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Wassim Jabi
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Jun 14, 2022
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,93 @@ | ||
| import sys | ||
| sys.path.append(r"D:\Anaconda3\envs\py310\Lib\site-packages") | ||
| import bpy | ||
| from bpy.props import IntProperty, FloatProperty, StringProperty, BoolProperty, EnumProperty | ||
| from sverchok.node_tree import SverchCustomTreeNode | ||
| from sverchok.data_structure import updateNode | ||
|
|
||
| import dgl | ||
| import pickle | ||
| import topologic | ||
| from dgl.data import DGLDataset | ||
| import torch | ||
| import numpy as np | ||
|
|
||
| from . import Replication | ||
|
|
||
| def processItem(item): | ||
| dgl_labels, dgl_predictions = item | ||
|
|
||
| num_correct = 0 | ||
| for i in range(len(dgl_predictions)): | ||
| if dgl_predictions[i] == dgl_labels[i]: | ||
| num_correct = num_correct + 1 | ||
| size = len(dgl_predictions) | ||
| return [size, num_correct, len(dgl_predictions)- num_correct, num_correct / len(dgl_predictions)] | ||
|
|
||
| replication = [("Default", "Default", "", 1),("Trim", "Trim", "", 2),("Iterate", "Iterate", "", 3),("Repeat", "Repeat", "", 4),("Interlace", "Interlace", "", 5)] | ||
|
|
||
| class SvDGLAccuracy(bpy.types.Node, SverchCustomTreeNode): | ||
| """ | ||
| Triggers: Topologic | ||
| Tooltip: Outputs the accuracy of the input predictions based on the input labels | ||
| """ | ||
| bl_idname = 'SvDGLAccuracy' | ||
| bl_label = 'DGL.Accuracy' | ||
| Replication: EnumProperty(name="Replication", description="Replication", default="Default", items=replication, update=updateNode) | ||
| LabelProp: IntProperty(name='Label', description='The actual label of the graph', default=0, update=updateNode) | ||
| PredictionProp: IntProperty(name='Prediction', description='The predicted label of the graph', default=0, update=updateNode) | ||
|
|
||
| def sv_init(self, context): | ||
| self.inputs.new('SvStringsSocket', 'Label').prop_name='LabelProp' | ||
| self.inputs.new('SvStringsSocket', 'Prediction').prop_name='PredictionProp' | ||
| self.outputs.new('SvStringsSocket', 'Size') | ||
| self.outputs.new('SvStringsSocket', 'Correct') | ||
| self.outputs.new('SvStringsSocket', 'Wrong') | ||
| self.outputs.new('SvStringsSocket', 'Accuracy') | ||
|
|
||
| def draw_buttons(self, context, layout): | ||
| layout.prop(self, "Replication",text="") | ||
|
|
||
| def process(self): | ||
| if not any(socket.is_linked for socket in self.outputs): | ||
| return | ||
| if not any(socket.is_linked for socket in self.inputs): | ||
| self.outputs['Accuracy'].sv_set([]) | ||
| return | ||
| labelList = self.inputs['Label'].sv_get(deepcopy=True) | ||
| predictionList = self.inputs['Prediction'].sv_get(deepcopy=True) | ||
| inputs = [labelList, predictionList] | ||
| if ((self.Replication) == "Default"): | ||
| inputs = Replication.iterate(inputs) | ||
| inputs = Replication.transposeList(inputs) | ||
| if ((self.Replication) == "Trim"): | ||
| inputs = Replication.trim(inputs) | ||
| inputs = Replication.transposeList(inputs) | ||
| elif ((self.Replication) == "Iterate"): | ||
| inputs = Replication.iterate(inputs) | ||
| inputs = Replication.transposeList(inputs) | ||
| elif ((self.Replication) == "Repeat"): | ||
| inputs = Replication.repeat(inputs) | ||
| inputs = Replication.transposeList(inputs) | ||
| elif ((self.Replication) == "Interlace"): | ||
| inputs = list(Replication.interlace(inputs)) | ||
| sizeList = [] | ||
| correctList = [] | ||
| wrongList = [] | ||
| accuracyList = [] | ||
| for anInput in inputs: | ||
| size, correct, wrong, accuracy = processItem(anInput) | ||
| sizeList.append(size) | ||
| correctList.append(correct) | ||
| wrongList.append(wrong) | ||
| accuracyList.append(accuracy) | ||
| self.outputs['Size'].sv_set(sizeList) | ||
| self.outputs['Correct'].sv_set(correctList) | ||
| self.outputs['Wrong'].sv_set(wrongList) | ||
| self.outputs['Accuracy'].sv_set(accuracyList) | ||
|
|
||
| def register(): | ||
| bpy.utils.register_class(SvDGLAccuracy) | ||
|
|
||
| def unregister(): | ||
| bpy.utils.unregister_class(SvDGLAccuracy) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| import bpy | ||
| from bpy.props import IntProperty, FloatProperty, StringProperty, BoolProperty, EnumProperty | ||
| from sverchok.node_tree import SverchCustomTreeNode | ||
| from sverchok.data_structure import updateNode | ||
|
|
||
| import torch | ||
| from . import Replication | ||
|
|
||
| def processItem(item): | ||
| """ | ||
| Parameters | ||
| ---------- | ||
| model_checkpoint_path : str | ||
| Path for the entire model | ||
| Returns | ||
| ------- | ||
| The classifier model | ||
| """ | ||
| return torch.load(item) | ||
|
|
||
| class SvDGLClassifierByFilePath(bpy.types.Node, SverchCustomTreeNode): | ||
| """ | ||
| Triggers: Topologic | ||
| Tooltip: Outputs the model saved at the input file path | ||
| """ | ||
|
|
||
| bl_idname = 'SvDGLClassifierByFilePath' | ||
| bl_label = 'DGL.ClassifierByFilePath' | ||
| FilePathProp: StringProperty(name="File Path", description="Path from which to load the classifier.", update=updateNode) | ||
|
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||
| def sv_init(self, context): | ||
| self.inputs.new('SvFilePathSocket', 'File Path').prop_name='FilePathProp' | ||
| self.outputs.new('SvStringsSocket', 'Classifier') | ||
|
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||
| def process(self): | ||
| if not any(socket.is_linked for socket in self.outputs): | ||
| return | ||
|
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| filePathList = self.inputs['File Path'].sv_get(deepcopy=True) | ||
| filePathList = Replication.flatten(filePathList) | ||
| outputs = [] | ||
| for anInput in filePathList: | ||
| outputs.append(processItem(anInput)) | ||
| self.outputs['Classifier'].sv_set(outputs) | ||
|
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| def register(): | ||
| bpy.utils.register_class(SvDGLClassifierByFilePath) | ||
|
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||
| def unregister(): | ||
| bpy.utils.unregister_class(SvDGLClassifierByFilePath) |
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