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gradcam.py
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gradcam.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import print_function
import mxnet as mx
import mxnet.ndarray as nd
from mxnet import gluon
from mxnet import autograd
from mxnet.gluon import nn
import numpy as np
import cv2
class ReluOp(mx.operator.CustomOp):
"""Modified ReLU as described in section 3.4 in https://arxiv.org/abs/1412.6806.
This is used for guided backpropagation to get gradients of the image w.r.t activations.
This Operator will do a regular backpropagation if `guided_backprop` is set to False
and a guided packpropagation if `guided_backprop` is set to True. Check gradcam_demo.py
for an example usage."""
guided_backprop = False
def forward(self, is_train, req, in_data, out_data, aux):
x = in_data[0]
y = nd.maximum(x, nd.zeros_like(x))
self.assign(out_data[0], req[0], y)
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
if ReluOp.guided_backprop:
# Get output and gradients of output
y = out_data[0]
dy = out_grad[0]
# Zero out the negatives in the gradients of the output
dy_positives = nd.maximum(dy, nd.zeros_like(dy))
# What output values were greater than 0?
y_ones = y.__gt__(0)
# Mask out the values for which at least one of dy or y is negative
dx = dy_positives * y_ones
self.assign(in_grad[0], req[0], dx)
else:
# Regular backward for ReLU
x = in_data[0]
x_gt_zero = x.__gt__(0)
dx = out_grad[0] * x_gt_zero
self.assign(in_grad[0], req[0], dx)
def set_guided_backprop(mode=True):
ReluOp.guided_backprop = mode
@mx.operator.register("relu")
class ReluProp(mx.operator.CustomOpProp):
def __init__(self):
super(ReluProp, self).__init__(True)
def infer_shape(self, in_shapes):
data_shape = in_shapes[0]
output_shape = data_shape
return (data_shape,), (output_shape,), ()
def create_operator(self, ctx, in_shapes, in_dtypes):
return ReluOp()
class Activation(mx.gluon.HybridBlock):
@staticmethod
def set_guided_backprop(mode=False):
ReluOp.guided_backprop = mode
def __init__(self, act_type, **kwargs):
assert act_type == 'relu'
super(Activation, self).__init__(**kwargs)
def hybrid_forward(self, F, x):
return F.Custom(x, op_type='relu')
class Conv2D(mx.gluon.HybridBlock):
"""Wrapper on top of gluon.nn.Conv2D to capture the output and gradients of output of a Conv2D
layer in a network. Use `set_capture_layer_name` to select the layer
whose outputs and gradients of outputs need to be captured. After the backward pass,
`conv_output` will contain the output and `conv_output.grad` will contain the
output's gradients. Check gradcam_demo.py for example usage."""
conv_output = None
capture_layer_name = None
def __init__(self, channels, kernel_size, strides=(1, 1), padding=(0, 0),
dilation=(1, 1), groups=1, layout='NCHW',
activation=None, use_bias=True, weight_initializer=None,
bias_initializer='zeros', in_channels=0, **kwargs):
super(Conv2D, self).__init__(**kwargs)
self.conv = nn.Conv2D(channels, kernel_size, strides=strides, padding=padding,
dilation=dilation, groups=groups, layout=layout,
activation=activation, use_bias=use_bias, weight_initializer=weight_initializer,
bias_initializer=bias_initializer, in_channels=in_channels)
def hybrid_forward(self, F, x):
out = self.conv(x)
name = self._prefix[:-1]
if name == Conv2D.capture_layer_name:
out.attach_grad()
Conv2D.conv_output = out
return out
def set_capture_layer_name(name):
Conv2D.capture_layer_name = name
def _get_grad(net, image, class_id=None, conv_layer_name=None, image_grad=False):
"""This is an internal helper function that can be used for either of these
but not both at the same time:
1. Record the output and gradient of output of an intermediate convolutional layer.
2. Record the gradients of the image.
Parameters
----------
image : NDArray
Image to visuaize. This is an NDArray with the preprocessed image.
class_id : int
Category ID this image belongs to. If not provided,
network's prediction will be used.
conv_layer_name: str
Name of the convolutional layer whose output and output's gradients need to be acptured.
image_grad: bool
Whether to capture gradients of the image."""
if image_grad:
image.attach_grad()
Conv2D.capture_layer_name = None
Activation.set_guided_backprop(True)
else:
# Tell convviz.Conv2D which layer's output and gradient needs to be recorded
Conv2D.capture_layer_name = conv_layer_name
Activation.set_guided_backprop(False)
# Run the network
with autograd.record(train_mode=False):
out = net(image)
# If user didn't provide a class id, we'll use the class that the network predicted
if class_id == None:
model_output = out.asnumpy()
class_id = np.argmax(model_output)
# Create a one-hot target with class_id and backprop with the created target
one_hot_target = mx.nd.one_hot(mx.nd.array([class_id]), 1000)
out.backward(one_hot_target, train_mode=False)
if image_grad:
return image.grad[0].asnumpy()
else:
# Return the recorded convolution output and gradient
conv_out = Conv2D.conv_output
return conv_out[0].asnumpy(), conv_out.grad[0].asnumpy()
def get_conv_out_grad(net, image, class_id=None, conv_layer_name=None):
"""Get the output and gradients of output of a convolutional layer.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provided,
network's prediction will be used.
conv_layer_name: str
Name of the convolutional layer whose output and output's gradients need to be acptured."""
return _get_grad(net, image, class_id, conv_layer_name, image_grad=False)
def get_image_grad(net, image, class_id=None):
"""Get the gradients of the image.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provided,
network's prediction will be used."""
return _get_grad(net, image, class_id, image_grad=True)
def grad_to_image(gradient):
"""Convert gradients of image obtained using `get_image_grad`
into image. This shows parts of the image that is most strongly activating
the output neurons."""
gradient = gradient - gradient.min()
gradient /= gradient.max()
gradient = np.uint8(gradient * 255).transpose(1, 2, 0)
gradient = gradient[..., ::-1]
return gradient
def get_cam(imggrad, conv_out):
"""Compute CAM. Refer section 3 of https://arxiv.org/abs/1610.02391 for details"""
weights = np.mean(imggrad, axis=(1, 2))
cam = np.ones(conv_out.shape[1:], dtype=np.float32)
for i, w in enumerate(weights):
cam += w * conv_out[i, :, :]
cam = cv2.resize(cam, (imggrad.shape[1], imggrad.shape[2]))
cam = np.maximum(cam, 0)
cam = (cam - np.min(cam)) / (np.max(cam) - np.min(cam))
cam = np.uint8(cam * 255)
return cam
def get_guided_grad_cam(cam, imggrad):
"""Compute Guided Grad-CAM. Refer section 3 of https://arxiv.org/abs/1610.02391 for details"""
return np.multiply(cam, imggrad)
def get_img_heatmap(orig_img, activation_map):
"""Draw a heatmap on top of the original image using intensities from activation_map"""
heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_COOL)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
img_heatmap = np.float32(heatmap) + np.float32(orig_img)
img_heatmap = img_heatmap / np.max(img_heatmap)
img_heatmap *= 255
return img_heatmap.astype(int)
def to_grayscale(cv2im):
"""Convert gradients to grayscale. This gives a saliency map."""
# How strongly does each position activate the output
grayscale_im = np.sum(np.abs(cv2im), axis=0)
# Normalize between min and 99th percentile
im_max = np.percentile(grayscale_im, 99)
im_min = np.min(grayscale_im)
grayscale_im = np.clip((grayscale_im - im_min) / (im_max - im_min), 0, 1)
grayscale_im = np.expand_dims(grayscale_im, axis=0)
return grayscale_im
def visualize(net, preprocessed_img, orig_img, conv_layer_name):
# Returns grad-cam heatmap, guided grad-cam, guided grad-cam saliency
imggrad = get_image_grad(net, preprocessed_img)
conv_out, conv_out_grad = get_conv_out_grad(net, preprocessed_img, conv_layer_name=conv_layer_name)
cam = get_cam(conv_out_grad, conv_out)
cam = cv2.resize(cam, (imggrad.shape[1], imggrad.shape[2]))
ggcam = get_guided_grad_cam(cam, imggrad)
img_ggcam = grad_to_image(ggcam)
img_heatmap = get_img_heatmap(orig_img, cam)
ggcam_gray = to_grayscale(ggcam)
img_ggcam_gray = np.squeeze(grad_to_image(ggcam_gray))
return img_heatmap, img_ggcam, img_ggcam_gray