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import torch.nn as nn
import math
import torch
from IndicPhotoOCR.detection import east_config as cfg
from IndicPhotoOCR.detection import east_utils as utils
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = round(inp * expand_ratio)
self.use_res_connect = self.stride == 1 and inp == oup
if expand_ratio == 1:
self.conv = nn.Sequential(
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
else:
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, width_mult=1.):
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 32
last_channel = 1280
interverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
# [6, 320, 1, 1],
]
# building first layer
# assert input_size % 32 == 0
input_channel = int(input_channel * width_mult)
self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
self.features = [conv_bn(3, input_channel, 2)]
# building inverted residual blocks
for t, c, n, s in interverted_residual_setting:
output_channel = int(c * width_mult)
for i in range(n):
if i == 0:
self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
else:
self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
input_channel = output_channel
# make it nn.Sequential
self.features = nn.Sequential(*self.features)
self._initialize_weights()
def forward(self, x):
x = self.features(x)
# x = x.mean(3).mean(2)
# x = self.classifier(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def mobilenet(pretrained=True, **kwargs):
"""
Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = MobileNetV2()
if pretrained:
model_dict = model.state_dict()
pretrained_dict = torch.load(cfg.pretrained_basemodel_path,map_location=torch.device('cpu'), weights_only=True)
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
# state_dict = torch.load(cfg.pretrained_basemodel_path) # add map_location='cpu' if no gpu
# model.load_state_dict(state_dict)
return model
class East(nn.Module):
def __init__(self):
super(East, self).__init__()
self.mobilenet = mobilenet(True)
# self.si for stage i
self.s1 = nn.Sequential(*list(self.mobilenet.children())[0][0:4])
self.s2 = nn.Sequential(*list(self.mobilenet.children())[0][4:7])
self.s3 = nn.Sequential(*list(self.mobilenet.children())[0][7:14])
self.s4 = nn.Sequential(*list(self.mobilenet.children())[0][14:17])
self.conv1 = nn.Conv2d(160+96, 128, 1)
self.bn1 = nn.BatchNorm2d(128)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(128, 128, 3, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.relu2 = nn.ReLU()
self.conv3 = nn.Conv2d(128+32, 64, 1)
self.bn3 = nn.BatchNorm2d(64)
self.relu3 = nn.ReLU()
self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
self.bn4 = nn.BatchNorm2d(64)
self.relu4 = nn.ReLU()
self.conv5 = nn.Conv2d(64+24, 64, 1)
self.bn5 = nn.BatchNorm2d(64)
self.relu5 = nn.ReLU()
self.conv6 = nn.Conv2d(64, 32, 3, padding=1)
self.bn6 = nn.BatchNorm2d(32)
self.relu6 = nn.ReLU()
self.conv7 = nn.Conv2d(32, 32, 3, padding=1)
self.bn7 = nn.BatchNorm2d(32)
self.relu7 = nn.ReLU()
self.conv8 = nn.Conv2d(32, 1, 1)
self.sigmoid1 = nn.Sigmoid()
self.conv9 = nn.Conv2d(32, 4, 1)
self.sigmoid2 = nn.Sigmoid()
self.conv10 = nn.Conv2d(32, 1, 1)
self.sigmoid3 = nn.Sigmoid()
self.unpool1 = nn.Upsample(scale_factor=2, mode='bilinear')
self.unpool2 = nn.Upsample(scale_factor=2, mode='bilinear')
self.unpool3 = nn.Upsample(scale_factor=2, mode='bilinear')
# utils.init_weights([self.conv1,self.conv2,self.conv3,self.conv4,
# self.conv5,self.conv6,self.conv7,self.conv8,
# self.conv9,self.conv10,self.bn1,self.bn2,
# self.bn3,self.bn4,self.bn5,self.bn6,self.bn7])
def forward(self, images):
images = utils.mean_image_subtraction(images)
f0 = self.s1(images)
f1 = self.s2(f0)
f2 = self.s3(f1)
f3 = self.s4(f2)
# _, f = self.mobilenet(images)
h = f3 # bs 2048 w/32 h/32
g = (self.unpool1(h)) # bs 2048 w/16 h/16
c = self.conv1(torch.cat((g, f2), 1))
c = self.bn1(c)
c = self.relu1(c)
h = self.conv2(c) # bs 128 w/16 h/16
h = self.bn2(h)
h = self.relu2(h)
g = self.unpool2(h) # bs 128 w/8 h/8
c = self.conv3(torch.cat((g, f1), 1))
c = self.bn3(c)
c = self.relu3(c)
h = self.conv4(c) # bs 64 w/8 h/8
h = self.bn4(h)
h = self.relu4(h)
g = self.unpool3(h) # bs 64 w/4 h/4
c = self.conv5(torch.cat((g, f0), 1))
c = self.bn5(c)
c = self.relu5(c)
h = self.conv6(c) # bs 32 w/4 h/4
h = self.bn6(h)
h = self.relu6(h)
g = self.conv7(h) # bs 32 w/4 h/4
g = self.bn7(g)
g = self.relu7(g)
F_score = self.conv8(g) # bs 1 w/4 h/4
F_score = self.sigmoid1(F_score)
geo_map = self.conv9(g)
geo_map = self.sigmoid2(geo_map) * 512
angle_map = self.conv10(g)
angle_map = self.sigmoid3(angle_map)
angle_map = (angle_map - 0.5) * math.pi / 2
F_geometry = torch.cat((geo_map, angle_map), 1) # bs 5 w/4 h/4
return F_score, F_geometry
model=East()
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