rebuild

tools/fp32comparefp16.py

@@ -0,0 +1,112 @@
+import os
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from test_ori import group_image, init_model, featurize
+from config import config as conf
+import json
+import os.path as osp
+
+def compare_fp16_fp32(values_pf16, values_pf32, dataTest):
+    if dataTest:
+        norm_values_pf16 = torch.norm(values_pf16, p=2)
+        norm_values_pf32 = torch.norm(values_pf32, p=2)
+        euclidean_distance = torch.norm(norm_values_pf16 - norm_values_pf32, p=2)
+        print(f"欧几里得距离: {euclidean_distance}")
+        cosine_sim = torch.dot(values_pf16.float(), values_pf32) / (norm_values_pf16 * norm_values_pf32)
+        print(f"余弦相似度: {cosine_sim}")
+    else:
+
+        pass
+def cosin_metric(x1, x2, fp32=True):
+    if fp32:
+        return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
+    else:
+        x1_fp16 = x1.astype(np.float16)
+        x2_fp16 = x2.astype(np.float16)
+        # print(type(x1))
+        # pdb.set_trace()
+        return np.dot(x1_fp16, x2_fp16) / (np.linalg.norm(x1_fp16) * np.linalg.norm(x2_fp16))
+def deal_group_pair(pairList1, pairList2):
+    one_similarity_fp16, one_similarity_fp32, allsimilarity_fp32, allsimilarity_fp16 = [], [], [], []
+    for pair1 in pairList1:
+        for pair2 in pairList2:
+            # similarity = cosin_metric(pair1.cpu().numpy(), pair2.cpu().numpy())
+            one_similarity_fp32.append(cosin_metric(pair1.cpu().numpy(), pair2.cpu().numpy(), True))
+            one_similarity_fp16.append(cosin_metric(pair1.cpu().numpy(), pair2.cpu().numpy(), False))
+        allsimilarity_fp32.append(one_similarity_fp32)
+        allsimilarity_fp16.append(one_similarity_fp16)
+        one_similarity_fp16, one_similarity_fp32 = [], []
+    return np.array(allsimilarity_fp32), np.array(allsimilarity_fp16)
+
+def compute_group_accuracy(content_list_read, model):
+    allSimilarity, allLabel = [], []
+    Same, Cross = [], []
+    flag_same = True
+    flag_diff = True
+    for data_loaded in content_list_read:
+        one_group_list = []
+        try:
+            if (flag_same and str(data_loaded[-1]) == '1') or (flag_diff and str(data_loaded[-1]) == '0'):
+                for i in range(2):
+                    images = [osp.join(conf.test_val, img) for img in data_loaded[i]]
+                    group = group_image(images, conf.test_batch_size)
+                    d = featurize(group[0], conf.test_transform, model, conf.device)
+                    one_group_list.append(d.values())
+                if str(data_loaded[-1]) == '1':
+                    flag_same = False
+                    allsimilarity_fp32, allsimilarity_fp16 = deal_group_pair(one_group_list[0], one_group_list[1])
+                    print('fp32 same-- >', allsimilarity_fp32)
+                    print('fp16 same-- >', allsimilarity_fp16)
+                else:
+                    flag_diff = False
+                    allsimilarity_fp32, allsimilarity_fp16 = deal_group_pair(one_group_list[0], one_group_list[1])
+                    print('fp32 diff-- >', allsimilarity_fp32)
+                    print('fp16 diff-- >', allsimilarity_fp16)
+        except Exception as e:
+            continue
+        # print(allSimilarity)
+        # print(allLabel)
+    return allSimilarity, allLabel
+def get_feature_list(imgPth):
+    imgs = get_files(imgPth)
+    group = group_image(imgs, conf.test_batch_size)
+    model = init_model()
+    model.eval()
+    fe = featurize(group[0], conf.test_transform, model, conf.device)
+    return fe
+
+
+def get_files(imgPth):
+    imgsList = []
+    for img in os.walk(imgPth):
+        for img_name in img[2]:
+            img_path = os.sep.join([img[0], img_name])
+            imgsList.append(img_path)
+    return imgsList
+import pdb
+
+def compare(imgPth, group=False):
+    model = init_model()
+    model.eval()
+    if not group:
+        values_pf16, values_pf32 = [], []
+        fe = get_feature_list(imgPth)
+        # pdb.set_trace()
+        values_pf32 += [value.cpu() for value in fe.values()]
+        values_pf16 += [value.cpu().half() for value in fe.values()]
+        for value_pf16, value_pf32 in zip(values_pf16, values_pf32):
+            compare_fp16_fp32(value_pf16, value_pf32, dataTest=True)
+    else:
+        filename = conf.test_group_json
+        with open(filename, 'r', encoding='utf-8') as file:
+            content_list_read = json.load(file)
+        compute_group_accuracy(content_list_read, model)
+        pass
+
+
+if __name__ == '__main__':
+    imgPth = './data/test/inner/3701375401900'
+    compare(imgPth)