# Copyright (c) 2021 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 import collections import copy import os import os.path as osp import six import numpy as np import paddle from paddle.static import InputSpec import ppdet from ppdet.modeling.proposal_generator.target_layer import BBoxAssigner, MaskAssigner import paddlex import paddlex.utils.logging as logging from paddlex.cv.transforms.operators import _NormalizeBox, _PadBox, _BboxXYXY2XYWH from paddlex.cv.transforms.batch_operators import BatchCompose, BatchRandomResize, BatchRandomResizeByShort, _BatchPadding, _Gt2YoloTarget from paddlex.cv.transforms import arrange_transforms from .base import BaseModel from .utils.det_metrics import VOCMetric, COCOMetric from .utils.ema import ExponentialMovingAverage from paddlex.utils.checkpoint import det_pretrain_weights_dict __all__ = [ "YOLOv3", "FasterRCNN", "PPYOLO", "PPYOLOTiny", "PPYOLOv2", "MaskRCNN" ] class BaseDetector(BaseModel): def __init__(self, model_name, num_classes=80, **params): self.init_params.update(locals()) del self.init_params['params'] super(BaseDetector, self).__init__('detector') if not hasattr(ppdet.modeling, model_name): raise Exception("ERROR: There's no model named {}.".format( model_name)) self.model_name = model_name self.num_classes = num_classes self.labels = None self.net = self.build_net(**params) def build_net(self, **params): with paddle.utils.unique_name.guard(): net = ppdet.modeling.__dict__[self.model_name](**params) return net def get_test_inputs(self, image_shape): input_spec = [{ "image": InputSpec( shape=[None, 3] + image_shape, name='image', dtype='float32'), "im_shape": InputSpec( shape=[None, 2], name='im_shape', dtype='float32'), "scale_factor": InputSpec( shape=[None, 2], name='scale_factor', dtype='float32') }] return input_spec def _get_backbone(self, backbone_name, **params): backbone = getattr(ppdet.modeling, backbone_name)(**params) return backbone def run(self, net, inputs, mode): net_out = net(inputs) if mode in ['train', 'eval']: outputs = net_out else: for key in ['im_shape', 'scale_factor']: net_out[key] = inputs[key] outputs = dict() for key in net_out: outputs[key] = net_out[key].numpy() return outputs def default_optimizer(self, parameters, learning_rate, warmup_steps, warmup_start_lr, lr_decay_epochs, lr_decay_gamma, num_steps_each_epoch): boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs] values = [(lr_decay_gamma**i) * learning_rate for i in range(len(lr_decay_epochs) + 1)] scheduler = paddle.optimizer.lr.PiecewiseDecay( boundaries=boundaries, values=values) if warmup_steps > 0: if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch: logging.error( "In function train(), parameters should satisfy: " "warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset", exit=False) logging.error( "See this doc for more information: " "https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice", exit=False) scheduler = paddle.optimizer.lr.LinearWarmup( learning_rate=scheduler, warmup_steps=warmup_steps, start_lr=warmup_start_lr, end_lr=learning_rate) optimizer = paddle.optimizer.Momentum( scheduler, momentum=.9, weight_decay=paddle.regularizer.L2Decay(coeff=1e-04), parameters=parameters) return optimizer def train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, optimizer=None, save_interval_epochs=1, log_interval_steps=10, save_dir='output', pretrain_weights='IMAGENET', learning_rate=.001, warmup_steps=0, warmup_start_lr=0.0, lr_decay_epochs=(216, 243), lr_decay_gamma=0.1, metric=None, use_ema=False, early_stop=False, early_stop_patience=5, use_vdl=True): """ Train the model. Args: num_epochs(int): The number of epochs. train_dataset(paddlex.dataset): Training dataset. train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64. eval_dataset(paddlex.dataset, optional): Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None. optimizer(paddle.optimizer.Optimizer or None, optional): Optimizer used for training. If None, a default optimizer is used. Defaults to None. save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1. log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10. save_dir(str, optional): Directory to save the model. Defaults to 'output'. pretrain_weights(str or None, optional): None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'. learning_rate(float, optional): Learning rate for training. Defaults to .001. warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0. warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0.. lr_decay_epochs(list or tuple, optional): Epoch milestones for learning rate decay. Defaults to (216, 243). lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay. Defaults to .1. metric({'VOC', 'COCO', None}, optional): Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None. use_ema(bool, optional): Whether to use exponential moving average strategy. Defaults to False. early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False. early_stop_patience(int, optional): Early stop patience. Defaults to 5. use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True. """ if train_dataset.__class__.__name__ == 'VOCDetection': train_dataset.data_fields = { 'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'difficult' } elif train_dataset.__class__.__name__ == 'CocoDetection': if self.__class__.__name__ == 'MaskRCNN': train_dataset.data_fields = { 'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'gt_poly', 'is_crowd' } else: train_dataset.data_fields = { 'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'is_crowd' } if metric is None: if eval_dataset.__class__.__name__ == 'VOCDetection': self.metric = 'voc' elif eval_dataset.__class__.__name__ == 'CocoDetection': self.metric = 'coco' else: assert metric.lower() in ['coco', 'voc'], \ "Evaluation metric {} is not supported, please choose form 'COCO' and 'VOC'" self.metric = metric.lower() train_dataset.batch_transforms = self._compose_batch_transform( train_dataset.transforms, mode='train') self.labels = train_dataset.labels # build optimizer if not defined if optimizer is None: num_steps_each_epoch = len(train_dataset) // train_batch_size self.optimizer = self.default_optimizer( parameters=self.net.parameters(), learning_rate=learning_rate, warmup_steps=warmup_steps, warmup_start_lr=warmup_start_lr, lr_decay_epochs=lr_decay_epochs, lr_decay_gamma=lr_decay_gamma, num_steps_each_epoch=num_steps_each_epoch) else: self.optimizer = optimizer # initiate weights if pretrain_weights is not None and not osp.exists(pretrain_weights): if pretrain_weights not in det_pretrain_weights_dict['_'.join( [self.model_name, self.backbone_name])]: logging.warning( "Path of pretrain_weights('{}') does not exist!".format( pretrain_weights)) pretrain_weights = det_pretrain_weights_dict['_'.join( [self.model_name, self.backbone_name])][0] logging.warning("Pretrain_weights is forcibly set to '{}'. " "If don't want to use pretrain weights, " "set pretrain_weights to be None.".format( pretrain_weights)) pretrained_dir = osp.join(save_dir, 'pretrain') self.net_initialize( pretrain_weights=pretrain_weights, save_dir=pretrained_dir) if use_ema: ema = ExponentialMovingAverage( decay=.9998, model=self.net, use_thres_step=True) else: ema = None # start train loop self.train_loop( num_epochs=num_epochs, train_dataset=train_dataset, train_batch_size=train_batch_size, eval_dataset=eval_dataset, save_interval_epochs=save_interval_epochs, log_interval_steps=log_interval_steps, save_dir=save_dir, ema=ema, early_stop=early_stop, early_stop_patience=early_stop_patience, use_vdl=use_vdl) def evaluate(self, eval_dataset, batch_size=1, metric=None, return_details=False): """ Evaluate the model. Args: eval_dataset(paddlex.dataset): Evaluation dataset. batch_size(int, optional): Total batch size among all cards used for evaluation. Defaults to 1. metric({'VOC', 'COCO', None}, optional): Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None. return_details(bool, optional): Whether to return evaluation details. Defaults to False. Returns: collections.OrderedDict with key-value pairs: {"mAP(0.50, 11point)":`mean average precision`}. """ if eval_dataset.__class__.__name__ == 'VOCDetection': eval_dataset.data_fields = { 'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'difficult' } elif eval_dataset.__class__.__name__ == 'CocoDetection': if self.__class__.__name__ == 'MaskRCNN': eval_dataset.data_fields = { 'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'gt_poly', 'is_crowd' } else: eval_dataset.data_fields = { 'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'is_crowd' } eval_dataset.batch_transforms = self._compose_batch_transform( eval_dataset.transforms, mode='eval') arrange_transforms( model_type=self.model_type, transforms=eval_dataset.transforms, mode='eval') self.net.eval() nranks = paddle.distributed.get_world_size() local_rank = paddle.distributed.get_rank() if nranks > 1: # Initialize parallel environment if not done. if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized( ): paddle.distributed.init_parallel_env() if batch_size > 1: logging.warning( "Detector only supports single card evaluation with batch_size=1 " "during evaluation, so batch_size is forcibly set to 1.") batch_size = 1 if nranks < 2 or local_rank == 0: self.eval_data_loader = self.build_data_loader( eval_dataset, batch_size=batch_size, mode='eval') is_bbox_normalized = False if eval_dataset.batch_transforms is not None: is_bbox_normalized = any( isinstance(t, _NormalizeBox) for t in eval_dataset.batch_transforms.batch_transforms) if metric is None: if getattr(self, 'metric', None) is not None: if self.metric == 'voc': eval_metric = VOCMetric( labels=eval_dataset.labels, coco_gt=copy.deepcopy(eval_dataset.coco_gt), is_bbox_normalized=is_bbox_normalized, classwise=False) else: eval_metric = COCOMetric( coco_gt=copy.deepcopy(eval_dataset.coco_gt), classwise=False) else: if eval_dataset.__class__.__name__ == 'VOCDetection': eval_metric = VOCMetric( labels=eval_dataset.labels, coco_gt=copy.deepcopy(eval_dataset.coco_gt), is_bbox_normalized=is_bbox_normalized, classwise=False) elif eval_dataset.__class__.__name__ == 'CocoDetection': eval_metric = COCOMetric( coco_gt=copy.deepcopy(eval_dataset.coco_gt), classwise=False) else: assert metric.lower() in ['coco', 'voc'], \ "Evaluation metric {} is not supported, please choose form 'COCO' and 'VOC'" if metric.lower() == 'coco': eval_metric = COCOMetric( coco_gt=copy.deepcopy(eval_dataset.coco_gt), classwise=False) else: eval_metric = VOCMetric( labels=eval_dataset.labels, is_bbox_normalized=is_bbox_normalized, classwise=False) scores = collections.OrderedDict() logging.info( "Start to evaluate(total_samples={}, total_steps={})...". format(eval_dataset.num_samples, eval_dataset.num_samples)) with paddle.no_grad(): for step, data in enumerate(self.eval_data_loader): outputs = self.run(self.net, data, 'eval') eval_metric.update(data, outputs) eval_metric.accumulate() self.eval_details = eval_metric.details scores.update(eval_metric.get()) eval_metric.reset() if return_details: return scores, self.eval_details return scores def predict(self, img_file, transforms=None): """ Do inference. Args: img_file(List[np.ndarray or str], str or np.ndarray): img_file(list or str or np.array)： Image path or decoded image data in a BGR format, which also could constitute a list, meaning all images to be predicted as a mini-batch. transforms(paddlex.transforms.Compose or None, optional): Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None. Returns: If img_file is a string or np.array, the result is a list of dict with key-value pairs: {"category_id": `category_id`, "category": `category`, "bbox": `[x, y, w, h]`, "score": `score`}. If img_file is a list, the result is a list composed of dicts with the corresponding fields: category_id(int): the predicted category ID category(str): category name bbox(list): bounding box in [x, y, w, h] format score(str): confidence """ if transforms is None and not hasattr(self, 'test_transforms'): raise Exception("transforms need to be defined, now is None.") if transforms is None: transforms = self.test_transforms if isinstance(img_file, (str, np.ndarray)): images = [img_file] else: images = img_file batch_samples = self._preprocess(images, transforms) self.net.eval() outputs = self.run(self.net, batch_samples, 'test') prediction = self._postprocess(outputs) if isinstance(img_file, (str, np.ndarray)): prediction = prediction[0] return prediction def _preprocess(self, images, transforms): arrange_transforms( model_type=self.model_type, transforms=transforms, mode='test') batch_samples = list() for im in images: sample = {'image': im} batch_samples.append(transforms(sample)) batch_transforms = self._compose_batch_transform(transforms, 'test') batch_samples = batch_transforms(batch_samples) for k, v in batch_samples.items(): batch_samples[k] = paddle.to_tensor(v) return batch_samples def _postprocess(self, batch_pred): infer_result = {} if 'bbox' in batch_pred: bboxes = batch_pred['bbox'] bbox_nums = batch_pred['bbox_num'] det_res = [] k = 0 for i in range(len(bbox_nums)): det_nums = bbox_nums[i] for j in range(det_nums): dt = bboxes[k] k = k + 1 num_id, score, xmin, ymin, xmax, ymax = dt.tolist() if int(num_id) < 0: continue category = self.labels[int(num_id)] w = xmax - xmin h = ymax - ymin bbox = [xmin, ymin, w, h] dt_res = { 'category_id': int(num_id), 'category': category, 'bbox': bbox, 'score': score } det_res.append(dt_res) infer_result['bbox'] = det_res if 'mask' in batch_pred: masks = batch_pred['mask'] bboxes = batch_pred['bbox'] mask_nums = batch_pred['bbox_num'] seg_res = [] k = 0 for i in range(len(mask_nums)): det_nums = mask_nums[i] for j in range(det_nums): mask = masks[k].astype(np.uint8) score = float(bboxes[k][1]) label = int(bboxes[k][0]) k = k + 1 if label == -1: continue category = self.labels[int(label)] import pycocotools.mask as mask_util rle = mask_util.encode( np.array( mask[:, :, None], order="F", dtype="uint8"))[0] if six.PY3: if 'counts' in rle: rle['counts'] = rle['counts'].decode("utf8") sg_res = { 'category': category, 'segmentation': rle, 'score': score } seg_res.append(sg_res) infer_result['mask'] = seg_res bbox_num = batch_pred['bbox_num'] results = [] start = 0 for num in bbox_num: end = start + num curr_res = infer_result['bbox'][start:end] if 'mask' in infer_result: mask_res = infer_result['mask'][start:end] for box, mask in zip(curr_res, mask_res): box.update(mask) results.append(curr_res) start = end return results class YOLOv3(BaseDetector): def __init__(self, num_classes=80, backbone='MobileNetV1', anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]], ignore_threshold=0.7, nms_score_threshold=0.01, nms_topk=1000, nms_keep_topk=100, nms_iou_threshold=0.45, label_smooth=False): self.init_params = locals() if backbone not in [ 'MobileNetV1', 'MobileNetV1_ssld', 'MobileNetV3', 'MobileNetV3_ssld', 'DarkNet53', 'ResNet50_vd_dcn', 'ResNet34' ]: raise ValueError( "backbone: {} is not supported. Please choose one of " "('MobileNetV1', 'MobileNetV1_ssld', 'MobileNetV3', 'MobileNetV3_ssld', 'DarkNet53', 'ResNet50_vd_dcn', 'ResNet34')". format(backbone)) if paddlex.env_info['place'] == 'gpu' and paddlex.env_info[ 'num'] > 1 and not os.environ.get('PADDLEX_EXPORT_STAGE'): norm_type = 'sync_bn' else: norm_type = 'bn' self.backbone_name = backbone if 'MobileNetV1' in backbone: norm_type = 'bn' backbone = self._get_backbone('MobileNet', norm_type=norm_type) elif 'MobileNetV3' in backbone: backbone = self._get_backbone( 'MobileNetV3', norm_type=norm_type, feature_maps=[7, 13, 16]) elif backbone == 'ResNet50_vd_dcn': backbone = self._get_backbone( 'ResNet', norm_type=norm_type, variant='d', return_idx=[1, 2, 3], dcn_v2_stages=[3], freeze_at=-1, freeze_norm=False) elif backbone == 'ResNet34': backbone = self._get_backbone( 'ResNet', depth=34, norm_type=norm_type, return_idx=[1, 2, 3], freeze_at=-1, freeze_norm=False, norm_decay=0.) else: backbone = self._get_backbone('DarkNet', norm_type=norm_type) neck = ppdet.modeling.YOLOv3FPN( norm_type=norm_type, in_channels=[i.channels for i in backbone.out_shape]) loss = ppdet.modeling.YOLOv3Loss( num_classes=num_classes, ignore_thresh=ignore_threshold, label_smooth=label_smooth) yolo_head = ppdet.modeling.YOLOv3Head( in_channels=[i.channels for i in neck.out_shape], anchors=anchors, anchor_masks=anchor_masks, num_classes=num_classes, loss=loss) post_process = ppdet.modeling.BBoxPostProcess( decode=ppdet.modeling.YOLOBox(num_classes=num_classes), nms=ppdet.modeling.MultiClassNMS( score_threshold=nms_score_threshold, nms_top_k=nms_topk, keep_top_k=nms_keep_topk, nms_threshold=nms_iou_threshold)) params = { 'backbone': backbone, 'neck': neck, 'yolo_head': yolo_head, 'post_process': post_process } super(YOLOv3, self).__init__( model_name='YOLOv3', num_classes=num_classes, **params) self.anchors = anchors self.anchor_masks = anchor_masks def _compose_batch_transform(self, transforms, mode='train'): if mode == 'train': default_batch_transforms = [ _BatchPadding( pad_to_stride=-1, pad_gt=False), _NormalizeBox(), _PadBox(getattr(self, 'num_max_boxes', 50)), _BboxXYXY2XYWH(), _Gt2YoloTarget( anchor_masks=self.anchor_masks, anchors=self.anchors, downsample_ratios=getattr(self, 'downsample_ratios', [32, 16, 8]), num_classes=self.num_classes) ] else: default_batch_transforms = [ _BatchPadding( pad_to_stride=-1, pad_gt=False) ] custom_batch_transforms = [] for i, op in enumerate(transforms.transforms): if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)): if mode != 'train': raise Exception( "{} cannot be present in the {} transforms. ".format( op.__class__.__name__, mode) + "Please check the {} transforms.".format(mode)) custom_batch_transforms.insert(0, copy.deepcopy(op)) batch_transforms = BatchCompose(custom_batch_transforms + default_batch_transforms) return batch_transforms class FasterRCNN(BaseDetector): def __init__(self, num_classes=80, backbone='ResNet50', with_fpn=True, aspect_ratios=[0.5, 1.0, 2.0], anchor_sizes=[[32], [64], [128], [256], [512]], keep_top_k=100, nms_threshold=0.5, score_threshold=0.05, fpn_num_channels=256, rpn_batch_size_per_im=256, rpn_fg_fraction=0.5, test_pre_nms_top_n=None, test_post_nms_top_n=1000): self.init_params = locals() if backbone not in [ 'ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet34', 'ResNet34_vd', 'ResNet101', 'ResNet101_vd' ]: raise ValueError( "backbone: {} is not supported. Please choose one of " "('ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet34', 'ResNet34_vd', " "'ResNet101', 'ResNet101_vd')".format(backbone)) self.backbone_name = backbone + '_fpn' if with_fpn else backbone if backbone == 'ResNet50_vd_ssld': if not with_fpn: logging.warning( "Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True". format(backbone)) with_fpn = True backbone = self._get_backbone( 'ResNet', variant='d', norm_type='bn', freeze_at=0, return_idx=[0, 1, 2, 3], num_stages=4, lr_mult_list=[0.05, 0.05, 0.1, 0.15]) elif 'ResNet50' in backbone: if with_fpn: backbone = self._get_backbone( 'ResNet', variant='d' if '_vd' in backbone else 'b', norm_type='bn', freeze_at=0, return_idx=[0, 1, 2, 3], num_stages=4) else: backbone = self._get_backbone( 'ResNet', variant='d' if '_vd' in backbone else 'b', norm_type='bn', freeze_at=0, return_idx=[2], num_stages=3) elif 'ResNet34' in backbone: if not with_fpn: logging.warning( "Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True". format(backbone)) with_fpn = True backbone = self._get_backbone( 'ResNet', depth=34, variant='d' if 'vd' in backbone else 'b', norm_type='bn', freeze_at=0, return_idx=[0, 1, 2, 3], num_stages=4) else: if not with_fpn: logging.warning( "Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True". format(backbone)) with_fpn = True backbone = self._get_backbone( 'ResNet', depth=101, variant='d' if 'vd' in backbone else 'b', norm_type='bn', freeze_at=0, return_idx=[0, 1, 2, 3], num_stages=4) rpn_in_channel = backbone.out_shape[0].channels if with_fpn: neck = ppdet.modeling.FPN( in_channels=[i.channels for i in backbone.out_shape], out_channel=fpn_num_channels, spatial_scales=[1.0 / i.stride for i in backbone.out_shape]) rpn_in_channel = neck.out_shape[0].channels anchor_generator_cfg = { 'aspect_ratios': aspect_ratios, 'anchor_sizes': anchor_sizes, 'strides': [4, 8, 16, 32, 64] } train_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 2000, 'post_nms_top_n': 1000, 'topk_after_collect': True } test_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 1000 if test_pre_nms_top_n is None else test_pre_nms_top_n, 'post_nms_top_n': test_post_nms_top_n } head = ppdet.modeling.TwoFCHead(out_channel=1024) roi_extractor_cfg = { 'resolution': 7, 'spatial_scale': [1. / i.stride for i in neck.out_shape], 'sampling_ratio': 0, 'aligned': True } with_pool = False else: neck = None anchor_generator_cfg = { 'aspect_ratios': aspect_ratios, 'anchor_sizes': anchor_sizes, 'strides': [16] } train_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 12000, 'post_nms_top_n': 2000, 'topk_after_collect': False } test_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 6000 if test_pre_nms_top_n is None else test_pre_nms_top_n, 'post_nms_top_n': test_post_nms_top_n } head = ppdet.modeling.Res5Head() roi_extractor_cfg = { 'resolution': 14, 'spatial_scale': [1. / i.stride for i in backbone.out_shape], 'sampling_ratio': 0, 'aligned': True } with_pool = True rpn_target_assign_cfg = { 'batch_size_per_im': rpn_batch_size_per_im, 'fg_fraction': rpn_fg_fraction, 'negative_overlap': .3, 'positive_overlap': .7, 'use_random': True } rpn_head = ppdet.modeling.RPNHead( anchor_generator=anchor_generator_cfg, rpn_target_assign=rpn_target_assign_cfg, train_proposal=train_proposal_cfg, test_proposal=test_proposal_cfg, in_channel=rpn_in_channel) bbox_assigner = BBoxAssigner(num_classes=num_classes) bbox_head = ppdet.modeling.BBoxHead( head=head, in_channel=head.out_shape[0].channels, roi_extractor=roi_extractor_cfg, with_pool=with_pool, bbox_assigner=bbox_assigner, num_classes=num_classes) bbox_post_process = ppdet.modeling.BBoxPostProcess( num_classes=num_classes, decode=ppdet.modeling.RCNNBox(num_classes=num_classes), nms=ppdet.modeling.MultiClassNMS( score_threshold=score_threshold, keep_top_k=keep_top_k, nms_threshold=nms_threshold)) params = { 'backbone': backbone, 'neck': neck, 'rpn_head': rpn_head, 'bbox_head': bbox_head, 'bbox_post_process': bbox_post_process } self.with_fpn = with_fpn super(FasterRCNN, self).__init__( model_name='FasterRCNN', num_classes=num_classes, **params) def _compose_batch_transform(self, transforms, mode='train'): if mode == 'train': default_batch_transforms = [ _BatchPadding( pad_to_stride=32 if self.with_fpn else -1, pad_gt=True) ] else: default_batch_transforms = [ _BatchPadding( pad_to_stride=32 if self.with_fpn else -1, pad_gt=False) ] custom_batch_transforms = [] for i, op in enumerate(transforms.transforms): if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)): if mode != 'train': raise Exception( "{} cannot be present in the {} transforms. ".format( op.__class__.__name__, mode) + "Please check the {} transforms.".format(mode)) custom_batch_transforms.insert(0, copy.deepcopy(op)) batch_transforms = BatchCompose(custom_batch_transforms + default_batch_transforms) return batch_transforms class PPYOLO(YOLOv3): def __init__(self, num_classes=80, backbone='ResNet50_vd_dcn', anchors=None, anchor_masks=None, use_coord_conv=True, use_iou_aware=True, use_spp=True, use_drop_block=True, scale_x_y=1.05, ignore_threshold=0.7, label_smooth=False, use_iou_loss=True, use_matrix_nms=True, nms_score_threshold=0.01, nms_topk=-1, nms_keep_topk=100, nms_iou_threshold=0.45): self.init_params = locals() if backbone not in [ 'ResNet50_vd_dcn', 'ResNet18_vd', 'MobileNetV3_large', 'MobileNetV3_small' ]: raise ValueError( "backbone: {} is not supported. Please choose one of " "('ResNet50_vd_dcn', 'ResNet18_vd', 'MobileNetV3_large', 'MobileNetV3_small')". format(backbone)) self.backbone_name = backbone if paddlex.env_info['place'] == 'gpu' and paddlex.env_info[ 'num'] > 1 and not os.environ.get('PADDLEX_EXPORT_STAGE'): norm_type = 'sync_bn' else: norm_type = 'bn' if anchors is None and anchor_masks is None: if 'MobileNetV3' in backbone: anchors = [[11, 18], [34, 47], [51, 126], [115, 71], [120, 195], [254, 235]] anchor_masks = [[3, 4, 5], [0, 1, 2]] elif backbone == 'ResNet50_vd_dcn': anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] else: anchors = [[10, 14], [23, 27], [37, 58], [81, 82], [135, 169], [344, 319]] anchor_masks = [[3, 4, 5], [0, 1, 2]] elif anchors is None or anchor_masks is None: raise ValueError("Please define both anchors and anchor_masks.") if backbone == 'ResNet50_vd_dcn': backbone = self._get_backbone( 'ResNet', variant='d', norm_type=norm_type, return_idx=[1, 2, 3], dcn_v2_stages=[3], freeze_at=-1, freeze_norm=False, norm_decay=0.) downsample_ratios = [32, 16, 8] elif backbone == 'ResNet18_vd': backbone = self._get_backbone( 'ResNet', depth=18, variant='d', norm_type=norm_type, return_idx=[2, 3], freeze_at=-1, freeze_norm=False, norm_decay=0.) downsample_ratios = [32, 16, 8] elif backbone == 'MobileNetV3_large': backbone = self._get_backbone( 'MobileNetV3', model_name='large', norm_type=norm_type, scale=1, with_extra_blocks=False, extra_block_filters=[], feature_maps=[13, 16]) downsample_ratios = [32, 16] elif backbone == 'MobileNetV3_small': backbone = self._get_backbone( 'MobileNetV3', model_name='small', norm_type=norm_type, scale=1, with_extra_blocks=False, extra_block_filters=[], feature_maps=[9, 12]) downsample_ratios = [32, 16] neck = ppdet.modeling.PPYOLOFPN( norm_type=norm_type, in_channels=[i.channels for i in backbone.out_shape], coord_conv=use_coord_conv, drop_block=use_drop_block, spp=use_spp, conv_block_num=0 if ('MobileNetV3' in self.backbone_name or self.backbone_name == 'ResNet18_vd') else 2) loss = ppdet.modeling.YOLOv3Loss( num_classes=num_classes, ignore_thresh=ignore_threshold, downsample=downsample_ratios, label_smooth=label_smooth, scale_x_y=scale_x_y, iou_loss=ppdet.modeling.IouLoss( loss_weight=2.5, loss_square=True) if use_iou_loss else None, iou_aware_loss=ppdet.modeling.IouAwareLoss(loss_weight=1.0) if use_iou_aware else None) yolo_head = ppdet.modeling.YOLOv3Head( in_channels=[i.channels for i in neck.out_shape], anchors=anchors, anchor_masks=anchor_masks, num_classes=num_classes, loss=loss, iou_aware=use_iou_aware) if use_matrix_nms: nms = ppdet.modeling.MatrixNMS( keep_top_k=nms_keep_topk, score_threshold=nms_score_threshold, post_threshold=.05 if 'MobileNetV3' in self.backbone_name else .01, nms_top_k=nms_topk, background_label=-1) else: nms = ppdet.modeling.MultiClassNMS( score_threshold=nms_score_threshold, nms_top_k=nms_topk, keep_top_k=nms_keep_topk, nms_threshold=nms_iou_threshold) post_process = ppdet.modeling.BBoxPostProcess( decode=ppdet.modeling.YOLOBox( num_classes=num_classes, conf_thresh=.005 if 'MobileNetV3' in self.backbone_name else .01, scale_x_y=scale_x_y), nms=nms) params = { 'backbone': backbone, 'neck': neck, 'yolo_head': yolo_head, 'post_process': post_process } super(YOLOv3, self).__init__( model_name='YOLOv3', num_classes=num_classes, **params) self.anchors = anchors self.anchor_masks = anchor_masks self.downsample_ratios = downsample_ratios self.model_name = 'PPYOLO' class PPYOLOTiny(YOLOv3): def __init__(self, num_classes=80, backbone='MobileNetV3', anchors=[[10, 15], [24, 36], [72, 42], [35, 87], [102, 96], [60, 170], [220, 125], [128, 222], [264, 266]], anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]], use_iou_aware=False, use_spp=True, use_drop_block=True, scale_x_y=1.05, ignore_threshold=0.5, label_smooth=False, use_iou_loss=True, use_matrix_nms=False, nms_score_threshold=0.005, nms_topk=1000, nms_keep_topk=100, nms_iou_threshold=0.45): self.init_params = locals() if backbone != 'MobileNetV3': logging.warning( "PPYOLOTiny only supports MobileNetV3 as backbone. " "Backbone is forcibly set to MobileNetV3.") self.backbone_name = 'MobileNetV3' if paddlex.env_info['place'] == 'gpu' and paddlex.env_info[ 'num'] > 1 and not os.environ.get('PADDLEX_EXPORT_STAGE'): norm_type = 'sync_bn' else: norm_type = 'bn' backbone = self._get_backbone( 'MobileNetV3', model_name='large', norm_type=norm_type, scale=.5, with_extra_blocks=False, extra_block_filters=[], feature_maps=[7, 13, 16]) downsample_ratios = [32, 16, 8] neck = ppdet.modeling.PPYOLOTinyFPN( detection_block_channels=[160, 128, 96], in_channels=[i.channels for i in backbone.out_shape], spp=use_spp, drop_block=use_drop_block) loss = ppdet.modeling.YOLOv3Loss( num_classes=num_classes, ignore_thresh=ignore_threshold, downsample=downsample_ratios, label_smooth=label_smooth, scale_x_y=scale_x_y, iou_loss=ppdet.modeling.IouLoss( loss_weight=2.5, loss_square=True) if use_iou_loss else None, iou_aware_loss=ppdet.modeling.IouAwareLoss(loss_weight=1.0) if use_iou_aware else None) yolo_head = ppdet.modeling.YOLOv3Head( in_channels=[i.channels for i in neck.out_shape], anchors=anchors, anchor_masks=anchor_masks, num_classes=num_classes, loss=loss, iou_aware=use_iou_aware) if use_matrix_nms: nms = ppdet.modeling.MatrixNMS( keep_top_k=nms_keep_topk, score_threshold=nms_score_threshold, post_threshold=.05, nms_top_k=nms_topk, background_label=-1) else: nms = ppdet.modeling.MultiClassNMS( score_threshold=nms_score_threshold, nms_top_k=nms_topk, keep_top_k=nms_keep_topk, nms_threshold=nms_iou_threshold) post_process = ppdet.modeling.BBoxPostProcess( decode=ppdet.modeling.YOLOBox( num_classes=num_classes, conf_thresh=.005, downsample_ratio=32, clip_bbox=True, scale_x_y=scale_x_y), nms=nms) params = { 'backbone': backbone, 'neck': neck, 'yolo_head': yolo_head, 'post_process': post_process } super(YOLOv3, self).__init__( model_name='YOLOv3', num_classes=num_classes, **params) self.anchors = anchors self.anchor_masks = anchor_masks self.downsample_ratios = downsample_ratios self.num_max_boxes = 100 self.model_name = 'PPYOLOTiny' class PPYOLOv2(YOLOv3): def __init__(self, num_classes=80, backbone='ResNet50_vd_dcn', anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]], use_iou_aware=True, use_spp=True, use_drop_block=True, scale_x_y=1.05, ignore_threshold=0.7, label_smooth=False, use_iou_loss=True, use_matrix_nms=True, nms_score_threshold=0.01, nms_topk=-1, nms_keep_topk=100, nms_iou_threshold=0.45): self.init_params = locals() if backbone not in ['ResNet50_vd_dcn', 'ResNet101_vd_dcn']: raise ValueError( "backbone: {} is not supported. Please choose one of " "('ResNet50_vd_dcn', 'ResNet18_vd')".format(backbone)) self.backbone_name = backbone if paddlex.env_info['place'] == 'gpu' and paddlex.env_info[ 'num'] > 1 and not os.environ.get('PADDLEX_EXPORT_STAGE'): norm_type = 'sync_bn' else: norm_type = 'bn' if backbone == 'ResNet50_vd_dcn': backbone = self._get_backbone( 'ResNet', variant='d', norm_type=norm_type, return_idx=[1, 2, 3], dcn_v2_stages=[3], freeze_at=-1, freeze_norm=False, norm_decay=0.) downsample_ratios = [32, 16, 8] elif backbone == 'ResNet101_vd_dcn': backbone = self._get_backbone( 'ResNet', depth=101, variant='d', norm_type=norm_type, return_idx=[1, 2, 3], dcn_v2_stages=[3], freeze_at=-1, freeze_norm=False, norm_decay=0.) downsample_ratios = [32, 16, 8] neck = ppdet.modeling.PPYOLOPAN( norm_type=norm_type, in_channels=[i.channels for i in backbone.out_shape], drop_block=use_drop_block, block_size=3, keep_prob=.9, spp=use_spp) loss = ppdet.modeling.YOLOv3Loss( num_classes=num_classes, ignore_thresh=ignore_threshold, downsample=downsample_ratios, label_smooth=label_smooth, scale_x_y=scale_x_y, iou_loss=ppdet.modeling.IouLoss( loss_weight=2.5, loss_square=True) if use_iou_loss else None, iou_aware_loss=ppdet.modeling.IouAwareLoss(loss_weight=1.0) if use_iou_aware else None) yolo_head = ppdet.modeling.YOLOv3Head( in_channels=[i.channels for i in neck.out_shape], anchors=anchors, anchor_masks=anchor_masks, num_classes=num_classes, loss=loss, iou_aware=use_iou_aware, iou_aware_factor=.5) if use_matrix_nms: nms = ppdet.modeling.MatrixNMS( keep_top_k=nms_keep_topk, score_threshold=nms_score_threshold, post_threshold=.01, nms_top_k=nms_topk, background_label=-1) else: nms = ppdet.modeling.MultiClassNMS( score_threshold=nms_score_threshold, nms_top_k=nms_topk, keep_top_k=nms_keep_topk, nms_threshold=nms_iou_threshold) post_process = ppdet.modeling.BBoxPostProcess( decode=ppdet.modeling.YOLOBox( num_classes=num_classes, conf_thresh=.01, downsample_ratio=32, clip_bbox=True, scale_x_y=scale_x_y), nms=nms) params = { 'backbone': backbone, 'neck': neck, 'yolo_head': yolo_head, 'post_process': post_process } super(YOLOv3, self).__init__( model_name='YOLOv3', num_classes=num_classes, **params) self.anchors = anchors self.anchor_masks = anchor_masks self.downsample_ratios = downsample_ratios self.num_max_boxes = 100 self.model_name = 'PPYOLOv2' class MaskRCNN(BaseDetector): def __init__(self, num_classes=80, backbone='ResNet50_vd', with_fpn=True, aspect_ratios=[0.5, 1.0, 2.0], anchor_sizes=[[32], [64], [128], [256], [512]], keep_top_k=100, nms_threshold=0.5, score_threshold=0.05, fpn_num_channels=256, rpn_batch_size_per_im=256, rpn_fg_fraction=0.5, test_pre_nms_top_n=None, test_post_nms_top_n=1000): self.init_params = locals() if backbone not in [ 'ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet101', 'ResNet101_vd' ]: raise ValueError( "backbone: {} is not supported. Please choose one of " "('ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet101', 'ResNet101_vd')". format(backbone)) self.backbone_name = backbone + '_fpn' if with_fpn else backbone if backbone == 'ResNet50': if with_fpn: backbone = self._get_backbone( 'ResNet', norm_type='bn', freeze_at=0, return_idx=[0, 1, 2, 3], num_stages=4) else: backbone = self._get_backbone( 'ResNet', norm_type='bn', freeze_at=0, return_idx=[2], num_stages=3) elif 'ResNet50_vd' in backbone: if not with_fpn: logging.warning( "Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True". format(backbone)) with_fpn = True backbone = self._get_backbone( 'ResNet', variant='d', norm_type='bn', freeze_at=0, return_idx=[0, 1, 2, 3], num_stages=4, lr_mult_list=[0.05, 0.05, 0.1, 0.15] if '_ssld' in backbone else [1.0, 1.0, 1.0, 1.0]) else: if not with_fpn: logging.warning( "Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True". format(backbone)) with_fpn = True backbone = self._get_backbone( 'ResNet', variant='d' if '_vd' in backbone else 'b', depth=101, norm_type='bn', freeze_at=0, return_idx=[0, 1, 2, 3], num_stages=4) rpn_in_channel = backbone.out_shape[0].channels if with_fpn: neck = ppdet.modeling.FPN( in_channels=[i.channels for i in backbone.out_shape], out_channel=fpn_num_channels, spatial_scales=[1.0 / i.stride for i in backbone.out_shape]) rpn_in_channel = neck.out_shape[0].channels anchor_generator_cfg = { 'aspect_ratios': aspect_ratios, 'anchor_sizes': anchor_sizes, 'strides': [4, 8, 16, 32, 64] } train_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 2000, 'post_nms_top_n': 1000, 'topk_after_collect': True } test_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 1000 if test_pre_nms_top_n is None else test_pre_nms_top_n, 'post_nms_top_n': test_post_nms_top_n } bb_head = ppdet.modeling.TwoFCHead( in_channel=neck.out_shape[0].channels, out_channel=1024) bb_roi_extractor_cfg = { 'resolution': 7, 'spatial_scale': [1. / i.stride for i in neck.out_shape], 'sampling_ratio': 0, 'aligned': True } with_pool = False m_head = ppdet.modeling.MaskFeat( in_channel=neck.out_shape[0].channels, out_channel=256, num_convs=4) m_roi_extractor_cfg = { 'resolution': 14, 'spatial_scale': [1. / i.stride for i in neck.out_shape], 'sampling_ratio': 0, 'aligned': True } mask_assigner = MaskAssigner( num_classes=num_classes, mask_resolution=28) share_bbox_feat = False else: neck = None anchor_generator_cfg = { 'aspect_ratios': aspect_ratios, 'anchor_sizes': anchor_sizes, 'strides': [16] } train_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 12000, 'post_nms_top_n': 2000, 'topk_after_collect': False } test_proposal_cfg = { 'min_size': 0.0, 'nms_thresh': .7, 'pre_nms_top_n': 6000 if test_pre_nms_top_n is None else test_pre_nms_top_n, 'post_nms_top_n': test_post_nms_top_n } bb_head = ppdet.modeling.Res5Head() bb_roi_extractor_cfg = { 'resolution': 14, 'spatial_scale': [1. / i.stride for i in backbone.out_shape], 'sampling_ratio': 0, 'aligned': True } with_pool = True m_head = ppdet.modeling.MaskFeat( in_channel=bb_head.out_shape[0].channels, out_channel=256, num_convs=0) m_roi_extractor_cfg = { 'resolution': 14, 'spatial_scale': [1. / i.stride for i in backbone.out_shape], 'sampling_ratio': 0, 'aligned': True } mask_assigner = MaskAssigner( num_classes=num_classes, mask_resolution=14) share_bbox_feat = True rpn_target_assign_cfg = { 'batch_size_per_im': rpn_batch_size_per_im, 'fg_fraction': rpn_fg_fraction, 'negative_overlap': .3, 'positive_overlap': .7, 'use_random': True } rpn_head = ppdet.modeling.RPNHead( anchor_generator=anchor_generator_cfg, rpn_target_assign=rpn_target_assign_cfg, train_proposal=train_proposal_cfg, test_proposal=test_proposal_cfg, in_channel=rpn_in_channel) bbox_assigner = BBoxAssigner(num_classes=num_classes) bbox_head = ppdet.modeling.BBoxHead( head=bb_head, in_channel=bb_head.out_shape[0].channels, roi_extractor=bb_roi_extractor_cfg, with_pool=with_pool, bbox_assigner=bbox_assigner, num_classes=num_classes) mask_head = ppdet.modeling.MaskHead( head=m_head, roi_extractor=m_roi_extractor_cfg, mask_assigner=mask_assigner, share_bbox_feat=share_bbox_feat, num_classes=num_classes) bbox_post_process = ppdet.modeling.BBoxPostProcess( num_classes=num_classes, decode=ppdet.modeling.RCNNBox(num_classes=num_classes), nms=ppdet.modeling.MultiClassNMS( score_threshold=score_threshold, keep_top_k=keep_top_k, nms_threshold=nms_threshold)) mask_post_process = ppdet.modeling.MaskPostProcess(binary_thresh=.5) params = { 'backbone': backbone, 'neck': neck, 'rpn_head': rpn_head, 'bbox_head': bbox_head, 'mask_head': mask_head, 'bbox_post_process': bbox_post_process, 'mask_post_process': mask_post_process } self.with_fpn = with_fpn super(MaskRCNN, self).__init__( model_name='MaskRCNN', num_classes=num_classes, **params) def _compose_batch_transform(self, transforms, mode='train'): if mode == 'train': default_batch_transforms = [ _BatchPadding( pad_to_stride=32 if self.with_fpn else -1, pad_gt=True) ] else: default_batch_transforms = [ _BatchPadding( pad_to_stride=32 if self.with_fpn else -1, pad_gt=False) ] custom_batch_transforms = [] for i, op in enumerate(transforms.transforms): if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)): if mode != 'train': raise Exception( "{} cannot be present in the {} transforms. ".format( op.__class__.__name__, mode) + "Please check the {} transforms.".format(mode)) custom_batch_transforms.insert(0, copy.deepcopy(op)) batch_transforms = BatchCompose(custom_batch_transforms + default_batch_transforms) return batch_transforms