本文整理汇总了Python中dataset.Dataset.VOC_SIZE方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.VOC_SIZE方法的具体用法？Python Dataset.VOC_SIZE怎么用？Python Dataset.VOC_SIZE使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类dataset.Dataset的用法示例。
在下文中一共展示了Dataset.VOC_SIZE方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: compute_statistics
# 需要导入模块: from dataset import Dataset [as 别名]
# 或者: from dataset.Dataset import VOC_SIZE [as 别名]
def compute_statistics(src_cfg, **kwargs):
""" Computes sufficient statistics needed for the bag-of-words or
Fisher vector model.
"""
# Default parameters.
ip_type = kwargs.get("ip_type", "dense5.track15mbh")
suffix = kwargs.get("suffix", "")
dataset = Dataset(src_cfg, ip_type=ip_type, suffix=suffix)
nr_clusters = kwargs.get("nr_clusters", 128)
dataset.VOC_SIZE = nr_clusters
model_type = kwargs.get("model_type", "fv")
worker_type = kwargs.get("worker_type", "normal")
outfilename = kwargs.get("outfilename", "stats.tmp")
if worker_type == "normal":
worker = compute_statistics_from_video_worker
outfilename = kwargs.get("outfilename", "stats.tmp")
elif worker_type == "per_slice":
from per_slice.compute_sstats_worker import compute_statistics_worker
worker = compute_statistics_worker
if kwargs.has_key("spm"):
from spatial_pyramids import compute_statistics_worker
worker = compute_statistics_worker
outfilename = "stats.tmp_spm%d%d%d" % kwargs.get("spm")
fn_pca = os.path.join(dataset.FEAT_DIR, "pca", "pca_64.pkl")
pca = kwargs.get("pca", load_pca(fn_pca))
fn_gmm = os.path.join(dataset.FEAT_DIR, "gmm", "gmm_%d" % nr_clusters)
gmm = kwargs.get("gmm", load_gmm(fn_gmm))
descs_to_sstats = Model(model_type, gmm).descs_to_sstats
nr_processes = kwargs.get("nr_processes", multiprocessing.cpu_count())
train_samples, train_labels = dataset.get_data("train")
test_samples, test_labels = dataset.get_data("test")
_samples = train_samples + test_samples
_labels = train_labels + test_labels
samples, labels = get_tupled_data(_samples, _labels)
sstats_out = SstatsMap(os.path.join(dataset.FEAT_DIR, "statistics_k_%d" % nr_clusters, outfilename))
if nr_processes > 1:
import multiprocessing as mp
processes = []
nr_samples_per_process = len(samples) // nr_processes + 1
for ii in xrange(nr_processes):
process = mp.Process(
target=worker,
args=(
dataset,
samples[ii * nr_samples_per_process : (ii + 1) * nr_samples_per_process],
labels[ii * nr_samples_per_process : (ii + 1) * nr_samples_per_process],
sstats_out,
descs_to_sstats,
pca,
gmm,
),
kwargs=kwargs,
)
processes.append(process)
process.start()
# Wait for jobs to finish.
for process in processes:
process.join()
else:
# We use this special case, because it makes possible to debug.
worker(dataset, samples, labels, sstats_out, descs_to_sstats, pca, gmm, **kwargs)