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_model(train_dir=None,
validation_dir=None,
debug=True,
resume_ckpt=None,
resume_meta=None,
margin=.4):
config = GEDIconfig()
if resume_meta is not None:
config = np.load(resume_meta).item()
assert margin is not None, 'Need a margin for the loss.'
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
ratio = meta_data['ratio']
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
]
[tf_fun.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, train_times = inputs(
train_data,
config.train_batch,
im_shape,
config.model_image_size,
# max_value=max_value,
# min_value=min_value,
train=config.data_augmentations,
num_epochs=config.epochs,
normalize=config.normalize,
return_filename=True)
val_images, val_labels, val_times = inputs(
validation_data,
config.validation_batch,
im_shape,
config.model_image_size,
# max_value=max_value,
# min_value=min_value,
num_epochs=config.epochs,
normalize=config.normalize,
return_filename=True)
train_image_list, val_image_list = [], []
for idx in range(int(train_images.get_shape()[1])):
train_image_list += [tf.gather(train_images, idx, axis=1)]
val_image_list += [tf.gather(val_images, idx, axis=1)]
tf.summary.image('train_image_frame_%s' % idx,
train_image_list[idx])
tf.summary.image('validation_image_frame_%s' % idx,
val_image_list[idx])
# Prepare model on GPU
config.l2_norm = False
config.norm_axis = 0
config.dist_fun = 'pearson'
config.per_batch = False
config.include_GEDI = True # False
config.output_shape = 32
config.margin = margin
with tf.device('/gpu:0'):
with tf.variable_scope('match'):
# Build matching model for frame 0
model_0 = vgg16.model_struct(
vgg16_npy_path=config.gedi_weight_path) # ,
frame_activity = []
model_activity = model_0.build(train_image_list[0],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = tf_fun.l2_normalize(model_activity,
axis=config.norm_axis)
frame_activity += [model_activity]
with tf.variable_scope('match', reuse=tf.AUTO_REUSE):
# Build matching model for other frames
for idx in range(1, len(train_image_list)):
model_activity = model_0.build(
train_image_list[idx],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = tf_fun.l2_normalize(model_activity,
axis=config.norm_axis)
frame_activity += [model_activity]
if config.dist_fun == 'l2':
pos = tf_fun.l2_dist(frame_activity[0], frame_activity[1], axis=1)
neg = tf_fun.l2_dist(frame_activity[0], frame_activity[2], axis=1)
elif config.dist_fun == 'pearson':
pos = tf_fun.pearson_dist(frame_activity[0],
frame_activity[1],
axis=1)
neg = tf_fun.pearson_dist(frame_activity[0],
frame_activity[2],
axis=1)
else:
raise NotImplementedError(config.dist_fun)
if config.per_batch:
loss = tf.maximum(tf.reduce_mean(pos - neg) + margin, 0.)
else:
loss = tf.reduce_mean(tf.maximum(pos - neg + margin, 0.))
tf.summary.scalar('Triplet_loss', loss)
# Weight decay
if config.wd_layers is not None:
_, l2_wd_layers = tf_fun.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]
if len(l2_wd_layers) > 0:
loss += (config.wd_penalty *
tf.add_n([tf.nn.l2_loss(x) for x in l2_wd_layers]))
# Optimize
train_op = tf.train.AdamOptimizer(config.new_lr).minimize(loss)
train_accuracy = tf.reduce_mean(
tf.cast(
tf.equal(
tf.nn.relu(tf.sign(neg - pos)), # 1 if pos < neg
tf.cast(tf.ones_like(train_labels), tf.float32)),
tf.float32))
tf.summary.scalar('training_accuracy', train_accuracy)
# Setup validation op
if validation_data is not False:
with tf.variable_scope('match', tf.AUTO_REUSE) as match:
# Build matching model for frame 0
match.reuse_variables()
val_model_0 = vgg16.model_struct(
vgg16_npy_path=config.gedi_weight_path)
val_frame_activity = []
model_activity = val_model_0.build(
val_image_list[0],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = tf_fun.l2_normalize(model_activity,
axis=config.norm_axis)
val_frame_activity += [model_activity]
# Build matching model for other frames
for idx in range(1, len(train_image_list)):
model_activity = val_model_0.build(
val_image_list[idx],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = tf_fun.l2_normalize(
model_activity, axis=config.norm_axis)
val_frame_activity += [model_activity]
if config.dist_fun == 'l2':
val_pos = tf_fun.l2_dist(val_frame_activity[0],
val_frame_activity[1],
axis=1)
val_neg = tf_fun.l2_dist(val_frame_activity[0],
val_frame_activity[2],
axis=1)
elif config.dist_fun == 'pearson':
val_pos = tf_fun.pearson_dist(val_frame_activity[0],
val_frame_activity[1],
axis=1)
val_neg = tf_fun.pearson_dist(val_frame_activity[0],
val_frame_activity[2],
axis=1)
if config.per_batch:
val_loss = tf.maximum(
tf.reduce_mean(val_pos - val_neg) + margin, 0.)
else:
val_loss = tf.reduce_mean(
tf.maximum(val_pos - val_neg + margin, 0.))
tf.summary.scalar('Validation_triplet_loss', val_loss)
# Calculate validation accuracy
val_accuracy = tf.reduce_mean(
tf.cast(
tf.equal(tf.nn.relu(tf.sign(val_neg - val_pos)),
tf.cast(tf.ones_like(val_labels), tf.float32)),
tf.float32))
tf.summary.scalar('val_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)
# Train operations
train_dict = {
'train_op': train_op,
'loss': loss,
'pos': pos,
'neg': neg,
'train_accuracy': train_accuracy,
'val_accuracy': val_accuracy,
}
val_dict = {'val_accuracy': val_accuracy}
if debug:
for idx in range(len(train_image_list)):
train_dict['train_im_%s' % idx] = train_image_list[idx]
for idx in range(len(val_image_list)):
val_dict['val_im_%s' % idx] = val_image_list[idx]
# Resume training if requested
if resume_ckpt is not None:
print('*' * 50)
print('Resuming training from: %s' % resume_ckpt)
print('*' * 50)
saver.restore(sess, resume_ckpt)
# Start training loop
np.save(out_dir + 'meta_info', config)
step, losses = 0, []
top_vals = np.asarray([0])
try:
# print response
while not coord.should_stop():
start_time = time.time()
train_values = sess.run(train_dict.values())
it_train_dict = {
k: v
for k, v in zip(train_dict.keys(), train_values)
}
losses += [it_train_dict['loss']]
duration = time.time() - start_time
if np.isnan(it_train_dict['loss']).sum():
assert not np.isnan(it_train_dict['loss']),\
'Model loss = NaN'
if step % config.validation_steps == 0:
if validation_data is not False:
val_accs = []
for vit in range(config.validation_iterations):
val_values = sess.run(val_dict.values())
it_val_dict = {
k: v
for k, v in zip(val_dict.keys(), val_values)
}
val_accs += [it_val_dict['val_accuracy']]
val_acc = np.nanmean(val_accs)
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,
it_train_dict['loss'], config.train_batch / duration,
float(duration), it_train_dict['train_accuracy'],
it_train_dict['val_accuracy'], summary_dir))
# Save the model checkpoint if it's the best yet
top_vals = top_vals[:config.num_keep_checkpoints]
check_val = val_acc > top_vals
if check_val.sum():
saver.save(sess,
os.path.join(config.train_checkpoint,
'model_' + str(step) + '.ckpt'),
global_step=step)
# Store the new validation accuracy
top_vals = np.append(top_vals, 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,
it_train_dict['loss'], config.train_batch / duration,
float(duration), it_train_dict['train_accuracy']))
# 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,
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
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 > config.mask_timepoint_value]
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, train_gedi_images = 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,
return_gedi=config.include_GEDI_in_tfrecords,
return_extra_gfp=config.extra_image,
return_GEDI_derivative=True)
val_images, val_labels, val_gedi_images = 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,
return_gedi=config.include_GEDI_in_tfrecords,
return_extra_gfp=config.extra_image,
return_GEDI_derivative=True)
if config.include_GEDI_in_tfrecords:
extra_im_name = 'GEDI at current timepoint'
else:
extra_im_name = 'next gfp timepoint'
tf.summary.image('train images', train_images)
tf.summary.image('validation images', val_images)
tf.summary.image('train %s' % extra_im_name, train_gedi_images)
tf.summary.image('validation %s' % extra_im_name, val_gedi_images)
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn') as scope:
if config.ordinal_classification is None:
vgg_output = 2 # Sign of derivative (inf norm)
train_labels = tf.cast(tf.sign(train_labels), tf.int32)
val_labels = tf.cast(tf.sign(val_labels), tf.int32)
elif config.ordinal_classification == 'regression':
vgg_output = 1
else:
raise RuntimeError(
'config.ordinal_classification must be sign or regression.'
)
vgg = vgg16.model_struct()
train_mode = tf.get_variable(name='training', initializer=True)
# Mask NAN images from loss
image_nan = tf.reduce_sum(
tf.cast(tf.is_nan(train_images), tf.float32),
reduction_indices=[1, 2, 3])
gedi_nan = tf.reduce_sum(
tf.cast(tf.is_nan(train_gedi_images), tf.float32),
reduction_indices=[1, 2, 3],
keep_dims=True)
image_mask = tf.cast(tf.equal(image_nan, 0.), tf.float32)
gedi_nan = tf.cast(tf.equal(gedi_nan, 0.), tf.float32)
train_images = tf.where(
tf.is_nan(train_images),
tf.zeros_like(train_images),
train_images)
train_gedi_images = tf.where(
tf.is_nan(train_gedi_images),
tf.zeros_like(train_gedi_images),
train_gedi_images)
train_images = tf.concat([train_images, train_images, train_images], axis=3)
val_images = tf.concat([val_images, val_images, val_images], axis=3)
vgg.build(
train_images, output_shape=vgg_output,
train_mode=train_mode, batchnorm=config.batchnorm_layers)
# Prepare the cost function
if config.ordinal_classification is None:
# Encode y w/ k-hot and yhat w/ sigmoid ce. units capture dist.
cost = softmax_cost(
vgg.fc8,
train_labels,
mask=image_mask)
elif config.ordinal_classification == 'regression':
cost = tf.nn.l2_loss(tf.squeeze(vgg.fc8) - train_labels)
class_loss = cost
tf.summary.scalar("cce cost", cost)
# Weight decay
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]
if len(l2_wd_layers) > 0:
cost += (config.wd_penalty * tf.add_n(
[tf.nn.l2_loss(x) for x in l2_wd_layers]))
# Optimize
train_op = tf.train.AdamOptimizer(config.new_lr).minimize(cost)
if config.ordinal_classification is None:
train_accuracy = class_accuracy(
vgg.prob, train_labels) # training accuracy
elif config.ordinal_classification == 'regression':
train_accuracy = tf.nn.l2_loss(
tf.squeeze(vgg.fc8) - train_labels)
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.model_struct(
fine_tune_layers=config.fine_tune_layers)
val_vgg.build(val_images, output_shape=vgg_output)
# Calculate validation accuracy
if config.ordinal_classification is None:
val_accuracy = class_accuracy(val_vgg.prob, val_labels)
elif config.ordinal_classification == 'regression':
val_accuracy = tf.nn.l2_loss(tf.squeeze(val_vgg.fc8) - 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)
# Restore model if requested
if config.restore_path is not None:
print '-' * 60
print 'Restoring from a previous model: %s' % config.restore_path
print '-' * 60
saver.restore(sess, config.restore_path)
# 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
if np.isnan(loss_value).sum():
import ipdb;ipdb.set_trace()
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 | '
'Training %s = %s | Training class loss = %s | '
'Validation accuracy = %s | Validation %s = %s | '
'logdir = %s')
print (format_str % (
datetime.now(), step, loss_value,
config.train_batch / duration, float(duration),
train_acc, extra_im_name, 0.,
0., val_acc, extra_im_name,
0., 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 | '
'Training %s = %s | Training class loss = %s')
print (format_str % (datetime.now(), step, loss_value,
config.train_batch / duration,
float(duration), train_acc,
extra_im_name, 0.,
0.))
# 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 test_placeholder(
image_path,
model_file,
model_meta,
out_dir,
n_images=3,
first_n_images=1,
debug=True,
margin=.1,
autopsy_csv=None,
C=1,
k_folds=10,
embedding_type='tsne',
autopsy_model='match'):
config = GEDIconfig()
assert margin is not None, 'Need a margin for the loss.'
assert image_path is not None, 'Provide a path to an image directory.'
assert model_file is not None, 'Provide a path to the model file.'
try:
# Load the model's config
config = np.load(model_meta).item()
except:
print 'Could not load model config, falling back to default config.'
config.model_image_size[-1] = 1
try:
# Load autopsy information
autopsy_data = pd.read_csv(autopsy_csv)
except IOError:
print 'Unable to load autopsy file.'
if not hasattr(config, 'include_GEDI'):
raise RuntimeError('You need to pass the correct meta file.')
config.include_GEDI = True
config.l2_norm = False
config.dist_fun = 'pearson'
config.per_batch = False
config.output_shape = 32
config.margin = 0.1
if os.path.isdir(image_path):
combined_files = np.asarray(
glob(os.path.join(image_path, '*%s' % config.raw_im_ext)))
else:
combined_files = [image_path]
if len(combined_files) == 0:
raise RuntimeError('Could not find any files. Check your image path.')
# 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(out_dir, dt_dataset)
dir_list = [out_dir]
[tf_fun.make_dir(d) for d in dir_list]
# Prepare data on CPU
with tf.device('/cpu:0'):
images = []
for idx in range(first_n_images):
images += [tf.placeholder(
tf.float32,
shape=[None] + config.model_image_size,
name='images_%s' % idx)]
# Prepare model on GPU
with tf.device('/gpu:0'):
if autopsy_model == 'match':
from models import matching_vgg16 as model_type
with tf.variable_scope('match'):
# Build matching model for frame 0
model_0 = model_type.model_struct(
vgg16_npy_path=config.gedi_weight_path) # ,
frame_activity = []
model_activity = model_0.build(
images[0],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = [model_activity]
frame_activity += [model_activity]
if first_n_images > 1:
with tf.variable_scope('match', reuse=tf.AUTO_REUSE):
# Build matching model for other frames
for idx in range(1, len(images)):
model_activity = model_0.build(
images[idx],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = tf_fun.l2_normalize(
model_activity)
frame_activity += [model_activity]
if config.dist_fun == 'l2':
pos = tf_fun.l2_dist(
frame_activity[0],
frame_activity[1], axis=1)
neg = tf_fun.l2_dist(
frame_activity[0],
frame_activity[2], axis=1)
elif config.dist_fun == 'pearson':
pos = tf_fun.pearson_dist(
frame_activity[0],
frame_activity[1],
axis=1)
neg = tf_fun.pearson_dist(
frame_activity[0],
frame_activity[2],
axis=1)
model_activity = pos - neg # Store the difference in distances
elif autopsy_model == 'GEDI' or autopsy_model == 'gedi':
from models import baseline_vgg16 as model_type
model = model_type.model_struct(
vgg16_npy_path=config.gedi_weight_path) # ,
model.build(
images[0],
output_shape=config.output_shape)
model_activity = model.fc7
else:
raise NotImplementedError(autopsy_model)
if config.validation_batch > len(combined_files):
print (
'Trimming validation_batch size to %s '
'(same as # of files).' % len(combined_files))
config.validation_batch = len(combined_files)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# 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()))
# Set up exemplar threading
if autopsy_model == 'match':
saver.restore(sess, model_file)
start_time = time.time()
num_batches = np.floor(
len(combined_files) / float(
config.validation_batch)).astype(int)
score_array, file_array = [], []
for image_batch, file_batch in tqdm(
image_batcher(
start=0,
num_batches=num_batches,
images=combined_files,
config=config,
first_n_images=first_n_images,
n_images=n_images),
total=num_batches):
for im_head in images:
feed_dict = {
im_head: image_batch
}
activity = sess.run(
model_activity,
feed_dict=feed_dict)
score_array += [activity]
file_array += [file_batch]
print 'Image processing %d took %.1f seconds' % (
idx, time.time() - start_time)
sess.close()
score_array = np.concatenate(score_array, axis=0)
score_array = score_array.reshape(-1, score_array.shape[-1])
file_array = np.concatenate(file_array, axis=0)
# Save everything
np.savez(
os.path.join(out_dir, 'validation_accuracies'),
score_array=score_array,
file_array=file_array)
if first_n_images == 1:
# Derive pathologies from file names
pathologies = []
for f in combined_files:
sf = f.split(os.path.sep)[-1].split('_')
line = sf[1]
# time_col = sf[2]
well = sf[4]
disease = autopsy_data[
np.logical_and(
autopsy_data['line'] == line,
autopsy_data['wells'] == well)]['type']
try:
disease = disease.as_matrix()[0]
except:
disease = 'Not_found'
pathologies += [disease]
pathologies = np.asarray(pathologies)[:len(score_array)]
mu = score_array.mean(0)
sd = score_array.std(0)
z_score_array = (score_array - mu) / (sd + 1e-4)
if embedding_type == 'TSNE' or embedding_type == 'tsne':
emb = manifold.TSNE(n_components=2, init='pca', random_state=0)
elif embedding_type == 'PCA' or embedding_type == 'pca':
emb = PCA(n_components=2, svd_solver='randomized', random_state=0)
elif embedding_type == 'spectral':
emb = manifold.SpectralEmbedding(n_components=2, random_state=0)
y = emb.fit_transform(score_array)
# Do a classification analysis
labels = np.unique(pathologies.reshape(-1, 1), return_inverse=True)[1]
# Run SVM
svm = LinearSVC(C=C, dual=False, class_weight='balanced')
clf = make_pipeline(preprocessing.StandardScaler(), svm)
predictions = cross_val_predict(clf, score_array, labels, cv=k_folds)
cv_performance = metrics.accuracy_score(predictions, labels)
clf.fit(score_array, labels)
# mu = dec_scores.mean(0)
# sd = dec_scores.std(0)
print '%s-fold SVM performance: accuracy = %s%%' % (
k_folds,
np.mean(cv_performance * 100))
np.savez(
os.path.join(out_dir, 'svm_data'),
yhat=score_array,
y=labels,
cv_performance=cv_performance,
# mu=mu,
# sd=sd,
C=C)
# Ouput csv
df = pd.DataFrame(
np.hstack((
y,
pathologies.reshape(-1, 1),
file_array.reshape(-1, 1))),
columns=['dim1', 'dim2', 'pathology', 'filename'])
out_name = os.path.join(out_dir, 'raw_embedding.csv')
df.to_csv(out_name)
print 'Saved csv to: %s' % out_name
create_figs(
emb=emb,
out_dir=out_dir,
out_name=out_name,
embedding_type=embedding_type,
embedding_name='raw_embedding')
# Now work on zscored data
y = emb.fit_transform(z_score_array)
# Ouput csv
df = pd.DataFrame(
np.hstack((
y,
pathologies.reshape(-1, 1),
file_array.reshape(-1, 1))),
columns=['dim1', 'dim2', 'pathology', 'filename'])
out_name = os.path.join(out_dir, 'embedding.csv')
df.to_csv(out_name)
print 'Saved csv to: %s' % out_name
# Create plot
create_figs(
emb=emb,
out_dir=out_dir,
out_name=out_name,
embedding_type=embedding_type,
embedding_name='normalized_embedding')
else:
# Do a classification (sign of the score)
decisions = np.sign(score_array)
df = pd.DataFrame(
np.hstack(decisions, score_array),
columns=['Decisions', 'Scores'])
df.to_csv(
os.path.join(
out_dir, 'tracking_model_scores.csv'))
