yangxue0827
commented
2 years ago Closed sangheonEN closed 2 years ago
yangxue0827
commented
2 years ago yangxue0827
commented
2 years ago It seems that there is no problem, you can compare it with the above.
sangheonEN
commented
2 years ago Thanks you. and one more question.
There is no background label in the dataset I have, but when adding DATASET information in label_dict.py, can I do it in the form below?
Can it be executed even if there is no label at the 0th index?
"back_ground = 0"
When I run it, draw_box_in_img.py throws an error txt = self.label_name_map[label] + ':' + str(round(score, 2)) KeyError: 5 .
yangxue0827
commented
2 years ago "back_ground = 0" is required
sangheonEN
commented
2 years ago If there is no background label in the annotation data, can it not be executed?
yangxue0827
commented
2 years ago I am not sure.
sangheonEN
commented
2 years ago I get the same error even after adding.
yangxue0827
commented
2 years ago Check your DTASET_NAME, and make sure the code is looking for this name_label_map.
yangxue0827
commented
2 years ago May be there is another dataset name startwith LEE before this one.
sangheonEN
commented
2 years ago Even though I set DATASET_NAME = "LEE" in cfgs.py, it is strange; Where can I find the data set to start with?
And don't you load the input data with the data converted to tfrecord?
sangheonEN
commented
2 years ago Total Error comment below:
Traceback (most recent call last): File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call return fn(*args) File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1319, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.UnknownError: KeyError: 5 Traceback (most recent call last):
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 207, in call ret = func(*args)
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 249, in draw_boxes_with_label_and_scores self.draw_label_with_scores(draw_obj, box, a_label, a_score, color='White')
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 163, in draw_label_with_scores txt = self.label_name_map[label] + ':' + str(round(score, 2))
KeyError: 5
[[{{node tower_0/PyFunc_1}}]]During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 164, in
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 207, in call ret = func(*args)
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 249, in draw_boxes_with_label_and_scores self.draw_label_with_scores(draw_obj, box, a_label, a_score, color='White')
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 163, in draw_label_with_scores txt = self.label_name_map[label] + ':' + str(round(score, 2))
KeyError: 5
[[node tower_0/PyFunc_1 (defined at /home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/show_box_in_tensor.py:66) ]]Caused by op 'tower_0/PyFunc_1', defined at:
File "train.py", line 164, in
UnknownError (see above for traceback): KeyError: 5 Traceback (most recent call last):
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 207, in call ret = func(*args)
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 249, in draw_boxes_with_label_and_scores self.draw_label_with_scores(draw_obj, box, a_label, a_score, color='White')
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 163, in draw_label_with_scores txt = self.label_name_map[label] + ':' + str(round(score, 2))
KeyError: 5
yangxue0827
commented
2 years ago paste your label_dict.py here.
sangheonEN
commented
2 years ago # -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
class_names = [
'back_ground', 'person', 'bicycle', 'car', 'motorcycle',
'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench',
'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant',
'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove',
'skateboard', 'surfboard', 'tennis racket', 'bottle',
'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli',
'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet',
'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator',
'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']
classes_originID = {
'person': 1, 'bicycle': 2, 'car': 3, 'motorcycle': 4,
'airplane': 5, 'bus': 6, 'train': 7, 'truck': 8, 'boat': 9,
'traffic light': 10, 'fire hydrant': 11, 'stop sign': 13,
'parking meter': 14, 'bench': 15, 'bird': 16, 'cat': 17,
'dog': 18, 'horse': 19, 'sheep': 20, 'cow': 21, 'elephant': 22,
'bear': 23, 'zebra': 24, 'giraffe': 25, 'backpack': 27,
'umbrella': 28, 'handbag': 31, 'tie': 32, 'suitcase': 33,
'frisbee': 34, 'skis': 35, 'snowboard': 36, 'sports ball': 37,
'kite': 38, 'baseball bat': 39, 'baseball glove': 40,
'skateboard': 41, 'surfboard': 42, 'tennis racket': 43,
'bottle': 44, 'wine glass': 46, 'cup': 47, 'fork': 48,
'knife': 49, 'spoon': 50, 'bowl': 51, 'banana': 52, 'apple': 53,
'sandwich': 54, 'orange': 55, 'broccoli': 56, 'carrot': 57,
'hot dog': 58, 'pizza': 59, 'donut': 60, 'cake': 61,
'chair': 62, 'couch': 63, 'potted plant': 64, 'bed': 65,
'dining table': 67, 'toilet': 70, 'tv': 72, 'laptop': 73,
'mouse': 74, 'remote': 75, 'keyboard': 76, 'cell phone': 77,
'microwave': 78, 'oven': 79, 'toaster': 80, 'sink': 81,
'refrigerator': 82, 'book': 84, 'clock': 85, 'vase': 86,
'scissors': 87, 'teddy bear': 88, 'hair drier': 89,
'toothbrush': 90}
class LabelMap(object):
def __init__(self, cfgs):
self.cfgs = cfgs
def coco_name2abel(self):
# originID_classes = {item: key for key, item in classes_originID.items()}
name_label_map = dict(zip(class_names, range(len(class_names))))
return name_label_map
def name2label(self):
if self.cfgs.DATASET_NAME in ['WIDER', 'FDDB']:
name_label_map = {
'back_ground': 0,
'face': 1
}
elif self.cfgs.DATASET_NAME in ['ICDAR2015', 'MSRA-TD500', 'MLT', 'Total_Text']:
name_label_map = {
'back_ground': 0,
'text': 1
}
elif self.cfgs.DATASET_NAME in ['HRSC2016', 'HRSID']:
name_label_map = {
'back_ground': 0,
'ship': 1
}
elif self.cfgs.DATASET_NAME.startswith('OHD-SJTU-ALL'):
name_label_map = {
'back_ground': 0,
'small-vehicle': 1,
'ship': 2,
'plane': 3,
'large-vehicle': 4,
'helicopter': 5,
'harbor': 6,
}
elif self.cfgs.DATASET_NAME.startswith('OHD-SJTU'):
name_label_map = {
'back_ground': 0,
'ship': 1,
'plane': 2
}
elif self.cfgs.DATASET_NAME.startswith('SSDD++'):
name_label_map = {
'back_ground': 0,
'ship': 1
}
elif self.cfgs.DATASET_NAME.startswith('SKU110K-R'):
name_label_map = {
'back_ground': 0,
'commodity': 1
}
elif self.cfgs.DATASET_NAME.startswith('UCAS-AOD'):
name_label_map = {
'back_ground': 0,
'car': 1,
'plane': 2
}
elif self.cfgs.DATASET_NAME.startswith('DOTA'):
name_label_map = {
'back_ground': 0,
'roundabout': 1,
'tennis-court': 2,
'swimming-pool': 3,
'storage-tank': 4,
'soccer-ball-field': 5,
'small-vehicle': 6,
'ship': 7,
'plane': 8,
'large-vehicle': 9,
'helicopter': 10,
'harbor': 11,
'ground-track-field': 12,
'bridge': 13,
'basketball-court': 14,
'baseball-diamond': 15
}
if self.cfgs.DATASET_NAME == 'DOTA1.5':
name_label_map['container-crane'] = 16
if self.cfgs.DATASET_NAME == 'DOTA2.0':
name_label_map['container-crane'] = 16
name_label_map['airport'] = 17
name_label_map['helipad'] = 18
elif self.cfgs.DATASET_NAME == 'coco':
name_label_map = self.coco_name2abel()
elif self.cfgs.DATASET_NAME == 'pascal':
name_label_map = {
'back_ground': 0,
'aeroplane': 1,
'bicycle': 2,
'bird': 3,
'boat': 4,
'bottle': 5,
'bus': 6,
'car': 7,
'cat': 8,
'chair': 9,
'cow': 10,
'diningtable': 11,
'dog': 12,
'horse': 13,
'motorbike': 14,
'person': 15,
'pottedplant': 16,
'sheep': 17,
'sofa': 18,
'train': 19,
'tvmonitor': 20
}
elif self.cfgs.DATASET_NAME.startswith('DIOR'):
name_label_map = {
'back_ground': 0,
'airplane': 1,
'airport': 2,
'baseballfield': 3,
'basketballcourt': 4,
'bridge': 5,
'chimney': 6,
'dam': 7,
'Expressway-Service-area': 8,
'Expressway-toll-station': 9,
'golffield': 10,
'groundtrackfield': 11,
'harbor': 12,
'overpass': 13,
'ship': 14,
'stadium': 15,
'storagetank': 16,
'tenniscourt': 17,
'trainstation': 18,
'vehicle': 19,
'windmill': 20,
# 'swimmingpool': 21,
# 'soccerballfield': 22,
# 'volleyballcourt': 23,
# 'roundabout': 24,
# 'container-crane': 25,
# 'helipad': 26,
# 'rugbyfield': 27
}
elif self.cfgs.DATASET_NAME == 'bdd100k':
name_label_map = {
'back_ground': 0,
'bus': 1,
'traffic light': 2,
'traffic sign': 3,
'person': 4,
'bike': 5,
'truck': 6,
'motor': 7,
'car': 8,
'train': 9,
'rider': 10
}
elif self.cfgs.DATASET_NAME == 'LEE':
name_label_map = {
'back_ground': 0,
'container': 1,
'oil-tanker': 2,
'aircraft-carrier': 3,
'maritime-vessels': 4,
'war-ship': 5
}
else:
name_label_map = {}
assert 'please set label dict!'
return name_label_map
def label2name(self):
label_name_map = {}
for name, label in self.name2label().items():
label_name_map[label] = name
return label_name_map
Also paste your cfgs.py here.
sangheonEN
commented
2 years ago # -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
from configs._base_.models.retinanet_r50_fpn import *
# from configs._base_.datasets.dota_detection import *
from configs._base_.datasets.lee_detection import *
from configs._base_.schedules.schedule_1x import *
from alpharotate.utils.pretrain_zoo import PretrainModelZoo
# schedule
BATCH_SIZE = 1
GPU_GROUP = "0"
NUM_GPU = len(GPU_GROUP.strip().split(','))
SAVE_WEIGHTS_INTE = 20673
DECAY_STEP = np.array(DECAY_EPOCH, np.int32) * SAVE_WEIGHTS_INTE
MAX_ITERATION = SAVE_WEIGHTS_INTE * MAX_EPOCH
WARM_SETP = int(WARM_EPOCH * SAVE_WEIGHTS_INTE)
# dataset
DATASET_NAME = 'LEE'
# model
# backbone
pretrain_zoo = PretrainModelZoo()
PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
# bbox head
METHOD = 'R'
ANCHOR_RATIOS = [1, 1 / 3., 3.]
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 1.0 / 5.0
REG_LOSS_MODE = None
VERSION = 'RetinaNet_DOTA_1x_20210617'
"""
RetinaNet-R + 90
FLOPs: 512292484; Trainable params: 34524216
AP50:95: [0.6500133314849993, 0.6221115492957145, 0.5783876659070408, 0.5151213008971589, 0.43878296014912604,
0.3368465304363474, 0.22462667183919535, 0.11392563249065944, 0.03363650833577883, 0.002065178825137116]
mmAP: 0.35155173296611575
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
{'0.95': {'bridge': 0.004132231404958678, 'small-vehicle': 9.587038324185702e-05, 'roundabout': 0.0010822510822510823, 'tennis-court': 0.0213903743315508, 'ground-track-field': 0.0, 'basketball-court': 0.0, 'mAP': 0.002065178825137116, 'ship': 0.00033732501264968796, 'large-vehicle': 0.0002361275088547816, 'plane': 0.0008340283569641369, 'helicopter': 0.0, 'harbor': 9.362419249133976e-05, 'baseball-diamond': 0.0, 'storage-tank': 0.002775850104094379, 'swimming-pool': 0.0, 'soccer-ball-field': 0.0}, '0.6': {'bridge': 0.2180613029626103, 'small-vehicle': 0.48955296733750253, 'roundabout': 0.5743715824779693, 'tennis-court': 0.8988701575269984, 'ground-track-field': 0.499228771859173, 'basketball-court': 0.5809814425549822, 'mAP': 0.5783876659070408, 'ship': 0.7601302692501211, 'large-vehicle': 0.7307283640874089, 'plane': 0.8942021611064075, 'helicopter': 0.3072975896469221, 'harbor': 0.3269043269672149, 'baseball-diamond': 0.6129045619299885, 'storage-tank': 0.734913760456796, 'swimming-pool': 0.4140610594981138, 'soccer-ball-field': 0.6336066709434047}, '0.8': {'bridge': 0.09090909090909091, 'small-vehicle': 0.11325009395748946, 'roundabout': 0.21515151515151515, 'tennis-court': 0.7921941866364417, 'ground-track-field': 0.2096861471861472, 'basketball-court': 0.24332649806334017, 'mAP': 0.22462667183919535, 'ship': 0.26291307136684317, 'large-vehicle': 0.18348068300629328, 'plane': 0.40802398035426435, 'helicopter': 0.0303030303030303, 'harbor': 0.03896103896103896, 'baseball-diamond': 0.14207792207792208, 'storage-tank': 0.3159039855551453, 'swimming-pool': 0.014925373134328358, 'soccer-ball-field': 0.3082934609250399}, '0.75': {'bridge': 0.10214211076280041, 'small-vehicle': 0.19545376634203668, 'roundabout': 0.28496697538050925, 'tennis-court': 0.8550385251444838, 'ground-track-field': 0.31532093174321907, 'basketball-court': 0.4440063904349619, 'mAP': 0.3368465304363474, 'ship': 0.47083599337171966, 'large-vehicle': 0.3676002962555352, 'plane': 0.6242575391640972, 'helicopter': 0.04145169898594556, 'harbor': 0.09856749311294766, 'baseball-diamond': 0.2940265227697746, 'storage-tank': 0.4576325952192638, 'swimming-pool': 0.11452184179456906, 'soccer-ball-field': 0.3868752760633457}, '0.65': {'bridge': 0.1715728715728716, 'small-vehicle': 0.38714087806405156, 'roundabout': 0.48752732344925004, 'tennis-court': 0.8963243210004948, 'ground-track-field': 0.46565355697662375, 'basketball-court': 0.55128536051835, 'mAP': 0.5151213008971589, 'ship': 0.730472420280297, 'large-vehicle': 0.6571471502556688, 'plane': 0.8841534431766825, 'helicopter': 0.24147205105658032, 'harbor': 0.23720190635365604, 'baseball-diamond': 0.49210312524188676, 'storage-tank': 0.6753273086899294, 'swimming-pool': 0.30443298751756787, 'soccer-ball-field': 0.5450048093034745}, '0.5': {'bridge': 0.2812488756837439, 'small-vehicle': 0.6122705620423831, 'roundabout': 0.6293850696709108, 'tennis-court': 0.9013591967565613, 'ground-track-field': 0.5750856173584699, 'basketball-court': 0.6068251479708417, 'mAP': 0.6500133314849993, 'ship': 0.8601726316408385, 'large-vehicle': 0.7834049829446229, 'plane': 0.8961770992768222, 'helicopter': 0.3507698724777362, 'harbor': 0.5232626569936892, 'baseball-diamond': 0.6772717890839638, 'storage-tank': 0.7775776682818869, 'swimming-pool': 0.5425412825833318, 'soccer-ball-field': 0.7328475195091895}, '0.7': {'bridge': 0.12460815047021945, 'small-vehicle': 0.2845134660892606, 'roundabout': 0.3834644013719063, 'tennis-court': 0.8925703337005814, 'ground-track-field': 0.38178255494732427, 'basketball-court': 0.5012620086155817, 'mAP': 0.43878296014912604, 'ship': 0.6196205362182818, 'large-vehicle': 0.5254475720737497, 'plane': 0.7717130392038616, 'helicopter': 0.21916545239320936, 'harbor': 0.148302763837693, 'baseball-diamond': 0.394255080184464, 'storage-tank': 0.5963860132836875, 'swimming-pool': 0.22030141020234578, 'soccer-ball-field': 0.5183516196447231}, '0.55': {'bridge': 0.2446933360819818, 'small-vehicle': 0.5572753205907381, 'roundabout': 0.6127007450546773, 'tennis-court': 0.9013591967565613, 'ground-track-field': 0.5460695154831853, 'basketball-court': 0.598903178921024, 'mAP': 0.6221115492957145, 'ship': 0.8395840401868254, 'large-vehicle': 0.747613676166832, 'plane': 0.895943123347051, 'helicopter': 0.3439557489426765, 'harbor': 0.4312764134413297, 'baseball-diamond': 0.6530921050594436, 'storage-tank': 0.7668861840981078, 'swimming-pool': 0.4949977385816317, 'soccer-ball-field': 0.6973229167236527}, '0.9': {'bridge': 0.004132231404958678, 'small-vehicle': 0.003952569169960474, 'roundabout': 0.012396694214876032, 'tennis-court': 0.2869467055233188, 'ground-track-field': 0.045454545454545456, 'basketball-court': 0.012121212121212121, 'mAP': 0.03363650833577883, 'ship': 0.0303030303030303, 'large-vehicle': 0.00306894905544568, 'plane': 0.024475524475524476, 'helicopter': 0.0, 'harbor': 0.00267379679144385, 'baseball-diamond': 0.012987012987012986, 'storage-tank': 0.0202020202020202, 'swimming-pool': 0.0003787878787878788, 'soccer-ball-field': 0.045454545454545456}, '0.85': {'bridge': 0.025974025974025972, 'small-vehicle': 0.0303030303030303, 'roundabout': 0.11091872433199819, 'tennis-court': 0.6556119429916903, 'ground-track-field': 0.08181818181818182, 'basketball-court': 0.1098124098124098, 'mAP': 0.11392563249065944, 'ship': 0.11035613870665417, 'large-vehicle': 0.028601992732106313, 'plane': 0.16961143393022934, 'helicopter': 0.006493506493506493, 'harbor': 0.012987012987012986, 'baseball-diamond': 0.06296914095079233, 'storage-tank': 0.14633945289682992, 'swimming-pool': 0.0036784025223331584, 'soccer-ball-field': 0.1534090909090909}, 'mmAP': 0.35155173296611575}
"""
lee_detection.py
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
DATASET_NAME = 'LEE'
CLASS_NUM = 6
PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR
PIXEL_MEAN_ = [0.485, 0.456, 0.406] # resnet mean
PIXEL_STD = [0.229, 0.224, 0.225]
IMG_SHORT_SIDE_LEN = 800
IMG_MAX_LENGTH = 800
# data augmentation
IMG_ROTATE = False
RGB2GRAY = False
VERTICAL_FLIP = False
HORIZONTAL_FLIP = True
IMAGE_PYRAMID = False
One thing to consider is that the back_ground label is not defined in the dataset I have created. In actual annotation label data, only labels 1 to 5 exist.
yangxue0827
commented
2 years ago back_ground represents the background area, which is included in all images. It doesn't require you to annotate.
yangxue0827
commented
2 years ago CLASS_NUM = 6 --> CLASS_NUM = 5
sangheonEN
commented
2 years ago
CLASS_NUM = 6-->CLASS_NUM = 5
same error
sangheonEN
commented
2 years ago txt = self.label_name_map[label] + ':' + str(round(score, 2))
score is 0.xx? so, 0.1, 0.2, 0.3, 0.4 ...
Not 1, 2, 3, 4
yangxue0827
commented
2 years ago the error is caused by label. You may need to print the DATASET_NAME to ensure which name_label_map and label_name_map are used.
sangheonEN
commented
2 years ago 'motor': 7,
'car': 8,
'train': 9,
'rider': 10
}
elif self.cfgs.DATASET_NAME == 'LEE':
name_label_map = {
'back_ground': 0,
'container': 1,
'oil-tanker': 2,
'aircraft-carrier': 3,
'maritime-vessels': 4,
'war-ship': 5
}
else:
name_label_map = {}
assert 'please set label dict!'
while(1):
print(name_label_map)
return name_label_mapprint name_label_map in loop
however, not print name_label_map
yangxue0827
commented
2 years ago print at def __init__(self, cfgs):
yangxue0827
commented
2 years ago print(self.cfgs.DATASET_NAME)
sangheonEN
commented
2 years ago print at
def __init__(self, cfgs):
i don't know this method. def init(self, cfgs): build? any .py code?
yangxue0827
commented
2 years ago class LabelMap(object):
def __init__(self, cfgs):
self.cfgs = cfgs
print(self.cfgs.DATASET_NAME)# -*- coding:utf-8 -*-
# Author: Xue Yang <yangxue-2019-sjtu@sjtu.edu.cn>, <yangxue0827@126.com>
# License: Apache-2.0 license
# Copyright (c) SJTU. ALL rights reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
sys.path.append("../../")
from tools.train_base import Train
from configs import cfgs
from alpharotate.libs.models.detectors.retinanet import build_whole_network
from alpharotate.libs.utils.coordinate_convert import backward_convert, get_horizen_minAreaRectangle
from alpharotate.utils.pretrain_zoo import PretrainModelZoo
os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP
class TrainRetinaNet(Train):
def get_gtboxes_and_label(self, gtboxes_and_label_h, gtboxes_and_label_r, num_objects):
return gtboxes_and_label_h[:int(num_objects), :].astype(np.float32), \
gtboxes_and_label_r[:int(num_objects), :].astype(np.float32)
def main(self):
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
print(self.cfgs.DATASET_NAME)
with tf.Graph().as_default() as graph, tf.device('/cpu:0'):
d; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/home/jeongseunghyun/Rotation_Detector/RotationDetection-main
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
LEE
I Train retinanet. where use name_label_map and label_name_map??
yangxue0827
commented
2 years ago print your name_label_map and label_name_map at label_dict.py.
sangheonEN
commented
2 years ago name_label_map = {
'back_ground': 0,
'bus': 1,
'traffic light': 2,
'traffic sign': 3,
'person': 4,
'bike': 5,
'truck': 6,
'motor': 7,
'car': 8,
'train': 9,
'rider': 10
}
elif self.cfgs.DATASET_NAME == 'LEE':
name_label_map = {
'back_ground': 0,
'container': 1,
'oil-tanker': 2,
'aircraft-carrier': 3,
'maritime-vessels': 4,
'war-ship': 5
}
else:
name_label_map = {}
assert 'please set label dict!'
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
print(name_label_map.keys())
return name_label_map
def label2name(self):
label_name_map = {}
for name, label in self.name2label().items():
label_name_map[label] = name
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
print(label_name_map.key())
return label_name_mapbut it is not print label_both name_map and name_label_map.
i think the code doesn't seem to run to that part.
yangxue0827
commented
2 years ago it's really weird
sangheonEN
commented
2 years ago it's really weird
i think so.. it's late today and next day i will try again... and then i will comment here. thank you. please not close the issue. I must address this problem. hmm. If you are develop env is anaconda? my env is anaconda.
yangxue0827
commented
2 years ago OK. This problem should have little to do with the development environment.
sangheonEN
commented
2 years ago UnknownError (see above for traceback): KeyError: 5 Traceback (most recent call last):
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py", line 207, in call ret = func(*args)
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 249, in draw_boxes_with_label_and_scores self.draw_label_with_scores(draw_obj, box, a_label, a_score, color='White')
File "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py", line 163, in draw_label_with_scores txt = self.label_name_map[label] + ':' + str(round(score, 2))
This problem path is "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/utils/draw_box_in_img.py"
**so, I aproach that path. and then i find the path that "/home/jeongseunghyun/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/libs/label_name_dict/label_dict.py"
In conclusion, i went ahead and made only changes to the label_dict.py code stored in the local path. However, when i installed alpharotate, I installed it with the pip install alpharotate command in the anaconda virtual env, so i had to change the alpharotate label_dict.py that exists in the anaconda env path.**
yangxue0827
commented
2 years ago try
pip uninstall alpharotate
to use the local alpharotate folder
sangheonEN
commented
2 years ago try
pip uninstall alpharotateto use the local alpharotate folder
I solve the error using below solution
I installed it with the pip install alpharotate command in the anaconda virtual env, so i had to change the alpharotate label_dict.py that exists in the anaconda env path.
and, first issue's solution is same thing.
147 Line. feature, pa_mask = self.build_backbone(input_img_batch)
If i change the code and tools, I must use the code that below path. "/anaconda3/envs/rotation_detector_2/lib/python3.6/site-packages/alpharotate/"
yangxue0827
commented
2 years ago pip install alpharotate is not suitable for dev.
Problem path: RotationDetection/alpharotate/libs/models/detectors/scrdet/build_whole_network.py
147 Line. feature, pa_mask = self.build_backbone(input_img_batch)
Since only one variable is returned, why store it in two variables?
It is error that "ValueError: too many values to unpack"
what is pa_mask parameter's mean and rule?