fit_generator(self, generator, steps_per_epoch, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, class_weight=None, max_q_size=10, workers=1, pickle_safe=False, initial_epoch=0) # 案例: def generate_arrays_from_file(path): while 1: f = open(path) ...
WeightDataType ExecuteDeviceSelectMode TuningStrategy ImageType ExecuteDevice DeviceMemoryReusePlan 结构体类 AippChannelSwapPara AippCropPara AippCscPara AippDtcPara AippInputShape AippPaddingPara AippResizePara BuildOptions DynamicShapeConfig NativeHandle 模型管家V2接口 Overview 模...
classes=1000):# 检查weight与分类设置是否正确ifweightsnotin{'imagenet',None}:raiseValueError('The `weights` argument should be either ''`None` (random initialization) or `imagenet` ''(pre-training on ImageNet).')ifweights =='imagenet'andinclude_topandclasses !=1000:raiseValueError('If using...
测试时,使用多卡加载模型时,删掉'module.'前缀;或者用单卡加载模型进行测试。 # 删掉'module.'前缀model_cascade1.load_state_dict(get_loaded_dict(weight_c1), strict=True)defget_loaded_dict(weight_path): state_dict = torch.load(weight_path)# 检查是否有 'module.' 前缀has_module_prefix =any(key...
class LeNet5(nn.Cell): # 定义算子 def __init__(self, num_class=10, num_channel=3): super(LeNet5_2, self).__init__() self.conv1 = nn.Conv2d(num_channel, 16, 3, stride=1, pad_mode='pad', padding=1, weight_init=Normal(0.02)) self.conv2 = nn.Conv2d(16, 32, 3, str...
🍊Lightweight Training: Supports lightweight fine-tuning methods like LoRA, QLoRA, DoRA, LoRA+, ReFT, RS-LoRA, LLaMAPro, Adapter, GaLore, Q-Galore, LISA, UnSloth, Liger-Kernel. Distributed Training: Supports distributed data parallel (DDP), device_map simple model parallelism, DeepSpeed ZeRO...
TrainLimit( batch_size_limit=None, max_seq_len_options=None, epoch_limit=None, learning_rate_limit=(3e-05, 0.001), log_steps_limit=None, warmup_ratio_limit=None, weight_decay_limit=None, lora_rank_options=None, lora_alpha_options=None, lora_dropout_limit=None, scheduler_name_options=Non...
--train_data ./toy_finetune_data.jsonl \--learning_rate6e-5\--fp16 \--num_train_epochs5\--per_device_train_batch_size {batch size;set1fortoy data} \--gradient_accumulation_steps4\--dataloader_drop_last True \--train_group_si...
model_json=self.model.to_json()withopen(model_path+"/model_json.json","w")asjson_file:json_file.write(model_json)self.model.save_weights(model_path+'/model_weight.h5')self.model.save(model_path+'/model.h5') 识别模块 加载权重及模型结构 ...
代码非常的简单,首先rpartition()方法,使用右侧第一个分隔符将字符串target分为三个部分,分隔符左侧,分隔符,分隔符右侧。以"features.conv1.weight"为例,module_path = "features.conv1",param_name = "weight",_代表不需要这个参数,不保存,就是那个分隔符"."。