warmup1.py

# -*- coding: utf-8 -*-
"""Untitled

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1JDQjeuRBusQaEUEwkTGnq_SrS4IzlMUE
"""

import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import matplotlib
matplotlib.use("Agg")

import keras
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from keras.preprocessing import image
import numpy as np
import pandas as pd
model = VGG16(weights = 'imagenet')

val_path = '/mnt/fast-data16/datasets/ILSVRC/2012/clsloc/val/'
n_val = len(os.listdir(val_path))
print('num of validation files: ', n_val)

classes = os.listdir(val_path)
val_path_list = []
for class_file in classes:
  path = val_path + class_file + '/'
  images_files = os.listdir(path)
  for image_file in images_files:
    val_path_list.append(path+ image_file)
  
def get_prediction(file_path):
  img = image.load_img(file_path, target_size=(224, 224))
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  preds = model.predict(preprocess_input(x))
  return decode_predictions(preds, top=5)[0]

results = []
for i in range(len(val_path_list)):
  if i%1000 == 0:
    print ('now predicting', i, 'th image')
  results.append(get_prediction(val_path_list[i]))

save_path = '/home/xiao/warmup1'
df = pd.DataFrame(results)
df.to_csv(os.path.join(save_path, 'warmup1_prediction.csv'))
print ('YEAH!')