occlusion.py 文件源码

def Occlusion_exp(image,occluding_size,occluding_stride,model,preprocess,classes,groundTruth):    
    img = np.copy(image)
    height, width,_= img.shape
    output_height = int(math.ceil((height-occluding_size)/occluding_stride+1))
    output_width = int(math.ceil((width-occluding_size)/occluding_stride+1))
    ocludedImages=[]
    for h in range(output_height):
        for w in range(output_width):
            #occluder region
            h_start = h*occluding_stride
            w_start = w*occluding_stride
            h_end = min(height, h_start + occluding_size)
            w_end = min(width, w_start + occluding_size)

            input_image = copy.copy(img)
            input_image[h_start:h_end,w_start:w_end,:] =  0
            ocludedImages.append(preprocess(Image.fromarray(input_image)))

    L = np.empty(output_height*output_width)
    L.fill(groundTruth)
    L = torch.from_numpy(L)
    tensor_images = torch.stack([img for img in ocludedImages])
    dataset = torch.utils.data.TensorDataset(tensor_images,L) 
    dataloader = torch.utils.data.DataLoader(dataset,batch_size=5,shuffle=False, num_workers=8) 

    heatmap=np.empty(0)
    model.eval()
    for data in dataloader:
        images, labels = data

        if use_gpu:
            images, labels = (images.cuda()), (labels.cuda(async=True))

        outputs = model(Variable(images))
        m = nn.Softmax()
        outputs=m(outputs)
        if use_gpu:   
            outs=outputs.cpu()
        heatmap = np.concatenate((heatmap,outs[0:outs.size()[0],groundTruth].data.numpy()))

    return heatmap.reshape((output_height, output_width))