def train_vgg16(train_dir=None, validation_dir=None):
config = GEDIconfig()
if train_dir is None: # Use globals
train_data = os.path.join(config.tfrecord_dir,
config.tf_record_names['train'])
meta_data = np.load(
os.path.join(config.tfrecord_dir,
'%s_%s' % (config.tvt_flags[0], config.max_file)))
else:
meta_data = np.load(
os.path.join(train_dir,
'%s_%s' % (config.tvt_flags[0], config.max_file)))
# Prepare image normalization values
if config.max_gedi is None:
max_value = np.nanmax(meta_data['max_array']).astype(np.float32)
if max_value == 0:
max_value = None
print('Derived max value is 0')
else:
print('Normalizing with empirical max.')
if 'min_array' in meta_data.keys():
min_value = np.min(meta_data['min_array']).astype(np.float32)
print('Normalizing with empirical min.')
else:
min_value = None
print('Not normalizing with a min.')
else:
max_value = config.max_gedi
min_value = config.min_gedi
print('max value', max_value)
ratio = meta_data['ratio']
if config.encode_time_of_death:
tod = pd.read_csv(config.encode_time_of_death)
tod_data = tod['dead_tp'].as_matrix()
mask = np.isnan(tod_data).astype(int) + (
tod['plate_well_neuron'] == 'empty').as_matrix().astype(int)
tod_data = tod_data[mask == 0]
tod_data = tod_data[tod_data <
10] # throw away values have a high number
config.output_shape = len(np.unique(tod_data))
ratio = class_weight.compute_class_weight('balanced',
np.sort(np.unique(tod_data)),
tod_data)
flip_ratio = False
else:
flip_ratio = True
print('Ratio is: %s' % ratio)
if validation_dir is None: # Use globals
validation_data = os.path.join(config.tfrecord_dir,
config.tf_record_names['val'])
elif validation_dir is False:
pass # Do not use validation data during training
# Make output directories if they do not exist
dt_stamp = re.split(
'\.', str(datetime.now()))[0].\
replace(' ', '_').replace(':', '_').replace('-', '_')
dt_dataset = config.which_dataset + '_' + dt_stamp + '/'
config.train_checkpoint = os.path.join(config.train_checkpoint,
dt_dataset) # timestamp this run
out_dir = os.path.join(config.results, dt_dataset)
dir_list = [
config.train_checkpoint, config.train_summaries, config.results,
out_dir
]
[make_dir(d) for d in dir_list]
# im_shape = get_image_size(config)
im_shape = config.gedi_image_size
print('-' * 60)
print('Training model:' + dt_dataset)
print('-' * 60)
# Prepare data on CPU
assert os.path.exists(train_data)
assert os.path.exists(validation_data)
assert os.path.exists(config.vgg16_weight_path)
with tf.device('/cpu:0'):
train_images, train_labels = inputs(train_data,
config.train_batch,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
train=config.data_augmentations,
num_epochs=config.epochs,
normalize=config.normalize)
val_images, val_labels = inputs(validation_data,
config.validation_batch,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
num_epochs=config.epochs,
normalize=config.normalize)
tf.summary.image('train images', train_images)
tf.summary.image('validation images', val_images)
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn') as scope:
if config.ordinal_classification == 'ordinal': # config.output_shape > 2: # Hardcoded fix for timecourse pred
vgg_output = config.output_shape * 2
elif config.ordinal_classification == 'regression':
vgg_output = 1
elif config.ordinal_classification is None:
vgg_output = config.output_shape
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
train_mode = tf.get_variable(name='training', initializer=True)
vgg.build(train_images,
output_shape=vgg_output,
train_mode=train_mode,
batchnorm=config.batchnorm_layers)
# Prepare the cost function
if config.ordinal_classification == 'ordinal':
# Encode y w/ k-hot and yhat w/ sigmoid ce. units capture dist.
enc = tf.concat([
tf.reshape(tf.range(0, config.output_shape), [1, -1])
for x in range(config.train_batch)
],
axis=0)
enc_train_labels = tf.cast(
tf.greater_equal(enc, tf.expand_dims(train_labels,
axis=1)), tf.float32)
split_labs = tf.split(enc_train_labels,
config.output_shape,
axis=1)
res_output = tf.reshape(
vgg.fc8, [config.train_batch, 2, config.output_shape])
split_logs = tf.split(res_output, config.output_shape, axis=2)
if config.balance_cost:
cost = tf.add_n([
tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(tf.cast(tf.squeeze(s), tf.int32),
2),
logits=tf.squeeze(l)) * r
for s, l, r in zip(split_labs, split_logs, ratio)
])
else:
cost = tf.add_n([
tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(tf.cast(tf.squeeze(s), tf.int32),
2),
logits=tf.squeeze(l))
for s, l in zip(split_labs, split_logs)
])
cost = tf.reduce_mean(cost)
elif config.ordinal_classification == 'regression':
if config.balance_cost:
weight_vec = tf.gather(train_labels, ratio)
cost = tf.reduce_mean(
tf.pow(
(vgg.fc8) - tf.cast(train_labels, tf.float32), 2) *
weight_vec)
else:
cost = tf.nn.l2_loss((vgg.fc8) -
tf.cast(train_labels, tf.float32))
else:
if config.balance_cost:
cost = softmax_cost(vgg.fc8,
train_labels,
ratio=ratio,
flip_ratio=flip_ratio)
else:
cost = softmax_cost(vgg.fc8, train_labels)
tf.summary.scalar("cost", cost)
# Finetune the learning rates
if config.wd_layers is not None:
_, l2_wd_layers = fine_tune_prepare_layers(
tf.trainable_variables(), config.wd_layers)
l2_wd_layers = [
x for x in l2_wd_layers if 'biases' not in x.name
]
cost += (config.wd_penalty *
tf.add_n([tf.nn.l2_loss(x) for x in l2_wd_layers]))
# for all variables in trainable variables
# print name if there's duplicates you f****d up
other_opt_vars, ft_opt_vars = fine_tune_prepare_layers(
tf.trainable_variables(), config.fine_tune_layers)
if config.optimizer == 'adam':
train_op = ft_non_optimized(cost, other_opt_vars, ft_opt_vars,
tf.train.AdamOptimizer,
config.hold_lr, config.new_lr)
elif config.optimizer == 'sgd':
train_op = ft_non_optimized(cost, other_opt_vars, ft_opt_vars,
tf.train.GradientDescentOptimizer,
config.hold_lr, config.new_lr)
if config.ordinal_classification == 'ordinal':
arg_guesses = tf.cast(
tf.reduce_sum(tf.squeeze(tf.argmax(res_output, axis=1)),
reduction_indices=[1]), tf.int32)
train_accuracy = tf.reduce_mean(
tf.cast(tf.equal(arg_guesses, train_labels), tf.float32))
elif config.ordinal_classification == 'regression':
train_accuracy = tf.reduce_mean(
tf.cast(
tf.equal(tf.cast(tf.round(vgg.prob), tf.int32),
train_labels), tf.float32))
else:
train_accuracy = class_accuracy(
vgg.prob, train_labels) # training accuracy
tf.summary.scalar("training accuracy", train_accuracy)
# Setup validation op
if validation_data is not False:
scope.reuse_variables()
# Validation graph is the same as training except no batchnorm
val_vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
val_vgg.build(val_images, output_shape=vgg_output)
# Calculate validation accuracy
if config.ordinal_classification == 'ordinal':
val_res_output = tf.reshape(
val_vgg.fc8,
[config.validation_batch, 2, config.output_shape])
val_arg_guesses = tf.cast(
tf.reduce_sum(tf.squeeze(
tf.argmax(val_res_output, axis=1)),
reduction_indices=[1]), tf.int32)
val_accuracy = tf.reduce_mean(
tf.cast(tf.equal(val_arg_guesses, val_labels),
tf.float32))
elif config.ordinal_classification == 'regression':
val_accuracy = tf.reduce_mean(
tf.cast(
tf.equal(tf.cast(tf.round(val_vgg.prob), tf.int32),
val_labels), tf.float32))
else:
val_accuracy = class_accuracy(val_vgg.prob, val_labels)
tf.summary.scalar("validation accuracy", val_accuracy)
# Set up summaries and saver
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=config.keep_checkpoints)
summary_op = tf.summary.merge_all()
# Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# Need to initialize both of these if supplying num_epochs to inputs
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
summary_dir = os.path.join(config.train_summaries,
config.which_dataset + '_' + dt_stamp)
summary_writer = tf.summary.FileWriter(summary_dir, sess.graph)
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Start training loop
np.save(out_dir + 'meta_info', config)
step, losses = 0, [] # val_max = 0
try:
# print response
while not coord.should_stop():
start_time = time.time()
_, loss_value, train_acc = sess.run(
[train_op, cost, train_accuracy])
losses.append(loss_value)
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % config.validation_steps == 0:
if validation_data is not False:
_, val_acc = sess.run([train_op, val_accuracy])
else:
val_acc -= 1 # Store every checkpoint
# Summaries
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Training status and validation accuracy
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; '
'%.3f sec/batch) | Training accuracy = %s | '
'Validation accuracy = %s | logdir = %s')
print(format_str %
(datetime.now(), step,
loss_value, config.train_batch / duration,
float(duration), train_acc, val_acc, summary_dir))
# Save the model checkpoint if it's the best yet
if 1: # val_acc >= val_max:
saver.save(sess,
os.path.join(config.train_checkpoint,
'model_' + str(step) + '.ckpt'),
global_step=step)
# Store the new max validation accuracy
# val_max = val_acc
else:
# Training status
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; '
'%.3f sec/batch) | Training accuracy = %s')
print(format_str %
(datetime.now(), step, loss_value, config.train_batch /
duration, float(duration), train_acc))
# End iteration
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (config.epochs, step))
finally:
coord.request_stop()
np.save(os.path.join(config.tfrecord_dir, 'training_loss'), losses)
coord.join(threads)
sess.close()
def train_vgg16(train_dir=None, validation_dir=None):
config = GEDIconfig()
if train_dir is None: # Use globals
train_data = os.path.join(config.tfrecord_dir, 'train.tfrecords')
meta_data = np.load(
os.path.join(
config.tfrecord_dir, config.tvt_flags[0] + '_' +
config.max_file))
else:
meta_data = np.load(
os.path.join(
train_dir, config.tvt_flags[0] + '_' + config.max_file))
# Prepare image normalization values
max_value = np.nanmax(meta_data['max_array']).astype(np.float32)
if max_value == 0:
max_value = None
print 'Derived max value is 0'
else:
print 'Normalizing with empirical max.'
if 'min_array' in meta_data.keys():
min_value = np.min(meta_data['min_array']).astype(np.float32)
print 'Normalizing with empirical min.'
else:
min_value = None
print 'Not normalizing with a min.'
ratio = meta_data['ratio']
print 'Ratio is: %s' % ratio
if validation_dir is None: # Use globals
validation_data = os.path.join(config.tfrecord_dir, 'val.tfrecords')
elif validation_dir is False:
pass # Do not use validation data during training
# Make output directories if they do not exist
dt_stamp = re.split(
'\.', str(datetime.now()))[0].\
replace(' ', '_').replace(':', '_').replace('-', '_')
dt_dataset = config.which_dataset + '_' + dt_stamp + '/'
config.train_checkpoint = os.path.join(
config.train_checkpoint, dt_dataset) # timestamp this run
out_dir = os.path.join(config.results, dt_dataset)
dir_list = [
config.train_checkpoint, config.train_summaries,
config.results, out_dir]
[make_dir(d) for d in dir_list]
# im_shape = get_image_size(config)
im_shape = config.gedi_image_size
print '-'*60
print('Training model:' + dt_dataset)
print '-'*60
# Prepare data on CPU
with tf.device('/cpu:0'):
train_images, train_labels = inputs(
train_data,
config.train_batch,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
train=config.data_augmentations,
num_epochs=config.epochs,
normalize=config.normalize)
val_images, val_labels = inputs(
validation_data,
config.validation_batch,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
num_epochs=config.epochs,
normalize=config.normalize)
tf.summary.image('train images', train_images)
tf.summary.image('validation images', val_images)
# Prepare model on GPU
num_timepoints = len(config.channel)
with tf.device('/gpu:0'):
with tf.variable_scope('cnn') as scope:
# Prepare the loss function
if config.balance_cost:
cost_fun = lambda yhat, y: softmax_cost(yhat, y, ratio=ratio)
else:
cost_fun = lambda yhat, y: softmax_cost(yhat, y)
def cond(i, state, images, output, loss, num_timepoints): # NOT CORRECT
return tf.less(i, num_timepoints)
def body(
i,
images,
label,
cell,
state,
output,
loss,
vgg,
train_mode,
output_shape,
batchnorm_layers,
cost_fun,
score_layer='fc7'):
vgg.build(
images[i],
output_shape=config.output_shape,
train_mode=train_mode,
batchnorm=config.batchnorm_layers)
it_output, state = cell(vgg[score_layer], state)
output= output.write(i, it_output)
it_loss = cost_fun(output, label)
loss = loss.write(i, it_loss)
return (i+1, images, label, cell, state, output, loss, vgg, train_mode, output_shape, batchnorm_layers)
# Prepare LSTM loop
train_mode = tf.get_variable(name='training', initializer=True)
cell = lstm_layers(layer_units=config.lstm_units)
output = tf.TensorArray(tf.float32, num_timepoints) # output array for LSTM loop -- one slot per timepoint
loss = tf.TensorArray(tf.float32, num_timepoints) # loss for LSTM loop -- one slot per timepoint
i = tf.constant(0) # iterator for LSTM loop
loop_vars = [i, images, label, cell, state, output, loss, vgg, train_mode, output_shape, batchnorm_layers, cost_fun]
# Run the lstm
processed_list = tf.while_loop(
cond=cond,
body=body,
loop_vars=loop_vars,
back_prop=True,
swap_memory=False)
output_cell = processed_list[3]
output_state = processed_list[4]
output_activity = processed_list[5]
cost = processed_list[6]
# Optimize
combined_cost = tf.reduce_sum(cost)
tf.summary.scalar('cost', combined_cost)
import ipdb;ipdb.set_trace() # Need to make sure lstm weights are being trained
other_opt_vars, ft_opt_vars = fine_tune_prepare_layers(
tf.trainable_variables(), config.fine_tune_layers)
if config.optimizer == 'adam':
train_op = ft_non_optimized(
cost, other_opt_vars, ft_opt_vars,
tf.train.AdamOptimizer, config.hold_lr, config.new_lr)
elif config.optimizer == 'sgd':
train_op = ft_non_optimized(
cost, other_opt_vars, ft_opt_vars,
tf.train.GradientDescentOptimizer,
config.hold_lr, config.new_lr)
train_accuracy = class_accuracy(
vgg.prob, train_labels) # training accuracy
tf.summary.scalar("training accuracy", train_accuracy)
# Setup validation op
if validation_data is not False:
scope.reuse_variables()
# Validation graph is the same as training except no batchnorm
val_vgg = vgg16.Vgg16(
vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
val_vgg.build(val_images, output_shape=config.output_shape)
# Calculate validation accuracy
val_accuracy = class_accuracy(val_vgg.prob, val_labels)
tf.summary.scalar("validation accuracy", val_accuracy)
def test_vgg16(
which_dataset,
validation_data=None,
model_dir=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Find model checkpoints
ckpts, ckpt_names = find_ckpts(config)
#Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
config.validation_batch,
im_shape,
config.model_image_size[:2],
num_epochs=1)
#Prepare model on GPU
with tf.device('/gpu:0'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
validation_mode = tf.Variable(False, name='training')
vgg.build(val_images,
output_shape=config.output_shape,
train_mode=validation_mode)
#Setup validation op
eval_accuracy = class_accuracy(vgg.prob,
val_labels) #training accuracy now...
#Set up saver
saver = tf.train.Saver(tf.all_variables())
#Loop through each checkpoint, loading the model weights, then testing the entire validation set
ckpt_accs = []
for idx in tqdm(range(len(ckpts))):
accs = []
try:
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.initialize_all_variables(),
tf.initialize_local_variables())
) #need to initialize both if supplying num_epochs to inputs
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, ckpts[idx])
start_time = time.time()
while not coord.should_stop():
accs = np.append(accs, sess.run([eval_accuracy]))
except tf.errors.OutOfRangeError:
ckpt_accs.append(accs)
print('Batch %d took %.1f seconds', idx, time.time() - start_time)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
#Plot everything
plot_accuracies(ckpt_accs, ckpt_names,
out_dir + 'validation_accuracies.png')
np.savez(out_dir + 'validation_accuracies',
ckpt_accs=ckpt_accs,
ckpt_names=ckpt_names)
def train_vgg16(which_dataset,train_data=None,validation_data=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if train_data == None: #Use globals
train_data = config.tfrecord_dir + 'train.tfrecords'
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
dt_stamp = re.split(' ',str(datetime.now()))[0].replace('-','_')
config.train_checkpoint = os.path.join(config.train_checkpoint,config.which_dataset + '_' + dt_stamp) #timestamp this run
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.train_checkpoint,config.train_summaries,config.results,out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Prepare data on CPU
with tf.device('/cpu:0'):
train_images, train_labels = inputs(train_data, config.train_batch, im_shape, config.model_image_size[:2], train=config.data_augmentations, num_epochs=config.epochs)
tf.image_summary('train images', train_images)
#Prepare model on GPU
with tf.device('/gpu:0'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,fine_tune_layers=config.fine_tune_layers)
train_mode = tf.Variable(True, name='training')
vgg.build(train_images,output_shape=config.output_shape,train_mode=train_mode)
#Prepare the cost function
cost = softmax_cost(vgg.fc8, train_labels)
tf.scalar_summary("cost", cost)
#Finetune the learning rates
other_opt_vars,ft_opt_vars = fine_tune_prepare_layers(tf.trainable_variables(),config.fine_tune_layers) #for all variables in trainable variables, print name if there's duplicates you f****d up
train_op = ft_non_optimized(cost,other_opt_vars,ft_opt_vars,tf.train.AdamOptimizer,config.hold_lr,config.new_lr) #actually is faster :)
#Setup validation op
eval_accuracy = class_accuracy(vgg.prob,train_labels) #training accuracy now...
tf.scalar_summary("train accuracy", eval_accuracy)
#Set up summaries and saver
saver = tf.train.Saver(tf.all_variables(),max_to_keep=100)
summary_op = tf.merge_all_summaries()
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement = True))
sess.run(tf.group(tf.initialize_all_variables(), tf.initialize_local_variables())) #need to initialize both if supplying num_epochs to inputs
summary_writer = tf.train.SummaryWriter(os.path.join(config.train_summaries,config.which_dataset + '_' + dt_stamp), sess.graph)
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
#Start training loop
try:
step = 0
losses = []
accs=[]
while not coord.should_stop():
start_time = time.time()
_, loss_value, acc = sess.run([train_op, cost, eval_accuracy])
losses.append(loss_value)
accs.append(acc)
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 100 == 0:
if step % 500 == 0:
#Summaries
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
else:
#Training status
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
config.train_batch / duration, float(duration)))
# Save the model checkpoint periodically.
if step % 500 == 0 and step>0 :
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
if np.average(accs[-500:-1])>0.95:
coord.request_stop()
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (config.epochs, step))
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
np.save(out_dir + 'training_loss',losses)
def train_att(which_dataset,train_data=None,validation_data=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if train_data == None: #Use globals
train_data = config.tfrecord_dir + 'train.tfrecords'
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
dt_stamp = re.split(' ',str(datetime.now()))[0].replace('-','_')
config.train_checkpoint = os.path.join(config.train_checkpoint,config.which_dataset + '_' + dt_stamp) #timestamp this run
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.train_checkpoint,config.train_summaries,config.results,out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Prepare data on CPU
with tf.device('/cpu:0'):
train_images, train_labels = inputs(train_data, config.train_batch, im_shape, config.model_image_size[:2], train=config.data_augmentations, num_epochs=config.epochs)
tf.image_summary('train images', train_images)
#Prepare model on GPU
with tf.device('/gpu:0'):
att_model = att.Attention()
train_mode = tf.Variable(True, name='training')
att_model.build(train_images,enc_size=config.enc_size,read_n=config.read_n,T=config.T,output_shape=config.output_shape,train_mode=train_mode)
#Prepare the cost function
cost = softmax_cost(att_model.fc2, train_labels)
tf.scalar_summary("cost", cost)
#print type(tf.trainable_variables()[0])
print [x.name for x in tf.trainable_variables()]
#Finetune the learning rates
optimizer=tf.train.AdamOptimizer(learning_rate=config.new_lr,beta1=0.5)
grads=optimizer.compute_gradients(cost)
#v_i=att_model.v_i
for i,(g,v) in enumerate(grads):
if g is not None:
grads[i]=(tf.clip_by_norm(g,5),v) # clip gradients
train_op=optimizer.apply_gradients(grads)
#print a
#Setup validation op
eval_accuracy = class_accuracy(att_model.prob,train_labels) #training accuracy now...
tf.scalar_summary("train accuracy", eval_accuracy)
#Set up summaries and saver
saver = tf.train.Saver(tf.all_variables(),max_to_keep=100)
summary_op = tf.merge_all_summaries()
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement = True))
sess.run(tf.group(tf.initialize_all_variables(), tf.initialize_local_variables())) #need to initialize both if supplying num_epochs to inputs
summary_writer = tf.train.SummaryWriter(os.path.join(config.train_summaries,config.which_dataset + '_' + dt_stamp), sess.graph)
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
#Start training loop
try:
step = 0
losses = []
accs=[]
while not coord.should_stop():
start_time = time.time()
_,loss_value, acc= sess.run([train_op, cost, eval_accuracy])
#print v_j
losses.append(loss_value)
accs.append(acc)
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 100 == 0:
if step % 2000 == 0:
#Summaries
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
else:
#Training status
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
config.train_batch / duration, float(duration)))
if np.average(accs[-100:-1])>0.9 or np.average(losses[-100:-1])<0.1:
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
coord.request_stop()
# Save the model checkpoint periodically.
if step % 1000 == 0 and step>0 :
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (config.epochs, step))
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
np.save(out_dir + 'training_loss',losses)
def test_vgg16(validation_data, model_dir, which_set, selected_ckpts):
config = GEDIconfig()
blur_kernel = config.hm_blur
if validation_data is None: # Use globals
validation_data = os.path.join(config.tfrecord_dir,
config.tf_record_names[which_set])
meta_data = np.load(
os.path.join(config.tfrecord_dir, 'val_%s' % config.max_file))
else:
meta_data = np.load('%s_maximum_value.npz' %
validation_data.split('.tfrecords')[0])
label_list = os.path.join(
config.processed_image_patch_dir,
'list_of_' + '_'.join(x
for x in config.image_prefixes) + '_labels.txt')
with open(label_list) as f:
file_pointers = [l.rstrip('\n') for l in f.readlines()]
# Prepare image normalization values
try:
max_value = np.max(meta_data['max_array']).astype(np.float32)
except:
max_value = np.asarray([config.max_gedi])
try:
min_value = np.max(meta_data['min_array']).astype(np.float32)
except:
min_value = np.asarray([config.min_gedi])
# Find model checkpoints
ds_dt_stamp = re.split('/', model_dir)[-1]
out_dir = os.path.join(config.results, ds_dt_stamp + '/')
try:
config = np.load(os.path.join(out_dir, 'meta_info.npy')).item()
# Make sure this is always at 1
config.validation_batch = 1
print '-' * 60
print 'Loading config meta data for:%s' % out_dir
print '-' * 60
except:
print '-' * 60
print 'Using config from gedi_config.py for model:%s' % out_dir
print '-' * 60
# Make output directories if they do not exist
im_shape = config.gedi_image_size
# Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
1,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
num_epochs=1,
normalize=config.normalize)
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(val_images, output_shape=config.output_shape)
# Setup validation op
preds = tf.argmax(vgg.prob, 1)
targets = tf.cast(val_labels, dtype=tf.int64)
grad_labels = tf.one_hot(val_labels,
config.output_shape,
dtype=tf.float32)
heatmap_op = tf.gradients(vgg.fc8 * grad_labels, val_images)[0]
# Set up saver
saver = tf.train.Saver(tf.global_variables())
ckpts = [selected_ckpts]
# Loop through each checkpoint then test the entire validation set
print '-' * 60
print 'Beginning evaluation on ckpt: %s' % ckpts
print '-' * 60
yhat, y, tn_hms, tp_hms, fn_hms, fp_hms = [], [], [], [], [], []
tn_ims, tp_ims, fn_ims, fp_ims = [], [], [], []
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
try:
# Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, c)
start_time = time.time()
while not coord.should_stop():
tyh, ty, thm, tim = sess.run(
[preds, targets, heatmap_op, val_images])
tyh = tyh[0]
ty = ty[0]
tim = (tim / tim.max()).squeeze()
yhat += [tyh]
y += [ty]
if tyh == ty and not tyh: # True negative
tn_hms += [hm_normalize(thm)]
tn_ims += [tim]
elif tyh == ty and tyh: # True positive
tp_hms += [hm_normalize(thm)]
tp_ims += [tim]
elif tyh != ty and not tyh: # False negative
fn_hms += [hm_normalize(thm)]
fn_ims += [tim]
elif tyh != ty and tyh: # False positive
fp_hms += [hm_normalize(thm)]
fp_ims += [tim]
except tf.errors.OutOfRangeError:
print 'Batch %d took %.1f seconds' % (idx,
time.time() - start_time)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
# Plot images -- add to a dict and incorporate file_pointers
dir_pointer = os.path.join(config.heatmap_source_images, ds_dt_stamp)
stem_dirs = ['tn', 'tp', 'fn', 'fp']
dir_list = [dir_pointer]
dir_list += [os.path.join(dir_pointer, x) for x in stem_dirs]
[make_dir(d) for d in dir_list]
loop_plot(tn_ims,
tn_hms,
'True negative',
os.path.join(dir_pointer, 'tn'),
blur=blur_kernel)
loop_plot(tp_ims,
tp_hms,
'True positive',
os.path.join(dir_pointer, 'tp'),
blur=blur_kernel)
loop_plot(fn_ims,
fn_hms,
'False negative',
os.path.join(dir_pointer, 'fn'),
blur=blur_kernel)
loop_plot(fp_ims,
fp_hms,
'False positive',
os.path.join(dir_pointer, 'fp'),
blur=blur_kernel)
def test_vgg16(validation_data, model_dir, which_set, selected_ckpts=-1):
config = GEDIconfig()
if validation_data is None: # Use globals
validation_data = os.path.join(config.tfrecord_dir,
config.tf_record_names[which_set])
meta_data = np.load(
os.path.join(config.tfrecord_dir, 'val_%s' % config.max_file))
else:
meta_data = np.load('%s_maximum_value.npz' %
validation_data.split('.tfrecords')[0])
label_list = os.path.join(
config.processed_image_patch_dir,
'list_of_' + '_'.join(x
for x in config.image_prefixes) + '_labels.txt')
with open(label_list) as f:
file_pointers = [l.rstrip('\n') for l in f.readlines()]
# Prepare image normalization values
try:
max_value = np.max(meta_data['max_array']).astype(np.float32)
except:
max_value = np.asarray([config.max_gedi])
try:
min_value = np.max(meta_data['min_array']).astype(np.float32)
except:
min_value = np.asarray([config.min_gedi])
# Find model checkpoints
ckpts, ckpt_names = find_ckpts(config, model_dir)
ds_dt_stamp = re.split('/', ckpts[0])[-2]
out_dir = os.path.join(config.results, ds_dt_stamp)
try:
config = np.load(os.path.join(out_dir, 'meta_info.npy')).item()
# Make sure this is always at 1
config.validation_batch = 1
print('-' * 60)
print('Loading config meta data for:%s' % out_dir)
print('-' * 60)
except:
print('-' * 60)
print('Using config from gedi_config.py for model:%s' % out_dir)
print('-' * 60)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# im_shape = get_image_size(config)
im_shape = config.gedi_image_size
# Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
1,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
num_epochs=1,
normalize=config.normalize)
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(val_images, output_shape=config.output_shape)
# Setup validation op
scores = vgg.prob
preds = tf.argmax(vgg.prob, 1)
targets = tf.cast(val_labels, dtype=tf.int64)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Loop through each checkpoint then test the entire validation set
ckpt_yhat, ckpt_y, ckpt_scores = [], [], []
print('-' * 60)
print('Beginning evaluation')
print('-' * 60)
if selected_ckpts is not None:
# Select a specific ckpt
if selected_ckpts < 0:
ckpts = ckpts[selected_ckpts:]
else:
ckpts = ckpts[:selected_ckpts]
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
dec_scores, yhat, y = [], [], []
try:
# Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, c)
start_time = time.time()
while not coord.should_stop():
sc, tyh, ty = sess.run([scores, preds, targets])
dec_scores = np.append(dec_scores, sc)
yhat = np.append(yhat, tyh)
y = np.append(y, ty)
except tf.errors.OutOfRangeError:
ckpt_yhat.append(yhat)
ckpt_y.append(y)
ckpt_scores.append(dec_scores)
print('Iteration accuracy: %s' % np.mean(yhat == y))
print('Iteration pvalue: %.5f' %
randomization_test(y=y, yhat=yhat))
print('Batch %d took %.1f seconds' %
(idx, time.time() - start_time))
finally:
coord.request_stop()
coord.join(threads)
sess.close()
# Save everything
np.savez(os.path.join(out_dir, 'validation_accuracies'),
ckpt_yhat=ckpt_yhat,
ckpt_y=ckpt_y,
ckpt_scores=ckpt_scores,
ckpt_names=ckpt_names,
file_pointers=file_pointers)
# Also save a csv with item/guess pairs
try:
trimmed_files = [re.split('/', x)[-1] for x in file_pointers]
trimmed_files = np.asarray(trimmed_files)
dec_scores = np.asarray(dec_scores)
yhat = np.asarray(yhat)
df = pd.DataFrame(
np.hstack((trimmed_files.reshape(-1, 1), yhat.reshape(-1, 1),
dec_scores.reshape(dec_scores.shape[0] // 2, 2))),
columns=['files', 'guesses', 'score dead', 'score live'])
df.to_csv(os.path.join(out_dir, 'prediction_file.csv'))
print('Saved csv to: %s' % out_dir)
except:
print('X' * 60)
print('Could not save a spreadsheet of file info')
print('X' * 60)
# Plot everything
try:
plot_accuracies(ckpt_y, ckpt_yhat, config, ckpt_names,
os.path.join(out_dir, 'validation_accuracies.png'))
plot_std(ckpt_y, ckpt_yhat, ckpt_names,
os.path.join(out_dir, 'validation_stds.png'))
plot_cms(ckpt_y, ckpt_yhat, config,
os.path.join(out_dir, 'confusion_matrix.png'))
plot_pr(ckpt_y, ckpt_yhat, ckpt_scores,
os.path.join(out_dir, 'precision_recall.png'))
plot_cost(os.path.join(out_dir, 'training_loss.npy'), ckpt_names,
os.path.join(out_dir, 'training_costs.png'))
except:
print('X' * 60)
print('Could not locate the loss numpy')
print('X' * 60)
def test_vgg16(
which_dataset,
validation_data=None,
model_dir=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Find model checkpoints
ckpts, ckpt_names = find_ckpts(config)
print ckpts, ckpt_names
#Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
config.validation_batch,
im_shape,
config.model_image_size[:2],
num_epochs=1)
#Prepare model on GPU
with tf.device('/gpu:0'):
att_model = att.Attention()
validation_mode = tf.Variable(False, name='training')
att_model.build(val_images,
enc_size=config.enc_size,
read_n=config.read_n,
T=config.T,
output_shape=config.output_shape,
train_mode=validation_mode)
image_0 = val_images
image_1 = att_model.image_show
image_loc = att_model.location
# print image_0.get_shape()
# print image_1[0].get_shape()
#Setup validation op
eval_accuracy = class_accuracy(att_model.prob,
val_labels) #training accuracy now...
#Set up saver
saver = tf.train.Saver(tf.all_variables())
#Loop through each checkpoint, loading the model weights, then testing the entire validation set
ckpt_accs = []
max_acc = 0
max_ind = 0
max_show_0 = []
max_show_1 = []
max_loc = []
for idx in tqdm(range(len(ckpts))):
print ckpts[idx]
accs = []
show_0 = np.array([])
show_1 = np.array([])
show_loc = np.array([])
try:
#print type(show_0)
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.initialize_all_variables(),
tf.initialize_local_variables())
) #need to initialize both if supplying num_epochs to inputs
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, ckpts[idx])
start_time = time.time()
while not coord.should_stop():
#print '1'
#print type(accs)
acc, aa, bb, cc = sess.run(
[eval_accuracy, image_0, image_1, image_loc])
accs = np.append(accs, acc)
if accs[-1] > 0.8 and show_0.shape[-1] < 5:
#print show_0.shape[-1]
#print aa.shape, bb
aa = aa
bb = bb
(x1, x2, x3, x4) = aa.shape
(y1, y2, y3, y4) = bb.shape
(z1, z2, z3) = cc.shape
aa = np.reshape(aa, (x1, x2, x3, x4, 1))
bb = np.reshape(bb, (y1, y2, y3, y4, 1))
cc = np.reshape(cc, (z1, z2, z3, 1))
if show_0.shape[0] <= 2:
#print sess.run([image_1])
show_0 = aa
show_1 = bb
show_loc = cc
else:
#print sess.run([image_0])[0].shape, show_0.shape
show_0 = np.concatenate((show_0, aa), 4)
show_1 = np.concatenate((show_1, bb), 4)
show_loc = np.concatenate((show_loc, cc), 3)
except tf.errors.OutOfRangeError:
if np.mean(accs) > max_acc:
max_acc = np.mean(accs)
max_ind = idx
max_show_0 = show_0
max_show_1 = show_1
max_loc = show_loc
ckpt_accs.append(accs)
print('Batch %d took %.1f seconds', idx, time.time() - start_time)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
print ckpt_accs, ckpt_names
#Plot everything
plot_accuracies(ckpt_accs, ckpt_names,
out_dir + 'validation_accuracies.png')
np.savez(out_dir + 'validation_accuracies',
ckpt_accs=ckpt_accs,
ckpt_names=ckpt_names)
np.savez(out_dir + 'att_verification_' + which_dataset,
max_show_0=max_show_0,
max_show_1=max_show_1,
max_loc=max_loc)
for idx in range(len(ckpts)):
if idx != max_ind:
os.remove(ckpts[idx] + '.data-00000-of-00001')
os.remove(ckpts[idx] + '.meta')
