forked from PulseFocusPlatform/PulseFocusPlatform
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from paddle import fluid
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from ppdet.core.workspace import register
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__all__ = ['FusedSemanticHead']
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@register
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class FusedSemanticHead(object):
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def __init__(self, semantic_num_class=183):
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super(FusedSemanticHead, self).__init__()
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self.semantic_num_class = semantic_num_class
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def get_out(self,
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fpn_feats,
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out_c=256,
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num_convs=4,
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fusion_level='fpn_res3_sum'):
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new_feat = fpn_feats[fusion_level]
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new_feat_list = [new_feat, ]
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target_shape = fluid.layers.shape(new_feat)[2:]
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for k, v in fpn_feats.items():
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if k != fusion_level:
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v = fluid.layers.resize_bilinear(
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v, target_shape, align_corners=True)
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v = fluid.layers.conv2d(v, out_c, 1)
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new_feat_list.append(v)
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new_feat = fluid.layers.sum(new_feat_list)
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for i in range(num_convs):
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new_feat = fluid.layers.conv2d(new_feat, out_c, 3, padding=1)
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# conv embedding
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semantic_feat = fluid.layers.conv2d(new_feat, out_c, 1)
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# conv logits
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seg_pred = fluid.layers.conv2d(new_feat, self.semantic_num_class, 1)
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return semantic_feat, seg_pred
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def get_loss(self, logit, label, ignore_index=255):
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label = fluid.layers.resize_nearest(label,
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fluid.layers.shape(logit)[2:])
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label = fluid.layers.reshape(label, [-1, 1])
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label = fluid.layers.cast(label, 'int64')
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logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
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logit = fluid.layers.reshape(logit, [-1, self.semantic_num_class])
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loss, probs = fluid.layers.softmax_with_cross_entropy(
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logit,
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label,
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soft_label=False,
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ignore_index=ignore_index,
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return_softmax=True)
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ignore_mask = (label.astype('int32') != 255).astype('int32')
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if ignore_mask is not None:
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ignore_mask = fluid.layers.cast(ignore_mask, 'float32')
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ignore_mask = fluid.layers.reshape(ignore_mask, [-1, 1])
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loss = loss * ignore_mask
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avg_loss = fluid.layers.mean(loss) / fluid.layers.mean(ignore_mask)
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ignore_mask.stop_gradient = True
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else:
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avg_loss = fluid.layers.mean(loss)
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label.stop_gradient = True
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return avg_loss
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