{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from utils import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Application architectures deep dive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computer vision"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### cnn_learner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'cut': -2,\n",
" 'split': <function fastai2.vision.learner._resnet_split(m)>,\n",
" 'stats': ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])}"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_meta[resnet50]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sequential(\n",
" (0): AdaptiveConcatPool2d(\n",
" (ap): AdaptiveAvgPool2d(output_size=1)\n",
" (mp): AdaptiveMaxPool2d(output_size=1)\n",
" )\n",
" (1): full: False\n",
" (2): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (3): Dropout(p=0.25, inplace=False)\n",
" (4): Linear(in_features=20, out_features=512, bias=False)\n",
" (5): ReLU(inplace=True)\n",
" (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (7): Dropout(p=0.5, inplace=False)\n",
" (8): Linear(in_features=512, out_features=2, bias=False)\n",
")"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"create_head(20,2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### unet_learner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A Siamese network"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from fastai2.vision.all import *\n",
"path = untar_data(URLs.PETS)\n",
"files = get_image_files(path/\"images\")\n",
"\n",
"class SiameseImage(Tuple):\n",
" def show(self, ctx=None, **kwargs): \n",
" img1,img2,same_breed = self\n",
" if not isinstance(img1, Tensor):\n",
" if img2.size != img1.size: img2 = img2.resize(img1.size)\n",
" t1,t2 = tensor(img1),tensor(img2)\n",
" t1,t2 = t1.permute(2,0,1),t2.permute(2,0,1)\n",
" else: t1,t2 = img1,img2\n",
" line = t1.new_zeros(t1.shape[0], t1.shape[1], 10)\n",
" return show_image(torch.cat([t1,line,t2], dim=2), \n",
" title=same_breed, ctx=ctx)\n",
" \n",
"def label_func(fname):\n",
" return re.match(r'^(.*)_\\d+.jpg$', fname.name).groups()[0]\n",
"\n",
"class SiameseTransform(Transform):\n",
" def __init__(self, files, label_func, splits):\n",
" self.labels = files.map(label_func).unique()\n",
" self.lbl2files = {l: L(f for f in files if label_func(f) == l) for l in self.labels}\n",
" self.label_func = label_func\n",
" self.valid = {f: self._draw(f) for f in files[splits[1]]}\n",
" \n",
" def encodes(self, f):\n",
" f2,t = self.valid.get(f, self._draw(f))\n",
" img1,img2 = PILImage.create(f),PILImage.create(f2)\n",
" return SiameseImage(img1, img2, t)\n",
" \n",
" def _draw(self, f):\n",
" same = random.random() < 0.5\n",
" cls = self.label_func(f)\n",
" if not same: cls = random.choice(L(l for l in self.labels if l != cls)) \n",
" return random.choice(self.lbl2files[cls]),same\n",
" \n",
"splits = RandomSplitter()(files)\n",
"tfm = SiameseTransform(files, label_func, splits)\n",
"tls = TfmdLists(files, tfm, splits=splits)\n",
"dls = tls.dataloaders(after_item=[Resize(224), ToTensor], \n",
" after_batch=[IntToFloatTensor, Normalize.from_stats(*imagenet_stats)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class SiameseModel(Module):\n",
" def __init__(self, encoder, head):\n",
" self.encoder,self.head = encoder,head\n",
" \n",
" def forward(self, x1, x2):\n",
" ftrs = torch.cat([self.encoder(x1), self.encoder(x2)], dim=1)\n",
" return self.head(ftrs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"encoder = create_body(resnet34, cut=-2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"head = create_head(512*4, 2, ps=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = SiameseModel(encoder, head)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def loss_func(out, targ):\n",
" return nn.CrossEntropyLoss()(out, targ.long())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def siamese_splitter(model):\n",
" return [params(model.encoder), params(model.head)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn = Learner(dls, model, loss_func=loss_func, \n",
" splitter=siamese_splitter, metrics=accuracy)\n",
"learn.freeze()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.367015</td>\n",
" <td>0.281242</td>\n",
" <td>0.885656</td>\n",
" <td>00:26</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.307688</td>\n",
" <td>0.214721</td>\n",
" <td>0.915426</td>\n",
" <td>00:26</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.275221</td>\n",
" <td>0.170615</td>\n",
" <td>0.936401</td>\n",
" <td>00:26</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.223771</td>\n",
" <td>0.159633</td>\n",
" <td>0.943843</td>\n",
" <td>00:26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.fit_one_cycle(4, 3e-3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.212744</td>\n",
" <td>0.159033</td>\n",
" <td>0.944520</td>\n",
" <td>00:35</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.201893</td>\n",
" <td>0.159615</td>\n",
" <td>0.942490</td>\n",
" <td>00:35</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.204606</td>\n",
" <td>0.152338</td>\n",
" <td>0.945196</td>\n",
" <td>00:36</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.213203</td>\n",
" <td>0.148346</td>\n",
" <td>0.947903</td>\n",
" <td>00:36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.unfreeze()\n",
"learn.fit_one_cycle(4, slice(1e-6,1e-4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Natural language processing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tabular"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wrapping up architectures"
]
},
{
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}