From 6220fd3a860e32596e6b83a9583a9ee58ef7424d Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Wed, 27 Oct 2021 15:32:49 +0800 Subject: [PATCH] Revert " [PaddlePaddle Hackathon] Task 66 (#4317)" (#4374) This reverts commit 47fc188b65ef8950ac7eb9ab383a2ac7c1cb5c39. --- ...er_rcnn_r34_fpn_multiscaletest_1x_coco.yml | 22 ------- .../multi_scale_test_config.md | 45 ------------- .../multi_scale_test_config_cn.md | 45 ------------- ppdet/data/transform/batch_operators.py | 12 +--- ppdet/engine/trainer.py | 12 +--- ppdet/metrics/metrics.py | 7 +- ppdet/modeling/architectures/meta_arch.py | 52 +-------------- .../imgs/coco2017_val2017_000000000139.jpg | Bin 161811 -> 0 bytes .../imgs/coco2017_val2017_000000000724.jpg | Bin 130107 -> 0 bytes ppdet/modeling/tests/test_mstest.py | 60 ------------------ 10 files changed, 6 insertions(+), 249 deletions(-) delete mode 100644 configs/faster_rcnn/faster_rcnn_r34_fpn_multiscaletest_1x_coco.yml delete mode 100644 docs/tutorials/config_annotation/multi_scale_test_config.md delete mode 100644 docs/tutorials/config_annotation/multi_scale_test_config_cn.md delete mode 100644 ppdet/modeling/tests/imgs/coco2017_val2017_000000000139.jpg delete mode 100644 ppdet/modeling/tests/imgs/coco2017_val2017_000000000724.jpg delete mode 100644 ppdet/modeling/tests/test_mstest.py diff --git a/configs/faster_rcnn/faster_rcnn_r34_fpn_multiscaletest_1x_coco.yml b/configs/faster_rcnn/faster_rcnn_r34_fpn_multiscaletest_1x_coco.yml deleted file mode 100644 index 559d5f1fe..000000000 --- a/configs/faster_rcnn/faster_rcnn_r34_fpn_multiscaletest_1x_coco.yml +++ /dev/null @@ -1,22 +0,0 @@ -_BASE_: [ - 'faster_rcnn_r34_fpn_1x_coco.yml', -] - -pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet34_pretrained.pdparams -weights: output/faster_rcnn_r34_fpn_multiscaletest_1x_coco/model_final - -EvalReader: - sample_transforms: - - Decode: {} -# - Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True} - - MultiscaleTestResize: {origin_target_size: [800, 1333], target_size: [700 , 900], use_flip: False} - - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - - Permute: {} - -TestReader: - sample_transforms: - - Decode: {} -# - Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True} - - MultiscaleTestResize: {origin_target_size: [800, 1333], target_size: [700 , 900], use_flip: False} - - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - - Permute: {} \ No newline at end of file diff --git a/docs/tutorials/config_annotation/multi_scale_test_config.md b/docs/tutorials/config_annotation/multi_scale_test_config.md deleted file mode 100644 index 5d553ca0d..000000000 --- a/docs/tutorials/config_annotation/multi_scale_test_config.md +++ /dev/null @@ -1,45 +0,0 @@ -# Multi Scale Test Configuration - -Tags: Configuration - ---- -```yaml - -##################################### Multi scale test configuration ##################################### - -EvalReader: - sample_transforms: - - Decode: {} - - MultiscaleTestResize: {origin_target_size: [800, 1333], target_size: [700 , 900]} - - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - - Permute: {} - -TestReader: - sample_transforms: - - Decode: {} - - MultiscaleTestResize: {origin_target_size: [800, 1333], target_size: [700 , 900]} - - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - - Permute: {} -``` - ---- - -Multi Scale Test is a TTA (Test Time Augmentation) method, it can improve object detection performance. - -The input image will be scaled into different scales, then model generated predictions (bboxes) at different scales, finally all the predictions will be combined to generate final prediction. (Here **NMS** is used to aggregate the predictions.) - -## _MultiscaleTestResize_ option - -`MultiscaleTestResize` option is used to enable multi scale test prediction. - -`origin_target_size: [800, 1333]` means the input image will be scaled to 800 (for short edge) and 1333 (max edge length cannot be greater than 1333) at first - -`target_size: [700 , 900]` property is used to specify different scales. - -It can be plugged into evaluation process or test (inference) process, by adding `MultiscaleTestResize` entry to `EvalReader.sample_transforms` or `TestReader.sample_transforms` - ---- - -###Note - -Now only CascadeRCNN, FasterRCNN and MaskRCNN are supported for multi scale testing. And batch size must be 1. \ No newline at end of file diff --git a/docs/tutorials/config_annotation/multi_scale_test_config_cn.md b/docs/tutorials/config_annotation/multi_scale_test_config_cn.md deleted file mode 100644 index acf942c35..000000000 --- a/docs/tutorials/config_annotation/multi_scale_test_config_cn.md +++ /dev/null @@ -1,45 +0,0 @@ -# 多尺度测试的配置 - -标签: 配置 - ---- -```yaml - -##################################### 多尺度测试的配置 ##################################### - -EvalReader: - sample_transforms: - - Decode: {} - - MultiscaleTestResize: {origin_target_size: [800, 1333], target_size: [700 , 900]} - - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - - Permute: {} - -TestReader: - sample_transforms: - - Decode: {} - - MultiscaleTestResize: {origin_target_size: [800, 1333], target_size: [700 , 900]} - - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - - Permute: {} -``` - ---- - -多尺度测试是一种TTA方法(测试时增强),可以用于提高目标检测的准确率 - -输入图像首先被缩放为不同尺度的图像,然后模型对这些不同尺度的图像进行预测,最后将这些不同尺度上的预测结果整合为最终预测结果。(这里使用了**NMS**来整合不同尺度的预测结果) - -## _MultiscaleTestResize_ 选项 - -`MultiscaleTestResize` 选项用于开启多尺度测试. - -`origin_target_size: [800, 1333]` 项代表输入图像首先缩放为短边为800,最长边不超过1333. - -`target_size: [700 , 900]` 项设置不同的预测尺度。 - -通过在`EvalReader.sample_transforms`或`TestReader.sample_transforms`中设置`MultiscaleTestResize`项,可以在评估过程或预测过程中开启多尺度测试。 - ---- - -###注意 - -目前多尺度测试只支持CascadeRCNN, FasterRCNN and MaskRCNN网络, 并且batch size需要是1. \ No newline at end of file diff --git a/ppdet/data/transform/batch_operators.py b/ppdet/data/transform/batch_operators.py index e988e7201..bacd53b75 100644 --- a/ppdet/data/transform/batch_operators.py +++ b/ppdet/data/transform/batch_operators.py @@ -16,8 +16,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import typing - try: from collections.abc import Sequence except Exception: @@ -60,13 +58,7 @@ class PadBatch(BaseOperator): """ coarsest_stride = self.pad_to_stride - # multi scale input is nested list - if isinstance(samples, typing.Sequence) and len(samples) > 0 and isinstance(samples[0], typing.Sequence): - inner_samples = samples[0] - else: - inner_samples = samples - - max_shape = np.array([data['image'].shape for data in inner_samples]).max( + max_shape = np.array([data['image'].shape for data in samples]).max( axis=0) if coarsest_stride > 0: max_shape[1] = int( @@ -74,7 +66,7 @@ class PadBatch(BaseOperator): max_shape[2] = int( np.ceil(max_shape[2] / coarsest_stride) * coarsest_stride) - for data in inner_samples: + for data in samples: im = data['image'] im_c, im_h, im_w = im.shape[:] padding_im = np.zeros( diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py index 353797309..edb02709b 100644 --- a/ppdet/engine/trainer.py +++ b/ppdet/engine/trainer.py @@ -22,7 +22,6 @@ import copy import time import numpy as np -import typing from PIL import Image import paddle @@ -429,11 +428,7 @@ class Trainer(object): for metric in self._metrics: metric.update(data, outs) - # multi-scale inputs: all inputs have same im_id - if isinstance(data, typing.Sequence): - sample_num += data[0]['im_id'].numpy().shape[0] - else: - sample_num += data['im_id'].numpy().shape[0] + sample_num += data['im_id'].numpy().shape[0] self._compose_callback.on_step_end(self.status) self.status['sample_num'] = sample_num @@ -476,10 +471,7 @@ class Trainer(object): outs = self.model(data) for key in ['im_shape', 'scale_factor', 'im_id']: - if isinstance(data, typing.Sequence): - outs[key] = data[0][key] - else: - outs[key] = data[key] + outs[key] = data[key] for key, value in outs.items(): if hasattr(value, 'numpy'): outs[key] = value.numpy() diff --git a/ppdet/metrics/metrics.py b/ppdet/metrics/metrics.py index ea9861319..65b18efd8 100644 --- a/ppdet/metrics/metrics.py +++ b/ppdet/metrics/metrics.py @@ -21,7 +21,6 @@ import sys import json import paddle import numpy as np -import typing from .map_utils import prune_zero_padding, DetectionMAP from .coco_utils import get_infer_results, cocoapi_eval @@ -98,11 +97,7 @@ class COCOMetric(Metric): for k, v in outputs.items(): outs[k] = v.numpy() if isinstance(v, paddle.Tensor) else v - # multi-scale inputs: all inputs have same im_id - if isinstance(inputs, typing.Sequence): - im_id = inputs[0]['im_id'] - else: - im_id = inputs['im_id'] + im_id = inputs['im_id'] outs['im_id'] = im_id.numpy() if isinstance(im_id, paddle.Tensor) else im_id diff --git a/ppdet/modeling/architectures/meta_arch.py b/ppdet/modeling/architectures/meta_arch.py index d914b4e98..00085428d 100644 --- a/ppdet/modeling/architectures/meta_arch.py +++ b/ppdet/modeling/architectures/meta_arch.py @@ -2,13 +2,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np import paddle import paddle.nn as nn -import typing - from ppdet.core.workspace import register -from static.ppdet.utils.post_process import nms __all__ = ['BaseArch'] @@ -29,53 +25,7 @@ class BaseArch(nn.Layer): if self.training: out = self.get_loss() else: - inputs_list = [] - # multi-scale input - if not isinstance(inputs, typing.Sequence): - inputs_list.append(inputs) - else: - inputs_list.extend(inputs) - - outs = [] - for inp in inputs_list: - self.inputs = inp - outs.append(self.get_pred()) - - # multi-scale test - if len(outs)>1: - out = self.merge_multi_scale_predictions(outs) - else: - out = outs[0] - return out - - def merge_multi_scale_predictions(self, outs): - # default values for architectures not included in following list - num_classes = 80 - nms_threshold = 0.5 - keep_top_k = 100 - - if self.__class__.__name__ in ('CascadeRCNN', 'FasterRCNN', 'MaskRCNN'): - num_classes = self.bbox_head.num_classes - keep_top_k = self.bbox_post_process.nms.keep_top_k - nms_threshold = self.bbox_post_process.nms.nms_threshold - else: - raise Exception("Multi scale test only supports CascadeRCNN, FasterRCNN and MaskRCNN for now") - - final_boxes = [] - all_scale_outs = paddle.concat([o['bbox'] for o in outs]).numpy() - for c in range(num_classes): - idxs = all_scale_outs[:, 0] == c - if np.count_nonzero(idxs) == 0: - continue - r = nms(all_scale_outs[idxs, 1:], nms_threshold) - final_boxes.append(np.concatenate([np.full((r.shape[0], 1), c), r], 1)) - out = np.concatenate(final_boxes) - out = np.concatenate(sorted(out, key=lambda e: e[1])[-keep_top_k:]).reshape((-1, 6)) - out = { - 'bbox': paddle.to_tensor(out), - 'bbox_num': paddle.to_tensor(np.array([out.shape[0], ])) - } - + out = self.get_pred() return out def build_inputs(self, data, input_def): diff --git a/ppdet/modeling/tests/imgs/coco2017_val2017_000000000139.jpg b/ppdet/modeling/tests/imgs/coco2017_val2017_000000000139.jpg deleted file mode 100644 index 19023f718333c56c70776c79201dc03d742c1ed3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 161811 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