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调试

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This commit is contained in:
552068321@qq.com 2022-11-08 08:59:45 +08:00
parent a78ef10b8a
commit 9e5134628e
3 changed files with 2 additions and 8 deletions
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1
app/controller/AlgorithmController.py
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@ -249,7 +249,6 @@ def train_R0DY(params_str, id):
params.read_from_str(params_str) params.read_from_str(params_str)
print(params.get('device').default) print(params.get('device').default)
data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('CLASS_NAMES').value) data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('CLASS_NAMES').value)
print('---------------------',data_list)
weights = params.get('resumeModPath').value # 初始化模型绝对路径 weights = params.get('resumeModPath').value # 初始化模型绝对路径
img_size = params.get('img_size').value img_size = params.get('img_size').value
savemodel = os.path.splitext(params.get('saveModDir').value)[0] + '_' + str(img_size) + '.pt' # 模型命名加上图像参数 savemodel = os.path.splitext(params.get('saveModDir').value)[0] + '_' + str(img_size) + '.pt' # 模型命名加上图像参数

4
app/yolov5/train_server.py
View File

@ -77,7 +77,6 @@ def yaml_rewrite(file='data.yaml',data_list=[]):
f.close() f.close()
classes = class_dict["classes"] classes = class_dict["classes"]
nc, names = (len(classes), classes) nc, names = (len(classes), classes)
print("names##############",names)
coco_dict['train'] = data_list[0] coco_dict['train'] = data_list[0]
coco_dict['val'] = data_list[1] coco_dict['val'] = data_list[1]
label_name = {} label_name = {}
@ -85,7 +84,6 @@ def yaml_rewrite(file='data.yaml',data_list=[]):
label_name[i] = names[i] label_name[i] = names[i]
#print(label_name) #print(label_name)
coco_dict['names'] = label_name coco_dict['names'] = label_name
print('coco_names################:',coco_dict['names'])
with open(file, 'w') as f: with open(file, 'w') as f:
yaml.safe_dump(coco_dict, f, sort_keys=False) yaml.safe_dump(coco_dict, f, sort_keys=False)
@ -306,7 +304,7 @@ def train(hyp, opt, device, data_list,id,callbacks): # hyp is path/to/hyp.yaml
num_train_img=train_num, num_train_img=train_num,
train_mod_savepath=best) train_mod_savepath=best)
@algorithm_process_value_websocket() # @algorithm_process_value_websocket()
def report_cellback(i, num_epochs, reportAccu): def report_cellback(i, num_epochs, reportAccu):
report.rate_of_progess = ((i + 1) / num_epochs) * 100 report.rate_of_progess = ((i + 1) / num_epochs) * 100
report.progress = (i + 1) report.progress = (i + 1)

5
app/yolov5/utils/dataloaders.py
View File

@ -428,13 +428,11 @@ class LoadImagesAndLabels(Dataset):
# Check cache # Check cache
self.label_files = img2label_paths(self.im_files) # labels self.label_files = img2label_paths(self.im_files) # labels
print('labelfile############',self.label_files)
cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
try: try:
cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict
assert cache['version'] == self.cache_version # matches current version assert cache['version'] == self.cache_version # matches current version
assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical has
print('load_cache#########')
except Exception: except Exception:
cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops
@ -449,7 +447,6 @@ class LoadImagesAndLabels(Dataset):
# Read cache # Read cache
[cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items
print('@@@@@@@@@@@@@@@@@@@@@')
labels, shapes, self.segments = zip(*cache.values()) labels, shapes, self.segments = zip(*cache.values())
nl = len(np.concatenate(labels, 0)) # number of labels nl = len(np.concatenate(labels, 0)) # number of labels
assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}' assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}'