diff --git a/dmlc-core b/dmlc-core index 424d0d5b6d16..5aeced5b84d1 160000 --- a/dmlc-core +++ b/dmlc-core @@ -1 +1 @@ -Subproject commit 424d0d5b6d16097769d1f30a956e0d25ccc1930d +Subproject commit 5aeced5b84d14e7ac50e82445eaeab1587313221 diff --git a/example/README.md b/example/README.md index c55cb460eeb8..0765885349f8 100644 --- a/example/README.md +++ b/example/README.md @@ -2,6 +2,14 @@ MXNet Examples ============== This folder contains examples of MXNet. +Notebooks +-------- +* [composite symbol](composite_symbol.ipynb) gives you a demo of how to composite a symbolic Inception-BatchNorm Network +* [cifar-10 recipe](cifar-recipe.ipynb) gives you a step by step demo of how to use MXNet +* [cifar-100](cifar-100.ipynb) gives you a demo of how to train a 75.68% accuracy CIFAR-100 model +* [predict with pretained model](predict-with-pretrained-model.ipynb) gives you a demo of use a pretrained Inception-BN Network + + Contents -------- * [mnist](mnist) gives examples on training mnist. @@ -13,4 +21,3 @@ Python Howto [Python Howto](python-howto) is a folder containing short snippet of code introducing a certain feature of mxnet. - diff --git a/example/notebooks/CIFAR-100.ipynb b/example/notebooks/cifar-100.ipynb similarity index 100% rename from example/notebooks/CIFAR-100.ipynb rename to example/notebooks/cifar-100.ipynb diff --git a/example/notebooks/cifar-recipe.ipynb b/example/notebooks/cifar-recipe.ipynb index 05097b026042..d21b2d6716d6 100644 --- a/example/notebooks/cifar-recipe.ipynb +++ b/example/notebooks/cifar-recipe.ipynb @@ -410,9 +410,12 @@ "fea_symbol = internals[\"global_avg_output\"]\n", "\n", "# Make a new model by using an internal symbol. We can reuse all parameters from model we trained before\n", - "# In this case, we must set ```allow_extra_params``` to True\n", + "# In this case, we must set ```allow_extra_params``` to True \n", + "# Because we don't need params of FullyConnected Layer\n", + "\n", "feature_extractor = mx.model.FeedForward(ctx=mx.gpu(), symbol=fea_symbol, \n", - " arg_params=model.arg_params, aux_params=model.aux_params,\n", + " arg_params=model.arg_params,\n", + " aux_params=model.aux_params,\n", " allow_extra_params=True)\n", "# Predict as normal\n", "global_pooling_feature = feature_extractor.predict(test_dataiter)\n", diff --git a/example/notebooks/predict-with-pretrained-model.ipynb b/example/notebooks/predict-with-pretrained-model.ipynb new file mode 100644 index 000000000000..36425997d36d --- /dev/null +++ b/example/notebooks/predict-with-pretrained-model.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use Pretrained Inception-BatchNorm Network\n", + "----\n", + "\n", + "In this example we will demo how to use a pretrained Inception-BatchNorm Network. \n", + "\n", + "The network is described in \n", + "\n", + "Ioffe, Sergey, and Christian Szegedy. \"Batch normalization: Accelerating deep network training by reducing internal covariate shift.\" arXiv preprint arXiv:1502.03167 (2015).\n", + "\n", + "For network structure, you can visualize it in [Composite Symbol Demo](composite_symbol.ipynb)\n", + "\n", + "The pre-trained Inception-BatchNorm network is able to be downloaded from:\n", + "[http://webdocs.cs.ualberta.ca/~bx3/data/Inception.zip](http://webdocs.cs.ualberta.ca/~bx3/data/Inception.zip)\n", + "This model achieves Top-1 Accuracy: 70% and Top-5 Accuracy: 89.9%\n", + "\n", + "Note: This network is trained by using very simple augmentation (random flip + random crop). We will release model with a little bit more augmentation (which achieves better validation score)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import mxnet as mx\n", + "import logging\n", + "import numpy as np\n", + "# Note: The decoded image should be in BGR channel (opencv output)\n", + "# For RGB output such as from skimage, we need to convert it to BGR\n", + "# WRONG channel will lead to WRONG result\n", + "from skimage import io, transform\n", + "\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.DEBUG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load the pre-trained model\n", + "prefix = \"Inception/Inception_BN\"\n", + "num_round = 39\n", + "model = mx.model.FeedForward.load(prefix, num_round, ctx=mx.gpu())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# load mean image\n", + "mean_img = mx.nd.load(\"Inception/mean_224.nd\")[\"mean_img\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# if you like, you can plot the network\n", + "# mx.viz.plot_network(model.symbol, shape={\"data\" : (1, 3, 224, 224)})" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# load synset (text label)\n", + "synset = [l.strip() for l in open('Inception/synset.txt').readlines()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we will show how to use this model to classify image. The image was taken in Notre-Dame Basilica, Montreal." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Image Shape: (3648, 5472, 3)\n", + "Top1: n07930864 cup\n", + "Top5: ['n07930864 cup', 'n04330267 stove', 'n02948072 candle, taper, wax light', 'n04286575 spotlight, spot', 'n02815834 beaker']\n" + ] + }, + { + "data": { + "image/png": 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nJwN9Hzg9UU5OVqzWcP5gzRATDx8+BApxEHePSiKo5yLs8471AHup6yJHIiCj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\nvUEJFvB/UefBJBx3HC44hULB6AqrBn9QDdY4XgkNHRwcxiCOxeNz9a9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t4cN3EpBauPTO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Uk5YsWPwPPsxl1rPAfOkpNaE9ck3qd6C6fsJEW5UyMwfN7j3ZmbqOhiNpWei/NMipMuWrrU2Skl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\nDWcc02lY0R2OMO7kzAP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def PreprocessImage(path, show_img=False):\n", + " # load image\n", + " img = io.imread(path)\n", + " print(\"Original Image Shape: \", img.shape)\n", + " # we crop image from center\n", + " short_egde = min(img.shape[:2])\n", + " yy = int((img.shape[0] - short_egde) / 2)\n", + " xx = int((img.shape[1] - short_egde) / 2)\n", + " crop_img = img[yy : yy + short_egde, xx : xx + short_egde]\n", + " # resize to 224, 224\n", + " resized_img = transform.resize(crop_img, (224, 224))\n", + " if show_img:\n", + " io.imshow(resized_img)\n", + " # convert to numpy.ndarray\n", + " sample = np.asarray(resized_img) * 256\n", + " # swap channel from RGB to BGR\n", + " sample = sample[:, :, [2,1,0]]\n", + " # swap axes to make image from (224, 224, 4) to (3, 224, 224)\n", + " sample = np.swapaxes(sample, 0, 2)\n", + " sample = np.swapaxes(sample, 1, 2)\n", + " # sub mean \n", + " normed_img = sample - mean_img.asnumpy()\n", + " normed_img.resize(1, 3, 224, 224)\n", + " return normed_img\n", + "\n", + "# Get preprocessed batch (single image batch)\n", + "batch = PreprocessImage('./DSC01012.JPG', True)\n", + "# Get prediction probability of 1000 classes from model\n", + "prob = model.predict(batch)[0]\n", + "# Argsort, get prediction index from largest prob to lowest\n", + "pred = np.argsort(prob)[::-1]\n", + "# Get top1 label\n", + "top1 = synset[pred[0]]\n", + "print(\"Top1: \", top1)\n", + "# Get top5 label\n", + "top5 = [synset[pred[i]] for i in range(5)]\n", + "print(\"Top5: \", top5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "To extract feature, it is in similar to steps of [CIFAR-10](cifar-recipe.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 1024, 1, 1)\n" + ] + } + ], + "source": [ + "# get internals from model's symbol\n", + "internals = model.symbol.get_internals()\n", + "# get feature layer symbol out of internals\n", + "fea_symbol = internals[\"global_pool_output\"]\n", + "# Make a new model by using an internal symbol. We can reuse all parameters from model we trained before\n", + "# In this case, we must set ```allow_extra_params``` to True\n", + "# Because we don't need params from FullyConnected symbol\n", + "feature_extractor = mx.model.FeedForward(ctx=mx.gpu(), symbol=fea_symbol, \n", + " arg_params=model.arg_params, aux_params=model.aux_params,\n", + " allow_extra_params=True)\n", + "# predict feature\n", + "global_pooling_feature = feature_extractor.predict(batch)\n", + "print(global_pooling_feature.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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": "3.4.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py index e53a46770464..4f3cdba7fb1c 100644 --- a/python/mxnet/metric.py +++ b/python/mxnet/metric.py @@ -52,6 +52,22 @@ def update(self, label, pred): self.sum_metric += numpy.sum(py == label) self.num_inst += label.size +class LogLoss(EvalMetric): + """Calculate logloss""" + def __init__(self): + self.eps = 1e-15 + super(LogLoss, self).__init__('logloss') + + def update(self, label, pred): + # pylint: disable=invalid-name + pred = pred.asnumpy() + label = label.asnumpy().astype('int32') + for i in range(label.size): + p = pred[i][label[i]] + assert(numpy.isnan(p) == False) + p = max(min(p, 1 - self.eps), self.eps) + self.sum_metric += -numpy.log(p) + self.num_inst += label.size class CustomMetric(EvalMetric): """Custom evaluation metric that takes a NDArray function. @@ -110,5 +126,7 @@ def create(metric): raise TypeError('metric should either be callable or str') if metric == 'acc' or metric == 'accuracy': return Accuracy() + elif metric == 'logloss': + return LogLoss() else: raise ValueError('Cannot find metric %s' % metric) diff --git a/python/mxnet/model.py b/python/mxnet/model.py index 270b462af987..8f67bb14e50d 100644 --- a/python/mxnet/model.py +++ b/python/mxnet/model.py @@ -511,7 +511,8 @@ def _init_predictor(self, input_shape): for name, value in list(zip(self.symbol.list_arguments(), pred_exec.arg_arrays)): if not self._is_data_arg(name): - assert name in self.arg_params + if not name in self.arg_params: + raise ValueError("%s not exist in arg_params" % name) self.arg_params[name].copyto(value) for name, value in list(zip(self.symbol.list_auxiliary_states(), pred_exec.aux_arrays)): assert name in self.aux_params