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transfer_config.yaml
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transfer_config.yaml
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# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unlesee you know exactly what you are doing)
enable_modelarts: False
# url for modelarts
data_url: ""
train_url: ""
checkpoint_url: ""
# path for local
data_path: ""
output_path: ""
load_path: ""
device_target: "CPU"
enable_profiling: False
# ======================================================================================
# common options
num_classes: 5
label_smooth_factor: 0.1
model_size: "2.0x"
device_id: 0
dataset_path: "./flower_photos/"
# ======================================================================================
# Training options
is_transfer: True
epoch_size: 100
save_ckpt_path: "./ckpt/"
save_checkpoint: True
amp_level: "O0"
is_distributed: False
resume: "/"
# Dataset config
batch_size: 128
# learning rate config
decay_method: "cosine"
lr_init: 0.00
lr_max: 0.50
lr_end: 0.00
warmup_epochs: 4
loss_scale: 1024
# optimization config
weight_decay: 0.00004
momentum: 0.9
# ======================================================================================
# Eval options
ckpt_path: "./ckpt/shufflenetv1_transfer_best.ckpt"
# ======================================================================================
# export options
file_name: "shufflenetv1"
file_format: "MINDIR"
---
# Help description for each configuration
enable_modelarts: "Whether training on modelarts default: False"
data_url: "Url for modelarts"
train_url: "Url for modelarts"
data_path: "The location of input data"
output_pah: "The location of the output file"
device_target: "device id of GPU or Ascend. (Default: None)"
enable_profiling: "Whether enable profiling while training default: False"
is_distributed: "distributed training"
resume: "resume training with existed checkpoint"
model_size: "shuffleNetV1 model size choices 2.0x, 1.5x, 1.0x, 0.5x"
device_id: "device id"
file_name: "output file name"
file_format: "file format choices [AIR MINDIR ONNX]"