Annotated adversarially sampled negative pairs.

adversarial_sampling.py

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+from argparse import ArgumentParser
+from glob import glob
+import os
+
+import numpy as np
+from sklearn.utils import shuffle
+import matplotlib.pyplot as plt
+import pandas as pd
+from tqdm import tqdm
+from PIL import Image
+import matplotlib.pyplot as plt
+
+def load_image(filename):
+    try:
+        with open(filename, "rb") as f:
+            image = Image.open(f)
+            return image.convert("RGB")
+    except UserWarning as e:
+        print(filename)
+        input("Something wrong happens while loading image: {} {}".format(filename, str(e)))
+
+# Example Model definition
+class Model(object):
+    def __init__(self, dirname):
+        import animecv
+
+        self.encoder = animecv.general.create_OML_ImageFolder_Encoder(dirname)
+        self.encoder.to("cuda")
+
+    # img: PIL image
+    def encode(self, img):
+        vecs = self.encoder.encode([img]).detach().cpu().numpy()
+        return vecs[0]
+
+if __name__=="__main__":
+    parser = ArgumentParser()
+
+    parser.add_argument("--test-pairs", help="CSV file which lists test image pairs.")
+    parser.add_argument("--test-dataset-dir", help="Directory of test images.")
+    parser.add_argument("--ignore-list", default=None, help="List of images which should be ignored during pair sampling.")
+
+    parser.add_argument("--out-fn", default="adversarial.csv")
+
+    parser.add_argument("--n-negative", type=int, default=3000)
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.out_fn):
+        if args.ignore_list is not None:
+            df = pd.read_csv(args.ignore_list, header=None)
+            ignore_list = set(df.values.flatten().tolist())
+        else:
+            ignore_list = set()
+
+        # Generate adversarial negative pairs.
+        model = Model("0206_resnet152")
+
+        images = glob(os.path.join(args.test_dataset_dir, "**"), recursive=True)
+        images = [fn for fn in images if os.path.isfile(fn)]
+        labels = [fn.split(os.path.sep)[-2] for fn in images]
+
+        vecs = []
+        for fn in tqdm(images):
+            img = load_image(fn)
+            vecs.append(model.encode(img).reshape((1,-1)))
+        vecs = np.concatenate(vecs, axis=0)
+
+        scores = np.sum(vecs[:,np.newaxis,:] * vecs[np.newaxis,:,:], axis=2)
+
+        negative_pairs = []
+        n_img = scores.shape[0]
+        sorted_idx = np.argsort(-scores, axis=None).tolist()
+        strip_len = len(args.test_dataset_dir + os.path.sep)
+        while len(negative_pairs) < args.n_negative:
+            idx = sorted_idx.pop(0)
+            i,j = idx // n_img, idx % n_img
+            if i<=j:
+                continue
+            if labels[i] == labels[j]:
+                continue
+            if os.path.basename(images[i]) in ignore_list:
+                continue
+            if os.path.basename(images[j]) in ignore_list:
+                continue
+            negative_pairs.append((images[i][strip_len:], images[j][strip_len:], 0, -1, 0))
+
+        # Reuse positive pairs.
+        positive_pairs = []
+        df = pd.read_csv(args.test_pairs)
+        for pathA, pathB in df[df["label"]==1][["pathA", "pathB"]].values:
+            #print(pathA, pathB)
+            positive_pairs.append((pathA, pathB, 1, -1, 0))
+
+        pairs = shuffle(positive_pairs + negative_pairs)
+
+        df = pd.DataFrame(pairs, columns=["pathA", "pathB", "label", "human_prediction", "invalid"])
+        df.to_csv(args.out_fn, index=False)
+    else:
+        print("Reload")
+        df = pd.read_csv(args.out_fn)
+
+    for i_row in tqdm(list(range(df.values.shape[0]))):
+        pathA, pathB, label, pred, invalid = df.loc[i_row].values
+        #print(pathA, pathB)
+        if pred >= 0:
+            continue
+        else:
+            im1 = np.array(Image.open(os.path.join(args.test_dataset_dir, pathA)))
+            im2 = np.array(Image.open(os.path.join(args.test_dataset_dir, pathB)))
+            ax = plt.subplot(1,2,1)
+            ax.imshow(im1)
+            ax = plt.subplot(1,2,2)
+            ax.imshow(im2)
+            plt.draw()
+            plt.pause(0.001)
+            cmd = input("correct?[y/n]: ")
+            if cmd=="y":
+                pred = 1
+            elif cmd=="n":
+                pred = 0
+            else:
+                pred = 0
+                df.loc[i_row, "invalid"] = 1
+            df.loc[i_row, "human_prediction"] = pred
+            df.to_csv(args.out_fn, index=False)
+            plt.close()

dataset/ignore_images.csv

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+3196587.jpg
+2715436.jpg
+4073331.jpg
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+1375976.jpg
+2022189.jpg
+3679066.jpg
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+2557668.jpg
+3037438.jpg
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+131866.jpg
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+2679171.jpg
+1297445.jpg
+3654015.jpg
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+911056.jpg
+2590354.jpg
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+456627.jpg
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+790640.jpg
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+1446387.jpg
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+2679325.jpg
+2132647.jpg
+664895.jpg
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+3564166.jpg
+2017193.jpg
+3306763.jpg
+3603215.jpg
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+2917089.jpg
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+511047.jpg
+560368.jpg
+836229.jpg
+1288484.jpg
+481397.jpg
+1239831.jpg
+3865565.jpg
+2518166.jpg
+1610891.jpg
+1449964.jpg
+710851.jpg
+3910247.jpg
+4174543.jpg
+2039196.jpg
+651377.jpg
+2132651.jpg