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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import sys
from contextlib import contextmanager
import cv2
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim.nets import resnet_v1
import tensorpack as tp
import tensorpack.utils.viz as viz
IMAGE_SIZE = 224
@contextmanager
def guided_relu():
"""
Returns:
A context where the gradient of :meth:`tf.nn.relu` is replaced by
guided back-propagation, as described in the paper:
`Striving for Simplicity: The All Convolutional Net
<https://arxiv.org/abs/1412.6806>`_
"""
from tensorflow.python.ops import gen_nn_ops # noqa
@tf.RegisterGradient("GuidedReLU")
def GuidedReluGrad(op, grad):
return tf.where(0. < grad,
gen_nn_ops._relu_grad(grad, op.outputs[0]),
tf.zeros(grad.get_shape()))
g = tf.get_default_graph()
with g.gradient_override_map({'Relu': 'GuidedReLU'}):
yield
def saliency_map(output, input, name="saliency_map"):
"""
Produce a saliency map as described in the paper:
`Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
<https://arxiv.org/abs/1312.6034>`_.
The saliency map is the gradient of the max element in output w.r.t input.
Returns:
tf.Tensor: the saliency map. Has the same shape as input.
"""
max_outp = tf.reduce_max(output, 1)
saliency_op = tf.gradients(max_outp, input)[:][0]
return tf.identity(saliency_op, name=name)
class Model(tp.ModelDescBase):
def inputs(self):
return [tf.TensorSpec((IMAGE_SIZE, IMAGE_SIZE, 3), tf.float32, 'image')]
def build_graph(self, orig_image):
mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])
with guided_relu():
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=False)):
image = tf.expand_dims(orig_image - mean, 0)
logits, _ = resnet_v1.resnet_v1_50(image, 1000)
saliency_map(logits, orig_image, name="saliency")
def run(model_path, image_path):
predictor = tp.OfflinePredictor(tp.PredictConfig(
model=Model(),
session_init=tp.get_model_loader(model_path),
input_names=['image'],
output_names=['saliency']))
im = cv2.imread(image_path)
assert im is not None and im.ndim == 3, image_path
# resnet expect RGB inputs of 224x224x3
im = cv2.resize(im, (IMAGE_SIZE, IMAGE_SIZE))
im = im.astype(np.float32)[:, :, ::-1]
saliency_images = predictor(im)[0]
abs_saliency = np.abs(saliency_images).max(axis=-1)
pos_saliency = np.maximum(0, saliency_images)
neg_saliency = np.maximum(0, -saliency_images)
pos_saliency -= pos_saliency.min()
pos_saliency /= pos_saliency.max()
cv2.imwrite('pos.jpg', pos_saliency * 255)
neg_saliency -= neg_saliency.min()
neg_saliency /= neg_saliency.max()
cv2.imwrite('neg.jpg', neg_saliency * 255)
abs_saliency = viz.intensity_to_rgb(abs_saliency, normalize=True)[:, :, ::-1] # bgr
cv2.imwrite("abs-saliency.jpg", abs_saliency)
rsl = im * 0.2 + abs_saliency * 0.8
cv2.imwrite("blended.jpg", rsl)
if __name__ == '__main__':
if len(sys.argv) != 2:
tp.logger.error("Usage: {} image.jpg".format(sys.argv[0]))
sys.exit(1)
run("resnet_v1_50.ckpt", sys.argv[1])
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