_values":model_outputs["fg_rgb_values"].detach().clone(),
"normal_values": model_outputs["normal_values"].detach().clone(),
"acc_map": model_outputs["acc_map"].detach().clone(),
**batch_targets})
img_size = results[0]["img_size"]
rgb_pred = torch.cat([result["rgb_values"] for result in results], dim=0)
rgb_pred = rgb_pred.reshape(*img_size, -1)
fg_rgb_pred = torch.cat([result["fg_rgb_values"] for result in results], dim=0)
fg_rgb_pred = fg_rgb_pred.reshape(*img_size, -1)
normal_pred = torch.cat([result["normal_values"] for result in results], dim=0)
normal_pred = (normal_pred.reshape(*img_size, -1) + 1) / 2
pred_mask = torch.cat([result["acc_map"] for result in results], dim=0)
pred_mask = pred_mask.reshape(*img_size, -1)
if results[0]['rgb'] is not None:
rgb_gt = torch.cat([result["rgb"] for result in results], dim=1).squeeze(0)
rgb_gt = rgb_gt.reshape(*img_size, -1)
rgb = torch.cat([rgb_gt, rgb_pred], dim=0).cpu().numpy()
else:
rgb = torch.cat([rgb_pred], dim=0).cpu().numpy()
if 'normal' in results[0].keys():
normal_gt = torch.cat([result["normal"] for result in results], dim=1).squeeze(0)
normal_gt = (normal_gt.reshape(*img_size, -1) + 1) / 2
normal = torch.cat([normal_gt, normal_pred], dim=0).cpu().numpy()
else:
normal = torch.cat([normal_pred], dim=0).cpu().numpy()
rgb = (rgb * 255).astype(np.uint8)
fg_rgb = torch.cat([fg_rgb_pred], dim=0).cpu().numpy()
fg_rgb = (fg_rgb * 255).astype(np.uint8)
normal = (normal * 255).astype(np.uint8)
cv2.imwrite(f"test_mask/{int(idx.cpu().numpy()):04d}.png", pred_mask.cpu().numpy() * 255)
cv2.imwrite(f"test_rendering/{int(idx.cpu().numpy()):04d}.png", rgb[:, :, ::-1])
cv2.imwrite(f"test_normal/{int(idx.cpu().numpy()):04d}.png", normal[:, :, ::-1])
cv2.imwrite(f"test_fg_rendering/{int(idx.cpu().numpy()):04d}.png", fg_rgb[:, :, ::-1]) |