# Copyright (c) 2019 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import OrderedDict import paddle.fluid as fluid from ppdet.experimental import mixed_precision_global_state from ppdet.core.workspace import register __all__ = ['FCOS'] @register class FCOS(object): """ FCOS architecture, see https://arxiv.org/abs/1904.01355 Args: backbone (object): backbone instance fpn (object): feature pyramid network instance fcos_head (object): `FCOSHead` instance """ __category__ = 'architecture' __inject__ = ['backbone', 'fpn', 'fcos_head'] def __init__(self, backbone, fpn, fcos_head): super(FCOS, self).__init__() self.backbone = backbone self.fpn = fpn self.fcos_head = fcos_head def build(self, feed_vars, mode='train'): im = feed_vars['image'] im_info = feed_vars['im_info'] mixed_precision_enabled = mixed_precision_global_state() is not None # cast inputs to FP16 if mixed_precision_enabled: im = fluid.layers.cast(im, 'float16') # backbone body_feats = self.backbone(im) # cast features back to FP32 if mixed_precision_enabled: body_feats = OrderedDict((k, fluid.layers.cast(v, 'float32')) for k, v in body_feats.items()) # FPN body_feats, spatial_scale = self.fpn.get_output(body_feats) # fcosnet head if mode == 'train': tag_labels = [] tag_bboxes = [] tag_centerness = [] for i in range(len(self.fcos_head.fpn_stride)): # reg_target, labels, scores, centerness k_lbl = 'labels{}'.format(i) if k_lbl in feed_vars: tag_labels.append(feed_vars[k_lbl]) k_box = 'reg_target{}'.format(i) if k_box in feed_vars: tag_bboxes.append(feed_vars[k_box]) k_ctn = 'centerness{}'.format(i) if k_ctn in feed_vars: tag_centerness.append(feed_vars[k_ctn]) # tag_labels, tag_bboxes, tag_centerness loss = self.fcos_head.get_loss(body_feats, tag_labels, tag_bboxes, tag_centerness) total_loss = fluid.layers.sum(list(loss.values())) loss.update({'loss': total_loss}) return loss else: pred = self.fcos_head.get_prediction(body_feats, im_info) return pred def _inputs_def(self, image_shape, fields): im_shape = [None] + image_shape # yapf: disable inputs_def = { 'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0}, 'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0}, 'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0}, 'im_id': {'shape': [None, 1], 'dtype': 'int64', 'lod_level': 0}, 'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1}, 'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1}, 'gt_score': {'shape': [None, 1], 'dtype': 'float32', 'lod_level': 1}, 'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1}, 'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1} } # yapf: disable if 'fcos_target' in fields: targets_def = { 'labels0': {'shape': [None, None, None, 1], 'dtype': 'int32', 'lod_level': 0}, 'reg_target0': {'shape': [None, None, None, 4], 'dtype': 'float32', 'lod_level': 0}, 'centerness0': {'shape': [None, None, None, 1], 'dtype': 'float32', 'lod_level': 0}, 'labels1': {'shape': [None, None, None, 1], 'dtype': 'int32', 'lod_level': 0}, 'reg_target1': {'shape': [None, None, None, 4], 'dtype': 'float32', 'lod_level': 0}, 'centerness1': {'shape': [None, None, None, 1], 'dtype': 'float32', 'lod_level': 0}, 'labels2': {'shape': [None, None, None, 1], 'dtype': 'int32', 'lod_level': 0}, 'reg_target2': {'shape': [None, None, None, 4], 'dtype': 'float32', 'lod_level': 0}, 'centerness2': {'shape': [None, None, None, 1], 'dtype': 'float32', 'lod_level': 0}, 'labels3': {'shape': [None, None, None, 1], 'dtype': 'int32', 'lod_level': 0}, 'reg_target3': {'shape': [None, None, None, 4], 'dtype': 'float32', 'lod_level': 0}, 'centerness3': {'shape': [None, None, None, 1], 'dtype': 'float32', 'lod_level': 0}, 'labels4': {'shape': [None, None, None, 1], 'dtype': 'int32', 'lod_level': 0}, 'reg_target4': {'shape': [None, None, None, 4], 'dtype': 'float32', 'lod_level': 0}, 'centerness4': {'shape': [None, None, None, 1], 'dtype': 'float32', 'lod_level': 0}, } # yapf: enable # downsample = 128 for k, stride in enumerate(self.fcos_head.fpn_stride): k_lbl = 'labels{}'.format(k) k_box = 'reg_target{}'.format(k) k_ctn = 'centerness{}'.format(k) grid_y = image_shape[-2] // stride if image_shape[-2] else None grid_x = image_shape[-1] // stride if image_shape[-1] else None if grid_x is not None: num_pts = grid_x * grid_y num_dim2 = 1 else: num_pts = None num_dim2 = None targets_def[k_lbl]['shape'][1] = num_pts targets_def[k_box]['shape'][1] = num_pts targets_def[k_ctn]['shape'][1] = num_pts targets_def[k_lbl]['shape'][2] = num_dim2 targets_def[k_box]['shape'][2] = num_dim2 targets_def[k_ctn]['shape'][2] = num_dim2 inputs_def.update(targets_def) return inputs_def def build_inputs( self, image_shape=[3, None, None], fields=['image', 'im_info', 'fcos_target'], # for-train use_dataloader=True, iterable=False): inputs_def = self._inputs_def(image_shape, fields) if "fcos_target" in fields: for i in range(len(self.fcos_head.fpn_stride)): fields.extend( ['labels%d' % i, 'reg_target%d' % i, 'centerness%d' % i]) fields.remove('fcos_target') feed_vars = OrderedDict([(key, fluid.data( name=key, shape=inputs_def[key]['shape'], dtype=inputs_def[key]['dtype'], lod_level=inputs_def[key]['lod_level'])) for key in fields]) loader = fluid.io.DataLoader.from_generator( feed_list=list(feed_vars.values()), capacity=16, use_double_buffer=True, iterable=iterable) if use_dataloader else None return feed_vars, loader def train(self, feed_vars): return self.build(feed_vars, 'train') def eval(self, feed_vars): return self.build(feed_vars, 'test') def test(self, feed_vars): return self.build(feed_vars, 'test')