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resnet.b512.baseline.py
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resnet.b512.baseline.py
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#!/usr/bin/env python
# File: resnet.b512.baseline.py
# from yuxin
import sys
import argparse
import numpy as np
import os
from itertools import count
import tensorflow as tf
from tensorpack import *
from tensorpack.models import *
from tensorpack.callbacks import *
from tensorpack.train import TrainConfig, SyncMultiGPUTrainerParameterServer
from tensorpack.dataflow import imgaug
from tensorpack.tfutils import argscope, get_model_loader
from tensorpack.tfutils.summary import *
from tensorpack.utils.gpu import get_nr_gpu
from imagenet_utils import (
ImageNetModel,
get_imagenet_dataflow,
eval_on_ILSVRC12,
fbresnet_augmentor)
from resnet_model import (
resnet_group, resnet_basicblock, resnet_bottleneck)
TOTAL_BATCH_SIZE = 512
BASE_LR = 0.1 * (512 // 256)
class Model(ImageNetModel):
def get_logits(self, image):
group_func = resnet_group
block_func = resnet_bottleneck
num_blocks = [3, 4, 6, 3]
with argscope(
[Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm],
data_format='NCHW'), \
argscope(Conv2D, nl=tf.identity, use_bias=False,
W_init=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
logits = (LinearWrap(image)
.Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)
.MaxPooling('pool0', shape=3, stride=2, padding='SAME')
.apply(group_func, 'group0', block_func, 64, num_blocks[0], 1)
.apply(group_func, 'group1', block_func, 128, num_blocks[1], 2)
.apply(group_func, 'group2', block_func, 256, num_blocks[2], 2)
.apply(group_func, 'group3', block_func, 512, num_blocks[3], 2)
.GlobalAvgPooling('gap')
.FullyConnected('linear', 1000, nl=tf.identity)())
return logits
def get_data(name, batch):
isTrain = name == 'train'
global args
augmentors = fbresnet_augmentor(isTrain)
if isTrain:
print("Training batch:", batch)
return get_imagenet_dataflow(args.data, name, batch, augmentors)
else:
imagenet1k = get_imagenet_dataflow(args.data, name, batch, augmentors)
return imagenet1k
def get_config(model):
nr_tower = max(get_nr_gpu(), 1)
batch = TOTAL_BATCH_SIZE // nr_tower
logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
dataset_train = get_data('train', batch)
dataset_val = get_data('val', batch)
infs = [ClassificationError('wrong-top1', 'val-error-top1'),
ClassificationError('wrong-top5', 'val-error-top5')]
callbacks = [
ModelSaver(),
GPUUtilizationTracker(),
ScheduledHyperParamSetter('learning_rate',
[(0, 0.1), (3, BASE_LR)], interp='linear'),
ScheduledHyperParamSetter('learning_rate',
[(30, BASE_LR * 1e-1), (60, BASE_LR * 1e-2), (80, BASE_LR * 1e-3)]),
PeriodicTrigger(
DataParallelInferenceRunner(
dataset_val, infs, list(range(nr_tower))),
every_k_epochs=1),
]
input = QueueInput(dataset_train)
input = StagingInput(input, nr_stage=1)
return TrainConfig(
model=model,
data=input,
callbacks=callbacks,
steps_per_epoch=1281167 // TOTAL_BATCH_SIZE,
max_epoch=100,
)
if __name__ == '__main__':
from os.path import expanduser
home = expanduser("~")
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--data', default=home+'/data/imagenet',
help='ILSVRC dataset dir')
parser.add_argument('--load', help='load model')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--logdir', default='train_log/tmp')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
model = Model()
if args.eval:
batch = 128 # something that can run on one gpu
ds = get_data('val', batch)
eval_on_ILSVRC12(
model,
get_model_loader(args.load), ds,
['input', 'label0'],
['wrong-top1', 'wrong-top5'])
else:
logger.set_logger_dir(args.logdir, 'd')
config = get_config(model)
if args.load:
config.session_init = get_model_loader(args.load)
nr_tower = max(get_nr_gpu(), 1)
launch_train_with_config(config,
SyncMultiGPUTrainerReplicated(nr_tower))