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borisdayma/test_TfmdList.ipynb

Created January 21, 2020 04:25
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test_TfmdList.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example extracted from [Pets tutorial](https://dev.fast.ai/tutorial.pets#Try-1:-State-outside-class)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from fastai2.data.all import *\n",
"from fastai2.vision.core import *\n",
"from fastai2.vision.data import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"source = untar_data(URLs.PETS)/\"images\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"items = get_image_files(source)\n",
"split_idx = RandomSplitter()(items)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def resized_image(fn:Path, sz=128):\n",
" x = Image.open(fn).convert('RGB').resize((sz,sz))\n",
" # Convert image to tensor for modeling\n",
" return tensor(array(x)).permute(2,0,1).float()/255."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class TitledImage(Tuple):\n",
" _tensor_cls = TensorImage # not sure it is needed\n",
" def show(self, ctx=None, **kwargs): show_titled_image(self, ctx=ctx, **kwargs)\n",
" \n",
"# not sure this helps\n",
"@ToTensor\n",
"def encodes(self, o:TitledImage): return o._tensor_cls(image2tensor(o))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"img = resized_image(items[0])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"TitledImage(img,'test').show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class PetTfm(Transform):\n",
" def __init__(self, vocab, o2i, lblr): self.vocab,self.o2i,self.lblr = vocab,o2i,lblr\n",
" def encodes(self, o): return resized_image(o), self.o2i[self.lblr(o)]\n",
" def decodes(self, x): return TitledImage(x[0],self.vocab[x[1]])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"labeller = RegexLabeller(pat = r'/([^/]+)_\\d+.jpg$')\n",
"vals = list(map(labeller, items[split_idx[0]]))\n",
"vocab,o2i = uniqueify(vals, sort=True, bidir=True)\n",
"pets = PetTfm(vocab,o2i,labeller)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"dsrc = TfmdList(items, tfms=pets)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([[[0.7765, 0.7882, 0.7804, ..., 0.8706, 0.8627, 0.8667],\n",
" [0.7765, 0.7961, 0.5020, ..., 0.8784, 0.8706, 0.8941],\n",
" [0.7843, 0.7843, 0.7804, ..., 0.9020, 0.8745, 0.8706],\n",
" ...,\n",
" [0.9294, 0.9412, 0.8627, ..., 0.8275, 0.8824, 0.8706],\n",
" [0.9451, 0.8667, 0.8863, ..., 0.8157, 0.8549, 0.8314],\n",
" [0.8824, 0.9373, 0.8667, ..., 0.8510, 0.8824, 0.8471]],\n",
" \n",
" [[0.8196, 0.8314, 0.8314, ..., 0.8863, 0.8784, 0.8824],\n",
" [0.8196, 0.8392, 0.5529, ..., 0.8941, 0.8863, 0.9098],\n",
" [0.8275, 0.8275, 0.8314, ..., 0.9176, 0.8902, 0.8863],\n",
" ...,\n",
" [0.7294, 0.7373, 0.6549, ..., 0.5686, 0.6157, 0.6000],\n",
" [0.7412, 0.6549, 0.6627, ..., 0.5490, 0.5765, 0.5647],\n",
" [0.6745, 0.7059, 0.6235, ..., 0.5843, 0.6039, 0.5804]],\n",
" \n",
" [[0.8431, 0.8549, 0.8549, ..., 0.8902, 0.8824, 0.8863],\n",
" [0.8431, 0.8627, 0.5765, ..., 0.8980, 0.8902, 0.9137],\n",
" [0.8510, 0.8510, 0.8549, ..., 0.9216, 0.8941, 0.8902],\n",
" ...,\n",
" [0.5216, 0.5529, 0.4824, ..., 0.3804, 0.4314, 0.4353],\n",
" [0.5569, 0.4745, 0.4863, ..., 0.3647, 0.4039, 0.3882],\n",
" [0.5176, 0.5333, 0.4314, ..., 0.3922, 0.4235, 0.3961]]]), 32)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_sample = dsrc[0]\n",
"data_sample"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Displaying picture works correctly\n",
"dsrc.tfms.show(data_sample)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# We can even call directly `show`, meaning the \"decoder\" is called (only place where TitledImage is used)\n",
"dsrc.show(data_sample)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Let's create a databunch\n",
"databunch = dsrc.databunch(bs=4)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Pipeline.decode of Pipeline: (#1) [PetTfm: True (object,object) -> encodes (object,object) -> decodes]>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this is the method that we would like to be used by the databunch prior to displaying samples\n",
"dsrc.tfms.decode"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Tensor' object has no attribute 'show'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-9f15518344df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# We cannot display a batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatabunch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/data/fastai2/fastai2/data/core.py\u001b[0m in \u001b[0;36mshow_batch\u001b[0;34m(self, b, max_n, ctxs, show, **kwargs)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_show_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_show_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctxs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mctxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshow_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctxs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastcore/fastcore/dispatch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMethodType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastai2/fastai2/data/core.py\u001b[0m in \u001b[0;36mshow_batch\u001b[0;34m(x, y, samples, ctxs, max_n, **kwargs)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mctxs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mctxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnones\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mctxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitemgot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mctxs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mctxs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastai2/fastai2/data/core.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mctxs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mctxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnones\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mctxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitemgot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mctxs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mctxs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'show'"
]
}
],
"source": [
"# We cannot display a batch\n",
"batch = databunch.show_batch()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([[[[0.5725, 0.5804, 0.6118, ..., 0.7922, 0.8157, 0.8118],\n",
" [0.5686, 0.5686, 0.5098, ..., 0.7961, 0.8039, 0.8000],\n",
" [0.5647, 0.5843, 0.4392, ..., 0.8078, 0.8000, 0.8039],\n",
" ...,\n",
" [0.2706, 0.4157, 0.4471, ..., 0.7412, 0.7961, 0.7961],\n",
" [0.4157, 0.3294, 0.7490, ..., 0.7569, 0.7922, 0.7765],\n",
" [0.3804, 0.7412, 0.7098, ..., 0.7686, 0.7608, 0.7608]],\n",
" \n",
" [[0.4039, 0.4078, 0.4314, ..., 0.7922, 0.8118, 0.8078],\n",
" [0.4000, 0.4078, 0.3569, ..., 0.7961, 0.8000, 0.7961],\n",
" [0.3961, 0.4157, 0.2980, ..., 0.8078, 0.7961, 0.8000],\n",
" ...,\n",
" [0.2824, 0.4078, 0.4510, ..., 0.7333, 0.7922, 0.7882],\n",
" [0.4235, 0.3333, 0.7569, ..., 0.7490, 0.7882, 0.7686],\n",
" [0.3882, 0.7569, 0.7216, ..., 0.7608, 0.7529, 0.7529]],\n",
" \n",
" [[0.2275, 0.2314, 0.2392, ..., 0.8000, 0.8314, 0.8314],\n",
" [0.2353, 0.2353, 0.2039, ..., 0.8039, 0.8196, 0.8196],\n",
" [0.2314, 0.2471, 0.1647, ..., 0.8157, 0.8157, 0.8235],\n",
" ...,\n",
" [0.3176, 0.4588, 0.4314, ..., 0.7373, 0.7725, 0.7922],\n",
" [0.4745, 0.3490, 0.7137, ..., 0.7608, 0.7686, 0.7725],\n",
" [0.4392, 0.7020, 0.6392, ..., 0.7725, 0.7725, 0.7725]]],\n",
" \n",
" \n",
" [[[0.3255, 0.3020, 0.2706, ..., 0.2118, 0.2039, 0.2039],\n",
" [0.3176, 0.2902, 0.2784, ..., 0.2118, 0.2118, 0.2235],\n",
" [0.3176, 0.2941, 0.2784, ..., 0.2118, 0.2078, 0.2157],\n",
" ...,\n",
" [0.0745, 0.0863, 0.0941, ..., 0.0471, 0.0471, 0.0627],\n",
" [0.0824, 0.0980, 0.0627, ..., 0.0235, 0.0196, 0.0235],\n",
" [0.0784, 0.1137, 0.1098, ..., 0.0196, 0.0196, 0.0196]],\n",
" \n",
" [[0.2980, 0.2863, 0.2549, ..., 0.1725, 0.1647, 0.1608],\n",
" [0.3098, 0.2824, 0.2627, ..., 0.1647, 0.1686, 0.1608],\n",
" [0.3098, 0.2863, 0.2627, ..., 0.1647, 0.1647, 0.1608],\n",
" ...,\n",
" [0.0549, 0.0667, 0.0745, ..., 0.0431, 0.0471, 0.0549],\n",
" [0.0510, 0.0784, 0.0471, ..., 0.0157, 0.0118, 0.0157],\n",
" [0.0588, 0.1098, 0.0745, ..., 0.0118, 0.0118, 0.0118]],\n",
" \n",
" [[0.3255, 0.2902, 0.2510, ..., 0.1373, 0.1569, 0.1529],\n",
" [0.3294, 0.2863, 0.2510, ..., 0.1647, 0.1608, 0.1608],\n",
" [0.3216, 0.2980, 0.2667, ..., 0.1725, 0.1569, 0.1569],\n",
" ...,\n",
" [0.0392, 0.0549, 0.0627, ..., 0.0275, 0.0549, 0.0667],\n",
" [0.0431, 0.0667, 0.0431, ..., 0.0196, 0.0157, 0.0196],\n",
" [0.0471, 0.0941, 0.0784, ..., 0.0157, 0.0157, 0.0157]]],\n",
" \n",
" \n",
" [[[0.4275, 0.4196, 0.4588, ..., 0.6706, 0.6745, 0.6235],\n",
" [0.4353, 0.4314, 0.4627, ..., 0.6745, 0.6627, 0.6000],\n",
" [0.4353, 0.4431, 0.4667, ..., 0.6510, 0.6000, 0.6039],\n",
" ...,\n",
" [0.6980, 0.6863, 0.6824, ..., 0.7490, 0.7882, 0.7882],\n",
" [0.7373, 0.7216, 0.7098, ..., 0.8235, 0.8471, 0.8471],\n",
" [0.7529, 0.7333, 0.7294, ..., 0.8392, 0.8706, 0.8667]],\n",
" \n",
" [[0.4627, 0.4510, 0.4863, ..., 0.4863, 0.4784, 0.4275],\n",
" [0.4706, 0.4667, 0.4941, ..., 0.4902, 0.4588, 0.3961],\n",
" [0.4667, 0.4706, 0.4824, ..., 0.4549, 0.4039, 0.4000],\n",
" ...,\n",
" [0.5647, 0.5451, 0.5373, ..., 0.6510, 0.6824, 0.6784],\n",
" [0.5922, 0.5765, 0.5725, ..., 0.7059, 0.7216, 0.7216],\n",
" [0.6196, 0.5961, 0.5922, ..., 0.7216, 0.7451, 0.7412]],\n",
" \n",
" [[0.2549, 0.2510, 0.3137, ..., 0.4078, 0.3882, 0.3373],\n",
" [0.2549, 0.2510, 0.3020, ..., 0.4118, 0.3725, 0.3098],\n",
" [0.2667, 0.2667, 0.2824, ..., 0.3647, 0.2980, 0.3059],\n",
" ...,\n",
" [0.4275, 0.4196, 0.4235, ..., 0.5333, 0.5765, 0.5843],\n",
" [0.4784, 0.4627, 0.4549, ..., 0.6039, 0.6235, 0.6235],\n",
" [0.5098, 0.4784, 0.4745, ..., 0.6275, 0.6471, 0.6431]]],\n",
" \n",
" \n",
" [[[0.0745, 0.1255, 0.1020, ..., 0.1137, 0.1176, 0.0510],\n",
" [0.0392, 0.1725, 0.1843, ..., 0.1843, 0.1451, 0.0157],\n",
" [0.0549, 0.7294, 0.4863, ..., 0.1725, 0.3647, 0.0000],\n",
" ...,\n",
" [0.0510, 0.9255, 0.9059, ..., 0.9569, 0.9412, 0.0000],\n",
" [0.0471, 0.1765, 0.1804, ..., 0.1765, 0.1725, 0.0118],\n",
" [0.0275, 0.0000, 0.0078, ..., 0.0039, 0.0039, 0.0000]],\n",
" \n",
" [[0.0745, 0.1255, 0.1098, ..., 0.1216, 0.1176, 0.0510],\n",
" [0.0392, 0.1725, 0.1765, ..., 0.1608, 0.1882, 0.0157],\n",
" [0.0549, 0.7451, 0.5176, ..., 0.0000, 0.2863, 0.0000],\n",
" ...,\n",
" [0.0510, 0.9529, 0.9451, ..., 0.9569, 0.9412, 0.0000],\n",
" [0.0471, 0.1725, 0.1765, ..., 0.1765, 0.1725, 0.0118],\n",
" [0.0275, 0.0039, 0.0078, ..., 0.0039, 0.0039, 0.0000]],\n",
" \n",
" [[0.0745, 0.1255, 0.1059, ..., 0.1098, 0.1176, 0.0510],\n",
" [0.0392, 0.1647, 0.1804, ..., 0.1765, 0.1725, 0.0157],\n",
" [0.0549, 0.7569, 0.5255, ..., 0.0000, 0.2902, 0.0000],\n",
" ...,\n",
" [0.0510, 0.9765, 0.9804, ..., 1.0000, 0.9725, 0.0000],\n",
" [0.0549, 0.1647, 0.1686, ..., 0.1686, 0.1725, 0.0118],\n",
" [0.0275, 0.0118, 0.0078, ..., 0.0000, 0.0039, 0.0000]]]],\n",
" device='cuda:0'), tensor([ 0, 34, 16, 36], device='cuda:0'))"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# However we can get a batch correctly\n",
"batch = databunch.one_batch()\n",
"batch"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Pipeline.decode of Pipeline: (#1) [Transform: False (object,object) -> noop ]>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this method is called by databunch before display but does not contain any operation\n",
"databunch.after_item.decode\n",
"\n",
"# Note: I also tried the following line but it does not work either as the transform is already applied\n",
"# databunch = dsrc.databunch(after_item=pets)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# let's try to force it\n",
"databunch.after_item.decode = dsrc.tfms.decode"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "invalid literal for int() with base 10: 's'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-57f6d15947f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# the method we wanted is called -> PetTfm.decodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# however it raises another unexpected error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdatabunch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/data/fastai2/fastai2/data/core.py\u001b[0m in \u001b[0;36mshow_batch\u001b[0;34m(self, b, max_n, ctxs, show, **kwargs)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_show_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_show_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctxs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mctxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshow_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctxs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastai2/fastai2/data/core.py\u001b[0m in \u001b[0;36m_pre_show_batch\u001b[0;34m(self, b, max_n)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'show'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0mits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdetuplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_inp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdetuplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_inp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastai2/fastai2/data/core.py\u001b[0m in \u001b[0;36m_decode_batch\u001b[0;34m(self, b, max_n, full)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_item\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'decode'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfull\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_to_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_n\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_pre_show_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastcore/fastcore/foundation.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, f, *args, **kwargs)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 361\u001b[0m else f.__getitem__)\n\u001b[0;32m--> 362\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnegate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/data/fastcore/fastcore/foundation.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(self, i)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'iloc'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmask2idxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 322\u001b[0m return (self.items.iloc[list(i)] if hasattr(self.items,'iloc')\n\u001b[1;32m 323\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'__array__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastcore/fastcore/foundation.py\u001b[0m in \u001b[0;36mmask2idxs\u001b[0;34m(mask)\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'item'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNoneType\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/fastcore/fastcore/foundation.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'item'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNoneType\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 's'"
]
}
],
"source": [
"# the method we wanted is called -> PetTfm.decodes\n",
"# however it raises another unexpected error\n",
"databunch.show_batch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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