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yang-zhang/fastai-RNNLearner-getpreds-debug.ipynb

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fastai-RNNLearner-getpreds-debug.ipynb
{
"cells": [
{
"metadata": {
"scrolled": false,
"trusted": true
},
"cell_type": "code",
"source": "%load_ext autoreload\n%autoreload 2\n\nimport inspect\nfrom fastai.text import * \nimport fastai; fastai.__version__",
"execution_count": 1,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 1,
"data": {
"text/plain": "'1.0.39.dev0'"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## current"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "path = untar_data(URLs.IMDB_SAMPLE)\n\ndf = pd.read_csv(path/'texts.csv')\n\n# Language model data\ndata_lm = TextLMDataBunch.from_csv(path, 'texts.csv')",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Classifier model data\ndata_clas = TextClasDataBunch.from_csv(path, 'texts.csv', vocab=data_lm.train_ds.vocab, bs=4)",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "learn = language_model_learner(data_lm, drop_mult=0.5)\nlearn.save_encoder('ft_enc')",
"execution_count": 4,
"outputs": [
{
"output_type": "error",
"ename": "RuntimeError",
"evalue": "CUDA error: out of memory",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-da6e0b4785d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlanguage_model_learner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_lm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_mult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\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 2\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_encoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ft_enc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/git/fastai/fastai/text/learner.py\u001b[0m in \u001b[0;36mlanguage_model_learner\u001b[0;34m(data, bptt, emb_sz, nh, nl, pad_token, drop_mult, tie_weights, bias, qrnn, pretrained_model, pretrained_fnames, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m model = get_language_model(vocab_size, emb_sz, nh, nl, pad_token, input_p=dps[0], output_p=dps[1],\n\u001b[1;32m 132\u001b[0m weight_p=dps[2], embed_p=dps[3], hidden_p=dps[4], tie_weights=tie_weights, bias=bias, qrnn=qrnn)\n\u001b[0;32m--> 133\u001b[0;31m \u001b[0mlearn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLanguageLearner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbptt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlm_split\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 134\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpretrained_model\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muntar_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\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[0;32m/data/git/fastai/fastai/text/learner.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, model, bptt, split_func, clip, adjust, alpha, beta, metrics, **kwargs)\u001b[0m\n\u001b[1;32m 49\u001b[0m def __init__(self, data:DataBunch, model:nn.Module, bptt:int=70, split_func:OptSplitFunc=None, clip:float=None,\n\u001b[1;32m 50\u001b[0m adjust:bool=False, alpha:float=2., beta:float=1., metrics=None, **kwargs):\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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 52\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRNNTrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbptt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madjust\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0madjust\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 53\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGradientClipping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclip\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<string>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, model, opt_func, loss_func, metrics, true_wd, bn_wd, wd, train_bn, path, model_dir, callback_fns, callbacks, layer_groups)\u001b[0m\n",
"\u001b[0;32m/data/git/fastai/fastai/basic_train.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\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 140\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\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 142\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/anaconda3/envs/fastaidev/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 377\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\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 380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_backward_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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/anaconda3/envs/fastaidev/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\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 184\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\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--> 185\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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 186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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/data/anaconda3/envs/fastaidev/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\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 184\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\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--> 185\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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 186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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/data/anaconda3/envs/fastaidev/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;31m# Tensors stored in modules are graph leaves, and we don't\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;31m# want to create copy nodes, so we have to unpack the data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\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 192\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/anaconda3/envs/fastaidev/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\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--> 377\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\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 378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: CUDA error: out of memory"
]
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn = text_classifier_learner(data_clas, drop_mult=0.5)\nlearn.load_encoder('ft_enc')",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "`get_preds` works for `DatasetType.Valid`"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pred_val, y_val=learn.get_preds(DatasetType.Valid, ordered=True)\n\npred_val.shape, y_val.shape",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "assert np.alltrue(learn.data.valid_ds.y.items==y_val.numpy())",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "`get_preds` works for `DatasetType.Train` for `ordered=False`"
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "pred_trn, y_trn=learn.get_preds(DatasetType.Train, ordered=False)\npred_trn.shape, y_trn.shape, len(learn.data.train_ds)",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "`get_preds` does not work for `DatasetType.Train` for `ordered=True`"
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "pred_trn, y_trn=learn.get_preds(DatasetType.Train, ordered=True)\npred_trn.shape, y_trn.shape, len(learn.data.train_ds)",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## This change generate the desired result, but is there a real solution?"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Code change in `TextClasDataBunch.create`"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "print(inspect.getsource(TextClasDataBunch.create))",
"execution_count": 11,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " @classmethod\n def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', bs=64, pad_idx=1, pad_first=True,\n no_check:bool=False, **kwargs) -> DataBunch:\n \"Function that transform the `datasets` in a `DataBunch` for classification.\"\n datasets = cls._init_ds(train_ds, valid_ds, test_ds)\n collate_fn = partial(pad_collate, pad_idx=pad_idx, pad_first=pad_first)\n train_sampler = SortishSampler(datasets[0].x, key=lambda t: len(datasets[0][t][0].data), bs=bs//2)\n # I removed >>>\n # train_dl = DataLoader(datasets[0], batch_size=bs//2, sampler=train_sampler, drop_last=True, **kwargs)\n # <<< I removed\n # I added >>> \n train_dl = DataLoader(datasets[0], batch_size=bs//2, sampler=train_sampler, drop_last=False, **kwargs)\n # <<< I added >>> \n dataloaders = [train_dl]\n for ds in datasets[1:]:\n lengths = [len(t) for t in ds.x.items]\n sampler = SortSampler(ds.x, key=lengths.__getitem__)\n dataloaders.append(DataLoader(ds, batch_size=bs, sampler=sampler, **kwargs))\n return cls(*dataloaders, path=path, collate_fn=collate_fn, no_check=no_check)\n\n"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Code change in `SortishSampler`"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "print(inspect.getsource(SortishSampler))",
"execution_count": 12,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "class SortishSampler(Sampler):\n \"Go through the text data by order of length with a bit of randomness.\"\n\n def __init__(self, data_source:NPArrayList, key:KeyFunc, bs:int):\n self.data_source,self.key,self.bs = data_source,key,bs\n\n def __len__(self) -> int: return len(self.data_source)\n\n def __iter__(self):\n \n # I added >>> \n np.random.seed(42)\n # <<< I added\n idxs = np.random.permutation(len(self.data_source))\n sz = self.bs*50\n ck_idx = [idxs[i:i+sz] for i in range(0, len(idxs), sz)]\n sort_idx = np.concatenate([sorted(s, key=self.key, reverse=True) for s in ck_idx])\n sz = self.bs\n ck_idx = [sort_idx[i:i+sz] for i in range(0, len(sort_idx), sz)]\n max_ck = np.argmax([self.key(ck[0]) for ck in ck_idx]) # find the chunk with the largest key,\n ck_idx[0],ck_idx[max_ck] = ck_idx[max_ck],ck_idx[0] # then make sure it goes first.\n sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([],dtype=np.int)\n sort_idx = np.concatenate((ck_idx[0], sort_idx))\n return iter(sort_idx)\n\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "path = untar_data(URLs.IMDB_SAMPLE)\n\ndf = pd.read_csv(path/'texts.csv')\n\n# Language model data\ndata_lm = TextLMDataBunch.from_csv(path, 'texts.csv')",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Classifier model data\ndata_clas = TextClasDataBunch.from_csv(path, 'texts.csv', vocab=data_lm.train_ds.vocab, bs=32)",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn = language_model_learner(data_lm, drop_mult=0.5)\nlearn.save_encoder('ft_enc')",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn = text_classifier_learner(data_clas, drop_mult=0.5)\nlearn.load_encoder('ft_enc')",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pred_val, y_val=learn.get_preds(DatasetType.Valid, ordered=True)\n\npred_val.shape, y_val.shape",
"execution_count": 17,
"outputs": [
{
"data": {
"text/plain": "(torch.Size([201, 2]), torch.Size([201]))"
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "assert np.alltrue(learn.data.valid_ds.y.items==y_val.numpy())",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "pred_trn, y_trn=learn.get_preds(DatasetType.Train, ordered=False)\npred_trn.shape, y_trn.shape, len(learn.data.train_ds)",
"execution_count": 20,
"outputs": [
{
"data": {
"text/plain": "(torch.Size([799, 2]), torch.Size([799]), 799)"
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "pred_trn, y_trn=learn.get_preds(DatasetType.Train, ordered=True)\npred_trn.shape, y_trn.shape, len(learn.data.train_ds)",
"execution_count": 21,
"outputs": [
{
"data": {
"text/plain": "(torch.Size([799, 2]), torch.Size([799]), 799)"
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "assert np.alltrue(learn.data.train_ds.y.items==y_trn.numpy())",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "markdown",
"source": "## Solution"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "path = untar_data(URLs.IMDB_SAMPLE)\n\ndf = pd.read_csv(path/'texts.csv')\n\n# Language model data\ndata_lm = TextLMDataBunch.from_csv(path, 'texts.csv')",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Classifier model data\ndata_clas = TextClasDataBunch.from_csv(path, 'texts.csv', vocab=data_lm.train_ds.vocab, bs=4)",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn = language_model_learner(data_lm, drop_mult=0.5)\nlearn.save_encoder('ft_enc')",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn = text_classifier_learner(data_clas, drop_mult=0.5)\nlearn.load_encoder('ft_enc')",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "pred_fix, y_fix=learn.get_preds(DatasetType.Fix, ordered=True)\npred_fix.shape, y_fix.shape, len(learn.data.train_ds)",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 11,
"data": {
"text/plain": "(torch.Size([799, 2]), torch.Size([799]), 799)"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "assert np.alltrue(learn.data.train_ds.y.items==y_fix.numpy())",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "pred_fix, y_fix=learn.get_preds(DatasetType.Fix)\npred_fix.shape, y_fix.shape, len(learn.data.train_ds)",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 14,
"data": {
"text/plain": "(torch.Size([799, 2]), torch.Size([799]), 799)"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "assert np.alltrue(learn.data.train_ds.y.items==y_fix.numpy())",
"execution_count": 15,
"outputs": [
{
"output_type": "error",
"ename": "AssertionError",
"evalue": "",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-5868b068c5fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malltrue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0my_fix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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[0;31mAssertionError\u001b[0m: "
]
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
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