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Grad-CAM++ 1d v2.py
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Grad-CAM++ 1d v2.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def target_category_loss(x, category_index, nb_classes):
return torch.mul(x, F.one_hot(category_index, nb_classes))
def target_category_loss_output_shape(input_shape):
return input_shape
def normalize(x):
# utility function to normalize a tensor by its L2 norm
return x / (torch.sqrt(torch.mean(torch.square(x))) + 1e-5)
# def resize_1d(array, shape):
# res = np.zeros(shape)
# if array.shape[0] >= shape:
# ratio = array.shape[0]/shape
# for i in range(array.shape[0]):
# res[int(i/ratio)] += array[i]*(1-(i/ratio-int(i/ratio)))
# if int(i/ratio) != shape-1:
# res[int(i/ratio)+1] += array[i]*(i/ratio-int(i/ratio))
# else:
# res[int(i/ratio)] += array[i]*(i/ratio-int(i/ratio))
# res = res[::-1]
# array = array[::-1]
# for i in range(array.shape[0]):
# res[int(i/ratio)] += array[i]*(1-(i/ratio-int(i/ratio)))
# if int(i/ratio) != shape-1:
# res[int(i/ratio)+1] += array[i]*(i/ratio-int(i/ratio))
# else:
# res[int(i/ratio)] += array[i]*(i/ratio-int(i/ratio))
# res = res[::-1]/(2*ratio)
# array = array[::-1]
# else:
# ratio = shape/array.shape[0]
# left = 0
# right = 1
# for i in range(shape):
# if left < int(i/ratio):
# left += 1
# right += 1
# if right > array.shape[0]-1:
# res[i] += array[left]
# else:
# res[i] += array[right] * \
# (i - left * ratio)/ratio+array[left]*(right*ratio-i)/ratio
# res = res[::-1]
# array = array[::-1]
# left = 0
# right = 1
# for i in range(shape):
# if left < int(i/ratio):
# left += 1
# right += 1
# if right > array.shape[0]-1:
# res[i] += array[left]
# else:
# res[i] += array[right] * \
# (i - left * ratio)/ratio+array[left]*(right*ratio-i)/ratio
# res = res[::-1]/2
# array = array[::-1]
# return res
class ActivationsAndGradients:
""" Class for extracting activations and
registering gradients from targetted intermediate layers """
def __init__(self, model, target_layer):
self.model = model
self.gradients = []
self.activations = []
target_layer.register_forward_hook(self.save_activation)
target_layer.register_full_backward_hook(self.save_gradient)
def save_activation(self, module, input, output):
self.activations.append(output)
def save_gradient(self, module, grad_input, grad_output):
# Gradients are computed in reverse order
self.gradients = [grad_output[0]] + self.gradients
def __call__(self, x):
self.gradients = []
self.activations = []
return self.model(x)
class BaseCAM:
def __init__(self, model, target_layer, use_cuda=False):
self.model = model.eval()
self.target_layer = target_layer
self.cuda = use_cuda
if self.cuda:
self.model = model.cuda()
self.activations_and_grads = ActivationsAndGradients(self.model, target_layer)
def forward(self, input_img):
return self.model(input_img)
def get_cam_weights(self,
input_tensor,
target_category,
activations,
grads):
raise Exception("Not Implemented")
def get_loss(self, output, target_category):
# print(output.size())
return output[target_category]
def __call__(self, input_tensor, target_category=None):
if self.cuda:
input_tensor = input_tensor.cuda()
output = self.activations_and_grads(input_tensor)
if target_category is None:
output = output.squeeze()
target_category = np.argmax(output.cpu().data.numpy())
# print(output)
# print(target_category)
self.model.zero_grad()
loss = self.get_loss(output, target_category)
loss.backward(retain_graph=True)
activations = self.activations_and_grads.activations[-1].cpu().data.numpy()[0, :]
grads = self.activations_and_grads.gradients[-1].cpu().data.numpy()[0, :]
#weights = np.mean(grads, axis=(0))
weights = self.get_cam_weights(input_tensor, target_category, activations, grads)
cam = np.zeros(activations.shape[1:], dtype=np.float32)
for i, w in enumerate(weights):
cam += w * activations[i, :]
# cam = activations.T.dot(weights)
# cam = activations.dot(weights)
# cam = activations.dot(weights)
# print(input_tensor.shape[1])
# print(cam.shape)
# x = np.arange(0, 247, 1)
# plt.plot(x, cam.reshape(-1, 1))
# sns.set()
# ax = sns.heatmap(cam.reshape(-1, 1).T)
#cam = cv2.resize(cam, input_tensor.shape[1:][::-1])
#cam = resize_1d(cam, (input_tensor.shape[2]))
cam = np.interp(np.linspace(0, cam.shape[0], input_tensor.shape[2]), np.linspace(0, cam.shape[0], cam.shape[0]), cam) #Change it to the interpolation algorithm that numpy comes with.
#cam = np.maximum(cam, 0)
# cam = np.expand_dims(cam, axis=1)
# ax = sns.heatmap(cam)
# plt.show()
# cam = cam - np.min(cam)
# cam = cam / np.max(cam)
heatmap = (cam - np.min(cam)) / (np.max(cam) - np.min(cam) + 1e-10)#归一化处理
# heatmap = (cam - np.mean(cam, axis=-1)) / (np.std(cam, axis=-1) + 1e-10)
print(heatmap.shape)
return heatmap
class GradCAM(BaseCAM):
def __init__(self, model, target_layer, use_cuda=False):
super(GradCAM, self).__init__(model, target_layer, use_cuda)
def get_cam_weights(self, input_tensor,
target_category,
activations,
grads):
grads_power_2 = grads ** 2
grads_power_3 = grads_power_2 * grads
sum_activations = np.sum(activations, axis=1)
eps = 0.000001
aij = grads_power_2 / (2 * grads_power_2 + sum_activations[:, None] * grads_power_3 + eps)
aij = np.where(grads != 0, aij, 0)
weights = np.maximum(grads, 0) * aij
weights = np.sum(weights, axis=1)
return weights
# from pytorch_grad_cam.utils.image import preprocess_image
model = Net1()
model.load_state_dict(torch.load('./data7/G0503_02.pt')) #Load your own pretrained model
target_layer = model.p3_0
net = GradCAM(model, target_layer)
from settest import Test
# from scipy.fftpack import fft
input_tensor = Test.Data[0:1, :]
input_tensor = input_tensor.float()
#plt.figure(figsize=(5, 1))
output = net(input_tensor)
import scipy.io as scio
input_tensor = input_tensor.numpy().squeeze()
dataNew = "G:\\datanew.mat"
scio.savemat(dataNew, mdict={'cam': output, 'data': input_tensor})