config.py

import imgaug  # https://github.com/aleju/imgaug
from imgaug import augmenters as iaa
import imgaug as ia
import os

####
class Config(object):
    def __init__(self, _args=None):
        if _args is not None:
            self.__dict__.update(_args.__dict__)
        self.seed = self.seed
        self.init_lr = 1.0e-3
        self.lr_steps = 20  # decrease at every n-th epoch
        self.gamma = 0.2
        self.train_batch_size = 32
        self.infer_batch_size = 64
        self.nr_classes = 4
        self.nr_epochs = 60
        self.epoch_length = 50

        # nr of processes for parallel processing input
        self.nr_procs_train = 8
        self.nr_procs_valid = 8

        self.nr_fold = 5
        self.fold_idx = 0
        self.cross_valid = False

        self.load_network = False
        self.save_net_path = ""

        #
        self.dataset = 'colon_manual'
        self.logging = True  # True for debug run only

        # self.log_path = '/media/data1/trinh_2021/data/workspace_data/join_learning_2021/colon/ordinalforcancer_v0/'
        self.log_path = _args.log_path

        self.chkpts_prefix = 'model'
        if _args is not None:
            self.__dict__.update(_args.__dict__)
            self.task_type = self.run_info.split('_')[0]
            self.loss_type = self.run_info.replace(self.task_type + "_", "")
        self.model_name = f'/{self.task_type}_{self.loss_type}_cancer_Effi_seed{self.seed}_BS64'
        self.log_dir = self.log_path + self.model_name
        print(self.model_name)

    def train_augmentors(self):
        if self.dataset == "prostate_uhu":
            shape_augs = [
                iaa.Resize(0.5, interpolation='nearest'),
                iaa.CropToFixedSize(width=350, height=350),
            ]
        else:
            shape_augs = [
                ]

        sometimes = lambda aug: iaa.Sometimes(0.2, aug)
        input_augs = iaa.Sequential(
            [
                # apply the following augmenters to most images
                iaa.Fliplr(0.5),  # horizontally flip 50% of all images
                iaa.Flipud(0.5),  # vertically flip 50% of all images
                sometimes(iaa.Affine(
                    rotate=(-45, 45),  # rotate by -45 to +45 degrees
                    shear=(-16, 16),  # shear by -16 to +16 degrees
                    order=[0, 1],  # use nearest neighbour or bilinear interpolation (fast)
                    cval=(0, 255),  # if mode is constant, use a cval between 0 and 255
                    mode='symmetric'
                    # use any of scikit-image's warping modes (see 2nd image from the top for examples)
                )),
                # execute 0 to 5 of the following (less important) augmenters per image
                # don't execute all of them, as that would often be way too strong
                iaa.SomeOf((0, 5),
                           [
                               iaa.OneOf([
                                   iaa.GaussianBlur((0, 3.0)),  # blur images with a sigma between 0 and 3.0
                                   iaa.AverageBlur(k=(2, 7)),
                                   # blur image using local means with kernel sizes between 2 and 7
                                   iaa.MedianBlur(k=(3, 11)),
                                   # blur image using local medians with kernel sizes between 2 and 7
                               ]),
                               iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
                               # add gaussian noise to images
                               iaa.Dropout((0.01, 0.1), per_channel=0.5),  # randomly remove up to 10% of the pixels
                               # change brightness of images (by -10 to 10 of original value)
                               iaa.AddToHueAndSaturation((-20, 20)),  # change hue and saturation
                               iaa.LinearContrast((0.5, 2.0), per_channel=0.5),  # improve or worsen the contrast
                           ],
                           random_order=True
                           )
            ],
            random_order=True
        )
        return shape_augs, input_augs

    ####
    def infer_augmentors(self):
        if self.dataset in ["prostate_uhu", "prostate_ubc"]:
            shape_augs = [
                iaa.Resize(0.5, interpolation='nearest'),
                iaa.CropToFixedSize(width=350, height=350, position="center"),
            ]
        else:
            shape_augs = [
                # iaa.Resize(0.5, interpolation='nearest'),
                # iaa.CropToFixedSize(width=350, height=350, position="center"),
                ]
        return shape_augs, None