def get_images_with_filenames(filenames):
#### read a mini batch data
inputs, targets = load_images(filenames, deterministic=True)
new_inputs = []
new_targets = []
for image, target in zip(inputs, targets):
ims, trs = testtime_augmentation(image[0],
target) # Take color channel of image
new_inputs += ims
new_targets += trs
new_filenames = []
for fname in filenames:
for i in range(int(len(new_inputs) / len(filenames))):
new_filenames.append(fname)
return np.array(new_inputs,
dtype=np.float32), np.array(new_targets,
dtype=np.int32), new_filenames
def get_images_with_filenames(filenames):
inputs, targets = load_images(filenames, deterministic=True)
new_inputs = []
new_targets = []
for image, target in zip(inputs, targets):
ims, trs = augment.testtime_augmentation(image[0], target) #Take color channel of image
new_inputs += ims
new_targets+=trs
new_filenames = []
for fname in filenames:
for i in range(int(len(new_inputs)/len(filenames))):
new_filenames.append(fname)
return np.array(new_inputs,dtype=np.float32),np.array(new_targets,dtype=np.int32), new_filenames
def main():
model_path = sys.argv[1]
net_file = sys.argv[2]
### load all predicting filenames
filenames = glob.glob(P.FILENAMES_PREDICTION)
batch_size = P.BATCH_SIZE_PREDICTION
### get augmentation number
test_im = np.zeros((64, 64))
n_testtime_augmentation = len(testtime_augmentation(test_im, 0)[0])
#### get the parallel data generator
gen = ParallelBatchIterator(get_images_with_filenames,
filenames,
ordered=True,
batch_size=batch_size //
(3 * n_testtime_augmentation),
multiprocess=False,
n_producers=12)
### get wide resnet caffe model
caffe_net = caffe.Net(net_file, model_path, caffe.TEST)
### do forward pass
all_probabilities = []
all_filenames = []
print('begin predicting...')
for i, batch in tqdm(enumerate(gen)):
data, label, fnames = batch
### pass data and label to caff net, please set the batch size to 12(atomic batchsize) for both prediction batch size and train batch size
if data.shape[0] == batch_size:
caffe_net.blobs['data'].data[...] = data.astype(np.float32,
copy=False)
caffe_net.blobs['label'].data[...] = label.astype(np.float32,
copy=False)
caffe_net.forward()
softmax_out = caffe_net.blobs['prob'].data.copy()
all_probabilities += list(softmax_out[:, 1].tolist())
all_filenames += list(fnames)
# print("one batch done")
else:
break
### for all filenames get the probalilities(3 * n_testtime_augmentation)
d = {f: [] for f in filenames}
for probability, f in zip(all_probabilities, all_filenames):
d[f].append(probability)
### the code will not run unless the batch size is not the atomic batch size(3 * n_testtime_augmentation)
for key in d.keys():
if len(d[key]) == 0:
d.pop(key)
print('end predicting')
print('write to csv')
cur_dir = os.path.dirname(os.path.abspath(__file__))
candidates = pd.read_csv(
os.path.join(cur_dir,
'./../../../data/finalizedcandidates_unet_01.csv'))
data = []
### get the mean prob for each filename and get the row number of the candidate
for x in d.iteritems():
fname, probabilities = x
prob = np.mean(probabilities)
candidates_row = int(os.path.split(fname)[1].replace('.pkl.gz',
'')) - 2
print candidates_row
data.append(
list(candidates.iloc[candidates_row].values)[:-1] + [str(prob)])
### write the prob to a .csv
submission = pd.DataFrame(
columns=['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability'],
data=data)
submission_path = os.path.join(cur_dir,
'./../../../data/submission_subset01.csv')
submission.to_csv(
submission_path,
columns=['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability'])
print('finished!')
print "Loading saved model", model_save_file
with np.load(model_save_file) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(network, param_values)
print "Defining updates.."
train_fn, val_fn, l_r = resnet.define_updates(network, input_var, target_var)
in_pattern = '/media/diskB/guido/luna16/candidates_v2_0.5mm_96x96_xy_xz_yz/subset[{}]/*/*.pkl.gz'.format(subsets)
filenames = glob(in_pattern)
batch_size = 1200
multiprocess = True
test_im = np.zeros((64,64))
n_testtime_augmentation = len(augment.testtime_augmentation(test_im, 0)[0])
def get_images_with_filenames(filenames):
inputs, targets = load_images(filenames, deterministic=True)
new_inputs = []
new_targets = []
for image, target in zip(inputs, targets):
ims, trs = augment.testtime_augmentation(image[0], target) #Take color channel of image
new_inputs += ims
new_targets+=trs
new_filenames = []
for fname in filenames:
for i in range(int(len(new_inputs)/len(filenames))):
lasagne.layers.set_all_param_values(network, param_values)
print "Defining updates.."
train_fn, val_fn, l_r = resnet.define_updates(network, input_var,
target_var)
#in_pattern = '../../data/cadV2_0.5mm_64x64_xy_xz_yz/subset[{}]/*/*.pkl.gz'.format(subsets)
in_pattern = '/scratch-shared/vdgugten/data/cadOWN_0.5mm_96x96_xy_xz_yz/subset[{}]/*/*.pkl.gz'.format(
subsets)
filenames = glob(in_pattern)
batch_size = 1200
multiprocess = True
test_im = np.zeros((64, 64))
n_testtime_augmentation = len(augment.testtime_augmentation(test_im, 0)[0])
def get_images_with_filenames(filenames):
inputs, targets = load_images(filenames, deterministic=True)
new_inputs = []
new_targets = []
for image, target in zip(inputs, targets):
ims, trs = augment.testtime_augmentation(
image[0], target) #Take color channel of image
new_inputs += ims
new_targets += trs
new_filenames = []
for fname in filenames:
from skimage import util
import matplotlib.pyplot as plt
from matplotlib.pyplot import imread
import scipy
import numpy as np
if __name__ == "__main__":
image = imread("example.jpg")
# print image
image = image[:, :, 1]
# plt.subplot(211)
# image=np.array(image)
# print image.shape
# plt.imshow(list(image))
# image2 = augment.augment([image])
# plt.subplot(212)
# plt.imshow(image2[0])
# print image2[0].shape
# plt.show()
images = augment.testtime_augmentation(np.array(image))
print len(images)
plt.subplot(221)
plt.imshow(images[0])
plt.subplot(222)
plt.imshow(images[1])
plt.subplot(223)
plt.imshow(images[2])
plt.subplot(224)
plt.imshow(images[3])
plt.show()
import matplotlib.pyplot as plt
from matplotlib.pyplot import imread
import scipy
import numpy as np
if __name__ == "__main__":
image = imread("example.jpg")
#print image
image = image[:,:,1]
# plt.subplot(211)
# image=np.array(image)
# print image.shape
# plt.imshow(list(image))
# image2 = augment.augment([image])
# plt.subplot(212)
# plt.imshow(image2[0])
# print image2[0].shape
# plt.show()
images = augment.testtime_augmentation(np.array(image))
print len(images)
plt.subplot(221)
plt.imshow(images[0])
plt.subplot(222)
plt.imshow(images[1])
plt.subplot(223)
plt.imshow(images[2])
plt.subplot(224)
plt.imshow(images[3])
plt.show()