提交 c7f64eeb 编写于 作者: M michaelowenliu

add comomon usages in models such as loss and prediction computing

上级 e3340a1e
# -*- encoding: utf-8 -*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# 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.functional as F
from paddle import fluid
from paddle.fluid import dygraph
from paddle.fluid.dygraph import Conv2D
from paddle.nn import SyncBatchNorm as BatchNorm
from dygraph.models.architectures import layer_utils
class FCNHead(fluid.dygraph.Layer):
"""
The FCNHead implementation used in auxilary layer
Args:
in_channels (int): the number of input channels
out_channels (int): the number of output channels
"""
def __init__(self, in_channels, out_channels):
super(FCNHead, self).__init__()
inter_channels = in_channels // 4
self.conv_bn_relu = layer_utils.ConvBnRelu(num_channels=in_channels,
num_filters=inter_channels,
filter_size=3,
padding=1)
self.conv = Conv2D(num_channels=inter_channels,
num_filters=out_channels,
filter_size=1)
def forward(self, x):
x = self.conv_bn_relu(x)
x = F.dropout(x, p=0.1)
x = self.conv(x)
return x
def get_loss(logit, label, ignore_index=255, EPS=1e-5):
"""
compute forward loss of the model
Args:
logit (tensor): the logit of model output
label (tensor): ground truth
Returns:
avg_loss (tensor): forward loss
"""
logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
label = fluid.layers.transpose(label, [0, 2, 3, 1])
mask = label != ignore_index
mask = fluid.layers.cast(mask, 'float32')
loss, probs = fluid.layers.softmax_with_cross_entropy(
logit,
label,
ignore_index=ignore_index,
return_softmax=True,
axis=-1)
loss = loss * mask
avg_loss = paddle.mean(loss) / (paddle.mean(mask) + EPS)
label.stop_gradient = True
mask.stop_gradient = True
return avg_loss
def get_pred_score_map(logit):
"""
Get prediction and score map output in inference phase.
Args:
logit (tensor): output logit of network
Returns:
pred (tensor): predition map
score_map (tensor): score map
"""
score_map = F.softmax(logit, axis=1)
score_map = fluid.layers.transpose(score_map, [0, 2, 3, 1])
pred = fluid.layers.argmax(score_map, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
return pred, score_map
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