File size: 2,952 Bytes
37b3db0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
import os
import inspect
from collections import OrderedDict
from dataclasses import dataclass
import sys
sys.path.append("src")
from training.params import get_default_params
@dataclass
class Config:
tokenizer_context_length = 256
eval_freq = 1
text_encoder_model_name = 'microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract'
distributed_engine = "ddp"
train_data = None
val_data = None
train_num_samples = None
val_num_samples = None
dataset_type = "auto"
dataset_resampled = False
csv_separator = "\t"
csv_img_key = "filepath"
csv_caption_key = "title"
imagenet_val = "/datasets01/imagenet_full_size/061417/val"
imagenet_v2 = None
logs = "./logs/"
log_local = False
name = None
workers = 8
batch_size = 64
epochs = 32
lr = None
beta1 = None
beta2 = None
eps = None
wd = 0.2
warmup = 2000 # 10000
use_bn_sync = False
skip_scheduler = False
save_frequency = 10
save_most_recent = True # False
zeroshot_frequency = 1
val_frequency = 1
resume = None
precision = "amp"
clip_model = "CLIP"
model = "RN50"
pretrained = ''
pretrained_image = False
lock_image = False
lock_image_unlocked_groups = 0
lock_image_freeze_bn_stats = False
grad_checkpointing = False
local_loss = False
gather_with_grad = False
force_quick_gelu = False
torchscript = False
trace = False
dist_url = "env://"
dist_backend = "nccl"
report_to = ""
wandb_notes = ''
debug = False
copy_codebase = False
horovod = False
ddp_static_graph = False
no_set_device_rank = False
seed = 0
norm_gradient_clip = None
train_data_upsampling_factors = None
def __post_init__(self):
args = self
args.name = self.__class__.__name__
args.output_dir = os.path.join(args.logs, args.name)
for name, val in get_default_params(args.model).items():
if getattr(args, name) is None:
setattr(args, name, val)
def parse_start_end(shards):
start, end = os.path.basename(shards).split("{")[1].split("}")[0].split("..")
return int(start), int(end)
def search_config(config_name):
import importlib
project_dir = os.path.dirname(__file__)
all_configs = {}
for code in os.listdir(project_dir):
if code.endswith(".py") and code.startswith("run_configs"):
module = importlib.import_module(code[:-3])
for _config_name in dir(module):
if _config_name in ["Config"] or _config_name.startswith("__") or _config_name.startswith("run_config"):
continue
if _config_name not in all_configs:
all_configs[_config_name] = module
print(f"launching {config_name} from {all_configs[config_name].__file__}")
config = getattr(all_configs[config_name], config_name)()
return config
|