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