OSPP: Development of Federated Incremental Learning for Label Scarcity: Base on KubeEdge-Ianvs #143
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Signed-off-by: Marchons <[email protected]> Signed-off-by: Marchons <[email protected]>
new file: core/testcasecontroller/algorithm/paradigm/federated_learning/federeated_learning.py Signed-off-by: Marchons <[email protected]> Signed-off-by: Marchons <[email protected]>
Signed-off-by: Marchons <[email protected]> Signed-off-by: Marchons <[email protected]>
Signed-off-by: Marchons <[email protected]> Signed-off-by: Marchons <[email protected]>
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There are three CI issues that remain to be resolved, mainly for Pylint, see https://github.com/kubeedge/ianvs/actions/runs/11137579503?pr=143
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Signed-off-by: Marchons <[email protected]> Signed-off-by: Marchons <[email protected]>
Signed-off-by: Yu Fan <[email protected]>
Signed-off-by: Marchons <[email protected]>
| def augment_for_cifar(self, images: np.ndarray): | ||
| return self.augment_shift(self.augment_mirror(images), 4) | ||
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| def augment_for_svhn(self, images: np.ndarray): |
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| self.rand_augment = Rand_Augment() | ||
| if self.dataset_name in ["cifar10", "cifar100", "svhn"]: | ||
| self.input_shape = (32, 32, 3) | ||
| elif self.dataset_name == "stl10": |
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Unused dataset augmentation code should be removed
| information needed to compute system metrics. | ||
| """ | ||
| # init client wait for connection | ||
| # self.init_client() |
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| if not len(clients): | ||
| return self.weights | ||
| self.total_size = sum([c.num_samples for c in clients]) | ||
| # print(next(iter(clients)).weights) |
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| prob = tf.nn.softmax(pred, axis=1) | ||
| pred = tf.argmax(prob, axis=1) | ||
| pred = tf.cast(pred, dtype=tf.int32) | ||
| # pred = tf.cast(tf.argmax(logits, axis=1), tf.int32) |
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| # print(f' grad_diff shape {grad.shape} and type(grad) {type(grad)}') | ||
| opt.apply_gradients(zip([grad], [dummy_data])) | ||
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| # if iter == self.Iteration - 1: |
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| # print(f"in evalute data: {data}") | ||
| for i, (x, y) in enumerate(data): | ||
| logits = self.model(x, training=False) | ||
| # prob = tf.nn.softmax(logits, axis=1) |
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| self.fc = Dense(num_classes, activation='softmax') | ||
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| def call(self, inputs): | ||
| # print(type(self.feature)) |
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| import tensorflow as tf | ||
| import keras | ||
| # import keras |
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| class_labels = np.unique(y_train) # 获取所有类别 | ||
| train_class_dict = {label: [] for label in class_labels} | ||
| test_class_dict = {label: [] for label in class_labels} | ||
| # train_cnt = 0 |
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What type of PR is this?
/kind feature
What this PR does / why we need it:
This PR add two main feature:
Besides this PR also add the example of these two paradigm.
Which issue(s) this PR fixes:
related issue: #97
Fixes #