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main.py
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import numpy as np
import random
from common.arguments import parse_args
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import os
import sys
import errno
import math
from einops import rearrange, repeat
from copy import deepcopy
from common.camera import *
import collections
from common.ddhpose import *
from common.loss import *
from common.generators import ChunkedGenerator_Seq, UnchunkedGenerator_Seq
from time import time
from common.utils import *
from common.logging import Logger
from torch.utils.tensorboard import SummaryWriter
from datetime import datetime
import random
#cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
args = parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.evaluate != '':
description = "Evaluate!"
elif args.evaluate == '':
description = "Train!"
# initial setting
TIMESTAMP = "{0:%Y%m%dT%H-%M-%S/}".format(datetime.now())
# tensorboard
if not args.nolog:
writer = SummaryWriter(args.log+'_'+TIMESTAMP)
writer.add_text('description', description)
writer.add_text('command', 'python ' + ' '.join(sys.argv))
# logging setting
logfile = os.path.join(args.log+'_'+TIMESTAMP, 'logging.log')
sys.stdout = Logger(logfile)
print(description)
print('python ' + ' '.join(sys.argv))
print("CUDA Device Count: ", torch.cuda.device_count())
print(args)
manualSeed = 1
random.seed(manualSeed)
torch.manual_seed(manualSeed)
np.random.seed(manualSeed)
torch.cuda.manual_seed_all(manualSeed)
# if not assign checkpoint path, Save checkpoint file into log folder
if args.checkpoint=='':
args.checkpoint = args.log+'_'+TIMESTAMP
try:
# Create checkpoint directory if it does not exist
os.makedirs(args.checkpoint)
except OSError as e:
if e.errno != errno.EEXIST:
raise RuntimeError('Unable to create checkpoint directory:', args.checkpoint)
# dataset loading
print('Loading dataset...')
dataset_path = 'data/data_3d_' + args.dataset + '.npz'
if args.dataset == 'h36m':
from common.h36m_dataset import Human36mDataset
dataset = Human36mDataset(dataset_path)
elif args.dataset.startswith('humaneva'):
from common.humaneva_dataset import HumanEvaDataset
dataset = HumanEvaDataset(dataset_path)
elif args.dataset.startswith('custom'):
from common.custom_dataset import CustomDataset
dataset = CustomDataset('data/data_2d_' + args.dataset + '_' + args.keypoints + '.npz')
else:
raise KeyError('Invalid dataset')
print('Preparing data...')
for subject in dataset.subjects():
for action in dataset[subject].keys():
anim = dataset[subject][action]
if 'positions' in anim:
positions_3d = []
for cam in anim['cameras']:
pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation'])
pos_3d[:, 1:] -= pos_3d[:, :1] # Remove global offset, but keep trajectory in first position
positions_3d.append(pos_3d)
anim['positions_3d'] = positions_3d
print('Loading 2D detections...')
keypoints = np.load('data/data_2d_' + args.dataset + '_' + args.keypoints + '.npz', allow_pickle=True)
keypoints_metadata = keypoints['metadata'].item()
keypoints_symmetry = keypoints_metadata['keypoints_symmetry']
kps_left, kps_right = list(keypoints_symmetry[0]), list(keypoints_symmetry[1])
joints_left, joints_right = list(dataset.skeleton().joints_left()), list(dataset.skeleton().joints_right())
keypoints = keypoints['positions_2d'].item()
###################
for subject in dataset.subjects():
assert subject in keypoints, 'Subject {} is missing from the 2D detections dataset'.format(subject)
for action in dataset[subject].keys():
assert action in keypoints[subject], 'Action {} of subject {} is missing from the 2D detections dataset'.format(action, subject)
if 'positions_3d' not in dataset[subject][action]:
continue
for cam_idx in range(len(keypoints[subject][action])):
# We check for >= instead of == because some videos in H3.6M contain extra frames
mocap_length = dataset[subject][action]['positions_3d'][cam_idx].shape[0]
assert keypoints[subject][action][cam_idx].shape[0] >= mocap_length
if keypoints[subject][action][cam_idx].shape[0] > mocap_length:
# Shorten sequence
keypoints[subject][action][cam_idx] = keypoints[subject][action][cam_idx][:mocap_length]
assert len(keypoints[subject][action]) == len(dataset[subject][action]['positions_3d'])
for subject in keypoints.keys():
for action in keypoints[subject]:
for cam_idx, kps in enumerate(keypoints[subject][action]):
# Normalize camera frame
cam = dataset.cameras()[subject][cam_idx]
kps[..., :2] = normalize_screen_coordinates(kps[..., :2], w=cam['res_w'], h=cam['res_h'])
keypoints[subject][action][cam_idx] = kps
subjects_train = args.subjects_train.split(',')
subjects_semi = [] if not args.subjects_unlabeled else args.subjects_unlabeled.split(',')
if not args.render:
subjects_test = args.subjects_test.split(',')
else:
subjects_test = [args.viz_subject]
def fetch(subjects, action_filter=None, subset=1, parse_3d_poses=True):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
for subject in subjects:
for action in keypoints[subject].keys():
if action_filter is not None:
found = False
for a in action_filter:
if action.startswith(a):
found = True
break
if not found:
continue
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
if subject in dataset.cameras():
cams = dataset.cameras()[subject]
assert len(cams) == len(poses_2d), 'Camera count mismatch'
for cam in cams:
if 'intrinsic' in cam:
out_camera_params.append(cam['intrinsic'])
if parse_3d_poses and 'positions_3d' in dataset[subject][action]:
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
if len(out_camera_params) == 0:
out_camera_params = None
if len(out_poses_3d) == 0:
out_poses_3d = None
stride = args.downsample
if subset < 1:
for i in range(len(out_poses_2d)):
n_frames = int(round(len(out_poses_2d[i])//stride * subset)*stride)
start = deterministic_random(0, len(out_poses_2d[i]) - n_frames + 1, str(len(out_poses_2d[i])))
out_poses_2d[i] = out_poses_2d[i][start:start+n_frames:stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][start:start+n_frames:stride]
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
action_filter = None if args.actions == '*' else args.actions.split(',')
if action_filter is not None:
print('Selected actions:', action_filter)
cameras_valid, poses_valid, poses_valid_2d = fetch(subjects_test, action_filter)
# set receptive_field as number assigned
receptive_field = args.number_of_frames
print('INFO: Receptive field: {} frames'.format(receptive_field))
if not args.nolog:
writer.add_text(args.log+'_'+TIMESTAMP + '/Receptive field', str(receptive_field))
pad = (receptive_field -1) // 2 # Padding on each side
min_loss = args.min_loss
width = cam['res_w']
height = cam['res_h']
num_joints = keypoints_metadata['num_joints']
print('Loading bone index...')
boneindextemp = args.boneindex_h36m.split(',')
boneindex = []
for i in range(0,len(boneindextemp),2):
boneindex.append([int(boneindextemp[i]), int(boneindextemp[i+1])])
model_pos_train = DDHPose(args, joints_left, joints_right, is_train=True)
model_pos_test_temp = DDHPose(args,joints_left, joints_right, is_train=False)
model_pos = DDHPose(args,joints_left, joints_right, is_train=False, num_proposals=args.num_proposals, sampling_timesteps=args.sampling_timesteps)
causal_shift = 0
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params/1000000, 'Million')
if not args.nolog:
writer.add_text(args.log+'_'+TIMESTAMP + '/Trainable parameter count', str(model_params/1000000) + ' Million')
# make model parallel
if torch.cuda.is_available():
model_pos = nn.DataParallel(model_pos)
model_pos = model_pos.cuda()
model_pos_train = nn.DataParallel(model_pos_train)
model_pos_train = model_pos_train.cuda()
model_pos_test_temp = nn.DataParallel(model_pos_test_temp)
model_pos_test_temp = model_pos_test_temp.cuda()
if args.resume or args.evaluate:
chk_filename = os.path.join(args.checkpoint, args.resume if args.resume else args.evaluate)
# chk_filename = args.resume or args.evaluate
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_pos_train.load_state_dict(checkpoint['model_pos'], strict=False)
model_pos.load_state_dict(checkpoint['model_pos'], strict=False)
test_generator = UnchunkedGenerator_Seq(cameras_valid, poses_valid, poses_valid_2d,
pad=pad, causal_shift=causal_shift, augment=False,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
print('INFO: Testing on {} frames'.format(test_generator.num_frames()))
if not args.nolog:
writer.add_text(args.log+'_'+TIMESTAMP + '/Testing Frames', str(test_generator.num_frames()))
def eval_data_prepare(receptive_field, inputs_2d, inputs_3d):
assert inputs_2d.shape[:-1] == inputs_3d.shape[:-1], "2d and 3d inputs shape must be same! "+str(inputs_2d.shape)+str(inputs_3d.shape)
inputs_2d_p = torch.squeeze(inputs_2d)
inputs_3d_p = torch.squeeze(inputs_3d)
if inputs_2d_p.shape[0] / receptive_field > inputs_2d_p.shape[0] // receptive_field:
out_num = inputs_2d_p.shape[0] // receptive_field+1
elif inputs_2d_p.shape[0] / receptive_field == inputs_2d_p.shape[0] // receptive_field:
out_num = inputs_2d_p.shape[0] // receptive_field
eval_input_2d = torch.empty(out_num, receptive_field, inputs_2d_p.shape[1], inputs_2d_p.shape[2])
eval_input_3d = torch.empty(out_num, receptive_field, inputs_3d_p.shape[1], inputs_3d_p.shape[2])
for i in range(out_num-1):
eval_input_2d[i,:,:,:] = inputs_2d_p[i*receptive_field:i*receptive_field+receptive_field,:,:]
eval_input_3d[i,:,:,:] = inputs_3d_p[i*receptive_field:i*receptive_field+receptive_field,:,:]
if inputs_2d_p.shape[0] < receptive_field:
from torch.nn import functional as F
pad_right = receptive_field-inputs_2d_p.shape[0]
inputs_2d_p = rearrange(inputs_2d_p, 'b f c -> f c b')
inputs_2d_p = F.pad(inputs_2d_p, (0,pad_right), mode='replicate')
# inputs_2d_p = np.pad(inputs_2d_p, ((0, receptive_field-inputs_2d_p.shape[0]), (0, 0), (0, 0)), 'edge')
inputs_2d_p = rearrange(inputs_2d_p, 'f c b -> b f c')
if inputs_3d_p.shape[0] < receptive_field:
pad_right = receptive_field-inputs_3d_p.shape[0]
inputs_3d_p = rearrange(inputs_3d_p, 'b f c -> f c b')
inputs_3d_p = F.pad(inputs_3d_p, (0,pad_right), mode='replicate')
inputs_3d_p = rearrange(inputs_3d_p, 'f c b -> b f c')
eval_input_2d[-1,:,:,:] = inputs_2d_p[-receptive_field:,:,:]
eval_input_3d[-1,:,:,:] = inputs_3d_p[-receptive_field:,:,:]
return eval_input_2d, eval_input_3d
def lxd2Threedim(boneindex, bone_length, bonedir):
skeleton_3d = torch.zeros_like(bonedir).cuda()
p_loc = skeleton_3d.clone()
for idx in range(len(boneindex)):
cidx = boneindex[idx][1]
pidx = boneindex[idx][0]
skeleton_3d[:,:,cidx] = p_loc[:,:,pidx] + bone_length[:,:,idx+1]*bonedir[:,:,idx+1]
p_loc[:,:,cidx] = skeleton_3d[:,:,cidx]
return skeleton_3d
def getbonelength(seq, boneindex):
bs = seq.size(0)
ss = seq.size(1)
seq = seq.view(-1,seq.size(2),seq.size(3))
bone = []
for index in boneindex:
bone.append(seq[:,index[1]] - seq[:,index[0]])
bone = torch.stack(bone,1)
bone = torch.pow(torch.pow(bone,2).sum(2),0.5)
bone = bone.view(bs,ss, bone.size(1),1)
return bone
def getbonedirect(seq, boneindex):
bs = seq.size(0)
ss = seq.size(1)
seq = seq.view(-1,seq.size(2),seq.size(3))
bone = []
for index in boneindex:
bone.append(seq[:,index[1]] - seq[:,index[0]])
bonedirect = torch.stack(bone,1)
bonesum = torch.pow(torch.pow(bonedirect,2).sum(2), 0.5).unsqueeze(2)
bonedirect = bonedirect/bonesum
bonedirect = bonedirect.view(bs,ss,-1,3)
return bonedirect
###################
# Training start
if not args.evaluate:
cameras_train, poses_train, poses_train_2d = fetch(subjects_train, action_filter, subset=args.subset)
lr = args.learning_rate
optimizer = optim.AdamW(model_pos_train.parameters(), lr=lr, weight_decay=0.1)
lr_decay = args.lr_decay
losses_3d_train = []
losses_3d_pos_train = []
losses_3d_diff_train = []
losses_3d_train_eval = []
losses_3d_valid = []
losses_3d_depth_valid = []
epoch = 0
best_epoch = 0
initial_momentum = 0.1
final_momentum = 0.001
# get training data
train_generator = ChunkedGenerator_Seq(args.batch_size//args.stride, cameras_train, poses_train, poses_train_2d, args.number_of_frames,
pad=pad, causal_shift=causal_shift, shuffle=True, augment=args.data_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
train_generator_eval = UnchunkedGenerator_Seq(cameras_train, poses_train, poses_train_2d,
pad=pad, causal_shift=causal_shift, augment=False)
print('INFO: Training on {} frames'.format(train_generator_eval.num_frames()))
if not args.nolog:
writer.add_text(args.log+'_'+TIMESTAMP + '/Training Frames', str(train_generator_eval.num_frames()))
if args.resume:
epoch = checkpoint['epoch']
if 'optimizer' in checkpoint and checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
train_generator.set_random_state(checkpoint['random_state'])
else:
print('WARNING: this checkpoint does not contain an optimizer state. The optimizer will be reinitialized.')
if not args.coverlr:
lr = checkpoint['lr']
print('** Note: reported losses are averaged over all frames.')
print('** The final evaluation will be carried out after the last training epoch.')
# Pos model only
while epoch < args.epochs:
start_time = time()
epoch_loss_3d_train = 0
epoch_loss_3d_pos_train = 0
epoch_loss_3d_diff_train = 0
epoch_loss_traj_train = 0
epoch_loss_2d_train_unlabeled = 0
N = 0
N_semi = 0
model_pos_train.train()
iteration = 0
num_batches = train_generator.batch_num()
# Just train 1 time, for quick debug
quickdebug=args.debug
for cameras_train, batch_3d, batch_2d,in train_generator.next_epoch():
if iteration % 1000 == 0:
print("%d/%d"% (iteration, num_batches))
if cameras_train is not None:
cameras_train = torch.from_numpy(cameras_train.astype('float32'))
inputs_3d = torch.from_numpy(batch_3d.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
if cameras_train is not None:
cameras_train = cameras_train.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
optimizer.zero_grad()
predicted_3d_pos = model_pos_train(inputs_2d, inputs_3d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
# get bone length
inputs_3d_length = getbonelength(inputs_3d, boneindex).mean(1)
predicted_3d_length = getbonelength(predicted_3d_pos, boneindex).mean(1)
loss_length = args.wl*torch.pow(inputs_3d_length - predicted_3d_length,2).mean()
# get bone dir
inputs_3d_bonedir = getbonedirect(inputs_3d, boneindex)
predicted_bonedir = getbonedirect(predicted_3d_pos, boneindex)
loss_dir = args.wd*torch.pow(inputs_3d_bonedir - predicted_bonedir,2).sum(3).mean()
loss_total = loss_3d_pos + loss_length + loss_dir
loss_total.backward(loss_total.clone().detach())
loss_total = torch.mean(loss_total)
epoch_loss_3d_train += inputs_3d.shape[0] * inputs_3d.shape[1] * loss_total.item()
epoch_loss_3d_pos_train += inputs_3d.shape[0] * inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
optimizer.step()
iteration += 1
if quickdebug:
if N==inputs_3d.shape[0] * inputs_3d.shape[1]:
break
losses_3d_train.append(epoch_loss_3d_train / N)
losses_3d_pos_train.append(epoch_loss_3d_pos_train / N)
# torch.cuda.empty_cache()
# End-of-epoch evaluation
with torch.no_grad():
model_pos_test_temp.load_state_dict(model_pos_train.state_dict(), strict=False)
model_pos_test_temp.eval()
epoch_loss_3d_valid = None
epoch_loss_3d_depth_valid = 0
epoch_loss_traj_valid = 0
epoch_loss_2d_valid = 0
epoch_loss_3d_vel = 0
N = 0
iteration = 0
if not args.no_eval:
# Evaluate on test set
for cam, batch, batch_2d, in test_generator.next_epoch():
inputs_3d = torch.from_numpy(batch.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
##### apply test-time-augmentation (following Videopose3d)
inputs_2d_flip = inputs_2d.clone()
inputs_2d_flip[:, :, :, 0] *= -1
inputs_2d_flip[:, :, kps_left + kps_right, :] = inputs_2d_flip[:, :, kps_right + kps_left, :]
##### convert size
inputs_3d_p = inputs_3d
inputs_2d, inputs_3d = eval_data_prepare(receptive_field, inputs_2d, inputs_3d_p)
inputs_2d_flip, _ = eval_data_prepare(receptive_field, inputs_2d_flip, inputs_3d_p)
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_2d_flip = inputs_2d_flip.cuda()
inputs_3d[:, :, 0] = 0
predicted_3d_pos = model_pos_test_temp(inputs_2d, inputs_3d,
input_2d_flip=inputs_2d_flip) # b, t, h, f, j, c
predicted_3d_pos[:, :, :, :, 0] = 0
error = mpjpe_diffusion(predicted_3d_pos, inputs_3d)
if iteration == 0:
epoch_loss_3d_valid = inputs_3d.shape[0] * inputs_3d.shape[1] * error.clone()
else:
epoch_loss_3d_valid += inputs_3d.shape[0] * inputs_3d.shape[1] * error.clone()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
iteration += 1
if quickdebug:
if N == inputs_3d.shape[0] * inputs_3d.shape[1]:
break
losses_3d_valid.append(epoch_loss_3d_valid / N)
elapsed = (time() - start_time) / 60
if args.no_eval:
print('[%d] time %.2f lr %f 3d_train %f 3d_pos_train %f 3d_diff_train %f' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_pos_train[-1] * 1000,
losses_3d_diff_train[-1] * 1000
))
log_path = os.path.join(args.checkpoint, 'training_log.txt')
f = open(log_path, mode='a')
f.write('[%d] time %.2f lr %f 3d_train %f 3d_pos_train %f 3d_diff_train %f\n' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_pos_train[-1] * 1000,
losses_3d_diff_train[-1] * 1000
))
f.close()
else:
print('[%d] time %.2f lr %f 3d_train %f 3d_pos_train %f 3d_pos_valid %f' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_pos_train[-1] * 1000,
losses_3d_valid[-1][0] * 1000
))
log_path = os.path.join(args.checkpoint, 'training_log.txt')
f = open(log_path, mode='a')
f.write('[%d] time %.2f lr %f 3d_train %f 3d_pos_train %f 3d_pos_valid %f\n' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_pos_train[-1] * 1000,
losses_3d_valid[-1][0] * 1000
))
f.close()
if not args.nolog:
#writer.add_scalar("Loss/3d training eval loss", losses_3d_train_eval[-1] * 1000, epoch+1)
writer.add_scalar("Loss/3d validation loss", losses_3d_valid[-1] * 1000, epoch+1)
if not args.nolog:
writer.add_scalar("Loss/3d training loss", losses_3d_train[-1] * 1000, epoch+1)
writer.add_scalar("Parameters/learing rate", lr, epoch+1)
writer.add_scalar('Parameters/training time per epoch', elapsed, epoch+1)
# Decay learning rate exponentially
lr *= lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
epoch += 1
# Decay BatchNorm momentum
# momentum = initial_momentum * np.exp(-epoch/args.epochs * np.log(initial_momentum/final_momentum))
# model_pos_train.set_bn_momentum(momentum)
# Save checkpoint if necessary
if epoch % args.checkpoint_frequency == 0:
chk_path = os.path.join(args.checkpoint, 'epoch_{}.bin'.format(epoch))
print('Saving checkpoint to', chk_path)
torch.save({
'epoch': epoch,
'lr': lr,
'random_state': train_generator.random_state(),
'optimizer': optimizer.state_dict(),
'model_pos': model_pos_train.state_dict(),
# 'min_loss': min_loss
# 'model_traj': model_traj_train.state_dict() if semi_supervised else None,
# 'random_state_semi': semi_generator.random_state() if semi_supervised else None,
}, chk_path)
#### save best checkpoint
best_chk_path = os.path.join(args.checkpoint, 'best_epoch.bin')
if losses_3d_valid[-1][0] * 1000 < min_loss:
min_loss = losses_3d_valid[-1] * 1000
best_epoch = epoch
print("save best checkpoint")
torch.save({
'epoch': epoch,
'lr': lr,
'random_state': train_generator.random_state(),
'optimizer': optimizer.state_dict(),
'model_pos': model_pos_train.state_dict(),
# 'model_traj': model_traj_train.state_dict() if semi_supervised else None,
# 'random_state_semi': semi_generator.random_state() if semi_supervised else None,
}, best_chk_path)
f = open(log_path, mode='a')
f.write('best epoch\n')
f.close()
# Save training curves after every epoch, as .png images (if requested)
if args.export_training_curves and epoch > 3:
if 'matplotlib' not in sys.modules:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure()
epoch_x = np.arange(3, len(losses_3d_train)) + 1
plt.plot(epoch_x, losses_3d_train[3:], '--', color='C0')
plt.plot(epoch_x, losses_3d_train_eval[3:], color='C0')
plt.plot(epoch_x, losses_3d_valid[3:], color='C1')
plt.legend(['3d train', '3d train (eval)', '3d valid (eval)'])
plt.ylabel('MPJPE (m)')
plt.xlabel('Epoch')
plt.xlim((3, epoch))
plt.savefig(os.path.join(args.checkpoint, 'loss_3d.png'))
plt.close('all')
# Training end
# Evaluate
def evaluate(test_generator, action=None, return_predictions=False, use_trajectory_model=False, newmodel=None):
epoch_loss_3d_pos = torch.zeros(args.sampling_timesteps).cuda()
epoch_loss_3d_pos_h = torch.zeros(args.sampling_timesteps).cuda()
epoch_loss_3d_pos_mean = torch.zeros(args.sampling_timesteps).cuda()
epoch_loss_3d_pos_select = torch.zeros(args.sampling_timesteps).cuda()
epoch_loss_3d_pos_p2 = torch.zeros(args.sampling_timesteps)
epoch_loss_3d_pos_h_p2 = torch.zeros(args.sampling_timesteps)
epoch_loss_3d_pos_mean_p2 = torch.zeros(args.sampling_timesteps)
epoch_loss_3d_pos_select_p2 = torch.zeros(args.sampling_timesteps)
with torch.no_grad():
if newmodel is not None:
print('Loading comparison model')
model_eval = newmodel
chk_file_path = '/mnt/data3/home/zjl/workspace/3dpose/PoseFormer/checkpoint/train_pf_00/epoch_60.bin'
print('Loading evaluate checkpoint of comparison model', chk_file_path)
checkpoint = torch.load(chk_file_path, map_location=lambda storage, loc: storage)
model_eval.load_state_dict(checkpoint['model_pos'], strict=False)
model_eval.eval()
else:
model_eval = model_pos
if not use_trajectory_model:
# load best checkpoint
if args.evaluate == '':
chk_file_path = os.path.join(args.checkpoint, 'best_epoch.bin' )
print('Loading best checkpoint', chk_file_path)
elif args.evaluate != '':
chk_file_path = os.path.join(args.checkpoint, args.evaluate)
print('Loading evaluate checkpoint', chk_file_path)
checkpoint = torch.load(chk_file_path, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_eval.load_state_dict(checkpoint['model_pos'])
model_eval.eval()
# else:
# model_traj.eval()
N = 0
iteration = 0
#num_batches = test_generator.batch_num()
quickdebug=args.debug
for cam, batch, batch_2d in test_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
inputs_3d = torch.from_numpy(batch.astype('float32'))
cam = torch.from_numpy(cam.astype('float32'))
##### apply test-time-augmentation (following Videopose3d)
inputs_2d_flip = inputs_2d.clone()
inputs_2d_flip [:, :, :, 0] *= -1
inputs_2d_flip[:, :, kps_left + kps_right,:] = inputs_2d_flip[:, :, kps_right + kps_left,:]
##### convert size
inputs_3d_p = inputs_3d
if newmodel is not None:
def eval_data_prepare_pf(receptive_field, inputs_2d, inputs_3d):
inputs_2d_p = torch.squeeze(inputs_2d)
inputs_3d_p = inputs_3d.permute(1,0,2,3)
padding = int(receptive_field//2)
inputs_2d_p = rearrange(inputs_2d_p, 'b f c -> f c b')
inputs_2d_p = F.pad(inputs_2d_p, (padding,padding), mode='replicate')
inputs_2d_p = rearrange(inputs_2d_p, 'f c b -> b f c')
out_num = inputs_2d_p.shape[0] - receptive_field + 1
eval_input_2d = torch.empty(out_num, receptive_field, inputs_2d_p.shape[1], inputs_2d_p.shape[2])
for i in range(out_num):
eval_input_2d[i,:,:,:] = inputs_2d_p[i:i+receptive_field, :, :]
return eval_input_2d, inputs_3d_p
inputs_2d, inputs_3d = eval_data_prepare_pf(81, inputs_2d, inputs_3d_p)
inputs_2d_flip, _ = eval_data_prepare_pf(81, inputs_2d_flip, inputs_3d_p)
else:
inputs_2d, inputs_3d = eval_data_prepare(receptive_field, inputs_2d, inputs_3d_p)
inputs_2d_flip, _ = eval_data_prepare(receptive_field, inputs_2d_flip, inputs_3d_p)
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
inputs_2d_flip = inputs_2d_flip.cuda()
inputs_3d = inputs_3d.cuda()
cam = cam.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
bs = args.batch_size
total_batch = (inputs_3d.shape[0] + bs - 1) // bs
for batch_cnt in range(total_batch):
if (batch_cnt + 1) * bs > inputs_3d.shape[0]:
inputs_2d_single = inputs_2d[batch_cnt * bs:]
inputs_2d_flip_single = inputs_2d_flip[batch_cnt * bs:]
inputs_3d_single = inputs_3d[batch_cnt * bs:]
inputs_traj_single = inputs_traj[batch_cnt * bs:]
else:
inputs_2d_single = inputs_2d[batch_cnt * bs:(batch_cnt+1) * bs]
inputs_2d_flip_single = inputs_2d_flip[batch_cnt * bs:(batch_cnt+1) * bs]
inputs_3d_single = inputs_3d[batch_cnt * bs:(batch_cnt+1) * bs]
inputs_traj_single = inputs_traj[batch_cnt * bs:(batch_cnt + 1) * bs]
predicted_3d_pos_single = model_eval(inputs_2d_single, inputs_3d_single, input_2d_flip=inputs_2d_flip_single) #b, t, h, f, j, c
predicted_3d_pos_single[:, :, :, :, 0] = 0
if return_predictions:
return predicted_3d_pos_single.squeeze().cpu().numpy()
# 2d reprojection
b_sz, t_sz, h_sz, f_sz, j_sz, c_sz =predicted_3d_pos_single.shape
inputs_traj_single_all = inputs_traj_single.unsqueeze(1).unsqueeze(1).repeat(1, t_sz, h_sz, 1, 1, 1)
predicted_3d_pos_abs_single = predicted_3d_pos_single + inputs_traj_single_all
predicted_3d_pos_abs_single = predicted_3d_pos_abs_single.reshape(b_sz*t_sz*h_sz*f_sz, j_sz, c_sz)
cam_single_all = cam.repeat(b_sz*t_sz*h_sz*f_sz, 1)
reproject_2d =project_to_2d(predicted_3d_pos_abs_single, cam_single_all)
reproject_2d = reproject_2d.reshape(b_sz, t_sz, h_sz, f_sz, j_sz, 2)
error = mpjpe_diffusion_all_min(predicted_3d_pos_single, inputs_3d_single) # J-Best
error_h = mpjpe_diffusion(predicted_3d_pos_single, inputs_3d_single) # P-Best
error_mean = mpjpe_diffusion_all_min(predicted_3d_pos_single, inputs_3d_single, mean_pos=True) # P-Agg
error_reproj_select = mpjpe_diffusion_reproj(predicted_3d_pos_single, inputs_3d_single, reproject_2d, inputs_2d_single) # J-Agg
epoch_loss_3d_pos += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * error.clone()
epoch_loss_3d_pos_h += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * error_h.clone()
epoch_loss_3d_pos_mean += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * error_mean.clone()
epoch_loss_3d_pos_select += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * error_reproj_select.clone()
if args.p2:
error_p2 = p_mpjpe_diffusion_all_min(predicted_3d_pos_single, inputs_3d_single)
error_h_p2 = p_mpjpe_diffusion(predicted_3d_pos_single, inputs_3d_single)
error_mean_p2 = p_mpjpe_diffusion_all_min(predicted_3d_pos_single, inputs_3d_single, mean_pos=True)
error_reproj_select_p2 = p_mpjpe_diffusion_reproj(predicted_3d_pos_single, inputs_3d_single, reproject_2d, inputs_2d_single)
epoch_loss_3d_pos_p2 += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * torch.from_numpy(error_p2)
epoch_loss_3d_pos_h_p2 += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * torch.from_numpy(error_h_p2)
epoch_loss_3d_pos_mean_p2 += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * torch.from_numpy(error_mean_p2)
epoch_loss_3d_pos_select_p2 += inputs_3d_single.shape[0] * inputs_3d_single.shape[1] * torch.from_numpy(error_reproj_select_p2)
N += inputs_3d_single.shape[0] * inputs_3d_single.shape[1]
if quickdebug:
if N == inputs_3d_single.shape[0] * inputs_3d_single.shape[1]:
break
if quickdebug:
if N == inputs_3d_single.shape[0] * inputs_3d_single.shape[1]:
break
log_path = os.path.join(args.checkpoint, 'h36m_test_log_H%d_K%d.txt' %(args.num_proposals, args.sampling_timesteps))
f = open(log_path, mode='a')
if action is None:
print('----------')
else:
print('----'+action+'----')
f.write('----'+action+'----\n')
e1 = (epoch_loss_3d_pos / N)*1000
e1_h = (epoch_loss_3d_pos_h / N) * 1000
e1_mean = (epoch_loss_3d_pos_mean / N) * 1000
e1_select = (epoch_loss_3d_pos_select / N) * 1000
if args.p2:
e2 = (epoch_loss_3d_pos_p2 / N) * 1000
e2_h = (epoch_loss_3d_pos_h_p2 / N) * 1000
e2_mean = (epoch_loss_3d_pos_mean_p2 / N) * 1000
e2_select = (epoch_loss_3d_pos_select_p2 / N) * 1000
print('Test time augmentation:', test_generator.augment_enabled())
for ii in range(e1.shape[0]):
print('step %d : Protocol #1 Error (MPJPE) J_Best:' % ii, e1[ii].item(), 'mm')
f.write('step %d : Protocol #1 Error (MPJPE) J_Best: %f mm\n' % (ii, e1[ii].item()))
print('step %d : Protocol #1 Error (MPJPE) P_Best:' % ii, e1_h[ii].item(), 'mm')
f.write('step %d : Protocol #1 Error (MPJPE) P_Best: %f mm\n' % (ii, e1_h[ii].item()))
print('step %d : Protocol #1 Error (MPJPE) P_Agg:' % ii, e1_mean[ii].item(), 'mm')
f.write('step %d : Protocol #1 Error (MPJPE) P_Agg: %f mm\n' % (ii, e1_mean[ii].item()))
print('step %d : Protocol #1 Error (MPJPE) J_Agg:' % ii, e1_select[ii].item(), 'mm')
f.write('step %d : Protocol #1 Error (MPJPE) J_Agg: %f mm\n' % (ii, e1_select[ii].item()))
if args.p2:
print('step %d : Protocol #2 Error (MPJPE) J_Best:' % ii, e2[ii].item(), 'mm')
f.write('step %d : Protocol #2 Error (MPJPE) J_Best: %f mm\n' % (ii, e2[ii].item()))
print('step %d : Protocol #2 Error (MPJPE) P_Best:' % ii, e2_h[ii].item(), 'mm')
f.write('step %d : Protocol #2 Error (MPJPE) P_Best: %f mm\n' % (ii, e2_h[ii].item()))
print('step %d : Protocol #2 Error (MPJPE) P_Agg:' % ii, e2_mean[ii].item(), 'mm')
f.write('step %d : Protocol #2 Error (MPJPE) P_Agg: %f mm\n' % (ii, e2_mean[ii].item()))
print('step %d : Protocol #2 Error (MPJPE) J_Agg:' % ii, e2_select[ii].item(), 'mm')
f.write('step %d : Protocol #2 Error (MPJPE) J_Agg: %f mm\n' % (ii, e2_select[ii].item()))
print('----------')
f.write('----------\n')
f.close()
if args.p2:
return e1, e1_h, e1_mean, e1_select, e2, e2_h, e2_mean, e2_select
else:
return e1, e1_h, e1_mean, e1_select
if args.render:
print('Rendering...')
input_keypoints = keypoints[args.viz_subject][args.viz_action][args.viz_camera].copy()
ground_truth = None
if args.viz_subject in dataset.subjects() and args.viz_action in dataset[args.viz_subject]:
if 'positions_3d' in dataset[args.viz_subject][args.viz_action]:
ground_truth = dataset[args.viz_subject][args.viz_action]['positions_3d'][args.viz_camera].copy()
if ground_truth is None:
print('INFO: this action is unlabeled. Ground truth will not be rendered.')
gen = UnchunkedGenerator_Seq(None, [ground_truth], [input_keypoints],
pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
prediction = evaluate(gen, return_predictions=True)
if args.compare:
from common.model_poseformer import PoseTransformer
model_pf = PoseTransformer(num_frame=81, num_joints=17, in_chans=2, num_heads=8, mlp_ratio=2., qkv_bias=False, qk_scale=None,drop_path_rate=0.1)
if torch.cuda.is_available():
model_pf = nn.DataParallel(model_pf)
model_pf = model_pf.cuda()
prediction_pf = evaluate(gen, newmodel=model_pf, return_predictions=True)
# ### reshape prediction_pf as ground truth
# if ground_truth.shape[0] / receptive_field > ground_truth.shape[0] // receptive_field:
# batch_num = (ground_truth.shape[0] // receptive_field) +1
# prediction_pf_2 = np.empty_like(ground_truth)
# for i in range(batch_num-1):
# prediction_pf_2[i*receptive_field:(i+1)*receptive_field,:,:] = prediction_pf[i,:,:,:]
# left_frames = ground_truth.shape[0] - (batch_num-1)*receptive_field
# prediction_pf_2[-left_frames:,:,:] = prediction_pf[-1,-left_frames:,:,:]
# prediction_pf = prediction_pf_2
# elif ground_truth.shape[0] / receptive_field == ground_truth.shape[0] // receptive_field:
# prediction_pf.reshape(ground_truth.shape[0], 17, 3)
# if model_traj is not None and ground_truth is None:
# prediction_traj = evaluate(gen, return_predictions=True, use_trajectory_model=True)
# prediction += prediction_traj
### reshape prediction as ground truth
if ground_truth.shape[0] / receptive_field > ground_truth.shape[0] // receptive_field:
batch_num = (ground_truth.shape[0] // receptive_field) +1
prediction2 = np.empty_like(ground_truth)
for i in range(batch_num-1):
prediction2[i*receptive_field:(i+1)*receptive_field,:,:] = prediction[i,:,:,:]
left_frames = ground_truth.shape[0] - (batch_num-1)*receptive_field
prediction2[-left_frames:,:,:] = prediction[-1,-left_frames:,:,:]
prediction = prediction2
elif ground_truth.shape[0] / receptive_field == ground_truth.shape[0] // receptive_field:
prediction.reshape(ground_truth.shape[0], 17, 3)
if args.viz_export is not None:
print('Exporting joint positions to', args.viz_export)
# Predictions are in camera space
np.save(args.viz_export, prediction)
if args.viz_output is not None:
if ground_truth is not None:
# Reapply trajectory
trajectory = ground_truth[:, :1]
ground_truth[:, 1:] += trajectory
prediction += trajectory
if args.compare:
prediction_pf += trajectory
# Invert camera transformation
cam = dataset.cameras()[args.viz_subject][args.viz_camera]
if ground_truth is not None:
if args.compare:
prediction_pf = camera_to_world(prediction_pf, R=cam['orientation'], t=cam['translation'])
prediction = camera_to_world(prediction, R=cam['orientation'], t=cam['translation'])
ground_truth = camera_to_world(ground_truth, R=cam['orientation'], t=cam['translation'])
else:
# If the ground truth is not available, take the camera extrinsic params from a random subject.
# They are almost the same, and anyway, we only need this for visualization purposes.
for subject in dataset.cameras():
if 'orientation' in dataset.cameras()[subject][args.viz_camera]:
rot = dataset.cameras()[subject][args.viz_camera]['orientation']
break
if args.compare:
prediction_pf = camera_to_world(prediction_pf, R=rot, t=0)
prediction_pf[:, :, 2] -= np.min(prediction_pf[:, :, 2])
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
if args.compare:
anim_output = {'PoseFormer': prediction_pf}
anim_output['Ours'] = prediction
# print(prediction_pf.shape, prediction.shape)
else:
# anim_output = {'Reconstruction': prediction}
anim_output = {'Reconstruction': ground_truth + np.random.normal(loc=0.0, scale=0.1, size=[ground_truth.shape[0], 17, 3])}
if ground_truth is not None and not args.viz_no_ground_truth:
anim_output['Ground truth'] = ground_truth
input_keypoints = image_coordinates(input_keypoints[..., :2], w=cam['res_w'], h=cam['res_h'])
from common.visualization import render_animation
render_animation(input_keypoints, keypoints_metadata, anim_output,
dataset.skeleton(), dataset.fps(), args.viz_bitrate, cam['azimuth'], args.viz_output,
limit=args.viz_limit, downsample=args.viz_downsample, size=args.viz_size,
input_video_path=args.viz_video, viewport=(cam['res_w'], cam['res_h']),
input_video_skip=args.viz_skip)
else:
print('Evaluating...')
all_actions = {}
all_actions_flatten = []
all_actions_by_subject = {}
for subject in subjects_test:
if subject not in all_actions_by_subject:
all_actions_by_subject[subject] = {}
for action in dataset[subject].keys():
action_name = action.split(' ')[0]
if action_name not in all_actions:
all_actions[action_name] = []
if action_name not in all_actions_by_subject[subject]:
all_actions_by_subject[subject][action_name] = []
all_actions[action_name].append((subject, action))
all_actions_flatten.append((subject, action))
all_actions_by_subject[subject][action_name].append((subject, action))
def fetch_actions(actions):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
for subject, action in actions:
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
if subject in dataset.cameras():
cams = dataset.cameras()[subject]
assert len(cams) == len(poses_2d), 'Camera count mismatch'
for cam in cams:
if 'intrinsic' in cam:
out_camera_params.append(cam['intrinsic'])
stride = args.downsample
if stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
def run_evaluation(actions, action_filter=None):
errors_p1 = []
errors_p1_h = []
errors_p1_mean = []
errors_p1_select = []
errors_p2 = []