mirror of
https://github.com/open-mmlab/mmocr.git
synced 2025-06-03 21:54:47 +08:00
124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
from mmdet.core import BitmapMasks
|
|
from mmocr.models.common.losses import DiceLoss
|
|
from mmocr.models.textrecog.losses import (CAFCNLoss, CELoss, CTCLoss, SARLoss,
|
|
TFLoss)
|
|
|
|
|
|
def test_ctc_loss():
|
|
# test CTCLoss
|
|
ctc_loss = CTCLoss()
|
|
outputs = torch.zeros(2, 40, 37)
|
|
targets_dict = {
|
|
'flatten_targets': torch.IntTensor([1, 2, 3, 4, 5]),
|
|
'target_lengths': torch.LongTensor([2, 3])
|
|
}
|
|
|
|
losses = ctc_loss(outputs, targets_dict)
|
|
assert isinstance(losses, dict)
|
|
assert 'loss_ctc' in losses
|
|
assert torch.allclose(losses['loss_ctc'],
|
|
torch.tensor(losses['loss_ctc'].item()).float())
|
|
|
|
|
|
def test_ce_loss():
|
|
with pytest.raises(AssertionError):
|
|
CELoss(ignore_index='ignore')
|
|
with pytest.raises(AssertionError):
|
|
CELoss(reduction=1)
|
|
with pytest.raises(AssertionError):
|
|
CELoss(reduction='avg')
|
|
|
|
ce_loss = CELoss(ignore_index=0)
|
|
outputs = torch.rand(1, 10, 37)
|
|
targets_dict = {
|
|
'padded_targets': torch.LongTensor([[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]])
|
|
}
|
|
losses = ce_loss(outputs, targets_dict)
|
|
assert isinstance(losses, dict)
|
|
assert 'loss_ce' in losses
|
|
print(losses['loss_ce'].size())
|
|
assert losses['loss_ce'].size(1) == 10
|
|
|
|
|
|
def test_sar_loss():
|
|
outputs = torch.rand(1, 10, 37)
|
|
targets_dict = {
|
|
'padded_targets': torch.LongTensor([[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]])
|
|
}
|
|
sar_loss = SARLoss()
|
|
new_output, new_target = sar_loss.format(outputs, targets_dict)
|
|
assert new_output.shape == torch.Size([1, 37, 9])
|
|
assert new_target.shape == torch.Size([1, 9])
|
|
|
|
|
|
def test_tf_loss():
|
|
with pytest.raises(AssertionError):
|
|
TFLoss(flatten=1.0)
|
|
|
|
outputs = torch.rand(1, 10, 37)
|
|
targets_dict = {
|
|
'padded_targets': torch.LongTensor([[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]])
|
|
}
|
|
tf_loss = TFLoss(flatten=False)
|
|
new_output, new_target = tf_loss.format(outputs, targets_dict)
|
|
assert new_output.shape == torch.Size([1, 37, 9])
|
|
assert new_target.shape == torch.Size([1, 9])
|
|
|
|
|
|
def test_cafcn_loss():
|
|
with pytest.raises(AssertionError):
|
|
CAFCNLoss(alpha='1')
|
|
with pytest.raises(AssertionError):
|
|
CAFCNLoss(attn_s2_downsample_ratio='2')
|
|
with pytest.raises(AssertionError):
|
|
CAFCNLoss(attn_s3_downsample_ratio='1.5')
|
|
with pytest.raises(AssertionError):
|
|
CAFCNLoss(seg_downsample_ratio='1.5')
|
|
with pytest.raises(AssertionError):
|
|
CAFCNLoss(attn_s2_downsample_ratio=2)
|
|
with pytest.raises(AssertionError):
|
|
CAFCNLoss(attn_s3_downsample_ratio=1.5)
|
|
with pytest.raises(AssertionError):
|
|
CAFCNLoss(seg_downsample_ratio=1.5)
|
|
|
|
bsz = 1
|
|
H = W = 64
|
|
out_neck = (torch.ones(bsz, 1, H // 4, W // 4) * 0.5,
|
|
torch.ones(bsz, 1, H // 8, W // 8) * 0.5,
|
|
torch.ones(bsz, 1, H // 8, W // 8) * 0.5,
|
|
torch.ones(bsz, 1, H // 8, W // 8) * 0.5,
|
|
torch.ones(bsz, 1, H // 2, W // 2) * 0.5)
|
|
out_head = torch.rand(bsz, 37, H // 2, W // 2)
|
|
|
|
attn_tgt = np.zeros((H, W), dtype=np.float32)
|
|
segm_tgt = np.zeros((H, W), dtype=np.float32)
|
|
mask = np.ones((H, W), dtype=np.float32)
|
|
gt_kernels = BitmapMasks([attn_tgt, segm_tgt, mask], H, W)
|
|
|
|
cafcn_loss = CAFCNLoss()
|
|
losses = cafcn_loss(out_neck, out_head, [gt_kernels])
|
|
assert isinstance(losses, dict)
|
|
assert 'loss_seg' in losses
|
|
assert torch.allclose(losses['loss_seg'],
|
|
torch.tensor(losses['loss_seg'].item()).float())
|
|
|
|
|
|
def test_dice_loss():
|
|
with pytest.raises(AssertionError):
|
|
DiceLoss(eps='1')
|
|
|
|
dice_loss = DiceLoss()
|
|
pred = torch.rand(1, 1, 32, 32)
|
|
gt = torch.rand(1, 1, 32, 32)
|
|
|
|
loss = dice_loss(pred, gt, None)
|
|
assert isinstance(loss, torch.Tensor)
|
|
|
|
mask = torch.rand(1, 1, 1, 1)
|
|
loss = dice_loss(pred, gt, mask)
|
|
assert isinstance(loss, torch.Tensor)
|