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import torch
import torch.nn as nn
import torch.nn.functional as F
from backbones.upsample_head import SimpleUpsampleHead
class SimpleDetectionDecoder(nn.Module):
def __init__(self, feature_channel=256):
nn.Module.__init__(self)
self.feature_channel = feature_channel
self.head_layer = self.create_head_layer()
self.pred_layers = nn.ModuleDict(self.create_pred_layers())
def create_head_layer(self):
return SimpleUpsampleHead(
self.feature_channel,
[self.feature_channel, self.feature_channel // 2, self.feature_channel // 4]
)
def create_pred_layer(self, channels):
return nn.Sequential(
nn.Conv2d(self.feature_channel // 4, channels, kernel_size=1, stride=1, padding=0, bias=False),
)
def create_pred_layers(self):
return {}
def postprocess_pred(self, pred):
return pred
def calculate_losses(self, preds, label):
raise NotImplementedError()
def forward(self, input, label, meta, train):
feature = self.head_layer(input)
pred = {}
for name, pred_layer in self.pred_layers.items():
pred[name] = pred_layer(feature)
if train:
losses = self.calculate_losses(pred, label)
pred = self.postprocess_pred(pred)
loss = sum(losses.values())
return loss, pred, losses
else:
pred = self.postprocess_pred(pred)
return pred
class SimpleSegDecoder(SimpleDetectionDecoder):
def create_pred_layers(self):
return {
'heatmap': self.create_pred_layer(1)
}
def postprocess_pred(self, pred):
pred['heatmap'] = F.sigmoid(pred['heatmap'])
return pred
def calculate_losses(self, pred, label):
heatmap = label['heatmap']
heatmap_weight = label['heatmap_weight']
heatmap_pred = pred['heatmap']
heatmap_loss = F.binary_cross_entropy_with_logits(heatmap_pred, heatmap, reduction='none')
heatmap_loss = (heatmap_loss * heatmap_weight).mean(dim=(1, 2, 3))
return {
'heatmap_loss': heatmap_loss,
}
class SimpleEASTDecoder(SimpleDetectionDecoder):
def __init__(self, feature_channels=256, densebox_ratio=1000.0, densebox_rescale_factor=512):
SimpleDetectionDecoder.__init__(self, feature_channels)
self.densebox_ratio = densebox_ratio
self.densebox_rescale_factor = densebox_rescale_factor
def create_pred_layers(self):
return {
'heatmap': self.create_pred_layer(1),
'densebox': self.create_pred_layer(8),
}
def postprocess_pred(self, pred):
pred['heatmap'] = F.sigmoid(pred['heatmap'])
pred['densebox'] = pred['densebox'] * self.densebox_rescale_factor
return pred
def calculate_losses(self, pred, label):
heatmap = label['heatmap']
heatmap_weight = label['heatmap_weight']
densebox = label['densebox'] / self.densebox_rescale_factor
densebox_weight = label['densebox_weight']
heatmap_pred = pred['heatmap']
densebox_pred = pred['densebox']
heatmap_loss = F.binary_cross_entropy_with_logits(heatmap_pred, heatmap, reduction='none')
heatmap_loss = (heatmap_loss * heatmap_weight).mean(dim=(1, 2, 3))
densebox_loss = F.mse_loss(densebox_pred, densebox, reduction='none')
densebox_loss = (densebox_loss * densebox_weight).mean(dim=(1, 2, 3)) * self.densebox_ratio
return {
'heatmap_loss': heatmap_loss,
'densebox_loss': densebox_loss,
}
class SimpleTextsnakeDecoder(SimpleDetectionDecoder):
def __init__(self, feature_channels=256, radius_ratio=10.0):
SimpleDetectionDecoder.__init__(self, feature_channels)
self.radius_ratio = radius_ratio
def create_pred_layers(self):
return {
'heatmap': self.create_pred_layer(1),
'radius': self.create_pred_layer(1),
}
def postprocess_pred(self, pred):
pred['heatmap'] = F.sigmoid(pred['heatmap'])
pred['radius'] = torch.exp(pred['radius'])
return pred
def calculate_losses(self, pred, label):
heatmap = label['heatmap']
heatmap_weight = label['heatmap_weight']
radius = torch.log(label['radius'] + 1)
radius_weight = label['radius_weight']
heatmap_pred = pred['heatmap']
radius_pred = pred['radius']
heatmap_loss = F.binary_cross_entropy_with_logits(heatmap_pred, heatmap, reduction='none')
heatmap_loss = (heatmap_loss * heatmap_weight).mean(dim=(1, 2, 3))
radius_loss = F.smooth_l1_loss(radius_pred, radius, reduction='none')
radius_loss = (radius_loss * radius_weight).mean(dim=(1, 2, 3)) * self.radius_ratio
return {
'heatmap_loss': heatmap_loss,
'radius_loss': radius_loss,
}
class SimpleMSRDecoder(SimpleDetectionDecoder):
def __init__(self, feature_channels=256, offset_ratio=1000.0, offset_rescale_factor=512):
SimpleDetectionDecoder.__init__(self, feature_channels)
self.offset_ratio = offset_ratio
self.offset_rescale_factor = offset_rescale_factor
def create_pred_layers(self):
return {
'heatmap': self.create_pred_layer(1),
'offset': self.create_pred_layer(2),
}
def postprocess_pred(self, pred):
pred['heatmap'] = F.sigmoid(pred['heatmap'])
pred['offset'] = pred['offset'] * self.offset_rescale_factor
return pred
def calculate_losses(self, pred, label):
heatmap = label['heatmap']
heatmap_weight = label['heatmap_weight']
offset = label['offset'] / self.offset_rescale_factor
offset_weight = label['offset_weight']
heatmap_pred = pred['heatmap']
offset_pred = pred['offset']
heatmap_loss = F.binary_cross_entropy_with_logits(heatmap_pred, heatmap, reduction='none')
heatmap_loss = (heatmap_loss * heatmap_weight).mean(dim=(1, 2, 3))
offset_loss = F.mse_loss(offset_pred, offset, reduction='none')
offset_loss = (offset_loss * offset_weight).mean(dim=(1, 2, 3)) * self.offset_ratio
return {
'heatmap_loss': heatmap_loss,
'offset_loss': offset_loss,
}
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