Scaling up - Githubissues

First issue in scaling up:

Looks like one needs to analyze all the photos to train with, to get preprocess() factors like in this example:

http://blog.outcome.io/pytorch-quick-start-classifying-an-image/

Seems someone would have written a program by now to take a set of images and output the numbers for

normalize = transforms.Normalize(
   mean=[0.485, 0.456, 0.406],
   std=[0.229, 0.224, 0.225]
)
preprocess = transforms.Compose([
   transforms.Scale(256),
   transforms.CenterCrop(224),
   transforms.ToTensor(),
   normalize
])

Here's a stab at it:

import numpy as np
from PIL import Image

pic_dir = '~/images/prun299'
fileList = pic_dir + '/files'

pixCount = 0
RGB = [0.0, 0.0, 0.0]

with open(fileList) as fp:
    for line in fp:
        file = pic_dir + "/" + line.rstrip()
        try:
            im = Image.open(file)
        except Exception, e:
            print "None: %s %s" % (file, str(e))
            continue

        for x in range(im.width):
            for y in range(im.height):
                pix = im.getpixel((x, y))
                RGB[0] += pix[0]
                RGB[1] += pix[1]
                RGB[2] += pix[2]
        pixCount += im.width * im.height
        im.close()

RGB[0] /= pixCount
RGB[1] /= pixCount
RGB[2] /= pixCount

DEV = [0.0, 0.0, 0.0]

print('pass 2')

with open(fileList) as fp:
    for line in fp:
        #print('line ' + line)
        file = pic_dir + "/" + line.rstrip()
        try:
            im = Image.open(file)
        except:
            continue

        #print('file ' + file)
        for x in range(im.width):
            for y in range(im.height):
                pix = im.getpixel((x, y))
                d = RGB[0] - pix[0]
                DEV[0] += d * d
                d = RGB[1] - pix[1]
                DEV[1] += d * d
                d = RGB[2] - pix[2]
                DEV[2] += d * d
         im.close()

DEV[0] /= pixCount
DEV[1] /= pixCount
DEV[2] /= pixCount
DEV = np.sqrt(DEV)

RGB[0] /= 255
RGB[1] /= 255
RGB[2] /= 255

DEV[0] /= 255
DEV[1] /= 255
DEV[2] /= 255

print('mean=[' + str(RGB[0]) + ', ' + str(RGB[1]) + ', ' + str(RGB[2]) + '],')
print('std=[' + str(DEV[0]) + ', ' + str(DEV[1]) + ', ' + str(DEV[2]) + ']')

#  6764 files:
# mean=[0.3876046197, 0.3751385941, 0.3667266388],
# std=[0.2649736267, 0.2584158245, 0.2701408752]

Resulting in this initial loader, keras version works, this one untested.

from torchvision import models, transforms
from PIL import Image

pair_dir = '~/pb'
pic_dir = '~/images/prun299'
image_size = 299

normalize = transforms.Normalize(
 mean=[0.3876046197, 0.3751385941, 0.3667266388],
 std=[0.2649736267, 0.2584158245, 0.2701408752]
)
preprocess = transforms.Compose([
   transforms.Scale(256),
   transforms.CenterCrop(image_size),
   transforms.ToTensor(),
   normalize
])

file_map = {}

def load_preproc():
    print('Loading pics')
    scan_file(pair_dir + '/test.neg')
    scan_file(pair_dir + '/test.pos')
    scan_file(pair_dir + '/train.pos')
    scan_file(pair_dir + '/train.neg')

def scan_file(fname):
    print('Scan file: ' + fname)
    ct = 0
    ct2 = 0
    with open(fname) as fp:
        for line in fp:
            fname1, fname2 = line.split()
            if (type(file_map.get(fname1, None)) is NoneType):
                ct += 1
                img_pil = Image.open(pic_dir + '/' + fname1)
                img_tensor = preprocess(img_pil)
                img_tensor.unsqueeze_(0)
                file_map[fname1] = img_tensor
            else:
                ct2 += 1

            if (type(file_map.get(fname2, None)) is NoneType):
                ct += 1
                img_pil = Image.open(pic_dir + '/' + fname2)
                img_tensor = preprocess(img_pil)
                img_tensor.unsqueeze_(0)
                file_map[fname2] = img_tensor
            else:
                ct2 += 1

    print('    loaded: ' + str(ct) + ' already loaded: ' + str(ct2))

Here's how the keras file-load-preproc portion looks:

from keras.preprocessing import image
from keras.applications.inception_v3 import preprocess_input

                im1 = image.load_img(pic_dir + '/' + fname1, target_size=input_dim)
                x = image.img_to_array(im1)
                x = np.expand_dims(x, axis=0)
                x = preprocess_input(x)[0]
                file_map[fname1] = x

sanyam5 / arc-pytorch

Scaling up #4