Upload hybridnets/autoanchor.py

hybridnets/autoanchor.py

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+# Auto-anchor utils
+
+import numpy as np
+import torch
+import yaml
+from scipy.cluster.vq import kmeans
+from tqdm import tqdm
+import math
+
+
+def check_anchor_order(anchors, anchor_grid, stride):
+    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+    a = anchor_grid.prod(-1).view(-1)  # anchor area
+    da = a[-1] - a[0]  # delta a
+    ds = stride[-1] - stride[0]  # delta s
+    if da.sign() != ds.sign():  # same order
+        print('Reversing anchor order')
+        anchors[:] = anchors.flip(0)
+        anchor_grid[:] = anchor_grid.flip(0)
+    return anchors, anchor_grid, stride
+
+
+def run_anchor(logger, dataset, thr=4.0, imgsz=640):
+    # default_anchors = [[3, 9, 5, 11, 4, 20], [7, 18, 6, 39, 12, 31], [19, 50, 38, 81, 68, 157]]
+    # nl = len(default_anchors)  # number of detection layers 3
+    # na = len(default_anchors[0]) // 2  # number of anchors 3
+    # anchors = torch.tensor(default_anchors,
+    #                        device=torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    #                        ).float().view(nl, -1, 2)
+    # anchor_num = na * nl
+    anchor_num = 9
+    new_anchors = kmean_anchors(dataset, n=anchor_num, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+
+    scales = [0, None, None]
+    scales[1] = math.log2(np.mean(new_anchors[1::3][:, 0] / new_anchors[0::3][:, 0]))
+    scales[2] = math.log2(np.mean(new_anchors[2::3][:, 0] / new_anchors[0::3][:, 0]))
+    scales = [round(2 ** x, 2) for x in scales]
+
+    normalized_anchors = new_anchors / np.sqrt(new_anchors.prod(axis=1, keepdims=True))
+    ratios = [(1.0, 1.0), None, None]
+    ratios[1] = (np.mean(normalized_anchors[:, 0]), np.mean(normalized_anchors[:, 1]))
+    ratios[2] = (np.mean(normalized_anchors[:, 1]), np.mean(normalized_anchors[:, 0]))
+    ratios = [(round(x, 2), round(y, 2)) for x, y in ratios]
+    print("New scales:", scales)
+    print("New ratios:", ratios)
+    print('New anchors saved to model. Update model config to use these anchors in the future.')
+    return str(scales), str(ratios)
+
+
+def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
+    """ Creates kmeans-evolved anchors from training dataset
+
+        Arguments:
+            path: path to dataset *.yaml, or a loaded dataset
+            n: number of anchors
+            img_size: image size used for training
+            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
+            gen: generations to evolve anchors using genetic algorithm
+            verbose: print all results
+
+        Return:
+            k: kmeans evolved anchors
+
+        Usage:
+            from utils.autoanchor import *; _ = kmean_anchors()
+    """
+    thr = 1. / thr
+
+    def metric(k, wh):  # compute metrics
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        # x = wh_iou(wh, torch.tensor(k))  # iou metric
+        return x, x.max(1)[0]  # x, best_x
+
+    def anchor_fitness(k):  # mutation fitness
+        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
+        return (best * (best > thr).float()).mean()  # fitness
+
+    def print_results(k):
+        k = k[np.argsort(k.prod(1))]  # sort small to large
+        x, best = metric(k, wh0)
+        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
+        print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
+        print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
+              (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
+        for i, x in enumerate(k):
+            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
+        return k
+
+    if isinstance(path, str):  # not class
+        raise TypeError('Dataset must be class, but found str')
+    else:
+        dataset = path  # dataset
+
+    labels = [db['label'] for db in dataset.db]
+    labels = np.vstack(labels)
+    if not (labels[:, 1:] <= 1).all():
+        # normalize label
+        labels[:, [2, 4]] /= dataset.shapes[0]
+        labels[:, [1, 3]] /= dataset.shapes[1]
+    # Get label wh
+    shapes = img_size * dataset.shapes / dataset.shapes.max()
+    # wh0 = np.concatenate([l[:, 3:5] * shapes for l in labels])  # wh
+    wh0 = labels[:, 3:5] * shapes
+    # Filter
+    i = (wh0 < 3.0).any(1).sum()
+    if i:
+        print('WARNING: Extremely small objects found. '
+              '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
+    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
+
+    # Kmeans calculation
+    print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
+    s = wh.std(0)  # sigmas for whitening
+    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
+    k *= s
+    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
+    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
+    k = print_results(k)
+
+    # Plot
+    # k, d = [None] * 20, [None] * 20
+    # for i in tqdm(range(1, 21)):
+    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
+    # ax = ax.ravel()
+    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
+    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
+    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
+    # fig.savefig('wh.png', dpi=200)
+
+    # Evolve
+    npr = np.random
+    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
+    pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm')  # progress bar
+    for _ in pbar:
+        v = np.ones(sh)
+        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
+            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
+        kg = (k.copy() * v).clip(min=2.0)
+        fg = anchor_fitness(kg)
+        if fg > f:
+            f, k = fg, kg.copy()
+            pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
+            if verbose:
+                print_results(k)
+
+    return print_results(k)