diff --git a/docs/Examples/HED.rst b/docs/Examples/HED.rst index f27d7ed..1be8c14 100644 --- a/docs/Examples/HED.rst +++ b/docs/Examples/HED.rst @@ -28,13 +28,13 @@ model. import torch import torch.nn as nn import matplotlib.pyplot as plt - from examples.hed.datasets import get_dataset, split_equation - from examples.models.nn import SymbolNet + from datasets import get_dataset, split_equation + from models.nn import SymbolNet from abl.learning import ABLModel, BasicNN - from examples.hed.reasoning import HedKB, HedReasoner + from reasoning import HedKB, HedReasoner from abl.data.evaluation import ReasoningMetric, SymbolAccuracy from abl.utils import ABLLogger, print_log - from examples.hed.bridge import HedBridge + from bridge import HedBridge Working with Data ----------------- diff --git a/docs/Examples/HWF.rst b/docs/Examples/HWF.rst index 613c2e7..275fe0e 100644 --- a/docs/Examples/HWF.rst +++ b/docs/Examples/HWF.rst @@ -26,8 +26,8 @@ machine learning model. import torch import torch.nn as nn import matplotlib.pyplot as plt - from examples.hwf.datasets import get_dataset - from examples.models.nn import SymbolNet + from datasets import get_dataset + from models.nn import SymbolNet from abl.learning import ABLModel, BasicNN from abl.reasoning import KBBase, Reasoner from abl.data.evaluation import ReasoningMetric, SymbolAccuracy diff --git a/docs/Examples/MNISTAdd.rst b/docs/Examples/MNISTAdd.rst index 148d213..0f7eb2d 100644 --- a/docs/Examples/MNISTAdd.rst +++ b/docs/Examples/MNISTAdd.rst @@ -27,8 +27,8 @@ machine learning model. from torch.optim import RMSprop, lr_scheduler - from examples.mnist_add.datasets import get_dataset - from examples.models.nn import LeNet5 + from datasets import get_dataset + from models.nn import LeNet5 from abl.learning import ABLModel, BasicNN from abl.reasoning import KBBase, Reasoner from abl.data.evaluation import ReasoningMetric, SymbolAccuracy diff --git a/docs/Examples/Zoo.rst b/docs/Examples/Zoo.rst index c5acead..cb77f9f 100644 --- a/docs/Examples/Zoo.rst +++ b/docs/Examples/Zoo.rst @@ -22,9 +22,9 @@ further update the learning model. import os.path as osp import numpy as np from sklearn.ensemble import RandomForestClassifier - from examples.zoo.get_dataset import load_and_preprocess_dataset, split_dataset + from get_dataset import load_and_preprocess_dataset, split_dataset from abl.learning import ABLModel - from examples.zoo.kb import ZooKB + from kb import ZooKB from abl.reasoning import Reasoner from abl.data.evaluation import ReasoningMetric, SymbolAccuracy from abl.utils import ABLLogger, print_log, confidence_dist diff --git a/docs/Intro/Quick-Start.rst b/docs/Intro/Quick-Start.rst index 1c70e85..fddc2ce 100644 --- a/docs/Intro/Quick-Start.rst +++ b/docs/Intro/Quick-Start.rst @@ -21,7 +21,7 @@ In the MNIST Addition task, the data loading looks like .. code:: python - from examples.mnist_add.datasets.get_mnist_add import get_mnist_add + from datasets.get_mnist_add import get_mnist_add # train_data and test_data are tuples in the format (X, gt_pseudo_label, Y) # If get_pseudo_label is set to False, the gt_pseudo_label in each tuple will be None. @@ -38,7 +38,7 @@ In this example, we build a simple LeNet5 network as the base model. .. code:: python - from examples.models.nn import LeNet5 + from models.nn import LeNet5 # The number of pseudo-labels is 10 cls = LeNet5(num_classes=10) diff --git a/examples/hed/bridge.py b/examples/hed/bridge.py index 2a27908..dd98025 100644 --- a/examples/hed/bridge.py +++ b/examples/hed/bridge.py @@ -11,9 +11,9 @@ from abl.learning import ABLModel, BasicNN from abl.reasoning import Reasoner from abl.data.structures import ListData from abl.utils import print_log -from examples.hed.datasets import get_pretrain_data -from examples.hed.utils import InfiniteSampler, gen_mappings -from examples.models.nn import SymbolNetAutoencoder +from datasets import get_pretrain_data +from utils import InfiniteSampler, gen_mappings +from models.nn import SymbolNetAutoencoder class HedBridge(SimpleBridge): diff --git a/examples/hed/hed.ipynb b/examples/hed/hed.ipynb index 8f9d898..6413289 100644 --- a/examples/hed/hed.ipynb +++ b/examples/hed/hed.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -23,14 +23,13 @@ "import torch.nn as nn\n", "import matplotlib.pyplot as plt\n", "\n", - "from abl.learning import ABLModel, BasicNN\n", - "from abl.utils import ABLLogger, print_log\n", - "\n", - "from bridge import HedBridge\n", - "from consistency_metric import ConsistencyMetric\n", "from datasets import get_dataset, split_equation\n", "from models.nn import SymbolNet\n", - "from reasoning import HedKB, HedReasoner" + "from abl.learning import ABLModel, BasicNN\n", + "from reasoning import HedKB, HedReasoner\n", + "from consistency_metric import ConsistencyMetric\n", + "from abl.utils import ABLLogger, print_log\n", + "from bridge import HedBridge" ] }, { @@ -49,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -121,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -242,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -272,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -298,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -323,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -347,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -366,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -383,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/examples/hed/main.py b/examples/hed/main.py index b3952bd..07a6321 100644 --- a/examples/hed/main.py +++ b/examples/hed/main.py @@ -4,14 +4,13 @@ import argparse import torch import torch.nn as nn -from abl.learning import ABLModel, BasicNN -from abl.data.evaluation import ReasoningMetric, SymbolAccuracy -from abl.utils import ABLLogger, print_log - -from bridge import HedBridge from datasets import get_dataset, split_equation from models.nn import SymbolNet +from abl.learning import ABLModel, BasicNN from reasoning import HedKB, HedReasoner +from consistency_metric import ConsistencyMetric +from abl.utils import ABLLogger, print_log +from bridge import HedBridge def main(): @@ -82,7 +81,7 @@ def main(): reasoner = HedReasoner(kb, dist_func="hamming", use_zoopt=True, max_revision=args.max_revision) ### Building Evaluation Metrics - metric_list = [SymbolAccuracy(prefix="hed"), ReasoningMetric(kb=kb, prefix="hed")] + metric_list = [ConsistencyMetric(kb=kb)] ### Bridge Learning and Reasoning bridge = HedBridge(model, reasoner, metric_list) diff --git a/examples/hed/models/nn.py b/examples/hed/models/nn.py index 7aa9994..f65b9e0 100644 --- a/examples/hed/models/nn.py +++ b/examples/hed/models/nn.py @@ -1,16 +1,3 @@ -# coding: utf-8 -# ================================================================# -# Copyright (C) 2021 Freecss All rights reserved. -# -# File Name ：lenet5.py -# Author ：freecss -# Email ：karlfreecss@gmail.com -# Created Date ：2021/03/03 -# Description ： -# -# ================================================================# - - import torch from torch import nn diff --git a/examples/hwf/hwf.ipynb b/examples/hwf/hwf.ipynb index 2be427a..584c736 100644 --- a/examples/hwf/hwf.ipynb +++ b/examples/hwf/hwf.ipynb @@ -24,14 +24,13 @@ "import torch.nn as nn\n", "import matplotlib.pyplot as plt\n", "\n", + "from datasets import get_dataset\n", + "from models.nn import SymbolNet\n", "from abl.learning import ABLModel, BasicNN\n", "from abl.reasoning import KBBase, Reasoner\n", "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n", "from abl.utils import ABLLogger, print_log\n", - "from abl.bridge import SimpleBridge\n", - "\n", - "from datasets import get_dataset\n", - "from models.nn import SymbolNet" + "from abl.bridge import SimpleBridge" ] }, { diff --git a/examples/hwf/main.py b/examples/hwf/main.py index 0f9dfe7..dbfcc5c 100644 --- a/examples/hwf/main.py +++ b/examples/hwf/main.py @@ -1,19 +1,18 @@ -import os.path as osp import argparse +import os.path as osp import numpy as np import torch from torch import nn +from datasets import get_dataset +from models.nn import SymbolNet from abl.learning import ABLModel, BasicNN from abl.reasoning import KBBase, GroundKB, Reasoner from abl.data.evaluation import ReasoningMetric, SymbolAccuracy from abl.utils import ABLLogger, print_log from abl.bridge import SimpleBridge -from datasets import get_dataset -from models.nn import SymbolNet - class HwfKB(KBBase): def __init__( diff --git a/examples/hwf/models/nn.py b/examples/hwf/models/nn.py index 2b6da47..875283f 100644 --- a/examples/hwf/models/nn.py +++ b/examples/hwf/models/nn.py @@ -1,16 +1,3 @@ -# coding: utf-8 -# ================================================================# -# Copyright (C) 2021 Freecss All rights reserved. -# -# File Name ：lenet5.py -# Author ：freecss -# Email ：karlfreecss@gmail.com -# Created Date ：2021/03/03 -# Description ： -# -# ================================================================# - - import torch from torch import nn diff --git a/examples/mnist_add/main.py b/examples/mnist_add/main.py index 4322254..fbb1bfc 100644 --- a/examples/mnist_add/main.py +++ b/examples/mnist_add/main.py @@ -5,14 +5,13 @@ import torch from torch import nn from torch.optim import RMSprop, lr_scheduler -from abl.bridge import SimpleBridge -from abl.data.evaluation import ReasoningMetric, SymbolAccuracy +from datasets import get_dataset +from models.nn import LeNet5 from abl.learning import ABLModel, BasicNN from abl.reasoning import GroundKB, KBBase, PrologKB, Reasoner +from abl.data.evaluation import ReasoningMetric, SymbolAccuracy from abl.utils import ABLLogger, print_log - -from datasets import get_dataset -from models.nn import LeNet5 +from abl.bridge import SimpleBridge class AddKB(KBBase): diff --git a/examples/mnist_add/mnist_add.ipynb b/examples/mnist_add/mnist_add.ipynb index c0af396..b43a81b 100644 --- a/examples/mnist_add/mnist_add.ipynb +++ b/examples/mnist_add/mnist_add.ipynb @@ -25,14 +25,13 @@ "\n", "from torch.optim import RMSprop, lr_scheduler\n", "\n", - "from abl.bridge import SimpleBridge\n", - "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n", + "from datasets import get_dataset\n", + "from models.nn import LeNet5\n", "from abl.learning import ABLModel, BasicNN\n", "from abl.reasoning import KBBase, Reasoner\n", + "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n", "from abl.utils import ABLLogger, print_log\n", - "\n", - "from datasets import get_dataset\n", - "from models.nn import LeNet5" + "from abl.bridge import SimpleBridge" ] }, { diff --git a/examples/mnist_add/models/nn.py b/examples/mnist_add/models/nn.py index 5fb7f3e..182eb51 100644 --- a/examples/mnist_add/models/nn.py +++ b/examples/mnist_add/models/nn.py @@ -1,16 +1,3 @@ -# coding: utf-8 -# ================================================================# -# Copyright (C) 2021 Freecss All rights reserved. -# -# File Name ：lenet5.py -# Author ：freecss -# Email ：karlfreecss@gmail.com -# Created Date ：2021/03/03 -# Description ： -# -# ================================================================# - - import numpy as np import torch from torch import nn @@ -45,50 +32,3 @@ class LeNet5(nn.Module): x = self.fc2(x) x = self.fc3(x) return x - - -class SymbolNet(nn.Module): - def __init__(self, num_classes=4, image_size=(28, 28, 1)): - super(SymbolNet, self).__init__() - self.conv1 = nn.Sequential( - nn.Conv2d(1, 32, 5, stride=1), - nn.ReLU(), - nn.MaxPool2d(kernel_size=2, stride=2), - nn.BatchNorm2d(32, momentum=0.99, eps=0.001), - ) - self.conv2 = nn.Sequential( - nn.Conv2d(32, 64, 5, padding=2, stride=1), - nn.ReLU(), - nn.MaxPool2d(kernel_size=2, stride=2), - nn.BatchNorm2d(64, momentum=0.99, eps=0.001), - ) - - num_features = 64 * (image_size[0] // 4 - 1) * (image_size[1] // 4 - 1) - self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU()) - self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU()) - self.fc3 = nn.Sequential(nn.Linear(84, num_classes)) - - def forward(self, x): - x = self.conv1(x) - x = self.conv2(x) - x = torch.flatten(x, 1) - x = self.fc1(x) - x = self.fc2(x) - x = self.fc3(x) - return x - - -class SymbolNetAutoencoder(nn.Module): - def __init__(self, num_classes=4, image_size=(28, 28, 1)): - super(SymbolNetAutoencoder, self).__init__() - self.base_model = SymbolNet(num_classes, image_size) - self.softmax = nn.Softmax(dim=1) - self.fc1 = nn.Sequential(nn.Linear(num_classes, 100), nn.ReLU()) - self.fc2 = nn.Sequential(nn.Linear(100, image_size[0] * image_size[1]), nn.ReLU()) - - def forward(self, x): - x = self.base_model(x) - # x = self.softmax(x) - x = self.fc1(x) - x = self.fc2(x) - return x diff --git a/examples/models/nn.py b/examples/models/nn.py deleted file mode 100644 index 5fb7f3e..0000000 --- a/examples/models/nn.py +++ /dev/null @@ -1,94 +0,0 @@ -# coding: utf-8 -# ================================================================# -# Copyright (C) 2021 Freecss All rights reserved. -# -# File Name ：lenet5.py -# Author ：freecss -# Email ：karlfreecss@gmail.com -# Created Date ：2021/03/03 -# Description ： -# -# ================================================================# - - -import numpy as np -import torch -from torch import nn - - -class LeNet5(nn.Module): - def __init__(self, num_classes=10, image_size=(28, 28)): - super(LeNet5, self).__init__() - self.conv1 = nn.Sequential( - nn.Conv2d(1, 6, 3, padding=1), - nn.ReLU(), - nn.MaxPool2d(kernel_size=2, stride=2), - ) - self.conv2 = nn.Sequential( - nn.Conv2d(6, 16, 3), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2) - ) - self.conv3 = nn.Sequential(nn.Conv2d(16, 16, 3), nn.ReLU()) - - feature_map_size = (np.array(image_size) // 2 - 2) // 2 - 2 - num_features = 16 * feature_map_size[0] * feature_map_size[1] - - self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU()) - self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU()) - self.fc3 = nn.Linear(84, num_classes) - - def forward(self, x): - x = self.conv1(x) - x = self.conv2(x) - x = self.conv3(x) - x = torch.flatten(x, 1) - x = self.fc1(x) - x = self.fc2(x) - x = self.fc3(x) - return x - - -class SymbolNet(nn.Module): - def __init__(self, num_classes=4, image_size=(28, 28, 1)): - super(SymbolNet, self).__init__() - self.conv1 = nn.Sequential( - nn.Conv2d(1, 32, 5, stride=1), - nn.ReLU(), - nn.MaxPool2d(kernel_size=2, stride=2), - nn.BatchNorm2d(32, momentum=0.99, eps=0.001), - ) - self.conv2 = nn.Sequential( - nn.Conv2d(32, 64, 5, padding=2, stride=1), - nn.ReLU(), - nn.MaxPool2d(kernel_size=2, stride=2), - nn.BatchNorm2d(64, momentum=0.99, eps=0.001), - ) - - num_features = 64 * (image_size[0] // 4 - 1) * (image_size[1] // 4 - 1) - self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU()) - self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU()) - self.fc3 = nn.Sequential(nn.Linear(84, num_classes)) - - def forward(self, x): - x = self.conv1(x) - x = self.conv2(x) - x = torch.flatten(x, 1) - x = self.fc1(x) - x = self.fc2(x) - x = self.fc3(x) - return x - - -class SymbolNetAutoencoder(nn.Module): - def __init__(self, num_classes=4, image_size=(28, 28, 1)): - super(SymbolNetAutoencoder, self).__init__() - self.base_model = SymbolNet(num_classes, image_size) - self.softmax = nn.Softmax(dim=1) - self.fc1 = nn.Sequential(nn.Linear(num_classes, 100), nn.ReLU()) - self.fc2 = nn.Sequential(nn.Linear(100, image_size[0] * image_size[1]), nn.ReLU()) - - def forward(self, x): - x = self.base_model(x) - # x = self.softmax(x) - x = self.fc1(x) - x = self.fc2(x) - return x diff --git a/examples/zoo/main.py b/examples/zoo/main.py index d86fd3a..f5c86eb 100644 --- a/examples/zoo/main.py +++ b/examples/zoo/main.py @@ -4,9 +4,9 @@ import argparse import numpy as np from sklearn.ensemble import RandomForestClassifier -from examples.zoo.get_dataset import load_and_preprocess_dataset, split_dataset +from get_dataset import load_and_preprocess_dataset, split_dataset from abl.learning import ABLModel -from examples.zoo.kb import ZooKB +from kb import ZooKB from abl.reasoning import Reasoner from abl.data.evaluation import ReasoningMetric, SymbolAccuracy from abl.utils import ABLLogger, print_log, confidence_dist diff --git a/examples/zoo/zoo.ipynb b/examples/zoo/zoo.ipynb index 6af591d..ca43dd8 100644 --- a/examples/zoo/zoo.ipynb +++ b/examples/zoo/zoo.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21,9 +21,9 @@ "import os.path as osp\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestClassifier\n", - "from examples.zoo.get_dataset import load_and_preprocess_dataset, split_dataset\n", + "from get_dataset import load_and_preprocess_dataset, split_dataset\n", "from abl.learning import ABLModel\n", - "from examples.zoo.kb import ZooKB\n", + "from kb import ZooKB\n", "from abl.reasoning import Reasoner\n", "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n", "from abl.utils import ABLLogger, print_log, confidence_dist\n", @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -366,7 +366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.18" }, "orig_nbformat": 4, "vscode": {