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# Copyright (c) 2011, Alex Krizhevsky ([email protected])
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as n
import os
from time import time, asctime, localtime, strftime
from numpy.random import randn, rand
from numpy import s_, dot, tile, zeros, ones, zeros_like, array, ones_like
from util import *
from data import *
from options import *
from math import ceil, floor, sqrt
from data import DataProvider, dp_types
import sys
import shutil
import platform
from os import linesep as NL
class ModelStateException(Exception):
pass
# GPU Model interface
class IGPUModel:
def __init__(self, model_name, op, load_dic, filename_options=None, dp_params={}):
# these are input parameters
self.model_name = model_name
self.op = op
self.options = op.options
self.load_dic = load_dic
self.filename_options = filename_options
self.dp_params = dp_params
self.get_gpus()
self.fill_excused_options()
#assert self.op.all_values_given()
for o in op.get_options_list():
setattr(self, o.name, o.value)
# these are things that the model must remember but they're not input parameters
if load_dic:
self.model_state = load_dic["model_state"]
self.save_file = self.options["load_file"].value
if not os.path.isdir(self.save_file):
self.save_file = os.path.dirname(self.save_file)
else:
self.model_state = {}
if filename_options is not None:
self.save_file = model_name + "_" + '_'.join(['%s_%s' % (char, self.options[opt].get_str_value()) for opt, char in filename_options]) + '_' + strftime('%Y-%m-%d_%H.%M.%S')
self.model_state["train_outputs"] = []
self.model_state["test_outputs"] = []
self.model_state["epoch"] = 1
self.model_state["batchnum"] = self.train_batch_range[0]
self.init_data_providers()
if load_dic:
self.train_data_provider.advance_batch()
# model state often requries knowledge of data provider, so it's initialized after
try:
self.init_model_state()
except ModelStateException, e:
print e
sys.exit(1)
for var, val in self.model_state.iteritems():
setattr(self, var, val)
self.import_model()
self.init_model_lib()
def import_model(self):
print "========================="
print "Importing %s C++ module" % ('_' + self.model_name)
self.libmodel = __import__('_' + self.model_name)
def fill_excused_options(self):
pass
def init_data_providers(self):
self.dp_params['convnet'] = self
try:
self.test_data_provider = DataProvider.get_instance(self.data_path, self.test_batch_range,
type=self.dp_type, dp_params=self.dp_params, test=True)
self.train_data_provider = DataProvider.get_instance(self.data_path, self.train_batch_range,
self.model_state["epoch"], self.model_state["batchnum"],
type=self.dp_type, dp_params=self.dp_params, test=False)
except DataProviderException, e:
print "Unable to create data provider: %s" % e
self.print_data_providers()
sys.exit()
def init_model_state(self):
pass
def init_model_lib(self):
pass
def start(self):
if self.test_only:
self.test_outputs += [self.get_test_error()]
self.print_test_results()
sys.exit(0)
self.train()
def train(self):
print "========================="
print "Training %s" % self.model_name
self.op.print_values()
print "========================="
self.print_model_state()
print "Running on CUDA device(s) %s" % ", ".join("%d" % d for d in self.device_ids)
print "Current time: %s" % asctime(localtime())
print "Saving checkpoints to %s" % os.path.join(self.save_path, self.save_file)
print "========================="
next_data = self.get_next_batch()
while self.epoch <= self.num_epochs:
data = next_data
self.epoch, self.batchnum = data[0], data[1]
self.print_iteration()
sys.stdout.flush()
compute_time_py = time()
self.start_batch(data)
# load the next batch while the current one is computing
next_data = self.get_next_batch()
batch_output = self.finish_batch()
self.train_outputs += [batch_output]
self.print_train_results()
if self.get_num_batches_done() % self.testing_freq == 0:
self.sync_with_host()
self.test_outputs += [self.get_test_error()]
self.print_test_results()
self.print_test_status()
self.conditional_save()
self.print_train_time(time() - compute_time_py)
self.cleanup()
def cleanup(self):
sys.exit(0)
def sync_with_host(self):
self.libmodel.syncWithHost()
def print_model_state(self):
pass
def get_num_batches_done(self):
return len(self.train_batch_range) * (self.epoch - 1) + self.batchnum - self.train_batch_range[0] + 1
def get_next_batch(self, train=True):
dp = self.train_data_provider
if not train:
dp = self.test_data_provider
return self.parse_batch_data(dp.get_next_batch(), train=train)
def parse_batch_data(self, batch_data, train=True):
return batch_data[0], batch_data[1], batch_data[2]['data']
def start_batch(self, batch_data, train=True):
self.libmodel.startBatch(batch_data[2], not train)
def finish_batch(self):
return self.libmodel.finishBatch()
def print_iteration(self):
print "\t%d.%d..." % (self.epoch, self.batchnum),
def print_train_time(self, compute_time_py):
print "(%.3f sec)" % (compute_time_py)
def print_train_results(self):
batch_error = self.train_outputs[-1][0]
if not (batch_error > 0 and batch_error < 2e20):
print "Crazy train error: %.6f" % batch_error
self.cleanup()
print "Train error: %.6f " % (batch_error),
def print_test_results(self):
batch_error = self.test_outputs[-1][0]
print "%s\t\tTest error: %.6f" % (NL, batch_error),
def print_test_status(self):
status = (len(self.test_outputs) == 1 or self.test_outputs[-1][0] < self.test_outputs[-2][0]) and "ok" or "WORSE"
print status,
def conditional_save(self):
batch_error = self.test_outputs[-1][0]
if batch_error > 0 and batch_error < self.max_test_err:
self.save_state()
else:
print "\tTest error > %g, not saving." % self.max_test_err,
def aggregate_test_outputs(self, test_outputs):
test_error = tuple([sum(t[r] for t in test_outputs) / (1 if self.test_one else len(self.test_batch_range)) for r in range(len(test_outputs[-1]))])
return test_error
def get_test_error(self):
next_data = self.get_next_batch(train=False)
test_outputs = []
while True:
data = next_data
self.start_batch(data, train=False)
load_next = not self.test_one and data[1] < self.test_batch_range[-1]
if load_next: # load next batch
next_data = self.get_next_batch(train=False)
test_outputs += [self.finish_batch()]
if self.test_only: # Print the individual batch results for safety
print "batch %d: %s" % (data[1], str(test_outputs[-1]))
if not load_next:
break
sys.stdout.flush()
return self.aggregate_test_outputs(test_outputs)
def set_var(self, var_name, var_val):
setattr(self, var_name, var_val)
self.model_state[var_name] = var_val
return var_val
def get_var(self, var_name):
return self.model_state[var_name]
def has_var(self, var_name):
return var_name in self.model_state
def save_state(self):
for att in self.model_state:
if hasattr(self, att):
self.model_state[att] = getattr(self, att)
dic = {"model_state": self.model_state,
"op": self.op}
layers = {"layers": self.model_state['layers']}
conv = {"conv": self.model_state['layers'][2]}
convBiases = {"biases": self.model_state['layers'][2]['biases']}
# objectFileName = r'./Data.txt'
# f = file(objectFileName, 'w')
# # cPickle .dump(dic, f)
# #
# # cPickle .dump(layers, f)
# #
# # cPickle .dump(conv, f)
#
# cPickle .dump(dic, f)
# f.close()
#
# obj2 = cPickle.load(open(objectFileName, "r"))
#
# # print obj2['biases']
# # print obj2['biases'].size
# # print obj2['biases'][0][0]
# # print obj2['biases'][1][0]
#
# # zbfState = obj2["model_state"]
# # zbfLayers = zbfState["layers"]
#
# print obj2["model_state"]["layers"]
##########################################################################################
# myfile = open('testWeight.txt', 'w')
# zbfState = dic["model_state"]
# zbfLayers = zbfState["layers"]
#
# myfile.write("0\n")
# myfile.write("32 ")
# myfile.write("32 ")
# myfile.write("3"+"\n")
#
# for numLayer in range(2,16):
# zbfNum = zbfLayers[numLayer]
# type = zbfNum["type"]
# print type
#
# if type == "conv":
# myfile.write("1\n")
#
# filterSize = zbfNum["filterSize"][0]
# myfile.write(str(filterSize)+" ")
#
# filters = zbfNum["filters"]
# myfile.write(str(filters)+" ")
#
# stride = zbfNum["stride"][0]
# myfile.write(str(stride)+"\n")
#
# channels = zbfNum["channels"][0]
# # print zbfNum["stride"][0]
#
# zbfBiases = zbfNum["biases"]
# for i in range(filters):
# myfile.write(str(zbfBiases[i][0])+" ")
# myfile.write("\n")
#
# zbfWeight = zbfNum["weights"]
#
# for j in range(filterSize*filterSize*channels):
# for i in range(filters):
# myfile.write(str(zbfWeight[0][j][i])+" ")
# myfile.write("\n")
# if type == "neuron":
# myfile.write("256\n")
#
# if type == "pool":
# myfile.write("3\n")
# sizeX = zbfNum["sizeX"]
# myfile.write(str(sizeX)+" ")
#
# stride = zbfNum["stride"]
# myfile.write(str(stride)+"\n")
#
# if type == "cmrnorm":
# myfile.write("4\n")
# k = 1
# myfile.write(str(k)+" ")
#
# size = zbfNum["size"]
# myfile.write(str(size)+"\n")
#
# scale = zbfNum["scale"]
# myfile.write(str(scale)+" ")
#
# pow = zbfNum["pow"]
# myfile.write(str(pow)+"\n")
#
# if type == "local":
# myfile.write("2\n")
#
# filterSize = zbfNum["filterSize"][0]
# myfile.write(str(filterSize)+" ")
#
#
# filters = zbfNum["filters"]
# myfile.write(str(filters)+" ")
#
# stride = zbfNum["stride"][0]
# myfile.write(str(stride)+"\n")
#
# channels = zbfNum["channels"][0]
# # print zbfNum["stride"][0]
# imagePixels = zbfNum["imgPixels"][0]
#
# filterPixels = zbfNum["filterPixels"][0]
# filterChannels = zbfNum["filterChannels"][0]
#
# biases = zbfNum["biases"]
# for i in range(filters*channels):
# myfile.write(str(biases[i][0])+" ")
# myfile.write("\n")
#
# zbfWeight = zbfNum["weights"]
# # print zbfWeight[0].T
# for j in range(channels*filterChannels*filterPixels):
# for i in range(filters):
# myfile.write(str(zbfWeight[0][j][i])+" ")
# myfile.write("\n")
#
# if type == "fc":
# myfile.write("5\n")
#
# output = zbfNum["outputs"]
# myfile.write(str(output)+" ")
#
# biases = zbfNum["biases"]
# for i in range(output):
# myfile.write(str(biases[0][i])+" ")
# myfile.write("\n")
#
# zbfWeight = zbfNum["weights"]
#
# for j in range(8*8*32):
# for i in range(output):
# myfile.write(str(zbfWeight[0][j][i])+" ")
# myfile.write("\n")
#
# if type == "softmax":
# myfile.write("6\n")
#
# myfile.close();
####################################################################################
checkpoint_dir = os.path.join(self.save_path, self.save_file)
checkpoint_file = "%d.%d" % (self.epoch, self.batchnum)
checkpoint_file_full_path = os.path.join(checkpoint_dir, checkpoint_file)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
pickle(checkpoint_file_full_path, dic,compress=self.zip_save)
for f in sorted(os.listdir(checkpoint_dir), key=alphanum_key):
if sum(os.path.getsize(os.path.join(checkpoint_dir, f2)) for f2 in os.listdir(checkpoint_dir)) > self.max_filesize_mb*1024*1024 and f != checkpoint_file:
os.remove(os.path.join(checkpoint_dir, f))
else:
break
@staticmethod
def load_checkpoint(load_dir):
if os.path.isdir(load_dir):
return unpickle(os.path.join(load_dir, sorted(os.listdir(load_dir), key=alphanum_key)[-1]))
return unpickle(load_dir)
@staticmethod
def get_options_parser():
op = OptionsParser()
op.add_option("f", "load_file", StringOptionParser, "Load file", default="", excuses=OptionsParser.EXCLUDE_ALL)
op.add_option("train-range", "train_batch_range", RangeOptionParser, "Data batch range: training")
op.add_option("test-range", "test_batch_range", RangeOptionParser, "Data batch range: testing")
op.add_option("data-provider", "dp_type", StringOptionParser, "Data provider", default="default")
op.add_option("test-freq", "testing_freq", IntegerOptionParser, "Testing frequency", default=25)
op.add_option("epochs", "num_epochs", IntegerOptionParser, "Number of epochs", default=500)
op.add_option("data-path", "data_path", StringOptionParser, "Data path")
op.add_option("save-path", "save_path", StringOptionParser, "Save path")
op.add_option("max-filesize", "max_filesize_mb", IntegerOptionParser, "Maximum save file size (MB)", default=5000)
op.add_option("max-test-err", "max_test_err", FloatOptionParser, "Maximum test error for saving")
op.add_option("num-gpus", "num_gpus", IntegerOptionParser, "Number of GPUs", default=1)
op.add_option("test-only", "test_only", BooleanOptionParser, "Test and quit?", default=0)
op.add_option("zip-save", "zip_save", BooleanOptionParser, "Compress checkpoints?", default=0)
op.add_option("test-one", "test_one", BooleanOptionParser, "Test on one batch at a time?", default=1)
op.add_option("gpu", "gpu", ListOptionParser(IntegerOptionParser), "GPU override", default=OptionExpression("[-1] * num_gpus"))
return op
@staticmethod
def print_data_providers():
print "Available data providers:"
for dp, desc in dp_types.iteritems():
print " %s: %s" % (dp, desc)
def get_gpus(self):
self.device_ids = [get_gpu_lock(g) for g in self.op.get_value('gpu')]
if GPU_LOCK_NO_LOCK in self.device_ids:
print "Not enough free GPUs!"
sys.exit()
@staticmethod
def parse_options(op):
try:
load_dic = None
options = op.parse()
if options["load_file"].value_given:
load_dic = IGPUModel.load_checkpoint(options["load_file"].value)
##########################################################################################
if 0 :
myfile = open('testWeight.txt', 'w')
zbfState = load_dic["model_state"]
zbfLayers = zbfState["layers"]
myfile.write("0\n")
myfile.write("32 ")
myfile.write("32 ")
myfile.write("3"+"\n")
datapath = 'F:/convnet-RGBRGB/cuda-convnet/data/cifar-10-py-colmajor/batches.meta'
dictionary = unpickle(datapath)
data_mean = dictionary.get('data_mean')
for i in range(3072):
myfile.write(str(data_mean[i][0])+" ")
print data_mean[i][0]
myfile.write("\n")
for numLayer in range(2,16):
zbfNum = zbfLayers[numLayer]
type = zbfNum["type"]
print type
if type == "conv":
myfile.write("1\n")
filterSize = zbfNum["filterSize"][0]
myfile.write(str(filterSize)+" ")
filters = zbfNum["filters"]
myfile.write(str(filters)+" ")
stride = zbfNum["stride"][0]
myfile.write(str(stride)+"\n")
channels = zbfNum["channels"][0]
# print zbfNum["stride"][0]
zbfBiases = zbfNum["biases"]
for i in range(filters):
myfile.write(str(zbfBiases[i][0])+" ")
myfile.write("\n")
zbfWeight = zbfNum["weights"]
for j in range(filterSize*filterSize*channels):
for i in range(filters):
myfile.write(str(zbfWeight[0][j][i])+" ")
myfile.write("\n")
if type == "neuron":
myfile.write("256\n")
if type == "pool":
myfile.write("3\n")
sizeX = zbfNum["sizeX"]
myfile.write(str(sizeX)+" ")
stride = zbfNum["stride"]
myfile.write(str(stride)+"\n")
if type == "cmrnorm":
myfile.write("4\n")
k = 1
myfile.write(str(k)+" ")
size = zbfNum["size"]
myfile.write(str(size)+"\n")
scale = zbfNum["scale"]
myfile.write(str(scale)+" ")
pow = zbfNum["pow"]
myfile.write(str(pow)+"\n")
if type == "local":
myfile.write("2\n")
filterSize = zbfNum["filterSize"][0]
myfile.write(str(filterSize)+" ")
filters = zbfNum["filters"]
myfile.write(str(filters)+" ")
stride = zbfNum["stride"][0]
myfile.write(str(stride)+"\n")
channels = zbfNum["channels"][0]
# print zbfNum["stride"][0]
imagePixels = zbfNum["imgPixels"][0]
filterPixels = zbfNum["filterPixels"][0]
filterChannels = zbfNum["filterChannels"][0]
biases = zbfNum["biases"]
for i in range(filters*channels):
myfile.write(str(biases[i][0])+" ")
myfile.write("\n")
zbfWeight = zbfNum["weights"]
# print zbfWeight[0].T
for j in range(channels*filterChannels*filterPixels):
for i in range(filters):
myfile.write(str(zbfWeight[0][j][i])+" ")
myfile.write("\n")
if type == "fc":
myfile.write("5\n")
output = zbfNum["outputs"]
myfile.write(str(output)+" ")
biases = zbfNum["biases"]
for i in range(output):
myfile.write(str(biases[0][i])+" ")
myfile.write("\n")
zbfWeight = zbfNum["weights"]
for j in range(8*8*32):
for i in range(output):
myfile.write(str(zbfWeight[0][j][i])+" ")
myfile.write("\n")
if type == "softmax":
myfile.write("6\n")
myfile.close();
####################################################################################
old_op = load_dic["op"]
old_op.merge_from(op)
op = old_op
op.eval_expr_defaults()
return op, load_dic
except OptionMissingException, e:
print e
op.print_usage()
except OptionException, e:
print e
except UnpickleError, e:
print "Error loading checkpoint:"
print e
sys.exit()