LoadImageD performance

@drbeh @Nic-Ma Can I ask, why is LoadImageD with WSIReader is slower than WSIReader directly (both use tiffile backend) https://github.com/Project-MONAI/MONAI/issues/3251

start = time.time()

wsi_reader =WSIReader( backend="TiffFile", level=1)
transform =  LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True)

wsi_time = []
transform_time=[]

for i in range(100):
    start = time.time()
    img_obj = wsi_reader.read(image_path)
    image = wsi_reader.get_data(img_obj)[0]
    wsi_time.append(time.time()-start)

    start = time.time()
    image = transform({"image" : image_path})['image']
    transform_time.append(time.time()-start)


print('wsi time', sum(wsi_time[1:])/(len(wsi_time)-1), 'transform_time time', sum(transform_time[1:])/(len(transform_time)-1))

wsi time 0.02141376216002185 transform_time time 0.026847357701773596

which is 20% slower, and my overall code runs 10% slower

[cgohlke]

numpy.ndarray.astype makes a copy by default. Try the copy=False parameter.

[myron]

wsi_reader =WSIReader( backend="TiffFile", level=1)
transform =  LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True)

wsi_time = []
wsi_time2 = []
wsi_time3 = []
transform_time=[]

for i in range(100):
    start = time.time()
    img_obj = wsi_reader.read(image_path)
    image = wsi_reader.get_data(img_obj)[0]
    wsi_time.append(time.time()-start)


    start = time.time()
    img_obj = wsi_reader.read(image_path)
    image = wsi_reader.get_data(img_obj)[0].astype(np.uint8)
    wsi_time2.append(time.time()-start)

    start = time.time()
    img_obj = wsi_reader.read(image_path)
    image = wsi_reader.get_data(img_obj)[0]
    if image.dtype!=np.uint8:
        image = image.astype(np.uint8)
    wsi_time3.append(time.time()-start)


    start = time.time()
    image = transform({"image" : image_path})['image']
    transform_time.append(time.time()-start)


print('wsi time', sum(wsi_time[1:])/(len(wsi_time)-1), 'wsi time2', sum(wsi_time2[1:])/(len(wsi_time2)-1), 'wsi time3', sum(wsi_time3[1:])/(len(wsi_time2)-1), 'transform_time time', sum(transform_time[1:])/(len(transform_time)-1))

result wsi time 0.022537204954359267 wsi time2 0.028219649285981148 wsi time3 0.022044388934819387 transform_time time 0.027675144600145744

summary: adding .astype(np.uint8) indeed makes it slower even if data is already np.uint8 we can solve it by checking for the type first

    if image.dtype!=np.uint8:
        image = image.astype(np.uint8)

this runs fast. I suggest we make a PR for it, we can compare if dtype matches the provided type, and if they don’t match do the conversion