in eft/apps/eval_export.py [0:0]
def run_evaluation(model, dataset_name, dataset, result_file,
batch_size=32, img_res=224,
num_workers=32, shuffle=False, log_freq=50, bVerbose= True):
"""Run evaluation on the datasets and metrics we report in the paper. """
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# # Transfer model to the GPU
# model.to(device)
# Load SMPL model
global g_smpl_neutral, g_smpl_male, g_smpl_female
if g_smpl_neutral is None:
g_smpl_neutral = SMPL(config.SMPL_MODEL_DIR,
create_transl=False).to(device)
g_smpl_male = SMPL(config.SMPL_MODEL_DIR,
gender='male',
create_transl=False).to(device)
g_smpl_female = SMPL(config.SMPL_MODEL_DIR,
gender='female',
create_transl=False).to(device)
smpl_neutral = g_smpl_neutral
smpl_male = g_smpl_male
smpl_female = g_smpl_female
else:
smpl_neutral = g_smpl_neutral
smpl_male = g_smpl_male
smpl_female = g_smpl_female
# renderer = PartRenderer()
# Regressor for H36m joints
J_regressor = torch.from_numpy(np.load(config.JOINT_REGRESSOR_H36M)).float()
save_results = result_file is not None
# Disable shuffling if you want to save the results
if save_results:
shuffle=False
# Create dataloader for the dataset
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
# Pose metrics
# MPJPE and Reconstruction error for the non-parametric and parametric shapes
# mpjpe = np.zeros(len(dataset))
# recon_err = np.zeros(len(dataset))
quant_mpjpe = {}#np.zeros(len(dataset))
quant_recon_err = {}#np.zeros(len(dataset))
mpjpe = np.zeros(len(dataset))
recon_err = np.zeros(len(dataset))
mpjpe_smpl = np.zeros(len(dataset))
recon_err_smpl = np.zeros(len(dataset))
# Shape metrics
# Mean per-vertex error
shape_err = np.zeros(len(dataset))
shape_err_smpl = np.zeros(len(dataset))
# Mask and part metrics
# Accuracy
accuracy = 0.
parts_accuracy = 0.
# True positive, false positive and false negative
tp = np.zeros((2,1))
fp = np.zeros((2,1))
fn = np.zeros((2,1))
parts_tp = np.zeros((7,1))
parts_fp = np.zeros((7,1))
parts_fn = np.zeros((7,1))
# Pixel count accumulators
pixel_count = 0
parts_pixel_count = 0
# Store SMPL parameters
output_pred_pose = np.zeros((len(dataset), 72))
output_pred_betas = np.zeros((len(dataset), 10))
output_pred_camera = np.zeros((len(dataset), 3))
output_pred_joints = np.zeros((len(dataset), 14, 3))
output_gt_pose = np.zeros((len(dataset), 72))
output_gt_betas = np.zeros((len(dataset), 10))
output_gt_joints = np.zeros((len(dataset), 14, 3))
output_error_MPJPE = np.zeros((len(dataset)))
output_error_recon = np.zeros((len(dataset)))
output_imgNames =[]
output_cropScale = np.zeros((len(dataset)))
output_cropCenter = np.zeros((len(dataset), 2))
outputStartPointer = 0
eval_pose = False
eval_masks = False
eval_parts = False
# Choose appropriate evaluation for each dataset
if dataset_name == 'h36m-p1' or dataset_name == 'h36m-p2' or dataset_name == '3dpw' or dataset_name == 'mpi-inf-3dhp':
eval_pose = True
elif dataset_name == 'lsp':
eval_masks = True
eval_parts = True
annot_path = config.DATASET_FOLDERS['upi-s1h']
joint_mapper_h36m = constants.H36M_TO_J17 if dataset_name == 'mpi-inf-3dhp' else constants.H36M_TO_J14
joint_mapper_gt = constants.J24_TO_J17 if dataset_name == 'mpi-inf-3dhp' else constants.J24_TO_J14
# Iterate over the entire dataset
for step, batch in enumerate(tqdm(data_loader, desc='Eval', total=len(data_loader))):
# Get ground truth annotations from the batch
imgName = batch['imgname'][0]
seqName = os.path.basename ( os.path.dirname(imgName) )
gt_pose = batch['pose'].to(device)
gt_betas = batch['betas'].to(device)
gt_vertices = smpl_neutral(betas=gt_betas, body_pose=gt_pose[:, 3:], global_orient=gt_pose[:, :3]).vertices
images = batch['img'].to(device)
gender = batch['gender'].to(device)
curr_batch_size = images.shape[0]
with torch.no_grad():
pred_rotmat, pred_betas, pred_camera = model(images)
pred_output = smpl_neutral(betas=pred_betas, body_pose=pred_rotmat[:,1:], global_orient=pred_rotmat[:,0].unsqueeze(1), pose2rot=False)
pred_vertices = pred_output.vertices
# 3D pose evaluation
if eval_pose:
# Regressor broadcasting
J_regressor_batch = J_regressor[None, :].expand(pred_vertices.shape[0], -1, -1).to(device)
# Get 14 ground truth joints
if 'h36m' in dataset_name or 'mpi-inf' in dataset_name:
gt_keypoints_3d = batch['pose_3d'].cuda()
gt_keypoints_3d = gt_keypoints_3d[:, joint_mapper_gt, :-1]
# For 3DPW get the 14 common joints from the rendered shape
else:
gt_vertices = smpl_male(global_orient=gt_pose[:,:3], body_pose=gt_pose[:,3:], betas=gt_betas).vertices
gt_vertices_female = smpl_female(global_orient=gt_pose[:,:3], body_pose=gt_pose[:,3:], betas=gt_betas).vertices
gt_vertices[gender==1, :, :] = gt_vertices_female[gender==1, :, :]
gt_keypoints_3d = torch.matmul(J_regressor_batch, gt_vertices)
gt_pelvis = gt_keypoints_3d[:, [0],:].clone()
gt_keypoints_3d = gt_keypoints_3d[:, joint_mapper_h36m, :]
gt_keypoints_3d = gt_keypoints_3d - gt_pelvis
if False:
from renderer import viewer2D
from renderer import glViewer
import humanModelViewer
batchNum = gt_pose.shape[0]
for i in range(batchNum):
smpl_face = humanModelViewer.GetSMPLFace()
meshes_gt = {'ver': gt_vertices[i].cpu().numpy()*100, 'f': smpl_face}
meshes_pred = {'ver': pred_vertices[i].cpu().numpy()*100, 'f': smpl_face}
glViewer.setMeshData([meshes_gt, meshes_pred], bComputeNormal= True)
glViewer.show(5)
# Get 14 predicted joints from the mesh
pred_keypoints_3d = torch.matmul(J_regressor_batch, pred_vertices)
# if save_results:
# pred_joints[step * batch_size:step * batch_size + curr_batch_size, :, :] = pred_keypoints_3d.cpu().numpy()
pred_pelvis = pred_keypoints_3d[:, [0],:].clone()
pred_keypoints_3d = pred_keypoints_3d[:, joint_mapper_h36m, :]
pred_keypoints_3d = pred_keypoints_3d - pred_pelvis
#Visualize GT mesh and SPIN output mesh
if False:
from renderer import viewer2D
from renderer import glViewer
import humanModelViewer
gt_keypoints_3d_vis = gt_keypoints_3d.cpu().numpy()
gt_keypoints_3d_vis = np.reshape(gt_keypoints_3d_vis, (gt_keypoints_3d_vis.shape[0],-1)) #N,14x3
gt_keypoints_3d_vis = np.swapaxes(gt_keypoints_3d_vis, 0,1) *100
pred_keypoints_3d_vis = pred_keypoints_3d.cpu().numpy()
pred_keypoints_3d_vis = np.reshape(pred_keypoints_3d_vis, (pred_keypoints_3d_vis.shape[0],-1)) #N,14x3
pred_keypoints_3d_vis = np.swapaxes(pred_keypoints_3d_vis, 0,1) *100
# output_sample = output_sample[ : , np.newaxis]*0.1
# gt_sample = gt_sample[: , np.newaxis]*0.1
# (skelNum, dim, frames)
glViewer.setSkeleton( [gt_keypoints_3d_vis, pred_keypoints_3d_vis] ,jointType='smplcoco')#(skelNum, dim, frames)
glViewer.show()
# Absolute error (MPJPE)
error = torch.sqrt(((pred_keypoints_3d - gt_keypoints_3d) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
# mpjpe[step * batch_size:step * batch_size + curr_batch_size] = error
# Reconstuction_error
r_error = reconstruction_error(pred_keypoints_3d.cpu().numpy(), gt_keypoints_3d.cpu().numpy(), reduction=None)
# recon_err[step * batch_size:step * batch_size + curr_batch_size] = r_error
for ii, p in enumerate(batch['imgname'][:len(r_error)]):
seqName = os.path.basename( os.path.dirname(p))
# quant_mpjpe[step * batch_size:step * batch_size + curr_batch_size] = error
if seqName not in quant_mpjpe.keys():
quant_mpjpe[seqName] = []
quant_recon_err[seqName] = []
quant_mpjpe[seqName].append(error[ii])
quant_recon_err[seqName].append(r_error[ii])
# Reconstuction_error
# quant_recon_err[step * batch_size:step * batch_size + curr_batch_size] = r_error
list_mpjpe = np.hstack([ quant_mpjpe[k] for k in quant_mpjpe])
list_reconError = np.hstack([ quant_recon_err[k] for k in quant_recon_err])
if bVerbose:
print(">>> {} : MPJPE {:.02f} mm, error: {:.02f} mm | Total MPJPE {:.02f} mm, error {:.02f} mm".format(seqName, np.mean(error)*1000, np.mean(r_error)*1000, np.hstack(list_mpjpe).mean()*1000, np.hstack(list_reconError).mean()*1000) )
# print("MPJPE {}, error: {}".format(np.mean(error)*100, np.mean(r_error)*100))
# If mask or part evaluation, render the mask and part images
# if eval_masks or eval_parts:
# mask, parts = renderer(pred_vertices, pred_camera)
# Mask evaluation (for LSP)
if eval_masks:
center = batch['center'].cpu().numpy()
scale = batch['scale'].cpu().numpy()
# Dimensions of original image
orig_shape = batch['orig_shape'].cpu().numpy()
for i in range(curr_batch_size):
# After rendering, convert imate back to original resolution
pred_mask = uncrop(mask[i].cpu().numpy(), center[i], scale[i], orig_shape[i]) > 0
# Load gt mask
gt_mask = cv2.imread(os.path.join(annot_path, batch['maskname'][i]), 0) > 0
# Evaluation consistent with the original UP-3D code
accuracy += (gt_mask == pred_mask).sum()
pixel_count += np.prod(np.array(gt_mask.shape))
for c in range(2):
cgt = gt_mask == c
cpred = pred_mask == c
tp[c] += (cgt & cpred).sum()
fp[c] += (~cgt & cpred).sum()
fn[c] += (cgt & ~cpred).sum()
f1 = 2 * tp / (2 * tp + fp + fn)
# Part evaluation (for LSP)
if eval_parts:
center = batch['center'].cpu().numpy()
scale = batch['scale'].cpu().numpy()
orig_shape = batch['orig_shape'].cpu().numpy()
for i in range(curr_batch_size):
pred_parts = uncrop(parts[i].cpu().numpy().astype(np.uint8), center[i], scale[i], orig_shape[i])
# Load gt part segmentation
gt_parts = cv2.imread(os.path.join(annot_path, batch['partname'][i]), 0)
# Evaluation consistent with the original UP-3D code
# 6 parts + background
for c in range(7):
cgt = gt_parts == c
cpred = pred_parts == c
cpred[gt_parts == 255] = 0
parts_tp[c] += (cgt & cpred).sum()
parts_fp[c] += (~cgt & cpred).sum()
parts_fn[c] += (cgt & ~cpred).sum()
gt_parts[gt_parts == 255] = 0
pred_parts[pred_parts == 255] = 0
parts_f1 = 2 * parts_tp / (2 * parts_tp + parts_fp + parts_fn)
parts_accuracy += (gt_parts == pred_parts).sum()
parts_pixel_count += np.prod(np.array(gt_parts.shape))
# Print intermediate results during evaluation
if bVerbose:
if step % log_freq == log_freq - 1:
if eval_pose:
print('MPJPE: ' + str(1000 * mpjpe[:step * batch_size].mean()))
print('Reconstruction Error: ' + str(1000 * recon_err[:step * batch_size].mean()))
print()
if eval_masks:
print('Accuracy: ', accuracy / pixel_count)
print('F1: ', f1.mean())
print()
if eval_parts:
print('Parts Accuracy: ', parts_accuracy / parts_pixel_count)
print('Parts F1 (BG): ', parts_f1[[0,1,2,3,4,5,6]].mean())
print()
if save_results:
rot_pad = torch.tensor([0,0,1], dtype=torch.float32, device=device).view(1,3,1)
rotmat = torch.cat((pred_rotmat.view(-1, 3, 3), rot_pad.expand(curr_batch_size * 24, -1, -1)), dim=-1)
pred_pose = tgm.rotation_matrix_to_angle_axis(rotmat).contiguous().view(-1, 72)
output_pred_pose[outputStartPointer:outputStartPointer+curr_batch_size, :] = pred_pose.cpu().numpy()
output_pred_betas[outputStartPointer:outputStartPointer+curr_batch_size, :] = pred_betas.cpu().numpy()
output_pred_camera[outputStartPointer:outputStartPointer+curr_batch_size, :] = pred_camera.cpu().numpy()
output_pred_pose[outputStartPointer:outputStartPointer+curr_batch_size, :] = pred_pose.cpu().numpy()
output_pred_betas[outputStartPointer:outputStartPointer+curr_batch_size, :] = pred_betas.cpu().numpy()
output_pred_camera[outputStartPointer:outputStartPointer+curr_batch_size, :] = pred_camera.cpu().numpy()
output_pred_joints[outputStartPointer:outputStartPointer+curr_batch_size, :] = pred_keypoints_3d.cpu().numpy()
output_gt_pose[outputStartPointer:outputStartPointer+curr_batch_size, :] = gt_pose.cpu().numpy()
output_gt_betas[outputStartPointer:outputStartPointer+curr_batch_size, :] = gt_betas.cpu().numpy()
output_gt_joints[outputStartPointer:outputStartPointer+curr_batch_size, :] = gt_keypoints_3d.cpu().numpy()
output_error_MPJPE[outputStartPointer:outputStartPointer+curr_batch_size,] = error *1000
output_error_recon[outputStartPointer:outputStartPointer+curr_batch_size] = r_error*1000
output_cropScale[outputStartPointer:outputStartPointer+curr_batch_size] = batch['scale'].cpu().numpy()
output_cropCenter[outputStartPointer:outputStartPointer+curr_batch_size, :] = batch['center'].cpu().numpy()
output_imgNames +=batch['imgname']
outputStartPointer +=curr_batch_size
# if outputStartPointer>100: #Debug
# break
# if len(output_imgNames) < output_pred_pose.shape[0]:
output ={}
finalLen = len(output_imgNames)
output['imageNames'] = output_imgNames
output['pred_pose'] = output_pred_pose[:finalLen]
output['pred_betas'] = output_pred_betas[:finalLen]
output['pred_camera'] = output_pred_camera[:finalLen]
output['pred_joints'] = output_pred_joints[:finalLen]
output['gt_pose'] = output_gt_pose[:finalLen]
output['gt_betas'] = output_gt_betas[:finalLen]
output['gt_joints'] = output_gt_joints[:finalLen]
output['error_MPJPE'] = output_error_MPJPE[:finalLen]
output['error_recon'] = output_error_recon[:finalLen]
output['cropScale'] = output_cropScale[:finalLen]
output['cropCenter'] = output_cropCenter[:finalLen]
# Save reconstructions to a file for further processing
if save_results:
import pickle
# np.savez(result_file, pred_joints=pred_joints, pred_pose=pred_pose, pred_betas=pred_betas, pred_camera=pred_camera)
with open(result_file,'wb') as f:
pickle.dump(output, f)
f.close()
print("Saved to:{}".format(result_file))
# Print final results during evaluation
if bVerbose:
print('*** Final Results ***')
print()
if eval_pose:
# if bVerbose:
# print('MPJPE: ' + str(1000 * mpjpe.mean()))
# print('Reconstruction Error: ' + str(1000 * recon_err.mean()))
# print()
list_mpjpe = np.hstack([ quant_mpjpe[k] for k in quant_mpjpe])
list_reconError = np.hstack([ quant_recon_err[k] for k in quant_recon_err])
output_str ='SeqNames; '
for seq in quant_mpjpe:
output_str += seq + ';'
output_str +='\n MPJPE; '
quant_mpjpe_avg_mm = np.hstack(list_mpjpe).mean()*1000
output_str += "Avg {:.02f} mm; ".format( quant_mpjpe_avg_mm)
for seq in quant_mpjpe:
output_str += '{:.02f}; '.format(1000 * np.hstack(quant_mpjpe[seq]).mean())
output_str +='\n Recon Error; '
quant_recon_error_avg_mm = np.hstack(list_reconError).mean()*1000
output_str +="Avg {:.02f}mm; ".format( quant_recon_error_avg_mm )
for seq in quant_recon_err:
output_str += '{:.02f}; '.format(1000 * np.hstack(quant_recon_err[seq]).mean())
if bVerbose:
print(output_str)
else:
print(">>> Test on 3DPW: MPJPE: {} | quant_recon_error_avg_mm: {}".format(quant_mpjpe_avg_mm, quant_recon_error_avg_mm) )
return quant_mpjpe_avg_mm, quant_recon_error_avg_mm
if bVerbose:
if eval_masks:
print('Accuracy: ', accuracy / pixel_count)
print('F1: ', f1.mean())
print()
if eval_parts:
print('Parts Accuracy: ', parts_accuracy / parts_pixel_count)
print('Parts F1 (BG): ', parts_f1[[0,1,2,3,4,5,6]].mean())
print()
return -1 #Should return something