#我没有看明白load_checkpoint参数和checkpoint_name_or_path参数哪个是加载训练生成的权重文件
```yaml
seed: 0
run_mode: 'eval'
output_dir: './output' # path to save checkpoint/strategy
load_checkpoint: ''
src_strategy_path_or_dir: ''
auto_trans_ckpt: False # If true, auto transform load_checkpoint to load in distributed model
only_save_strategy: False
resume_training: False
# context
context:
mode: 0 #0--Graph Mode; 1--Pynative Mode
device_target: "Ascend"
enable_graph_kernel: False
graph_kernel_flags: "--opt_level=0"
max_call_depth: 10000
save_graphs: False
device_id: 0
# aicc
remote_save_url: "Please input obs url on AICC platform."
# runner
runner_config:
epochs: 30
batch_size: 32
image_size: 224
sink_mode: False
sink_size: 2
# parallel
use_parallel: False
parallel:
parallel_mode: 0 # 0-data parallel, 1-semi-auto parallel, 2-auto parallel, 3-hybrid parallel
gradients_mean: True
enable_alltoall: False
full_batch: False
search_mode: "sharding_propagation"
enable_parallel_optimizer: False
strategy_ckpt_save_file: "./ckpt_strategy.ckpt"
parallel_config:
data_parallel: 1
model_parallel: 1
expert_parallel: 1
pipeline_stage: 1
micro_batch_num: 1
gradient_aggregation_group: 4
micro_batch_interleave_num: 1
# recompute
recompute_config:
recompute: False
parallel_optimizer_comm_recompute: False
mp_comm_recompute: True
recompute_slice_activation: False
# autotune
auto_tune: False
filepath_prefix: './autotune'
autotune_per_step: 10
# profile
profile: False
profile_start_step: 1
profile_stop_step: 10
init_start_profile: False
profile_communication: False
profile_memory: True
# Trainer
trainer:
type: ZeroShotImageClassificationTrainer
model_name: 'clip_vit_b_32'
# eval dataset
eval_dataset: &eval_dataset
data_loader:
type: Cifar100DataLoader
dataset_dir: 'cifar-100-python'
column_names: ["image", "text", "label"]
stage: 'test'
fine_label: True
shuffle: False
hypothesis_template: "a picture of {}"
tokenizer:
type: CLIPTokenizer
pad_token: '!'
text_transforms:
type: TokenizerForward
max_length: 77
padding: "max_length"
transforms:
- type: ToPIL
- type: Resize
size: 224
interpolation: 'linear'
- type: CenterCrop
size: 224
- type: ToTensor
- type: Normalize
mean: [0.48145466, 0.4578275, 0.40821073]
std: [0.26862954, 0.26130258, 0.27577711]
is_hwc: False
num_parallel_workers: 8
python_multiprocessing: False
drop_remainder: False
batch_size: 32
repeat: 1
numa_enable: False
prefetch_size: 30
seed: 2022
eval_dataset_task:
type: ZeroShotImageClassificationDataset
dataset_config: *eval_dataset
# model
model:
arch:
type: CLIPModel
model_config:
type: CLIPConfig
text_config:
type: CLIPTextConfig
vocab_size: 49408
hidden_size: 512
intermediate_size: 2048
num_hidden_layers: 12
num_attention_heads: 8
max_position_embeddings: 77
hidden_act: "quick_gelu"
attention_dropout: 0.0
drop_out: 0.0
initializer_range: 0.02
initializer_factor: 1.0
vision_config:
type: CLIPVisionConfig
hidden_size: 768
intermediate_size: 3072
num_hidden_layers: 12
num_attention_heads: 12
image_size: 224
patch_size: 32
hidden_act: "quick_gelu"
dropout: 0.0
attention_dropout: 0.0
initializer_range: 0.02
initializer_factor: 1.0
dtype: float16 # type of tensors
checkpoint_name_or_path: clip_vit_b_32 # the loaded model type
projection_dim: 512 # feature dims
logit_scale_init_value: 2.6592
# lr sechdule
layer_scale: False
layer_decay: 0.65
# optimizer
optimizer:
type: adamw
weight_decay: 0.001
learning_rate: 0.00001
lr_scale: False
lr_scale_factor: 256
# callbacks
callbacks:
- type: MFLossMonitor
- type: CheckpointMointor
prefix: "mindformers"
save_checkpoint_steps: 100
integrated_save: True
async_save: False
- type: ObsMonitor
eval_callbacks:
- type: ObsMonitor
# metric
metric:
type: 'Accuracy'
# processor
processor:
type: CLIPProcessor
image_processor:
type: CLIPImageProcessor
image_resolution: 224 # input image size
tokenizer:
type: CLIPTokenizer
pad_token: '!' # corresponding token id is 0
top_k: 3
save_file: 'results.txt'
#save_file: '/home/ma-user/work/mindformers/results.txt'
```
