f3c77b1750
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads. - Added a comprehensive training utility for the model, including loss functions and training steps. - Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
import plotly.graph_objects as go
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# Data from the provided JSON
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data = {
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"wifi_same": {"AP": 43.5, "AP@50": 87.2, "AP@75": 44.6, "AP-m": 38.1, "AP-l": 46.4},
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"image_same": {"AP": 84.7, "AP@50": 94.4, "AP@75": 77.1, "AP-m": 70.3, "AP-l": 83.8},
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"wifi_diff": {"AP": 27.3, "AP@50": 51.8, "AP@75": 24.2, "AP-m": 22.1, "AP-l": 28.6}
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}
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# Extract metrics and values
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metrics = list(data["wifi_same"].keys())
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wifi_same_values = list(data["wifi_same"].values())
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image_same_values = list(data["image_same"].values())
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wifi_diff_values = list(data["wifi_diff"].values())
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# Define colors from the brand palette - using darker color for WiFi Diff
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colors = ['#1FB8CD', '#FFC185', '#5D878F']
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# Create the grouped bar chart
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fig = go.Figure()
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# Add bars for each method with hover data
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fig.add_trace(go.Bar(
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name='WiFi Same',
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x=metrics,
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y=wifi_same_values,
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marker_color=colors[0],
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hovertemplate='<b>WiFi Same</b><br>Metric: %{x}<br>Score: %{y}<extra></extra>'
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))
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fig.add_trace(go.Bar(
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name='Image Same',
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x=metrics,
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y=image_same_values,
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marker_color=colors[1],
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hovertemplate='<b>Image Same</b><br>Metric: %{x}<br>Score: %{y}<extra></extra>'
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))
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fig.add_trace(go.Bar(
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name='WiFi Diff',
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x=metrics,
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y=wifi_diff_values,
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marker_color=colors[2],
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hovertemplate='<b>WiFi Diff</b><br>Metric: %{x}<br>Score: %{y}<extra></extra>'
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))
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# Update layout
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fig.update_layout(
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title='DensePose Performance Comparison',
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xaxis_title='AP Metrics',
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yaxis_title='Score',
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barmode='group',
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legend=dict(orientation='h', yanchor='bottom', y=1.05, xanchor='center', x=0.5),
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plot_bgcolor='rgba(0,0,0,0)',
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paper_bgcolor='white'
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)
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# Add grid for better readability
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fig.update_yaxes(showgrid=True, gridcolor='lightgray')
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fig.update_xaxes(showgrid=False)
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# Save the chart
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fig.write_image('densepose_performance_chart.png') |