vashineyu

def gradcam_plus(model, im, class_select, layer, image_size, preproc_fn, alpha=0.6, filter_threshold=0.5):
    """GradCAM method for visualizing input saliency.
    Args:
      model: keras model
      im: single image (with only RGB, [H,W,C])
      class_select: class to show
      layer: layer name
      image_size: tuple of image H,W
      preproc_fn: preprocessing function
      alpha: alpha

vashineyu

# CAM
import cv2
from PIL import Image
import tensorflow as tf
from tensorflow.python.keras import backend as K

def grad_cam_keras(model, im, class_select, layer, image_size, preproc_fn, alpha=0.6, filter_threshold=0.5):
    """GradCAM method for visualizing input saliency.
    Args:
      model: keras model

vashineyu

import sys
import json
from config_initalize import get_cfg_defaults
cfg = get_cfg_defaults()
sys.path.append(cfg.LAB_TOOLS)
print(cfg)
from organize_data import initalize_folders, search_raw_files, Link_origin_to_soft, merge_dicts
initalize_folders(cfg)

raw_files = []

vashineyu

import glob
import os
import shutil

def initalize_folders(cfg):
    """Initalize orgainzed folder.
    
    Args:
      cfg (object): configuration object
    """

vashineyu

import sys
import json
from config_initalize import get_cfg_defaults
cfg = get_cfg_defaults()
sys.path.append(cfg.LAB_TOOLS)
print(cfg)
from organize_data import initalize_folders, search_raw_files, Link_origin_to_soft, merge_dicts
initalize_folders(cfg)

raw_files = []

vashineyu

import glob
import os
import shutil

def initalize_folders(cfg):
    """Initalize orgainzed folder.
    
    Args:
      cfg (object): configuration object
    """

vashineyu

class GetDataset():
    """Claim Dataset object for inferencing.
    
    Args:
        slide_name (str): full path to slide
        f_inputs_preproc (function): preprocessing function to array
        patch_size (int): patch size of array
        stride (int): stride of image when get patches
        level (int): get patch at level
    

vashineyu

def get_large_image(slide_handler, dpl_list, sz = 256):
    """
    Given partial coordinate list, open them at once and return a large image.
    This is a modification from last version, which dynamically generate patches from slide and predict. However, that method request a interacte with openslide object too frequently, which cause a core-dump. In this version, we read the slide image as a whole then split them with normal operations.
    Args:
        - slide_handler: openslide object (SLIDE_OPENER object)
        - dpl_list: list of coordinates in tuple
        - sz: patch size
    Return:
        - large image: numpy array of partial WSI in high resolution (about 1/n_gpus patches of a WSI)

vashineyu

"""Data generator for model

GetDataset: Get single data with next
Customized dataloader: Compose multiple dataset object together and put them into multi-processing flow

ver1. all patches were taken from single slide util N patches have been taken.
"""
import cv2
import os
import json

vashineyu

# model.py

import tensorflow as tf
import tensorflow.keras.layers as layers
import tensorflow.keras.models as models
import tensorflow.nn as F

class Conv_bn_relu(models.Model):
    """Stack blocks of Conv2D->BN->relu.