Transfer learning... twice?

Part 1 (2019)
8

So, in the end, this is how my code looks like:

First, transfer learning (follows notebook from lesson3),

  1. Load Resnet50 with the weights from imagenet:

    from fastai.vision import *
    from fastai import *

    path = Config.data_path()/‘MYPATH’
    path.mkdir(parents=True, exist_ok=True)

    df = pd.read_csv(path/‘train_v2.csv’)
    tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)
    np.random.seed(42)
    src = (ImageList.from_csv(path, ‘train_v2.csv’, folder=‘train-jpg’, suffix=’.jpg’)
    .split_by_rand_pct(0.2)
    .label_from_df(label_delim=’ '))
    data = (src.transform(tfms, size=128)
    .databunch().normalize(imagenet_stats))

    arch = models.resnet50
    acc_02 = partial(accuracy_thresh, thresh=0.2)
    f_score = partial(fbeta, thresh=0.2)
    learn = cnn_learner(data, arch, metrics=[acc_02, f_score])

  2. Then, fit the model to the planet database.

    learn.lr_find()

    lr = 0.01
    learn.fit_one_cycle(5, slice(lr))

    learn.model[-1][-1]=nn.Linear(in_features=512,out_features=5, bias=True)
    learn.save(‘Planetstage-1-rn50’)

Notice that before saving we change the number of categories that the model outputs so we can then open it with the other data (we change from 7 to 5).

I also re-run the whole thing changing the last bit with:

learn.unfreeze()
learn.lr_find()

learn.fit_one_cycle(5, slice(1e-5, lr/5))

learn.model[-1][-1]=nn.Linear(in_features=512,out_features=5, bias=True)
learn.save('Planetstage-2-rn50')

Second, Re-train the model fitted for “Planet” with my images

Then I loaded my images with the tweaked models (do not worry about the “self” parts, this is inside of a python class, they mainly carry the information on where to find the images):

    path = Config.data_path()/self.path
    path.mkdir(parents=True, exist_ok=True)

    df = pd.read_csv(self.path+self.labelFileName)
    df.head()

    tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)
    np.random.seed(42)
    src = (ImageList.from_csv(path, self.labelFileName, folder=self.imageDir,      suffix=self.suffix).split_by_rand_pct(0.2).label_from_df(label_delim=' '))
    data = (src.transform(tfms, size=128).databunch().normalize(imagenet_stats))
    arch = models.resnet50
    acc_02 = partial(accuracy_thresh, thresh=0.2)
    f_score = partial(fbeta, thresh=0.2)
    learn = cnn_learner(data, arch, metrics=[acc_02, f_score])
    learn.load(self.modelFile)

And then I was able to fit the model again.

Hope this helps.