forked from mindspore-Ecosystem/mindspore
!19388 repair maskrcnn export & transformer
Merge pull request !19388 from huchunmei/maskrcnn
This commit is contained in:
Gitee
commit
1f2080f860
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@ -19,8 +19,8 @@ ann_file: "./annotations/instances_val2017.json"
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modelarts_dataset_unzip_name: 'cocodataset'
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need_modelarts_dataset_unzip: True
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img_path: '' # "image file path."
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result_path: '' # "result file path."
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img_path: ''
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result_path: ''
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# Training options
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img_width: 1280
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@ -30,10 +30,15 @@ flip_ratio: 0.5
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expand_ratio: 1.0
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max_instance_count: 128
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mask_shape: (28, 28)
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mask_shape: [28, 28]
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# anchor
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feature_shapes: [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)]
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feature_shapes:
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- [192, 320]
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- [96, 160]
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- [48, 80]
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- [24, 40]
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- [12, 20]
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anchor_scales: [8]
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anchor_ratios: [0.5, 1.0, 2.0]
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anchor_strides: [4, 8, 16, 32, 64]
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@ -72,7 +77,7 @@ activate_num_classes: 2
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use_sigmoid_cls: True
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# roi_align
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roi_layer: dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
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roi_layer: {type: 'RoIAlign', out_size: 7, mask_out_size: 14, sample_num: 2}
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roi_align_out_channels: 256
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roi_align_featmap_strides: [4, 8, 16, 32]
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roi_align_finest_scale: 56
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@ -127,7 +132,7 @@ base_lr: 0.02
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base_step: 58633
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total_epoch: 13
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warmup_step: 500
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warmup_ratio: 1/3.0
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warmup_ratio: 0.333333
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sgd_momentum: 0.9
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# train
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@ -142,11 +147,11 @@ save_checkpoint_epochs: 1
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keep_checkpoint_max: 12
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save_checkpoint_path: "./"
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mindrecord_dir: "./MindRecord_COCO" # "/home/mask_rcnn/MindRecord_COCO2017_Train"
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mindrecord_dir: "./MindRecord_COCO"
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train_data_type: "train2017"
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val_data_type: "val2017"
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instance_set: "annotations/instances_{}.json"
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coco_classes: ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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coco_classes: ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
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'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
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'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
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@ -160,7 +165,7 @@ coco_classes: ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane
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'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
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'refrigerator', 'book', 'clock', 'vase', 'scissors',
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'teddy bear', 'hair drier', 'toothbrush')
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'teddy bear', 'hair drier', 'toothbrush']
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num_classes: 81
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only_create_dataset: False
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@ -173,7 +178,6 @@ device_num: 1
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rank_id: 0
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# maskrcnn export
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batch_size_export: 2
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file_name: "maskrcnn"
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file_format: "AIR"
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ckpt_file: '/cache/data/cocodataset/ckpt_maskrcnn/mask_rcnn-12_7393.ckpt'
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@ -16,7 +16,6 @@
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"""Evaluation for MaskRcnn"""
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import os
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import time
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import re
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import numpy as np
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from src.model_utils.config import config
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@ -34,13 +33,6 @@ from mindspore.common import set_seed
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set_seed(1)
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lss = [int(re.findall(r'[0-9]+', i)[0]) for i in config.feature_shapes]
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config.feature_shapes = [(lss[2*i], lss[2*i+1]) for i in range(int(len(lss)/2))]
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config.roi_layer = dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
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config.warmup_ratio = 1/3.0
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config.mask_shape = (28, 28)
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def maskrcnn_eval(dataset_path, ckpt_path, ann_file):
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"""MaskRcnn evaluation."""
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ds = create_maskrcnn_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)
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@ -14,7 +14,6 @@
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# ============================================================================
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"""export checkpoint file into air, onnx, mindir models"""
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import re
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import numpy as np
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from src.model_utils.config import config
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from src.model_utils.device_adapter import get_device_id
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@ -23,15 +22,6 @@ from src.maskrcnn.mask_rcnn_r50 import MaskRcnn_Infer
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from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
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lss = [int(re.findall(r'[0-9]+', i)[0]) for i in config.feature_shapes]
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config.feature_shapes = [(lss[2*i], lss[2*i+1]) for i in range(int(len(lss)/2))]
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config.roi_layer = dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
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config.warmup_ratio = 1/3.0
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config.mask_shape = (28, 28)
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train_cls = [i for i in re.findall(r'[a-zA-Z\s]+', config.coco_classes) if i != ' ']
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config.coco_classes = np.array(train_cls)
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config.batch_size = config.batch_size_export
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if not config.enable_modelarts:
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config.ckpt_file = config.ckpt_file_local
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@ -45,6 +35,7 @@ def modelarts_process():
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@moxing_wrapper(pre_process=modelarts_process)
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def export_maskrcnn():
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""" export_maskrcnn """
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config.test_batch_size = config.batch_size
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net = MaskRcnn_Infer(config=config)
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param_dict = load_checkpoint(config.ckpt_file)
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@ -17,7 +17,6 @@
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from __future__ import division
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import os
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import re
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import numpy as np
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from numpy import random
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import cv2
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@ -29,8 +28,6 @@ from mindspore.mindrecord import FileWriter
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from .model_utils.config import config
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config.mask_shape = (28, 28)
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def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
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"""Calculate the ious between each bbox of bboxes1 and bboxes2.
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@ -390,10 +387,7 @@ def create_coco_label(is_training):
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data_type = config.train_data_type
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# Classes need to train or test.
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# train_cls = config.coco_classes
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train_cls = [i for i in re.findall(r'[a-zA-Z\s]+', config.coco_classes) if i != ' ']
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train_cls = np.array(train_cls)
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print(train_cls)
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train_cls = config.coco_classes
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train_cls_dict = {}
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for i, cls in enumerate(train_cls):
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@ -114,7 +114,7 @@ class RcnnCls(nn.Cell):
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self.train_batch_size = batch_size
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self.test_batch_size = cfg.test_batch_size
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self.fpn_cls = FpnCls(self.in_channels, self.rcnn_fc_out_channels, self.num_classes, cfg.roi_layer["out_size"])
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self.fpn_cls = FpnCls(self.in_channels, self.rcnn_fc_out_channels, self.num_classes, cfg.roi_layer.out_size)
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self.relu = P.ReLU()
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self.logicaland = P.LogicalAnd()
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self.loss_cls = P.SoftmaxCrossEntropyWithLogits()
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@ -88,9 +88,9 @@ class SingleRoIExtractor(nn.Cell):
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self.out_channels = out_channels
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self.featmap_strides = featmap_strides
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self.num_levels = len(self.featmap_strides)
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self.out_size = roi_layer['mask_out_size'] if mask else roi_layer['out_size']
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self.out_size = config.roi_layer.mask_out_size if mask else config.roi_layer.out_size
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self.mask = mask
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self.sample_num = roi_layer['sample_num']
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self.sample_num = config.roi_layer.sample_num
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self.roi_layers = self.build_roi_layers(self.featmap_strides)
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self.roi_layers = L.CellList(self.roi_layers)
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@ -17,7 +17,6 @@
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import time
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import os
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import re
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from src.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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@ -40,12 +39,6 @@ from mindspore.communication.management import get_rank, get_group_size
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set_seed(1)
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lss = [int(re.findall(r'[0-9]+', i)[0]) for i in config.feature_shapes]
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config.feature_shapes = [(lss[2*i], lss[2*i+1]) for i in range(int(len(lss)/2))]
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config.roi_layer = dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
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config.warmup_ratio = 1/3.0
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config.mask_shape = (28, 28)
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def modelarts_pre_process():
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def unzip(zip_file, save_dir):
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import zipfile
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@ -189,7 +189,6 @@ python eval.py > eval.log 2>&1 &
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├─__init__.py
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├─beam_search.py
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├─dataset.py
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├─eval_config.py
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├─lr_schedule.py
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├─process_output.py
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├─tokenization.py
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@ -244,15 +243,12 @@ options:
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#### Running Options
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```text
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config.py:
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default_config.yaml:
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transformer_network version of Transformer model: base | large, default is large
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init_loss_scale_value initial value of loss scale: N, default is 2^10
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scale_factor factor used to update loss scale: N, default is 2
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scale_window steps for once updatation of loss scale: N, default is 2000
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optimizer optimizer used in the network: Adam, default is "Adam"
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eval_config.py:
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transformer_network version of Transformer model: base | large, default is large
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data_file data file: PATH
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model_file checkpoint file to be loaded: PATH
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output_file output file of evaluation: PATH
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@ -313,7 +309,7 @@ Parameters for learning rate:
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## [Training Process](#contents)
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- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
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- Set options in `default_config.yaml`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
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- Run `run_standalone_train.sh` for non-distributed training of Transformer model.
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@ -331,7 +327,7 @@ Parameters for learning rate:
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## [Evaluation Process](#contents)
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- Set options in `eval_config.py`. Make sure the 'data_file', 'model_file' and 'output_file' are set to your own path.
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- Set options in `default_config.yaml`. Make sure the 'data_file', 'model_file' and 'output_file' are set to your own path.
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- Run `eval.py` for evaluation of Transformer model.
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@ -422,7 +418,7 @@ There are three random situations:
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- Initialization of some model weights.
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- Dropout operations.
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Some seeds have already been set in train.py to avoid the randomness of dataset shuffle and weight initialization. If you want to disable dropout, please set the corresponding dropout_prob parameter to 0 in src/config.py.
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Some seeds have already been set in train.py to avoid the randomness of dataset shuffle and weight initialization. If you want to disable dropout, please set the corresponding dropout_prob parameter to 0 in default_config.yaml.
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## [ModelZoo Homepage](#contents)
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@ -195,7 +195,6 @@ python eval.py > eval.log 2>&1 &
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├─__init__.py
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├─beam_search.py
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├─dataset.py
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├─eval_config.py
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├─lr_schedule.py
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├─process_output.py
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├─tokenization.py
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@ -250,15 +249,12 @@ options:
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#### 运行选项
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```text
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config.py:
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default_config.yaml:
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transformer_network version of Transformer model: base | large, default is large
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init_loss_scale_value initial value of loss scale: N, default is 2^10
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scale_factor factor used to update loss scale: N, default is 2
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scale_window steps for once updatation of loss scale: N, default is 2000
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optimizer optimizer used in the network: Adam, default is "Adam"
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eval_config.py:
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transformer_network version of Transformer model: base | large, default is large
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data_file data file: PATH
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model_file checkpoint file to be loaded: PATH
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output_file output file of evaluation: PATH
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@ -320,7 +316,7 @@ Parameters for learning rate:
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### 训练过程
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- 在`config.py`中设置选项，包括loss_scale、学习率和网络超参数。点击[这里](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html)查看更多数据集信息。
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- 在`default_config.yaml`中设置选项，包括loss_scale、学习率和网络超参数。点击[这里](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html)查看更多数据集信息。
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- 运行`run_standalone_train.sh`，进行Transformer模型的非分布式训练。
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@ -338,7 +334,7 @@ Parameters for learning rate:
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### 评估过程
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- 在`eval_config.py`中设置选项。确保已设置了‘data_file'、'model_file’和'output_file'文件路径。
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- 在`default_config.yaml`中设置选项。确保已设置了‘data_file'、'model_file’和'output_file'文件路径。
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- 运行`eval.py`，评估Transformer模型。
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@ -429,7 +425,7 @@ bash run_infer_310.sh [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
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- 初始化部分模型权重
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- 随机失活运行
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train.py已经设置了一些种子，避免数据集轮换和权重初始化的随机性。若需关闭随机失活，将src/config.py中相应的dropout_prob参数设置为0。
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train.py已经设置了一些种子，避免数据集轮换和权重初始化的随机性。若需关闭随机失活，将default_config.yaml中相应的dropout_prob参数设置为0。
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## ModelZoo主页
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@ -16,8 +16,8 @@
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if [ $# != 5 ] ; then
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echo "=============================================================================================================="
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echo "Please run the script as: "
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echo "sh run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_PATH RANK_TABLE_FILE CONFIG_PATH"
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echo "for example: sh run_distribute_pretrain.sh 8 52 /path/ende-l128-mindrecord00 /path/hccl.json ./default_config_large.yaml"
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echo "sh run_distribute_train_ascend.sh DEVICE_NUM EPOCH_SIZE DATA_PATH RANK_TABLE_FILE CONFIG_PATH"
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echo "for example: sh run_distribute_train_ascend.sh 8 52 /path/ende-l128-mindrecord00 /path/hccl.json ./default_config_large.yaml"
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echo "It is better to use absolute path."
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echo "=============================================================================================================="
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exit 1;
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@ -64,8 +64,7 @@ do
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--checkpoint_path="" \
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--save_checkpoint_steps=2500 \
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--save_checkpoint_num=30 \
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--data_path=$DATA_PATH \
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--bucket_boundaries=[16,32,48,64,128] > log.txt 2>&1 &
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--data_path=$DATA_PATH > log.txt 2>&1 &
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cd ../
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done
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cd ..
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@ -17,7 +17,7 @@ if [ $# != 4 ] ; then
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echo "=============================================================================================================="
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||||
echo "Please run the script as: "
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echo "sh run_distribute_train_gpu.sh DEVICE_NUM EPOCH_SIZE DATA_PATH CONFIG_PATH"
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echo "for example: sh run_distribute_pretrain.sh 8 55 /path/ende-l128-mindrecord00 ./default_config_large_gpu.yaml"
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echo "for example: sh run_distribute_train_gpu.sh 8 55 /path/ende-l128-mindrecord00 ./default_config_large_gpu.yaml"
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echo "It is better to use absolute path."
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echo "=============================================================================================================="
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exit 1;
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@ -47,5 +47,4 @@ mpirun -n $RANK_SIZE \
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--checkpoint_path="" \
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--save_checkpoint_steps=2500 \
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--save_checkpoint_num=30 \
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--data_path=$DATA_PATH \
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--bucket_boundaries=[16,32,48,64,128] > log.txt 2>&1 &
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--data_path=$DATA_PATH > log.txt 2>&1 &
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@ -48,8 +48,7 @@ if [ $DEVICE_TARGET == 'Ascend' ];then
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--checkpoint_path="" \
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--save_checkpoint_steps=2500 \
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--save_checkpoint_num=30 \
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--data_path=$DATA_PATH \
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--bucket_boundaries=[16,32,48,64,128] > log.txt 2>&1 &
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--data_path=$DATA_PATH > log.txt 2>&1 &
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elif [ $DEVICE_TARGET == 'GPU' ];then
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export CUDA_VISIBLE_DEVICES="$2"
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||||
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@ -64,8 +63,7 @@ elif [ $DEVICE_TARGET == 'GPU' ];then
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--checkpoint_path="" \
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||||
--save_checkpoint_steps=2500 \
|
||||
--save_checkpoint_num=30 \
|
||||
--data_path=$DATA_PATH \
|
||||
--bucket_boundaries=[16,32,48,64,128] > log.txt 2>&1 &
|
||||
--data_path=$DATA_PATH > log.txt 2>&1 &
|
||||
else
|
||||
echo "Not supported device target."
|
||||
fi
|
||||
|
|
|
|||
Loading…
Reference in New Issue