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hed.py
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#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# File: hed.py
# Author: Yuxin Wu <[email protected]>
import cv2
import tensorflow as tf
import argparse
from six.moves import zip
import os
from tensorpack import *
from tensorpack.dataflow import dataset
from tensorpack.utils.gpu import get_nr_gpu
from tensorpack.tfutils import optimizer
from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
def class_balanced_sigmoid_cross_entropy(logits, label, name='cross_entropy_loss'):
"""
The class-balanced cross entropy loss,
as in `Holistically-Nested Edge Detection
<http://arxiv.org/abs/1504.06375>`_.
Args:
logits: of shape (b, ...).
label: of the same shape. the ground truth in {0,1}.
Returns:
class-balanced cross entropy loss.
"""
with tf.name_scope('class_balanced_sigmoid_cross_entropy'):
y = tf.cast(label, tf.float32)
count_neg = tf.reduce_sum(1. - y)
count_pos = tf.reduce_sum(y)
beta = count_neg / (count_neg + count_pos)
pos_weight = beta / (1 - beta)
cost = tf.nn.weighted_cross_entropy_with_logits(logits=logits, targets=y, pos_weight=pos_weight)
cost = tf.reduce_mean(cost * (1 - beta))
zero = tf.equal(count_pos, 0.0)
return tf.where(zero, 0.0, cost, name=name)
class Model(ModelDesc):
def _get_inputs(self):
return [InputDesc(tf.float32, [None, None, None, 3], 'image'),
InputDesc(tf.int32, [None, None, None], 'edgemap')]
def _build_graph(self, inputs):
image, edgemap = inputs
image = image - tf.constant([104, 116, 122], dtype='float32')
edgemap = tf.expand_dims(edgemap, 3, name='edgemap4d')
def branch(name, l, up):
with tf.variable_scope(name):
l = Conv2D('convfc', l, 1, kernel_shape=1, nl=tf.identity,
use_bias=True,
W_init=tf.constant_initializer(),
b_init=tf.constant_initializer())
while up != 1:
l = BilinearUpSample('upsample{}'.format(up), l, 2)
up = up / 2
return l
with argscope(Conv2D, kernel_shape=3, nl=tf.nn.relu):
l = Conv2D('conv1_1', image, 64)
l = Conv2D('conv1_2', l, 64)
b1 = branch('branch1', l, 1)
l = MaxPooling('pool1', l, 2)
l = Conv2D('conv2_1', l, 128)
l = Conv2D('conv2_2', l, 128)
b2 = branch('branch2', l, 2)
l = MaxPooling('pool2', l, 2)
l = Conv2D('conv3_1', l, 256)
l = Conv2D('conv3_2', l, 256)
l = Conv2D('conv3_3', l, 256)
b3 = branch('branch3', l, 4)
l = MaxPooling('pool3', l, 2)
l = Conv2D('conv4_1', l, 512)
l = Conv2D('conv4_2', l, 512)
l = Conv2D('conv4_3', l, 512)
b4 = branch('branch4', l, 8)
l = MaxPooling('pool4', l, 2)
l = Conv2D('conv5_1', l, 512)
l = Conv2D('conv5_2', l, 512)
l = Conv2D('conv5_3', l, 512)
b5 = branch('branch5', l, 16)
final_map = Conv2D('convfcweight',
tf.concat([b1, b2, b3, b4, b5], 3), 1, 1,
W_init=tf.constant_initializer(0.2),
use_bias=False, nl=tf.identity)
costs = []
for idx, b in enumerate([b1, b2, b3, b4, b5, final_map]):
output = tf.nn.sigmoid(b, name='output{}'.format(idx + 1))
xentropy = class_balanced_sigmoid_cross_entropy(
b, edgemap,
name='xentropy{}'.format(idx + 1))
costs.append(xentropy)
# some magic threshold
pred = tf.cast(tf.greater(output, 0.5), tf.int32, name='prediction')
wrong = tf.cast(tf.not_equal(pred, edgemap), tf.float32)
wrong = tf.reduce_mean(wrong, name='train_error')
if get_current_tower_context().is_training:
wd_w = tf.train.exponential_decay(2e-4, get_global_step_var(),
80000, 0.7, True)
wd_cost = tf.multiply(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
costs.append(wd_cost)
add_param_summary(('.*/W', ['histogram'])) # monitor W
self.cost = tf.add_n(costs, name='cost')
add_moving_summary(costs + [wrong, self.cost])
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=3e-5, trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(
opt, [gradproc.ScaleGradient(
[('convfcweight.*', 0.1), ('conv5_.*', 5)])])
def get_data(name):
isTrain = name == 'train'
ds = dataset.BSDS500(name, shuffle=True)
class CropMultiple16(imgaug.ImageAugmentor):
def _get_augment_params(self, img):
newh = img.shape[0] // 16 * 16
neww = img.shape[1] // 16 * 16
assert newh > 0 and neww > 0
diffh = img.shape[0] - newh
h0 = 0 if diffh == 0 else self.rng.randint(diffh)
diffw = img.shape[1] - neww
w0 = 0 if diffw == 0 else self.rng.randint(diffw)
return (h0, w0, newh, neww)
def _augment(self, img, param):
h0, w0, newh, neww = param
return img[h0:h0 + newh, w0:w0 + neww]
if isTrain:
shape_aug = [
imgaug.RandomResize(xrange=(0.7, 1.5), yrange=(0.7, 1.5),
aspect_ratio_thres=0.15),
imgaug.RotationAndCropValid(90),
CropMultiple16(),
imgaug.Flip(horiz=True),
imgaug.Flip(vert=True)
]
else:
# the original image shape (321x481) in BSDS is not a multiple of 16
IMAGE_SHAPE = (320, 480)
shape_aug = [imgaug.CenterCrop(IMAGE_SHAPE)]
ds = AugmentImageComponents(ds, shape_aug, (0, 1), copy=False)
def f(m): # thresholding
m[m >= 0.50] = 1
m[m < 0.50] = 0
return m
ds = MapDataComponent(ds, f, 1)
if isTrain:
augmentors = [
imgaug.Brightness(63, clip=False),
imgaug.Contrast((0.4, 1.5)),
]
ds = AugmentImageComponent(ds, augmentors, copy=False)
ds = BatchDataByShape(ds, 8, idx=0)
ds = PrefetchDataZMQ(ds, 1)
else:
ds = BatchData(ds, 1)
return ds
def view_data():
ds = RepeatedData(get_data('train'), -1)
ds.reset_state()
for ims, edgemaps in ds.get_data():
for im, edgemap in zip(ims, edgemaps):
assert im.shape[0] % 16 == 0 and im.shape[1] % 16 == 0, im.shape
cv2.imshow("im", im / 255.0)
cv2.waitKey(1000)
cv2.imshow("edge", edgemap)
cv2.waitKey(1000)
def get_config():
logger.auto_set_dir()
dataset_train = get_data('train')
steps_per_epoch = dataset_train.size() * 40
dataset_val = get_data('val')
return TrainConfig(
dataflow=dataset_train,
callbacks=[
ModelSaver(),
ScheduledHyperParamSetter('learning_rate', [(30, 6e-6), (45, 1e-6), (60, 8e-7)]),
HumanHyperParamSetter('learning_rate'),
InferenceRunner(dataset_val,
BinaryClassificationStats('prediction', 'edgemap4d'))
],
model=Model(),
steps_per_epoch=steps_per_epoch,
max_epoch=100,
)
def run(model_path, image_path, output):
pred_config = PredictConfig(
model=Model(),
session_init=get_model_loader(model_path),
input_names=['image'],
output_names=['output' + str(k) for k in range(1, 7)])
predictor = OfflinePredictor(pred_config)
im = cv2.imread(image_path)
assert im is not None
im = cv2.resize(
im, (im.shape[1] // 16 * 16, im.shape[0] // 16 * 16)
)[None, :, :, :].astype('float32')
outputs = predictor(im)
if output is None:
for k in range(6):
pred = outputs[k][0]
cv2.imwrite("out{}.png".format(
'-fused' if k == 5 else str(k + 1)), pred * 255)
else:
pred = outputs[5][0]
cv2.imwrite(output, pred * 255)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--view', help='view dataset', action='store_true')
parser.add_argument('--run', help='run model on images')
parser.add_argument('--output', help='fused output filename. default to out-fused.png')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.view:
view_data()
elif args.run:
run(args.load, args.run, args.output)
else:
config = get_config()
if args.load:
config.session_init = get_model_loader(args.load)
launch_train_with_config(
config,
SyncMultiGPUTrainer(max(get_nr_gpu(), 1)))