jimmy15923

import sys
sys.path.append("/data/jimmy15923/CGMH_NPC_PROGRAM/ndpi2dzi/") ## set path for ndpi
import ndpread
import json
import numpy as np
import pickle
import cv2
from matplotlib.path import Path
import json
import matplotlib.pyplot as plt

jimmy15923

"""
Tesing code for IBM LMS / CUDA Unified Memory
Run this script with CUDA Unified Memory by
```
python LMS_UM_test.py --image_size=224 --batch_size=256 --gpu_id=1 --cuda_memory=5
```

Run this script with IBM Large Model Support
```
python LMS_UM_test.py --image_size=224 --batch_size=256 --gpu_id=1 --use_lms=True

jimmy15923

"""
Tesing code for CUDA unified memory
Run this script with CUDA unified memory by
```
python cuda_unified_test.py --image_size=224 --batch_size=256 --gpu_id=1 --cuda_memory=5
```
"""

import numpy as np
import time

jimmy15923

import numpy as np
import tensorflow as tf
import os

# FLAGS
tf.logging.set_verbosity(tf.logging.INFO)
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('f', '', 'kernel')
tf.app.flags.DEFINE_string("gpu_id", "0", "idx of GPU using")
tf.app.flags.DEFINE_integer("batch_size", 512, "Batch size")

jimmy15923

import tensorflow as tf
import numpy as np
import six

def _bn_relu(input):
    """Helper to build a BN -> relu block (by @raghakot)."""
    norm = tf.keras.layers.BatchNormalization(axis=CHANNEL_AXIS)(input) 
    return tf.keras.layers.Activation("relu")(norm) #Activation("relu")(norm)

jimmy15923

def resize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square"):
    """Resizes an image keeping the aspect ratio unchanged.
    min_dim: if provided, resizes the image such that it's smaller
        dimension == min_dim
    max_dim: if provided, ensures that the image longest side doesn't
        exceed this value.
    min_scale: if provided, ensure that the image is scaled up by at least
        this percent even if min_dim doesn't require it.
    mode: Resizing mode.
        none: No resizing. Return the image unchanged.

jimmy15923

import keras.layers as KL
import keras.engine as KE
import keras.models as KM

from keras.engine import Layer, InputSpec
from keras import initializers, regularizers, constraints
from keras import backend as K
from keras.utils.generic_utils import get_custom_objects

class BatchNorm(KL.BatchNormalization):

jimmy15923

import tensorflow as tf
import keras
import keras.backend as K
import keras.layers as KL
import keras.engine as KE
import keras.models as KM

from keras.engine import Layer, InputSpec
from keras import initializers, regularizers, constraints
from keras import backend as K

jimmy15923

def grad_cam(img, model):
    """Gradient Activation Map for keras model
    # Arguments
        img: image to plot gradcam
        model: keras model
    # Returns
        gradcam image
    """  
  
    img = np.expand_dims(img, 0)

jimmy15923

import sys
sys.path.append("/mnt/deep-learning/usr/jimmy15923/keras_rcnn_family")
import json
import numpy as np
from collections import Counter
from scipy.ndimage.measurements import label
from slide_reader import *
import glob
import matplotlib.pyplot as plt