gpu
Browse files- .gitignore +2 -0
- app.py +5 -5
.gitignore
ADDED
@@ -0,0 +1,2 @@
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.vscode
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.tmp/res
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app.py
CHANGED
@@ -2,7 +2,7 @@
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import time
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import os
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import gradio as gr
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-
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from pnpxai.core.experiment.auto_explanation import AutoExplanationForImageClassification
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from pnpxai.core.detector.detector import extract_graph_data, symbolic_trace
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import matplotlib.pyplot as plt
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@@ -343,7 +343,7 @@ class Experiment(Component):
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_plots += [gr.Image(value=None, label="Blank", visible=False)] * ((buffer_n_rows - n_rows) * PLOT_PER_LINE)
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return _plots
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-
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def render_plots(data_id, *metric_inputs):
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# Clear Cache Files
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# print(f"GPU Check: {torch.cuda.is_available()}")
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@@ -485,7 +485,7 @@ class ExplainerCheckbox(Component):
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idx = [metric.__class__.__name__ for metric in metric_info[0]].index(metric_name)
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return metric_info[1][idx]
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-
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def optimize(self):
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# if self.explainer_name in ["Lime", "KernelShap", "IntegratedGradients"]:
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# gr.Info("Lime, KernelShap and IntegratedGradients currently do not support hyperparameter optimization.")
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@@ -649,8 +649,8 @@ from torch.utils.data import DataLoader
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from helpers import get_imagenet_dataset, get_torchvision_model, denormalize_image
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os.environ['GRADIO_TEMP_DIR'] = '.tmp'
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-
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device = torch.device("cpu")
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def target_visualizer(x): return dataset.dataset.idx_to_label(x.item())
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import time
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import os
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import gradio as gr
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import spaces
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from pnpxai.core.experiment.auto_explanation import AutoExplanationForImageClassification
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from pnpxai.core.detector.detector import extract_graph_data, symbolic_trace
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import matplotlib.pyplot as plt
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_plots += [gr.Image(value=None, label="Blank", visible=False)] * ((buffer_n_rows - n_rows) * PLOT_PER_LINE)
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return _plots
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@spaces.GPU
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def render_plots(data_id, *metric_inputs):
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# Clear Cache Files
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# print(f"GPU Check: {torch.cuda.is_available()}")
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idx = [metric.__class__.__name__ for metric in metric_info[0]].index(metric_name)
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return metric_info[1][idx]
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@spaces.GPU
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def optimize(self):
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# if self.explainer_name in ["Lime", "KernelShap", "IntegratedGradients"]:
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# gr.Info("Lime, KernelShap and IntegratedGradients currently do not support hyperparameter optimization.")
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from helpers import get_imagenet_dataset, get_torchvision_model, denormalize_image
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os.environ['GRADIO_TEMP_DIR'] = '.tmp'
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# device = torch.device("cpu")
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def target_visualizer(x): return dataset.dataset.idx_to_label(x.item())
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