def generate_trajs(self, data, boxes):
with torch.no_grad():
num_objs = boxes.shape[2]
g_idx = np.array([[i, j, 1] for i in range(num_objs)
for j in range(num_objs) if j != i])
g_idx = torch.from_numpy(g_idx[None].repeat(data.shape[0], 0))
rois = xyxy_to_rois(boxes,
batch=data.shape[0],
time_step=data.shape[1],
num_devices=self.num_gpus)
outputs = self.model(data,
rois,
num_rollouts=self.pred_rollout,
g_idx=g_idx)
outputs = {
'boxes': outputs['boxes'].cpu().numpy(),
'masks': outputs['masks'].cpu().numpy(),
'score': outputs['score'].cpu().numpy(),
}
outputs['boxes'][..., 0::2] *= self.input_width
outputs['boxes'][..., 1::2] *= self.input_height
outputs['boxes'] = xywh2xyxy(outputs['boxes'].reshape(
-1, 4)).reshape((data.shape[0], -1, num_objs, 4))
return outputs['boxes'], outputs['masks'], outputs['score']
def train_epoch(self):
for batch_idx, (data, data_t, rois, gt_boxes, gt_masks, valid,
g_idx) in enumerate(self.train_loader):
self._adjust_learning_rate()
data, data_t = data.to(self.device), data_t.to(self.device)
rois = xyxy_to_rois(rois,
batch=data.shape[0],
time_step=data.shape[1],
num_devices=self.num_gpus)
self.optim.zero_grad()
outputs = self.model(data,
rois,
num_rollouts=self.ptrain_size,
g_idx=g_idx,
x_t=data_t,
phase='train')
labels = {
'boxes': gt_boxes.to(self.device),
'masks': gt_masks.to(self.device),
'valid': valid.to(self.device),
}
loss = self.loss(outputs, labels, 'train')
loss.backward()
self.optim.step()
# this is an approximation for printing; the dataset size may not divide the batch size
self.iterations += self.batch_size
print_msg = ""
print_msg += f"{self.epochs:03}/{self.iterations // 1000:04}k"
print_msg += f" | "
mean_loss = np.mean(
np.array(self.box_p_step_losses[:self.ptrain_size]) /
self.loss_cnt) * 1e3
print_msg += f"{mean_loss:.3f} | "
print_msg += f" | ".join([
"{:.3f}".format(self.losses[name] * 1e3 / self.loss_cnt)
for name in self.loss_name
])
speed = self.loss_cnt / (timer() - self.time)
eta = (self.max_iters - self.iterations) / speed / 3600
print_msg += f" | speed: {speed:.1f} | eta: {eta:.2f} h"
print_msg += (
" " * (os.get_terminal_size().columns - len(print_msg) - 10))
tprint(print_msg)
if self.iterations % self.val_interval == 0:
self.snapshot()
self.val()
self._init_loss()
self.model.train()
if self.iterations >= self.max_iters:
print('\r', end='')
print(f'{self.best_mean:.3f}')
break
def val(self):
self.model.eval()
self._init_loss()
if C.RPIN.VAE:
losses = dict.fromkeys(self.loss_name, 0.0)
box_p_step_losses = [0.0 for _ in range(self.ptest_size)]
masks_step_losses = [0.0 for _ in range(self.ptest_size)]
for batch_idx, (data, _, rois, gt_boxes, gt_masks, valid, g_idx,
seq_l) in enumerate(self.val_loader):
tprint(f'eval: {batch_idx}/{len(self.val_loader)}')
with torch.no_grad():
if C.RPIN.ROI_MASKING or C.RPIN.ROI_CROPPING:
# data should be (b x t x o x c x h x w)
data = data.permute(
(0, 2, 1, 3, 4, 5)) # (b, o, t, c, h, w)
data = data.reshape((data.shape[0] * data.shape[1], ) +
data.shape[2:]) # (b*o, t, c, h, w)
data = data.to(self.device)
rois = xyxy_to_rois(rois,
batch=data.shape[0],
time_step=data.shape[1],
num_devices=self.num_gpus)
labels = {
'boxes': gt_boxes.to(self.device),
'masks': gt_masks.to(self.device),
'valid': valid.to(self.device),
'seq_l': seq_l.to(self.device),
}
outputs = self.model(data,
rois,
num_rollouts=self.ptest_size,
g_idx=g_idx,
phase='test')
self.loss(outputs, labels, 'test')
# VAE multiple runs
if C.RPIN.VAE:
vae_best_mean = np.mean(
np.array(self.box_p_step_losses[:self.ptest_size]) /
self.loss_cnt) * 1e3
losses_t = self.losses.copy()
box_p_step_losses_t = self.box_p_step_losses.copy()
masks_step_losses_t = self.masks_step_losses.copy()
for i in range(9):
outputs = self.model(data,
rois,
num_rollouts=self.ptest_size,
g_idx=g_idx,
phase='test')
self.loss(outputs, labels, 'test')
mean_loss = np.mean(
np.array(self.box_p_step_losses[:self.ptest_size])
/ self.loss_cnt) * 1e3
if mean_loss < vae_best_mean:
losses_t = self.losses.copy()
box_p_step_losses_t = self.box_p_step_losses.copy()
masks_step_losses_t = self.masks_step_losses.copy()
vae_best_mean = mean_loss
self._init_loss()
for k, v in losses.items():
losses[k] += losses_t[k]
for i in range(len(box_p_step_losses)):
box_p_step_losses[i] += box_p_step_losses_t[i]
masks_step_losses[i] += masks_step_losses_t[i]
if C.RPIN.VAE:
self.losses = losses.copy()
self.box_p_step_losses = box_p_step_losses.copy()
self.loss_cnt = len(self.val_loader)
print('\r', end='')
print_msg = ""
print_msg += f"{self.epochs:03}/{self.iterations // 1000:04}k"
print_msg += f" | "
mean_loss = np.mean(
np.array(self.box_p_step_losses[:self.ptest_size]) /
self.loss_cnt) * 1e3
print_msg += f"{mean_loss:.3f} | "
if mean_loss < self.best_mean:
self.snapshot('ckpt_best.path.tar')
self.best_mean = mean_loss
print_msg += f" | ".join([
"{:.3f}".format(self.losses[name] * 1e3 / self.loss_cnt)
for name in self.loss_name
])
if C.RPIN.SEQ_CLS_LOSS_WEIGHT:
print_msg += f" | {self.fg_correct / (self.fg_num + 1e-9):.3f} | {self.bg_correct / (self.bg_num + 1e-9):.3f}"
# print_msg += (" " * (os.get_terminal_size().columns - len(print_msg) - 10))
self.logger.info(print_msg)
def test(self):
self.model.eval()
if C.RPIN.VAE:
losses = dict.fromkeys(self.loss_name, 0.0)
box_p_step_losses = [0.0 for _ in range(self.ptest_size)]
masks_step_losses = [0.0 for _ in range(self.ptest_size)]
for batch_idx, (data, _, rois, gt_boxes, gt_masks, valid,
g_idx) in enumerate(self.val_loader):
with torch.no_grad():
data = data.to(self.device)
rois = xyxy_to_rois(rois,
batch=data.shape[0],
time_step=data.shape[1],
num_devices=self.num_gpus)
labels = {
'boxes': gt_boxes.to(self.device),
'masks': gt_masks.to(self.device),
'valid': valid.to(self.device),
}
outputs = self.model(data,
rois,
num_rollouts=self.ptest_size,
g_idx=g_idx,
phase='test')
self.loss(outputs, labels, 'test')
# VAE multiple runs
if C.RPIN.VAE:
vae_best_mean = np.mean(
np.array(self.box_p_step_losses[:self.ptest_size]) /
self.loss_cnt) * 1e3
losses_t = self.losses.copy()
box_p_step_losses_t = self.box_p_step_losses.copy()
masks_step_losses_t = self.masks_step_losses.copy()
for i in range(99):
outputs = self.model(data,
rois,
num_rollouts=self.ptest_size,
g_idx=g_idx,
phase='test')
self.loss(outputs, labels, 'test')
mean_loss = np.mean(
np.array(self.box_p_step_losses[:self.ptest_size])
/ self.loss_cnt) * 1e3
if mean_loss < vae_best_mean:
losses_t = self.losses.copy()
box_p_step_losses_t = self.box_p_step_losses.copy()
masks_step_losses_t = self.masks_step_losses.copy()
vae_best_mean = mean_loss
self._init_loss()
for k, v in losses.items():
losses[k] += losses_t[k]
for i in range(len(box_p_step_losses)):
box_p_step_losses[i] += box_p_step_losses_t[i]
masks_step_losses[i] += masks_step_losses_t[i]
tprint(f'eval: {batch_idx}/{len(self.val_loader)}:' + ' ' * 20)
if self.plot_image > 0:
outputs = {
'boxes':
outputs['boxes'].cpu().numpy(),
'masks':
outputs['masks'].cpu().numpy()
if C.RPIN.MASK_LOSS_WEIGHT else None,
}
outputs['boxes'][..., 0::2] *= self.input_width
outputs['boxes'][..., 1::2] *= self.input_height
outputs['boxes'] = xywh2xyxy(outputs['boxes'].reshape(
-1, 4)).reshape(
(data.shape[0], -1, C.RPIN.MAX_NUM_OBJS, 4))
labels = {
'boxes': labels['boxes'].cpu().numpy(),
'masks': labels['masks'].cpu().numpy(),
}
labels['boxes'][..., 0::2] *= self.input_width
labels['boxes'][..., 1::2] *= self.input_height
labels['boxes'] = xywh2xyxy(labels['boxes'].reshape(
-1, 4)).reshape(
(data.shape[0], -1, C.RPIN.MAX_NUM_OBJS, 4))
for i in range(rois.shape[0]):
batch_size = C.SOLVER.BATCH_SIZE if not C.RPIN.VAE else 1
plot_image_idx = batch_size * batch_idx + i
if plot_image_idx < self.plot_image:
tprint(f'plotting: {plot_image_idx}' + ' ' * 20)
video_idx, img_idx = self.val_loader.dataset.video_info[
plot_image_idx]
video_name = self.val_loader.dataset.video_list[
video_idx]
v = valid[i].numpy().astype(np.bool)
pred_boxes_i = outputs['boxes'][i][:, v]
gt_boxes_i = labels['boxes'][i][:, v]
if 'PHYRE' in C.DATA_ROOT:
im_data = phyre.observations_to_float_rgb(
np.load(video_name).astype(
np.uint8))[..., ::-1]
a, b, c = video_name.split('/')[5:8]
output_name = f'{a}_{b}_{c.replace(".npy", "")}'
bg_image = np.load(video_name).astype(np.uint8)
for fg_id in [1, 2, 3, 5]:
bg_image[bg_image == fg_id] = 0
bg_image = phyre.observations_to_float_rgb(
bg_image)
else:
bg_image = None
image_list = sorted(
glob(
f'{video_name}/*{self.val_loader.dataset.image_ext}'
))
im_name = image_list[img_idx]
output_name = '_'.join(
im_name.split('.')[0].split('/')[-2:])
# deal with image data here
gt_boxes_i = labels['boxes'][i][:, v]
im_data = get_im_data(im_name, gt_boxes_i[None,
0:1],
C.DATA_ROOT,
self.high_resolution_plot)
if self.high_resolution_plot:
scale_w = im_data.shape[1] / self.input_width
scale_h = im_data.shape[0] / self.input_height
pred_boxes_i[..., [0, 2]] *= scale_w
pred_boxes_i[..., [1, 3]] *= scale_h
gt_boxes_i[..., [0, 2]] *= scale_w
gt_boxes_i[..., [1, 3]] *= scale_h
pred_masks_i = None
if C.RPIN.MASK_LOSS_WEIGHT:
pred_masks_i = outputs['masks'][i][:, v]
plot_rollouts(im_data,
pred_boxes_i,
gt_boxes_i,
pred_masks_i,
labels['masks'][i][:, v],
output_dir=self.output_dir,
output_name=output_name,
bg_image=bg_image)
if C.RPIN.VAE:
self.losses = losses.copy()
self.box_p_step_losses = box_p_step_losses.copy()
self.loss_cnt = len(self.val_loader)
print('\r', end='')
print_msg = ""
mean_loss = np.mean(
np.array(self.box_p_step_losses[:self.ptest_size]) /
self.loss_cnt) * 1e3
print_msg += f"{mean_loss:.3f} | "
print_msg += f" | ".join([
"{:.3f}".format(self.losses[name] * 1e3 / self.loss_cnt)
for name in self.loss_name
])
pprint(print_msg)