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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.core import BitmapMasks
import mmocr.models.textdet.losses as losses
def test_panloss():
panloss = losses.PANLoss()
# test bitmasks2tensor
mask = [[1, 0, 1], [1, 1, 1], [0, 0, 1]]
target = [[1, 0, 1, 0, 0], [1, 1, 1, 0, 0], [0, 0, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
masks = [np.array(mask)]
bitmasks = BitmapMasks(masks, 3, 3)
target_sz = (6, 5)
results = panloss.bitmasks2tensor([bitmasks], target_sz)
assert len(results) == 1
assert torch.sum(torch.abs(results[0].float() -
torch.Tensor(target))).item() == 0
def test_textsnakeloss():
textsnakeloss = losses.TextSnakeLoss()
# test balanced_bce_loss
pred = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=torch.float)
target = torch.tensor([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=torch.long)
mask = torch.tensor([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=torch.long)
bce_loss = textsnakeloss.balanced_bce_loss(pred, target, mask).item()
assert np.allclose(bce_loss, 0)
def test_fcenetloss():
k = 5
fcenetloss = losses.FCELoss(fourier_degree=k, num_sample=10)
input_shape = (1, 3, 64, 64)
(n, c, h, w) = input_shape
# test ohem
pred = torch.ones((200, 2), dtype=torch.float)
target = torch.ones(200, dtype=torch.long)
target[20:] = 0
mask = torch.ones(200, dtype=torch.long)
ohem_loss1 = fcenetloss.ohem(pred, target, mask)
ohem_loss2 = fcenetloss.ohem(pred, target, 1 - mask)
assert isinstance(ohem_loss1, torch.Tensor)
assert isinstance(ohem_loss2, torch.Tensor)
# test forward
preds = []
for i in range(n):
scale = 8 * 2**i
pred = []
pred.append(torch.rand(n, 4, h // scale, w // scale))
pred.append(torch.rand(n, 4 * k + 2, h // scale, w // scale))
preds.append(pred)
p3_maps = []
p4_maps = []
p5_maps = []
for _ in range(n):
p3_maps.append(np.random.random((5 + 4 * k, h // 8, w // 8)))
p4_maps.append(np.random.random((5 + 4 * k, h // 16, w // 16)))
p5_maps.append(np.random.random((5 + 4 * k, h // 32, w // 32)))
loss = fcenetloss(preds, 0, p3_maps, p4_maps, p5_maps)
assert isinstance(loss, dict)
def test_drrgloss():
drrgloss = losses.DRRGLoss()
assert np.allclose(drrgloss.ohem_ratio, 3.0)
# test balance_bce_loss
pred = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=torch.float)
target = torch.tensor([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=torch.long)
mask = torch.tensor([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=torch.long)
bce_loss = drrgloss.balance_bce_loss(pred, target, mask).item()
assert np.allclose(bce_loss, 0)
# test balance_bce_loss with positive_count equal to zero
pred = torch.ones((16, 16), dtype=torch.float)
target = torch.ones((16, 16), dtype=torch.long)
mask = torch.zeros((16, 16), dtype=torch.long)
bce_loss = drrgloss.balance_bce_loss(pred, target, mask).item()
assert np.allclose(bce_loss, 0)
# test gcn_loss
gcn_preds = torch.tensor([[0., 1.], [1., 0.]])
labels = torch.tensor([1, 0], dtype=torch.long)
gcn_loss = drrgloss.gcn_loss((gcn_preds, labels))
assert gcn_loss.item()
# test bitmasks2tensor
mask = [[1, 0, 1], [1, 1, 1], [0, 0, 1]]
target = [[1, 0, 1, 0, 0], [1, 1, 1, 0, 0], [0, 0, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
masks = [np.array(mask)]
bitmasks = BitmapMasks(masks, 3, 3)
target_sz = (6, 5)
results = drrgloss.bitmasks2tensor([bitmasks], target_sz)
assert len(results) == 1
assert torch.sum(torch.abs(results[0].float() -
torch.Tensor(target))).item() == 0
# test forward
target_maps = [BitmapMasks([np.random.randn(20, 20)], 20, 20)]
target_masks = [BitmapMasks([np.ones((20, 20))], 20, 20)]
gt_masks = [BitmapMasks([np.ones((20, 20))], 20, 20)]
preds = (torch.randn((1, 6, 20, 20)), (gcn_preds, labels))
loss_dict = drrgloss(preds, 1., target_masks, target_masks, gt_masks,
target_maps, target_maps, target_maps, target_maps)
assert isinstance(loss_dict, dict)
assert 'loss_text' in loss_dict.keys()
assert 'loss_center' in loss_dict.keys()
assert 'loss_height' in loss_dict.keys()
assert 'loss_sin' in loss_dict.keys()
assert 'loss_cos' in loss_dict.keys()
assert 'loss_gcn' in loss_dict.keys()
# test forward with downsample_ratio less than 1.
target_maps = [BitmapMasks([np.random.randn(40, 40)], 40, 40)]
target_masks = [BitmapMasks([np.ones((40, 40))], 40, 40)]
gt_masks = [BitmapMasks([np.ones((40, 40))], 40, 40)]
preds = (torch.randn((1, 6, 20, 20)), (gcn_preds, labels))
loss_dict = drrgloss(preds, 0.5, target_masks, target_masks, gt_masks,
target_maps, target_maps, target_maps, target_maps)
assert isinstance(loss_dict, dict)
# test forward with blank gt_mask.
target_maps = [BitmapMasks([np.random.randn(20, 20)], 20, 20)]
target_masks = [BitmapMasks([np.ones((20, 20))], 20, 20)]
gt_masks = [BitmapMasks([np.zeros((20, 20))], 20, 20)]
preds = (torch.randn((1, 6, 20, 20)), (gcn_preds, labels))
loss_dict = drrgloss(preds, 1., target_masks, target_masks, gt_masks,
target_maps, target_maps, target_maps, target_maps)
assert isinstance(loss_dict, dict)
def test_dice_loss():
pred = torch.Tensor([[[-1000, -1000, -1000], [-1000, -1000, -1000],
[-1000, -1000, -1000]]])
target = torch.Tensor([[[0, 0, 0], [0, 0, 0], [0, 0, 0]]])
mask = torch.Tensor([[[1, 1, 1], [1, 1, 1], [1, 1, 1]]])
pan_loss = losses.PANLoss()
dice_loss = pan_loss.dice_loss_with_logits(pred, target, mask)
assert np.allclose(dice_loss.item(), 0)
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