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train_supermask.py
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train_supermask.py
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# Copyright (c) 2019 Uber Technologies, Inc.
# Licensed under the Uber Non-Commercial License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at the root directory of this project.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from __future__ import division
from ast import literal_eval
import tensorflow as tf
import numpy as np
import time
import h5py
import argparse
import os
import sys
import masked_networks
from tf_plus import learning_phase, batchnorm_learning_phase
from tf_plus import sess_run_dict, add_classification_losses
from tf_plus import summarize_weights
def make_parser():
parser = argparse.ArgumentParser()
# inputs
parser.add_argument('--train_h5', type=str, required=True)
parser.add_argument('--test_h5', type=str, required=True)
parser.add_argument('--input_dim', type=str, default='28,28,1', help='mnist: 28,28,1; cifar: 32,32,3')
parser.add_argument('--init_weights_h5', type=str, required=False)
parser.add_argument('--load', type=str, help='Load checkpoint weight file')
parser.add_argument('--resume', type=str, help='Load checkpoint weight file and resume training from there')
# model architecture
parser.add_argument('--arch', type=str, default='fc', choices=('fc_mask', 'conv2_mask', 'conv4_mask', 'conv6_mask'), help='network architecture')
# training params
parser.add_argument('--opt', type=str, default='sgd', choices=('sgd', 'rmsprop', 'adam'))
parser.add_argument('--lr', type=float, default=.01, help='suggested: .01 sgd, .001 rmsprop, .0001 adam')
parser.add_argument('--decay_schedule', type=str, default='-1', help='comma separated decay learning rate. allcnn: 200,250,300')
parser.add_argument('--mom', type=float, default=.9, help='momentum (only has effect for sgd/rmsprop)')
parser.add_argument('--l2', type=float, default=0)
parser.add_argument('--num_epochs', type=int, default=1, help='number of epochs')
parser.add_argument('--train_batch_size', type=int, default=250)
parser.add_argument('--large_batch_size', type=int, default=11000, help='use mnist: 11000, cifar: 5000')
parser.add_argument('--test_batch_size', type=int, default=0) # do 0 for all
parser.add_argument('--no_shuffle', action='store_true')
parser.add_argument('--shuffle_seed', type=int, default=-1, help='seed if you want to shuffle batches')
parser.add_argument('--tf_seed', type=int, default=-1, help='tensorflow random seed')
# eval and outputs
parser.add_argument('--print_every', type=int, default=100, help='print status update every n iterations')
parser.add_argument('--output_dir', type=str, default=os.environ.get('GIT_RESULTS_MANAGER_DIR', None), help='output directory')
parser.add_argument('--eval_every', type=int, default=20, help='eval on entire set')
parser.add_argument('--log_every', type=int, default=5, help='save tb batch acc/loss every n iterations')
parser.add_argument('--save_weights', action='store_true', help='save gradients and weights to file')
parser.add_argument('--save_every', type=int, default=1, help='save gradients every n iterations (averaged)') # kinda deprecated
# supermask configs
parser.add_argument('--sigmoid_bias', type=float, default=0, help='rounding bias (masks initialized with this)')
parser.add_argument('--round_mask', action='store_true', help='round masks instead of bernoulli sample')
parser.add_argument('--signed_constant', action='store_true', help='make network weights signed constant')
parser.add_argument('--signed_constant_multiplier', type=float, default=1.0, help='Value of multiplier to the default as signed constant (std of each layer init)')
parser.add_argument('--dynamic_scaling', action='store_true', help='dynamically determine singed constant multiplier based on percentage of masked weights')
return parser
def read_input_data(filename):
input_file = h5py.File(filename, 'r')
x = np.array(input_file['images'])
y = np.array(input_file['labels'])
input_file.close()
return x, y
################# model setup, after architecture is already created
def init_model(model, args):
img_size = tuple([None] + [int(dim) for dim in args.input_dim.split(',')])
input_images = tf.placeholder(dtype='float32', shape=img_size)
input_labels = tf.placeholder(dtype='int64', shape=(None,))
model.a('input_images', input_images)
model.a('input_labels', input_labels)
model.a('logits', model(input_images)) # logits is y_pred
def define_training(model, args):
# define optimizer
input_lr = tf.placeholder(tf.float32, shape=[]) # placeholder for dynamic learning rate
model.a('input_lr', input_lr)
if args.opt == 'sgd':
optimizer = tf.train.MomentumOptimizer(input_lr, args.mom)
elif args.opt == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(input_lr, momentum=args.mom)
elif args.opt == 'adam':
optimizer = tf.train.AdamOptimizer(input_lr)
model.a('optimizer', optimizer)
# This adds prob, cross_ent, loss_cross_ent, class_prediction,
# prediction_correct, accuracy, loss, (loss_reg) in tf_nets/losses.py
add_classification_losses(model, model.input_labels)
model.a('train_step', optimizer.minimize(model.loss, var_list=model.trainable_weights))
print('All model weights:')
summarize_weights(model.trainable_weights)
################# methods used for freezing layers
# returns list of variables as np arrays in their original shape
def split_and_shape(one_time_slice, shapes):
variables = []
offset = 0
for shape in shapes:
num_params = np.prod(shape)
variables.append(one_time_slice[offset : offset + num_params].reshape(shape))
offset += num_params
return variables
def load_initial_weights(sess, model, args):
if not args.init_weights_h5.endswith('/weights'):
h5file = os.path.join(args.init_weights_h5, 'weights')
else:
h5file = args.init_weights_h5
hf_weights = h5py.File(h5file, 'r')
init_weights_flat = hf_weights.get('all_weights')[0]
shapes = [literal_eval(s) for s in hf_weights.attrs['var_shapes'].decode('utf-8').split(';')]
hf_weights.close()
weight_values = split_and_shape(init_weights_flat, shapes)
for i, w in enumerate(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)):
#if 'mask' not in w.name: # HACK for biased masks
print('loading weights for layer {}: {}'.format(i, w.name))
w.load(weight_values[i], session=sess)
return
################# util for training/eval portion
# flatten and concatentate list of tensors into one np vector
def flatten_all(tensors):
return np.concatenate([tensor.eval().flatten() for tensor in tensors])
# eval on whole train/test set occasionally, for tuning purposes
def eval_on_entire_dataset(sess, model, input_x, input_y, batch_size, tb_prefix_and_iter, tb_writer):
#grad_sums = np.zeros(dim_sum)
num_batches = int(input_y.shape[0] / batch_size)
total_acc = 0
total_loss = 0
total_loss_no_reg = 0 # loss without counting l2 penalty
for i in range(num_batches):
# slice indices (should be large)
s_start = batch_size * i
s_end = s_start + batch_size
fetch_dict = {
'accuracy': model.accuracy,
'loss': model.loss,
'loss_no_reg': model.loss_cross_ent}
result_dict = sess_run_dict(sess, fetch_dict, feed_dict={
model.input_images: input_x[s_start:s_end],
model.input_labels: input_y[s_start:s_end],
learning_phase(): 0,
batchnorm_learning_phase(): 1}) # do not use nor update moving averages
total_acc += result_dict['accuracy']
total_loss += result_dict['loss']
total_loss_no_reg += result_dict['loss_no_reg']
acc = total_acc / num_batches
loss = total_loss / num_batches
loss_no_reg = total_loss_no_reg / num_batches
# tensorboard
if tb_writer:
tb_prefix, iterations = tb_prefix_and_iter
summary = tf.Summary()
summary.value.add(tag='%s_acc' % tb_prefix, simple_value=acc)
summary.value.add(tag='%s_loss' % tb_prefix, simple_value=loss)
summary.value.add(tag='%s_loss_no_reg' % tb_prefix, simple_value=loss_no_reg)
tb_writer.add_summary(summary, iterations)
return acc, loss_no_reg
#################
def eval(sess, model, train_x, train_y, test_x, test_y, args, tb_writer, iterations):
timerstart = time.time()
# eval on entire train set
tb_prefix_and_iter = ('eval_train', iterations) if tb_writer else (None, None)
cur_train_acc, cur_train_loss = eval_on_entire_dataset(sess, model, train_x, train_y,
args.large_batch_size, tb_prefix_and_iter, tb_writer)
# eval on entire test/val set
tb_prefix_and_iter = ('eval_test', iterations) if tb_writer else (None, None)
cur_test_acc, cur_test_loss = eval_on_entire_dataset(sess, model, test_x, test_y,
args.test_batch_size, tb_prefix_and_iter, tb_writer)
print(('{}: train acc = {:.4f}, test acc = {:.4f}, '
+ 'train loss = {:.4f}, test loss = {:.4f} ({:.2f} s)').format(iterations,
cur_train_acc, cur_test_acc, cur_train_loss, cur_test_loss, time.time() - timerstart))
if 'mask' in args.arch:
percs, ones_all, size_all = [], 0, 0
for layer in model.trainable_weights:
assert 'mask' in layer.name, "Should be just training masks"
#if 'bias' in layer.name:
# #print('bias values: ', layer.eval())
# continue
mprobs = tf.stop_gradient(tf.nn.sigmoid(layer)).eval()
num_ones = mprobs.sum() # expected value
# old, wrong
#nparr = layer.eval() # before sigmoid
#num_ones = (nparr > 0).sum() + 0.5 * (nparr == 0).sum() # expected value
#percs.append(num_ones / nparr.size)
percs.append(num_ones / mprobs.size)
ones_all += num_ones
size_all += mprobs.size
print('[Est] percent of 1s in mask (per layer):', percs)
print('[Est] percent of 1s in mask (total):', ones_all/size_all)
if args.dynamic_scaling:
layer_ones = [layer.ones_in_mask for layer in list(model.layers) if
'conv2D' in layer.name or 'fc' in layer.name]
layer_mults = [layer.multiplier for layer in list(model.layers) if
'conv2D' in layer.name or 'fc' in layer.name]
layer_sizes = [tf.size(layer.kernel).eval() for layer in list(model.layers) if
'conv2D' in layer.name or 'fc' in layer.name]
l_ones = sess.run(layer_ones, feed_dict={learning_phase(): 0})
l_mults = sess.run(layer_mults, feed_dict={learning_phase(): 0})
print('[Act] percent of 1s in mask (per layer):', (np.array(l_ones) / np.array(layer_sizes)).tolist())
print('[Act] percent of 1s in mask (total):', np.sum(l_ones) / np.sum(layer_sizes))
print('layer signed constant multipliers:', l_mults)
return cur_train_acc, cur_test_acc, cur_train_loss, cur_test_loss
def train_and_eval(sess, model, train_x, train_y, test_x, test_y, tb_writer, dsets, args):
# constants
num_batches = int(train_y.shape[0] / args.train_batch_size)
print('Training batch size {}, number of iterations: {} per epoch, {} total'.format(
args.train_batch_size, num_batches, args.num_epochs*num_batches))
#dim_sum = sum([tf.size(var).eval() for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)])
# adaptive learning schedule
curr_lr = args.lr
decay_epochs = [int(ep) for ep in args.decay_schedule.split(',')]
if decay_epochs[-1] > 0:
decay_epochs.append(-1) # need to end with something small to stop the decay
decay_count = 0
# initializations
tb_summaries = tf.summary.merge(tf.get_collection('tb_train_step'))
shuffled_indices = np.arange(train_y.shape[0]) # for no shuffling
iterations = 0
chunks_written = 0 # for args.save_every batches
timerstart = time.time()
for epoch in range(args.num_epochs):
print('-' * 100)
print('epoch {} current lr {:.3g}'.format(epoch, curr_lr))
if not args.no_shuffle:
shuffled_indices = np.random.permutation(train_y.shape[0]) # for shuffled mini-batches
if epoch == decay_epochs[decay_count]:
curr_lr *= 0.1
decay_count += 1
for i in range(num_batches):
# store current weights and gradients
if args.save_weights and iterations % args.save_every == 0:
dsets['all_weights'][chunks_written] = flatten_all(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES))
chunks_written += 1
# less frequent, larger evals
if iterations % args.eval_every == 0:
args.verbose = True if epoch <3 else False
eval(sess, model, train_x, train_y, test_x, test_y, args, tb_writer, iterations)
if args.signed_constant and iterations < args.print_every * 3: # validate 3 times
print('Sanity check: signed constant values')
if args.signed_constant_multiplier:
print('Note: signed constant multiplier is {}'.format(args.signed_constant_multiplier))
if args.dynamic_scaling:
print('Note: dynamic signed constant multiplier')
for layer in list(model.layers):
if 'conv2D' in layer.name or 'fc' in layer.name:
#signed_kernel = layer.signed_kernel.eval()
signed_kernel = sess.run(layer.kernel, feed_dict={learning_phase(): 0})
print('Layer {} signed kernel shape {}, has unique values {}'.format(
layer.name, signed_kernel.shape, np.unique(signed_kernel).tolist()))
# current slice for input data
batch_indices = shuffled_indices[args.train_batch_size * i : args.train_batch_size * (i + 1)]
# training
fetch_dict = {'train_step': model.train_step}
fetch_dict.update(model.update_dict())
if iterations % args.log_every == 0:
fetch_dict.update({'tb': tb_summaries})
result_train = sess_run_dict(sess, fetch_dict, feed_dict={
model.input_images: train_x[batch_indices],
model.input_labels: train_y[batch_indices],
model.input_lr: curr_lr,
learning_phase(): 1,
batchnorm_learning_phase(): 1})
# log to tensorboard
if tb_writer and iterations % args.log_every == 0:
tb_writer.add_summary(result_train['tb'], iterations)
iterations += 1
# save final weight values
if args.save_weights:
dsets['all_weights'][chunks_written] = flatten_all(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES))
# save final evals
if iterations % args.eval_every == 0:
eval(sess, model, train_x, train_y, test_x, test_y, args, tb_writer, iterations)
if 'mask' in args.arch:
# for supermask: eval 10 times from different random bernoullies
testaccs = []
testlosses = []
for sample in range(10):
cur_test_acc, cur_test_loss = eval_on_entire_dataset(sess, model, test_x, test_y,
args.test_batch_size, ('eval_test', iterations), tb_writer)
testaccs.append(cur_test_acc)
testlosses.append(cur_test_loss)
print("all test accs:", testaccs)
print("all test losses:", testlosses)
print('final test acc = {:.5f}, test loss = {:.5f}'.format(np.mean(testaccs), np.mean(testlosses)))
percs, ones_all, size_all = [], 0, 0
for layer in model.trainable_weights:
mprobs = tf.stop_gradient(tf.nn.sigmoid(layer)).eval()
num_ones = mprobs.sum() # expected value
percs.append(num_ones / mprobs.size)
ones_all += num_ones
size_all += mprobs.size
#nparr = layer.eval()
#num_ones = (nparr > 0).sum() + 0.5 * (nparr == 0).sum() # expected value
#percs.append(num_ones / nparr.size)
#ones_all += num_ones
#size_all += nparr.size
print('[Est] percent of 1s in mask (per layer):', percs)
print('[Est] percent of 1s in mask (total):', ones_all/size_all)
if args.signed_constant: # validate in the end
print('Sanity check: signed constant values')
if args.dynamic_scaling:
print('Note: dynamic signed constant multiplier')
elif args.signed_constant_multiplier:
print('Note: signed constant multiplier is {}'.format(args.signed_constant_multiplier))
for layer in list(model.layers):
if 'conv2D' in layer.name or 'fc' in layer.name:
#signed_kernel = layer.signed_kernel.eval()
signed_kernel = sess.run(layer.kernel, feed_dict={learning_phase(): 0})
print('Layer {} signed kernel shape {}, has unique values {}'.format(
layer.name, signed_kernel.shape, np.unique(signed_kernel).tolist()))
def main():
parser = make_parser()
args = parser.parse_args()
if args.tf_seed != -1:
tf.random.set_random_seed(args.tf_seed)
if not args.no_shuffle and args.shuffle_seed != -1:
np.random.seed(args.shuffle_seed)
# load data
train_x, train_y = read_input_data(args.train_h5)
test_x, test_y = read_input_data(args.test_h5) # used as val for now
images_scale = np.max(train_x)
if images_scale > 1:
print('Normalizing images by a factor of {}'.format(images_scale))
train_x = train_x / images_scale
test_x = test_x / images_scale
if args.test_batch_size == 0:
args.test_batch_size = test_y.shape[0]
print('Data shapes:', train_x.shape, train_y.shape, test_x.shape, test_y.shape)
if train_y.shape[0] % args.train_batch_size != 0:
print("WARNING batch size doesn't divide train set evenly")
if train_y.shape[0] % args.large_batch_size != 0:
print("WARNING large batch size doesn't divide train set evenly")
if test_y.shape[0] % args.test_batch_size != 0:
print("WARNING batch size doesn't divide test set evenly")
# build model, masked networks
if args.arch == 'fc_mask':
model = masked_networks.build_fc_supermask(args)
elif args.arch == 'conv2_mask':
model = masked_networks.build_conv2_supermask(args)
elif args.arch == 'conv4_mask':
model = masked_networks.build_conv4_supermask(args)
elif args.arch == 'conv6_mask':
model = masked_networks.build_conv6_supermask(args)
else:
raise Error("Unknown architeciture {}".format(args.arch))
init_model(model, args)
define_training(model, args)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
if args.init_weights_h5:
load_initial_weights(sess, model, args)
for collection in ['tb_train_step']: # 'eval_train' and 'eval_test' added manually later
tf.summary.scalar(collection + '_acc', model.accuracy, collections=[collection])
tf.summary.scalar(collection + '_loss', model.loss, collections=[collection])
tb_writer, hf = None, None
dsets = {}
if args.output_dir:
tb_writer = tf.summary.FileWriter(args.output_dir, sess.graph)
# set up output for gradients/weights
if args.save_weights:
dim_sum = sum([tf.size(var).eval() for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)])
total_iters = args.num_epochs * int(train_y.shape[0] / args.train_batch_size)
total_chunks = int(total_iters / args.save_every)
hf = h5py.File(args.output_dir + '/weights', 'w-')
# write metadata
var_shapes = np.string_(';'.join([str(var.get_shape()) for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)]))
hf.attrs['var_shapes'] = var_shapes
var_names = np.string_(';'.join([str(var.name) for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)]))
hf.attrs['var_names'] = var_names
# all individual weights at every iteration, where all_weights[i] = weights before iteration i:
dsets['all_weights'] = hf.create_dataset('all_weights', (total_chunks + 1, dim_sum), dtype='f8', compression='gzip')
########## Run main thing ##########
if args.resume:
print('='*40, 'Resuming from ckpt', '='*40)
args.train_x, args.train_y, args.test_x, args.test_y = train_x, train_y, test_x, test_y
args.load = args.resume
load_ckpt_weights(sess, model, args)
print('=' * 100)
train_and_eval(sess, model, train_x, train_y, test_x, test_y, tb_writer, dsets, args)
if tb_writer:
tb_writer.close()
if hf:
hf.close()
if __name__ == '__main__':
main()