def run(which_dataset):
#Run build_image_data script
print('Organizing files')
#Make dirs if they do not exist
config = GEDIconfig(which_dataset=which_dataset)
dir_list = [
config.train_directory, config.validation_directory,
config.tfrecord_dir, config.train_checkpoint
]
[make_dir(d) for d in dir_list]
#Prepare lists with file pointers
files = get_file_list(config.GEDI_path, config.label_directories,
config.im_ext)
label_list = config.GEDI_path + 'list_of_' + config.which_dataset + '_labels.txt' #to be created by prepare
write_label_list(files, label_list)
#Copy data into the appropriate training/testing directories
hw = misc.imread(files[0]).shape
new_files = split_files(files, config.train_proportion, config.tvt_flags)
move_files(new_files['train'], config.train_directory)
#process_image_data('train',new_files,config.tfrecord_dir,config.im_ext,config.train_shards,hw,config.normalize)
simple_tf_records('train', new_files, config.tfrecord_dir, config.im_ext,
config.train_shards, hw, config.normalize)
if 'val' in config.tvt_flags:
move_files(new_files['val'], config.validation_directory)
#process_image_data('val',new_files,config.tfrecord_dir,config.im_ext,config.train_shards,hw,config.normalize)
simple_tf_records('val', new_files, config.tfrecord_dir, config.im_ext,
config.train_shards, hw, config.normalize)
if 'test' in config.tvt_flags:
move_files(new_files['test'], config.test_directory)
#process_image_data('test',new_files,config.tfrecord_dir,config.im_ext,config.train_shards,hw,config.normalize)
simple_tf_records('test', new_files, config.tfrecord_dir,
config.im_ext, config.train_shards, hw,
config.normalize)
# Finally, write the labels file:
labels_to_class_names = dict(
zip(range(len(config.label_directories)), config.label_directories))
write_label_file(labels_to_class_names, config.tfrecord_dir)
def test_vgg16(
which_dataset,
validation_data=None,
model_dir=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Find model checkpoints
ckpts, ckpt_names = find_ckpts(config)
#Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
config.validation_batch,
im_shape,
config.model_image_size[:2],
num_epochs=1)
#Prepare model on GPU
with tf.device('/gpu:0'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
validation_mode = tf.Variable(False, name='training')
vgg.build(val_images,
output_shape=config.output_shape,
train_mode=validation_mode)
#Setup validation op
eval_accuracy = class_accuracy(vgg.prob,
val_labels) #training accuracy now...
#Set up saver
saver = tf.train.Saver(tf.all_variables())
#Loop through each checkpoint, loading the model weights, then testing the entire validation set
ckpt_accs = []
for idx in tqdm(range(len(ckpts))):
accs = []
try:
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.initialize_all_variables(),
tf.initialize_local_variables())
) #need to initialize both if supplying num_epochs to inputs
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, ckpts[idx])
start_time = time.time()
while not coord.should_stop():
accs = np.append(accs, sess.run([eval_accuracy]))
except tf.errors.OutOfRangeError:
ckpt_accs.append(accs)
print('Batch %d took %.1f seconds', idx, time.time() - start_time)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
#Plot everything
plot_accuracies(ckpt_accs, ckpt_names,
out_dir + 'validation_accuracies.png')
np.savez(out_dir + 'validation_accuracies',
ckpt_accs=ckpt_accs,
ckpt_names=ckpt_names)
def train_vgg16(which_dataset,train_data=None,validation_data=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if train_data == None: #Use globals
train_data = config.tfrecord_dir + 'train.tfrecords'
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
dt_stamp = re.split(' ',str(datetime.now()))[0].replace('-','_')
config.train_checkpoint = os.path.join(config.train_checkpoint,config.which_dataset + '_' + dt_stamp) #timestamp this run
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.train_checkpoint,config.train_summaries,config.results,out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Prepare data on CPU
with tf.device('/cpu:0'):
train_images, train_labels = inputs(train_data, config.train_batch, im_shape, config.model_image_size[:2], train=config.data_augmentations, num_epochs=config.epochs)
tf.image_summary('train images', train_images)
#Prepare model on GPU
with tf.device('/gpu:0'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,fine_tune_layers=config.fine_tune_layers)
train_mode = tf.Variable(True, name='training')
vgg.build(train_images,output_shape=config.output_shape,train_mode=train_mode)
#Prepare the cost function
cost = softmax_cost(vgg.fc8, train_labels)
tf.scalar_summary("cost", cost)
#Finetune the learning rates
other_opt_vars,ft_opt_vars = fine_tune_prepare_layers(tf.trainable_variables(),config.fine_tune_layers) #for all variables in trainable variables, print name if there's duplicates you f****d up
train_op = ft_non_optimized(cost,other_opt_vars,ft_opt_vars,tf.train.AdamOptimizer,config.hold_lr,config.new_lr) #actually is faster :)
#Setup validation op
eval_accuracy = class_accuracy(vgg.prob,train_labels) #training accuracy now...
tf.scalar_summary("train accuracy", eval_accuracy)
#Set up summaries and saver
saver = tf.train.Saver(tf.all_variables(),max_to_keep=100)
summary_op = tf.merge_all_summaries()
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement = True))
sess.run(tf.group(tf.initialize_all_variables(), tf.initialize_local_variables())) #need to initialize both if supplying num_epochs to inputs
summary_writer = tf.train.SummaryWriter(os.path.join(config.train_summaries,config.which_dataset + '_' + dt_stamp), sess.graph)
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
#Start training loop
try:
step = 0
losses = []
accs=[]
while not coord.should_stop():
start_time = time.time()
_, loss_value, acc = sess.run([train_op, cost, eval_accuracy])
losses.append(loss_value)
accs.append(acc)
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 100 == 0:
if step % 500 == 0:
#Summaries
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
else:
#Training status
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
config.train_batch / duration, float(duration)))
# Save the model checkpoint periodically.
if step % 500 == 0 and step>0 :
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
if np.average(accs[-500:-1])>0.95:
coord.request_stop()
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (config.epochs, step))
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
np.save(out_dir + 'training_loss',losses)
def train_att(which_dataset,train_data=None,validation_data=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if train_data == None: #Use globals
train_data = config.tfrecord_dir + 'train.tfrecords'
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
dt_stamp = re.split(' ',str(datetime.now()))[0].replace('-','_')
config.train_checkpoint = os.path.join(config.train_checkpoint,config.which_dataset + '_' + dt_stamp) #timestamp this run
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.train_checkpoint,config.train_summaries,config.results,out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Prepare data on CPU
with tf.device('/cpu:0'):
train_images, train_labels = inputs(train_data, config.train_batch, im_shape, config.model_image_size[:2], train=config.data_augmentations, num_epochs=config.epochs)
tf.image_summary('train images', train_images)
#Prepare model on GPU
with tf.device('/gpu:0'):
att_model = att.Attention()
train_mode = tf.Variable(True, name='training')
att_model.build(train_images,enc_size=config.enc_size,read_n=config.read_n,T=config.T,output_shape=config.output_shape,train_mode=train_mode)
#Prepare the cost function
cost = softmax_cost(att_model.fc2, train_labels)
tf.scalar_summary("cost", cost)
#print type(tf.trainable_variables()[0])
print [x.name for x in tf.trainable_variables()]
#Finetune the learning rates
optimizer=tf.train.AdamOptimizer(learning_rate=config.new_lr,beta1=0.5)
grads=optimizer.compute_gradients(cost)
#v_i=att_model.v_i
for i,(g,v) in enumerate(grads):
if g is not None:
grads[i]=(tf.clip_by_norm(g,5),v) # clip gradients
train_op=optimizer.apply_gradients(grads)
#print a
#Setup validation op
eval_accuracy = class_accuracy(att_model.prob,train_labels) #training accuracy now...
tf.scalar_summary("train accuracy", eval_accuracy)
#Set up summaries and saver
saver = tf.train.Saver(tf.all_variables(),max_to_keep=100)
summary_op = tf.merge_all_summaries()
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement = True))
sess.run(tf.group(tf.initialize_all_variables(), tf.initialize_local_variables())) #need to initialize both if supplying num_epochs to inputs
summary_writer = tf.train.SummaryWriter(os.path.join(config.train_summaries,config.which_dataset + '_' + dt_stamp), sess.graph)
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
#Start training loop
try:
step = 0
losses = []
accs=[]
while not coord.should_stop():
start_time = time.time()
_,loss_value, acc= sess.run([train_op, cost, eval_accuracy])
#print v_j
losses.append(loss_value)
accs.append(acc)
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 100 == 0:
if step % 2000 == 0:
#Summaries
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
else:
#Training status
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
config.train_batch / duration, float(duration)))
if np.average(accs[-100:-1])>0.9 or np.average(losses[-100:-1])<0.1:
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
coord.request_stop()
# Save the model checkpoint periodically.
if step % 1000 == 0 and step>0 :
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (config.epochs, step))
saver.save(sess, os.path.join(config.train_checkpoint, 'model_' + str(step) + '.ckpt'),
global_step=step)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
np.save(out_dir + 'training_loss',losses)
image_filenames = glob.glob(labels + '/*')
labels = []
for im in image_filenames:
for k, v in in_dict.iteritems():
label_name = re.split('\d+', re.split('/', im)[-1])[0]
if label_name == k:
labels += [v]
return image_filenames, labels
if __name__ == "__main__":
config = GEDIconfig()
#Make directories if they haven't been made yet
dir_list = [config.heatmap_source_images, config.heatmap_dataset_images]
[make_dir(d) for d in dir_list]
image_filenames, image_labels = random_sample_images(
config.heatmap_image_labels, config.heatmap_image_dict,
config.heatmap_dataset_images, config.im_ext,
config.heatmap_image_amount)
#Run bubbles
generate_heatmaps_for_images(config,
None,
image_filenames,
image_labels,
'vgg16',
'bubbles',
'neg',
block_size=config.block_size,
block_stride=config.block_stride,
generate_plots=config.generate_plots,
def test_vgg16(validation_data, model_dir, selected_ckpts=-1):
config = GEDIconfig()
if validation_data is None: # Use globals
validation_data = config.tf_record_names['val']
meta_data = np.load(
os.path.join(config.tfrecord_dir, 'val_' + config.max_file))
else:
meta_data = np.load(
validation_data.split('.tfrecords')[0] + '_maximum_value.npz')
label_list = os.path.join(
config.processed_image_patch_dir,
'list_of_' + '_'.join(x
for x in config.image_prefixes) + '_labels.txt')
with open(label_list) as f:
file_pointers = [l.rstrip('\n') for l in f.readlines()]
# Prepare image normalization values
try:
max_value = np.max(meta_data['max_array']).astype(np.float32)
except:
max_value = np.asarray([config.max_gedi])
try:
min_value = np.max(meta_data['min_array']).astype(np.float32)
except:
min_value = np.asarray([config.min_gedi])
# Find model checkpoints
ckpts, ckpt_names = find_ckpts(config, model_dir)
ds_dt_stamp = re.split('/', ckpts[0])[-2]
out_dir = os.path.join(config.results, ds_dt_stamp + '/')
try:
config = np.load(os.path.join(out_dir, 'meta_info.npy')).item()
# Make sure this is always at 1
config.validation_batch = 1
print '-' * 60
print 'Loading config meta data for:%s' % out_dir
print '-' * 60
except:
print '-' * 60
print 'Using config from gedi_config.py for model:%s' % out_dir
print '-' * 60
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# im_shape = get_image_size(config)
im_shape = config.gedi_image_size
# Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
1,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
num_epochs=1,
normalize=config.normalize)
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(val_images, output_shape=config.output_shape)
# Setup validation op
scores = vgg.prob
preds = tf.argmax(vgg.prob, 1)
targets = tf.cast(val_labels, dtype=tf.int64)
oh_targets = tf.one_hot(val_labels, config.output_shape)
# Masked LRP op
heatmap = lrp.lrp(logits * oh_targets, -123.68, 255 - 123.68)
# heatmap = lrp.get_lrp_im(sess, F, images, y, img, lab)[0]
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Get class indices for all files
if use_true_label:
label_key = np.asarray(config.label_directories)
class_indices = [
np.where(config.which_dataset + '_' + fn == label_key)
for fn in labels
]
else:
class_indices = [None] * len(image_filenames)
# Loop through each checkpoint then test the entire validation set
ckpt_yhat, ckpt_y, ckpt_scores = [], [], []
print '-' * 60
print 'Beginning visualization'
print '-' * 60
if selected_ckpts is not None:
# Select a specific ckpt
if selected_ckpts < 0:
ckpts = ckpts[selected_ckpts:]
else:
ckpts = ckpts[:selected_ckpts]
dec_scores, yhat, y, yoh, ims, hms = [], [], [], [], [], []
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
try:
# Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, c)
start_time = time.time()
while not coord.should_stop():
sc, tyh, ty, tyoh, imb, ihm = sess.run(
[scores, preds, targets, oh_targets, val_images, heatmap])
dec_scores += [sc]
yhat += [tyh]
y += [ty]
yoh += [tyoh]
ims += [imb]
hms += [ihm]
except tf.errors.OutOfRangeError:
print 'Batch %d took %.1f seconds' % (idx,
time.time() - start_time)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def test_vgg16(
which_dataset,
validation_data=None,
model_dir=None): #Fine tuning defaults to wipe out the final two FCs
config = GEDIconfig(which_dataset)
if validation_data == None: #Use globals
validation_data = config.tfrecord_dir + 'val.tfrecords'
#Make output directories if they do not exist
out_dir = config.results + config.which_dataset + '/'
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
im_shape = get_image_size(config)
#Find model checkpoints
ckpts, ckpt_names = find_ckpts(config)
print ckpts, ckpt_names
#Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
config.validation_batch,
im_shape,
config.model_image_size[:2],
num_epochs=1)
#Prepare model on GPU
with tf.device('/gpu:0'):
att_model = att.Attention()
validation_mode = tf.Variable(False, name='training')
att_model.build(val_images,
enc_size=config.enc_size,
read_n=config.read_n,
T=config.T,
output_shape=config.output_shape,
train_mode=validation_mode)
image_0 = val_images
image_1 = att_model.image_show
image_loc = att_model.location
# print image_0.get_shape()
# print image_1[0].get_shape()
#Setup validation op
eval_accuracy = class_accuracy(att_model.prob,
val_labels) #training accuracy now...
#Set up saver
saver = tf.train.Saver(tf.all_variables())
#Loop through each checkpoint, loading the model weights, then testing the entire validation set
ckpt_accs = []
max_acc = 0
max_ind = 0
max_show_0 = []
max_show_1 = []
max_loc = []
for idx in tqdm(range(len(ckpts))):
print ckpts[idx]
accs = []
show_0 = np.array([])
show_1 = np.array([])
show_loc = np.array([])
try:
#print type(show_0)
#Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.initialize_all_variables(),
tf.initialize_local_variables())
) #need to initialize both if supplying num_epochs to inputs
#Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, ckpts[idx])
start_time = time.time()
while not coord.should_stop():
#print '1'
#print type(accs)
acc, aa, bb, cc = sess.run(
[eval_accuracy, image_0, image_1, image_loc])
accs = np.append(accs, acc)
if accs[-1] > 0.8 and show_0.shape[-1] < 5:
#print show_0.shape[-1]
#print aa.shape, bb
aa = aa
bb = bb
(x1, x2, x3, x4) = aa.shape
(y1, y2, y3, y4) = bb.shape
(z1, z2, z3) = cc.shape
aa = np.reshape(aa, (x1, x2, x3, x4, 1))
bb = np.reshape(bb, (y1, y2, y3, y4, 1))
cc = np.reshape(cc, (z1, z2, z3, 1))
if show_0.shape[0] <= 2:
#print sess.run([image_1])
show_0 = aa
show_1 = bb
show_loc = cc
else:
#print sess.run([image_0])[0].shape, show_0.shape
show_0 = np.concatenate((show_0, aa), 4)
show_1 = np.concatenate((show_1, bb), 4)
show_loc = np.concatenate((show_loc, cc), 3)
except tf.errors.OutOfRangeError:
if np.mean(accs) > max_acc:
max_acc = np.mean(accs)
max_ind = idx
max_show_0 = show_0
max_show_1 = show_1
max_loc = show_loc
ckpt_accs.append(accs)
print('Batch %d took %.1f seconds', idx, time.time() - start_time)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
print ckpt_accs, ckpt_names
#Plot everything
plot_accuracies(ckpt_accs, ckpt_names,
out_dir + 'validation_accuracies.png')
np.savez(out_dir + 'validation_accuracies',
ckpt_accs=ckpt_accs,
ckpt_names=ckpt_names)
np.savez(out_dir + 'att_verification_' + which_dataset,
max_show_0=max_show_0,
max_show_1=max_show_1,
max_loc=max_loc)
for idx in range(len(ckpts)):
if idx != max_ind:
os.remove(ckpts[idx] + '.data-00000-of-00001')
os.remove(ckpts[idx] + '.meta')
