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gscnn_dual_task_loss.py
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gscnn_dual_task_loss.py
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# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
@manager.LOSSES.add_component
class DualTaskLoss(nn.Layer):
"""
The dual task loss implement of GSCNN
Args:
ignore_index (int64): Specifies a target value that is ignored
and does not contribute to the input gradient. Default ``255``.
tau (float): the tau of gumbel softmax sample.
"""
def __init__(self, ignore_index=255, tau=0.5):
super().__init__()
self.ignore_index = ignore_index
self.tau = tau
def _gumbel_softmax_sample(self, logit, tau=1, eps=1e-10):
"""
Draw a sample from the Gumbel-Softmax distribution
based on
https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb
(MIT license)
"""
gumbel_noise = paddle.rand(logit.shape)
gumbel_noise = -paddle.log(eps - paddle.log(gumbel_noise + eps))
logit = logit + gumbel_noise
return F.softmax(logit / tau, axis=1)
def compute_grad_mag(self, x):
eps = 1e-6
n, c, h, w = x.shape
if h <= 1 or w <= 1:
raise ValueError(
'The width and height of tensor to compute grad must be greater than 1, but the shape is {}.'
.format(x.shape))
x = self.conv_tri(x, r=4)
kernel = [[-1, 0, 1]]
kernel = paddle.to_tensor(kernel).astype('float32')
kernel = 0.5 * kernel
kernel_x = paddle.concat([kernel.unsqueeze((0, 1))] * c, axis=0)
grad_x = F.conv2d(x, kernel_x, padding='same', groups=c)
kernel_y = paddle.concat([kernel.t().unsqueeze((0, 1))] * c, axis=0)
grad_y = F.conv2d(x, kernel_y, padding='same', groups=c)
mag = paddle.sqrt(grad_x * grad_x + grad_y * grad_y + eps)
return mag / mag.max()
def conv_tri(self, input, r):
"""
Convolves an image by a 2D triangle filter (the 1D triangle filter f is
[1:r r+1 r:-1:1]/(r+1)^2, the 2D version is simply conv2(f,f'))
"""
if r <= 1:
raise ValueError(
'`r` should be greater than 1, but it is {}.'.format(r))
kernel = [
list(range(1, r + 1)) + [r + 1] + list(reversed(range(1, r + 1)))
]
kernel = paddle.to_tensor(kernel).astype('float32')
kernel = kernel / (r + 1)**2
input_ = F.pad(input, [1, 1, 0, 0], mode='replicate')
input_ = F.pad(input_, [r, r, 0, 0], mode='reflect')
input_ = [input_[:, :, :, :r], input, input_[:, :, :, -r:]]
input_ = paddle.concat(input_, axis=3)
tem = input_.clone()
input_ = F.pad(input_, [0, 0, 1, 1], mode='replicate')
input_ = F.pad(input_, [0, 0, r, r], mode='reflect')
input_ = [input_[:, :, :r, :], tem, input_[:, :, -r:, :]]
input_ = paddle.concat(input_, axis=2)
c = input.shape[1]
kernel_x = paddle.concat([kernel.unsqueeze((0, 1))] * c, axis=0)
output = F.conv2d(input_, kernel_x, padding=0, groups=c)
kernel_y = paddle.concat([kernel.t().unsqueeze((0, 1))] * c, axis=0)
output = F.conv2d(output, kernel_y, padding=0, groups=c)
return output
def forward(self, logit, labels):
# import pdb; pdb.set_trace()
n, c, h, w = logit.shape
th = 1e-8
eps = 1e-10
if len(labels.shape) == 3:
labels = labels.unsqueeze(1)
mask = (labels != self.ignore_index)
mask.stop_gradient = True
logit = logit * mask
labels = labels * mask
if len(labels.shape) == 4:
labels = labels.squeeze(1)
labels.stop_gradient = True
labels = F.one_hot(labels, logit.shape[1]).transpose((0, 3, 1, 2))
labels.stop_gradient = True
g = self._gumbel_softmax_sample(logit, tau=self.tau)
g = self.compute_grad_mag(g)
g_hat = self.compute_grad_mag(labels)
loss = F.l1_loss(g, g_hat, reduction='none')
loss = loss * mask
g_mask = (g > th).astype('float32')
g_mask.stop_gradient = True
g_mask_sum = paddle.sum(g_mask)
loss_g = paddle.sum(loss * g_mask)
if g_mask_sum > eps:
loss_g = loss_g / g_mask_sum
g_hat_mask = (g_hat > th).astype('float32')
g_hat_mask.stop_gradient = True
g_hat_mask_sum = paddle.sum(g_hat_mask)
loss_g_hat = paddle.sum(loss * g_hat_mask)
if g_hat_mask_sum > eps:
loss_g_hat = loss_g_hat / g_hat_mask_sum
total_loss = 0.5 * loss_g + 0.5 * loss_g_hat
return total_loss