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train.py
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#! /usr/bin/env python
# Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import sys
import os
import gzip
import cPickle as pickle
import numpy as np
import h5py
from IPython import embed
import colorama
import tensorflow as tf
import keras.backend as K
lab_root = os.path.join(os.path.abspath(os.path.dirname(__file__)), '..')
sys.path.insert(1, lab_root)
from general.util import tic, toc, tic2, toc2, tic3, toc3, mkdir_p, WithTimer
from general.image_preproc import ImagePreproc
from general.stats_buddy import StatsBuddy
from general.tfutil import (get_collection_intersection_summary,
log_scalars, sess_run_dict,
summarize_weights, summarize_opt,
tf_assert_all_init,
tf_get_uninitialized_variables,
add_grad_summaries)
from keras_ext.util import setup_session_and_seeds, warn_misaligned_shapes
from model_builders import (build_model_mnist_fc_dir,
build_model_mnist_fc,
build_cnn_model_direct_mnist,
build_cnn_model_mnist,
build_LeNet_direct_mnist,
build_LeNet_mnist,
build_LeNet_direct_cifar,
build_LeNet_cifar,
build_model_cifar_fc_dir,
build_model_cifar_fc,
build_model_mnist_fc_fastfood,
build_model_cifar_fc_fastfood,
build_model_cifar_LeNet_fastfood,
build_alexnet_direct,
build_alexnet_fastfood,
build_squeezenet_direct,
build_model_mnist_LeNet_fastfood,
build_MLPLeNet_direct_mnist,
build_model_mnist_MLPLeNet_fastfood,
build_MLPLeNet_direct_cifar,
build_model_cifar_MLPLeNet_fastfood,
build_UntiedLeNet_direct_mnist,
build_model_mnist_UntiedLeNet_fastfood,
build_UntiedLeNet_direct_cifar,
build_model_cifar_UntiedLeNet_fastfood)
from standard_parser import make_standard_parser
arch_choices_direct = ['mnistfc_dir', 'cifarfc_dir', 'mnistconv_dir', 'mnistlenet_dir', 'cifarlenet_dir', 'alexnet_dir', 'squeeze_dir', 'mnistMLPlenet_dir', 'cifarMLPlenet_dir', 'mnistUntiedlenet_dir', 'cifarUntiedlenet_dir']
arch_choices_projected = ['mnistfc', 'cifarfc', 'mnistconv', 'mnistlenet', 'cifar', 'cifarlenet', 'alexnet', 'mnistMLPlenet', 'cifarMLPlenet', 'mnistUntiedlenet', 'cifarUntiedlenet']
arch_choices = arch_choices_direct + arch_choices_projected
class LRStepper(object):
def __init__(self, lr_init, lr_ratio, lr_epochs, lr_steps):
self.lr_init = lr_init
self.lr_ratio = lr_ratio
self.lr_epochs = lr_epochs
self.lr_steps = lr_steps
self.last_printed = None
def lr(self, buddy):
if self.lr_ratio == 0 or self.lr_epochs == 0:
return self.lr_init
ret = self.lr_init * self.lr_ratio ** int(min(buddy.epoch / self.lr_epochs, self.lr_steps))
if ret != self.last_printed:
print 'At epoch %d setting LR to %g' % (buddy.epoch, ret)
self.last_printed = ret
return ret
def main():
parser = make_standard_parser('Random Projection Experiments.', arch_choices=arch_choices)
parser.add_argument('--vsize', type=int, default=100, help='Dimension of intrinsic parmaeter space.')
parser.add_argument('--d_rate', '--dr', type=float, default=0.0, help='Dropout rate.')
parser.add_argument('--depth', type=int, default=2, help='Number of layers in FNN.')
parser.add_argument('--width', type=int, default=100, help='Width of layers in FNN.')
parser.add_argument('--minibatch', '--mb', type=int, default=128, help='Size of minibatch.')
parser.add_argument('--lr_ratio', '--lrr', type=float, default=.1, help='Ratio to decay LR by every LR_EPSTEP epochs.')
parser.add_argument('--lr_epochs', '--lrep', type=float, default=0, help='Decay LR every LR_EPSTEP epochs. 0 to turn off decay.')
parser.add_argument('--lr_steps', '--lrst', type=float, default=3, help='Max LR steps.')
parser.add_argument('--c1', type=int, default=6, help='Channels in first conv layer, for LeNet.')
parser.add_argument('--c2', type=int, default=16, help='Channels in second conv layer, for LeNet.')
parser.add_argument('--d1', type=int, default=120, help='Channels in first dense layer, for LeNet.')
parser.add_argument('--d2', type=int, default=84, help='Channels in second dense layer, for LeNet.')
parser.add_argument('--denseproj', action='store_true', help='Use a dense projection.')
parser.add_argument('--sparseproj', action='store_true', help='Use a sparse projection.')
parser.add_argument('--fastfoodproj', action='store_true', help='Use a fastfood projection.')
parser.add_argument('--partial_data', '--pd', type=float, default=1.0, help='Percentage of dataset.')
parser.add_argument('--skiptfevents', action='store_true', help='Skip writing tf events files even if output is used.')
args = parser.parse_args()
n_proj_specified = sum([args.denseproj, args.sparseproj, args.fastfoodproj])
if args.arch in arch_choices_projected:
assert n_proj_specified == 1, 'Arch "%s" requires projection. Specify exactly one of {denseproj, sparseproj, fastfoodproj} options.' % args.arch
else:
assert n_proj_specified == 0, 'Arch "%s" does not require projection, so do not specify any of {denseproj, sparseproj, fastfoodproj} options.' % args.arch
if args.denseproj:
proj_type = 'dense'
elif args.sparseproj:
proj_type = 'sparse'
else:
proj_type = 'fastfood'
train_style, val_style = ('', '') if args.nocolor else (colorama.Fore.BLUE, colorama.Fore.MAGENTA)
# Get a TF session registered with Keras and set numpy and TF seeds
sess = setup_session_and_seeds(args.seed)
# 0. LOAD DATA
train_h5 = h5py.File(args.train_h5, 'r')
train_x = train_h5['images']
train_y = train_h5['labels']
val_h5 = h5py.File(args.val_h5, 'r')
val_x = val_h5['images']
val_y = val_h5['labels']
if args.partial_data < 1.0:
n_train_ = int(train_y.size*args.partial_data)
n_test_ = int(val_y.size*args.partial_data)
train_x = train_x[:n_train_]
train_y = train_y[:n_train_]
val_x = val_x[:n_test_]
val_y = val_y[:n_test_]
# load into memory if less than 1 GB
if train_x.size * 4 + val_x.size * 4 < 1e9:
train_x, train_y = np.array(train_x), np.array(train_y)
val_x, val_y = np.array(val_x), np.array(val_y)
# 1. CREATE MODEL
randmirrors = False
randcrops = False
cropsize = None
with WithTimer('Make model'):
if args.arch == 'mnistfc_dir':
model = build_model_mnist_fc_dir(weight_decay=args.l2, depth=args.depth, width=args.width)
elif args.arch == 'mnistfc':
if proj_type == 'fastfood':
model = build_model_mnist_fc_fastfood(weight_decay=args.l2, vsize=args.vsize, depth=args.depth, width=args.width)
else:
model = build_model_mnist_fc(weight_decay=args.l2, vsize=args.vsize, depth=args.depth, width=args.width, proj_type=proj_type)
elif args.arch == 'mnistconv':
model = build_cnn_model_mnist(weight_decay=args.l2, vsize=args.vsize)
elif args.arch == 'mnistconv_dir':
model = build_cnn_model_direct_mnist(weight_decay=args.l2)
elif args.arch == 'cifarfc_dir':
model = build_model_cifar_fc_dir(weight_decay=args.l2, depth=args.depth, width=args.width)
elif args.arch == 'cifarfc':
if proj_type == 'fastfood':
model = build_model_cifar_fc_fastfood(weight_decay=args.l2, vsize=args.vsize, depth=args.depth, width=args.width)
else:
model = build_model_cifar_fc(weight_decay=args.l2, vsize=args.vsize, depth=args.depth, width=args.width, proj_type=proj_type)
elif args.arch == 'mnistlenet_dir':
model = build_LeNet_direct_mnist(weight_decay=args.l2, c1=args.c1, c2=args.c2, d1=args.d1, d2=args.d2)
elif args.arch == 'mnistMLPlenet_dir':
model = build_MLPLeNet_direct_mnist(weight_decay=args.l2)
elif args.arch == 'mnistMLPlenet':
if proj_type == 'fastfood':
model = build_model_mnist_MLPLeNet_fastfood(weight_decay=args.l2, vsize=args.vsize)
elif args.arch == 'mnistUntiedlenet_dir':
model = build_UntiedLeNet_direct_mnist(weight_decay=args.l2)
elif args.arch == 'mnistUntiedlenet':
if proj_type == 'fastfood':
model = build_model_mnist_UntiedLeNet_fastfood(weight_decay=args.l2, vsize=args.vsize)
elif args.arch == 'cifarMLPlenet_dir':
model = build_MLPLeNet_direct_cifar(weight_decay=args.l2)
elif args.arch == 'cifarMLPlenet':
if proj_type == 'fastfood':
model = build_model_cifar_MLPLeNet_fastfood(weight_decay=args.l2, vsize=args.vsize)
elif args.arch == 'cifarUntiedlenet_dir':
model = build_UntiedLeNet_direct_cifar(weight_decay=args.l2)
elif args.arch == 'cifarUntiedlenet':
if proj_type == 'fastfood':
model = build_model_cifar_UntiedLeNet_fastfood(weight_decay=args.l2, vsize=args.vsize)
elif args.arch == 'mnistlenet':
if proj_type == 'fastfood':
model = build_model_mnist_LeNet_fastfood(weight_decay=args.l2, vsize=args.vsize)
else:
model = build_LeNet_mnist(weight_decay=args.l2, vsize=args.vsize, proj_type=proj_type)
elif args.arch == 'cifarlenet_dir':
model = build_LeNet_direct_cifar(weight_decay=args.l2, d_rate=args.d_rate, c1=args.c1, c2=args.c2, d1=args.d1, d2=args.d2)
elif args.arch == 'cifarlenet':
if proj_type == 'fastfood':
model = build_model_cifar_LeNet_fastfood(weight_decay=args.l2, vsize=args.vsize, d_rate=args.d_rate, c1=args.c1, c2=args.c2, d1=args.d1, d2=args.d2)
else:
model = build_LeNet_cifar(weight_decay=args.l2, vsize=args.vsize, proj_type=proj_type, d_rate=args.d_rate)
elif args.arch == 'alexnet_dir':
model = build_alexnet_direct(weight_decay=args.l2, shift_in=np.array([104, 117, 123]))
args.shuffletrain = False
randmirrors = True
randcrops = True
cropsize = (227,227)
elif args.arch == 'squeeze_dir':
model = build_squeezenet_direct(weight_decay=args.l2, shift_in=np.array([104, 117, 123]))
args.shuffletrain = False
randmirrors = True
randcrops = True
cropsize = (224,224)
elif args.arch == 'alexnet':
if proj_type == 'fastfood':
model = build_alexnet_fastfood(weight_decay=args.l2, shift_in=np.array([104, 117, 123]), vsize=args.vsize)
else:
raise Exception('not implemented')
args.shuffletrain = False
randmirrors = True
randcrops = True
cropsize = (227,227)
else:
raise Exception('Unknown network architecture: %s' % args.arch)
print 'All model weights:'
total_params = summarize_weights(model.trainable_weights)
print 'Model summary:'
model.summary()
model.print_trainable_warnings()
input_lr = tf.placeholder(tf.float32, shape=[])
lr_stepper = LRStepper(args.lr, args.lr_ratio, args.lr_epochs, args.lr_steps)
# 2. COMPUTE GRADS AND CREATE OPTIMIZER
if args.opt == 'sgd':
opt = tf.train.MomentumOptimizer(input_lr, args.mom)
elif args.opt == 'rmsprop':
opt = tf.train.RMSPropOptimizer(input_lr, momentum=args.mom)
elif args.opt == 'adam':
opt = tf.train.AdamOptimizer(input_lr, args.beta1, args.beta2)
# Optimize w.r.t all trainable params in the model
grads_and_vars = opt.compute_gradients(model.v.loss, model.trainable_weights, gate_gradients=tf.train.Optimizer.GATE_GRAPH)
train_step = opt.apply_gradients(grads_and_vars)
add_grad_summaries(grads_and_vars)
summarize_opt(opt)
# 3. OPTIONALLY SAVE OR LOAD VARIABLES (e.g. model params, model running BN means, optimization momentum, ...) and then finalize initialization
saver = tf.train.Saver(max_to_keep=None) if (args.output or args.load) else None
if args.load:
ckptfile, miscfile = args.load.split(':')
# Restore values directly to graph
saver.restore(sess, ckptfile)
with gzip.open(miscfile) as ff:
saved = pickle.load(ff)
buddy = saved['buddy']
else:
buddy = StatsBuddy()
buddy.tic() # call if new run OR resumed run
# Initialize any missed vars (e.g. optimization momentum, ... if not loaded from checkpoint)
uninitialized_vars = tf_get_uninitialized_variables(sess)
init_missed_vars = tf.variables_initializer(uninitialized_vars, 'init_missed_vars')
sess.run(init_missed_vars)
# Print warnings about any TF vs. Keras shape mismatches
warn_misaligned_shapes(model)
# Make sure all variables, which are model variables, have been initialized (e.g. model params and model running BN means)
tf_assert_all_init(sess)
# 3.5 Normalize the overall basis matrix across the (multiple) unnormalized basis matrices for each layer
basis_matrices = []
normalizers = []
for layer in model.layers:
try:
basis_matrices.extend(layer.offset_creator.basis_matrices)
except AttributeError:
continue
try:
normalizers.extend(layer.offset_creator.basis_matrix_normalizers)
except AttributeError:
continue
if len(basis_matrices) > 0 and not args.load:
if proj_type == 'sparse':
# Norm of overall basis matrix rows (num elements in each sum == total parameters in model)
bm_row_norms = tf.sqrt(tf.add_n([tf.sparse_reduce_sum(tf.square(bm), 1) for bm in basis_matrices]))
# Assign `normalizer` Variable to these row norms to achieve normalization of the basis matrix
# in the TF computational graph
rescale_basis_matrices = [tf.assign(var, tf.reshape(bm_row_norms,var.shape)) for var in normalizers]
_ = sess.run(rescale_basis_matrices)
elif proj_type == 'dense':
bm_sums = [tf.reduce_sum(tf.square(bm), 1) for bm in basis_matrices]
divisor = tf.expand_dims(tf.sqrt(tf.add_n(bm_sums)), 1)
rescale_basis_matrices = [tf.assign(var, var / divisor) for var in basis_matrices]
_ = sess.run(rescale_basis_matrices)
else:
print '\nhere\n'
embed()
assert False, 'what to do with fastfood?'
# 4. SETUP TENSORBOARD LOGGING
train_histogram_summaries = get_collection_intersection_summary('train_collection', 'orig_histogram')
train_scalar_summaries = get_collection_intersection_summary('train_collection', 'orig_scalar')
val_histogram_summaries = get_collection_intersection_summary('val_collection', 'orig_histogram')
val_scalar_summaries = get_collection_intersection_summary('val_collection', 'orig_scalar')
param_histogram_summaries = get_collection_intersection_summary('param_collection', 'orig_histogram')
writer = None
if args.output:
mkdir_p(args.output)
if not args.skiptfevents:
writer = tf.summary.FileWriter(args.output, sess.graph)
# 5. TRAIN
train_iters = (train_y.shape[0] - 1) / args.minibatch + 1
val_iters = (val_y.shape[0] - 1) / args.minibatch + 1
impreproc = ImagePreproc()
if args.ipy:
print 'Embed: before train / val loop (Ctrl-D to continue)'
embed()
fastest_avg_iter_time = 1e9
while buddy.epoch < args.epochs + 1:
# How often to log data
do_log_params = lambda ep, it, ii: False
do_log_val = lambda ep, it, ii: True
do_log_train = lambda ep, it, ii: (it < train_iters and it & it-1 == 0 or it>=train_iters and it%train_iters == 0) # Log on powers of two then every epoch
# 0. Log params
if args.output and do_log_params(buddy.epoch, buddy.train_iter, 0) and param_histogram_summaries is not None and not args.skiptfevents:
params_summary_str, = sess.run([param_histogram_summaries])
writer.add_summary(params_summary_str, buddy.train_iter)
# 1. Evaluate val set performance
if not args.skipval:
tic2()
for ii in xrange(val_iters):
start_idx = ii * args.minibatch
batch_x = val_x[start_idx:start_idx + args.minibatch]
batch_y = val_y[start_idx:start_idx + args.minibatch]
if randcrops:
batch_x = impreproc.center_crops(batch_x, cropsize)
feed_dict = {
model.v.input_images: batch_x,
model.v.input_labels: batch_y,
K.learning_phase(): 0,
}
fetch_dict = model.trackable_dict
with WithTimer('sess.run val iter', quiet=not args.verbose):
result_val = sess_run_dict(sess, fetch_dict, feed_dict=feed_dict)
buddy.note_weighted_list(batch_x.shape[0], model.trackable_names, [result_val[k] for k in model.trackable_names], prefix='val_')
if args.output and not args.skiptfevents and do_log_val(buddy.epoch, buddy.train_iter, 0):
log_scalars(writer, buddy.train_iter,
{'mean_%s' % name: value for name, value in buddy.epoch_mean_list_re('^val_')},
prefix='buddy')
print ('\ntime: %f. after training for %d epochs:\n%3d val: %s (%.3gs/i)'
% (buddy.toc(), buddy.epoch, buddy.train_iter, buddy.epoch_mean_pretty_re('^val_', style=val_style), toc2() / val_iters))
# 2. Possiby Snapshot, possibly quit
if args.output and args.snapshot_to and args.snapshot_every:
snap_intermed = args.snapshot_every > 0 and buddy.train_iter % args.snapshot_every == 0
snap_end = buddy.epoch == args.epochs
if snap_intermed or snap_end:
# Snapshot
save_path = saver.save(sess, '%s/%s_%04d.ckpt' % (args.output, args.snapshot_to, buddy.epoch))
print 'snappshotted model to', save_path
with gzip.open('%s/%s_misc_%04d.pkl.gz' % (args.output, args.snapshot_to, buddy.epoch), 'w') as ff:
saved = {'buddy': buddy}
pickle.dump(saved, ff)
if buddy.epoch == args.epochs:
if args.ipy:
print 'Embed: at end of training (Ctrl-D to exit)'
embed()
break # Extra pass at end: just report val stats and skip training
# 3. Train on training set
#train_order = range(train_x.shape[0])
if args.shuffletrain:
train_order = np.random.permutation(train_x.shape[0])
tic3()
for ii in xrange(train_iters):
tic2()
start_idx = ii * args.minibatch
if args.shuffletrain:
batch_x = train_x[train_order[start_idx:start_idx + args.minibatch]]
batch_y = train_y[train_order[start_idx:start_idx + args.minibatch]]
else:
batch_x = train_x[start_idx:start_idx + args.minibatch]
batch_y = train_y[start_idx:start_idx + args.minibatch]
if randcrops:
batch_x = impreproc.random_crops(batch_x, cropsize, randmirrors)
feed_dict = {
model.v.input_images: batch_x,
model.v.input_labels: batch_y,
input_lr: lr_stepper.lr(buddy),
K.learning_phase(): 1,
}
fetch_dict = {'train_step': train_step}
fetch_dict.update(model.trackable_and_update_dict)
if args.output and not args.skiptfevents and do_log_train(buddy.epoch, buddy.train_iter, ii):
if param_histogram_summaries is not None:
fetch_dict.update({'param_histogram_summaries': param_histogram_summaries})
if train_histogram_summaries is not None:
fetch_dict.update({'train_histogram_summaries': train_histogram_summaries})
if train_scalar_summaries is not None:
fetch_dict.update({'train_scalar_summaries': train_scalar_summaries})
with WithTimer('sess.run train iter', quiet=not args.verbose):
result_train = sess_run_dict(sess, fetch_dict, feed_dict=feed_dict)
buddy.note_weighted_list(batch_x.shape[0], model.trackable_names, [result_train[k] for k in model.trackable_names], prefix='train_')
if do_log_train(buddy.epoch, buddy.train_iter, ii):
print ('%3d train: %s (%.3gs/i)' % (buddy.train_iter, buddy.epoch_mean_pretty_re('^train_', style=train_style), toc2()))
if args.output and not args.skiptfevents:
if param_histogram_summaries is not None:
hist_summary_str = result_train['param_histogram_summaries']
writer.add_summary(hist_summary_str, buddy.train_iter)
if train_histogram_summaries is not None:
hist_summary_str = result_train['train_histogram_summaries']
writer.add_summary(hist_summary_str, buddy.train_iter)
if train_scalar_summaries is not None:
scalar_summary_str = result_train['train_scalar_summaries']
writer.add_summary(scalar_summary_str, buddy.train_iter)
log_scalars(writer, buddy.train_iter,
{'batch_%s' % name: value for name, value in buddy.last_list_re('^train_')},
prefix='buddy')
if ii > 0 and ii % 100 == 0:
avg_iter_time = toc3() / 100; tic3()
fastest_avg_iter_time = min(fastest_avg_iter_time, avg_iter_time)
print ' %d: Average iteration time over last 100 train iters: %.3gs' % (ii, avg_iter_time)
buddy.inc_train_iter() # after finished training a mini-batch
buddy.inc_epoch() # after finished training whole pass through set
if args.output and not args.skiptfevents and do_log_train(buddy.epoch, buddy.train_iter, 0):
log_scalars(writer, buddy.train_iter,
{'mean_%s' % name: value for name,value in buddy.epoch_mean_list_re('^train_')},
prefix='buddy')
print '\nFinal'
print '%02d:%d val: %s' % (buddy.epoch, buddy.train_iter, buddy.epoch_mean_pretty_re('^val_', style=val_style))
print '%02d:%d train: %s' % (buddy.epoch, buddy.train_iter, buddy.epoch_mean_pretty_re('^train_', style=train_style))
print '\nfinal_stats epochs %g' % buddy.epoch
print 'final_stats iters %g' % buddy.train_iter
print 'final_stats time %g' % buddy.toc()
print 'final_stats total_params %g' % total_params
print 'final_stats fastest_avg_iter_time %g' % fastest_avg_iter_time
for name, value in buddy.epoch_mean_list_all():
print 'final_stats %s %g' % (name, value)
if args.output and not args.skiptfevents:
writer.close() # Flush and close
if __name__ == '__main__':
main()