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propagator.py
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propagator.py
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from re import L
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import os
import time
import tqdm
import mv_warp_func_gpu
from model import SoftPropagation, res_patch
from utils import AverageMeter, aggregate_wbg_channel
# softmax aggregation：
# https://openaccess.thecvf.com/content_cvpr_2018/papers/Oh_Fast_Video_Object_CVPR_2018_paper.pdf
def unpad(pred, pad_array):
if pad_array[2] + pad_array[3] > 0:
pred = pred[:, pad_array[2] : -pad_array[3], :]
if pad_array[0] + pad_array[1] > 0:
pred = pred[:, :, pad_array[0] : -pad_array[1]]
return pred
class Propagator(nn.Module):
def __init__(
self,
model_path,
save_inter_feat=False,
):
super(Propagator, self).__init__()
self.save_inter_feat = save_inter_feat
self.model = SoftPropagation()
state_dict = torch.load(model_path)
state_dict_ = {}
for k in list(state_dict.keys()):
state_dict_[k.replace('soft_propagation.','')]=state_dict.pop(k)
self.model.load_state_dict(state_dict_)
self.model.eval()
def propagate(
self,
key_masks,
key_pred4,
key_feat4,
cvf,
pad_array,
gt_shape,
low_level_extractor,
**kwargs
):
nb_frames = cvf["nb_frames"]
non_key_idx = cvf["non_key_idx"]
key_idx = cvf["key_idx"]
feat4_all = key_feat4.new_zeros((nb_frames,) + key_feat4.shape[-3:])
pred4_all = key_pred4.new_zeros((nb_frames,) + key_pred4.shape[-3:])
feat4_all[key_idx] = key_feat4
pred4_all[key_idx] = key_pred4
propagation_tmp = torch.cat((feat4_all, pred4_all), dim=1)
warp_t = AverageMeter()
out_masks = torch.zeros((nb_frames, 1, *gt_shape))
k = 0
#for i in tqdm.tqdm(cvf["decode_order"], desc="mv warp"):
for i in cvf["decode_order"]:
if i in non_key_idx:
torch.cuda.synchronize()
start = time.time()
output_t = mv_warp_func_gpu.forward(
propagation_tmp,
cvf["mv_x_L0"][i],
cvf["mv_y_L0"][i],
cvf["mv_x_L1"][i],
cvf["mv_y_L1"][i],
cvf["L0_ref"][i],
cvf["L1_ref"][i],
i,
)
propagation_tmp[i] = output_t
pred4 = propagation_tmp[i][256:].unsqueeze(0)
feat_prop = propagation_tmp[i][:256].unsqueeze(0)
feat = low_level_extractor(cvf["rgb_tensor"][i].unsqueeze(0))
residual = cvf["residual"][i].unsqueeze(0)
# find the nearest keyframe:
nearest_key = min(key_idx, key=lambda list_value : abs(list_value - i))
pred4_ref = propagation_tmp[nearest_key][256:].unsqueeze(0)
feat_ref = propagation_tmp[nearest_key][:256].unsqueeze(0)
pred_patched = res_patch(pred4, feat, pred4_ref, feat_ref, residual)
pred4_prop = aggregate_wbg_channel(pred4, keep_bg=True)
pred4_channel_pad = pred4_prop.new_zeros((1, 11, *pred4_prop.shape[-2:]))
pred4_dim0 = pred4_prop.shape[1]
pred4_channel_pad[:, :pred4_dim0] = pred4_prop
pred4 = self.model(feat, feat_prop, pred4_channel_pad, pred_patched)
pred = F.interpolate(
pred4,
scale_factor=4,
mode="bilinear",
align_corners=False,
)
pred = torch.sigmoid(pred)
pred = pred[:, : pred4_dim0 - 1]
mask = aggregate_wbg_channel(pred, keep_bg=True).squeeze(0)
mask = unpad(mask, pad_array)
out_masks[i] = torch.argmax(mask, dim=0)
torch.cuda.synchronize()
warp_t.update(time.time() - start)
else:
out_masks[i] = key_masks[k]
k = k + 1
warp_fps = 1 / warp_t.avg
del propagation_tmp
del cvf
torch.cuda.empty_cache()
print("Do propagation video at FPS {:.2f}.".format(warp_fps))
return out_masks.squeeze().cpu().numpy(), warp_t.sum