[BNN-PYNQ] Training script for cucumber9

cucumber9.py

#BSD 3-Clause License
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#Copyright (c) 2017, Xilinx Inc.
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#Based Matthieu Courbariaux's CIFAR-10 example code
#Copyright (c) 2015-2016, Matthieu Courbariaux
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from __future__ import print_function

import sys
import os
import time

import numpy as np
np.random.seed(1234) # for reproducibility?

import lasagne

# specifying the gpu to use
# import theano.sandbox.cuda
# theano.sandbox.cuda.use('gpu1') 
import theano
import theano.tensor as T

import cPickle as pickle
import gzip

import binary_net
import cnv

from pylearn2.datasets.zca_dataset import ZCA_Dataset   
from pylearn2.datasets.cifar10 import CIFAR10 
from pylearn2.utils import serial

from collections import OrderedDict

from pylearn2.datasets import DenseDesignMatrix

class CUCUMBER9(DenseDesignMatrix):
    def __init__(self, which_set):

        self.path = '/home/ubuntu/CUCUMBER-9/prototype_1'

        self.img_shape = (3, 32, 32)
        self.img_size = np.prod(self.img_shape)

        if which_set in {'train'}:
            X, y = self.load_data()
        elif which_set in {'test', 'valid'}:
            X, y = self.load_data_test()

        X = X.astype('float32')

        super(CUCUMBER9, self).__init__(X=X, y=y)

    def unpickle(self, file_name):
        with open(file_name, 'rb') as f:
            if sys.version_info.major == 2:
                return pickle.load(f)
            elif sys.version_info.major == 3:
                return pickle.load(f, encoding='latin-1')

    def load_data(self):

        nb_train_samples = 2475

        X = np.zeros((nb_train_samples, self.img_size), dtype='uint8')
        y = np.zeros((nb_train_samples,1), dtype='uint8')
        for i in range(1, 6):
            fpath = os.path.join(self.path, 'data_batch_' + str(i))
            batch_dict = self.unpickle(fpath)
            data = batch_dict['data']
            labels = batch_dict['labels']

            X[(i-1)*495:i*495, :] = data.reshape(495, self.img_size)
            y[(i-1)*495:i*495, 0] = labels

        return X, y

    def load_data_test(self):

        fpath = os.path.join(self.path, 'test_batch')
        batch_dict = self.unpickle(fpath)
        data = batch_dict['data']
        labels = batch_dict['labels']

        X = np.zeros((495, self.img_size), dtype='uint8')
        y = np.zeros((495,1), dtype='uint8')

        X = data.reshape(495, self.img_size)
        y[:, 0] = labels

        return X, y


if __name__ == "__main__":
    
    learning_parameters = OrderedDict()
    # BN parameters
    batch_size = 50
    print("batch_size = "+str(batch_size))
    # alpha is the exponential moving average factor
    learning_parameters.alpha = .1
    print("alpha = "+str(learning_parameters.alpha))
    learning_parameters.epsilon = 1e-4
    print("epsilon = "+str(learning_parameters.epsilon))
    
    # W_LR_scale = 1.    
    learning_parameters.W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper
    print("W_LR_scale = "+str(learning_parameters.W_LR_scale))
    
    # Training parameters
    num_epochs = 500
    print("num_epochs = "+str(num_epochs))
    
    # Decaying LR 
    LR_start = 0.001
    print("LR_start = "+str(LR_start))
    LR_fin = 0.0000003
    print("LR_fin = "+str(LR_fin))
    LR_decay = (LR_fin/LR_start)**(1./num_epochs)
    print("LR_decay = "+str(LR_decay))
    # BTW, LR decay might good for the BN moving average...
    
    save_path = "cucumber9_parameters.npz"
    print("save_path = "+str(save_path))
    
    train_set_size = 2475
    print("train_set_size = "+str(train_set_size))
    shuffle_parts = 1
    print("shuffle_parts = "+str(shuffle_parts))
    
    print('Loading CUCUMBER9 dataset...')
    
    train_set = CUCUMBER9(which_set="train")
    valid_set = CUCUMBER9(which_set="valid")
    test_set = CUCUMBER9(which_set="test")

       
    # bc01 format
    # Inputs in the range [-1,+1]
    # print("Inputs in the range [-1,+1]")
    train_set.X = np.reshape(np.subtract(np.multiply(2./255.,train_set.X),1.),(-1,3,32,32))
    valid_set.X = np.reshape(np.subtract(np.multiply(2./255.,valid_set.X),1.),(-1,3,32,32))
    test_set.X = np.reshape(np.subtract(np.multiply(2./255.,test_set.X),1.),(-1,3,32,32))
    
    # flatten targets
    train_set.y = np.hstack(train_set.y)
    valid_set.y = np.hstack(valid_set.y)
    test_set.y = np.hstack(test_set.y)
    
    # Onehot the targets
    train_set.y = np.float32(np.eye(9)[train_set.y])
    valid_set.y = np.float32(np.eye(9)[valid_set.y])
    test_set.y = np.float32(np.eye(9)[test_set.y])
    
    # for hinge loss
    train_set.y = 2* train_set.y - 1.
    valid_set.y = 2* valid_set.y - 1.
    test_set.y = 2* test_set.y - 1.

    print('Building the CNN...') 
    
    # Prepare Theano variables for inputs and targets
    input = T.tensor4('inputs')
    target = T.matrix('targets')
    LR = T.scalar('LR', dtype=theano.config.floatX)

    cnn = cnv.genCnv(input, 9, learning_parameters)

    train_output = lasagne.layers.get_output(cnn, deterministic=False)
    
    # squared hinge loss
    loss = T.mean(T.sqr(T.maximum(0.,1.-target*train_output)))
    
    # W updates
    W = lasagne.layers.get_all_params(cnn, binary=True)
    W_grads = binary_net.compute_grads(loss,cnn)
    updates = lasagne.updates.adam(loss_or_grads=W_grads, params=W, learning_rate=LR)
    updates = binary_net.clipping_scaling(updates,cnn)
    
    # other parameters updates
    params = lasagne.layers.get_all_params(cnn, trainable=True, binary=False)
    updates = OrderedDict(updates.items() + lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR).items())

    test_output = lasagne.layers.get_output(cnn, deterministic=True)
    test_loss = T.mean(T.sqr(T.maximum(0.,1.-target*test_output)))
    test_err = T.mean(T.neq(T.argmax(test_output, axis=1), T.argmax(target, axis=1)),dtype=theano.config.floatX)
    
    # Compile a function performing a training step on a mini-batch (by giving the updates dictionary) 
    # and returning the corresponding training loss:
    train_fn = theano.function([input, target, LR], loss, updates=updates)

    # Compile a second function computing the validation loss and accuracy:
    val_fn = theano.function([input, target], [test_loss, test_err])

    print('Training...')
    
    binary_net.train(
            train_fn,val_fn,
            cnn,
            batch_size,
            LR_start,LR_decay,
            num_epochs,
            train_set.X,train_set.y,
            valid_set.X,valid_set.y,
            test_set.X,test_set.y,
            save_path=save_path,
            shuffle_parts=shuffle_parts)