{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Play the RF mapping for one unit.\n",
"\n",
"Tony Fu, July 8, 2022\n",
"\"\"\"\n",
"import sys\n",
"\n",
"import numpy as np\n",
"from torchvision import models\n",
"from torchvision.models import AlexNet_Weights, VGG16_Weights\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as animation\n",
"%matplotlib ipympl\n",
"\n",
"sys.path.append('..')\n",
"from image import make_box\n",
"from mapping import BarRfMapperPz\n",
"import constants as c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"conv_i = 1\n",
"unit_i = 11\n",
"\n",
"import numpy as np\n",
"model = models.alexnet(weights=AlexNet_Weights.IMAGENET1K_V1)\n",
"bm = BarRfMapperPz(model, conv_i, (227, 227))\n",
"a = bm.animate(unit_i)\n",
"\n",
"fig, ax = plt.subplots()\n",
"im = ax.imshow(np.zeros((227, 227)), cmap='gray')\n",
" \n",
"def animate_func(frame):\n",
" vmin = im.get_array().min()\n",
" vmax = im.get_array().max() * 0.8\n",
" im.set_data(frame[0])\n",
" im.set_clim(vmin=vmin, vmax=vmax)\n",
" ax.set_title(f\"conv{conv_i+1} unit no.{unit_i} frame {frame[2]}, response = {frame[1]:.2f}\")\n",
"\n",
"ani = animation.FuncAnimation(\n",
" fig, animate_func, frames=a, interval=10, save_count=0, cache_frame_data=False, repeat=False)\n",
"\n",
"ax.add_patch(make_box(bm.box))\n",
"boundary = 10\n",
"plt.xlim([bm.box[1] - boundary, bm.box[3] + boundary])\n",
"plt.ylim([bm.box[0] - boundary, bm.box[2] + boundary])\n",
"ax.invert_yaxis()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The RF mapper is for Conv2 (not Conv1) with input shape (yn = 227, xn = 227).\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9271b18bdc3c485d84de976da360a979",
"version_major": 2,
"version_minor": 0
},
"image/png": 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",
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"\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img 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' width=1000.0/>\n",
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"conv_i = 1\n",
"unit_i = 0\n",
"\n",
"model = models.alexnet(weights=AlexNet_Weights.IMAGENET1K_V1)\n",
"bm = BarRfMapperPz(model, conv_i, (227, 227))\n",
"a = bm.animate(unit_i)\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1, 2)\n",
"fig.suptitle(f\"conv{conv_i+1} unit no.{unit_i}\", fontsize=20)\n",
"fig.set_size_inches(10, 5)\n",
"fig.suptitle(f\"conv{conv_i+1} unit no.{unit_i}\", fontsize=20)\n",
"ax1.clear()\n",
"ax2.clear()\n",
"im1 = ax1.imshow(np.zeros((227, 227)), cmap='gray', vmin=-1, vmax=1)\n",
"im2 = ax2.imshow(np.zeros((227, 227)), cmap='gray')\n",
" \n",
"def animate_func(frame):\n",
" im1.set_data(frame[3])\n",
" ax1.set_title(f\"response = {frame[1]:.2f}\")\n",
"\n",
" vmin = im2.get_array().min()\n",
" vmax = im2.get_array().max()\n",
" im2.set_data(frame[0])\n",
" im2.set_clim(vmin=vmin, vmax=vmax)\n",
" ax2.set_title(f\"frame {frame[2]}\")\n",
"\n",
"ani = animation.FuncAnimation(\n",
" fig, animate_func, frames=a, interval=10, save_count=0, cache_frame_data=False, repeat=False)\n",
"\n",
"ax1.add_patch(make_box(bm.box))\n",
"ax2.add_patch(make_box(bm.box))\n",
"boundary = bm.rf_size//2\n",
"ax1.set_xlim([bm.box[1] - boundary, bm.box[3] + boundary])\n",
"ax1.set_ylim([bm.box[0] - boundary, bm.box[2] + boundary])\n",
"ax2.set_xlim([bm.box[1] - boundary, bm.box[3] + boundary])\n",
"ax2.set_ylim([bm.box[0] - boundary, bm.box[2] + boundary])\n",
"ax1.invert_yaxis()\n",
"ax2.invert_yaxis()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The RF mapper is for Conv1 (not Conv0) with input shape (yn = 227, xn = 227).\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5f5917e4ab0472682f3f65e7414eede",
"version_major": 2,
"version_minor": 0
},
"image/png": 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width=1500.0/>\n",
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"conv_i = 0\n",
"unit_i = 6\n",
"\n",
"model = models.alexnet(weights=AlexNet_Weights.IMAGENET1K_V1)\n",
"bm = BarRfMapperPz(model, conv_i, (227, 227))\n",
"a = bm.animate(unit_i)\n",
"\n",
"fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
"fig.suptitle(f\"conv{conv_i+1} unit no.{unit_i}\", fontsize=20)\n",
"fig.set_size_inches(15, 5)\n",
"ax1.clear()\n",
"ax2.clear()\n",
"im1 = ax1.imshow(np.zeros((227, 227)), cmap='gray', vmin=-1, vmax=1)\n",
"im2 = ax2.imshow(np.zeros((227, 227)), cmap='gray')\n",
"im3 = ax3.bar(bm.thetas, np.zeros((len(bm.thetas))))\n",
"ax3.set_title(\"Theta tuning\")\n",
"ax3.set_xlabel(\"theta\")\n",
"ax3.set_ylabel(\"avg response\")\n",
" \n",
"def animate_func(frame):\n",
" im1.set_data(frame[3])\n",
" ax1.set_title(f\"response = {frame[1]:.2f}\")\n",
"\n",
" vmin = im2.get_array().min()\n",
" vmax = im2.get_array().max()\n",
" im2.set_data(frame[0])\n",
" im2.set_clim(vmin=vmin, vmax=vmax)\n",
" ax2.set_title(f\"frame {frame[2]}\")\n",
" \n",
" ax3.bar(bm.thetas, frame[4], width=0.6)\n",
"\n",
"ani = animation.FuncAnimation(\n",
" fig, animate_func, frames=a, interval=10, save_count=0, cache_frame_data=False, repeat=False)\n",
"\n",
"ax1.add_patch(make_box(bm.box))\n",
"ax2.add_patch(make_box(bm.box))\n",
"boundary = bm.rf_size//2\n",
"ax1.set_xlim([bm.box[1] - boundary, bm.box[3] + boundary])\n",
"ax1.set_ylim([bm.box[0] - boundary, bm.box[2] + boundary])\n",
"ax2.set_xlim([bm.box[1] - boundary, bm.box[3] + boundary])\n",
"ax2.set_ylim([bm.box[0] - boundary, bm.box[2] + boundary])\n",
"ax1.invert_yaxis()\n",
"ax2.invert_yaxis()\n",
"plt.show()"
]
}
],
"metadata": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
},
"kernelspec": {
"display_name": "Python 3.9.1 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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