mirror of
https://github.com/fastai/fastbook.git
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463 lines
269 KiB
Plaintext
463 lines
269 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hide_input": false
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},
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"outputs": [],
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"source": [
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"#hide\n",
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"from utils import *"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# CNN interpretation with CAM"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## CAM and hooks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: left;\">\n",
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" <th>epoch</th>\n",
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" <th>train_loss</th>\n",
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" <th>valid_loss</th>\n",
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" <th>error_rate</th>\n",
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" <th>time</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <td>0.181760</td>\n",
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" <td>0.032238</td>\n",
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" <td>0.009472</td>\n",
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" <td>00:14</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: left;\">\n",
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" <th>epoch</th>\n",
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" <th>train_loss</th>\n",
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" <th>valid_loss</th>\n",
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" <th>error_rate</th>\n",
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" <th>time</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <td>0.059119</td>\n",
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" <td>0.014090</td>\n",
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" <td>0.002706</td>\n",
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" <td>00:18</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"path = untar_data(URLs.PETS)/'images'\n",
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"def is_cat(x): return x[0].isupper()\n",
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"dls = ImageDataLoaders.from_name_func(\n",
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" path, get_image_files(path), valid_pct=0.2, seed=42,\n",
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" label_func=is_cat, item_tfms=Resize(224))\n",
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"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
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"learn.fine_tune(1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"img = PILImage.create('images/chapter1_cat_example.jpg')\n",
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"x, = first(dls.test_dl([img]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Hook():\n",
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" def hook_func(self, m, i, o): self.stored = o.detach().clone()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"hook_output = Hook()\n",
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"hook = learn.model[0].register_forward_hook(hook_output.hook_func)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with torch.no_grad(): output = learn.model.eval()(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"act = hook_output.stored[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[2.7374e-09, 1.0000e+00]], device='cuda:5')"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"F.softmax(output, dim=-1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(#2) [False,True]"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dls.vocab"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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|
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"torch.Size([1, 3, 224, 224])"
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]
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},
|
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|
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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|
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"source": [
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|
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"x.shape"
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|
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]
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|
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},
|
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|
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{
|
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|
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"cell_type": "code",
|
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|
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"execution_count": null,
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|
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"metadata": {},
|
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"outputs": [
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|
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{
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"data": {
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|
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"text/plain": [
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|
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"torch.Size([2, 7, 7])"
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|
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]
|
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|
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},
|
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|
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"execution_count": null,
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|
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"metadata": {},
|
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|
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"output_type": "execute_result"
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}
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|
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],
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"source": [
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"cam_map = torch.einsum('ck,kij->cij', learn.model[1][-1].weight, act)\n",
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|
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"cam_map.shape"
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]
|
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|
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},
|
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|
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{
|
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|
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"cell_type": "code",
|
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|
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"execution_count": null,
|
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|
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"metadata": {},
|
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|
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"outputs": [
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|
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{
|
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|
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"data": {
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|
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"image/png": "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"text/plain": [
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|
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"<Figure size 432x288 with 1 Axes>"
|
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|
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]
|
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|
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},
|
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|
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"metadata": {
|
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|
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"needs_background": "light"
|
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|
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},
|
||
|
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"output_type": "display_data"
|
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|
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}
|
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|
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],
|
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"source": [
|
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|
|
"x_dec = TensorImage(dls.train.decode((x,))[0][0])\n",
|
||
|
|
"_,ax = plt.subplots()\n",
|
||
|
|
"x_dec.show(ctx=ax)\n",
|
||
|
|
"ax.imshow(cam_map[0].detach().cpu(), alpha=0.6, extent=(0,224,224,0),\n",
|
||
|
|
" interpolation='bilinear', cmap='magma');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"hook.remove()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class Hook():\n",
|
||
|
|
" def __init__(self, m):\n",
|
||
|
|
" self.hook = m.register_forward_hook(self.hook_func) \n",
|
||
|
|
" def hook_func(self, m, i, o): self.stored = o.detach().clone()\n",
|
||
|
|
" def __enter__(self, *args): return self\n",
|
||
|
|
" def __exit__(self, *args): self.hook.remove()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"with Hook(learn.model[0]) as hook:\n",
|
||
|
|
" with torch.no_grad(): output = learn.model.eval()(x.cuda())\n",
|
||
|
|
" act = hook.stored"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Gradient CAM"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class HookBwd():\n",
|
||
|
|
" def __init__(self, m):\n",
|
||
|
|
" self.hook = m.register_backward_hook(self.hook_func) \n",
|
||
|
|
" def hook_func(self, m, gi, go): self.stored = go[0].detach().clone()\n",
|
||
|
|
" def __enter__(self, *args): return self\n",
|
||
|
|
" def __exit__(self, *args): self.hook.remove()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"cls = 1\n",
|
||
|
|
"with HookBwd(learn.model[0]) as hookg:\n",
|
||
|
|
" with Hook(learn.model[0]) as hook:\n",
|
||
|
|
" output = learn.model.eval()(x.cuda())\n",
|
||
|
|
" act = hook.stored\n",
|
||
|
|
" output[0,cls].backward()\n",
|
||
|
|
" grad = hookg.stored"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"w = grad[0].mean(dim=[1,2], keepdim=True)\n",
|
||
|
|
"cam_map = (w * act[0]).sum(0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"_,ax = plt.subplots()\n",
|
||
|
|
"x_dec.show(ctx=ax)\n",
|
||
|
|
"ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0),\n",
|
||
|
|
" interpolation='bilinear', cmap='magma');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"with HookBwd(learn.model[0][-2]) as hookg:\n",
|
||
|
|
" with Hook(learn.model[0][-2]) as hook:\n",
|
||
|
|
" output = learn.model.eval()(x.cuda())\n",
|
||
|
|
" act = hook.stored\n",
|
||
|
|
" output[0,cls].backward()\n",
|
||
|
|
" grad = hookg.stored"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"w = grad[0].mean(dim=[1,2], keepdim=True)\n",
|
||
|
|
"cam_map = (w * act[0]).sum(0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"_,ax = plt.subplots()\n",
|
||
|
|
"x_dec.show(ctx=ax)\n",
|
||
|
|
"ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0),\n",
|
||
|
|
" interpolation='bilinear', cmap='magma');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Questionnaire"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Further research"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"jupytext": {
|
||
|
|
"split_at_heading": true
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.7.4"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|