init

Author: caodoanh2001

att_utils.py

@@ -0,0 +1,114 @@
+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-4
+        self.lr_steps = 20  # decrease at every n-th epoch
+        self.gamma = 0.2
+        self.train_batch_size = 64
+        self.infer_batch_size = 1
+        self.nr_classes = 3
+        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 = '/data4/doanhbc/ViTOnly_prostate_hv/'
+        if not os.path.exists(self.log_path):
+            os.makedirs(self.log_path, exist_ok=True)
+
+        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_hv":
+            shape_augs = [
+                iaa.Resize(0.5, interpolation='nearest'),
+                iaa.CropToFixedSize(width=350, height=350),
+            ]
+        else:
+            shape_augs = [
+                # iaa.Resize(dict(height=384, width=384), interpolation='nearest')
+                iaa.Resize(dict(height=384, width=384), interpolation='nearest')
+            ]
+        #
+        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 == "prostate_hv":
+            shape_augs = [
+                iaa.Resize(0.5, interpolation='nearest'),
+                iaa.CropToFixedSize(width=350, height=350, position="center"),
+            ]
+        else:
+            shape_augs = [
+                # iaa.Resize(dict(height=384, width=384), interpolation='nearest')
+                iaa.Resize(dict(height=384, width=384), interpolation='nearest')
+            ]
+        return shape_augs, None
+
+###########################################################################