Repository: incubator-systemml Updated Branches: refs/heads/gh-pages b91f9bfec -> c4918f5b6
[SYSTEMML-692] Added initial version of DML generator for Caffe This experimental interface is called Caffe2DML and doesnot affect other functionality. - Updated the interface to match the Caffe specification as per @bertholdreinwald 's suggestion. - Added support for fine-tuning. - Added support for explain, statistics and gpu. Closes #422. Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/48ba441e Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/48ba441e Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/48ba441e Branch: refs/heads/gh-pages Commit: 48ba441ee3d3ad440322c72c6a3f8a55dfcd84bc Parents: b91f9bf Author: Niketan Pansare <npan...@us.ibm.com> Authored: Wed Apr 19 14:07:44 2017 -0800 Committer: Niketan Pansare <npan...@us.ibm.com> Committed: Wed Apr 19 15:07:43 2017 -0700 ---------------------------------------------------------------------- beginners-guide-caffe2dml.md | 124 ++++++++++++++++++++++++++++++++++++++ devdocs/deep-learning.md | 84 ++++++++++++++++++++++++++ 2 files changed, 208 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/48ba441e/beginners-guide-caffe2dml.md ---------------------------------------------------------------------- diff --git a/beginners-guide-caffe2dml.md b/beginners-guide-caffe2dml.md new file mode 100644 index 0000000..cfcc0cb --- /dev/null +++ b/beginners-guide-caffe2dml.md @@ -0,0 +1,124 @@ +--- +layout: global +title: Beginner's Guide for Caffe2DML users +description: Beginner's Guide for Caffe2DML users +--- +<!-- +{% comment %} +Licensed to the Apache Software Foundation (ASF) under one or more +contributor license agreements. See the NOTICE file distributed with +this work for additional information regarding copyright ownership. +The ASF licenses this file to you under the Apache License, Version 2.0 +(the "License"); you may not use this file except in compliance with +the License. You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +{% endcomment %} +--> + +* This will become a table of contents (this text will be scraped). +{:toc} + +<br/> + +## Introduction + +Caffe2DML is an experimental API that converts an Caffe specification to DML. + +## Frequently asked questions + +- How to set batch size ? + +Batch size is set in `data_param` of the Data layer: + + layer { + name: "mnist" + type: "Data" + top: "data" + top: "label" + data_param { + source: "mnist_train" + batch_size: 64 + backend: LMDB + } + } + +- How to set maximum number of iterations for training ? + +Caffe allows you to set the maximum number of iterations in solver specification + + # The maximum number of iterations + max_iter: 2000 + +- How to set the size of the validation dataset ? + +The size of the validation dataset is determined by the parameters `test_iter` and the batch size. For example: If the batch size is 64 and +`test_iter` is 10, then the validation size is 640. This setting generates following DML code internally: + + num_images = nrow(y_full) + BATCH_SIZE = 64 + num_validation = 10 * BATCH_SIZE + X = X_full[(num_validation+1):num_images,]; y = y_full[(num_validation+1):num_images,] + X_val = X_full[1:num_validation,]; y_val = y_full[1:num_validation,] + num_images = nrow(y) + +- How to monitor loss via command-line ? + +To monitor loss, please set following parameters in the solver specification + + # Display training loss and accuracy every 100 iterations + display: 100 + # Carry out validation every 500 training iterations and display validation loss and accuracy. + test_iter: 10 + test_interval: 500 + + - How to pass a single jpeg image to Caffe2DML for prediction ? + + from PIL import Image + import systemml as sml + from systemml.mllearn import Caffe2DML + img_shape = (3, 224, 224) + input_image = sml.convertImageToNumPyArr(Image.open(img_file_path), img_shape=img_shape) + resnet = Caffe2DML(sqlCtx, solver='ResNet_50_solver.proto', weights='ResNet_50_pretrained_weights', input_shape=img_shape) + resnet.predict(input_image) + +- How to prepare a directory of jpeg images for training with Caffe2DML ? + +The below example assumes that the input dataset has 2 labels `cat` and `dogs` and the filename has these labels as prefix. +We iterate through the directory and convert each jpeg image into pyspark.ml.linalg.Vector using pyspark. +These vectors are stored as DataFrame and randomized using Spark SQL's `orderBy(rand())` function. +The DataFrame is then saved in parquet format to reduce the cost of preprocessing for repeated training. + + from systemml.mllearn import Caffe2DML + from pyspark.sql import SQLContext + import numpy as np + import urllib, os, scipy.ndimage + from pyspark.ml.linalg import Vectors + from pyspark import StorageLevel + import systemml as sml + from pyspark.sql.functions import rand + # ImageNet specific parameters + img_shape = (3, 224, 224) + train_dir = '/home/biuser/dogs_vs_cats/train' + def getLabelFeatures(filename): + from PIL import Image + vec = Vectors.dense(sml.convertImageToNumPyArr(Image.open(os.path.join(train_dir, filename)), img_shape=img_shape)[0,:]) + if filename.lower().startswith('cat'): + return (1, vec) + elif filename.lower().startswith('dog'): + return (2, vec) + else: + raise ValueError('Expected the filename to start with either cat or dog') + + list_jpeg_files = os.listdir(train_dir) + # 10 files per partition + train_df = sc.parallelize(list_jpeg_files, int(len(list_jpeg_files)/10)).map(lambda filename : getLabelFeatures(filename)).toDF(['label', 'features']).orderBy(rand()) + # Optional: but helps seperates conversion-related from training + # Alternatively, this dataframe can be passed directly to `caffe2dml_model.fit(train_df)` + train_df.write.parquet('kaggle-cats-dogs.parquet') \ No newline at end of file http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/48ba441e/devdocs/deep-learning.md ---------------------------------------------------------------------- diff --git a/devdocs/deep-learning.md b/devdocs/deep-learning.md index 1fb951a..329c6c8 100644 --- a/devdocs/deep-learning.md +++ b/devdocs/deep-learning.md @@ -139,3 +139,87 @@ updates for the image: |-----------------|---------------------------------|-----------------| | `w3*y1 + w1*y3` | `w4*y1 + w3*y2 + w2*y3 + w1*y4` | `w4*y2 + w2*y4` | | `w3*y3` | `w4*y3 + w3*y4` | `w4*y4` | + +# Caffe2DML examples + +## Training using Caffe models on Lenet + +The below script also demonstrates how to save the trained model. + +```python +# Download the MNIST dataset +from mlxtend.data import mnist_data +import numpy as np +from sklearn.utils import shuffle +X, y = mnist_data() +X, y = shuffle(X, y) +num_classes = np.unique(y).shape[0] +img_shape = (1, 28, 28) + +# Split the data into training and test +n_samples = len(X) +X_train = X[:int(.9 * n_samples)] +y_train = y[:int(.9 * n_samples)] +X_test = X[int(.9 * n_samples):] +y_test = y[int(.9 * n_samples):] + +# Download the Lenet network +import urllib +urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/lenet/mnist/lenet.proto', 'lenet.proto') +urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/lenet/mnist/lenet_solver.proto', 'lenet_solver.proto') + +# Train Lenet On MNIST using scikit-learn like API +from systemml.mllearn import Caffe2DML +lenet = Caffe2DML(sqlCtx, solver='lenet_solver.proto').set(max_iter=500, debug=True).setStatistics(True) +print('Lenet score: %f' % lenet.fit(X_train, y_train).score(X_test, y_test)) + +# Save the trained model +lenet.save('lenet_model') +``` + +## Load the trained model and retrain (i.e. finetuning) + +```python +# Fine-tune the existing trained model +new_lenet = Caffe2DML(sqlCtx, solver='lenet_solver.proto', weights='lenet_model').set(max_iter=500, debug=True) +new_lenet.fit(X_train, y_train) +new_lenet.save('lenet_model') +``` + +## Perform prediction using the above trained model + +```python +# Use the new model for prediction +predict_lenet = Caffe2DML(sqlCtx, solver='lenet_solver.proto', weights='lenet_model') +print('Lenet score: %f' % predict_lenet.score(X_test, y_test)) +``` + +Similarly, you can perform prediction using the pre-trained ResNet network + +```python +from systemml.mllearn import Caffe2DML +from pyspark.sql import SQLContext +import numpy as np +import urllib, os, scipy.ndimage +from PIL import Image +import systemml as sml + +# ImageNet specific parameters +img_shape = (3, 224, 224) + +# Downloads a jpg image, resizes it to 224 and return as numpy array in N X CHW format +url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/5/58/MountainLion.jpg/312px-MountainLion.jpg' +outFile = 'test.jpg' +urllib.urlretrieve(url, outFile) +input_image = sml.convertImageToNumPyArr(Image.open(outFile), img_shape=img_shape) + +# Download the ResNet network +import urllib +urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/resnet/ilsvrc12/ResNet_50_network.proto', 'ResNet_50_network.proto') +urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/resnet/ilsvrc12/ResNet_50_solver.proto', 'ResNet_50_solver.proto') + +# Assumes that you have cloned the model_zoo repository +# git clone https://github.com/niketanpansare/model_zoo.git +resnet = Caffe2DML(sqlCtx, solver='ResNet_50_solver.proto', weights='~/model_zoo/caffe/vision/resnet/ilsvrc12/ResNet_50_pretrained_weights').set(input_shape=img_shape) +resnet.predict(input_image) +``` \ No newline at end of file
