Yolo segmentation hyperparameter Evolution

iamprakhargupta commented 1 year ago

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Question

I have multiple questions which I have since I am trying to run hyperparameter evolution for my segmentation model.

I do see that the segmentation code train.py has code for hyperparameter evolution

Q1: I see the fitness function comments below state that weights will be applied to P R metrics/mAP_0.5(B) and metrics/mAP_0.5:0.95 by slicing all rows and first 4 columns of x

def fitness(x):
    # Model fitness as a weighted combination of metrics
    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
    return (x[:, :4] * w).sum(1)

But in the train.py when we read the CSV we pass all the values of the csv to the fitness()

for _ in range(opt.evolve):  # generations to evolve
            if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate
                # Select parent(s)
                parent = 'single'  # parent selection method: 'single' or 'weighted'
                x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
                n = min(5, len(x))  # number of previous results to consider
                x = x[np.argsort(-fitness(x))][:n]  # top n mutations
                w = fitness(x) - fitness(x).min() + 1E-6  # weights (sum > 0)
                if parent == 'single' or len(x) == 1:
                    # x = x[random.randint(0, n - 1)]  # random selection
                    x = x[random.choices(range(n), weights=w)[0]]  # weighted selection
                elif parent == 'weighted':
                    x = (x * w.reshape(n, 1)).sum(0) / w.sum()

Now my question is how are weights being applied to my metrics because when I look at my evolve.csv

      train/box_loss,      train/seg_loss,      train/obj_loss,      train/cls_loss,metrics/precision(B),   metrics/recall(B),  metrics/mAP_0.5(B),metrics/mAP_0.5:0.95(B),metrics/precision(M),   metrics/recall(M),  metrics/mAP_0.5(M),metrics/mAP_0.5:0.95(M),        val/box_loss,        val/seg_loss,        val/obj_loss,        val/cls_loss,               x/lr0,               x/lr1,               x/lr2,                 lr0,                 lrf,            momentum,        weight_decay,       warmup_epochs,     warmup_momentum,      warmup_bias_lr,                 box,                 cls,              cls_pw,                 obj,              obj_pw,               iou_t,            anchor_t,            fl_gamma,               hsv_h,               hsv_s,               hsv_v,             degrees,           translate,               scale,               shear,         perspective,              flipud,              fliplr,              mosaic,               mixup,          copy_paste,             anchors
             0.82198,             0.64013,             0.71905,             0.44133,             0.77251,             0.55216,             0.59938,             0.27542,            0.044692,            0.048322,            0.030093,           0.0058852,               0.001,                0.01,               0.937,              0.0005,                   3,                 0.8,                 0.1,                0.05,                 0.5,                   1,                   1,                   1,                 0.2,                   4,                   0,               0.015,                 0.7,                 0.4,                   0,                 0.1,                 0.5,                   0,                   0,                   0,                 0.5,                   0,                   0,                   0,                   3
             0.82198,             0.64013,             0.71905,             0.44133,             0.77251,             0.55216,             0.59938,             0.27542,            0.044692,            0.048322,            0.030093,           0.0058852,               0.001,                0.01,               0.937,              0.0005,                   3,                 0.8,                 0.1,                0.05,                 0.5,                   1,                   1,                   1,                 0.2,                   4,                   0,               0.015,                 0.7,                 0.4,                   0,                 0.1,                 0.5,                   0,                   0,                   0,                 0.5,                   0,                   0,                   0,                   3
                   0,                   0,                   0,                   0,                   0,                   0,                   0,                   0,                 nan,                 nan,                 nan,                 nan,                 0.1,             0.04195,                 0.6,               0.001,             0.00589,             0.00104,             0.00957,                 0.2,                 0.2,                   2,                 0.8,                 0.5,                 0.1,                   2,                   1,                 0.1,                 0.9,                 0.2,                   4,                   0,             0.01544,             0.61419,               0.001,                   0,                 0.1,                 0.5,                   0,                   0,                   2
                   0,                   0,                   0,                   0,                   0,                   0,                   0,                   0,                 nan,                 nan,                 nan,                 nan,                 0.1,             0.04195,                 0.6,               0.001,             0.00589,             0.00104,             0.00957,                 0.2,                 0.2,                   2,                 0.8,                 0.5,                 0.1,                   2,                   1,                 0.1,                 0.9,                 0.2,                   4,                   0,             0.01544,             0.61419,               0.001,                   0,                 0.1,                 0.5,                   0,                   0,                   2
          0.00019934,            0.010324,          0.00010308,          2.0187e-05,          0.00031629,           0.0038312,          0.00017246,          2.0777e-05,             0.47588,             0.41791,           0.0052818,            0.011287,                 0.1,             0.04529,                 0.6,               0.001,             0.00658,             0.00101,                0.01,                 0.2,                 0.2,                   2,             0.77271,                 0.5,                 0.1,                   2,                   1,                 0.1,                 0.9,             0.20879,                   4,                   0,              0.0153,             0.69439,               0.001,                   0,                 0.1,             0.52331,                   0,                   0,                   2
          0.00019934,            0.010324,          0.00010308,          2.0187e-05,          0.00031629,           0.0038312,          0.00017246,          2.0777e-05,             0.47588,             0.41791,           0.0052818,            0.011287,                 0.1,             0.04529,                 0.6,               0.001,             0.00658,             0.00101,                0.01,                 0.2,                 0.2,                   2,             0.77271,                 0.5,                 0.1,                   2,                   1,                 0.1,                 0.9,             0.20879,                   4,                   0,              0.0153,             0.69439,               0.001,                   0,                 0.1,             0.52331,                   0,                   0,                   2

I see first four columns to be train/box_loss, train/seg_loss, train/obj_loss, train/cls_loss.

Q2: In the plot_evolve function inside utils We see we are trying to plot from index 7 onwards but based on the csv above the l0 (intial learning rate) index is 19 onwards so is it something designed for object detection and not for segmenation+detection

def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *; plot_evolve()
    # Plot evolve.csv hyp evolution results
    evolve_csv = Path(evolve_csv)
    data = pd.read_csv(evolve_csv)
    keys = [x.strip() for x in data.columns]
    x = data.values
    f = fitness(x)
    j = np.argmax(f)  # max fitness index
    plt.figure(figsize=(10, 12), tight_layout=True)
    matplotlib.rc('font', **{'size': 8})
    print(f'Best results from row {j} of {evolve_csv}:')
    for i, k in enumerate(keys[7:]):
        v = x[:, 7 + i]
        mu = v[j]  # best single result
        plt.subplot(6, 5, i + 1)
        plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
        plt.plot(mu, f.max(), 'k+', markersize=15)
        plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9})  # limit to 40 characters
        if i % 5 != 0:
            plt.yticks([])
        print(f'{k:>15}: {mu:.3g}')
    f = evolve_csv.with_suffix('.png')  # filename
    plt.savefig(f, dpi=200)
    plt.close()
    print(f'Saved {f}')

Also when I try running this code without index change (keeping 7 instead of 19) with the evolve.csv file it error out because in subplots we specify to include 30 subplots 5*6 but we get more than 30 and and will include more than 30 hyperparams.

Q3: I didn't understand why there are duplicate records for every evolution entry and also why we have NaN and nan entries(refer to csv attached)?

Additional

No response

github-actions[bot] commented 1 year ago

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iamprakhargupta commented 12 months ago

@glenn-jocher sorry for tagging you but are we sure that hyper parameter evolution is supported for segmentation as well

glenn-jocher commented 12 months ago

@iamprakhargupta hyperparameter Evolution in YOLOv5 is primarily designed for object detection tasks. While YOLOv5 itself does not include built-in support for segmentation tasks, you can still adapt the hyperparameter evolution technique for segmentation by modifying the training pipeline and loss functions.

For segmentation tasks, you might consider exploring other architectures specifically designed for this purpose, such as UNet or Deeplab, which have been widely used for image segmentation and provide better compatibility with hyperparameter evolution techniques.

Note that adapting hyperparameter evolution for segmentation can be a complex process and may require significant modifications to the codebase. It's always recommended to consult the documentation and resources available for the specific segmentation architecture you are using.

Feel free to reach out if you have any further questions or need assistance with implementing segmentation tasks in YOLOv5!

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