use eps instald of 0.001
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@ -25,7 +25,7 @@ class PSELoss(nn.Layer):
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ohem_ratio=3,
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ohem_ratio=3,
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kernel_sample_mask='pred',
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kernel_sample_mask='pred',
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reduction='sum',
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reduction='sum',
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**kwargs):
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eps=1e-6**kwargs):
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"""Implement PSE Loss.
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"""Implement PSE Loss.
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"""
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"""
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super(PSELoss, self).__init__()
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super(PSELoss, self).__init__()
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@ -34,6 +34,7 @@ class PSELoss(nn.Layer):
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self.ohem_ratio = ohem_ratio
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self.ohem_ratio = ohem_ratio
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self.kernel_sample_mask = kernel_sample_mask
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self.kernel_sample_mask = kernel_sample_mask
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self.reduction = reduction
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self.reduction = reduction
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self.eps = eps
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def forward(self, outputs, labels):
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def forward(self, outputs, labels):
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predicts = outputs['maps']
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predicts = outputs['maps']
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@ -92,8 +93,8 @@ class PSELoss(nn.Layer):
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target = target * mask
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target = target * mask
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a = paddle.sum(input * target, 1)
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a = paddle.sum(input * target, 1)
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b = paddle.sum(input * input, 1) + 0.001
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b = paddle.sum(input * input, 1) + self.eps
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c = paddle.sum(target * target, 1) + 0.001
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c = paddle.sum(target * target, 1) + self.eps
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d = (2 * a) / (b + c)
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d = (2 * a) / (b + c)
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return 1 - d
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return 1 - d
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@ -104,7 +105,6 @@ class PSELoss(nn.Layer):
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.astype('float32')))
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.astype('float32')))
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if pos_num == 0:
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if pos_num == 0:
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# selected_mask = gt_text.copy() * 0 # may be not good
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selected_mask = training_mask
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selected_mask = training_mask
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selected_mask = selected_mask.reshape(
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selected_mask = selected_mask.reshape(
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[1, selected_mask.shape[0], selected_mask.shape[1]]).astype(
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[1, selected_mask.shape[0], selected_mask.shape[1]]).astype(
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