def visualize_model(
live_ims,
dead_ims,
model_file,
output_folder,
num_channels,
smooth_iterations=50,
untargeted=False,
viz='none',
per_timepoint=True):
"""Train an SVM for your dataset on GEDI-model encodings."""
config = GEDIconfig()
if live_ims is None:
raise RuntimeError(
'You need to supply a directory path to the live images.')
if dead_ims is None:
raise RuntimeError(
'You need to supply a directory path to the dead images.')
live_files = glob(os.path.join(live_ims, '*%s' % config.raw_im_ext))
dead_files = glob(os.path.join(dead_ims, '*%s' % config.raw_im_ext))
combined_labels = np.concatenate((
np.zeros(len(live_files)),
np.ones(len(dead_files))))
combined_files = np.concatenate((live_files, dead_files))
if len(combined_files) == 0:
raise RuntimeError('Could not find any files. Check your image path.')
config = GEDIconfig()
model_file_path = os.path.sep.join(model_file.split(os.path.sep)[:-1])
meta_file_pointer = os.path.join(
model_file_path,
'train_maximum_value.npz')
if not os.path.exists(meta_file_pointer):
raise RuntimeError(
'Cannot find the training data meta file: train_maximum_value.npz'
'Closest I could find from directory %s was %s.'
'Download this from the link described in the README.md.'
% (model_file_path, glob(os.path.join(model_file_path, '*.npz'))))
meta_data = np.load(meta_file_pointer)
# Prepare image normalization values
training_max = np.max(meta_data['max_array']).astype(np.float32)
training_min = np.min(meta_data['min_array']).astype(np.float32)
# Find model checkpoints
ds_dt_stamp = re.split('/', model_file)[-2]
out_dir = os.path.join(config.results, ds_dt_stamp)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# Prepare data on CPU
images = tf.placeholder(
tf.float32,
shape=[None] + config.model_image_size,
name='images')
labels = tf.placeholder(
tf.int64,
shape=[None],
name='labels')
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.model_struct(
vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(
images,
output_shape=config.output_shape)
# Setup validation op
scores = vgg.fc7
preds = tf.argmax(vgg.prob, 1)
activity_pattern = vgg.fc8
if not untargeted:
oh_labels = tf.one_hot(labels, config.output_shape)
activity_pattern *= oh_labels
grad_image = tf.gradients(activity_pattern, images)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Loop through each checkpoint then test the entire validation set
ckpts = [model_file]
ckpt_yhat, ckpt_y, ckpt_scores = [], [], []
ckpt_file_array, ckpt_viz_images = [], []
print '-' * 60
print 'Beginning evaluation'
print '-' * 60
if config.validation_batch > len(combined_files):
print 'Trimming validation_batch to %s (same as # of files).' % len(
combined_files)
config.validation_batch = len(combined_files)
count = 0
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
dec_scores, yhat, y, file_array, viz_images = [], [], [], [], []
# 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
saver.restore(sess, c)
start_time = time.time()
num_batches = np.floor(
len(combined_files) / float(
config.validation_batch)).astype(int)
for image_batch, label_batch, file_batch in tqdm(
image_batcher(
start=0,
num_batches=num_batches,
images=combined_files,
labels=combined_labels,
config=config,
training_max=training_max,
training_min=training_min,
num_channels=num_channels,
per_timepoint=per_timepoint),
total=num_batches):
feed_dict = {
images: image_batch,
labels: label_batch
}
it_grads = np.zeros((image_batch.shape))
sc, tyh = sess.run(
[scores, preds],
feed_dict=feed_dict)
for idx in range(smooth_iterations):
feed_dict = {
images: add_noise(image_batch),
labels: label_batch
}
it_grad = sess.run(
grad_image,
feed_dict=feed_dict)
it_grads += it_grad[0]
it_grads /= smooth_iterations # Mean across iterations
it_grads = visualization_function(it_grads, viz)
# Save each grad individually
for grad_i, pred_i, file_i, label_i in zip(
it_grads, tyh, file_batch, label_batch):
out_pointer = os.path.join(
output_folder,
file_i.split(os.path.sep)[-1])
out_pointer = out_pointer.split('.')[0] + '.png'
f = plt.figure()
plt.imshow(grad_i)
plt.title('Pred=%s, label=%s' % (pred_i, label_batch))
plt.savefig(out_pointer)
plt.close(f)
# Plot a moisaic of the grads
if viz == 'none':
pos_grads = normalize(np.maximum(it_grads, 0))
neg_grads = normalize(np.minimum(it_grads, 0))
alpha_mosaic(
image_batch,
pos_grads,
'pos_batch_%s.pdf' % count,
title='Positive gradient overlays.',
rc=1,
cc=len(image_batch),
cmap=plt.cm.Reds)
alpha_mosaic(
image_batch,
neg_grads,
'neg_batch_%s.pdf' % count,
title='Negative gradient overlays.',
rc=1,
cc=len(image_batch),
cmap=plt.cm.Reds)
else:
alpha_mosaic(
image_batch,
it_grads,
'batch_%s.pdf' % count,
title='Gradient overlays.',
rc=1,
cc=len(image_batch),
cmap=plt.cm.Reds)
count += 1
# Store the results
dec_scores += [sc]
yhat = np.append(yhat, tyh)
y = np.append(y, label_batch)
file_array = np.append(file_array, file_batch)
viz_images += [it_grads]
ckpt_yhat.append(yhat)
ckpt_y.append(y)
ckpt_scores.append(dec_scores)
ckpt_file_array.append(file_array)
ckpt_viz_images.append(viz_images)
print 'Batch %d took %.1f seconds' % (
idx, time.time() - start_time)
sess.close()
# Save everything
np.savez(
os.path.join(out_dir, 'validation_accuracies'),
ckpt_yhat=ckpt_yhat,
ckpt_y=ckpt_y,
ckpt_scores=ckpt_scores,
ckpt_names=ckpts,
combined_files=ckpt_file_array,
ckpt_viz_images=ckpt_viz_images)
def test_vgg16(validation_data, model_dir, label_file, selected_ckpts=-1):
config = GEDIconfig()
# Load metas
meta_data = np.load(os.path.join(tf_dir, 'val_maximum_value.npz'))
max_value = np.max(meta_data['max_array']).astype(np.float32)
# 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, 'gfp_2017_02_19_17_41_19' + '/')
try:
config = np.load(os.path.join(out_dir, 'meta_info.npy')).item()
# Make sure this is always at 1
config.validation_batch = 64
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
sorted_index = np.argsort(np.asarray([int(x) for x in ckpt_names]))
ckpts = ckpts[sorted_index]
ckpt_names = ckpt_names[sorted_index]
# CSV file
svm_image_file = os.path.join(out_dir, 'svm_models.npz')
if svm_image_file == 2:
svm_image_data = np.load(svm_image_file)
image_array = svm_image_data['image_array']
label_vec = svm_image_data['label_vec']
tr_label_vec = svm_image_data['tr_label_vec']
else:
labels = pd.read_csv(
os.path.join(config.processed_image_patch_dir,
'LINCSproject_platelayout_trans.csv'))
label_vec = []
image_array = []
for idx, row in tqdm(labels.iterrows(), total=len(labels)):
path_wd = '*%s_%s*' % (row['Plate'], row['Sci_WellID'])
path_pointer = glob(os.path.join(image_dir, path_wd))
if len(path_pointer) > 0:
for p in path_pointer:
import ipdb
ipdb.set_trace()
label_vec.append(row['Sci_SampleID'])
label_vec = np.asarray(label_vec)
le = preprocessing.LabelEncoder()
tr_label_vec = le.fit_transform(label_vec)
np.savez(svm_image_file,
image_array=image_array,
label_vec=label_vec,
tr_label_vec=tr_label_vec)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# Make placeholder
val_images = tf.placeholder(tf.float32,
shape=[None] + config.model_image_size)
# 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)
validation_mode = tf.Variable(False, name='training')
# No batchnorms durign testing
vgg.build(val_images,
output_shape=config.output_shape,
train_mode=validation_mode)
# Set up saver
svm_feature_file = os.path.join(out_dir, 'svm_scores.npz')
if os.path.exists(svm_feature_file):
svm_features = np.load(svm_feature_file)
dec_scores = svm_features['dec_scores']
label_vec = svm_features['label_vec']
else:
saver = tf.train.Saver(tf.global_variables())
ckpts = [ckpts[selected_ckpts]]
image_array = np.asarray(image_array)
for idx, c in enumerate(ckpts):
dec_scores = []
# 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
saver.restore(sess, c)
num_batches = np.ceil(len(image_array) /
config.validation_batch).astype(int)
batch_idx = np.arange(num_batches).repeat(
num_batches)[:len(image_array)]
for bi in np.unique(batch_idx):
# move this above to image processing
batch_images = image_array[batch_idx == bi] / 255.
start_time = time.time()
sc = sess.run(vgg.fc7, feed_dict={val_images: batch_images})
dec_scores.append(sc)
print 'Batch %d took %.1f seconds' % (idx,
time.time() - start_time)
# Save everything
np.savez(svm_feature_file, dec_scores=dec_scores, label_vec=label_vec)
# Build SVM
dec_scores = np.concatenate(dec_scores[:], axis=0)
model_array, score_array, combo_array, masked_label_array = [], [], [], []
for combo in itertools.combinations(np.unique(label_vec), 2):
combo_array.append(combo)
mask = np.logical_or(label_vec == combo[0], label_vec == combo[1])
import ipdb
ipdb.set_trace()
masked_labels = label_vec[mask]
masked_scores = dec_scores[mask, :]
clf = SVC(kernel='linear', C=1)
scores = cross_val_score(clf, masked_scores, masked_labels, cv=5)
model_array.append(clf)
score_array.append(scores)
masked_label_array.append(masked_labels)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
# Save everything
np.savez(os.path.join(out_dir, 'svm_models'),
combo_array=combo_array,
model_array=model_array,
score_array=score_array,
masked_label_array=masked_label_array)
def test_vgg16(image_dir,
model_file,
output_csv='prediction_file',
training_max=None):
print(image_dir)
# tf.set_random_seed(0)
config = GEDIconfig()
if image_dir is None:
raise RuntimeError(
'You need to supply a directory path to the images.')
combined_files = np.asarray(
glob(os.path.join(image_dir, '*%s' % config.raw_im_ext)))
if len(combined_files) == 0:
raise RuntimeError('Could not find any files. Check your image path.')
config = GEDIconfig()
model_file_path = os.path.sep.join(model_file.split(os.path.sep)[:-1])
print('model file path', model_file_path)
meta_file_pointer = os.path.join(model_file_path,
'train_maximum_value.npz')
if not os.path.exists(meta_file_pointer):
raise RuntimeError(
'Cannot find the training data meta file: train_maximum_value.npz'
'Closest I could find from directory %s was %s.'
'Download this from the link described in the README.md.' %
(model_file_path, glob(os.path.join(model_file_path, '*.npz'))))
meta_data = np.load(meta_file_pointer)
# Prepare image normalization values
if training_max is None:
training_max = np.max(meta_data['max_array']).astype(np.float32)
training_min = np.min(meta_data['min_array']).astype(np.float32)
# Find model checkpoints
ds_dt_stamp = re.split('/', model_file)[-2]
out_dir = os.path.join(config.results, ds_dt_stamp)
print('out_dir', out_dir)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# Prepare data on CPU
if config.model_image_size[-1] < 3:
print('*' * 60)
print('Warning: model is expecting a H/W/1 image. '
'Do you mean to set the last dimension of '
'config.model_image_size to 3?')
print('*' * 60)
images = tf.placeholder(tf.float32,
shape=[None] + config.model_image_size,
name='images')
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.model_struct(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(images, output_shape=config.output_shape)
# Setup validation op
scores = vgg.prob
preds = tf.argmax(vgg.prob, 1)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Loop through each checkpoint then test the entire validation set
ckpts = [model_file]
ckpt_yhat, ckpt_y, ckpt_scores, ckpt_file_array = [], [], [], []
print('-' * 60)
print('Beginning evaluation')
print('-' * 60)
if config.validation_batch > len(combined_files):
print('Trimming validation_batch size to %s (same as # of files).' %
len(combined_files))
config.validation_batch = len(combined_files)
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
dec_scores, yhat, file_array = [], [], []
# Initialize the graph
# sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)) as sess:
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
# Set up exemplar threading
saver.restore(sess, c)
start_time = time.time()
num_batches = np.floor(
len(combined_files) /
float(config.validation_batch)).astype(int)
for image_batch, file_batch in tqdm(image_batcher(
start=0,
num_batches=num_batches,
images=combined_files,
config=config,
training_max=training_max,
training_min=training_min),
total=num_batches):
feed_dict = {images: image_batch}
sc, tyh = sess.run([scores, preds], feed_dict=feed_dict)
dec_scores = np.append(dec_scores, sc)
yhat = np.append(yhat, tyh)
file_array = np.append(file_array, file_batch)
ckpt_yhat.append(yhat)
ckpt_scores.append(dec_scores)
ckpt_file_array.append(file_array)
print('Batch %d took %.1f seconds' %
(idx, time.time() - start_time))
# sess.close()
# Save everything
print('Save npz.')
print(os.path.join(out_dir, 'validation_accuracies'))
np.savez(os.path.join(out_dir, 'validation_accuracies'),
ckpt_yhat=ckpt_yhat,
ckpt_scores=ckpt_scores,
ckpt_names=ckpts,
combined_files=ckpt_file_array)
# Also save a csv with item/guess pairs
try:
dec_scores = np.asarray(dec_scores)
yhat = np.asarray(yhat)
df = pd.DataFrame(np.hstack(
(np.asarray(ckpt_file_array).reshape(-1, 1), yhat.reshape(-1, 1),
dec_scores.reshape(dec_scores.shape[0] // 2, 2))),
columns=[
'files', 'live_guesses', 'classifier score dead',
'classifier score live'
])
output_name = image_dir.split('/')[-1]
if output_name is None or len(output_name) == 0:
output_name = 'output'
df.to_csv(os.path.join(out_dir, '%s.csv' % output_name))
print('Saved csv to: %s' %
os.path.join(out_dir, '%s.csv' % output_name))
except:
print('X' * 60)
print('Could not save a spreadsheet of file info')
print('X' * 60)
# Plot everything
try:
plot_accuracies(ckpt_y, ckpt_yhat, config, ckpts,
os.path.join(out_dir, 'validation_accuracies.png'))
plot_std(ckpt_y, ckpt_yhat, ckpts,
os.path.join(out_dir, 'validation_stds.png'))
plot_cms(ckpt_y, ckpt_yhat, config,
os.path.join(out_dir, 'confusion_matrix.png'))
plot_pr(ckpt_y, ckpt_yhat, ckpt_scores,
os.path.join(out_dir, 'precision_recall.png'))
# plot_cost(
# os.path.join(out_dir, 'training_loss.npy'), ckpts,
# os.path.join(out_dir, 'training_costs.png'))
except:
print('X' * 60)
print('Could not locate the loss numpy')
print('X' * 60)
def test_vgg16(validation_data, model_dir, which_set, selected_ckpts):
config = GEDIconfig()
blur_kernel = config.hm_blur
if validation_data is None: # Use globals
validation_data = os.path.join(config.tfrecord_dir,
config.tf_record_names[which_set])
meta_data = np.load(
os.path.join(config.tfrecord_dir, 'val_%s' % config.max_file))
else:
meta_data = np.load('%s_maximum_value.npz' %
validation_data.split('.tfrecords')[0])
label_list = os.path.join(
config.processed_image_patch_dir,
'list_of_' + '_'.join(x
for x in config.image_prefixes) + '_labels.txt')
with open(label_list) as f:
file_pointers = [l.rstrip('\n') for l in f.readlines()]
# Prepare image normalization values
try:
max_value = np.max(meta_data['max_array']).astype(np.float32)
except:
max_value = np.asarray([config.max_gedi])
try:
min_value = np.max(meta_data['min_array']).astype(np.float32)
except:
min_value = np.asarray([config.min_gedi])
# Find model checkpoints
ds_dt_stamp = re.split('/', model_dir)[-1]
out_dir = os.path.join(config.results, ds_dt_stamp + '/')
try:
config = np.load(os.path.join(out_dir, 'meta_info.npy')).item()
# Make sure this is always at 1
config.validation_batch = 1
print '-' * 60
print 'Loading config meta data for:%s' % out_dir
print '-' * 60
except:
print '-' * 60
print 'Using config from gedi_config.py for model:%s' % out_dir
print '-' * 60
# Make output directories if they do not exist
im_shape = config.gedi_image_size
# Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
1,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
num_epochs=1,
normalize=config.normalize)
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(val_images, output_shape=config.output_shape)
# Setup validation op
preds = tf.argmax(vgg.prob, 1)
targets = tf.cast(val_labels, dtype=tf.int64)
grad_labels = tf.one_hot(val_labels,
config.output_shape,
dtype=tf.float32)
heatmap_op = tf.gradients(vgg.fc8 * grad_labels, val_images)[0]
# Set up saver
saver = tf.train.Saver(tf.global_variables())
ckpts = [selected_ckpts]
# Loop through each checkpoint then test the entire validation set
print '-' * 60
print 'Beginning evaluation on ckpt: %s' % ckpts
print '-' * 60
yhat, y, tn_hms, tp_hms, fn_hms, fp_hms = [], [], [], [], [], []
tn_ims, tp_ims, fn_ims, fp_ims = [], [], [], []
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
try:
# Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, c)
start_time = time.time()
while not coord.should_stop():
tyh, ty, thm, tim = sess.run(
[preds, targets, heatmap_op, val_images])
tyh = tyh[0]
ty = ty[0]
tim = (tim / tim.max()).squeeze()
yhat += [tyh]
y += [ty]
if tyh == ty and not tyh: # True negative
tn_hms += [hm_normalize(thm)]
tn_ims += [tim]
elif tyh == ty and tyh: # True positive
tp_hms += [hm_normalize(thm)]
tp_ims += [tim]
elif tyh != ty and not tyh: # False negative
fn_hms += [hm_normalize(thm)]
fn_ims += [tim]
elif tyh != ty and tyh: # False positive
fp_hms += [hm_normalize(thm)]
fp_ims += [tim]
except tf.errors.OutOfRangeError:
print 'Batch %d took %.1f seconds' % (idx,
time.time() - start_time)
finally:
coord.request_stop()
coord.join(threads)
sess.close()
# Plot images -- add to a dict and incorporate file_pointers
dir_pointer = os.path.join(config.heatmap_source_images, ds_dt_stamp)
stem_dirs = ['tn', 'tp', 'fn', 'fp']
dir_list = [dir_pointer]
dir_list += [os.path.join(dir_pointer, x) for x in stem_dirs]
[make_dir(d) for d in dir_list]
loop_plot(tn_ims,
tn_hms,
'True negative',
os.path.join(dir_pointer, 'tn'),
blur=blur_kernel)
loop_plot(tp_ims,
tp_hms,
'True positive',
os.path.join(dir_pointer, 'tp'),
blur=blur_kernel)
loop_plot(fn_ims,
fn_hms,
'False negative',
os.path.join(dir_pointer, 'fn'),
blur=blur_kernel)
loop_plot(fp_ims,
fp_hms,
'False positive',
os.path.join(dir_pointer, 'fp'),
blur=blur_kernel)
def test_vgg16(validation_data, model_dir, which_set, selected_ckpts=-1):
config = GEDIconfig()
if validation_data is None: # Use globals
validation_data = os.path.join(config.tfrecord_dir,
config.tf_record_names[which_set])
meta_data = np.load(
os.path.join(config.tfrecord_dir, 'val_%s' % config.max_file))
else:
meta_data = np.load('%s_maximum_value.npz' %
validation_data.split('.tfrecords')[0])
label_list = os.path.join(
config.processed_image_patch_dir,
'list_of_' + '_'.join(x
for x in config.image_prefixes) + '_labels.txt')
with open(label_list) as f:
file_pointers = [l.rstrip('\n') for l in f.readlines()]
# Prepare image normalization values
try:
max_value = np.max(meta_data['max_array']).astype(np.float32)
except:
max_value = np.asarray([config.max_gedi])
try:
min_value = np.max(meta_data['min_array']).astype(np.float32)
except:
min_value = np.asarray([config.min_gedi])
# Find model checkpoints
ckpts, ckpt_names = find_ckpts(config, model_dir)
ds_dt_stamp = re.split('/', ckpts[0])[-2]
out_dir = os.path.join(config.results, ds_dt_stamp)
try:
config = np.load(os.path.join(out_dir, 'meta_info.npy')).item()
# Make sure this is always at 1
config.validation_batch = 1
print('-' * 60)
print('Loading config meta data for:%s' % out_dir)
print('-' * 60)
except:
print('-' * 60)
print('Using config from gedi_config.py for model:%s' % out_dir)
print('-' * 60)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# im_shape = get_image_size(config)
im_shape = config.gedi_image_size
# Prepare data on CPU
with tf.device('/cpu:0'):
val_images, val_labels = inputs(validation_data,
1,
im_shape,
config.model_image_size[:2],
max_value=max_value,
min_value=min_value,
num_epochs=1,
normalize=config.normalize)
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.Vgg16(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(val_images, output_shape=config.output_shape)
# Setup validation op
scores = vgg.prob
preds = tf.argmax(vgg.prob, 1)
targets = tf.cast(val_labels, dtype=tf.int64)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Loop through each checkpoint then test the entire validation set
ckpt_yhat, ckpt_y, ckpt_scores = [], [], []
print('-' * 60)
print('Beginning evaluation')
print('-' * 60)
if selected_ckpts is not None:
# Select a specific ckpt
if selected_ckpts < 0:
ckpts = ckpts[selected_ckpts:]
else:
ckpts = ckpts[:selected_ckpts]
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
dec_scores, yhat, y = [], [], []
try:
# Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
# Set up exemplar threading
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, c)
start_time = time.time()
while not coord.should_stop():
sc, tyh, ty = sess.run([scores, preds, targets])
dec_scores = np.append(dec_scores, sc)
yhat = np.append(yhat, tyh)
y = np.append(y, ty)
except tf.errors.OutOfRangeError:
ckpt_yhat.append(yhat)
ckpt_y.append(y)
ckpt_scores.append(dec_scores)
print('Iteration accuracy: %s' % np.mean(yhat == y))
print('Iteration pvalue: %.5f' %
randomization_test(y=y, yhat=yhat))
print('Batch %d took %.1f seconds' %
(idx, time.time() - start_time))
finally:
coord.request_stop()
coord.join(threads)
sess.close()
# Save everything
np.savez(os.path.join(out_dir, 'validation_accuracies'),
ckpt_yhat=ckpt_yhat,
ckpt_y=ckpt_y,
ckpt_scores=ckpt_scores,
ckpt_names=ckpt_names,
file_pointers=file_pointers)
# Also save a csv with item/guess pairs
try:
trimmed_files = [re.split('/', x)[-1] for x in file_pointers]
trimmed_files = np.asarray(trimmed_files)
dec_scores = np.asarray(dec_scores)
yhat = np.asarray(yhat)
df = pd.DataFrame(
np.hstack((trimmed_files.reshape(-1, 1), yhat.reshape(-1, 1),
dec_scores.reshape(dec_scores.shape[0] // 2, 2))),
columns=['files', 'guesses', 'score dead', 'score live'])
df.to_csv(os.path.join(out_dir, 'prediction_file.csv'))
print('Saved csv to: %s' % out_dir)
except:
print('X' * 60)
print('Could not save a spreadsheet of file info')
print('X' * 60)
# Plot everything
try:
plot_accuracies(ckpt_y, ckpt_yhat, config, ckpt_names,
os.path.join(out_dir, 'validation_accuracies.png'))
plot_std(ckpt_y, ckpt_yhat, ckpt_names,
os.path.join(out_dir, 'validation_stds.png'))
plot_cms(ckpt_y, ckpt_yhat, config,
os.path.join(out_dir, 'confusion_matrix.png'))
plot_pr(ckpt_y, ckpt_yhat, ckpt_scores,
os.path.join(out_dir, 'precision_recall.png'))
plot_cost(os.path.join(out_dir, 'training_loss.npy'), ckpt_names,
os.path.join(out_dir, 'training_costs.png'))
except:
print('X' * 60)
print('Could not locate the loss numpy')
print('X' * 60)
def test_vgg16(model_file,
trained_svm,
ims,
dead_ims=None,
output_csv='prediction_file',
training_max=None,
C=1e-3,
k_folds=10):
"""Test an SVM you've trained on a new dataset."""
config = GEDIconfig()
if ims is None:
raise RuntimeError(
'You need to supply a directory path to the images.')
if dead_ims is None:
print 'Assuming all of your images are in the ims folder' + \
'-- will not derive labels to calculate accuracy.'
# if not os.path.exists(trained_svm):
# raise RuntimeError(
# 'Cannot find the trained svm model. Check the path you passed.')
try:
clf = cPickle.load(open(trained_svm, 'rb'))
# clf = model_dict['clf']
# mu = model_dict['mu']
# sd = model_dict['sd']
except:
raise RuntimeError('Cannot find SVM file: %s' % trained_svm)
if dead_ims is not None:
live_files = glob(os.path.join(ims, '*%s' % config.raw_im_ext))
dead_files = glob(os.path.join(dead_ims, '*%s' % config.raw_im_ext))
combined_labels = np.concatenate(
(np.zeros(len(live_files)), np.ones(len(dead_files))))
combined_files = np.concatenate((live_files, dead_files))
else:
live_files = glob(os.path.join(ims, '*%s' % config.raw_im_ext))
combined_labels = None
combined_files = np.asarray(live_files)
if len(combined_files) == 0:
raise RuntimeError('Could not find any files. Check your image path.')
config = GEDIconfig()
model_file_path = os.path.sep.join(model_file.split(os.path.sep)[:-1])
meta_file_pointer = os.path.join(model_file_path,
'train_maximum_value.npz')
if not os.path.exists(meta_file_pointer):
raise RuntimeError(
'Cannot find the training data meta file: train_maximum_value.npz'
'Closest I could find from directory %s was %s.'
'Download this from the link described in the README.md.' %
(model_file_path, glob(os.path.join(model_file_path, '*.npz'))))
meta_data = np.load(meta_file_pointer)
# Prepare image normalization values
if training_max is None:
training_max = np.max(meta_data['max_array']).astype(np.float32)
training_min = np.min(meta_data['min_array']).astype(np.float32)
# Find model checkpoints
ds_dt_stamp = re.split('/', model_file)[-2]
out_dir = os.path.join(config.results, ds_dt_stamp)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# Prepare data on CPU
images = tf.placeholder(tf.float32,
shape=[None] + config.model_image_size,
name='images')
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.model_struct(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(images, output_shape=config.output_shape)
# Setup validation op
scores = vgg.fc7
preds = tf.argmax(vgg.prob, 1)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Loop through each checkpoint then test the entire validation set
ckpts = [model_file]
ckpt_yhat, ckpt_y, ckpt_scores, ckpt_file_array = [], [], [], []
print '-' * 60
print 'Beginning evaluation'
print '-' * 60
if config.validation_batch > len(combined_files):
print 'Trimming validation_batch size to %s (same as # of files).' % len(
combined_files)
config.validation_batch = len(combined_files)
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
dec_scores, yhat, y, file_array = [], [], [], []
# 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
saver.restore(sess, c)
start_time = time.time()
num_batches = np.floor(
len(combined_files) / float(config.validation_batch)).astype(int)
for image_batch, label_batch, file_batch in tqdm(image_batcher(
start=0,
num_batches=num_batches,
images=combined_files,
labels=combined_labels,
config=config,
training_max=training_max,
training_min=training_min),
total=num_batches):
feed_dict = {images: image_batch}
sc, tyh = sess.run([scores, preds], feed_dict=feed_dict)
dec_scores += [sc]
yhat = np.append(yhat, tyh)
y = np.append(y, label_batch)
file_array = np.append(file_array, file_batch)
ckpt_yhat.append(yhat)
ckpt_y.append(y)
ckpt_scores.append(dec_scores)
ckpt_file_array.append(file_array)
print 'Batch %d took %.1f seconds' % (idx, time.time() - start_time)
sess.close()
# Save everything
new_dt_string = re.split('\.', str(datetime.now()))[0].\
replace(' ', '_').replace(':', '_').replace('-', '_')
np.savez(os.path.join(out_dir, '%s_validation_accuracies' % new_dt_string),
ckpt_yhat=ckpt_yhat,
ckpt_y=ckpt_y,
ckpt_scores=ckpt_scores,
ckpt_names=ckpts,
combined_files=ckpt_file_array)
# Run SVM
all_scores = np.concatenate(dec_scores)
# all_scores = (all_scores - mu) / sd
predictions = clf.predict(all_scores)
if dead_ims is not None:
mean_acc = np.mean(predictions == y)
p_value = randomization_test(y=y, yhat=predictions)
print 'SVM performance: mean accuracy = %s%%, p = %.5f' % (mean_acc,
p_value)
df_col_label = 'true label'
else:
mean_acc, p_value = None, None
y = np.copy(yhat)
df_col_label = 'Dummy column (no labels supplied)'
np.savez(os.path.join(out_dir, '%s_svm_test_data' % new_dt_string),
yhat=yhat,
y=y,
scores=dec_scores,
ckpts=ckpts,
p_value=p_value)
# Also save a csv with item/guess pairs
trimmed_files = np.asarray([
x.split(os.path.sep)[-1] for x in np.asarray(ckpt_file_array).ravel()
])
yhat = np.asarray(yhat)
df = pd.DataFrame(
np.hstack((trimmed_files.reshape(-1, 1), yhat.reshape(-1, 1))),
# y.reshape(-1, 1))),
columns=['files', 'guesses']) # , df_col_label])
df.to_csv(os.path.join(out_dir, 'prediction_file.csv'))
print 'Saved csv to: %s' % out_dir
def test_placeholder(
image_path,
model_file,
model_meta,
out_dir,
n_images=3,
first_n_images=1,
debug=True,
margin=.1,
autopsy_csv=None,
C=1,
k_folds=10,
embedding_type='tsne',
autopsy_model='match'):
config = GEDIconfig()
assert margin is not None, 'Need a margin for the loss.'
assert image_path is not None, 'Provide a path to an image directory.'
assert model_file is not None, 'Provide a path to the model file.'
try:
# Load the model's config
config = np.load(model_meta).item()
except:
print 'Could not load model config, falling back to default config.'
config.model_image_size[-1] = 1
try:
# Load autopsy information
autopsy_data = pd.read_csv(autopsy_csv)
except IOError:
print 'Unable to load autopsy file.'
if not hasattr(config, 'include_GEDI'):
raise RuntimeError('You need to pass the correct meta file.')
config.include_GEDI = True
config.l2_norm = False
config.dist_fun = 'pearson'
config.per_batch = False
config.output_shape = 32
config.margin = 0.1
if os.path.isdir(image_path):
combined_files = np.asarray(
glob(os.path.join(image_path, '*%s' % config.raw_im_ext)))
else:
combined_files = [image_path]
if len(combined_files) == 0:
raise RuntimeError('Could not find any files. Check your image path.')
# Make output directories if they do not exist
dt_stamp = re.split(
'\.', str(datetime.now()))[0].\
replace(' ', '_').replace(':', '_').replace('-', '_')
dt_dataset = config.which_dataset + '_' + dt_stamp + '/'
config.train_checkpoint = os.path.join(
config.train_checkpoint, dt_dataset) # timestamp this run
out_dir = os.path.join(out_dir, dt_dataset)
dir_list = [out_dir]
[tf_fun.make_dir(d) for d in dir_list]
# Prepare data on CPU
with tf.device('/cpu:0'):
images = []
for idx in range(first_n_images):
images += [tf.placeholder(
tf.float32,
shape=[None] + config.model_image_size,
name='images_%s' % idx)]
# Prepare model on GPU
with tf.device('/gpu:0'):
if autopsy_model == 'match':
from models import matching_vgg16 as model_type
with tf.variable_scope('match'):
# Build matching model for frame 0
model_0 = model_type.model_struct(
vgg16_npy_path=config.gedi_weight_path) # ,
frame_activity = []
model_activity = model_0.build(
images[0],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = [model_activity]
frame_activity += [model_activity]
if first_n_images > 1:
with tf.variable_scope('match', reuse=tf.AUTO_REUSE):
# Build matching model for other frames
for idx in range(1, len(images)):
model_activity = model_0.build(
images[idx],
output_shape=config.output_shape,
include_GEDI=config.include_GEDI)
if config.l2_norm:
model_activity = tf_fun.l2_normalize(
model_activity)
frame_activity += [model_activity]
if config.dist_fun == 'l2':
pos = tf_fun.l2_dist(
frame_activity[0],
frame_activity[1], axis=1)
neg = tf_fun.l2_dist(
frame_activity[0],
frame_activity[2], axis=1)
elif config.dist_fun == 'pearson':
pos = tf_fun.pearson_dist(
frame_activity[0],
frame_activity[1],
axis=1)
neg = tf_fun.pearson_dist(
frame_activity[0],
frame_activity[2],
axis=1)
model_activity = pos - neg # Store the difference in distances
elif autopsy_model == 'GEDI' or autopsy_model == 'gedi':
from models import baseline_vgg16 as model_type
model = model_type.model_struct(
vgg16_npy_path=config.gedi_weight_path) # ,
model.build(
images[0],
output_shape=config.output_shape)
model_activity = model.fc7
else:
raise NotImplementedError(autopsy_model)
if config.validation_batch > len(combined_files):
print (
'Trimming validation_batch size to %s '
'(same as # of files).' % len(combined_files))
config.validation_batch = len(combined_files)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Initialize the graph
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# Need to initialize both of these if supplying num_epochs to inputs
sess.run(
tf.group(
tf.global_variables_initializer(),
tf.local_variables_initializer()))
# Set up exemplar threading
if autopsy_model == 'match':
saver.restore(sess, model_file)
start_time = time.time()
num_batches = np.floor(
len(combined_files) / float(
config.validation_batch)).astype(int)
score_array, file_array = [], []
for image_batch, file_batch in tqdm(
image_batcher(
start=0,
num_batches=num_batches,
images=combined_files,
config=config,
first_n_images=first_n_images,
n_images=n_images),
total=num_batches):
for im_head in images:
feed_dict = {
im_head: image_batch
}
activity = sess.run(
model_activity,
feed_dict=feed_dict)
score_array += [activity]
file_array += [file_batch]
print 'Image processing %d took %.1f seconds' % (
idx, time.time() - start_time)
sess.close()
score_array = np.concatenate(score_array, axis=0)
score_array = score_array.reshape(-1, score_array.shape[-1])
file_array = np.concatenate(file_array, axis=0)
# Save everything
np.savez(
os.path.join(out_dir, 'validation_accuracies'),
score_array=score_array,
file_array=file_array)
if first_n_images == 1:
# Derive pathologies from file names
pathologies = []
for f in combined_files:
sf = f.split(os.path.sep)[-1].split('_')
line = sf[1]
# time_col = sf[2]
well = sf[4]
disease = autopsy_data[
np.logical_and(
autopsy_data['line'] == line,
autopsy_data['wells'] == well)]['type']
try:
disease = disease.as_matrix()[0]
except:
disease = 'Not_found'
pathologies += [disease]
pathologies = np.asarray(pathologies)[:len(score_array)]
mu = score_array.mean(0)
sd = score_array.std(0)
z_score_array = (score_array - mu) / (sd + 1e-4)
if embedding_type == 'TSNE' or embedding_type == 'tsne':
emb = manifold.TSNE(n_components=2, init='pca', random_state=0)
elif embedding_type == 'PCA' or embedding_type == 'pca':
emb = PCA(n_components=2, svd_solver='randomized', random_state=0)
elif embedding_type == 'spectral':
emb = manifold.SpectralEmbedding(n_components=2, random_state=0)
y = emb.fit_transform(score_array)
# Do a classification analysis
labels = np.unique(pathologies.reshape(-1, 1), return_inverse=True)[1]
# Run SVM
svm = LinearSVC(C=C, dual=False, class_weight='balanced')
clf = make_pipeline(preprocessing.StandardScaler(), svm)
predictions = cross_val_predict(clf, score_array, labels, cv=k_folds)
cv_performance = metrics.accuracy_score(predictions, labels)
clf.fit(score_array, labels)
# mu = dec_scores.mean(0)
# sd = dec_scores.std(0)
print '%s-fold SVM performance: accuracy = %s%%' % (
k_folds,
np.mean(cv_performance * 100))
np.savez(
os.path.join(out_dir, 'svm_data'),
yhat=score_array,
y=labels,
cv_performance=cv_performance,
# mu=mu,
# sd=sd,
C=C)
# Ouput csv
df = pd.DataFrame(
np.hstack((
y,
pathologies.reshape(-1, 1),
file_array.reshape(-1, 1))),
columns=['dim1', 'dim2', 'pathology', 'filename'])
out_name = os.path.join(out_dir, 'raw_embedding.csv')
df.to_csv(out_name)
print 'Saved csv to: %s' % out_name
create_figs(
emb=emb,
out_dir=out_dir,
out_name=out_name,
embedding_type=embedding_type,
embedding_name='raw_embedding')
# Now work on zscored data
y = emb.fit_transform(z_score_array)
# Ouput csv
df = pd.DataFrame(
np.hstack((
y,
pathologies.reshape(-1, 1),
file_array.reshape(-1, 1))),
columns=['dim1', 'dim2', 'pathology', 'filename'])
out_name = os.path.join(out_dir, 'embedding.csv')
df.to_csv(out_name)
print 'Saved csv to: %s' % out_name
# Create plot
create_figs(
emb=emb,
out_dir=out_dir,
out_name=out_name,
embedding_type=embedding_type,
embedding_name='normalized_embedding')
else:
# Do a classification (sign of the score)
decisions = np.sign(score_array)
df = pd.DataFrame(
np.hstack(decisions, score_array),
columns=['Decisions', 'Scores'])
df.to_csv(
os.path.join(
out_dir, 'tracking_model_scores.csv'))
def test_vgg16(live_ims,
dead_ims,
model_file,
svm_model='svm_model',
output_csv='prediction_file',
training_max=None,
C=1e-3,
k_folds=10):
"""Train an SVM for your dataset on GEDI-model encodings."""
config = GEDIconfig()
if live_ims is None:
raise RuntimeError(
'You need to supply a directory path to the live images.')
if dead_ims is None:
raise RuntimeError(
'You need to supply a directory path to the dead images.')
live_files = glob(os.path.join(live_ims, '*%s' % config.raw_im_ext))
dead_files = glob(os.path.join(dead_ims, '*%s' % config.raw_im_ext))
combined_labels = np.concatenate(
(np.zeros(len(live_files)), np.ones(len(dead_files))))
combined_files = np.concatenate((live_files, dead_files))
if len(combined_files) == 0:
raise RuntimeError('Could not find any files. Check your image path.')
config = GEDIconfig()
model_file_path = os.path.sep.join(model_file.split(os.path.sep)[:-1])
meta_file_pointer = os.path.join(model_file_path,
'train_maximum_value.npz')
if not os.path.exists(meta_file_pointer):
raise RuntimeError(
'Cannot find the training data meta file: train_maximum_value.npz'
'Closest I could find from directory %s was %s.'
'Download this from the link described in the README.md.' %
(model_file_path, glob(os.path.join(model_file_path, '*.npz'))))
meta_data = np.load(meta_file_pointer)
# Prepare image normalization values
if training_max is None:
training_max = np.max(meta_data['max_array']).astype(np.float32)
training_min = np.min(meta_data['min_array']).astype(np.float32)
# Find model checkpoints
ds_dt_stamp = re.split('/', model_file)[-2]
out_dir = os.path.join(config.results, ds_dt_stamp)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# Prepare data on CPU
images = tf.placeholder(tf.float32,
shape=[None] + config.model_image_size,
name='images')
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.model_struct(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(images, output_shape=config.output_shape)
# Setup validation op
scores = vgg.fc7
preds = tf.argmax(vgg.prob, 1)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Loop through each checkpoint then test the entire validation set
ckpts = [model_file]
ckpt_yhat, ckpt_y, ckpt_scores, ckpt_file_array = [], [], [], []
print '-' * 60
print 'Beginning evaluation'
print '-' * 60
if config.validation_batch > len(combined_files):
print 'Trimming validation_batch size to %s (same as # of files).' % len(
combined_files)
config.validation_batch = len(combined_files)
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
dec_scores, yhat, y, file_array = [], [], [], []
# 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
saver.restore(sess, c)
start_time = time.time()
num_batches = np.floor(
len(combined_files) / float(config.validation_batch)).astype(int)
for image_batch, label_batch, file_batch in tqdm(image_batcher(
start=0,
num_batches=num_batches,
images=combined_files,
labels=combined_labels,
config=config,
training_max=training_max,
training_min=training_min),
total=num_batches):
feed_dict = {images: image_batch}
sc, tyh = sess.run([scores, preds], feed_dict=feed_dict)
dec_scores += [sc]
yhat = np.append(yhat, tyh)
y = np.append(y, label_batch)
file_array = np.append(file_array, file_batch)
ckpt_yhat.append(yhat)
ckpt_y.append(y)
ckpt_scores.append(dec_scores)
ckpt_file_array.append(file_array)
print 'Batch %d took %.1f seconds' % (idx, time.time() - start_time)
sess.close()
# Save everything
np.savez(os.path.join(out_dir, 'validation_accuracies'),
ckpt_yhat=ckpt_yhat,
ckpt_y=ckpt_y,
ckpt_scores=ckpt_scores,
ckpt_names=ckpts,
combined_files=ckpt_file_array)
# Run SVM
svm = LinearSVC(C=C, dual=False, class_weight='balanced')
clf = make_pipeline(preprocessing.StandardScaler(), svm)
predictions = cross_val_predict(clf,
np.concatenate(dec_scores),
y,
cv=k_folds)
cv_performance = metrics.accuracy_score(predictions, y)
p_value = randomization_test(y=y, yhat=predictions)
clf.fit(np.concatenate(dec_scores), y)
# mu = dec_scores.mean(0)
# sd = dec_scores.std(0)
print '%s-fold SVM performance: accuracy = %s%% , p = %.5f' % (
k_folds, np.mean(cv_performance * 100), p_value)
np.savez(
os.path.join(out_dir, 'svm_data'),
yhat=predictions,
y=y,
scores=dec_scores,
ckpts=ckpts,
cv_performance=cv_performance,
p_value=p_value,
k_folds=k_folds,
# mu=mu,
# sd=sd,
C=C)
# Also save a csv with item/guess pairs
try:
trimmed_files = [re.split('/', x)[-1] for x in combined_files]
trimmed_files = np.asarray(trimmed_files)
dec_scores = np.asarray(dec_scores)
yhat = np.asarray(yhat)
df = pd.DataFrame(np.hstack(
(trimmed_files.reshape(-1, 1), yhat.reshape(-1,
1), y.reshape(-1, 1))),
columns=['files', 'guesses', 'true label'])
df.to_csv(os.path.join(out_dir, 'prediction_file.csv'))
print 'Saved csv to: %s' % out_dir
except:
print 'X' * 60
print 'Could not save a spreadsheet of file info'
print 'X' * 60
# save the classifier
with open('%s.pkl' % svm_model, 'wb') as fid:
# model_dict = {
# 'model': clf,
# 'mu': mu,
# 'sd': sd
# }
cPickle.dump(clf, fid)
print 'Saved svm model to: %s.pkl' % svm_model
def test_vgg16(image_dir,
model_file,
autopsy_csv=None,
autopsy_path=None,
output_csv='prediction_file',
target_layer='fc7',
save_npy=False,
shuffle_images=True,
embedding_type='PCA'):
"""Testing function for pretrained vgg16."""
assert autopsy_csv is not None, 'You must pass an autopsy file name.'
assert autopsy_path is not None, 'You must pass an autopsy path.'
# Load autopsy information
autopsy_data = pd.read_csv(os.path.join(autopsy_path, autopsy_csv))
# Load config and begin preparing data
config = GEDIconfig()
if image_dir is None:
raise RuntimeError(
'You need to supply a directory path to the images.')
combined_files = np.asarray(
glob(os.path.join(image_dir, '*%s' % config.raw_im_ext)))
if shuffle_images:
combined_files = combined_files[np.random.permutation(
len(combined_files))]
if len(combined_files) == 0:
raise RuntimeError('Could not find any files. Check your image path.')
config = GEDIconfig()
meta_file_pointer = os.path.join(
model_file.split('/model')[0], 'train_maximum_value.npz')
if not os.path.exists(meta_file_pointer):
raise RuntimeError(
'Cannot find the training data meta file.'
'Download this from the link described in the README.md.')
meta_data = np.load(meta_file_pointer)
# Prepare image normalization values
training_max = np.max(meta_data['max_array']).astype(np.float32)
training_min = np.min(meta_data['min_array']).astype(np.float32)
# Find model checkpoints
ds_dt_stamp = re.split('/', model_file)[-2]
out_dir = os.path.join(config.results, ds_dt_stamp)
# Make output directories if they do not exist
dir_list = [config.results, out_dir]
[make_dir(d) for d in dir_list]
# Prepare data on CPU
images = tf.placeholder(tf.float32,
shape=[None] + config.model_image_size,
name='images')
# Prepare model on GPU
with tf.device('/gpu:0'):
with tf.variable_scope('cnn'):
vgg = vgg16.model_struct(vgg16_npy_path=config.vgg16_weight_path,
fine_tune_layers=config.fine_tune_layers)
vgg.build(images, output_shape=config.output_shape)
# Setup validation op
scores = vgg[target_layer]
preds = tf.argmax(vgg.prob, 1)
# Derive pathologies from file names
pathologies = []
for f in combined_files:
sf = f.split('/')[-1].split('_')
sf = '_'.join(sf[1:4])
it_path = autopsy_data[autopsy_data['plate_well_neuron'] ==
sf]['disease']
if not len(it_path):
it_path = 'Absent'
else:
it_path = it_path.as_matrix()[0]
pathologies += [it_path]
pathologies = np.asarray(pathologies)
# Set up saver
saver = tf.train.Saver(tf.global_variables())
# Loop through each checkpoint then test the entire validation set
ckpts = [model_file]
ckpt_yhat, ckpt_scores, ckpt_file_array = [], [], []
print '-' * 60
print 'Beginning evaluation'
print '-' * 60
if config.validation_batch > len(combined_files):
print 'Trimming validation_batch size to %s.' % len(combined_files)
config.validation_batch = len(combined_files)
for idx, c in tqdm(enumerate(ckpts), desc='Running checkpoints'):
dec_scores, yhat, file_array = [], [], []
# 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
saver.restore(sess, c)
start_time = time.time()
num_batches = np.floor(
len(combined_files) / float(config.validation_batch)).astype(int)
for image_batch, file_batch in tqdm(image_batcher(
start=0,
num_batches=num_batches,
images=combined_files,
config=config,
training_max=training_max,
training_min=training_min),
total=num_batches):
feed_dict = {images: image_batch}
sc, tyh = sess.run([scores, preds], feed_dict=feed_dict)
dec_scores += [sc]
yhat += [tyh]
file_array += [file_batch]
ckpt_yhat.append(yhat)
ckpt_scores.append(dec_scores)
ckpt_file_array.append(file_array)
print 'Batch %d took %.1f seconds' % (idx, time.time() - start_time)
sess.close()
# Create and plot an embedding
im_path_map = pathologies[:num_batches * config.validation_batch]
dec_scores = np.concatenate(dec_scores)
mu = dec_scores.mean(0)[None, :]
sd = dec_scores.std(0)[None, :]
dec_scores = (dec_scores - mu) / sd
yhat = np.concatenate(yhat)
file_array = np.concatenate(file_array)
if embedding_type == 'TSNE' or embedding_type == 'tsne':
emb = manifold.TSNE(n_components=2, init='pca', random_state=0)
elif embedding_type == 'PCA' or embedding_type == 'pca':
emb = PCA(n_components=2, svd_solver='randomized', random_state=0)
elif embedding_type == 'spectral':
emb = manifold.SpectralEmbedding(n_components=2, random_state=0)
y = emb.fit_transform(dec_scores)
# Ouput csv
df = pd.DataFrame(np.hstack(
(y, im_path_map.reshape(-1, 1), file_array.reshape(-1, 1))),
columns=['D1', 'D2', 'pathology', 'filename'])
out_name = os.path.join(out_dir, 'embedding.csv')
df.to_csv(out_name)
print 'Saved csv to: %s' % out_name
# Create plot
f, ax = plt.subplots()
unique_cats = np.unique(im_path_map)
h = []
for idx, cat in enumerate(unique_cats):
h += [
plt.scatter(y[im_path_map == cat, 0],
y[im_path_map == cat, 1],
c=plt.cm.Spectral(idx * 1000))
]
plt.legend(h, unique_cats)
plt.axis('tight')
plt.show()
plt.savefig('embedding.png')
plt.close(f)
# Save everything
if save_npy:
np.savez(os.path.join(out_dir, 'validation_accuracies'),
ckpt_yhat=ckpt_yhat,
ckpt_scores=ckpt_scores,
ckpt_names=ckpts,
combined_files=ckpt_file_array)
