def main(filelist):
filenames = imageData.get_files(filelist)
mean,std = compute_mean_std(filenames)
print('channel mean')
print(mean)
print('channel std')
print(std)
def _get_next_minibatch(self):
try:
dataBlob, labelBlob,_ = self.iterator.next()
except StopIteration:
filenames = imageData.get_files(self.config.get('file_list'))
labels = imageData.get_labels(self.config.get('file_list'))
self.iterator = iter(self.sampleIter(filenames,labels))
dataBlob, labelBlob,_ = self.iterator.next()
return {'data': dataBlob, 'labels': labelBlob }
def setup(self, bottom, top):
"""Setup the ResamplerDataLayer."""
# parse the layer parameter string
layer_config = self.param_str
self.config = imageUtil.load_module(layer_config).config
filenames = imageData.get_files(self.config.get('file_list'))
labels = imageData.get_labels(self.config.get('file_list'))
self.sampleIter = imageIterator.SharedImageIterator(self.config, deterministic=True,batch_size=self.config.get('batch_size'))
self.iterator = iter(self.sampleIter(filenames,labels))
self._name_to_top_map = {
'data': 0,
'labels': 1}
top[0].reshape(self.config.get('batch_size'), 3, self.config.get('h'), self.config.get('w'))
top[1].reshape(self.config.get('batch_size'))
def main(filelist):
filenames = imageData.get_files(filelist)
bs = 1000
batches = [filenames[i * bs : (i + 1) * bs]
for i in range(int(len(filenames) / bs) + 1)]
Us, evs = [], []
for batch in batches:
images = np.array([imageData.load_augment(f, 256, 256) for f in batch])
X = images.transpose(0, 2, 3, 1).reshape(-1, 3)
cov = np.dot(X.T, X) / X.shape[0]
U, S, V = np.linalg.svd(cov)
ev = np.sqrt(S)
Us.append(U)
evs.append(ev)
print('U')
print(np.mean(Us, axis=0))
print('eigenvalues')
print(np.mean(evs, axis=0))
