import tensorflow as tf
import sys
from tensorpack import ModelSaver
from tensorpack import ClassificationError
from tensorpack import EstimatedTimeLeft
from tensorpack import MinSaver
from tensorpack import ScheduledHyperParamSetter
from tensorpack import QueueInput
from tensorpack import StagingInput
from tensorpack import InferenceRunner
from tensorpack.models.regularize import regularize_cost
from tensorpack.models.regularize import l2_regularizer
from tensorpack.tfutils import optimizer
from tensorpack.tfutils import gradproc
from tensorpack.tfutils import get_model_loader
from tensorpack.tfutils.summary import add_moving_summary
from tensorpack.train.config import TrainConfig
from tensorpack.train.interface import launch_train_with_config
from tensorpack.train.model_desc import ModelDesc
from tensorpack.train.trainers import SyncMultiGPUTrainerParameterServer
from tensorpack.utils import logger
from tensorpack.utils.gpu import get_nr_gpu
from config import get_args
from data_loader import get_data
from evaluate import evaluate_wsol
from os.path import join as ospj
from util import image_summaries
import backbone as arch
_CKPT_NAMES = {
'resnet50_se': 'pretrained/ResNet50-SE.npz',
'vgg_gap': 'pretrained/vgg16.npz',
}
_WEIGHT_DECAY = {
'resnet50_se': 1e-4,
'vgg_gap': 5e-4,
}
_LR_SCALE = {
'resnet50_se': [('conv0.*', 0.1), ('group[0-2].*', 0.1)],
'vgg_gap': [('conv.*', 0.1), ('fc.*', 0.1)],
}
class Model(ModelDesc):
def __init__(self, args):
self.args = args
def inputs(self):
input_shape = [None, self.args.final_size, self.args.final_size, 3]
return [tf.placeholder(tf.uint8, input_shape, 'input'),
tf.placeholder(tf.int32, [None], 'label'),
tf.placeholder(tf.float32, [None, 2, 2], 'bbox')]
def build_graph(self, image, label, bbox):
image, label_onehot = self._pre_process_inputs(image, label)
logit, convmap = arch.__dict__[self.args.arch_name](image, self.args)
self._prepare_cam(logit, convmap, label_onehot)
loss = self._get_loss(logit, label)
return loss
def optimizer(self):
lr = tf.get_variable('learning_rate',
initializer=self.args.base_lr, trainable=False)
opt = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
add_moving_summary(lr)
if self.args.use_pretrained_model:
gradprocs = [gradproc.ScaleGradient(_LR_SCALE[self.args.arch_name])]
return optimizer.apply_grad_processors(opt, gradprocs)
else:
return opt
def _pre_process_inputs(self, image, label):
image = self._image_preprocess(image, is_bgr=True)
image = tf.transpose(image, [0, 3, 1, 2])
label_onehot = tf.one_hot(label, self.args.number_of_class)
image_summaries('input-images', image)
return image, label_onehot
def _image_preprocess(self, image, is_bgr=True):
with tf.name_scope('image_preprocess'):
if image.dtype.base_dtype != tf.float32:
image = tf.cast(image, tf.float32)
image = image * (1.0 / 255)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
if is_bgr:
mean = mean[::-1]
std = std[::-1]
image_mean = tf.constant(mean, dtype=tf.float32)
image_std = tf.constant(std, dtype=tf.float32)
image = (image - image_mean) / image_std
return image
def _prepare_cam(self, logits, convmaps, label_onehot):
_, indices = tf.nn.top_k(logits, 5)
tf.identity(indices, name='top5')
tf.identity(convmaps, name='actmap')
y_c = tf.reduce_sum(tf.multiply(logits, label_onehot), axis=1)
tf.identity(tf.gradients(y_c, convmaps)[0], name='grad')
def _get_loss(self, logits, label):
loss = self._compute_loss_and_error(logits, label)
wd_cost = regularize_cost('.*/W', l2_regularizer(
_WEIGHT_DECAY[self.args.arch_name]), name='l2_regularize_loss')
add_moving_summary(loss, wd_cost)
return tf.add_n([loss, wd_cost], name='cost')
def _compute_loss_and_error(self, logits, label):
def prediction_incorrect(logits_, label_, topk=1,
name='incorrect_vector'):
with tf.name_scope('prediction_incorrect'):
x = tf.logical_not(tf.nn.in_top_k(logits_, label_, topk))
return tf.cast(x, tf.float32, name=name)
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
loss = tf.reduce_mean(loss, name='xentropy-loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
return loss
def get_callbacks(dataset_val, option):
cls_metric = [ClassificationError('wrong-top1', 'val-error-top1'),
ClassificationError('wrong-top5', 'val-error-top5')]
callbacks = [
ModelSaver(max_to_keep=1, keep_checkpoint_every_n_hours=1000),
EstimatedTimeLeft(),
InferenceRunner(dataset_val, cls_metric),
MinSaver('val-error-top1'),
ScheduledHyperParamSetter('learning_rate',
[(0, option.base_lr),
(30, option.base_lr * 1e-1),
(60, option.base_lr * 1e-2),
(90, option.base_lr * 1e-3),
(100, option.base_lr * 1e-4)]),
]
return callbacks
def get_steps_per_epoch(option):
nr_gpu = get_nr_gpu()
total_batch = option.batch_size * nr_gpu
if option.dataset_name == 'CUB':
steps_per_epoch = 25 * (256 / total_batch) * option.stepscale
elif option.dataset_name == 'ILSVRC':
steps_per_epoch = 5000 * (256 / total_batch) * option.stepscale
else:
raise KeyError("Unavailable dataset: {}".format(option.dataset_name))
return int(steps_per_epoch)
def get_config(model, option):
dataset_train = get_data('train', option)
dataset_val = get_data('val', option)
callbacks = get_callbacks(dataset_val, option)
steps_per_epoch = get_steps_per_epoch(option)
return TrainConfig(
model=model,
data=StagingInput(QueueInput(dataset_train), nr_stage=1),
callbacks=callbacks,
steps_per_epoch=steps_per_epoch,
max_epoch=option.epoch,
)
def main():
args = get_args()
nr_gpu = get_nr_gpu()
args.batch_size = args.batch_size // nr_gpu
model = Model(args)
if args.evaluate:
evaluate_wsol(args, model, interval=False)
sys.exit()
logger.set_logger_dir(ospj('train_log', args.log_dir))
config = get_config(model, args)
if args.use_pretrained_model:
config.session_init = get_model_loader(_CKPT_NAMES[args.arch_name])
launch_train_with_config(config,
SyncMultiGPUTrainerParameterServer(nr_gpu))
evaluate_wsol(args, model, interval=True)
if __name__ == '__main__':
main()
