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AMILIO-Model-Playground/filter_experiments.ipynb
Iaphetes b7114d815f 2D model trial
2025-11-21 15:53:57 +01:00

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{
"cells": [
{
"cell_type": "code",
"id": "a853eaac-8442-45f9-a83a-65680038469c",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:07.412161Z",
"start_time": "2025-11-20T13:26:07.256191Z"
}
},
"source": [
"from experiment_loader import load_2d_experiment, load_3d_experiment\n",
"from modeling import *\n",
"import plotly.graph_objects as go\n",
"import pandas as pd\n",
"from plotly.subplots import make_subplots\n",
"PLOT_WIDTH = 2000\n",
"PLOT_HEIGHT = 1000\n",
"import scipy"
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "code",
"id": "dd678982",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:07.419831Z",
"start_time": "2025-11-20T13:26:07.417132Z"
}
},
"source": [
"def calculate_complexity(\n",
" input_shape: tuple, kernel_size: tuple, stride: tuple, filters: int, padding: str\n",
"):\n",
" if padding == \"valid\":\n",
" out_x = np.floor((input_shape[0] - kernel_size[0]) / stride[0]) + 1\n",
" out_y = np.floor((input_shape[1] - kernel_size[1]) / stride[1]) + 1\n",
" else:\n",
" out_x = np.floor((input_shape[0] - 1) / stride[0]) + 1\n",
" out_y = np.floor((input_shape[1] - 1) / stride[1]) + 1\n",
" return kernel_size[0] * kernel_size[1] * input_shape[2] * out_x * out_y * filters\n"
],
"outputs": [],
"execution_count": 2
},
{
"cell_type": "code",
"id": "81811eea-db29-4f40-989c-20e6a5ad0a52",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:08.091566Z",
"start_time": "2025-11-20T13:26:07.472362Z"
}
},
"source": [
"# lut_channel_sweep(channels: Union[range,list], base_path: str, lut_config: dict = {\"f\": 128, \"kernel_shape\": [3, 3], \"pads\": [1, 1], \"stride\": [1, 1], \"dilation\":[1, 1]}):\n",
"filters, filter_meas = load_2d_experiment(\"./lut_filter_sweep.csv\")\n",
"ops_cost = []\n",
"for filter, meas in zip(filters, filter_meas):\n",
"\n",
" ops_cost.append(calculate_complexity((256, 256, 128), kernel_size=(3, 3), stride=(1, 1), filters=filter, padding=\"zeros\") / meas )\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=filters, y=ops_cost, name=\"Channel Measurements by ops\", mode=\"markers\"))\n",
"\n",
"fig.update_layout(\n",
" autosize=False,\n",
" width=PLOT_WIDTH,\n",
" height=PLOT_HEIGHT,\n",
")"
],
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"mode": "markers",
"name": "Channel Measurements by ops",
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"metadata": {
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"source": [
"\n",
"\n",
"fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
"lv = filter_meas[0]\n",
"deltas = []\n",
"for meas in filter_meas[1:]:\n",
" deltas.append((meas / lv) * 10 - 10 )\n",
" lv = meas\n",
"\n",
"fig.update_layout(\n",
" scene=dict(\n",
" xaxis_title='channels',\n",
" yaxis_title='filters',\n",
" zaxis_title='ms'\n",
" ),\n",
" width=PLOT_WIDTH,\n",
" height=PLOT_HEIGHT / 3 * 1,\n",
" template='plotly_white',\n",
")\n",
"fig.add_trace(go.Scatter(x=filters, y=filter_meas, name=\"Layer Execution Time\", mode=\"markers\", marker=dict(size=4)), secondary_y=False)\n",
"fig.add_trace(go.Scatter(x=filters[1:], y=deltas, name=\"Point to point variance\", line=dict(color=\"lightgreen\")), secondary_y=True)\n",
"\n",
"fig.update_layout(\n",
" title_text=\"<b>Latency Measurements for a Singular Neural Network Layer With Changing Input Channels</b>\",\n",
" autosize=False,\n",
" width=PLOT_WIDTH,\n",
" height=PLOT_HEIGHT / 3 * 2,\n",
" legend_x=0,\n",
" legend_y=1,\n",
" template='plotly_white',\n",
"\n",
" font=dict(\n",
" size=16,\n",
" )\n",
")\n",
"fig.update_xaxes(title_text=\"<b>#Channels</b>\")\n",
"fig.update_yaxes(title_text=f\"\"\"<b style=\"color:blue\";>Layer Execution Time (ms)</b>\"\"\", secondary_y=False, range=[-10, 40])\n",
"fig.update_yaxes(title_text=f\"\"\"<b style=\"color:green\";>Point to point variance (%)</b>\"\"\", secondary_y=True, range=[-4, 40],)\n",
"fig.write_image(\"images/sweep.svg\", width=PLOT_WIDTH/ 3 * 2, height=PLOT_HEIGHT / 3 * 2, scale=2)\n",
"fig.show()"
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"title": {
"text": "<b style=\"color:blue\";>Layer Execution Time (ms)</b>"
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40
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"width": 2000,
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"title": {
"text": "<b>Latency Measurements for a Singular Neural Network Layer With Changing Input Channels</b>"
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},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:09.590849Z",
"start_time": "2025-11-20T13:26:09.251595Z"
}
},
"cell_type": "code",
"source": [
"import scipy\n",
"from collections import Counter\n",
"from itertools import repeat, chain\n",
"\n",
"peaks = scipy.signal.find_peaks(deltas, prominence=0.1)[0]\n",
"distances = []\n",
"\n",
"for (fpidx, first_peak) in enumerate(peaks):\n",
" for (spidx, second_peak) in enumerate(peaks[fpidx+1:]):\n",
" distances.append(int(second_peak - first_peak))\n",
"\n",
"sorted_distances = []\n",
"for d in list(chain.from_iterable(repeat(i, c) for i,c in Counter(distances).most_common())):\n",
" if d not in sorted_distances:\n",
" sorted_distances.append(d)\n",
"print(sorted_distances)\n",
"counted_distances = {int(d):distances.count(d) for d in distances}\n",
"print(counted_distances[2])"
],
"id": "c16f6f494fcd97f6",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[160, 64, 128, 32, 96, 192, 320, 224, 48, 112, 144, 176, 256, 16, 272, 288, 304, 336, 384, 208, 352, 80, 2, 5, 9, 53, 117, 149, 165, 181, 245, 277, 309, 325, 341, 373, 437, 469, 501, 3, 7, 51, 115, 147, 163, 179, 243, 275, 307, 323, 339, 371, 435, 467, 499, 4, 240, 368, 432, 464, 496, 44, 108, 140, 156, 172, 236, 268, 300, 316, 332, 364, 428, 460, 492, 416, 448]\n",
"1\n"
]
}
],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:09.618287Z",
"start_time": "2025-11-20T13:26:09.598836Z"
}
},
"cell_type": "code",
"source": [
"from scipy.fft import fft, fftfreq\n",
"import scipy\n",
"import numpy as np\n",
"\n",
"# Same signal creation...\n",
"\n",
"# Perform FFT\n",
"bins = 768\n",
"offset = 16\n",
"signal = deltas[offset:offset+bins]\n",
"print(len(signal))\n",
"print(32*16)\n",
"fft_result = fft(signal)\n",
"\n",
"n = len(fft_result)\n",
"frequencies = fftfreq(n, 1)[:n//2]\n",
"magnitude = 2 * np.abs(fft_result[:n//2]) / n\n",
"fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
"# fig.add_trace(go.Scatter(x=channels[1:], y=deltas, name=\"Point to point variance\", line=dict(color=\"lightgreen\")), secondary_y=True)\n",
"fig.add_trace(go.Scatter(x=frequencies, y=magnitude, name=\"Point to point variance\", line=dict(color=\"lightgreen\")), secondary_y=True)\n",
"mean_magnitude = np.mean(magnitude)\n",
"\n",
"for (mag, freq) in sorted(zip(magnitude, frequencies), reverse=True):\n",
" if freq > 0 and mag > mean_magnitude:\n",
" print(round(1 / freq), mag)\n",
"fig.show()"
],
"id": "efd335c6fc7769cb",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"metadata": {
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"start_time": "2025-11-20T13:26:09.653303Z"
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"execution_count": 7
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{
"cell_type": "code",
"id": "b615e79b-639f-4850-b9f8-5518ba98edd9",
"metadata": {
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"source": [
"first_order_approx = []\n",
"second_order_approx = []\n",
"pol_approx = []\n",
"\n",
"\n",
"\n",
"upper_sampled_filters = [129, 385]\n",
"lower_sampled_filters = [129, 385]\n",
"\n",
"upper_sampled_filter_meas = [filter_meas[filters.index(c)] for c in upper_sampled_filters]\n",
"lower_sampled_filter_meas = [filter_meas[filters.index(c)] for c in lower_sampled_filters]\n",
"\n",
"upper_m, upper_b = lin_interpol( upper_sampled_filters[0], upper_sampled_filters[1], upper_sampled_filter_meas[0], upper_sampled_filter_meas[1])\n",
"lower_m, lower_b = lin_interpol(lower_sampled_filters[0], lower_sampled_filters[1], lower_sampled_filter_meas[0], lower_sampled_filter_meas[1])\n",
"\n",
"r_c = filters\n",
"\n",
"r_v_upper = [calc_upper(c, upper_m, upper_b, 128) for c in r_c]\n",
"r_v_lower = [calc_lower(c, lower_m, lower_b, 128) for c in r_c]\n",
"r_v_mean = [calc_mean(c,upper_m, upper_b, lower_m, lower_b, 128) for c in r_c]\n",
"r_v_rect = [calc_rect(c, upper_m, upper_b, lower_m, lower_b, 128, 1) for c in r_c]\n",
"\n",
"err_upper = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_upper, filter_meas)]\n",
"err_lower = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_lower, filter_meas)]\n",
"err_mean = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_mean, filter_meas)]\n",
"err_rect = [(approx - meas) / meas for approx, meas in zip(r_v_rect, filter_meas)]\n",
"\n",
"# print(np.mean(err_sine))\n",
"print(np.mean(err_rect))\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=filters, y=filter_meas, name=\"Channel Measurements\", mode=\"markers\"))\n",
"\n",
"fig.add_trace(go.Scatter(x=list(range(filters[-1])), y=[c * upper_m + upper_b for c in range(filters[-1])], name=\"Upper Sampled Channels\"))\n",
"fig.add_trace(go.Scatter(x=list(range(filters[-1])), y=[c * lower_m + lower_b for c in range(filters[-1])], name=\"Lower Sampled Channels\"))\n",
"fig.add_trace(go.Scatter(x=r_c, y=r_v_mean, name=\"Mean Steps\"))\n",
"fig.add_trace(go.Scatter(x=r_c, y=r_v_rect, name=\"Rect Steps\"))\n",
"\n",
"fig.add_trace(go.Scatter(x=r_c, y=err_rect, name=\"Rect Errors\", mode=\"markers\"))\n",
"print(np.mean(np.abs(err_rect)))\n",
"fig.update_layout(\n",
" autosize=False,\n",
" width=PLOT_WIDTH,\n",
" height=PLOT_HEIGHT,\n",
"\n",
")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.008773193474599025\n",
"0.07652679728655397\n"
]
},
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{
"cell_type": "code",
"id": "069bce83-c4d8-4f36-8c27-d00269919c34",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:09.774182Z",
"start_time": "2025-11-20T13:26:09.770285Z"
}
},
"source": [
"\n",
"df = pd.read_csv('./lut_channel_filter_sweep.csv', usecols=[\"channels\", \"filters\", \"ms\"])\n",
"split_df = df.groupby('channels')"
],
"outputs": [],
"execution_count": 9
},
{
"cell_type": "code",
"id": "f48c666a-e44c-4381-871d-2443abde826f",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:09.933731Z",
"start_time": "2025-11-20T13:26:09.821346Z"
}
},
"source": [
"fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
"split_df = df.groupby('filters')\n",
"first = True\n",
"\n",
"deltas = calculate_deltas(list(split_df.get_group(100)['ms']))\n",
"all_errs = []\n",
"all_rect_errs = []\n",
"\n",
"color_tuples = [\n",
" (\"#000088\", \"#0000DD\", \"#880000\"), # Blue (max brightness), Green (max brightness)\n",
" (\"#0000CC\", \"#00CC00\", \"#550000\"),\n",
" (\"#0000BB\", \"#00BB00\", \"#330000\"),\n",
" (\"#0000AA\", \"#00AA00\", \"#880000\"),\n",
" (\"#000033\", \"#000099\", \"#330000\"),\n",
" (\"#000033\", \"#000099\", \"#330000\"),\n",
" (\"#000033\", \"#000099\", \"#330000\"),\n",
" (\"#000066\", \"#006600\", \"#880000\"),\n",
" (\"#000055\", \"#005500\", \"#880000\"),\n",
" (\"#000044\", \"#004400\", \"#880000\") # Blue (min brightness), Green (min brightness)\n",
"]\n",
"for (idx, (category, category_df)) in list(enumerate(split_df))[::5]:\n",
" # print(category_df)\n",
" # if not first:\n",
" # continue\n",
" upper_right = 195\n",
" upper_left = 129\n",
" lower_right = 192\n",
" lower_left = 126\n",
"\n",
" upper_right_meas = category_df.loc[category_df[\"channels\"] == upper_right][\"ms\"].values[0]\n",
" upper_left_meas = category_df.loc[category_df[\"channels\"] == upper_left][\"ms\"].values[0]\n",
"\n",
" lower_right_meas = category_df.loc[category_df[\"channels\"] == lower_right][\"ms\"].values[0]\n",
" lower_left_meas = category_df.loc[category_df[\"channels\"] == lower_left][\"ms\"].values[0]\n",
" upper_m, upper_b = lin_interpol( upper_sampled_filters[0], upper_sampled_filters[1], upper_sampled_filter_meas[0], upper_sampled_filter_meas[1])\n",
" r_c = list(category_df[\"channels\"])\n",
" r_v_rect = [calc_rect(c, upper_m, upper_b, lower_m, lower_b) for c in r_c] \n",
" err_rect = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_rect, filter_meas)]\n",
"\n",
"\n",
" upper_m, upper_b = lin_interpol(upper_left - 3, upper_right - 3, upper_left_meas, upper_right_meas)\n",
" lower_m, lower_b = lin_interpol(lower_left, lower_right, lower_left_meas, lower_right_meas)\n",
" start = list(category_df['channels'])[0]\n",
" end = list(category_df['channels'])[-1]\n",
" # r_c = list(range(start, end))\n",
"\n",
" r_v_upper = [calc_upper(c, upper_m, upper_b) for c in r_c]\n",
" r_v_lower = [calc_lower(c, lower_m, lower_b) for c in r_c]\n",
" r_v_rect = [calc_rect(c, upper_m, upper_b, lower_m, lower_b) for c in r_c]\n",
" lv = list(category_df['ms'])[0]\n",
" delta_approx = [lv]\n",
" for delta in deltas:\n",
" lv = delta * lv\n",
" delta_approx.append(lv)\n",
" \n",
" errs = compute_absolute_percentage_errors(list(category_df['ms']), delta_approx) # [np.abs(1 - (g / m)) * 100 for g, m in zip(delta_approx, list(category_df['ms']))]\n",
" rect_errs = compute_absolute_percentage_errors(list(category_df['ms']), r_v_rect)\n",
"\n",
" all_errs.append(np.mean(errs))\n",
" all_rect_errs.append(np.mean(rect_errs))\n",
" print(f\"{np.mean(errs)=}\")\n",
" print(f\"{np.mean(rect_errs)=}\")\n",
" fig.add_trace(go.Scatter(\n",
" # x=category_df['channels'],\n",
" x=category_df['channels'],\n",
" y=category_df['ms'],\n",
" # mode='markers',\n",
" marker=dict(\n",
" size=7,\n",
" color=color_tuples[idx][0]\n",
" ),\n",
" name=f\"#Filters:{category} Latency\",\n",
" mode='markers',\n",
" \n",
" ), \n",
" secondary_y=False\n",
" )\n",
" # fig.add_trace(\n",
" # go.Scatter(\n",
" # x=list(category_df['channels']),\n",
" # y=delta_approx,\n",
" # name=f\"Delta Latency approximation\",\n",
" # mode='lines',\n",
" # line=dict(\n",
" # width=.5,\n",
" # color=color_tuples[idx][1]\n",
" # ),\n",
" \n",
" # ), \n",
" # secondary_y=False\n",
" # )\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=list(category_df['channels']),\n",
" y=r_v_rect,\n",
" name=f\"Approximation\",\n",
" mode='lines',\n",
" line=dict(\n",
" dash=\"dot\",\n",
" width=.5,\n",
" color=color_tuples[idx][1]\n",
" ),\n",
" \n",
" ), \n",
" secondary_y=False\n",
" )\n",
" # fig.add_trace(\n",
" # go.Scatter(\n",
" # x=list(category_df['channels']),\n",
" # y=errs,\n",
" # name=f\"Delta Latency Approximation Error\",\n",
" # mode='lines',\n",
" # line=dict(\n",
" # width=.5,\n",
" # color=color_tuples[idx][2]\n",
" # ),\n",
" \n",
" # ),\n",
" # secondary_y=True\n",
" # )\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=list(category_df['channels']),\n",
" y=rect_errs,\n",
" name=f\"Error\",\n",
" mode='lines',\n",
" line=dict(\n",
" width=.5,\n",
" color=color_tuples[idx][2]\n",
" ),\n",
" \n",
" ),\n",
" secondary_y=True\n",
" )\n",
"\n",
" first = False\n",
"\n",
"print()\n",
"print(f\"{np.mean(all_errs)=}\")\n",
"print(f\"{np.mean(all_rect_errs)=}\")\n",
"fig.update_layout(\n",
" scene=dict(\n",
" xaxis_title='channels',\n",
" yaxis_title='filters',\n",
" zaxis_title='ms'\n",
" ),\n",
" width=PLOT_WIDTH,\n",
" height=PLOT_HEIGHT,\n",
" template='plotly_white',\n",
")\n",
"fig.update_xaxes(title_text=\"<b>#Channels</b>\")\n",
"\n",
"fig.update_yaxes(title_text=f\"\"\"<b style=\"color:blue\";>Layer Execution Time (ms)</b>\"\"\", secondary_y=False, range=[-5, 50])\n",
"fig.update_yaxes(title_text=f\"\"\"<b style=\"color:red\";>Absolute Approximation Error (%)</b>\"\"\", secondary_y=True, range=[-4, 40],)\n",
"fig.update_layout(\n",
" title_text=\"<b>Approximation using a modified stepfunction</b>\",\n",
" autosize=False,\n",
" width=PLOT_WIDTH / 2,\n",
" height=PLOT_HEIGHT / 3 * 2,\n",
" legend_x=0, \n",
" legend_y=1,\n",
" font=dict(\n",
" size=14,\n",
" )\n",
")\n",
"fig.write_image(\"images/step_approx.svg\", width=PLOT_WIDTH /2, height=PLOT_HEIGHT / 3 * 2, scale=2)\n",
"\n",
"fig.show()"
],
"outputs": [
{
"ename": "TypeError",
"evalue": "calc_rect() missing 2 required positional arguments: 'step_period' and 'rect_period'",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mTypeError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[10]\u001B[39m\u001B[32m, line 37\u001B[39m\n\u001B[32m 35\u001B[39m upper_m, upper_b = lin_interpol( upper_sampled_filters[\u001B[32m0\u001B[39m], upper_sampled_filters[\u001B[32m1\u001B[39m], upper_sampled_filter_meas[\u001B[32m0\u001B[39m], upper_sampled_filter_meas[\u001B[32m1\u001B[39m])\n\u001B[32m 36\u001B[39m r_c = \u001B[38;5;28mlist\u001B[39m(category_df[\u001B[33m\"\u001B[39m\u001B[33mchannels\u001B[39m\u001B[33m\"\u001B[39m])\n\u001B[32m---> \u001B[39m\u001B[32m37\u001B[39m r_v_rect = [\u001B[43mcalc_rect\u001B[49m\u001B[43m(\u001B[49m\u001B[43mc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mupper_m\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mupper_b\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlower_m\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlower_b\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m c \u001B[38;5;129;01min\u001B[39;00m r_c] \n\u001B[32m 38\u001B[39m err_rect = [np.abs((approx - meas) / meas) \u001B[38;5;28;01mfor\u001B[39;00m approx, meas \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(r_v_rect, filter_meas)]\n\u001B[32m 41\u001B[39m upper_m, upper_b = lin_interpol(upper_left - \u001B[32m3\u001B[39m, upper_right - \u001B[32m3\u001B[39m, upper_left_meas, upper_right_meas)\n",
"\u001B[31mTypeError\u001B[39m: calc_rect() missing 2 required positional arguments: 'step_period' and 'rect_period'"
]
}
],
"execution_count": 10
},
{
"cell_type": "code",
"id": "8c92e57c-3d5a-41f3-8225-d5ad2c50e650",
"metadata": {
"ExecuteTime": {
"end_time": "2025-11-20T13:26:09.944595098Z",
"start_time": "2025-11-20T07:43:24.882926Z"
}
},
"source": [
"fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
"split_df = df.groupby('filters')\n",
"first = True\n",
"\n",
"deltas = calculate_deltas(list(split_df.get_group(100)['ms']))\n",
"all_errs = []\n",
"all_rect_errs = []\n",
"\n",
"color_tuples = [\n",
" (\"#000088\", \"#0000DD\", \"#880000\"), # Blue (max brightness), Green (max brightness)\n",
" (\"#0000CC\", \"#00CC00\", \"#550000\"),\n",
" (\"#0000BB\", \"#00BB00\", \"#330000\"),\n",
" (\"#0000AA\", \"#00AA00\", \"#880000\"),\n",
" (\"#000033\", \"#000099\", \"#330000\"),\n",
" (\"#000033\", \"#000099\", \"#330000\"),\n",
" (\"#000033\", \"#000099\", \"#330000\"),\n",
" (\"#000066\", \"#006600\", \"#880000\"),\n",
" (\"#000055\", \"#005500\", \"#880000\"),\n",
" (\"#000044\", \"#004400\", \"#880000\") # Blue (min brightness), Green (min brightness)\n",
"]\n",
"for (idx, (category, category_df)) in list(enumerate(split_df))[::5]:\n",
" # print(category_df)\n",
" # if not first:\n",
" # continue\n",
" upper_right = 195\n",
" upper_left = 129\n",
" lower_right = 192\n",
" lower_left = 126\n",
"\n",
" upper_right_meas = category_df.loc[category_df[\"channels\"] == upper_right][\"ms\"].values[0]\n",
" upper_left_meas = category_df.loc[category_df[\"channels\"] == upper_left][\"ms\"].values[0]\n",
"\n",
" lower_right_meas = category_df.loc[category_df[\"channels\"] == lower_right][\"ms\"].values[0]\n",
" lower_left_meas = category_df.loc[category_df[\"channels\"] == lower_left][\"ms\"].values[0]\n",
" upper_m, upper_b = lin_interpol( upper_sampled_filters[0], upper_sampled_filters[1], upper_sampled_filter_meas[0], upper_sampled_filter_meas[1])\n",
" r_c = list(category_df[\"channels\"])\n",
" r_v_rect = [calc_rect(c, upper_m, upper_b, lower_m, lower_b) for c in r_c] \n",
" err_rect = [np.abs((approx - meas) / meas) for approx, meas in zip(r_v_rect, filter_meas)]\n",
"\n",
"\n",
" upper_m, upper_b = lin_interpol(upper_left - 3, upper_right - 3, upper_left_meas, upper_right_meas)\n",
" lower_m, lower_b = lin_interpol(lower_left, lower_right, lower_left_meas, lower_right_meas)\n",
" start = list(category_df['channels'])[0]\n",
" end = list(category_df['channels'])[-1]\n",
" # r_c = list(range(start, end))\n",
"\n",
" r_v_upper = [calc_upper(c, upper_m, upper_b) for c in r_c]\n",
" r_v_lower = [calc_lower(c, lower_m, lower_b) for c in r_c]\n",
" r_v_rect = [calc_rect(c, upper_m, upper_b, lower_m, lower_b) for c in r_c]\n",
" lv = list(category_df['ms'])[0]\n",
" delta_approx = [lv]\n",
" for delta in deltas:\n",
" lv = delta * lv\n",
" delta_approx.append(lv)\n",
" \n",
" errs = compute_absolute_percentage_errors(list(category_df['ms']), delta_approx) # [np.abs(1 - (g / m)) * 100 for g, m in zip(delta_approx, list(category_df['ms']))]\n",
" rect_errs = compute_absolute_percentage_errors(list(category_df['ms']), r_v_rect)\n",
"\n",
" all_errs.append(np.mean(errs))\n",
" all_rect_errs.append(np.mean(rect_errs))\n",
" print(f\"{np.mean(errs)=}\")\n",
" print(f\"{np.mean(rect_errs)=}\")\n",
" fig.add_trace(go.Scatter(\n",
" # x=category_df['channels'],\n",
" x=category_df['channels'],\n",
" y=category_df['ms'],\n",
" # mode='markers',\n",
" marker=dict(\n",
" size=7,\n",
" color=color_tuples[idx][0]\n",
" ),\n",
" name=f\"#Filters:{category} Latency\",\n",
" mode='markers',\n",
" \n",
" ), \n",
" secondary_y=False\n",
" )\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=list(category_df['channels']),\n",
" y=delta_approx,\n",
" name=f\"Approximation\",\n",
" mode='lines',\n",
" line=dict(\n",
" dash=\"dot\",\n",
" width=.5,\n",
" color=color_tuples[idx][1]\n",
" ),\n",
" \n",
" ), \n",
" secondary_y=False\n",
" )\n",
" # fig.add_trace(\n",
" # go.Scatter(\n",
" # x=list(category_df['channels']),\n",
" # y=r_v_rect,\n",
" # name=f\"Stepwise Latency approximation\",\n",
" # mode='lines',\n",
" # line=dict(\n",
" # width=.5,\n",
" # color=color_tuples[idx][1]\n",
" # ),\n",
" \n",
" # ), \n",
" # secondary_y=False\n",
" # )\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=list(category_df['channels']),\n",
" y=errs,\n",
" name=f\"Error\",\n",
" mode='lines',\n",
" line=dict(\n",
" width=.5,\n",
" color=color_tuples[idx][2]\n",
" ),\n",
" \n",
" ),\n",
" secondary_y=True\n",
" )\n",
" # fig.add_trace(\n",
" # go.Scatter(\n",
" # x=list(category_df['channels']),\n",
" # y=rect_errs,\n",
" # name=f\"Stepwise Latency Approximation Error\",\n",
" # mode='lines',\n",
" # line=dict(\n",
" # width=.5,\n",
" # color=color_tuples[idx][2]\n",
" # ),\n",
" \n",
" # ),\n",
" # secondary_y=True\n",
" # )\n",
"\n",
" first = False\n",
"\n",
"print()\n",
"print(f\"{np.mean(all_errs)=}\")\n",
"print(f\"{np.mean(all_rect_errs)=}\")\n",
"fig.update_layout(\n",
" scene=dict(\n",
" xaxis_title='channels',\n",
" yaxis_title='filters',\n",
" zaxis_title='ms'\n",
" ),\n",
" width=PLOT_WIDTH,\n",
" height=PLOT_HEIGHT,\n",
" template='plotly_white',\n",
" plot_bgcolor= \"rgba(0, 0, 0, 0)\",\n",
")\n",
"fig.update_xaxes(title_text=\"<b>#Channels</b>\")\n",
"\n",
"fig.update_yaxes(title_text=f\"\"\"<b style=\"color:blue\";>Layer Execution Time (ms)</b>\"\"\", secondary_y=False, range=[-5, 50])\n",
"fig.update_yaxes(title_text=f\"\"\"<b style=\"color:red\";>Absolute Approximation Error (%)</b>\"\"\", secondary_y=True, range=[-4, 40],)\n",
"fig.update_layout(\n",
" title_text=\"<b>Approximation using the point to point differences of reference sweep</b>\",\n",
" autosize=False,\n",
" width=PLOT_WIDTH / 2,\n",
" height=PLOT_HEIGHT / 3 * 2,\n",
" legend_x=0, \n",
" legend_y=1,\n",
" font=dict(\n",
" size=14,\n",
" ),\n",
" plot_bgcolor= \"rgba(0, 0, 0, 0)\",\n",
"\n",
")\n",
"fig.write_image(\"images/delta_approx.svg\", width=PLOT_WIDTH /2, height=PLOT_HEIGHT / 3 * 2, scale=2)\n",
"\n",
"fig.show()"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"np.mean(errs)=np.float64(2.768728199793735e-14)\n",
"np.mean(rect_errs)=np.float64(0.5195734160479627)\n",
"np.mean(errs)=np.float64(2.048453928944935)\n",
"np.mean(rect_errs)=np.float64(2.53793726954352)\n",
"\n",
"np.mean(all_errs)=np.float64(1.0242269644724813)\n",
"np.mean(all_rect_errs)=np.float64(1.5287553427957412)\n"
]
},
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"marker": {
"color": "#000088",
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},
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},
"width": 1000.0,
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{
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"metadata": {
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"start_time": "2025-11-20T07:43:26.146437Z"
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],
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