![]() ![]() (convOutputs, predictions) = gradModel(inputs) # associated with the specific class index # image through the gradient model, and grab the loss ![]() # cast the image tensor to a float-32 data type, pass the # record operations for automatic differentiation # final 4D layer in the network, and (3) the output of the # to our pre-trained model, (2) the output of the (presumably) # construct our gradient model by supplying (1) the inputs Cannot apply GradCAM.")ĭef compute_heatmap(self, image, eps=1e-8): Raise ValueError("Could not find 4D layer. # otherwise, we could not find a 4D layer so the GradCAM # check to see if the layer has a 4D output # by looping over the layers of the network in reverse orderįor layer in reversed(): # attempt to find the final convolutional layer in the network Self.layerName = self.find_target_layer() # if the layer name is None, attempt to automatically find # activation map, and the layer to be used when visualizing # store the model, the class index used to measure the class Gradcam.py from import Modelĭef _init_(self, model, classIdx, layerName=None): Output = imutils.resize(output, height=700) # display the original image and resulting heatmap and output image Heatmap = cv2.resize(heatmap, (orig.shape, orig.shape)) # and then overlay heatmap on top of the image # resize the resulting heatmap to the original input image dimensions # initialize our gradient class activation map and build the heatmap # decode the ImageNet predictions to obtain the human-readable labelĭecoded = imagenet_code_predictions(preds) # the class label index with the largest corresponding probability # use the network to make predictions on the input image and find Image = imagenet_utils.preprocess_input(image) # load the input image from disk (in Keras/TensorFlow format) and # resize the image to its target dimensions # load the original image from disk (in OpenCV format) and then # construct the argument parser and parse the argumentsĪp.add_argument("-i", "-image", required=True,Īp.add_argument("-m", "-model", type=str, default="vgg", I want to apply gradcam using my own model and not VGG16 or ResNet etc.Īpply_gradcam.py # import the necessary packagesįrom import ResNet50įrom import VGG16įrom import img_to_arrayįrom import load_imgįrom import imagenet_utilsįrom import load_model I have already trained a network and I have saved it in the form of mynetwork.model. ![]()
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