def config_forward_pass(config_filename, verbose=True, sample_ids=None):
"""
Performs a full forward pass for all samples specified within
a configuration file
sample_ids should be a list of ints describing the samples to run
"""
# parameters
config, params = front_end.parser(config_filename)
# set command line sample ids
if sample_ids is not None:
params[range_optionname] = sample_ids
# load network
net = netio.load_network(params, train=False)
output_patch_shape = params[outsz_optionname]
sample_outputs = {}
# Loop over sample range
for sample in params[range_optionname]:
print "Sample: %d" % sample
# read image stacks
# Note: preprocessing included within CSamples
# See CONSTANTS section above for optionname values
Dataset = front_end.ConfigSample(config, params, sample, net, output_patch_shape)
sample_outputs[sample] = generate_full_output(Dataset, net, params, params["dtype"], verbose=True)
# softmax if using softmax_loss
if "softmax" in params["cost_fn_str"]:
sample_outputs[sample] = run_softmax(sample_outputs[sample])
return sample_outputs
def config_forward_pass( config_filename, verbose=True, sample_ids=None ):
'''
Performs a full forward pass for all samples specified within
a configuration file
sample_ids should be a list of ints describing the samples to run
'''
# parameters
config, params = front_end.parser( config_filename )
# set command line sample ids
if sample_ids is not None:
params[range_optionname] = sample_ids
# load network
net = netio.load_network( params, train=False )
output_patch_shape = params[outsz_optionname]
sample_outputs = {}
#Loop over sample range
for sample in params[range_optionname]:
print "Sample: %d" % sample
# read image stacks
# Note: preprocessing included within CSamples
# See CONSTANTS section above for optionname values
Dataset = front_end.ConfigSample(config, params,
sample, net, output_patch_shape )
sample_outputs[sample] = generate_full_output(Dataset, net,
params['dtype'], verbose=True)
# softmax if using softmax_loss
if 'softmax' in params['cost_fn_str']:
sample_outputs[sample] = run_softmax(sample_outputs[sample])
return sample_outputs
def main(config_filename, sample_ids=None):
"""
Script functionality - runs config_forward_pass and saves the
output volumes
"""
config, params = front_end.parser(config_filename)
if sample_ids is None:
sample_ids = params[range_optionname]
for sample_id in sample_ids:
output_volume = config_forward_pass(config_filename, verbose=True, sample_ids=[sample_id])
print "Saving Output Volume %d..." % sample_id
save_sample_outputs(output_volume, params[output_prefix_optionname])
def main( config_filename, sample_ids=None ):
'''
Script functionality - runs config_forward_pass and saves the
output volumes
'''
config, params = front_end.parser( config_filename )
if sample_ids is None:
sample_ids = params[range_optionname]
for sample_id in sample_ids:
output_volume = config_forward_pass( config_filename, verbose=True, sample_ids=[sample_id])
print "Saving Output Volume %d..." % sample_id
save_sample_outputs( output_volume, params[output_prefix_optionname] )
def main( conf_file='config.cfg', logfile=None ):
#%% parameters
print "reading config parameters..."
config, pars = front_end.parser( conf_file )
if pars.has_key('logging') and pars['logging']:
print "recording configuration file..."
front_end.record_config_file( pars )
logfile = front_end.make_logfile_name( pars )
#%% create and initialize the network
if pars['train_load_net'] and os.path.exists(pars['train_load_net']):
print "loading network..."
net = netio.load_network( pars )
# load existing learning curve
lc = zstatistics.CLearnCurve( pars['train_load_net'] )
else:
if pars['train_seed_net'] and os.path.exists(pars['train_seed_net']):
print "seeding network..."
net = netio.load_network( pars, is_seed=True )
else:
print "initializing network..."
net = netio.init_network( pars )
# initalize a learning curve
lc = zstatistics.CLearnCurve()
# show field of view
print "field of view: ", net.get_fov()
print "output volume info: ", net.get_outputs_setsz()
# set some parameters
print 'setting up the network...'
vn = utils.get_total_num(net.get_outputs_setsz())
eta = pars['eta'] #/ vn
net.set_eta( eta )
net.set_momentum( pars['momentum'] )
net.set_weight_decay( pars['weight_decay'] )
# initialize samples
outsz = pars['train_outsz']
print "\n\ncreate train samples..."
smp_trn = front_end.CSamples(config, pars, pars['train_range'], net, outsz, logfile)
print "\n\ncreate test samples..."
smp_tst = front_end.CSamples(config, pars, pars['test_range'], net, outsz, logfile)
# initialization
elapsed = 0
err = 0
cls = 0
# interactive visualization
plt.ion()
plt.show()
# the last iteration we want to continue training
iter_last = lc.get_last_it()
print "start training..."
start = time.time()
print "start from ", iter_last+1
for i in xrange(iter_last+1, pars['Max_iter']+1):
vol_ins, lbl_outs, msks = smp_trn.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward( vol_ins )
# cost function and accumulate errors
props, cerr, grdts = pars['cost_fn']( props, lbl_outs )
err = err + cerr
cls = cls + cost_fn.get_cls(props, lbl_outs)
# mask process the gradient
grdts = utils.dict_mul(grdts, msks)
# run backward pass
grdts = utils.make_continuous(grdts, dtype=pars['dtype'])
net.backward( grdts )
if pars['is_malis'] :
malis_weights = cost_fn.malis_weight(props, lbl_outs)
grdts = utils.dict_mul(grdts, malis_weights)
if i%pars['Num_iter_per_test']==0:
# test the net
lc = test.znn_test(net, pars, smp_tst, vn, i, lc)
if i%pars['Num_iter_per_show']==0:
# anneal factor
eta = eta * pars['anneal_factor']
net.set_eta(eta)
# normalize
err = err / vn / pars['Num_iter_per_show']
cls = cls / vn / pars['Num_iter_per_show']
lc.append_train(i, err, cls)
# time
elapsed = time.time() - start
elapsed = elapsed / pars['Num_iter_per_show']
show_string = "iteration %d, err: %.3f, cls: %.3f, elapsed: %.1f s/iter, learning rate: %.6f"\
%(i, err, cls, elapsed, eta )
if pars.has_key('logging') and pars['logging']:
utils.write_to_log(logfile, show_string)
print show_string
if pars['is_visual']:
# show results To-do: run in a separate thread
front_end.inter_show(start, lc, eta, vol_ins, props, lbl_outs, grdts, pars)
if pars['is_rebalance'] and 'aff' not in pars['out_type']:
plt.subplot(247)
plt.imshow(msks.values()[0][0,0,:,:], interpolation='nearest', cmap='gray')
plt.xlabel('rebalance weight')
if pars['is_malis']:
plt.subplot(248)
plt.imshow(malis_weights.values()[0][0,0,:,:], interpolation='nearest', cmap='gray')
plt.xlabel('malis weight (log)')
plt.pause(2)
plt.show()
# reset err and cls
err = 0
cls = 0
# reset time
start = time.time()
if i%pars['Num_iter_per_save']==0:
# save network
netio.save_network(net, pars['train_save_net'], num_iters=i)
lc.save( pars, elapsed )
def main(conf_file='config.cfg', logfile=None):
#%% parameters
print "reading config parameters..."
config, pars = front_end.parser(conf_file)
if pars.has_key('logging') and pars['logging']:
print "recording configuration file..."
front_end.record_config_file(pars)
logfile = front_end.make_logfile_name(pars)
#%% create and initialize the network
if pars['train_load_net'] and os.path.exists(pars['train_load_net']):
print "loading network..."
net = netio.load_network(pars)
# load existing learning curve
lc = zstatistics.CLearnCurve(pars['train_load_net'])
else:
if pars['train_seed_net'] and os.path.exists(pars['train_seed_net']):
print "seeding network..."
net = netio.seed_network(pars, is_seed=True)
else:
print "initializing network..."
net = netio.init_network(pars)
# initalize a learning curve
lc = zstatistics.CLearnCurve()
# show field of view
print "field of view: ", net.get_fov()
print "output volume info: ", net.get_outputs_setsz()
# set some parameters
print 'setting up the network...'
vn = utils.get_total_num(net.get_outputs_setsz())
eta = pars['eta'] #/ vn
net.set_eta(eta)
net.set_momentum(pars['momentum'])
net.set_weight_decay(pars['weight_decay'])
# initialize samples
outsz = pars['train_outsz']
print "\n\ncreate train samples..."
smp_trn = front_end.CSamples(config, pars, pars['train_range'], net, outsz,
logfile)
print "\n\ncreate test samples..."
smp_tst = front_end.CSamples(config, pars, pars['test_range'], net, outsz,
logfile)
# initialization
elapsed = 0
err = 0
cls = 0
# interactive visualization
plt.ion()
plt.show()
# the last iteration we want to continue training
iter_last = lc.get_last_it()
print "start training..."
start = time.time()
print "start from ", iter_last + 1
for i in xrange(iter_last + 1, pars['Max_iter'] + 1):
vol_ins, lbl_outs, msks = smp_trn.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward(vol_ins)
# cost function and accumulate errors
props, cerr, grdts = pars['cost_fn'](props, lbl_outs)
err = err + cerr
cls = cls + cost_fn.get_cls(props, lbl_outs)
# mask process the gradient
grdts = utils.dict_mul(grdts, msks)
# run backward pass
grdts = utils.make_continuous(grdts, dtype=pars['dtype'])
net.backward(grdts)
if pars['is_malis']:
malis_weights = cost_fn.malis_weight(props, lbl_outs)
grdts = utils.dict_mul(grdts, malis_weights)
if i % pars['Num_iter_per_test'] == 0:
# test the net
lc = test.znn_test(net, pars, smp_tst, vn, i, lc)
if i % pars['Num_iter_per_show'] == 0:
# anneal factor
eta = eta * pars['anneal_factor']
net.set_eta(eta)
# normalize
err = err / vn / pars['Num_iter_per_show']
cls = cls / vn / pars['Num_iter_per_show']
lc.append_train(i, err, cls)
# time
elapsed = time.time() - start
elapsed = elapsed / pars['Num_iter_per_show']
show_string = "iteration %d, err: %.3f, cls: %.3f, elapsed: %.1f s/iter, learning rate: %.6f"\
%(i, err, cls, elapsed, eta )
if pars.has_key('logging') and pars['logging']:
utils.write_to_log(logfile, show_string)
print show_string
if pars['is_visual']:
# show results To-do: run in a separate thread
front_end.inter_show(start, lc, eta, vol_ins, props, lbl_outs,
grdts, pars)
if pars['is_rebalance'] and 'aff' not in pars['out_type']:
plt.subplot(247)
plt.imshow(msks.values()[0][0, 0, :, :],
interpolation='nearest',
cmap='gray')
plt.xlabel('rebalance weight')
if pars['is_malis']:
plt.subplot(248)
plt.imshow(malis_weights.values()[0][0, 0, :, :],
interpolation='nearest',
cmap='gray')
plt.xlabel('malis weight (log)')
plt.pause(2)
plt.show()
# reset err and cls
err = 0
cls = 0
# reset time
start = time.time()
if i % pars['Num_iter_per_save'] == 0:
# save network
netio.save_network(net, pars['train_save_net'], num_iters=i)
lc.save(pars, elapsed)