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))