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# -*- coding: utf-8 -*-
# @Time : 10/1/21
# @Author : GXYM
import torch
import torch.nn as nn
from IndicPhotoOCR.detection.textbpn.network.layers.model_block import FPN
from IndicPhotoOCR.detection.textbpn.cfglib.config import config as cfg
import numpy as np
from IndicPhotoOCR.detection.textbpn.network.layers.CircConv import DeepSnake
from IndicPhotoOCR.detection.textbpn.network.layers.GCN import GCN
from IndicPhotoOCR.detection.textbpn.network.layers.RNN import RNN
from IndicPhotoOCR.detection.textbpn.network.layers.Adaptive_Deformation import AdaptiveDeformation
# from IndicPhotoOCR.detection.textbpn.network.layers.Transformer_old import Transformer_old
from IndicPhotoOCR.detection.textbpn.network.layers.Transformer import Transformer
import cv2
from IndicPhotoOCR.detection.textbpn.util.misc import get_sample_point, fill_hole
from IndicPhotoOCR.detection.textbpn.network.layers.gcn_utils import get_node_feature, \
get_adj_mat, get_adj_ind, coord_embedding, normalize_adj
import torch.nn.functional as F
import time
class Evolution(nn.Module):
def __init__(self, node_num, adj_num, is_training=True, device=None, model="snake"):
super(Evolution, self).__init__()
self.node_num = node_num
self.adj_num = adj_num
self.device = device
self.is_training = is_training
self.clip_dis = 16
self.iter = 3
if model == "gcn":
self.adj = get_adj_mat(self.adj_num, self.node_num)
self.adj = normalize_adj(self.adj, type="DAD").float().to(self.device)
for i in range(self.iter):
evolve_gcn = GCN(36, 128)
self.__setattr__('evolve_gcn' + str(i), evolve_gcn)
elif model == "rnn":
self.adj = None
for i in range(self.iter):
evolve_gcn = RNN(36, 128)
self.__setattr__('evolve_gcn' + str(i), evolve_gcn)
elif model == "AD":
self.adj = get_adj_mat(self.adj_num, self.node_num)
self.adj = normalize_adj(self.adj, type="DAD").float().to(self.device)
for i in range(self.iter):
evolve_gcn = AdaptiveDeformation(36, 128)
self.__setattr__('evolve_gcn' + str(i), evolve_gcn)
# elif model == "BT_old":
# self.adj = None
# for i in range(self.iter):
# evolve_gcn = Transformer_old(36, 512, num_heads=8,
# dim_feedforward=2048, drop_rate=0.0, if_resi=True, block_nums=4)
# self.__setattr__('evolve_gcn' + str(i), evolve_gcn)
elif model == "BT":
self.adj = None
for i in range(self.iter):
evolve_gcn = Transformer(36, 128, num_heads=8,
dim_feedforward=1024, drop_rate=0.0, if_resi=True, block_nums=3)
self.__setattr__('evolve_gcn' + str(i), evolve_gcn)
else:
self.adj = get_adj_ind(self.adj_num, self.node_num, self.device)
for i in range(self.iter):
evolve_gcn = DeepSnake(state_dim=128, feature_dim=36, conv_type='dgrid')
self.__setattr__('evolve_gcn' + str(i), evolve_gcn)
for m in self.modules():
if isinstance(m, nn.Conv1d) or isinstance(m, nn.Conv2d):
m.weight.data.normal_(0.0, 0.02)
# nn.init.kaiming_normal_(m.weight, mode='fan_in')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
@staticmethod
def get_boundary_proposal(input=None, seg_preds=None, switch="gt"):
if switch == "gt":
inds = torch.where(input['ignore_tags'] > 0)
# if len(inds[0]) > 320:
# inds = (inds[0][:320], inds[1][:320])
init_polys = input['proposal_points'][inds]
else:
tr_masks = input['tr_mask'].cpu().numpy()
tcl_masks = seg_preds[:, 0, :, :].detach().cpu().numpy() > cfg.threshold
inds = []
init_polys = []
for bid, tcl_mask in enumerate(tcl_masks):
ret, labels = cv2.connectedComponents(tcl_mask.astype(np.uint8), connectivity=8)
for idx in range(1, ret):
text_mask = labels == idx
ist_id = int(np.sum(text_mask*tr_masks[bid])/np.sum(text_mask))-1
inds.append([bid, ist_id])
poly = get_sample_point(text_mask, cfg.num_points, cfg.approx_factor)
init_polys.append(poly)
inds = torch.from_numpy(np.array(inds)).permute(1, 0).to(input["img"].device)
init_polys = torch.from_numpy(np.array(init_polys)).to(input["img"].device)
return init_polys, inds, None
def get_boundary_proposal_eval(self, input=None, seg_preds=None):
# if cfg.scale > 1:
# seg_preds = F.interpolate(seg_preds, scale_factor=cfg.scale, mode='bilinear')
cls_preds = seg_preds[:, 0, :, :].detach().cpu().numpy()
dis_preds = seg_preds[:, 1, :, ].detach().cpu().numpy()
inds = []
init_polys = []
confidences = []
for bid, dis_pred in enumerate(dis_preds):
# # dis_mask = (dis_pred / np.max(dis_pred)) > cfg.dis_threshold
dis_mask = dis_pred > cfg.dis_threshold
# dis_mask = fill_hole(dis_mask)
ret, labels = cv2.connectedComponents(dis_mask.astype(np.uint8), connectivity=8, ltype=cv2.CV_16U)
for idx in range(1, ret):
text_mask = labels == idx
confidence = round(cls_preds[bid][text_mask].mean(), 3)
# 50 for MLT2017 and ArT (or DCN is used in backone); else is all 150;
# just can set to 50, which has little effect on the performance
if np.sum(text_mask) < 50/(cfg.scale*cfg.scale) or confidence < cfg.cls_threshold:
continue
confidences.append(confidence)
inds.append([bid, 0])
poly = get_sample_point(text_mask, cfg.num_points,
cfg.approx_factor, scales=np.array([cfg.scale, cfg.scale]))
init_polys.append(poly)
if len(inds) > 0:
inds = torch.from_numpy(np.array(inds)).permute(1, 0).to(input["img"].device, non_blocking=True)
init_polys = torch.from_numpy(np.array(init_polys)).to(input["img"].device, non_blocking=True).float()
else:
init_polys = torch.from_numpy(np.array(init_polys)).to(input["img"].device, non_blocking=True).float()
inds = torch.from_numpy(np.array(inds)).to(input["img"].device, non_blocking=True)
return init_polys, inds, confidences
def evolve_poly(self, snake, cnn_feature, i_it_poly, ind):
if len(i_it_poly) == 0:
return torch.zeros_like(i_it_poly)
h, w = cnn_feature.size(2)*cfg.scale, cnn_feature.size(3)*cfg.scale
node_feats = get_node_feature(cnn_feature, i_it_poly, ind, h, w)
i_poly = i_it_poly + torch.clamp(snake(node_feats, self.adj).permute(0, 2, 1), -self.clip_dis, self.clip_dis)
if self.is_training:
i_poly = torch.clamp(i_poly, 0, w-1)
else:
i_poly[:, :, 0] = torch.clamp(i_poly[:, :, 0], 0, w - 1)
i_poly[:, :, 1] = torch.clamp(i_poly[:, :, 1], 0, h - 1)
return i_poly
def forward(self, embed_feature, input=None, seg_preds=None, switch="gt"):
if self.is_training:
init_polys, inds, confidences = self.get_boundary_proposal(input=input, seg_preds=seg_preds, switch=switch)
# TODO sample fix number
else:
init_polys, inds, confidences = self.get_boundary_proposal_eval(input=input, seg_preds=seg_preds)
if init_polys.shape[0] == 0:
return [init_polys for i in range(self.iter+1)], inds, confidences
py_preds = [init_polys, ]
for i in range(self.iter):
evolve_gcn = self.__getattr__('evolve_gcn' + str(i))
init_polys = self.evolve_poly(evolve_gcn, embed_feature, init_polys, inds[0])
py_preds.append(init_polys)
return py_preds, inds, confidences
class TextNet(nn.Module):
def __init__(self, backbone='vgg', is_training=True):
super().__init__()
self.is_training = is_training
self.backbone_name = backbone
self.fpn = FPN(self.backbone_name, is_training=(not cfg.resume and is_training))
self.seg_head = nn.Sequential(
nn.Conv2d(32, 16, kernel_size=3, padding=2, dilation=2),
nn.PReLU(),
nn.Conv2d(16, 16, kernel_size=3, padding=4, dilation=4),
nn.PReLU(),
nn.Conv2d(16, 4, kernel_size=1, stride=1, padding=0),
)
self.BPN = Evolution(cfg.num_points, adj_num=4,
is_training=is_training, device=cfg.device, model="BT")
def load_model(self, model_path):
print('Loading from {}'.format(model_path))
state_dict = torch.load(model_path, map_location=torch.device(cfg.device))
self.load_state_dict(state_dict['model'], strict=(not self.is_training))
def forward(self, input_dict, test_speed=False):
output = {}
b, c, h, w = input_dict["img"].shape
if self.is_training or cfg.exp_name in ['ArT', 'MLT2017', "MLT2019"] or test_speed:
image = input_dict["img"]
else:
image = torch.zeros((b, c, cfg.test_size[1], cfg.test_size[1]), dtype=torch.float32).to(cfg.device)
image[:, :, :h, :w] = input_dict["img"][:, :, :, :]
up1, _, _, _, _ = self.fpn(image)
up1 = up1[:, :, :h // cfg.scale, :w // cfg.scale]
preds = self.seg_head(up1)
fy_preds = torch.cat([torch.sigmoid(preds[:, 0:2, :, :]), preds[:, 2:4, :, :]], dim=1)
cnn_feats = torch.cat([up1, fy_preds], dim=1)
py_preds, inds, confidences = self.BPN(cnn_feats, input=input_dict, seg_preds=fy_preds, switch="gt")
output["fy_preds"] = fy_preds
output["py_preds"] = py_preds
output["inds"] = inds
output["confidences"] = confidences
return output
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