计算两张图的余弦相似度

# 结果余弦相似度对比
import numpy as np
import pdb
 
def count_difference(groundtruth, inputs):
    statistical_method = {
        'cosine_similarity':
        lambda X1, X2: np.sum(X1 * X2) /
        (np.sqrt(np.sum(X1**2)) * np.sqrt(np.sum(X2**2))),
        'maximum_absolute_error':
        lambda X1, X2: np.max(np.abs(X1 - X2)).tolist(),
        'accumulated_relative_error':
        lambda X1, X2: np.sum(np.abs(np.nan_to_num((X1 - X2) / X1))),
        'relative_euclidean_distance':
        lambda X1, X2: np.sqrt(
            np.sum((X1 / np.sqrt(np.sum(X1**2)) - X2 / np.sqrt(np.sum(X2**2)))
                   **2)).tolist(),
        'kullback_leibler_divergence':
        lambda X1, X2: np.sum(X1 * np.nan_to_num(np.log(X1 / X2))),
        'standard_deviation':
        lambda *X: [(np.mean(x).tolist(), np.std(x).tolist()) for x in X]
    }
 
    reports = {}
    for input_key in groundtruth.keys():
        if input_key in inputs.keys():
            reports[input_key] = {}
            gt_input = groundtruth[input_key].reshape(-1, 1)
            compare_input = inputs[input_key].reshape(-1, 1)
            assert gt_input.size == compare_input.size# 要求对比的两张图尺寸一致
            for key, value in statistical_method.items():
                reports[input_key][key] = value(gt_input, compare_input)
    return reports
 
 
def main():
    
    a = np.fromfile("/home/wangmaolin/for_test/tofile/conv_82_memory", dtype=np.float32)
    print(a.shape)
    print(a.dtype)
    inputs = {"data": a}
 
    c = np.fromfile("/home/wangmaolin/for_test/onnx_output/onnx_output_conv_82", dtype=np.float32)
    print(c.shape)
    print(c.dtype)
    gt_inputs = {"data": c}
    
 
    report = count_difference(gt_inputs, inputs)
    print(report)
 
 
if __name__ == '__main__':
    main()