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train.py
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train.py
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from __future__ import print_function, division
import os
import logging
import yaml
import importlib
import mxnet as mx
import numpy as np
from easydict import EasyDict
from pprint import pprint
from scipy.linalg import block_diag
from utils.misc import clean_immediate_checkpoints
from utils.group_iterator import GroupIterator
from utils.initializer import InitWithArray
from utils.debug import forward_debug
from utils.memonger import search_plan
from utils.lr_scheduler import WarmupMultiFactorScheduler, ExponentialScheduler
# operators
import operators.triplet_loss
def build_network(symbol, num_id, p_size, **kwargs):
triplet_normalization = kwargs.get("triplet_normalization", False)
use_triplet = kwargs.get("use_triplet", False)
use_softmax = kwargs.get("use_softmax", False)
triplet_margin = kwargs.get("triplet_margin", 0.5)
label = mx.symbol.Variable(name="softmax_label")
group = [label]
pooling = mx.symbol.Pooling(data=symbol, kernel=(1, 1), global_pool=True, pool_type='max', name='global_pool')
flatten = mx.symbol.Flatten(data=pooling, name='flatten')
# triplet loss
if use_triplet:
data_triplet = mx.sym.L2Normalization(flatten, name="triplet_l2") if triplet_normalization else flatten
triplet = mx.symbol.Custom(data=data_triplet, p_size=p_size, margin=triplet_margin, op_type='TripletLoss',
name='triplet')
group.append(triplet)
# softmax cross entropy loss
if use_softmax:
fc = mx.symbol.FullyConnected(flatten, num_hidden=bottleneck_dims, name="bottleneck")
bn = mx.sym.BatchNorm(data=fc, fix_gamma=False, momentum=0.9, eps=2e-5, name='bottleneck_bn')
relu = mx.sym.Activation(data=bn, act_type='relu', name='bottleneck_relu')
# relu = bn
dropout = mx.symbol.Dropout(relu, p=dropout_ratio)
softmax_w = mx.symbol.Variable("softmax_weight", shape=(num_id, bottleneck_dims))
if softmax_weight_normalization:
softmax_w = mx.symbol.L2Normalization(softmax_w, name="softmax_weight_norm")
if softmax_feat_normalization:
data_softmax = mx.sym.L2Normalization(dropout, name="softmax_data_norm") * norm_scale
else:
data_softmax = dropout
softmax_fc = mx.symbol.FullyConnected(data_softmax, weight=softmax_w, num_hidden=num_id,
no_bias=True if softmax_weight_normalization else False,
name="softmax_fc")
softmax = mx.symbol.SoftmaxOutput(data=softmax_fc, label=label, name='softmax')
group.append(softmax)
return mx.symbol.Group(group)
def get_iterators(data_dir, p_size, k_size, crop_size, aug_dict, seed):
rand_mirror = aug_dict.get("rand_mirror", False)
rand_crop = aug_dict.get("rand_crop", False)
random_erasing = aug_dict.get("random_erasing", False)
resize_shorter = aug_dict.get("resize_shorter", None)
force_resize = aug_dict.get("force_resize", None)
train = GroupIterator(data_dir=os.path.join(data_dir, "bounding_box_train"), p_size=p_size, k_size=k_size,
num_worker=8, image_size=(crop_size * 2, crop_size), resize_shorter=resize_shorter,
force_resize=force_resize, rand_mirror=rand_mirror, rand_crop=rand_crop,
random_erasing=random_erasing, random_seed=seed)
# val = GroupIterator(data_dir=os.path.join(data_dir, "bounding_box_test"), p_size=p_size, k_size=k_size,
# image_size=(crop_size * 2, crop_size), resize_shorter=resize_shorter,
# force_resize=force_resize,rand_crop=False, rand_mirror=False, random_seed=seed)
return train, None
if __name__ == '__main__':
random_seed = 0
mx.random.seed(random_seed)
# load configuration
args = yaml.load(open("config.yml", "r"))
selected_dataset = args["dataset"]
datasets = ["duke", "market", "cuhk"]
args["prefix"] = selected_dataset + "/" + args["prefix"]
for dataset in datasets:
dataset_config = args.pop(dataset)
if dataset == selected_dataset:
args.update(dataset_config)
args = EasyDict(args)
pprint(args)
model_load_prefix = args.model_load_prefix
model_load_epoch = args.model_load_epoch
network = args.network
gpus = args.gpus
data_dir = args.data_dir
p_size = args.p_size
k_size = args.k_size
lr_step = args.lr_step
optmizer = args.optimizer
lr = args.lr
wd = args.wd
num_epoch = args.num_epoch
crop_size = args.crop_size
prefix = args.prefix
batch_size = p_size * k_size
use_softmax = args.use_softmax
# use_gcn = args.use_gcn
bottleneck_dims = args.bottleneck_dims
temperature = args.temperature
num_id = args.num_id
use_triplet = args.use_triplet
triplet_margin = args.triplet_margin
dropout_ratio = args.dropout_ratio
softmax_weight_normalization = args.softmax_weight_normalization
softmax_feat_normalization = args.softmax_feat_normalization
triplet_normalization = args.triplet_normalization
aug = args.aug
# residual = args.residual
memonger = args.memonger
begin_epoch = args.begin_epoch
# keep_diag = args.keep_diag
norm_scale = args.norm_scale
# config logger
logging.basicConfig(format="%(asctime)s %(message)s",
filename='log/%s/%s.log' % (selected_dataset, os.path.basename(prefix)), filemode='a')
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
logging.info(args)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.DEBUG)
logger.addHandler(stream_handler)
_, arg_params, aux_params = mx.model.load_checkpoint('%s' % model_load_prefix, model_load_epoch)
devices = [mx.gpu(int(i)) for i in gpus.split(',')]
train, val = get_iterators(data_dir=data_dir, p_size=p_size, k_size=k_size, crop_size=crop_size, aug_dict=aug,
seed=random_seed)
steps = [int(x) for x in lr_step.split(',')]
lr_scheduler = WarmupMultiFactorScheduler(step=[s * train.size for s in steps], factor=0.1, warmup=True,
warmup_lr=1e-4, warmup_step=train.size * 20, mode="gradual")
# lr_scheduler = ExponentialScheduler(base_lr=lr, exp=0.001, start_step=150 * train.size, end_step=300 * train.size)
init = mx.initializer.MSRAPrelu(factor_type='out', slope=0.0)
optimizer_params = {"learning_rate": lr,
"wd": wd,
"lr_scheduler": lr_scheduler,
"rescale_grad": 1.0 / batch_size,
"begin_num_update": begin_epoch * train.size}
symbol = importlib.import_module('symbols.symbol_' + network).get_symbol()
net = build_network(symbol=symbol, num_id=num_id, batch_size=batch_size, p_size=p_size,
softmax_weight_normalization=softmax_weight_normalization, norm_scale=norm_scale,
softmax_feat_normalization=softmax_feat_normalization,
triplet_normalization=triplet_normalization,
bottleneck_dims=bottleneck_dims, dropout_ratio=dropout_ratio,
use_softmax=use_softmax, use_triplet=use_triplet, triplet_margin=triplet_margin,
temperature=temperature)
if memonger:
net = search_plan(net, data=(batch_size, 3, crop_size * 2, crop_size), softmax_label=(batch_size,))
# Metric
metric_list = []
if use_softmax:
acc = mx.metric.Accuracy(output_names=["softmax_output"], label_names=["softmax_label"], name="acc")
ce_loss = mx.metric.CrossEntropy(output_names=["softmax_output"], label_names=["softmax_label"], name="ce")
metric_list.extend([acc, ce_loss])
if use_triplet:
triplet_loss = mx.metric.Loss(output_names=["triplet_output"], name="triplet")
metric_list.append(triplet_loss)
metric = mx.metric.CompositeEvalMetric(metrics=metric_list)
model = mx.mod.Module(symbol=net, context=devices, logger=logger)
model.fit(train_data=train,
eval_data=None,
eval_metric=metric,
validation_metric=metric,
arg_params=arg_params,
aux_params=aux_params,
allow_missing=True,
initializer=init,
optimizer=optmizer,
optimizer_params=optimizer_params,
num_epoch=num_epoch,
begin_epoch=begin_epoch,
batch_end_callback=mx.callback.Speedometer(batch_size=batch_size, frequent=5),
epoch_end_callback=mx.callback.do_checkpoint("models/" + prefix, period=10),
kvstore='device')
clean_immediate_checkpoints("models", prefix, num_epoch)