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Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/README.md

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+# Implementation of TransUNet Segmentation model on ACDC Dataset
+
+- to run directly on your system, please change the data path from "./data/ACDC" in test.py or train.py to the path to your Dataset.
+
+- this folder does NOT include the dataset due to size reasons.
+
+- test.py in this folder does NOT save the visualised images for every predicted file. Please modify and run the ACDCvisualiser.ipynb notebook to visualise your data slice by slice.
+
+- Download Google pre-trained ViT models
+  * [Get models in this link](https://console.cloud.google.com/storage/vit_models/): R50-ViT-B_16, ViT-B_16, ViT-L_16...
+  ```bash
+  wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz &&
+  mkdir ../model/vit_checkpoint/imagenet21k &&
+  mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npz
+  ```
+  I used the R50_ViT-B_16 model
+

Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/dataset_acdc.py

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+import itertools
+import os
+import random
+import re
+from glob import glob
+
+import cv2
+import h5py
+import numpy as np
+import torch
+from scipy import ndimage
+from scipy.ndimage.interpolation import zoom
+from torch.utils.data import Dataset
+from skimage import io
+import cv2
+class BaseDataSets(Dataset):
+    def __init__(self, base_dir=None, split='train', list_dir=None, transform=None):
+        self._base_dir = base_dir
+        self.sample_list = []
+        self.split = split
+        self.transform = transform
+        train_ids, val_ids, test_ids = self._get_ids()
+        if self.split.find('train') != -1:
+            print(f"Looking for slices in: {os.path.abspath('./data/ACDC_training_slices')}")
+            self.all_slices = os.listdir(
+                self._base_dir + "/ACDC_training_slices")
+            self.sample_list = []
+            for ids in train_ids:
+                new_data_list = list(filter(lambda x: re.match('{}.*'.format(ids), x) != None, self.all_slices))
+                self.sample_list.extend(new_data_list)
+
+        elif self.split.find('val') != -1:
+            self.all_volumes = os.listdir(
+                self._base_dir + "/ACDC_training_volumes")
+            self.sample_list = []
+            for ids in val_ids:
+                new_data_list = list(filter(lambda x: re.match('{}.*'.format(ids), x) != None, self.all_volumes))
+                self.sample_list.extend(new_data_list)
+
+        elif self.split.find('test') != -1:
+            self.all_volumes = os.listdir(
+                self._base_dir + "/ACDC_training_volumes")
+            self.sample_list = []
+            for ids in test_ids:
+                new_data_list = list(filter(lambda x: re.match('{}.*'.format(ids), x) != None, self.all_volumes))
+                self.sample_list.extend(new_data_list)
+
+        # if num is not None and self.split == "train":
+        #     self.sample_list = self.sample_list[:num]
+        print("total {} samples".format(len(self.sample_list)))
+
+    def _get_ids(self):
+        all_cases_set = ["patient{:0>3}".format(i) for i in range(1, 101)]
+        testing_set = ["patient{:0>3}".format(i) for i in range(1, 21)]
+        validation_set = ["patient{:0>3}".format(i) for i in range(21, 31)]
+        training_set = [i for i in all_cases_set if i not in testing_set+validation_set]
+
+        return [training_set, validation_set, testing_set]
+
+    def __len__(self):
+        return len(self.sample_list)
+
+    def __getitem__(self, idx):
+        case = self.sample_list[idx]
+
+        # image = h5f['image'][:]
+        # label = h5f['label'][:]
+        # sample = {'image': image, 'label': label}
+        if self.split == "train":
+            h5f = h5py.File(self._base_dir + "/ACDC_training_slices/{}".format(case), 'r')
+            image = h5f['image'][:]
+            label = h5f['label'][:]  # fix sup_type to label
+            sample = {'image': image, 'label': label}
+            sample = self.transform(sample)
+        else:
+            h5f = h5py.File(self._base_dir + "/ACDC_training_volumes/{}".format(case), 'r')
+            image = h5f['image'][:]
+            label = h5f['label'][:]
+            sample = {'image': image, 'label': label}
+        sample["idx"] = idx
+        sample['case_name'] = case.replace('.h5', '')
+        return sample
+
+
+def random_rot_flip(image, label):
+    k = np.random.randint(0, 4)
+    image = np.rot90(image, k)
+    label = np.rot90(label, k)
+    axis = np.random.randint(0, 2)
+    image = np.flip(image, axis=axis).copy()
+    label = np.flip(label, axis=axis).copy()
+    return image, label
+
+
+def random_rotate(image, label):
+    angle = np.random.randint(-20, 20)
+    image = ndimage.rotate(image, angle, order=0, reshape=False)
+    label = ndimage.rotate(label, angle, order=0, reshape=False)
+    return image, label
+
+
+class RandomGenerator(object):
+    def __init__(self, output_size):
+        self.output_size = output_size
+
+    def __call__(self, sample):
+        image, label = sample['image'], sample['label']
+        # ind = random.randrange(0, img.shape[0])
+        # image = img[ind, ...]
+        # label = lab[ind, ...]
+        if random.random() > 0.5:
+            image, label = random_rot_flip(image, label)
+        elif random.random() > 0.5:
+            image, label = random_rotate(image, label)
+        x, y = image.shape
+        if x != self.output_size[0] or y != self.output_size[1]:
+            image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=0)  # the default is 0
+            label = zoom( label, (self.output_size[0] / x, self.output_size[1] / y), order=0)
+
+        assert (image.shape[0] == self.output_size[0]) and (image.shape[1] == self.output_size[1])
+        image = torch.from_numpy(image.astype(np.float32)).unsqueeze(0)
+        label = torch.from_numpy(label.astype(np.uint8))
+        sample = {'image': image, 'label': label}
+        return sample
+
+
+def iterate_once(iterable):
+    return np.random.permutation(iterable)
+
+
+def iterate_eternally(indices):
+    def infinite_shuffles():
+        while True:
+            yield np.random.permutation(indices)
+    return itertools.chain.from_iterable(infinite_shuffles())
+
+
+def grouper(iterable, n):
+    "Collect data into fixed-length chunks or blocks"
+    # grouper('ABCDEFG', 3) --> ABC DEF"
+    args = [iter(iterable)] * n
+    return zip(*args)

Notes/GarvSachdev_Notes_and_Files/ACDC_Dataset_test/networks/vit_seg_configs.py

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+import ml_collections
+
+def get_b16_config():
+    """Returns the ViT-B/16 configuration."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (16, 16)})
+    config.hidden_size = 768
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 3072
+    config.transformer.num_heads = 12
+    config.transformer.num_layers = 12
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+
+    config.classifier = 'seg'
+    config.representation_size = None
+    config.resnet_pretrained_path = None
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_16.npz'
+    config.patch_size = 16
+
+    config.decoder_channels = (256, 128, 64, 16)
+    config.n_classes = 2
+    config.activation = 'softmax'
+    return config
+
+
+def get_testing():
+    """Returns a minimal configuration for testing."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (16, 16)})
+    config.hidden_size = 1
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 1
+    config.transformer.num_heads = 1
+    config.transformer.num_layers = 1
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+    config.classifier = 'token'
+    config.representation_size = None
+    return config
+
+def get_r50_b16_config():
+    """Returns the Resnet50 + ViT-B/16 configuration."""
+    config = get_b16_config()
+    config.patches.grid = (16, 16)
+    config.resnet = ml_collections.ConfigDict()
+    config.resnet.num_layers = (3, 4, 9)
+    config.resnet.width_factor = 1
+
+    config.classifier = 'seg'
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz'
+    config.decoder_channels = (256, 128, 64, 16)
+    config.skip_channels = [512, 256, 64, 16]
+    config.n_classes = 2
+    config.n_skip = 3
+    config.activation = 'softmax'
+
+    return config
+
+
+def get_b32_config():
+    """Returns the ViT-B/32 configuration."""
+    config = get_b16_config()
+    config.patches.size = (32, 32)
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_32.npz'
+    return config
+
+
+def get_l16_config():
+    """Returns the ViT-L/16 configuration."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (16, 16)})
+    config.hidden_size = 1024
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 4096
+    config.transformer.num_heads = 16
+    config.transformer.num_layers = 24
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+    config.representation_size = None
+
+    # custom
+    config.classifier = 'seg'
+    config.resnet_pretrained_path = None
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-L_16.npz'
+    config.decoder_channels = (256, 128, 64, 16)
+    config.n_classes = 2
+    config.activation = 'softmax'
+    return config
+
+
+def get_r50_l16_config():
+    """Returns the Resnet50 + ViT-L/16 configuration. customized """
+    config = get_l16_config()
+    config.patches.grid = (16, 16)
+    config.resnet = ml_collections.ConfigDict()
+    config.resnet.num_layers = (3, 4, 9)
+    config.resnet.width_factor = 1
+
+    config.classifier = 'seg'
+    config.resnet_pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz'
+    config.decoder_channels = (256, 128, 64, 16)
+    config.skip_channels = [512, 256, 64, 16]
+    config.n_classes = 2
+    config.activation = 'softmax'
+    return config
+
+
+def get_l32_config():
+    """Returns the ViT-L/32 configuration."""
+    config = get_l16_config()
+    config.patches.size = (32, 32)
+    return config
+
+
+def get_h14_config():
+    """Returns the ViT-L/16 configuration."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (14, 14)})
+    config.hidden_size = 1280
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 5120
+    config.transformer.num_heads = 16
+    config.transformer.num_layers = 32
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+    config.classifier = 'token'
+    config.representation_size = None
+
+    return config