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train_direct.py
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train_direct.py
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r'''
This script trains ResNet CNN models to estimate wealth for DHS and LSMS
locations. Model checkpoints and TensorBoard training logs are saved to
`out_dir`.
Usage:
python train_direct.py \
--label_name wealthpooled \
--model_name resnet --num_layers 18 \
--lr_decay 0.96 --batch_size 64 \
--gpu 0 --num_threads 5 \
--cache train train_eval val \
--augment True --eval_every 1 --print_every 40 \
--ooc {ooc} --max_epochs {max_epochs} \
--out_dir {out_dir} \
--keep_frac {keep_frac} --seed {seed} \
--experiment_name {experiment_name} \
--dataset {dataset} \
--ls_bands {ls_bands} --nl_band {nl_band} \
--lr {lr} --fc_reg {reg} --conv_reg {reg} \
--imagenet_weights_path {imagenet_weights_path} \
--hs_weight_init {hs_weight_init}
Prerequisites: download TFRecords, process them, and create incountry folds. See
`preprocessing/1_process_tfrecords.ipynb` and
`preprocessing/2_create_incountry_folds.ipynb`.
'''
from __future__ import annotations
import argparse
import json
import os
from pprint import pprint
import time
from typing import Any, Optional
from batchers import batcher, tfrecord_paths_utils
from models.resnet_model import Hyperspectral_Resnet
from utils.run import get_full_experiment_name
from utils.trainer import RegressionTrainer
import numpy as np
import tensorflow as tf
ROOT_DIR = os.path.dirname(__file__) # folder containing this file
def run_training(sess: tf.Session,
ooc: bool,
dataset: str,
keep_frac: float,
model_name: str,
model_params: dict[str, Any],
batch_size: int,
ls_bands: Optional[str],
nl_band: Optional[str],
label_name: str,
augment: bool,
learning_rate: float,
lr_decay: float,
max_epochs: int,
print_every: int,
eval_every: int,
num_threads: int,
cache: list[str],
out_dir: str,
init_ckpt_dir: Optional[str],
imagenet_weights_path: Optional[str],
hs_weight_init: Optional[str],
exclude_final_layer: bool
) -> None:
'''
Args
- sess: tf.Session
- ooc: bool, whether to use out-of-country split
- dataset: str
- keep_frac: float
- model_name: str, currently only 'resnet' is supported
- model_params: dict
- batch_size: int
- ls_bands: one of [None, 'rgb', 'ms']
- nl_band: one of [None, 'merge', 'split']
- label_name: str, name of the label in the TFRecord file
- augment: bool
- learning_rate: float
- lr_decay: float
- max_epochs: int
- print_every: int
- eval_every: int
- num_threads: int
- cache: list of str, names of dataset splits to cache in RAM
- out_dir: str, path to output directory for saving checkpoints and TensorBoard logs, must already exist
- init_ckpt_dir: str, path to checkpoint dir from which to load existing weights
- set to None to use ImageNet or random initialization
- imagenet_weights_path: str, path to pre-trained weights from ImageNet
- set to None to use saved ckpt or random initialization
- hs_weight_init: str, one of [None, 'random', 'same', 'samescaled']
- exclude_final_layer: bool, or None
'''
# ====================
# ERROR CHECKING
# ====================
assert os.path.exists(out_dir)
if model_name == 'resnet':
model_class = Hyperspectral_Resnet
else:
raise ValueError('Unknown model_name. Only "resnet" model currently supported.')
# ====================
# BATCHERS
# ====================
if ooc: # out-of-country split
if 'dhs' in dataset.lower():
train_tfrecord_paths = tfrecord_paths_utils.dhs_ooc(dataset, split='train')
val_tfrecord_paths = tfrecord_paths_utils.dhs_ooc(dataset, split='val')
else:
raise ValueError('out-of-country w/ LSMS is not currently supported')
else: # in-country split
if 'dhs' in dataset.lower():
paths = tfrecord_paths_utils.dhs_incountry(dataset, splits=['train', 'val'])
if 'lsms' in dataset.lower():
paths = tfrecord_paths_utils.lsms_incountry(dataset, splits=['train', 'val'])
train_tfrecord_paths = paths['train']
val_tfrecord_paths = paths['val']
num_train = len(train_tfrecord_paths)
num_val = len(val_tfrecord_paths)
# keep_frac affects sizes of both training and validation sets
if keep_frac < 1.0:
num_train = int(num_train * keep_frac)
num_val = int(num_val * keep_frac)
train_tfrecord_paths = np.random.choice(
train_tfrecord_paths, size=num_train, replace=False)
val_tfrecord_paths = np.random.choice(
val_tfrecord_paths, size=num_val, replace=False)
print('num_train:', num_train)
print('num_val:', num_val)
train_steps_per_epoch = int(np.ceil(num_train / batch_size))
val_steps_per_epoch = int(np.ceil(num_val / batch_size))
def get_batcher(tfrecord_paths: tf.Tensor, shuffle: bool, augment: bool,
epochs: int, cache: bool) -> batcher.Batcher:
return batcher.Batcher(
tfrecord_files=tfrecord_paths,
label_name=label_name,
ls_bands=ls_bands,
nl_band=nl_band,
batch_size=batch_size,
epochs=epochs,
normalize='DHS', # TODO
shuffle=shuffle,
augment=augment,
clipneg=True,
cache=cache,
num_threads=num_threads)
train_tfrecord_paths_ph = tf.placeholder(tf.string, shape=[None])
val_tfrecord_paths_ph = tf.placeholder(tf.string, shape=[None])
with tf.name_scope('train_batcher'):
train_batcher = get_batcher(
train_tfrecord_paths_ph,
shuffle=True,
augment=augment,
epochs=max_epochs,
cache='train' in cache)
train_init_iter, train_batch = train_batcher.get_batch()
with tf.name_scope('train_eval_batcher'):
train_eval_batcher = get_batcher(
train_tfrecord_paths_ph,
shuffle=False,
augment=False,
epochs=max_epochs + 1, # may need extra epoch at the end of training
cache='train_eval' in cache)
train_eval_init_iter, train_eval_batch = train_eval_batcher.get_batch()
with tf.name_scope('val_batcher'):
val_batcher = get_batcher(
val_tfrecord_paths_ph,
shuffle=False,
augment=False,
epochs=max_epochs + 1, # may need extra epoch at the end of training
cache='val' in cache)
val_init_iter, val_batch = val_batcher.get_batch()
# ====================
# MODEL
# ====================
print('Building model...', flush=True)
model_params['num_outputs'] = 1
with tf.variable_scope(tf.get_variable_scope()) as model_scope:
train_model = model_class(train_batch['images'], is_training=True, **model_params)
train_preds = tf.reshape(train_model.outputs, shape=[-1], name='train_preds')
with tf.variable_scope(model_scope, reuse=True):
train_eval_model = model_class(train_eval_batch['images'], is_training=False, **model_params)
train_eval_preds = tf.reshape(train_eval_model.outputs, shape=[-1], name='train_eval_preds')
with tf.variable_scope(model_scope, reuse=True):
val_model = model_class(val_batch['images'], is_training=False, **model_params)
val_preds = tf.reshape(val_model.outputs, shape=[-1], name='val_preds')
trainer = RegressionTrainer(
train_batch, train_eval_batch, val_batch,
train_model, train_eval_model, val_model,
train_preds, train_eval_preds, val_preds,
sess, train_steps_per_epoch, ls_bands, nl_band, learning_rate, lr_decay,
out_dir, init_ckpt_dir, imagenet_weights_path,
hs_weight_init, exclude_final_layer, image_summaries=False)
# initialize the training dataset iterator
sess.run([train_init_iter, train_eval_init_iter, val_init_iter], feed_dict={
train_tfrecord_paths_ph: train_tfrecord_paths,
val_tfrecord_paths_ph: val_tfrecord_paths
})
for epoch in range(max_epochs):
if epoch % eval_every == 0:
trainer.eval_train(max_nbatches=train_steps_per_epoch)
trainer.eval_val(max_nbatches=val_steps_per_epoch)
trainer.train_epoch(print_every)
trainer.eval_train(max_nbatches=train_steps_per_epoch)
trainer.eval_val(max_nbatches=val_steps_per_epoch)
trainer.log_results()
def run_training_wrapper(**params: Any) -> None:
'''
params is a dict with keys matching the arguments from _parse_args()
'''
start = time.time()
print('Current time:', start)
# print all of the flags
pprint(params)
# parameters that might be 'None'
none_params = ['ls_bands', 'nl_band', 'hs_weight_init',
'imagenet_weights_path', 'init_ckpt_dir']
for p in none_params:
if params[p] == 'None':
params[p] = None
# reset any existing graph
tf.reset_default_graph()
# set the random seeds
seed = params['seed']
np.random.seed(seed)
tf.set_random_seed(seed)
# create the output directory if needed
full_experiment_name = get_full_experiment_name(
params['experiment_name'], params['batch_size'],
params['fc_reg'], params['conv_reg'], params['lr'])
out_dir = os.path.join(params['out_dir'], full_experiment_name)
params_filepath = os.path.join(out_dir, 'params.json')
if os.path.exists(params_filepath):
print(f'Stopping. Found previous run at: {params_filepath}')
return
print(f'Outputs directory: {out_dir}')
os.makedirs(out_dir, exist_ok=True)
with open(params_filepath, 'w') as config_file:
json.dump(params, config_file, indent=4)
# Create session
# - MUST set os.environ['CUDA_VISIBLE_DEVICES'] before creating tf.Session
if params['gpu'] is None: # restrict to CPU only
os.environ['CUDA_VISIBLE_DEVICES'] = ''
else:
os.environ['CUDA_VISIBLE_DEVICES'] = str(params['gpu'])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
model_params = {
'fc_reg': params['fc_reg'],
'conv_reg': params['conv_reg'],
'use_dilated_conv_in_first_layer': False,
}
if params['model_name'] == 'resnet':
model_params['num_layers'] = params['num_layers']
run_training(
sess=sess,
ooc=params['ooc'],
dataset=params['dataset'],
keep_frac=params['keep_frac'],
model_name=params['model_name'],
model_params=model_params,
batch_size=params['batch_size'],
ls_bands=params['ls_bands'],
nl_band=params['nl_band'],
label_name=params['label_name'],
augment=params['augment'],
learning_rate=params['lr'],
lr_decay=params['lr_decay'],
max_epochs=params['max_epochs'],
print_every=params['print_every'],
eval_every=params['eval_every'],
num_threads=params['num_threads'],
cache=params['cache'],
out_dir=out_dir,
init_ckpt_dir=params['init_ckpt_dir'],
imagenet_weights_path=params['imagenet_weights_path'],
hs_weight_init=params['hs_weight_init'],
exclude_final_layer=params['exclude_final_layer'])
sess.close()
end = time.time()
print('End time:', end)
print('Time elasped (sec.):', end - start)
def _parse_args() -> argparse.Namespace:
"""Parses arguments."""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Run end-to-end training.')
# paths
parser.add_argument(
'--experiment_name', default='new_experiment',
help='name of experiment being run')
parser.add_argument(
'--out_dir', default=os.path.join(ROOT_DIR, 'outputs/'),
help='path to output directory for saving checkpoints and TensorBoard '
'logs')
# initialization
parser.add_argument(
'--init_ckpt_dir',
help='path to checkpoint prefix from which to initialize weights')
parser.add_argument(
'--imagenet_weights_path',
help='path to ImageNet weights for initialization')
parser.add_argument(
'--hs_weight_init', choices=[None, 'random', 'same', 'samescaled'],
help='method for initializing weights of non-RGB bands in 1st conv '
'layer')
parser.add_argument(
'--exclude_final_layer', action='store_true',
help='whether to use checkpoint to initialize final layer')
# learning parameters
parser.add_argument(
'--label_name', default='wealthpooled',
help='name of label to use from the TFRecord files')
parser.add_argument(
'--batch_size', type=int, default=64,
help='batch size')
parser.add_argument(
'--augment', action='store_true',
help='whether to use data augmentation')
parser.add_argument(
'--fc_reg', type=float, default=1e-3,
help='Regularization penalty factor for fully connected layers')
parser.add_argument(
'--conv_reg', type=float, default=1e-3,
help='Regularization penalty factor for convolution layers')
parser.add_argument(
'--lr', type=float, default=1e-3,
help='Learning rate for optimizer')
parser.add_argument(
'--lr_decay', type=float, default=1.0,
help='Decay rate of the learning rate')
# high-level model control
parser.add_argument(
'--model_name', default='resnet', choices=['resnet'],
help='name of model architecture')
# resnet-only params
parser.add_argument(
'--num_layers', type=int, default=18, choices=[18, 34, 50],
help='number of ResNet layers')
# data params
parser.add_argument(
'--dataset', default='DHS_OOC_A', # TODO: choices?
help='dataset to use')
parser.add_argument(
'--ooc', action='store_true',
help='whether to use out-of-country split')
parser.add_argument(
'--keep_frac', type=float, default=1.0,
help='fraction of training data to use')
parser.add_argument(
'--ls_bands', choices=[None, 'rgb', 'ms'],
help='Landsat bands to use')
parser.add_argument(
'--nl_band', choices=[None, 'merge', 'split'],
help='nightlights band')
# system
parser.add_argument(
'--gpu', type=int,
help='which GPU to use')
parser.add_argument(
'--num_threads', type=int, default=1,
help='number of threads for batcher')
parser.add_argument(
'--cache', nargs='*', default=[], choices=['train', 'train_eval', 'val'],
help='list of datasets to cache in memory')
# Misc
parser.add_argument(
'--max_epochs', type=int, default=150,
help='maximum number of epochs for training')
parser.add_argument(
'--eval_every', type=int, default=1,
help='evaluate the model on the validation set after every so many '
'epochs of training')
parser.add_argument(
'--print_every', type=int, default=40,
help='print training statistics after every so many steps')
parser.add_argument(
'--seed', type=int, default=123,
help='seed for random initialization and shuffling')
return parser.parse_args()
if __name__ == '__main__':
args = _parse_args()
run_training_wrapper(**vars(args))