| 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)) |
|
|
|
|
