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import os
import numpy as np
import cv2 as cv
from tqdm import tqdm
class ImageNet:
def __init__(self, root, size=224):
self.root = root
self.size = size
self.top1_acc = -1
self.top5_acc = -1
self.root_val = os.path.join(self.root, "ILSVRC", "Data", "CLS-LOC", "val")
self.val_label_file = os.path.join(self.root, "caffe_ilsvrc12", "val.txt")
self.val_label = self.load_label(self.val_label_file)
@property
def name(self):
return self.__class__.__name__
def load_label(self, label_file):
label = list()
with open(label_file, "r") as f:
for line in f:
line = line.strip()
key, value = line.split()
key = os.path.join(self.root_val, key)
value = int(value)
label.append([key, value])
return label
def eval(self, model):
top_1_hits = 0
top_5_hits = 0
pbar = tqdm(self.val_label)
pbar.set_description("Evaluating {} with {} val set".format(model.name, self.name))
for fn, label in pbar:
img = cv.imread(fn)
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
img = cv.resize(img, dsize=(256, 256))
img = img[16:240, 16:240, :]
pred = model.infer(img)
if label == pred[0][0]:
top_1_hits += 1
if label in pred[0]:
top_5_hits += 1
self.top1_acc = top_1_hits/(len(self.val_label) * 1.0)
self.top5_acc = top_5_hits/(len(self.val_label) * 1.0)
def get_result(self):
return self.top1_acc, self.top5_acc
def print_result(self):
print("Top-1 Accuracy: {:.2f}%; Top-5 Accuracy: {:.2f}%".format(self.top1_acc*100, self.top5_acc*100))
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