def main(experiment_name, list_experiments=False, gpu_device='/gpu:0'):
"""Create a tensorflow worker to run experiments in your DB."""
if list_experiments:
exps = db.list_experiments()
print '_' * 30
print 'Initialized experiments:'
print '_' * 30
for l in exps:
print l.values()[0]
print '_' * 30
print 'You can add to the DB with: '\
'python prepare_experiments.py --experiment=%s' % \
exps[0].values()[0]
return
if experiment_name is None:
print 'No experiment specified. Pulling one out of the DB.'
experiment_name = db.get_experiment_name()
# Prepare to run the model
config = Config()
condition_label = '%s_%s' % (experiment_name, py_utils.get_dt_stamp())
experiment_label = '%s' % (experiment_name)
log = logger.get(os.path.join(config.log_dir, condition_label))
experiment_dict = experiments.experiments()[experiment_name]()
config = add_to_config(d=experiment_dict, config=config) # Globals
config, exp_params = process_DB_exps(
experiment_name=experiment_name, log=log,
config=config) # Update config w/ DB params
dataset_module = py_utils.import_module(model_dir=config.dataset_info,
dataset=config.dataset)
dataset_module = dataset_module.data_processing() # hardcoded class name
train_data, train_means = get_data_pointers(
dataset=config.dataset,
base_dir=config.tf_records,
cv=dataset_module.folds.keys()[1], # TODO: SEARCH FOR INDEX.
log=log)
val_data, val_means = get_data_pointers(dataset=config.dataset,
base_dir=config.tf_records,
cv=dataset_module.folds.keys()[0],
log=log)
# Initialize output folders
dir_list = {
'checkpoints':
os.path.join(config.checkpoints, condition_label),
'summaries':
os.path.join(config.summaries, condition_label),
'condition_evaluations':
os.path.join(config.condition_evaluations, condition_label),
'experiment_evaluations':
os.path.join( # DEPRECIATED
config.experiment_evaluations, experiment_label),
'visualization':
os.path.join(config.visualizations, condition_label),
'weights':
os.path.join(config.condition_evaluations, condition_label, 'weights')
}
[py_utils.make_dir(v) for v in dir_list.values()]
# Prepare data loaders on the cpu
config.data_augmentations = py_utils.flatten_list(
config.data_augmentations, log)
with tf.device('/cpu:0'):
train_images, train_labels = data_loader.inputs(
dataset=train_data,
batch_size=config.batch_size,
model_input_image_size=dataset_module.model_input_image_size,
tf_dict=dataset_module.tf_dict,
data_augmentations=config.data_augmentations,
num_epochs=config.epochs,
tf_reader_settings=dataset_module.tf_reader,
shuffle=config.shuffle)
val_images, val_labels = data_loader.inputs(
dataset=val_data,
batch_size=config.batch_size,
model_input_image_size=dataset_module.model_input_image_size,
tf_dict=dataset_module.tf_dict,
data_augmentations=config.data_augmentations,
num_epochs=config.epochs,
tf_reader_settings=dataset_module.tf_reader,
shuffle=config.shuffle)
log.info('Created tfrecord dataloader tensors.')
# Load model specification
struct_name = config.model_struct.split(os.path.sep)[-1]
try:
model_dict = py_utils.import_module(
dataset=struct_name,
model_dir=os.path.join('models', 'structs',
experiment_name).replace(os.path.sep, '.'))
except IOError:
print 'Could not find the model structure: %s' % experiment_name
# Inject model_dict with hyperparameters if requested
model_dict.layer_structure = hp_opt_utils.inject_model_with_hps(
layer_structure=model_dict.layer_structure, exp_params=exp_params)
# Prepare model on GPU
with tf.device(gpu_device):
with tf.variable_scope('cnn') as scope:
# Training model
if len(dataset_module.output_size) > 1:
log.warning('Found > 1 dimension for your output size.'
'Converting to a scalar.')
dataset_module.output_size = np.prod(
dataset_module.output_size)
if hasattr(model_dict, 'output_structure'):
# Use specified output layer
output_structure = model_dict.output_structure
else:
output_structure = None
model = model_utils.model_class(
mean=train_means,
training=True,
output_size=dataset_module.output_size)
train_scores, model_summary = model.build(
data=train_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
log.info('Built training model.')
log.debug(json.dumps(model_summary, indent=4), verbose=0)
print_model_architecture(model_summary)
# Prepare the loss function
train_loss, _ = loss_utils.loss_interpreter(
logits=train_scores,
labels=train_labels,
loss_type=config.loss_function,
dataset_module=dataset_module)
# Add weight decay if requested
if len(model.regularizations) > 0:
train_loss = loss_utils.wd_loss(
regularizations=model.regularizations,
loss=train_loss,
wd_penalty=config.regularization_strength)
train_op = loss_utils.optimizer_interpreter(
loss=train_loss,
lr=config.lr,
optimizer=config.optimizer,
constraints=config.optimizer_constraints,
model=model)
log.info('Built training loss function.')
train_accuracy = eval_metrics.metric_interpreter(
metric=dataset_module.score_metric,
pred=train_scores,
labels=train_labels) # training accuracy
if int(train_images.get_shape()[-1]) <= 3:
tf.summary.image('train images', train_images)
tf.summary.scalar('training loss', train_loss)
tf.summary.scalar('training accuracy', train_accuracy)
log.info('Added training summaries.')
# Validation model
scope.reuse_variables()
val_model = model_utils.model_class(
mean=val_means,
training=True,
output_size=dataset_module.output_size)
val_scores, _ = val_model.build( # Ignore summary
data=val_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
log.info('Built validation model.')
val_loss, _ = loss_utils.loss_interpreter(
logits=val_scores,
labels=val_labels,
loss_type=config.loss_function,
dataset_module=dataset_module)
val_accuracy = eval_metrics.metric_interpreter(
metric=dataset_module.score_metric,
pred=val_scores,
labels=val_labels) # training accuracy
if int(train_images.get_shape()[-1]) <= 3:
tf.summary.image('val images', val_images)
tf.summary.scalar('validation loss', val_loss)
tf.summary.scalar('validation accuracy', val_accuracy)
log.info('Added validation summaries.')
# Set up summaries and saver
saver = tf.train.Saver(tf.global_variables())
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_writer = tf.summary.FileWriter(dir_list['summaries'], sess.graph)
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Create dictionaries of important training and validation information
train_dict = {
'train_loss': train_loss,
'train_accuracy': train_accuracy,
'train_images': train_images,
'train_labels': train_labels,
'train_op': train_op,
'train_scores': train_scores
}
val_dict = {
'val_loss': val_loss,
'val_accuracy': val_accuracy,
'val_images': val_images,
'val_labels': val_labels,
'val_scores': val_scores,
}
# Start training loop
np.save(
os.path.join(dir_list['condition_evaluations'],
'training_config_file'), config)
log.info('Starting training')
output_dict = training.training_loop(
config=config,
db=db,
coord=coord,
sess=sess,
summary_op=summary_op,
summary_writer=summary_writer,
saver=saver,
threads=threads,
summary_dir=dir_list['summaries'],
checkpoint_dir=dir_list['checkpoints'],
weight_dir=dir_list['weights'],
train_dict=train_dict,
val_dict=val_dict,
train_model=model,
val_model=val_model,
exp_params=exp_params)
log.info('Finished training.')
model_name = config.model_struct.replace('/', '_')
py_utils.save_npys(data=output_dict,
model_name=model_name,
output_string=dir_list['experiment_evaluations'])
def main(experiment_name,
list_experiments=False,
load_and_evaluate_ckpt=None,
config_file=None,
ckpt_file=None,
gpu_device='/gpu:0'):
"""Create a tensorflow worker to run experiments in your DB."""
if list_experiments:
exps = db.list_experiments()
print '_' * 30
print 'Initialized experiments:'
print '_' * 30
for l in exps:
print l.values()[0]
print '_' * 30
if len(exps) == 0:
print 'No experiments found.'
else:
print 'You can add to the DB with: '\
'python prepare_experiments.py --experiment=%s' % \
exps[0].values()[0]
return
if experiment_name is None:
print 'No experiment specified. Pulling one out of the DB.'
experiment_name = db.get_experiment_name()
# Prepare to run the model
config = Config()
condition_label = '%s_%s' % (experiment_name, py_utils.get_dt_stamp())
experiment_label = '%s' % (experiment_name)
log = logger.get(os.path.join(config.log_dir, condition_label))
experiment_dict = experiments.experiments()[experiment_name]()
config = add_to_config(d=experiment_dict, config=config) # Globals
config.load_and_evaluate_ckpt = load_and_evaluate_ckpt
config, exp_params = process_DB_exps(
experiment_name=experiment_name, log=log,
config=config) # Update config w/ DB params
config = np.load(config_file).item()
dataset_module = py_utils.import_module(model_dir=config.dataset_info,
dataset=config.dataset)
dataset_module = dataset_module.data_processing() # hardcoded class name
train_data, train_means_image, train_means_label = get_data_pointers(
dataset=config.dataset,
base_dir=config.tf_records,
cv=dataset_module.folds.keys()[1], # TODO: SEARCH FOR INDEX.
log=log)
val_data, val_means_image, val_means_label = get_data_pointers(
dataset=config.dataset,
base_dir=config.tf_records,
cv=dataset_module.folds.keys()[0],
log=log)
# Initialize output folders
dir_list = {
'checkpoints':
os.path.join(config.checkpoints, condition_label),
'summaries':
os.path.join(config.summaries, condition_label),
'condition_evaluations':
os.path.join(config.condition_evaluations, condition_label),
'experiment_evaluations':
os.path.join( # DEPRECIATED
config.experiment_evaluations, experiment_label),
'visualization':
os.path.join(config.visualizations, condition_label),
'weights':
os.path.join(config.condition_evaluations, condition_label, 'weights')
}
[py_utils.make_dir(v) for v in dir_list.values()]
# Prepare data loaders on the cpu
if all(isinstance(i, list) for i in config.data_augmentations):
if config.data_augmentations:
config.data_augmentations = py_utils.flatten_list(
config.data_augmentations, log)
config.epochs = 1
config.shuffle = False
with tf.device('/cpu:0'):
train_images, train_labels = data_loader.inputs(
dataset=train_data,
batch_size=config.batch_size,
model_input_image_size=dataset_module.model_input_image_size,
tf_dict=dataset_module.tf_dict,
data_augmentations=config.data_augmentations,
num_epochs=config.epochs,
tf_reader_settings=dataset_module.tf_reader,
shuffle=config.shuffle_train,
resize_output=config.resize_output)
if hasattr(config, 'val_augmentations'):
val_augmentations = config.val_augmentations
else:
val_augmentations = config.data_augmentations
val_images, val_labels = data_loader.inputs(
dataset=val_data,
batch_size=config.batch_size,
model_input_image_size=dataset_module.model_input_image_size,
tf_dict=dataset_module.tf_dict,
data_augmentations=['resize_and_crop'],
num_epochs=config.epochs,
tf_reader_settings=dataset_module.tf_reader,
shuffle=config.shuffle_val,
resize_output=config.resize_output)
log.info('Created tfrecord dataloader tensors.')
# Load model specification
struct_name = config.model_struct.split(os.path.sep)[-1]
try:
model_dict = py_utils.import_module(
dataset=struct_name,
model_dir=os.path.join('models', 'structs',
experiment_name).replace(os.path.sep, '.'))
except IOError:
print 'Could not find the model structure: %s in folder %s' % (
struct_name, experiment_name)
# Inject model_dict with hyperparameters if requested
model_dict.layer_structure = hp_opt_utils.inject_model_with_hps(
layer_structure=model_dict.layer_structure, exp_params=exp_params)
# Prepare model on GPU
with tf.device(gpu_device):
with tf.variable_scope('cnn') as scope:
# Normalize labels if needed
if 'normalize_labels' in exp_params.keys():
if exp_params['normalize_labels'] == 'zscore':
train_labels -= train_means_label['mean']
train_labels /= train_means_label['std']
log.info('Z-scoring labels.')
elif exp_params['normalize_labels'] == 'mean':
train_labels -= train_means_label['mean']
log.info('Mean-centering labels.')
# Training model
if len(dataset_module.output_size) == 2:
log.warning('Found > 1 dimension for your output size.'
'Converting to a scalar.')
dataset_module.output_size = np.prod(
dataset_module.output_size)
if hasattr(model_dict, 'output_structure'):
# Use specified output layer
output_structure = model_dict.output_structure
else:
output_structure = None
model = model_utils.model_class(
mean=train_means_image,
training=True,
output_size=dataset_module.output_size)
train_scores, model_summary = model.build(
data=train_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
eval_graph = tf.Graph()
with eval_graph.as_default():
with eval_graph.gradient_override_map({'selu': 'GradLRP'}):
train_grad_images = tf.gradients(
train_scores[0] * tf.cast(train_labels, tf.float32),
train_images)[0]
log.info('Built training model.')
log.debug(json.dumps(model_summary, indent=4), verbose=0)
print_model_architecture(model_summary)
# Check the shapes of labels and scores
if not isinstance(train_scores, list):
if len(train_scores.get_shape()) != len(
train_labels.get_shape()):
train_shape = train_scores.get_shape().as_list()
label_shape = train_labels.get_shape().as_list()
if len(train_shape) == 2 and len(
label_shape) == 1 and train_shape[-1] == 1:
train_labels = tf.expand_dims(train_labels, axis=-1)
elif len(train_shape) == 2 and len(
label_shape) == 1 and train_shape[-1] == 1:
train_scores = tf.expand_dims(train_scores, axis=-1)
# Prepare the loss function
train_loss, _ = loss_utils.loss_interpreter(
logits=train_scores, # TODO
labels=train_labels,
loss_type=config.loss_function,
weights=config.loss_weights,
dataset_module=dataset_module)
# Add loss tensorboard tracking
if isinstance(train_loss, list):
for lidx, tl in enumerate(train_loss):
tf.summary.scalar('training_loss_%s' % lidx, tl)
train_loss = tf.add_n(train_loss)
else:
tf.summary.scalar('training_loss', train_loss)
# Add weight decay if requested
if len(model.regularizations) > 0:
train_loss = loss_utils.wd_loss(
regularizations=model.regularizations,
loss=train_loss,
wd_penalty=config.regularization_strength)
train_op = loss_utils.optimizer_interpreter(
loss=train_loss,
lr=config.lr,
optimizer=config.optimizer,
constraints=config.optimizer_constraints,
model=model)
log.info('Built training loss function.')
# Add a score for the training set
train_accuracy = eval_metrics.metric_interpreter(
metric=dataset_module.score_metric, # TODO: Attach to exp cnfg
pred=train_scores, # TODO
labels=train_labels)
# Add aux scores if requested
train_aux = {}
if hasattr(dataset_module, 'aux_scores'):
for m in dataset_module.aux_scores:
train_aux[m] = eval_metrics.metric_interpreter(
metric=m, pred=train_scores,
labels=train_labels)[0] # TODO: Fix for multiloss
# Prepare remaining tensorboard summaries
if len(train_images.get_shape()) == 4:
tf_fun.image_summaries(train_images, tag='Training images')
if len(train_labels.get_shape()) > 2:
tf_fun.image_summaries(train_labels, tag='Training_targets')
tf_fun.image_summaries(train_scores,
tag='Training_predictions')
if isinstance(train_accuracy, list):
for tidx, ta in enumerate(train_accuracy):
tf.summary.scalar('training_accuracy_%s' % tidx, ta)
else:
tf.summary.scalar('training_accuracy', train_accuracy)
if config.pr_curve:
if isinstance(train_scores, list):
for pidx, train_score in enumerate(train_scores):
train_label = train_labels[:, pidx]
pr_summary.op(
tag='training_pr_%s' % pidx,
predictions=tf.cast(
tf.argmax(train_score, axis=-1), tf.float32),
labels=tf.cast(train_label, tf.bool),
display_name='training_precision_recall_%s' % pidx)
else:
pr_summary.op(tag='training_pr',
predictions=tf.cast(
tf.argmax(train_scores, axis=-1),
tf.float32),
labels=tf.cast(train_labels, tf.bool),
display_name='training_precision_recall')
log.info('Added training summaries.')
# Validation model
scope.reuse_variables()
val_model = model_utils.model_class(
mean=train_means_image, # Normalize with train data
training=False, # False,
output_size=dataset_module.output_size)
val_scores, _ = val_model.build( # Ignore summary
data=val_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
eval_graph = tf.Graph()
with eval_graph.as_default():
with eval_graph.gradient_override_map({'selu': 'GradLRP'}):
val_grad_images = tf.gradients(
val_scores[0] * tf.cast(val_labels, tf.float32),
val_images)[0]
log.info('Built validation model.')
# Check the shapes of labels and scores
if not isinstance(train_scores, list):
if len(val_scores.get_shape()) != len(val_labels.get_shape()):
val_shape = val_scores.get_shape().as_list()
val_label_shape = val_labels.get_shape().as_list()
if len(val_shape) == 2 and len(
val_label_shape) == 1 and val_shape[-1] == 1:
val_labels = tf.expand_dims(val_labels, axis=-1)
if len(val_shape) == 2 and len(
val_label_shape) == 1 and val_shape[-1] == 1:
val_scores = tf.expand_dims(val_scores, axis=-1)
val_loss, _ = loss_utils.loss_interpreter(
logits=val_scores,
labels=val_labels,
loss_type=config.loss_function,
weights=config.loss_weights,
dataset_module=dataset_module)
# Add loss tensorboard tracking
if isinstance(val_loss, list):
for lidx, tl in enumerate(val_loss):
tf.summary.scalar('validation_loss_%s' % lidx, tl)
val_loss = tf.add_n(val_loss)
else:
tf.summary.scalar('validation_loss', val_loss)
# Add a score for the validation set
val_accuracy = eval_metrics.metric_interpreter(
metric=dataset_module.score_metric, # TODO
pred=val_scores,
labels=val_labels)
# Add aux scores if requested
val_aux = {}
if hasattr(dataset_module, 'aux_scores'):
for m in dataset_module.aux_scores:
val_aux[m] = eval_metrics.metric_interpreter(
metric=m, pred=val_scores,
labels=val_labels)[0] # TODO: Fix for multiloss
# Prepare tensorboard summaries
if len(val_images.get_shape()) == 4:
tf_fun.image_summaries(val_images, tag='Validation')
if len(val_labels.get_shape()) > 2:
tf_fun.image_summaries(val_labels, tag='Validation_targets')
tf_fun.image_summaries(val_scores,
tag='Validation_predictions')
if isinstance(val_accuracy, list):
for vidx, va in enumerate(val_accuracy):
tf.summary.scalar('validation_accuracy_%s' % vidx, va)
else:
tf.summary.scalar('validation_accuracy', val_accuracy)
if config.pr_curve:
if isinstance(val_scores, list):
for pidx, val_score in enumerate(val_scores):
val_label = val_labels[:, pidx]
pr_summary.op(
tag='validation_pr_%s' % pidx,
predictions=tf.cast(tf.argmax(val_score, axis=-1),
tf.float32),
labels=tf.cast(val_label, tf.bool),
display_name='validation_precision_recall_%s' %
pidx)
else:
pr_summary.op(tag='validation_pr',
predictions=tf.cast(
tf.argmax(val_scores, axis=-1),
tf.float32),
labels=tf.cast(val_labels, tf.bool),
display_name='validation_precision_recall')
log.info('Added validation summaries.')
# Set up summaries and 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
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Create dictionaries of important training and validation information
train_dict = {
'train_loss': train_loss,
'train_images': train_images,
'train_labels': train_labels,
'train_op': train_op,
'train_scores': train_scores,
'train_grad_images': train_grad_images
}
val_dict = {
'val_loss': val_loss,
'val_images': val_images,
'val_labels': val_labels,
'val_scores': val_scores,
'val_grad_images': val_grad_images
}
if isinstance(train_accuracy, list):
for tidx, (ta, va) in enumerate(zip(train_accuracy, val_accuracy)):
train_dict['train_accuracy_%s' % tidx] = ta
val_dict['val_accuracy_%s' % tidx] = va
else:
train_dict['train_accuracy_0'] = train_accuracy
val_dict['val_accuracy_0'] = val_accuracy
if load_and_evaluate_ckpt is not None:
# Remove the train operation and add a ckpt pointer
del train_dict['train_op']
if hasattr(dataset_module, 'aux_score'):
# Attach auxillary scores to tensor dicts
for m in dataset_module.aux_scores:
train_dict['train_aux_%s' % m] = train_aux[m]
val_dict['val_aux_%s' % m] = val_aux[m]
# Start training loop
checkpoint_dir = dir_list['checkpoints']
step = 0
train_losses, train_accs, train_aux, timesteps = {}, {}, {}, {}
val_scores, val_aux, val_labels, val_grads = {}, {}, {}, {}
train_images, val_images = {}, {}
train_scores, train_labels = {}, {}
train_aux_check = np.any(['aux_score' in k for k in train_dict.keys()])
val_aux_check = np.any(['aux_score' in k for k in val_dict.keys()])
# Restore model
saver.restore(sess, ckpt_file)
# Start evaluation
try:
while not coord.should_stop():
start_time = time.time()
train_vars = sess.run(train_dict.values())
it_train_dict = {
k: v
for k, v in zip(train_dict.keys(), train_vars)
}
duration = time.time() - start_time
train_losses[step] = it_train_dict['train_loss']
train_accs[step] = it_train_dict['train_accuracy_0']
train_images[step] = it_train_dict['train_images']
train_labels[step] = it_train_dict['train_labels']
train_scores[step] = it_train_dict['train_scores']
timesteps[step] = duration
if train_aux_check:
# Loop through to find aux scores
it_train_aux = {
itk: itv
for itk, itv in it_train_dict.iteritems()
if 'aux_score' in itk
}
train_aux[step] = it_train_aux
assert not np.isnan(it_train_dict['train_loss']).any(
), 'Model diverged with loss = NaN'
if step % config.validation_iters == 0:
it_val_scores, it_val_labels, it_val_aux, it_val_grads, it_val_ims = [], [], [], [], []
for num_vals in range(config.num_validation_evals):
# Validation accuracy as the average of n batches
val_vars = sess.run(val_dict.values())
it_val_dict = {
k: v
for k, v in zip(val_dict.keys(), val_vars)
}
it_val_labels += [it_val_dict['val_labels']]
it_val_scores += [it_val_dict['val_scores']]
it_val_grads += [it_val_dict['val_grad_images']]
it_val_ims += [it_val_dict['val_images']]
if val_aux_check:
iva = {
itk: itv
for itk, itv in it_val_dict.iteritems()
if 'aux_score' in itk
}
it_val_aux += [iva]
val_scores[step] = it_val_scores
val_labels[step] = it_val_labels
val_aux[step] = it_val_aux
val_images[step] = it_val_grads
val_grads[step] = it_val_ims
# End iteration
step += 1
except tf.errors.OutOfRangeError:
print 'Done with evaluation for %d epochs, %d steps.' % (config.epochs,
step)
print 'Saved to: %s' % checkpoint_dir
finally:
coord.request_stop()
coord.join(threads)
sess.close()
import ipdb
ipdb.set_trace()
np.savez(
'val_imgs_grads',
val_images=val_images, # it_val_dict['val_images'],
val_grads=val_grads, # it_val_dict['val_grad_images'],
val_labels=val_labels, # it_val_dict['val_labels'],
val_scores=val_scores) # it_val_dict['val_scores'][0])
def main(
experiment_name,
list_experiments=False,
load_and_evaluate_ckpt=None,
placeholder_data=None,
grad_images=False,
gpu_device='/gpu:0'):
"""Create a tensorflow worker to run experiments in your DB."""
if list_experiments:
exps = db.list_experiments()
print '_' * 30
print 'Initialized experiments:'
print '_' * 30
for l in exps:
print l.values()[0]
print '_' * 30
if len(exps) == 0:
print 'No experiments found.'
else:
print 'You can add to the DB with: '\
'python prepare_experiments.py --experiment=%s' % \
exps[0].values()[0]
return
if experiment_name is None:
print 'No experiment specified. Pulling one out of the DB.'
experiment_name = db.get_experiment_name()
# Prepare to run the model
config = Config()
condition_label = '%s_%s' % (experiment_name, py_utils.get_dt_stamp())
experiment_label = '%s' % (experiment_name)
log = logger.get(os.path.join(config.log_dir, condition_label))
assert experiment_name is not None, 'Empty experiment name.'
experiment_dict = experiments.experiments()[experiment_name]()
config = add_to_config(d=experiment_dict, config=config) # Globals
config.load_and_evaluate_ckpt = load_and_evaluate_ckpt
if load_and_evaluate_ckpt is not None:
# Remove the train operation and add a ckpt pointer
from ops import evaluation
config, exp_params = process_DB_exps(
experiment_name=experiment_name,
log=log,
config=config) # Update config w/ DB params
dataset_module = py_utils.import_module(
model_dir=config.dataset_info,
dataset=config.dataset)
dataset_module = dataset_module.data_processing() # hardcoded class name
train_key = [k for k in dataset_module.folds.keys() if 'train' in k]
if not len(train_key):
train_key = 'train'
else:
train_key = train_key[0]
train_data, train_means_image, train_means_label = get_data_pointers(
dataset=config.dataset,
base_dir=config.tf_records,
cv=train_key,
log=log)
val_key = [k for k in dataset_module.folds.keys() if 'val' in k]
if not len(val_key):
val_key = 'train'
else:
val_key = val_key[0]
val_data, val_means_image, val_means_label = get_data_pointers(
dataset=config.dataset,
base_dir=config.tf_records,
cv=val_key,
log=log)
# Initialize output folders
dir_list = {
'checkpoints': os.path.join(
config.checkpoints, condition_label),
'summaries': os.path.join(
config.summaries, condition_label),
'condition_evaluations': os.path.join(
config.condition_evaluations, condition_label),
'experiment_evaluations': os.path.join( # DEPRECIATED
config.experiment_evaluations, experiment_label),
'visualization': os.path.join(
config.visualizations, condition_label),
'weights': os.path.join(
config.condition_evaluations, condition_label, 'weights')
}
[py_utils.make_dir(v) for v in dir_list.values()]
# Prepare data loaders on the cpu
if all(isinstance(i, list) for i in config.data_augmentations):
if config.data_augmentations:
config.data_augmentations = py_utils.flatten_list(
config.data_augmentations,
log)
if load_and_evaluate_ckpt is not None:
config.epochs = 1
config.train_shuffle = False
config.val_shuffle = False
with tf.device('/cpu:0'):
if placeholder_data:
placeholder_shape = placeholder_data['train_image_shape']
placeholder_dtype = placeholder_data['train_image_dtype']
original_train_images = tf.placeholder(
dtype=placeholder_dtype,
shape=placeholder_shape,
name='train_images')
placeholder_shape = placeholder_data['train_label_shape']
placeholder_dtype = placeholder_data['train_label_dtype']
original_train_labels = tf.placeholder(
dtype=placeholder_dtype,
shape=placeholder_shape,
name='train_labels')
placeholder_shape = placeholder_data['val_image_shape']
placeholder_dtype = placeholder_data['val_image_dtype']
original_val_images = tf.placeholder(
dtype=placeholder_dtype,
shape=placeholder_shape,
name='val_images')
placeholder_shape = placeholder_data['val_label_shape']
placeholder_dtype = placeholder_data['val_label_dtype']
original_val_labels = tf.placeholder(
dtype=placeholder_dtype,
shape=placeholder_shape,
name='val_labels')
# Apply augmentations
(
train_images,
train_labels
) = data_loader.placeholder_image_augmentations(
images=original_train_images,
model_input_image_size=dataset_module.model_input_image_size,
labels=original_train_labels,
data_augmentations=config.data_augmentations,
batch_size=config.batch_size)
(
val_images,
val_labels
) = data_loader.placeholder_image_augmentations(
images=original_val_images,
model_input_image_size=dataset_module.model_input_image_size,
labels=original_val_labels,
data_augmentations=config.data_augmentations,
batch_size=config.batch_size)
# Store in the placeholder dict
placeholder_data['train_images'] = original_train_images
placeholder_data['train_labels'] = original_train_labels
placeholder_data['val_images'] = original_val_images
placeholder_data['val_labels'] = original_val_labels
else:
train_images, train_labels = data_loader.inputs(
dataset=train_data,
batch_size=config.batch_size,
model_input_image_size=dataset_module.model_input_image_size,
tf_dict=dataset_module.tf_dict,
data_augmentations=config.data_augmentations,
num_epochs=config.epochs,
tf_reader_settings=dataset_module.tf_reader,
shuffle=config.shuffle_train,
resize_output=config.resize_output)
if hasattr(config, 'val_augmentations'):
val_augmentations = config.val_augmentations
else:
val_augmentations = config.data_augmentations
val_images, val_labels = data_loader.inputs(
dataset=val_data,
batch_size=config.batch_size,
model_input_image_size=dataset_module.model_input_image_size,
tf_dict=dataset_module.tf_dict,
data_augmentations=val_augmentations,
num_epochs=config.epochs,
tf_reader_settings=dataset_module.tf_reader,
shuffle=config.shuffle_val,
resize_output=config.resize_output)
log.info('Created tfrecord dataloader tensors.')
# Load model specification
struct_name = config.model_struct.split(os.path.sep)[-1]
try:
model_dict = py_utils.import_module(
dataset=struct_name,
model_dir=os.path.join(
'models',
'structs',
experiment_name).replace(os.path.sep, '.')
)
except IOError:
print 'Could not find the model structure: %s in folder %s' % (
struct_name,
experiment_name)
# Inject model_dict with hyperparameters if requested
model_dict.layer_structure = hp_opt_utils.inject_model_with_hps(
layer_structure=model_dict.layer_structure,
exp_params=exp_params)
# Prepare variables for the models
if len(dataset_module.output_size) == 2:
log.warning(
'Found > 1 dimension for your output size.'
'Converting to a scalar.')
dataset_module.output_size = np.prod(
dataset_module.output_size)
if hasattr(model_dict, 'output_structure'):
# Use specified output layer
output_structure = model_dict.output_structure
else:
output_structure = None
# Correct number of output neurons if needed
if config.dataloader_override and\
'weights' in output_structure[-1].keys():
output_neurons = output_structure[-1]['weights'][0]
size_check = output_neurons != dataset_module.output_size
fc_check = output_structure[-1]['layers'][0] == 'fc'
if size_check and fc_check:
output_structure[-1]['weights'][0] = dataset_module.output_size
log.warning('Adjusted output neurons from %s to %s.' % (
output_neurons,
dataset_module.output_size))
# Prepare model on GPU
if not hasattr(dataset_module, 'input_normalization'):
dataset_module.input_normalization = None
with tf.device(gpu_device):
with tf.variable_scope('cnn') as scope:
# Training model
model = model_utils.model_class(
mean=train_means_image,
training=True,
output_size=dataset_module.output_size,
input_normalization=dataset_module.input_normalization)
train_scores, model_summary, _ = model.build(
data=train_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
if grad_images:
oh_dims = int(train_scores.get_shape()[-1])
target_scores = tf.one_hot(train_labels, oh_dims) * train_scores
train_gradients = tf.gradients(target_scores, train_images)[0]
log.info('Built training model.')
log.debug(
json.dumps(model_summary, indent=4),
verbose=0)
print_model_architecture(model_summary)
# Normalize labels on GPU if needed
if 'normalize_labels' in exp_params.keys():
if exp_params['normalize_labels'] == 'zscore':
train_labels -= train_means_label['mean']
train_labels /= train_means_label['std']
val_labels -= train_means_label['mean']
val_labels /= train_means_label['std']
log.info('Z-scoring labels.')
elif exp_params['normalize_labels'] == 'mean':
train_labels -= train_means_label['mean']
val_labels -= val_means_label['mean']
log.info('Mean-centering labels.')
# Check the shapes of labels and scores
if not isinstance(train_scores, list):
if len(
train_scores.get_shape()) != len(
train_labels.get_shape()):
train_shape = train_scores.get_shape().as_list()
label_shape = train_labels.get_shape().as_list()
val_shape = val_scores.get_shape().as_list()
val_label_shape = val_labels.get_shape().as_list()
if len(
train_shape) == 2 and len(
label_shape) == 1 and train_shape[-1] == 1:
train_labels = tf.expand_dims(train_labels, axis=-1)
val_labels = tf.expand_dims(val_labels, axis=-1)
elif len(
train_shape) == 2 and len(
label_shape) == 1 and train_shape[-1] == 1:
train_scores = tf.expand_dims(train_scores, axis=-1)
val_scores = tf.expand_dims(val_scores, axis=-1)
# Prepare the loss function
train_loss, _ = loss_utils.loss_interpreter(
logits=train_scores, # TODO
labels=train_labels,
loss_type=config.loss_function,
weights=config.loss_weights,
dataset_module=dataset_module)
# Add loss tensorboard tracking
if isinstance(train_loss, list):
for lidx, tl in enumerate(train_loss):
tf.summary.scalar('training_loss_%s' % lidx, tl)
train_loss = tf.add_n(train_loss)
else:
tf.summary.scalar('training_loss', train_loss)
# Add weight decay if requested
if len(model.regularizations) > 0:
train_loss = loss_utils.wd_loss(
regularizations=model.regularizations,
loss=train_loss,
wd_penalty=config.regularization_strength)
assert config.lr is not None, 'No learning rate.' # TODO: Make a QC function
if config.lr > 1:
old_lr = config.lr
config.lr = loss_utils.create_lr_schedule(
train_batch=config.batch_size,
num_training=config.lr)
config.optimizer = 'momentum'
log.info('Forcing momentum classifier.')
else:
old_lr = None
train_op = loss_utils.optimizer_interpreter(
loss=train_loss,
lr=config.lr,
optimizer=config.optimizer,
constraints=config.optimizer_constraints,
model=model)
log.info('Built training loss function.')
# Add a score for the training set
train_accuracy = eval_metrics.metric_interpreter(
metric=dataset_module.score_metric, # TODO: Attach to exp cnfg
pred=train_scores, # TODO
labels=train_labels)
# Add aux scores if requested
train_aux = {}
if hasattr(dataset_module, 'aux_scores'):
for m in dataset_module.aux_scores:
train_aux[m] = eval_metrics.metric_interpreter(
metric=m,
pred=train_scores,
labels=train_labels) # [0] # TODO: Fix for multiloss
# Prepare remaining tensorboard summaries
if config.tensorboard_images:
if len(train_images.get_shape()) == 4:
tf_fun.image_summaries(train_images, tag='Training images')
if (np.asarray(
train_labels.get_shape().as_list()) > 1).sum() > 2:
tf_fun.image_summaries(
train_labels,
tag='Training_targets')
tf_fun.image_summaries(
train_scores,
tag='Training_predictions')
if isinstance(train_accuracy, list):
for tidx, ta in enumerate(train_accuracy):
tf.summary.scalar('training_accuracy_%s' % tidx, ta)
else:
tf.summary.scalar('training_accuracy', train_accuracy)
if config.pr_curve:
if isinstance(train_scores, list):
for pidx, train_score in enumerate(train_scores):
train_label = train_labels[:, pidx]
pr_summary.op(
tag='training_pr_%s' % pidx,
predictions=tf.cast(
tf.argmax(
train_score,
axis=-1),
tf.float32),
labels=tf.cast(train_label, tf.bool),
display_name='training_precision_recall_%s' % pidx)
else:
pr_summary.op(
tag='training_pr',
predictions=tf.cast(
tf.argmax(
train_scores,
axis=-1),
tf.float32),
labels=tf.cast(train_labels, tf.bool),
display_name='training_precision_recall')
log.info('Added training summaries.')
with tf.variable_scope('cnn', tf.AUTO_REUSE) as scope:
# Validation model
scope.reuse_variables()
val_model = model_utils.model_class(
mean=train_means_image, # Normalize with train data
training=False,
output_size=dataset_module.output_size,
input_normalization=dataset_module.input_normalization)
val_scores, _, _ = val_model.build( # Ignore summary
data=val_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
if grad_images:
oh_dims = int(val_scores.get_shape()[-1])
target_scores = tf.one_hot(val_labels, oh_dims) * val_scores
val_gradients = tf.gradients(target_scores, val_images)[0]
log.info('Built validation model.')
# Check the shapes of labels and scores
val_loss, _ = loss_utils.loss_interpreter(
logits=val_scores,
labels=val_labels,
loss_type=config.loss_function,
weights=config.loss_weights,
dataset_module=dataset_module)
# Add loss tensorboard tracking
if isinstance(val_loss, list):
for lidx, tl in enumerate(val_loss):
tf.summary.scalar('validation_loss_%s' % lidx, tl)
val_loss = tf.add_n(val_loss)
else:
tf.summary.scalar('validation_loss', val_loss)
# Add a score for the validation set
val_accuracy = eval_metrics.metric_interpreter(
metric=dataset_module.score_metric, # TODO
pred=val_scores,
labels=val_labels)
# Add aux scores if requested
val_aux = {}
if hasattr(dataset_module, 'aux_scores'):
for m in dataset_module.aux_scores:
val_aux[m] = eval_metrics.metric_interpreter(
metric=m,
pred=val_scores,
labels=val_labels) # [0] # TODO: Fix for multiloss
# Prepare tensorboard summaries
if config.tensorboard_images:
if len(val_images.get_shape()) == 4:
tf_fun.image_summaries(
val_images,
tag='Validation')
if (np.asarray(
val_labels.get_shape().as_list()) > 1).sum() > 2:
tf_fun.image_summaries(
val_labels,
tag='Validation_targets')
tf_fun.image_summaries(
val_scores,
tag='Validation_predictions')
if isinstance(val_accuracy, list):
for vidx, va in enumerate(val_accuracy):
tf.summary.scalar('validation_accuracy_%s' % vidx, va)
else:
tf.summary.scalar('validation_accuracy', val_accuracy)
if config.pr_curve:
if isinstance(val_scores, list):
for pidx, val_score in enumerate(val_scores):
val_label = val_labels[:, pidx]
pr_summary.op(
tag='validation_pr_%s' % pidx,
predictions=tf.cast(
tf.argmax(
val_score,
axis=-1),
tf.float32),
labels=tf.cast(val_label, tf.bool),
display_name='validation_precision_recall_%s' %
pidx)
else:
pr_summary.op(
tag='validation_pr',
predictions=tf.cast(
tf.argmax(
val_scores,
axis=-1),
tf.float32),
labels=tf.cast(val_labels, tf.bool),
display_name='validation_precision_recall')
log.info('Added validation summaries.')
# Set up summaries and saver
if not hasattr(config, 'max_to_keep'):
config.max_to_keep = None
saver = tf.train.Saver(
var_list=tf.global_variables(),
max_to_keep=config.max_to_keep)
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_writer = tf.summary.FileWriter(dir_list['summaries'], sess.graph)
# Set up exemplar threading
if placeholder_data:
coord, threads = None, None
else:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Create dictionaries of important training and validation information
train_dict = {
'train_loss': train_loss,
'train_images': train_images,
'train_labels': train_labels,
'train_op': train_op,
'train_scores': train_scores
}
val_dict = {
'val_loss': val_loss,
'val_images': val_images,
'val_labels': val_labels,
'val_scores': val_scores,
}
if grad_images:
train_dict['train_gradients'] = train_gradients
val_dict['val_gradients'] = val_gradients
if isinstance(train_accuracy, list):
for tidx, (ta, va) in enumerate(zip(train_accuracy, val_accuracy)):
train_dict['train_accuracy_%s' % tidx] = ta
val_dict['val_accuracy_%s' % tidx] = va
else:
train_dict['train_accuracy_0'] = train_accuracy
val_dict['val_accuracy_0'] = val_accuracy
if load_and_evaluate_ckpt is not None:
# Remove the train operation and add a ckpt pointer
del train_dict['train_op']
if hasattr(dataset_module, 'aux_score'):
# Attach auxillary scores to tensor dicts
for m in dataset_module.aux_scores:
train_dict['train_aux_%s' % m] = train_aux[m]
val_dict['val_aux_%s' % m] = val_aux[m]
# Start training loop
if old_lr is not None:
config.lr = old_lr
np.save(
os.path.join(
dir_list['condition_evaluations'], 'training_config_file'),
config)
log.info('Starting training')
if load_and_evaluate_ckpt is not None:
return evaluation.evaluation_loop(
config=config,
db=db,
coord=coord,
sess=sess,
summary_op=summary_op,
summary_writer=summary_writer,
saver=saver,
threads=threads,
summary_dir=dir_list['summaries'],
checkpoint_dir=dir_list['checkpoints'],
weight_dir=dir_list['weights'],
train_dict=train_dict,
val_dict=val_dict,
train_model=model,
val_model=val_model,
exp_params=exp_params,
placeholder_data=placeholder_data)
else:
output_dict = training.training_loop(
config=config,
db=db,
coord=coord,
sess=sess,
summary_op=summary_op,
summary_writer=summary_writer,
saver=saver,
threads=threads,
summary_dir=dir_list['summaries'],
checkpoint_dir=dir_list['checkpoints'],
weight_dir=dir_list['weights'],
train_dict=train_dict,
val_dict=val_dict,
train_model=model,
val_model=val_model,
exp_params=exp_params)
log.info('Finished training.')
model_name = config.model_struct.replace('/', '_')
if output_dict is not None:
py_utils.save_npys(
data=output_dict,
model_name=model_name,
output_string=dir_list['experiment_evaluations'])
def plot_fits(
experiment='760_cells_2017_11_04_16_29_09',
query_db=False,
num_models=3,
template_exp='ALLEN_selected_cells_1',
process_pnodes=False,
allen_dir='/home/drew/Documents/Allen_Brain_Observatory',
output_dir='tests/ALLEN_files',
stimulus_type='tfrecord',
top_n=1,
grad='lrp',
target_layer='conv1_1', # conv1_1, sep_conv1_1, dog1_1
target_model='conv2d'): # conv2d, sep_conv2d, dog
"""Plot fits across the RF.
experiment: Name of Allen experiment you're plotting.
query_db: Use data from DB versus data in Numpys.
num_models: The number of architectures you're testing.
template_exp: The name of the contextual_circuit model template used."""
sys.path.append(allen_dir)
from allen_config import Allen_Brain_Observatory_Config
if process_pnodes:
from pnodes_declare_datasets_loop import query_hp_hist, sel_exp_query
else:
from declare_datasets_loop import query_hp_hist, sel_exp_query
config = Config()
main_config = Allen_Brain_Observatory_Config()
db_config = credentials.postgresql_connection()
files = glob(
os.path.join(
allen_dir,
main_config.multi_exps,
experiment, '*.npz'))
assert len(files), 'Couldn\'t find files.'
out_data, xs, ys = [], [], []
perfs, model_types, exps, arg_perf = [], [], [], []
count = 0
for f in files:
data = np.load(f)
d = {
'x': data['rf_data'].item()['on_center_x'],
'y': data['rf_data'].item()['on_center_y'],
# x: files['dataset_method'].item()['x_min'],
# y: files['dataset_method'].item()['y_min'],
}
exp_name = {
'experiment_name': data['dataset_method'].item()[
'experiment_name']}
if query_db:
perf = query_hp_hist(
exp_name['experiment_name'],
db_config=db_config)
if perf is None:
print 'No fits for: %s' % exp_name['experiment_name']
else:
raise NotImplementedError
d['perf'] = perf
d['max_val'] = np.max(perf)
out_data += [d]
xs += [np.round(d['x'])]
ys += [np.round(d['y'])]
perfs += [np.max(d['perf'])]
count += 1
else:
data_files = glob(
os.path.join(
main_config.ccbp_exp_evals,
exp_name['experiment_name'],
'*val_losses.npy')) # Scores has preds, labels has GT
for gd in data_files:
mt = gd.split(
os.path.sep)[-1].split(
template_exp + '_')[-1].split('_' + 'val')[0]
it_data = np.load(gd).item()
sinds = np.asarray(it_data.keys())[np.argsort(it_data.keys())]
sit_data = [it_data[idx] for idx in sinds]
d['perf'] = sit_data
d['max_val'] = np.max(sit_data)
d['max_idx'] = np.argmax(sit_data)
d['mt'] = mt
out_data += [d]
xs += [np.round(d['x'])]
ys += [np.round(d['y'])]
perfs += [np.max(sit_data)]
arg_perf += [np.argmax(sit_data)]
exps += [gd.split(os.path.sep)[-2]]
model_types += [mt]
count += 1
# Package as a df
xs = np.round(np.asarray(xs)).astype(int)
ys = np.round(np.asarray(ys)).astype(int)
perfs = np.asarray(perfs)
arg_perf = np.asarray(arg_perf)
exps = np.asarray(exps)
model_types = np.asarray(model_types)
# Filter to only keep top-scoring values at each x/y (dirty trick)
fxs, fys, fperfs, fmodel_types, fexps, fargs = [], [], [], [], [], []
xys = np.vstack((xs, ys)).transpose()
cxy = np.ascontiguousarray( # Unique rows
xys).view(
np.dtype((np.void, xys.dtype.itemsize * xys.shape[1])))
_, idx = np.unique(cxy, return_index=True)
uxys = xys[idx]
scores = []
for xy in uxys:
sel_idx = (xys == xy).sum(axis=-1) == 2
sperfs = perfs[sel_idx]
sexps = exps[sel_idx]
sargs = arg_perf[sel_idx]
sel_mts = model_types[sel_idx]
# Only get top conv/sep spots
sperfs = sperfs[sel_mts != 'dog']
sperfs = sperfs[sel_mts != 'DoG']
scores += [sperfs.mean() / sperfs.std()]
best_fits = np.argmax(np.asarray(scores))
xs = np.asarray([uxys[best_fits][0]])
ys = np.asarray([uxys[best_fits][1]])
sel_idx = (xys == uxys[best_fits]).sum(axis=-1) == 2
perfs = np.asarray(perfs[sel_idx])
exps = np.asarray(exps[sel_idx])
model_types = np.asarray(model_types[sel_idx])
umt, model_types_inds = np.unique(model_types, return_inverse=True)
# Get weights for the top-n fitting models of each type
it_perfs = perfs[model_types == target_model]
it_exps = exps[model_types == target_model]
# it_args = arg_perf[model_types == target_model]
sorted_perfs = np.argsort(it_perfs)[::-1][:top_n]
for idx in sorted_perfs:
perf = sel_exp_query(
experiment_name=it_exps[idx],
model=target_model,
db_config=db_config)
# perf_steps = np.argsort([v['training_step'] for v in perf])[::-1]
perf_steps = [v['validation_loss'] for v in perf]
max_score = np.max(perf_steps)
arg_perf_steps = np.argmax(perf_steps)
sel_model = perf[arg_perf_steps] # perf_steps[it_args[idx]]]
print 'Using %s' % sel_model
model_file = sel_model['ckpt_file'].split('.')[0]
model_ckpt = '%s.ckpt-%s' % (
model_file,
model_file.split(os.path.sep)[-1].split('_')[-1])
model_meta = '%s.meta' % model_ckpt
# Pull stimuli
stim_dir = os.path.join(
main_config.tf_record_output,
sel_model['experiment_name'])
stim_files = glob(stim_dir + '*')
stim_meta_file = [x for x in stim_files if 'meta' in x][0]
# stim_val_data = [x for x in stim_files if 'val.tfrecords' in x][0]
stim_val_data = [x for x in stim_files if 'train.tfrecords' in x][0]
stim_val_mean = [x for x in stim_files if 'train_means' in x][0]
assert stim_meta_file is not None
assert stim_val_data is not None
assert stim_val_mean is not None
stim_meta_data = np.load(stim_meta_file).item()
rf_stim_meta_data = stim_meta_data['rf_data']
stim_mean_data = np.load(
stim_val_mean).items()[0][1].item()['image']['mean']
# Store sparse noise for reference
sparse_rf_on = {
'center_x': rf_stim_meta_data.get('on_center_x', None),
'center_y': rf_stim_meta_data.get('on_center_y', None),
'width_x': rf_stim_meta_data.get('on_width_x', None),
'width_y': rf_stim_meta_data.get('on_width_y', None),
'distance': rf_stim_meta_data.get('on_distance', None),
'area': rf_stim_meta_data.get('on_area', None),
'rotation': rf_stim_meta_data.get('on_rotation', None),
}
sparse_rf_off = {
'center_x': rf_stim_meta_data.get('off_center_x', None),
'center_y': rf_stim_meta_data.get('off_center_y', None),
'width_x': rf_stim_meta_data.get('off_width_x', None),
'width_y': rf_stim_meta_data.get('off_width_y', None),
'distance': rf_stim_meta_data.get('off_distance', None),
'area': rf_stim_meta_data.get('off_area', None),
'rotation': rf_stim_meta_data.get('off_rotation', None),
}
sparse_rf = {'on': sparse_rf_on, 'off': sparse_rf_off}
# Pull responses
dataset_module = py_utils.import_module(
model_dir=config.dataset_info,
dataset=sel_model['experiment_name'])
dataset_module = dataset_module.data_processing()
with tf.device('/cpu:0'):
if stimulus_type == 'sparse_noise':
pass
elif stimulus_type == 'drifting_grating':
pass
elif stimulus_type == 'tfrecord':
val_images, val_labels = data_loader.inputs(
dataset=stim_val_data,
batch_size=1,
model_input_image_size=dataset_module.model_input_image_size,
tf_dict=dataset_module.tf_dict,
data_augmentations=[None], # dataset_module.preprocess,
num_epochs=1,
tf_reader_settings=dataset_module.tf_reader,
shuffle=False
)
# Mean normalize
log = logger.get(os.path.join(output_dir, 'sta_logs', target_model))
data_dir = os.path.join(output_dir, 'data', target_model)
py_utils.make_dir(data_dir)
sys.path.append(os.path.join('models', 'structs', sel_model['experiment_name']))
model_dict = __import__(target_model)
if hasattr(model_dict, 'output_structure'):
# Use specified output layer
output_structure = model_dict.output_structure
else:
output_structure = None
model = model_utils.model_class(
mean=stim_mean_data,
training=True, # FIXME
output_size=dataset_module.output_size)
with tf.device('/gpu:0'):
with tf.variable_scope('cnn') as scope:
val_scores, model_summary = model.build(
data=val_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
if grad == 'vanilla':
grad_image = tf.gradients(model.output, val_images)[0]
elif grad == 'lrp':
eval_graph = tf.Graph()
with eval_graph.as_default():
with eval_graph.gradient_override_map(
{'Relu': 'GradLRP'}):
grad_image = tf.gradients(model.output, val_images)[0]
elif grad == 'cam':
eval_graph = tf.Graph()
with eval_graph.as_default():
with eval_graph.gradient_override_map(
{'Relu': 'GuidedRelu'}):
grad_image = tf.gradients(model.output, val_images)[0]
else:
raise NotImplementedError
print(json.dumps(model_summary, indent=4))
# Set up summaries and saver
saver = tf.train.Saver(tf.global_variables())
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())
)
saver.restore(sess, model_ckpt)
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if target_model == 'conv2d':
fname = [
x for x in tf.global_variables()
if 'conv1_1_filters:0' in x.name]
elif target_model == 'sep_conv2d':
fname = [
x for x in tf.global_variables()
if 'sep_conv1_1_filters:0' in x.name]
elif target_model == 'dog' or target_model == 'DoG':
fname = [
x for x in tf.global_variables()
if 'dog1_1_filters:0' in x.name]
else:
raise NotImplementedError
val_tensors = {
'images': val_images,
'labels': val_labels,
'filts': fname,
'responses': model.output, # model[target_layer],
'grads': grad_image
}
all_images, all_preds, all_grads, all_responses = [], [], [], []
step = 0
try:
while not coord.should_stop():
val_vals = sess.run(val_tensors.values())
val_dict = {k: v for k, v in zip(val_tensors.keys(), val_vals)}
all_images += [val_dict['images']]
all_responses += [val_dict['responses']]
all_preds += [val_dict['labels'].squeeze()]
all_grads += [val_dict['grads'].squeeze()]
print 'Finished step %s' % step
step += 1
except:
print 'Finished tfrecords'
finally:
coord.request_stop()
coord.join(threads)
sess.close()
# Process and save data
# if target_model != 'dog':
# filters = val_dict['filts'][0].squeeze().transpose(2, 0, 1)
all_images = np.concatenate(all_images).squeeze()
all_grads = np.asarray(all_grads)
all_preds = np.asarray(all_preds).reshape(-1, 1)
all_responses = np.asarray(all_responses).squeeze()
np.savez(
os.path.join(data_dir, 'data'),
images=all_images,
pred=all_preds,
# filters=filters,
grads=all_grads)
# if target_model != 'dog':
# save_mosaic(
# maps=filters, # [0].squeeze().transpose(2, 0, 1),
# output=os.path.join(data_dir, '%s_filters' % target_layer),
# rc=8,
# cc=4,
# title='%s filters' % (
# target_layer))
print 'Complete.'
def plot_fits(
experiment='760_cells_2017_11_04_16_29_09',
query_db=False,
template_exp='ALLEN_selected_cells_1',
process_pnodes=False,
allen_dir='/home/drew/Documents/Allen_Brain_Observatory',
output_dir='tests/ALLEN_files',
stimulus_dir='/media/data_cifs/AllenData/DataForTrain/all_stimulus_template',
stimulus_type='tfrecord',
top_n=100,
recalc=False,
preload_stim=False,
target_layer='conv1_1', # conv1_1, sep_conv1_1, dog1_1
target_model='conv2d'): # conv2d, sep_conv2d, dog
"""Plot fits across the RF.
experiment: Name of Allen experiment you're plotting.
query_db: Use data from DB versus data in Numpys.
num_models: The number of architectures you're testing.
template_exp: The name of the contextual_circuit model template used."""
sys.path.append(allen_dir)
from allen_config import Allen_Brain_Observatory_Config
if process_pnodes:
from pnodes_declare_datasets_loop import query_hp_hist, sel_exp_query
else:
from declare_datasets_loop import query_hp_hist, sel_exp_query
config = Config()
main_config = Allen_Brain_Observatory_Config()
db_config = credentials.postgresql_connection()
files = glob(
os.path.join(allen_dir, main_config.multi_exps, experiment, '*.npz'))
assert len(files), 'Couldn\'t find files.'
out_data, xs, ys = [], [], []
perfs, model_types, exps, arg_perf = [], [], [], []
count = 0
for f in files:
data = np.load(f)
d = {
'x': data['rf_data'].item()['on_center_x'],
'y': data['rf_data'].item()['on_center_y'],
# x: files['dataset_method'].item()['x_min'],
# y: files['dataset_method'].item()['y_min'],
}
exp_name = {
'experiment_name': data['dataset_method'].item()['experiment_name']
}
if query_db:
perf = query_hp_hist(exp_name['experiment_name'],
db_config=db_config)
if perf is None:
print 'No fits for: %s' % exp_name['experiment_name']
else:
raise NotImplementedError
d['perf'] = perf
d['max_val'] = np.max(perf)
out_data += [d]
xs += [np.round(d['x'])]
ys += [np.round(d['y'])]
perfs += [np.max(d['perf'])]
count += 1
else:
data_files = glob(
os.path.join(
main_config.ccbp_exp_evals, exp_name['experiment_name'],
'*val_losses.npy')) # Scores has preds, labels has GT
score_files = glob(
os.path.join(
main_config.ccbp_exp_evals, exp_name['experiment_name'],
'*val_scores.npy')) # Scores has preds, labels has GT
lab_files = glob(
os.path.join(
main_config.ccbp_exp_evals, exp_name['experiment_name'],
'*val_labels.npy')) # Scores has preds, labels has GT
for gd, sd, ld in zip(data_files, score_files, lab_files):
mt = gd.split(os.path.sep)[-1].split(template_exp +
'_')[-1].split('_' +
'val')[0]
if not recalc:
it_data = np.load(gd).item()
else:
lds = np.load(ld).item()
sds = np.load(sd).item()
it_data = {
k: np.corrcoef(lds[k], sds[k])[0, 1]
for k in sds.keys()
}
sinds = np.asarray(it_data.keys())[np.argsort(it_data.keys())]
sit_data = [it_data[idx] for idx in sinds]
d['perf'] = sit_data
d['max_val'] = np.max(sit_data)
d['max_idx'] = np.argmax(sit_data)
d['mt'] = mt
out_data += [d]
xs += [np.round(d['x'])]
ys += [np.round(d['y'])]
perfs += [np.max(sit_data)]
arg_perf += [np.argmax(sit_data)]
exps += [gd.split(os.path.sep)[-2]]
model_types += [mt]
count += 1
# Package as a df
xs = np.round(np.asarray(xs)).astype(int)
ys = np.round(np.asarray(ys)).astype(int)
perfs = np.asarray(perfs)
arg_perf = np.asarray(arg_perf)
exps = np.asarray(exps)
model_types = np.asarray(model_types)
# Filter to only keep top-scoring values at each x/y (dirty trick)
fxs, fys, fperfs, fmodel_types, fexps, fargs = [], [], [], [], [], []
xys = np.vstack((xs, ys)).transpose()
cxy = np.ascontiguousarray( # Unique rows
xys).view(np.dtype((np.void, xys.dtype.itemsize * xys.shape[1])))
_, idx = np.unique(cxy, return_index=True)
uxys = xys[idx]
for xy in uxys:
sel_idx = (xys == xy).sum(axis=-1) == 2
sperfs = perfs[sel_idx]
sexps = exps[sel_idx]
sargs = arg_perf[sel_idx]
sel_mts = model_types[sel_idx]
bp = np.argmax(sperfs)
fxs += [xy[0]]
fys += [xy[1]]
fperfs += [sperfs[bp]]
fargs += [sargs[bp]]
fmodel_types += [sel_mts[bp]]
fexps += [sexps[bp]]
xs = np.asarray(fxs)
ys = np.asarray(fys)
perfs = np.asarray(fperfs)
arg_perf = np.asarray(fargs)
exps = np.asarray(fexps)
model_types = np.asarray(fmodel_types)
umt, model_types_inds = np.unique(model_types, return_inverse=True)
# Get weights for the top-n fitting models of each type
it_perfs = perfs[model_types == target_model]
it_exps = exps[model_types == target_model]
# it_args = arg_perf[model_types == target_model]
sorted_perfs = np.argsort(it_perfs)[::-1][:top_n]
perf = sel_exp_query(experiment_name=it_exps[sorted_perfs[0]],
model=target_model,
db_config=db_config)
dummy_sel_model = perf[-1]
print 'Using %s' % dummy_sel_model
model_file = dummy_sel_model['ckpt_file'].split('.')[0]
model_ckpt = '%s.ckpt-%s' % (model_file, model_file.split(
os.path.sep)[-1].split('_')[-1])
model_meta = '%s.meta' % model_ckpt
# Pull responses
dataset_module = py_utils.import_module(
model_dir=config.dataset_info,
dataset=dummy_sel_model['experiment_name'])
dataset_module = dataset_module.data_processing()
with tf.device('/cpu:0'):
val_images = tf.placeholder(tf.float32,
shape=[1] +
[x for x in dataset_module.im_size])
# Pull stimuli
stim_dir = os.path.join(main_config.tf_record_output,
dummy_sel_model['experiment_name'])
stim_files = glob(stim_dir + '*')
stim_meta_file = [x for x in stim_files if 'meta' in x][0]
stim_val_data = [x for x in stim_files if 'train.tfrecords' in x][0]
stim_val_mean = [x for x in stim_files if 'train_means' in x][0]
assert stim_meta_file is not None
assert stim_val_data is not None
assert stim_val_mean is not None
stim_meta_data = np.load(stim_meta_file).item()
rf_stim_meta_data = stim_meta_data['rf_data']
stim_mean_data = np.load(
stim_val_mean).items()[0][1].item()['image']['mean']
# Mean normalize
log = logger.get(os.path.join(output_dir, 'sta_logs', target_model))
data_dir = os.path.join(output_dir, 'data', target_model)
py_utils.make_dir(data_dir)
sys.path.append(
os.path.join('models', 'structs', dummy_sel_model['experiment_name']))
model_dict = __import__(target_model)
if hasattr(model_dict, 'output_structure'):
# Use specified output layer
output_structure = model_dict.output_structure
else:
output_structure = None
model = model_utils.model_class(
mean=stim_mean_data,
training=True, # FIXME
output_size=dataset_module.output_size)
with tf.device('/gpu:0'):
with tf.variable_scope('cnn') as scope:
val_scores, model_summary = model.build(
data=val_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
grad_image = tf.gradients(model.output, val_images)[0]
print(json.dumps(model_summary, indent=4))
# Set up summaries and saver
saver = tf.train.Saver(tf.global_variables())
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()))
all_filters = []
all_rfs = []
max_scores = []
for idx in sorted_perfs:
perf = sel_exp_query(experiment_name=it_exps[idx],
model=target_model,
db_config=db_config)
# perf_steps = np.argsort([v['training_step'] for v in perf])[::-1]
perf_steps = [v['validation_loss'] for v in perf]
max_score = np.max(perf_steps)
arg_perf_steps = np.argmax(perf_steps)
sel_model = perf[arg_perf_steps] # perf_steps[it_args[idx]]]
print 'Using %s' % sel_model
model_file = sel_model['ckpt_file'].split('.')[0]
model_ckpt = '%s.ckpt-%s' % (model_file, model_file.split(
os.path.sep)[-1].split('_')[-1])
model_meta = '%s.meta' % model_ckpt
# Store sparse noise for reference
stim_dir = os.path.join(main_config.tf_record_output,
sel_model['experiment_name'])
stim_files = glob(stim_dir + '*')
stim_meta_file = [x for x in stim_files if 'meta' in x][0]
stim_val_data = [x for x in stim_files if 'train.tfrecords' in x][0]
stim_val_mean = [x for x in stim_files if 'train_means' in x][0]
assert stim_meta_file is not None
assert stim_val_data is not None
assert stim_val_mean is not None
stim_meta_data = np.load(stim_meta_file).item()
rf_stim_meta_data = stim_meta_data['rf_data']
stim_mean_data = np.load(
stim_val_mean).items()[0][1].item()['image']['mean']
rf_stim_meta_data = rf_stim_meta_data.values()[0][0]
sparse_rf_on = {
'center_x': rf_stim_meta_data.get('on_center_x', None),
'center_y': rf_stim_meta_data.get('on_center_y', None),
'width_x': rf_stim_meta_data.get('on_width_x', None),
'width_y': rf_stim_meta_data.get('on_width_y', None),
'distance': rf_stim_meta_data.get('on_distance', None),
'area': rf_stim_meta_data.get('on_area', None),
'rotation': rf_stim_meta_data.get('on_rotation', None),
}
sparse_rf_off = {
'center_x': rf_stim_meta_data.get('off_center_x', None),
'center_y': rf_stim_meta_data.get('off_center_y', None),
'width_x': rf_stim_meta_data.get('off_width_x', None),
'width_y': rf_stim_meta_data.get('off_width_y', None),
'distance': rf_stim_meta_data.get('off_distance', None),
'area': rf_stim_meta_data.get('off_area', None),
'rotation': rf_stim_meta_data.get('off_rotation', None),
}
sparse_rf = {'on': sparse_rf_on, 'off': sparse_rf_off}
# Set up exemplar threading
if target_model == 'conv2d':
fname = [
x for x in tf.global_variables()
if 'conv1_1_filters:0' in x.name
]
elif target_model == 'sep_conv2d':
fname = [
x for x in tf.global_variables()
if 'sep_conv1_1_filters:0' in x.name
]
elif target_model == 'dog' or target_model == 'DoG':
fname = [
x for x in tf.global_variables()
if 'dog1_1_filters:0' in x.name
]
else:
raise NotImplementedError
print 'Using %s' % sel_model
model_file = sel_model['ckpt_file'].split('.')[0]
model_ckpt = '%s.ckpt-%s' % (model_file, model_file.split(
os.path.sep)[-1].split('_')[-1])
saver.restore(sess, model_ckpt)
all_filters += [sess.run(fname)]
all_rfs += [sparse_rf]
max_scores += [max_score]
np.savez('tests/ALLEN_files/filters/%s_%s_recalc_%s' %
(experiment, target_model, recalc),
rfs=all_rfs,
perf=max_scores,
filters=all_filters)
print 'SAVED'
def plot_fits(
experiment='760_cells_2017_11_04_16_29_09',
query_db=False,
num_models=3,
template_exp='ALLEN_selected_cells_1',
process_pnodes=False,
allen_dir='/home/drew/Documents/Allen_Brain_Observatory',
output_dir='tests/ALLEN_files',
stimulus_dir='/media/data_cifs/AllenData/DataForTrain/all_stimulus_template',
stimulus_type='tfrecord',
top_n=0,
preload_stim=False,
target_layer='conv1_1', # conv1_1, sep_conv1_1, dog1_1
target_model='conv2d'): # conv2d, sep_conv2d, dog
"""Plot fits across the RF.
experiment: Name of Allen experiment you're plotting.
query_db: Use data from DB versus data in Numpys.
num_models: The number of architectures you're testing.
template_exp: The name of the contextual_circuit model template used."""
sys.path.append(allen_dir)
from allen_config import Allen_Brain_Observatory_Config
if process_pnodes:
from pnodes_declare_datasets_loop import query_hp_hist, sel_exp_query
else:
from declare_datasets_loop import query_hp_hist, sel_exp_query
config = Config()
main_config = Allen_Brain_Observatory_Config()
db_config = credentials.postgresql_connection()
files = glob(
os.path.join(
allen_dir,
main_config.multi_exps,
experiment, '*.npz'))
assert len(files), 'Couldn\'t find files.'
out_data, xs, ys = [], [], []
perfs, model_types, exps, arg_perf = [], [], [], []
count = 0
for f in files:
data = np.load(f)
d = {
'x': data['rf_data'].item()['on_center_x'],
'y': data['rf_data'].item()['on_center_y'],
# x: files['dataset_method'].item()['x_min'],
# y: files['dataset_method'].item()['y_min'],
}
exp_name = {
'experiment_name': data['dataset_method'].item()[
'experiment_name']}
if query_db:
perf = query_hp_hist(
exp_name['experiment_name'],
db_config=db_config)
if perf is None:
print 'No fits for: %s' % exp_name['experiment_name']
else:
raise NotImplementedError
d['perf'] = perf
d['max_val'] = np.max(perf)
out_data += [d]
xs += [np.round(d['x'])]
ys += [np.round(d['y'])]
perfs += [np.max(d['perf'])]
count += 1
else:
data_files = glob(
os.path.join(
main_config.ccbp_exp_evals,
exp_name['experiment_name'],
'*val_losses.npy')) # Scores has preds, labels has GT
for gd in data_files:
mt = gd.split(
os.path.sep)[-1].split(
template_exp + '_')[-1].split('_' + 'val')[0]
it_data = np.load(gd).item()
sinds = np.asarray(it_data.keys())[np.argsort(it_data.keys())]
sit_data = [it_data[idx] for idx in sinds]
d['perf'] = sit_data
d['max_val'] = np.max(sit_data)
d['max_idx'] = np.argmax(sit_data)
d['mt'] = mt
out_data += [d]
xs += [np.round(d['x'])]
ys += [np.round(d['y'])]
perfs += [np.max(sit_data)]
arg_perf += [np.argmax(sit_data)]
exps += [gd.split(os.path.sep)[-2]]
model_types += [mt]
count += 1
# Package as a df
xs = np.round(np.asarray(xs)).astype(int)
ys = np.round(np.asarray(ys)).astype(int)
perfs = np.asarray(perfs)
arg_perf = np.asarray(arg_perf)
exps = np.asarray(exps)
model_types = np.asarray(model_types)
# Filter to only keep top-scoring values at each x/y (dirty trick)
fxs, fys, fperfs, fmodel_types, fexps, fargs = [], [], [], [], [], []
xys = np.vstack((xs, ys)).transpose()
cxy = np.ascontiguousarray( # Unique rows
xys).view(
np.dtype((np.void, xys.dtype.itemsize * xys.shape[1])))
_, idx = np.unique(cxy, return_index=True)
uxys = xys[idx]
for xy in uxys:
sel_idx = (xys == xy).sum(axis=-1) == 2
sperfs = perfs[sel_idx]
sexps = exps[sel_idx]
sargs = arg_perf[sel_idx]
sel_mts = model_types[sel_idx]
bp = np.argmax(sperfs)
fxs += [xy[0]]
fys += [xy[1]]
fperfs += [sperfs[bp]]
fargs += [sargs[bp]]
fmodel_types += [sel_mts[bp]]
fexps += [sexps[bp]]
xs = np.asarray(fxs)
ys = np.asarray(fys)
perfs = np.asarray(fperfs)
arg_perf = np.asarray(fargs)
exps = np.asarray(fexps)
model_types = np.asarray(fmodel_types)
umt, model_types_inds = np.unique(model_types, return_inverse=True)
# Get weights for the top-n fitting models of each type
it_perfs = perfs[model_types == target_model]
it_exps = exps[model_types == target_model]
# it_args = arg_perf[model_types == target_model]
# sorted_perfs = np.argsort(it_perfs)[::-1][:top_n]
sorted_perfs = [np.argsort(it_perfs)[::-1][top_n]]
for idx in sorted_perfs:
perf = sel_exp_query(
experiment_name=it_exps[idx],
model=target_model,
db_config=db_config)
# perf_steps = np.argsort([v['training_step'] for v in perf])[::-1]
perf_steps = [v['validation_loss'] for v in perf]
max_score = np.max(perf_steps)
arg_perf_steps = np.argmax(perf_steps)
sel_model = perf[arg_perf_steps] # perf_steps[it_args[idx]]]
print 'Using %s' % sel_model
model_file = sel_model['ckpt_file'].split('.')[0]
model_ckpt = '%s.ckpt-%s' % (
model_file,
model_file.split(os.path.sep)[-1].split('_')[-1])
model_meta = '%s.meta' % model_ckpt
# Pull stimuli
stim_dir = os.path.join(
main_config.tf_record_output,
sel_model['experiment_name'])
stim_files = glob(stim_dir + '*')
stim_meta_file = [x for x in stim_files if 'meta' in x][0]
# stim_val_data = [x for x in stim_files if 'val.tfrecords' in x][0]
stim_val_data = [x for x in stim_files if 'train.tfrecords' in x][0]
stim_val_mean = [x for x in stim_files if 'train_means' in x][0]
assert stim_meta_file is not None
assert stim_val_data is not None
assert stim_val_mean is not None
stim_meta_data = np.load(stim_meta_file).item()
rf_stim_meta_data = stim_meta_data['rf_data']
stim_mean_data = np.load(
stim_val_mean).items()[0][1].item()['image']['mean']
# Store sparse noise for reference
sparse_rf_on = {
'center_x': rf_stim_meta_data.get('on_center_x', None),
'center_y': rf_stim_meta_data.get('on_center_y', None),
'width_x': rf_stim_meta_data.get('on_width_x', None),
'width_y': rf_stim_meta_data.get('on_width_y', None),
'distance': rf_stim_meta_data.get('on_distance', None),
'area': rf_stim_meta_data.get('on_area', None),
'rotation': rf_stim_meta_data.get('on_rotation', None),
}
sparse_rf_off = {
'center_x': rf_stim_meta_data.get('off_center_x', None),
'center_y': rf_stim_meta_data.get('off_center_y', None),
'width_x': rf_stim_meta_data.get('off_width_x', None),
'width_y': rf_stim_meta_data.get('off_width_y', None),
'distance': rf_stim_meta_data.get('off_distance', None),
'area': rf_stim_meta_data.get('off_area', None),
'rotation': rf_stim_meta_data.get('off_rotation', None),
}
sparse_rf = {'on': sparse_rf_on, 'off': sparse_rf_off}
# Pull responses
dataset_module = py_utils.import_module(
model_dir=config.dataset_info,
dataset=sel_model['experiment_name'])
dataset_module = dataset_module.data_processing()
with tf.device('/cpu:0'):
val_images = tf.placeholder(
tf.float32,
shape=[1] + [x for x in dataset_module.im_size])
# Mean normalize
log = logger.get(os.path.join(output_dir, 'sta_logs', target_model))
data_dir = os.path.join(output_dir, 'data', target_model)
py_utils.make_dir(data_dir)
sys.path.append(os.path.join('models', 'structs', sel_model['experiment_name']))
model_dict = __import__(target_model)
if hasattr(model_dict, 'output_structure'):
# Use specified output layer
output_structure = model_dict.output_structure
else:
output_structure = None
model = model_utils.model_class(
mean=stim_mean_data,
training=True, # FIXME
output_size=dataset_module.output_size)
with tf.device('/gpu:0'):
with tf.variable_scope('cnn') as scope:
val_scores, model_summary = model.build(
data=val_images,
layer_structure=model_dict.layer_structure,
output_structure=output_structure,
log=log,
tower_name='cnn')
grad_image = tf.gradients(model.output, val_images)[0]
print(json.dumps(model_summary, indent=4))
# Set up summaries and saver
saver = tf.train.Saver(tf.global_variables())
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())
)
saver.restore(sess, model_ckpt)
# Set up exemplar threading
if target_model == 'conv2d':
fname = [
x for x in tf.global_variables()
if 'conv1_1_filters:0' in x.name]
elif target_model == 'sep_conv2d':
fname = [
x for x in tf.global_variables()
if 'sep_conv1_1_filters:0' in x.name]
elif target_model == 'dog':
fname = [
x for x in tf.global_variables()
if 'dog1_1_filters:0' in x.name]
else:
raise NotImplementedError
val_tensors = {
'images': val_images,
# 'labels': val_labels,
'filts': fname,
'responses': model[target_layer],
'labels': model['output'],
'grads': grad_image
}
all_images, all_preds, all_grads, all_responses = [], [], [], []
num_steps = 10000
stimuli = os.path.join(
stimulus_dir,
'locally_sparse_noise_8deg_template.pkl')
stimuli = pickle.load(open(stimuli, 'rb'))[:num_steps]
ih, iw = 304, 608
ns, sh, sw = stimuli.shape
sh, sw = ih, iw
sh = int(sh)
sw = int(sw)
tb = int((ih - sh) // 2)
lr = int((iw - sw) // 2)
gns = 32
cnst = 127.5
for step in range(num_steps): # step, stim in enumerate(stimuli): # step in range(num_steps):
chosen_stim = np.random.permutation(ns)[0]
if preload_stim:
it_stim = stimuli[chosen_stim].astype(np.float32)
it_stim = it_stim.astype(np.float32)
noise_im = (misc.imresize(it_stim, [sh, sw], interp='nearest'))
noise_im = cv2.copyMakeBorder(
noise_im.squeeze(), tb, tb, lr, lr, cv2.BORDER_CONSTANT, value=cnst)
else:
stim_noise = scipy.sparse.csr_matrix(scipy.sparse.random(ih // gns, iw // gns, density=0.05)).todense()
stim_noise_mask = stim_noise == 0
stim_noise[stim_noise > 0.5] = 255.
stim_noise[stim_noise < 0.5] = 0.
stim_noise[stim_noise_mask] = cnst
noise_im = (misc.imresize(stim_noise.squeeze(), [sh, sw], interp='nearest'))
if np.random.rand() < 0.5:
noise_im = np.fliplr(noise_im)
if np.random.rand() < 0.5:
noise_im = np.flipud(noise_im)
# noise_im = (misc.imresize(it_stim, [ih, iw], interp='nearest'))[None, :, :, None]
noise_im = noise_im / 255.
noise_im = noise_im[None, :, :, None]
assert noise_im.max() <= 1
val_vals = sess.run(val_tensors.values(), feed_dict={val_images: noise_im})
val_dict = {k: v for k, v in zip(val_tensors.keys(), val_vals)}
all_images += [noise_im] # val_dict['images']]
# all_responses += [val_dict['responses']]
all_preds += [val_dict['labels'].squeeze()]
# all_grads += [val_dict['grads'].squeeze()]
print 'Finished step %s' % step
# Process and save data
all_images = np.concatenate(all_images).squeeze()
ev, vals = peakdet(all_preds, np.median(all_preds))
sp = np.zeros_like(all_preds)
sp[ev[:, 0].astype(int)] = 1
plt.imshow(np.matmul(all_images.reshape(all_images.shape[0], -1).transpose(), all_preds).reshape(ih, iw));plt.show()
plt.imshow(np.matmul(all_images.reshape(all_images.shape[0], -1).transpose(), sp).reshape(ih, iw));plt.show()
filters = val_dict['filts'][0].squeeze().transpose(2, 0, 1)
import ipdb;ipdb.set_trace()
all_grads = np.asarray(all_grads)
all_preds = np.asarray(all_preds).reshape(-1, 1)
import ipdb;ipdb.set_trace()
# res_f = all_responses.reshape(ne * h * w, k)
# res_g = res_grads.reshape(ne, rh * rw)
# i_cov = np.cov(res_i.transpose())
# f_cov = np.cov(res_f.transpose())
# g_cov = np.cov(res_g.transpose())
# sp = (all_preds > all_preds.mean()).astype(np.float32)
# res_i = res_g
# ev, vals = peakdet(all_preds, 0.5)
# sp = np.zeros_like(all_preds)
# sp[ev[:, 0].astype(int)] = 1
# slen = ne
# nsp = np.sum(sp) # number of spikes
# swid = rh * rw
# Msz = np.dot(np.dot(slen, swid), ne) # Size of full stimulus matrix
# rowlen = 1830 # np.dot(swid, ne) # Length of a single row of stimulus matrix
# Compute raw mean and covariance
# RawMu = np.mean(res_i, 0).T
# RawCov = np.dot(res_i.T, res_i) / (slen-1.) - (RawMu*np.vstack(RawMu)*slen) / (slen-1.)
# Compute spike-triggered mean and covariance
# iisp = np.nonzero((sp > 0.))
# spvec = sp[iisp]
# STA = np.divide(np.dot(spvec.T, res_i[iisp[0],:]).T, nsp)
# STC = np.dot(res_i[iisp[0],:].T, np.multiply(res_i[iisp[0],:], ml.repmat(spvec, rowlen, 1).T))/(nsp-1.) - (STA*np.vstack(STA)*nsp)/(nsp-1.)
# res_i_cov = np.matmul(res_i.transpose(), res_i)
# inv_res_i = np.linalg.pinv(res_i_cov)
# sta = inv_res_i * np.matmul(res_i.transpose(), all_preds)
# sta = inv_res_i * np.matmul(res_i.transpose(), spike_preds)
# sti = (1. / float(ne)) * (np.linalg.pinv(i_cov) * np.matmul(res_i, all_preds))
# sta = (1. / float(ne)) * (np.linalg.pinv(f_cov) * np.matmul(res_f, all_preds))
# sta = sta.reshape(h, w)
# stg = (1. / float(ne)) * np.matmul(all_grads.reshape(h * w, ne), all_preds)
# stg = stg.reshape(h, w)
np.savez(
os.path.join(data_dir, 'data'),
images=all_images,
pred=all_preds,
filters=filters,
STA=STA,
fits=fits,
grads=all_grads)
if target_model != 'dog':
save_mosaic(
maps=filters, # [0].squeeze().transpose(2, 0, 1),
output=os.path.join(data_dir, '%s_filters' % target_layer),
rc=8,
cc=4,
title='%s filters' % (
target_layer))
else:
import ipdb;ipdb.set_trace()
print 'Complete.'