Memory leak in Python application

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I am running maskrcnn_benchmark in the server and facing an issue of memory leak. I have made a flask application which when gets a request utilizes 800 MB of memory. On making another request the memory starts accumulating from 800 MB and goes further. On sending multiple requests simultaneously the program occupies all the allocated memory and exits.

I am not able to figure out where the memory is leaking. I am attaching some code snippets where I am using maskrcnn in my program.

def process_maskrcnn(self,front,side):        # try:        self.obj_maskrcnn.loader()        frontrcnnmask,frontmask = self.obj_maskrcnn.run_maskrcnn(front,'f')        sidercnnmask,sidemask = self.obj_maskrcnn.run_maskrcnn(side,'s')        cv2.imwrite("frontmaskrcnn.jpg",frontrcnnmask)        cv2.imwrite("sidemaskrcnn.jpg",sidercnnmask)        gc.collect()        return True,frontrcnnmask,sidercnnmask,frontmask,sidemaskdef run_maskrcnn(self, image,pose):        if pose == 'f':            predictions = self.compute_prediction(image)            top_predictions = self.select_top_predictions(predictions)            result = image.copy()            maskimg,maskimgall = self.overlay_mask(result, top_predictions)            # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))            # maskimg = cv2.dilate(maskimg, kernel)            # maskimgall = cv2.dilate(maskimgall, kernel)            if cfg.debug == 'True':                cv2.imwrite(self.outputdir +'/front.jpg', maskimg)            return maskimg, maskimgall        if pose == 's':            predictions = self.compute_prediction(image)            top_predictions = self.select_top_predictions(predictions)            result = image.copy()            maskimg, maskimgall = self.overlay_mask(result, top_predictions)            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))            maskimg = cv2.dilate(maskimg, kernel)            maskimgall = cv2.dilate(maskimgall, kernel)            if cfg.debug == 'True':                cv2.imwrite(self.outputdir +'/side.jpg', maskimg)            return maskimg, maskimgalldef compute_prediction(self, original_image):        image = self.transforms(original_image)        image_list = to_image_list(image, self.cfg.DATALOADER.SIZE_DIVISIBILITY)        image_list = image_list.to(self.device)        with torch.no_grad():            predictions = self.model(image_list)        predictions = [o.to(self.cpu_device) for o in predictions]        prediction = predictions[0]        height, width = original_image.shape[:-1]        prediction = prediction.resize((width, height))        if prediction.has_field("mask"):            masks = prediction.get_field("mask")            masks = self.masker([masks], [prediction])[0]            prediction.add_field("mask", masks)def select_top_predictions(self, predictions):        scores = predictions.get_field("scores")        keep = torch.nonzero(scores > self.confidence_threshold).squeeze(1)        predictions = predictions[keep]        scores = predictions.get_field("scores")        _, idx = scores.sort(0, descending=True)        return predictions[idx]

Here I would like to know how I can clear the memory after each request if it is possible to?

How we can explicitly delete the objects which are consuming memory at the end of each request?