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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | XueQiuTrader.get_entrust | 获取委托单(目前返回20次调仓的结果)
操作数量都按1手模拟换算的
:return: | easytrader/xqtrader.py | def get_entrust(self):
"""
获取委托单(目前返回20次调仓的结果)
操作数量都按1手模拟换算的
:return:
"""
xq_entrust_list = self._get_xq_history()
entrust_list = []
replace_none = lambda s: s or 0
for xq_entrusts in xq_entrust_list:
status = xq_entrusts["status"] # 调... | def get_entrust(self):
"""
获取委托单(目前返回20次调仓的结果)
操作数量都按1手模拟换算的
:return:
"""
xq_entrust_list = self._get_xq_history()
entrust_list = []
replace_none = lambda s: s or 0
for xq_entrusts in xq_entrust_list:
status = xq_entrusts["status"] # 调... | [
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train | XueQiuTrader.cancel_entrust | 对未成交的调仓进行伪撤单
:param entrust_no:
:return: | easytrader/xqtrader.py | def cancel_entrust(self, entrust_no):
"""
对未成交的调仓进行伪撤单
:param entrust_no:
:return:
"""
xq_entrust_list = self._get_xq_history()
is_have = False
for xq_entrusts in xq_entrust_list:
status = xq_entrusts["status"] # 调仓状态
for entrust i... | def cancel_entrust(self, entrust_no):
"""
对未成交的调仓进行伪撤单
:param entrust_no:
:return:
"""
xq_entrust_list = self._get_xq_history()
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status = xq_entrusts["status"] # 调仓状态
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train | XueQiuTrader.adjust_weight | 雪球组合调仓, weight 为调整后的仓位比例
:param stock_code: str 股票代码
:param weight: float 调整之后的持仓百分比， 0 - 100 之间的浮点数 | easytrader/xqtrader.py | def adjust_weight(self, stock_code, weight):
"""
雪球组合调仓, weight 为调整后的仓位比例
:param stock_code: str 股票代码
:param weight: float 调整之后的持仓百分比， 0 - 100 之间的浮点数
"""
stock = self._search_stock_info(stock_code)
if stock is None:
raise exceptions.TradeError(u"没有查询要... | def adjust_weight(self, stock_code, weight):
"""
雪球组合调仓, weight 为调整后的仓位比例
:param stock_code: str 股票代码
:param weight: float 调整之后的持仓百分比， 0 - 100 之间的浮点数
"""
stock = self._search_stock_info(stock_code)
if stock is None:
raise exceptions.TradeError(u"没有查询要... | [
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train | XueQiuTrader._trade | 调仓
:param security:
:param price:
:param amount:
:param volume:
:param entrust_bs:
:return: | easytrader/xqtrader.py | def _trade(self, security, price=0, amount=0, volume=0, entrust_bs="buy"):
"""
调仓
:param security:
:param price:
:param amount:
:param volume:
:param entrust_bs:
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调仓
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:param price:
:param amount:
:param volume:
:param entrust_bs:
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train | XueQiuTrader.buy | 买入卖出股票
:param security: 股票代码
:param price: 买入价格
:param amount: 买入股数
:param volume: 买入总金额 由 volume / price 取整， 若指定 price 则此参数无效
:param entrust_prop: | easytrader/xqtrader.py | def buy(self, security, price=0, amount=0, volume=0, entrust_prop=0):
"""买入卖出股票
:param security: 股票代码
:param price: 买入价格
:param amount: 买入股数
:param volume: 买入总金额 由 volume / price 取整， 若指定 price 则此参数无效
:param entrust_prop:
"""
return self._trade(security, pr... | def buy(self, security, price=0, amount=0, volume=0, entrust_prop=0):
"""买入卖出股票
:param security: 股票代码
:param price: 买入价格
:param amount: 买入股数
:param volume: 买入总金额 由 volume / price 取整， 若指定 price 则此参数无效
:param entrust_prop:
"""
return self._trade(security, pr... | [
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train | XueQiuTrader.sell | 卖出股票
:param security: 股票代码
:param price: 卖出价格
:param amount: 卖出股数
:param volume: 卖出总金额 由 volume / price 取整， 若指定 price 则此参数无效
:param entrust_prop: | easytrader/xqtrader.py | def sell(self, security, price=0, amount=0, volume=0, entrust_prop=0):
"""卖出股票
:param security: 股票代码
:param price: 卖出价格
:param amount: 卖出股数
:param volume: 卖出总金额 由 volume / price 取整， 若指定 price 则此参数无效
:param entrust_prop:
"""
return self._trade(security, pri... | def sell(self, security, price=0, amount=0, volume=0, entrust_prop=0):
"""卖出股票
:param security: 股票代码
:param price: 卖出价格
:param amount: 卖出股数
:param volume: 卖出总金额 由 volume / price 取整， 若指定 price 则此参数无效
:param entrust_prop:
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return self._trade(security, pri... | [
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train | JoinQuantFollower.follow | 跟踪joinquant对应的模拟交易，支持多用户多策略
:param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户
:param strategies: joinquant 的模拟交易地址，支持使用 [] 指定多个模拟交易,
地址类似 https://www.joinquant.com/algorithm/live/index?backtestId=xxx
:param track_interval: 轮训模拟交易时间，单位为秒
:param trade_cmd_expire_seconds: 交易指令过期时间,... | easytrader/joinquant_follower.py | def follow(
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trade_cmd_expire_seconds=120,
cmd_cache=True,
entrust_prop="limit",
send_interval=0,
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"""跟踪joinquant对应的模拟交易，支持多用户多策略
:param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户
:param ... | def follow(
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cmd_cache=True,
entrust_prop="limit",
send_interval=0,
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"""跟踪joinquant对应的模拟交易，支持多用户多策略
:param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户
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train | ClientTrader.connect | 直接连接登陆后的客户端
:param exe_path: 客户端路径类似 r'C:\\htzqzyb2\\xiadan.exe', 默认 r'C:\\htzqzyb2\\xiadan.exe'
:return: | easytrader/clienttrader.py | def connect(self, exe_path=None, **kwargs):
"""
直接连接登陆后的客户端
:param exe_path: 客户端路径类似 r'C:\\htzqzyb2\\xiadan.exe', 默认 r'C:\\htzqzyb2\\xiadan.exe'
:return:
"""
connect_path = exe_path or self._config.DEFAULT_EXE_PATH
if connect_path is None:
raise ValueE... | def connect(self, exe_path=None, **kwargs):
"""
直接连接登陆后的客户端
:param exe_path: 客户端路径类似 r'C:\\htzqzyb2\\xiadan.exe', 默认 r'C:\\htzqzyb2\\xiadan.exe'
:return:
"""
connect_path = exe_path or self._config.DEFAULT_EXE_PATH
if connect_path is None:
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train | ClientTrader.market_buy | 市价买入
:param security: 六位证券代码
:param amount: 交易数量
:param ttype: 市价委托类型，默认客户端默认选择，
深市可选 ['对手方最优价格', '本方最优价格', '即时成交剩余撤销', '最优五档即时成交剩余 '全额成交或撤销']
沪市可选 ['最优五档成交剩余撤销', '最优五档成交剩余转限价']
:return: {'entrust_no': '委托单号'} | easytrader/clienttrader.py | def market_buy(self, security, amount, ttype=None, **kwargs):
"""
市价买入
:param security: 六位证券代码
:param amount: 交易数量
:param ttype: 市价委托类型，默认客户端默认选择，
深市可选 ['对手方最优价格', '本方最优价格', '即时成交剩余撤销', '最优五档即时成交剩余 '全额成交或撤销']
沪市可选 ['最优五档成交剩余撤销', '最优五档成交剩余... | def market_buy(self, security, amount, ttype=None, **kwargs):
"""
市价买入
:param security: 六位证券代码
:param amount: 交易数量
:param ttype: 市价委托类型，默认客户端默认选择，
深市可选 ['对手方最优价格', '本方最优价格', '即时成交剩余撤销', '最优五档即时成交剩余 '全额成交或撤销']
沪市可选 ['最优五档成交剩余撤销', '最优五档成交剩余... | [
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train | ClientTrader.market_sell | 市价卖出
:param security: 六位证券代码
:param amount: 交易数量
:param ttype: 市价委托类型，默认客户端默认选择，
深市可选 ['对手方最优价格', '本方最优价格', '即时成交剩余撤销', '最优五档即时成交剩余 '全额成交或撤销']
沪市可选 ['最优五档成交剩余撤销', '最优五档成交剩余转限价']
:return: {'entrust_no': '委托单号'} | easytrader/clienttrader.py | def market_sell(self, security, amount, ttype=None, **kwargs):
"""
市价卖出
:param security: 六位证券代码
:param amount: 交易数量
:param ttype: 市价委托类型，默认客户端默认选择，
深市可选 ['对手方最优价格', '本方最优价格', '即时成交剩余撤销', '最优五档即时成交剩余 '全额成交或撤销']
沪市可选 ['最优五档成交剩余撤销', '最优五档成交剩... | def market_sell(self, security, amount, ttype=None, **kwargs):
"""
市价卖出
:param security: 六位证券代码
:param amount: 交易数量
:param ttype: 市价委托类型，默认客户端默认选择，
深市可选 ['对手方最优价格', '本方最优价格', '即时成交剩余撤销', '最优五档即时成交剩余 '全额成交或撤销']
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train | ClientTrader.market_trade | 市价交易
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:param amount: 交易数量
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市价交易
:param security: 六位证券代码
:param amount: 交易数量
:param ttype: 市价委托类型，默认客户端默认选择，
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沪市可选 ['最优五档成交剩余撤销', '最优五档成交... | def market_trade(self, security, amount, ttype=None, **kwargs):
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市价交易
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:param ttype: 市价委托类型，默认客户端默认选择，
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train | ClientTrader._set_market_trade_type | 根据选择的市价交易类型选择对应的下拉选项 | easytrader/clienttrader.py | def _set_market_trade_type(self, ttype):
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train | BaseLoginClientTrader.prepare | 登陆客户端
:param config_path: 登陆配置文件，跟参数登陆方式二选一
:param user: 账号
:param password: 明文密码
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"""
登陆客户端
:param config_path: 登陆配置文件，跟参数登陆方式二选一
:param user: 账号
:param password: 明文密码
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登陆客户端
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train | YHClientTrader.login | 登陆客户端
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:param exe_path: 客户端路径类似 'C:\\中国银河证券双子星3.2\\Binarystar.exe',
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:return: | easytrader/yh_clienttrader.py | def login(self, user, password, exe_path, comm_password=None, **kwargs):
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登陆客户端
:param user: 账号
:param password: 明文密码
:param exe_path: 客户端路径类似 'C:\\中国银河证券双子星3.2\\Binarystar.exe',
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:param comm_password: 通讯密码, 华泰需要，可不设
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"""
登陆客户端
:param user: 账号
:param password: 明文密码
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train | use | 用于生成特定的券商对象
:param broker:券商名支持 ['yh_client', '银河客户端'] ['ht_client', '华泰客户端']
:param debug: 控制 debug 日志的显示, 默认为 True
:param initial_assets: [雪球参数] 控制雪球初始资金，默认为一百万
:return the class of trader
Usage::
>>> import easytrader
>>> user = easytrader.use('xq')
>>> user.prepare('xq.... | easytrader/api.py | def use(broker, debug=True, **kwargs):
"""用于生成特定的券商对象
:param broker:券商名支持 ['yh_client', '银河客户端'] ['ht_client', '华泰客户端']
:param debug: 控制 debug 日志的显示, 默认为 True
:param initial_assets: [雪球参数] 控制雪球初始资金，默认为一百万
:return the class of trader
Usage::
>>> import easytrader
>>> user = easy... | def use(broker, debug=True, **kwargs):
"""用于生成特定的券商对象
:param broker:券商名支持 ['yh_client', '银河客户端'] ['ht_client', '华泰客户端']
:param debug: 控制 debug 日志的显示, 默认为 True
:param initial_assets: [雪球参数] 控制雪球初始资金，默认为一百万
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train | follower | 用于生成特定的券商对象
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:param initial_assets: [雪球参数] 控制雪球初始资金，默认为一万,
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Usage::
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:param initial_assets: [雪球参数] 控制雪球初始资金，默认为一万,
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Usage::
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train | CaffeLMDB | Read a Caffe LMDB file where each value contains a ``caffe.Datum`` protobuf.
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... | tensorpack/dataflow/format.py | def CaffeLMDB(lmdb_path, shuffle=True, keys=None):
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Read a Caffe LMDB file where each value contains a ``caffe.Datum`` protobuf.
Produces datapoints of the format: [HWC image, label].
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train | NvidiaDevice.memory | Memory information in bytes
Example:
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{'total': 4238016512L, 'used': 434831360L, 'free': 3803185152L}
Returns:
total/used/free memory in bytes | tensorpack/utils/nvml.py | def memory(self):
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Example:
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{'total': 4238016512L, 'used': 434831360L, 'free': 3803185152L}
Returns:
total/used/free memory in bytes
"""
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Example:
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{'total': 4238016512L, 'used': 434831360L, 'free': 3803185152L}
Returns:
total/used/free memory in bytes
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train | NvidiaDevice.utilization | Percent of time over the past second was utilized.
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Percent of time over the past second during which global (device) memory was being read or written
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train | NVMLContext.num_devices | Get number of devices | tensorpack/utils/nvml.py | def num_devices(self):
"""Get number of devices """
c_count = c_uint()
_check_return(_NVML.get_function(
"nvmlDeviceGetCount_v2")(byref(c_count)))
return c_count.value | def num_devices(self):
"""Get number of devices """
c_count = c_uint()
_check_return(_NVML.get_function(
"nvmlDeviceGetCount_v2")(byref(c_count)))
return c_count.value | [
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train | NVMLContext.device | Get a specific GPU device
Args:
idx: index of device
Returns:
NvidiaDevice: single GPU device | tensorpack/utils/nvml.py | def device(self, idx):
"""Get a specific GPU device
Args:
idx: index of device
Returns:
NvidiaDevice: single GPU device
"""
class GpuDevice(Structure):
pass
c_nvmlDevice_t = POINTER(GpuDevice)
c_index = c_uint(idx)
... | def device(self, idx):
"""Get a specific GPU device
Args:
idx: index of device
Returns:
NvidiaDevice: single GPU device
"""
class GpuDevice(Structure):
pass
c_nvmlDevice_t = POINTER(GpuDevice)
c_index = c_uint(idx)
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train | maybe_download_and_extract | Download and extract the tarball from Alex's website. Copied from tensorflow example | tensorpack/dataflow/dataset/cifar.py | def maybe_download_and_extract(dest_directory, cifar_classnum):
"""Download and extract the tarball from Alex's website. Copied from tensorflow example """
assert cifar_classnum == 10 or cifar_classnum == 100
if cifar_classnum == 10:
cifar_foldername = 'cifar-10-batches-py'
else:
cifar_f... | def maybe_download_and_extract(dest_directory, cifar_classnum):
"""Download and extract the tarball from Alex's website. Copied from tensorflow example """
assert cifar_classnum == 10 or cifar_classnum == 100
if cifar_classnum == 10:
cifar_foldername = 'cifar-10-batches-py'
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train | CifarBase.get_per_pixel_mean | Args:
names (tuple[str]): the names ('train' or 'test') of the datasets
Returns:
a mean image of all images in the given datasets, with size 32x32x3 | tensorpack/dataflow/dataset/cifar.py | def get_per_pixel_mean(self, names=('train', 'test')):
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Args:
names (tuple[str]): the names ('train' or 'test') of the datasets
Returns:
a mean image of all images in the given datasets, with size 32x32x3
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for name in names:
assert name ... | def get_per_pixel_mean(self, names=('train', 'test')):
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Args:
names (tuple[str]): the names ('train' or 'test') of the datasets
Returns:
a mean image of all images in the given datasets, with size 32x32x3
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train | CifarBase.get_per_channel_mean | Args:
names (tuple[str]): the names ('train' or 'test') of the datasets
Returns:
An array of three values as mean of each channel, for all images in the given datasets. | tensorpack/dataflow/dataset/cifar.py | def get_per_channel_mean(self, names=('train', 'test')):
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Args:
names (tuple[str]): the names ('train' or 'test') of the datasets
Returns:
An array of three values as mean of each channel, for all images in the given datasets.
"""
mean = self.get_per_p... | def get_per_channel_mean(self, names=('train', 'test')):
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Args:
names (tuple[str]): the names ('train' or 'test') of the datasets
Returns:
An array of three values as mean of each channel, for all images in the given datasets.
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train | maskrcnn_loss | Args:
mask_logits: #fg x #category xhxw
fg_labels: #fg, in 1~#class, int64
fg_target_masks: #fgxhxw, float32 | examples/FasterRCNN/model_mrcnn.py | def maskrcnn_loss(mask_logits, fg_labels, fg_target_masks):
"""
Args:
mask_logits: #fg x #category xhxw
fg_labels: #fg, in 1~#class, int64
fg_target_masks: #fgxhxw, float32
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num_fg = tf.size(fg_labels, out_type=tf.int64)
indices = tf.stack([tf.range(num_fg), fg_labels - 1]... | def maskrcnn_loss(mask_logits, fg_labels, fg_target_masks):
"""
Args:
mask_logits: #fg x #category xhxw
fg_labels: #fg, in 1~#class, int64
fg_target_masks: #fgxhxw, float32
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num_fg = tf.size(fg_labels, out_type=tf.int64)
indices = tf.stack([tf.range(num_fg), fg_labels - 1]... | [
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train | maskrcnn_upXconv_head | Args:
feature (NxCx s x s): size is 7 in C4 models and 14 in FPN models.
num_category(int):
num_convs (int): number of convolution layers
norm (str or None): either None or 'GN'
Returns:
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Args:
feature (NxCx s x s): size is 7 in C4 models and 14 in FPN models.
num_category(int):
num_convs (int): number of convolution layers
norm (str or None): either None or 'GN'
Returns:
mask... | def maskrcnn_upXconv_head(feature, num_category, num_convs, norm=None):
"""
Args:
feature (NxCx s x s): size is 7 in C4 models and 14 in FPN models.
num_category(int):
num_convs (int): number of convolution layers
norm (str or None): either None or 'GN'
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train | SVHNDigit.get_per_pixel_mean | Args:
names (tuple[str]): names of the dataset split
Returns:
a 32x32x3 image, the mean of all images in the given datasets | tensorpack/dataflow/dataset/svhn.py | def get_per_pixel_mean(names=('train', 'test', 'extra')):
"""
Args:
names (tuple[str]): names of the dataset split
Returns:
a 32x32x3 image, the mean of all images in the given datasets
"""
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assert name in ['train', 'test', '... | def get_per_pixel_mean(names=('train', 'test', 'extra')):
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Args:
names (tuple[str]): names of the dataset split
Returns:
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train | build_or_reuse_placeholder | Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
Args:
tensor_spec (tf.TensorSpec):
Returns:
tf.Tensor: | tensorpack/graph_builder/model_desc.py | def build_or_reuse_placeholder(tensor_spec):
"""
Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
Args:
tensor_spec (tf.TensorSpec):
Returns:
tf.Tensor:
"""
g = tfv1.get_default_graph()
name = tensor_spec.name
try:
te... | def build_or_reuse_placeholder(tensor_spec):
"""
Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
Args:
tensor_spec (tf.TensorSpec):
Returns:
tf.Tensor:
"""
g = tfv1.get_default_graph()
name = tensor_spec.name
try:
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The result is cached for each instance of :class:`ModelDescBase`. | tensorpack/graph_builder/model_desc.py | def get_input_signature(self):
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A list of :class:`tf.TensorSpec`, which describes the inputs of this model.
The result is cached for each instance of :class:`ModelDescBase`.
"""
with tf.Graph().as_default() as G: # create these placeholder in a tempo... | def get_input_signature(self):
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Returns:
A list of :class:`tf.TensorSpec`, which describes the inputs of this model.
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train | dependency_of_targets | Check that op is in the subgraph induced by the dependencies of targets.
The result is memoized.
This is useful if some SessionRunHooks should be run only together with certain ops.
Args:
targets: a tuple of ops or tensors. The targets to find dependencies of.
op (tf.Operation or tf.Tensor... | tensorpack/tfutils/dependency.py | def dependency_of_targets(targets, op):
"""
Check that op is in the subgraph induced by the dependencies of targets.
The result is memoized.
This is useful if some SessionRunHooks should be run only together with certain ops.
Args:
targets: a tuple of ops or tensors. The targets to find de... | def dependency_of_targets(targets, op):
"""
Check that op is in the subgraph induced by the dependencies of targets.
The result is memoized.
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train | dependency_of_fetches | Check that op is in the subgraph induced by the dependencies of fetches.
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fetches: An argument to `sess.run`. Nested structure will affect performance.
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fetches may have more general structure.
Args:
fetches: An argument to `sess.run`. Nested structure will affect performance.
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fetches: An argument to `sess.run`. Nested structure will affect performance.
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train | create_scalar_summary | Args:
name (str):
v (float): scalar value
Returns:
tf.Summary: a tf.Summary object with name and simple scalar value v. | tensorpack/tfutils/summary.py | def create_scalar_summary(name, v):
"""
Args:
name (str):
v (float): scalar value
Returns:
tf.Summary: a tf.Summary object with name and simple scalar value v.
"""
assert isinstance(name, six.string_types), type(name)
v = float(v)
s = tf.Summary()
s.value.add(tag=... | def create_scalar_summary(name, v):
"""
Args:
name (str):
v (float): scalar value
Returns:
tf.Summary: a tf.Summary object with name and simple scalar value v.
"""
assert isinstance(name, six.string_types), type(name)
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train | create_image_summary | Args:
name(str):
val(np.ndarray): 4D tensor of NHWC. assume RGB if C==3.
Can be either float or uint8. Range has to be [0,255].
Returns:
tf.Summary: | tensorpack/tfutils/summary.py | def create_image_summary(name, val):
"""
Args:
name(str):
val(np.ndarray): 4D tensor of NHWC. assume RGB if C==3.
Can be either float or uint8. Range has to be [0,255].
Returns:
tf.Summary:
"""
assert isinstance(name, six.string_types), type(name)
n, h, w, c ... | def create_image_summary(name, val):
"""
Args:
name(str):
val(np.ndarray): 4D tensor of NHWC. assume RGB if C==3.
Can be either float or uint8. Range has to be [0,255].
Returns:
tf.Summary:
"""
assert isinstance(name, six.string_types), type(name)
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train | add_tensor_summary | Summarize a tensor by different methods.
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types (list[str]): summary types, can be scalar/histogram/sparsity/mean/rms
name (str): summary name. Defaults to be the op name.
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"""
Summarize a tensor by different methods.
Args:
x (tf.Tensor): a tensor to summarize
types (list[str]): summary types, can be scalar/histogram/sparsity/mean/rms
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Summarize a tensor by different methods.
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x (tf.Tensor): a tensor to summarize
types (list[str]): summary types, can be scalar/histogram/sparsity/mean/rms
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train | add_activation_summary | Call :func:`add_tensor_summary` under a reused 'activation-summary' name scope.
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Args:
x (tf.Tensor): the tensor to summary.
types (list[str]): summary types, defaults to ``['sparsity', 'rms', 'histogram']``.
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This function is a no-op if not calling from main training tower.
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Call :func:`add_tensor_summary` under a reused 'activation-summary' name scope.
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train | add_param_summary | Add summary ops for all trainable variables matching the regex, under a
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train | add_moving_summary | Summarize the moving average for scalar tensors.
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Args:
args: scalar tensors to summarize
decay (float): the decay rate. Defaults to 0.95.
collection (str or None): the name of the collection to add EMA-maintaining ops.
... | tensorpack/tfutils/summary.py | def add_moving_summary(*args, **kwargs):
"""
Summarize the moving average for scalar tensors.
This function is a no-op if not calling from main training tower.
Args:
args: scalar tensors to summarize
decay (float): the decay rate. Defaults to 0.95.
collection (str or None): the ... | def add_moving_summary(*args, **kwargs):
"""
Summarize the moving average for scalar tensors.
This function is a no-op if not calling from main training tower.
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args: scalar tensors to summarize
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train | CascadeRCNNHead.run_head | Args:
proposals: BoxProposals
stage: 0, 1, 2
Returns:
FastRCNNHead
Nx4, updated boxes | examples/FasterRCNN/model_cascade.py | def run_head(self, proposals, stage):
"""
Args:
proposals: BoxProposals
stage: 0, 1, 2
Returns:
FastRCNNHead
Nx4, updated boxes
"""
reg_weights = tf.constant(cfg.CASCADE.BBOX_REG_WEIGHTS[stage], dtype=tf.float32)
pooled_fea... | def run_head(self, proposals, stage):
"""
Args:
proposals: BoxProposals
stage: 0, 1, 2
Returns:
FastRCNNHead
Nx4, updated boxes
"""
reg_weights = tf.constant(cfg.CASCADE.BBOX_REG_WEIGHTS[stage], dtype=tf.float32)
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train | CascadeRCNNHead.match_box_with_gt | Args:
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Returns:
BoxProposals | examples/FasterRCNN/model_cascade.py | def match_box_with_gt(self, boxes, iou_threshold):
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boxes: Nx4
Returns:
BoxProposals
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BoxProposals
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train | CascadeRCNNHead.decoded_output_boxes | Returns:
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"""
Returns:
Nx#classx4
"""
ret = self._cascade_boxes[-1]
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train | CascadeRCNNHead.output_scores | Returns:
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"""
Returns:
Nx#class
"""
scores = [head.output_scores('cascade_scores_stage{}'.format(idx + 1))
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Returns:
Nx#class
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scores = [head.output_scores('cascade_scores_stage{}'.format(idx + 1))
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train | do_visualize | Visualize some intermediate results (proposals, raw predictions) inside the pipeline. | examples/FasterRCNN/train.py | def do_visualize(model, model_path, nr_visualize=100, output_dir='output'):
"""
Visualize some intermediate results (proposals, raw predictions) inside the pipeline.
"""
df = get_train_dataflow() # we don't visualize mask stuff
df.reset_state()
pred = OfflinePredictor(PredictConfig(
m... | def do_visualize(model, model_path, nr_visualize=100, output_dir='output'):
"""
Visualize some intermediate results (proposals, raw predictions) inside the pipeline.
"""
df = get_train_dataflow() # we don't visualize mask stuff
df.reset_state()
pred = OfflinePredictor(PredictConfig(
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train | get_registered_layer | Args:
name (str): the name of the layer, e.g. 'Conv2D'
Returns:
the wrapped layer function, or None if not registered. | tensorpack/models/registry.py | def get_registered_layer(name):
"""
Args:
name (str): the name of the layer, e.g. 'Conv2D'
Returns:
the wrapped layer function, or None if not registered.
"""
ret = _LAYER_REGISTRY.get(name, None)
if ret == _NameConflict:
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"""
Args:
name (str): the name of the layer, e.g. 'Conv2D'
Returns:
the wrapped layer function, or None if not registered.
"""
ret = _LAYER_REGISTRY.get(name, None)
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train | layer_register | Args:
log_shape (bool): log input/output shape of this layer
use_scope (bool or None):
Whether to call this layer with an extra first argument as variable scope.
When set to None, it can be called either with or without
the scope name argument, depend on whether the f... | tensorpack/models/registry.py | def layer_register(
log_shape=False,
use_scope=True):
"""
Args:
log_shape (bool): log input/output shape of this layer
use_scope (bool or None):
Whether to call this layer with an extra first argument as variable scope.
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log_shape (bool): log input/output shape of this layer
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train | TowerTrainer.get_predictor | This method will build the trainer's tower function under ``TowerContext(is_training=False)``,
and returns a callable predictor with input placeholders & output tensors in this tower.
This method handles the common case of inference with the same tower function.
If you want to do inference with... | tensorpack/train/tower.py | def get_predictor(self, input_names, output_names, device=0):
"""
This method will build the trainer's tower function under ``TowerContext(is_training=False)``,
and returns a callable predictor with input placeholders & output tensors in this tower.
This method handles the common case o... | def get_predictor(self, input_names, output_names, device=0):
"""
This method will build the trainer's tower function under ``TowerContext(is_training=False)``,
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train | export_serving | Export trained model to use it in TensorFlow Serving or cloudML. | examples/basics/export-model.py | def export_serving(model_path):
"""Export trained model to use it in TensorFlow Serving or cloudML. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=InferenceOnlyModel(),
input_names=['input_img_bytes'],
output_names=['prediction_img_bytes'])
... | def export_serving(model_path):
"""Export trained model to use it in TensorFlow Serving or cloudML. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=InferenceOnlyModel(),
input_names=['input_img_bytes'],
output_names=['prediction_img_bytes'])
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train | export_compact | Export trained model to use it as a frozen and pruned inference graph in
mobile applications. | examples/basics/export-model.py | def export_compact(model_path):
"""Export trained model to use it as a frozen and pruned inference graph in
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model=Model(),
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pred_config = PredictConfig(
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train | apply | Run inference from a training model checkpoint. | examples/basics/export-model.py | def apply(model_path):
"""Run inference from a training model checkpoint. """
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train | apply_inference_graph | Run inference from a different graph, which receives encoded images buffers. | examples/basics/export-model.py | def apply_inference_graph(model_path):
"""Run inference from a different graph, which receives encoded images buffers. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=InferenceOnlyModel(),
input_names=['input_img_bytes'],
output_names=['predictio... | def apply_inference_graph(model_path):
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train | apply_compact | Run the pruned and frozen inference graph. | examples/basics/export-model.py | def apply_compact(graph_path):
"""Run the pruned and frozen inference graph. """
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# Note, we just load the graph and do *not* need to initialize anything.
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train | BatchNorm | Mostly equivalent to `tf.layers.batch_normalization`, but difference in
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1. Accepts `data_format` rather than `axis`. For 2D input, this argument will be ignored.
2. Default value for `momentum` and `epsilon` is different.
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gamma_initializer=tf.ones_initializer(),
data_format='channels_last',
internal_update=False):
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train | SelectComponent | Select / reorder components from datapoints.
Args:
ds (DataFlow): input DataFlow.
idxs (list[int]): a list of component indices.
Example:
.. code-block:: none
original df produces: [c1, c2, c3]
idxs: [2,1]
this df: [c3, c2] | tensorpack/dataflow/common.py | def SelectComponent(ds, idxs):
"""
Select / reorder components from datapoints.
Args:
ds (DataFlow): input DataFlow.
idxs (list[int]): a list of component indices.
Example:
.. code-block:: none
original df produces: [c1, c2, c3]
idxs: [2,1]
this df: [c3, c... | def SelectComponent(ds, idxs):
"""
Select / reorder components from datapoints.
Args:
ds (DataFlow): input DataFlow.
idxs (list[int]): a list of component indices.
Example:
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train | PrintData._analyze_input_data | Gather useful debug information from a datapoint.
Args:
entry: the datapoint component
k (int): index of this component in current datapoint
depth (int, optional): recursion depth
max_depth, max_list: same as in :meth:`__init__`.
Returns:
str... | tensorpack/dataflow/common.py | def _analyze_input_data(self, entry, k, depth=1, max_depth=3, max_list=3):
"""
Gather useful debug information from a datapoint.
Args:
entry: the datapoint component
k (int): index of this component in current datapoint
depth (int, optional): recursion depth
... | def _analyze_input_data(self, entry, k, depth=1, max_depth=3, max_list=3):
"""
Gather useful debug information from a datapoint.
Args:
entry: the datapoint component
k (int): index of this component in current datapoint
depth (int, optional): recursion depth
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train | RatioCounter.feed | Args:
cnt(int): the count of some event of interest.
tot(int): the total number of events. | tensorpack/utils/stats.py | def feed(self, count, total=1):
"""
Args:
cnt(int): the count of some event of interest.
tot(int): the total number of events.
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self._tot += total
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tot(int): the total number of events.
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self._tot += total
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train | BinaryStatistics.feed | Args:
pred (np.ndarray): binary array.
label (np.ndarray): binary array of the same size. | tensorpack/utils/stats.py | def feed(self, pred, label):
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Args:
pred (np.ndarray): binary array.
label (np.ndarray): binary array of the same size.
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assert pred.shape == label.shape, "{} != {}".format(pred.shape, label.shape)
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label (np.ndarray): binary array of the same size.
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train | OnlineMoments.feed | Args:
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train | apply_grad_processors | Wrapper around optimizers to apply gradient processors.
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Wrapper around optimizers to apply gradient processors.
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gradprocs (list[GradientProcessor]): gradient processors to add to the
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train | _paste_mask | Args:
box: 4 float
mask: MxM floats
shape: h,w
Returns:
A uint8 binary image of hxw. | examples/FasterRCNN/eval.py | def _paste_mask(box, mask, shape):
"""
Args:
box: 4 float
mask: MxM floats
shape: h,w
Returns:
A uint8 binary image of hxw.
"""
# int() is floor
# box fpcoor=0.0 -> intcoor=0.0
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Args:
box: 4 float
mask: MxM floats
shape: h,w
Returns:
A uint8 binary image of hxw.
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train | predict_image | Run detection on one image, using the TF callable.
This function should handle the preprocessing internally.
Args:
img: an image
model_func: a callable from the TF model.
It takes image and returns (boxes, probs, labels, [masks])
Returns:
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"""
Run detection on one image, using the TF callable.
This function should handle the preprocessing internally.
Args:
img: an image
model_func: a callable from the TF model.
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... | def predict_image(img, model_func):
"""
Run detection on one image, using the TF callable.
This function should handle the preprocessing internally.
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img: an image
model_func: a callable from the TF model.
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train | predict_dataflow | Args:
df: a DataFlow which produces (image, image_id)
model_func: a callable from the TF model.
It takes image and returns (boxes, probs, labels, [masks])
tqdm_bar: a tqdm object to be shared among multiple evaluation instances. If None,
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Return... | examples/FasterRCNN/eval.py | def predict_dataflow(df, model_func, tqdm_bar=None):
"""
Args:
df: a DataFlow which produces (image, image_id)
model_func: a callable from the TF model.
It takes image and returns (boxes, probs, labels, [masks])
tqdm_bar: a tqdm object to be shared among multiple evaluation i... | def predict_dataflow(df, model_func, tqdm_bar=None):
"""
Args:
df: a DataFlow which produces (image, image_id)
model_func: a callable from the TF model.
It takes image and returns (boxes, probs, labels, [masks])
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train | multithread_predict_dataflow | Running multiple `predict_dataflow` in multiple threads, and aggregate the results.
Args:
dataflows: a list of DataFlow to be used in :func:`predict_dataflow`
model_funcs: a list of callable to be used in :func:`predict_dataflow`
Returns:
list of dict, in the format used by
`De... | examples/FasterRCNN/eval.py | def multithread_predict_dataflow(dataflows, model_funcs):
"""
Running multiple `predict_dataflow` in multiple threads, and aggregate the results.
Args:
dataflows: a list of DataFlow to be used in :func:`predict_dataflow`
model_funcs: a list of callable to be used in :func:`predict_dataflow`... | def multithread_predict_dataflow(dataflows, model_funcs):
"""
Running multiple `predict_dataflow` in multiple threads, and aggregate the results.
Args:
dataflows: a list of DataFlow to be used in :func:`predict_dataflow`
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train | batch_flatten | Flatten the tensor except the first dimension. | tensorpack/models/fc.py | def batch_flatten(x):
"""
Flatten the tensor except the first dimension.
"""
shape = x.get_shape().as_list()[1:]
if None not in shape:
return tf.reshape(x, [-1, int(np.prod(shape))])
return tf.reshape(x, tf.stack([tf.shape(x)[0], -1])) | def batch_flatten(x):
"""
Flatten the tensor except the first dimension.
"""
shape = x.get_shape().as_list()[1:]
if None not in shape:
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train | FullyConnected | A wrapper around `tf.layers.Dense`.
One difference to maintain backward-compatibility:
Default weight initializer is variance_scaling_initializer(2.0).
Variable Names:
* ``W``: weights of shape [in_dim, out_dim]
* ``b``: bias | tensorpack/models/fc.py | def FullyConnected(
inputs,
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activation=None,
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kernel_initializer=None,
bias_initializer=tf.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
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train | MultiProcessPredictWorker._init_runtime | Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
have workers that run on multiGPUs | tensorpack/predict/concurrency.py | def _init_runtime(self):
""" Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
have workers that run on multiGPUs
"""
if self.idx != 0:
from tensorpack.models.registry import disable_layer_logging
disable_layer_logging()
self.predictor = ... | def _init_runtime(self):
""" Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
have workers that run on multiGPUs
"""
if self.idx != 0:
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disable_layer_logging()
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train | PredictorWorkerThread.fetch_batch | Fetch a batch of data without waiting | tensorpack/predict/concurrency.py | def fetch_batch(self):
""" Fetch a batch of data without waiting"""
inp, f = self.queue.get()
nr_input_var = len(inp)
batched, futures = [[] for _ in range(nr_input_var)], []
for k in range(nr_input_var):
batched[k].append(inp[k])
futures.append(f)
whi... | def fetch_batch(self):
""" Fetch a batch of data without waiting"""
inp, f = self.queue.get()
nr_input_var = len(inp)
batched, futures = [[] for _ in range(nr_input_var)], []
for k in range(nr_input_var):
batched[k].append(inp[k])
futures.append(f)
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train | MultiThreadAsyncPredictor.put_task | Same as in :meth:`AsyncPredictorBase.put_task`. | tensorpack/predict/concurrency.py | def put_task(self, dp, callback=None):
"""
Same as in :meth:`AsyncPredictorBase.put_task`.
"""
f = Future()
if callback is not None:
f.add_done_callback(callback)
self.input_queue.put((dp, f))
return f | def put_task(self, dp, callback=None):
"""
Same as in :meth:`AsyncPredictorBase.put_task`.
"""
f = Future()
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f.add_done_callback(callback)
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train | loads_msgpack | Args:
buf: the output of `dumps`. | tensorpack/utils/serialize.py | def loads_msgpack(buf):
"""
Args:
buf: the output of `dumps`.
"""
# Since 0.6, the default max size was set to 1MB.
# We change it to approximately 1G.
return msgpack.loads(buf, raw=False,
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buf: the output of `dumps`.
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train | BatchNorm | Almost equivalent to `tf.layers.batch_normalization`, but different (and more powerful)
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1. Accepts an alternative `data_format` option when `axis` is None. For 2D input, this argument will be ignored.
2. Default value for `momentum` and `epsilon` is different.
3. Default value for `tr... | tensorpack/models/batch_norm.py | def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
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beta_initializer=tf.zeros_initializer(),
gamma_initializer=tf.ones_initializer(),
virtual_batch_size=None,
data_format='channels_last',
... | def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
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beta_initializer=tf.zeros_initializer(),
gamma_initializer=tf.ones_initializer(),
virtual_batch_size=None,
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train | BatchRenorm | Batch Renormalization layer, as described in the paper:
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This implementation is a wrapper around `tf.layers.batch_normalization`.
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data_format='channels_last'):
"""
Batch Renormalization layer, as described in the paper:
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Batch Renormalization layer, as described in the paper:
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train | Model.generator | return an image generated from z | examples/GAN/DCGAN.py | def generator(self, z):
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nf = 64
l = FullyConnected('fc0', z, nf * 8 * 4 * 4, activation=tf.identity)
l = tf.reshape(l, [-1, 4, 4, nf * 8])
l = BNReLU(l)
with argscope(Conv2DTranspose, activation=BNReLU, kernel_size=4, strides=2):
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""" return an image generated from z"""
nf = 64
l = FullyConnected('fc0', z, nf * 8 * 4 * 4, activation=tf.identity)
l = tf.reshape(l, [-1, 4, 4, nf * 8])
l = BNReLU(l)
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train | Model.discriminator | return a (b, 1) logits | examples/GAN/DCGAN.py | def discriminator(self, imgs):
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nf = 64
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l = (LinearWrap(imgs)
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train | area | Args:
boxes: nx4 floatbox
Returns:
n | examples/FasterRCNN/utils/box_ops.py | def area(boxes):
"""
Args:
boxes: nx4 floatbox
Returns:
n
"""
x_min, y_min, x_max, y_max = tf.split(boxes, 4, axis=1)
return tf.squeeze((y_max - y_min) * (x_max - x_min), [1]) | def area(boxes):
"""
Args:
boxes: nx4 floatbox
Returns:
n
"""
x_min, y_min, x_max, y_max = tf.split(boxes, 4, axis=1)
return tf.squeeze((y_max - y_min) * (x_max - x_min), [1]) | [
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train | pairwise_intersection | Compute pairwise intersection areas between boxes.
Args:
boxlist1: Nx4 floatbox
boxlist2: Mx4
Returns:
a tensor with shape [N, M] representing pairwise intersections | examples/FasterRCNN/utils/box_ops.py | def pairwise_intersection(boxlist1, boxlist2):
"""Compute pairwise intersection areas between boxes.
Args:
boxlist1: Nx4 floatbox
boxlist2: Mx4
Returns:
a tensor with shape [N, M] representing pairwise intersections
"""
x_min1, y_min1, x_max1, y_max1 = tf.split(boxlist1, 4, axis=... | def pairwise_intersection(boxlist1, boxlist2):
"""Compute pairwise intersection areas between boxes.
Args:
boxlist1: Nx4 floatbox
boxlist2: Mx4
Returns:
a tensor with shape [N, M] representing pairwise intersections
"""
x_min1, y_min1, x_max1, y_max1 = tf.split(boxlist1, 4, axis=... | [
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train | pairwise_iou | Computes pairwise intersection-over-union between box collections.
Args:
boxlist1: Nx4 floatbox
boxlist2: Mx4
Returns:
a tensor with shape [N, M] representing pairwise iou scores. | examples/FasterRCNN/utils/box_ops.py | def pairwise_iou(boxlist1, boxlist2):
"""Computes pairwise intersection-over-union between box collections.
Args:
boxlist1: Nx4 floatbox
boxlist2: Mx4
Returns:
a tensor with shape [N, M] representing pairwise iou scores.
"""
intersections = pairwise_intersection(boxlist1, boxlist... | def pairwise_iou(boxlist1, boxlist2):
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Args:
boxlist1: Nx4 floatbox
boxlist2: Mx4
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a tensor with shape [N, M] representing pairwise iou scores.
"""
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train | sample | :param path: path to the model
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train | Maxout | Maxout as in the paper `Maxout Networks <http://arxiv.org/abs/1302.4389>`_.
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x (tf.Tensor): a NHWC or NC tensor. Channel has to be known.
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train | PReLU | Parameterized ReLU as in the paper `Delving Deep into Rectifiers: Surpassing
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train | BNReLU | A shorthand of BatchNormalization + ReLU. | tensorpack/models/nonlin.py | def BNReLU(x, name=None):
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train | GroupNorm | More code that reproduces the paper can be found at https://github.com/ppwwyyxx/GroupNorm-reproduce/. | examples/FasterRCNN/backbone.py | def GroupNorm(x, group=32, gamma_initializer=tf.constant_initializer(1.)):
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"""
shape = x.get_shape().as_list()
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chan = shape[1]
assert chan... | def GroupNorm(x, group=32, gamma_initializer=tf.constant_initializer(1.)):
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More code that reproduces the paper can be found at https://github.com/ppwwyyxx/GroupNorm-reproduce/.
"""
shape = x.get_shape().as_list()
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train | backbone_scope | Args:
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"""
Args:
freeze (bool): whether to freeze all the variables under the scope
"""
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freeze (bool): whether to freeze all the variables under the scope
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train | extract_images | Extract the images into a 4D uint8 numpy array [index, y, x, depth]. | tensorpack/dataflow/dataset/mnist.py | def extract_images(filename):
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magic = _read32(bytestream)
if magic != 2051:
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train | extract_labels | Extract the labels into a 1D uint8 numpy array [index]. | tensorpack/dataflow/dataset/mnist.py | def extract_labels(filename):
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with gzip.open(filename) as bytestream:
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if magic != 2049:
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train | create_dummy_class | When a dependency of a class is not available, create a dummy class which throws ImportError when used.
Args:
klass (str): name of the class.
dependency (str): name of the dependency.
Returns:
class: a class object | tensorpack/utils/develop.py | def create_dummy_class(klass, dependency):
"""
When a dependency of a class is not available, create a dummy class which throws ImportError when used.
Args:
klass (str): name of the class.
dependency (str): name of the dependency.
Returns:
class: a class object
"""
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When a dependency of a class is not available, create a dummy class which throws ImportError when used.
Args:
klass (str): name of the class.
dependency (str): name of the dependency.
Returns:
class: a class object
"""
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train | create_dummy_func | When a dependency of a function is not available, create a dummy function which throws ImportError when used.
Args:
func (str): name of the function.
dependency (str or list[str]): name(s) of the dependency.
Returns:
function: a function object | tensorpack/utils/develop.py | def create_dummy_func(func, dependency):
"""
When a dependency of a function is not available, create a dummy function which throws ImportError when used.
Args:
func (str): name of the function.
dependency (str or list[str]): name(s) of the dependency.
Returns:
function: a func... | def create_dummy_func(func, dependency):
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When a dependency of a function is not available, create a dummy function which throws ImportError when used.
Args:
func (str): name of the function.
dependency (str or list[str]): name(s) of the dependency.
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train | log_deprecated | Log deprecation warning.
Args:
name (str): name of the deprecated item.
text (str, optional): information about the deprecation.
eos (str, optional): end of service date such as "YYYY-MM-DD". | tensorpack/utils/develop.py | def log_deprecated(name="", text="", eos=""):
"""
Log deprecation warning.
Args:
name (str): name of the deprecated item.
text (str, optional): information about the deprecation.
eos (str, optional): end of service date such as "YYYY-MM-DD".
"""
assert name or text
if eo... | def log_deprecated(name="", text="", eos=""):
"""
Log deprecation warning.
Args:
name (str): name of the deprecated item.
text (str, optional): information about the deprecation.
eos (str, optional): end of service date such as "YYYY-MM-DD".
"""
assert name or text
if eo... | [
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train | deprecated | Args:
text, eos: same as :func:`log_deprecated`.
Returns:
a decorator which deprecates the function.
Example:
.. code-block:: python
@deprecated("Explanation of what to do instead.", "2017-11-4")
def foo(...):
pass | tensorpack/utils/develop.py | def deprecated(text="", eos=""):
"""
Args:
text, eos: same as :func:`log_deprecated`.
Returns:
a decorator which deprecates the function.
Example:
.. code-block:: python
@deprecated("Explanation of what to do instead.", "2017-11-4")
def foo(...):
... | def deprecated(text="", eos=""):
"""
Args:
text, eos: same as :func:`log_deprecated`.
Returns:
a decorator which deprecates the function.
Example:
.. code-block:: python
@deprecated("Explanation of what to do instead.", "2017-11-4")
def foo(...):
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train | QueueInput.refill_queue | Clear the queue, then call dataflow.__iter__() again and fill into the queue. | tensorpack/input_source/input_source.py | def refill_queue(self):
"""
Clear the queue, then call dataflow.__iter__() again and fill into the queue.
"""
self.thread.pause() # pause enqueue
opt = tfv1.RunOptions()
opt.timeout_in_ms = 2000 # 2s
sess = tfv1.get_default_session()
# dequeue until... | def refill_queue(self):
"""
Clear the queue, then call dataflow.__iter__() again and fill into the queue.
"""
self.thread.pause() # pause enqueue
opt = tfv1.RunOptions()
opt.timeout_in_ms = 2000 # 2s
sess = tfv1.get_default_session()
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train | QueueInput._create_ema_callback | Create a hook-only callback which maintain EMA of the queue size.
Also tf.summary.scalar the EMA. | tensorpack/input_source/input_source.py | def _create_ema_callback(self):
"""
Create a hook-only callback which maintain EMA of the queue size.
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"""
with self.cached_name_scope():
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train | BatchQueueInput._setup | shapes except for the batch dimension | tensorpack/input_source/input_source.py | def _setup(self, inputs):
logger.info("Setting up the queue for CPU prefetching ...")
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train | TFDatasetInput.dataflow_to_dataset | Wrap a dataflow to tf.data.Dataset.
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train | MultiTowerOfflinePredictor.get_predictor | Returns:
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train | intersection | Compute pairwise intersection areas between boxes.
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boxes1: a numpy array with shape [N, 4] holding N boxes
boxes2: a numpy array with shape [M, 4] holding M boxes
Returns:
a numpy array with shape [N*M] representing pairwise intersection area | examples/FasterRCNN/utils/np_box_ops.py | def intersection(boxes1, boxes2):
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Args:
boxes1: a numpy array with shape [N, 4] holding N boxes
boxes2: a numpy array with shape [M, 4] holding M boxes
Returns:
a numpy array with shape [N*M] representing pairwise intersection area
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boxes1: a numpy array with shape [N, 4] holding N boxes
boxes2: a numpy array with shape [M, 4] holding M boxes
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a numpy array with shape [N*M] representing pairwise intersection area
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train | iou | Computes pairwise intersection-over-union between box collections.
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boxes2: a numpy array with shape [M, 4] holding M boxes.
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boxes1: a numpy array with shape [N, 4] holding N boxes.
boxes2: a numpy array with shape [M, 4] holding M boxes.
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a numpy array with shape [N, M] representing pairwise iou scores.
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boxes1: a numpy array with shape [N, 4] holding N boxes.
boxes2: a numpy array with shape [M, 4] holding M boxes.
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a numpy array with shape [N, M] representing pairwise iou scores.
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train | ioa | Computes pairwise intersection-over-area between box collections.
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train | maybe_download | Download the data from Marlin's website, unless it's already here. | tensorpack/dataflow/dataset/caltech101.py | def maybe_download(url, work_directory):
"""Download the data from Marlin's website, unless it's already here."""
filename = url.split("/")[-1]
filepath = os.path.join(work_directory, filename)
if not os.path.exists(filepath):
logger.info("Downloading to {}...".format(filepath))
download... | def maybe_download(url, work_directory):
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filename = url.split("/")[-1]
filepath = os.path.join(work_directory, filename)
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train | ILSVRCMeta.get_synset_1000 | Returns:
dict: {cls_number: synset_id} | tensorpack/dataflow/dataset/ilsvrc.py | def get_synset_1000(self):
"""
Returns:
dict: {cls_number: synset_id}
"""
fname = os.path.join(self.dir, 'synsets.txt')
assert os.path.isfile(fname)
lines = [x.strip() for x in open(fname).readlines()]
return dict(enumerate(lines)) | def get_synset_1000(self):
"""
Returns:
dict: {cls_number: synset_id}
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fname = os.path.join(self.dir, 'synsets.txt')
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train | ILSVRCMeta.get_image_list | Args:
name (str): 'train' or 'val' or 'test'
dir_structure (str): same as in :meth:`ILSVRC12.__init__()`.
Returns:
list: list of (image filename, label) | tensorpack/dataflow/dataset/ilsvrc.py | def get_image_list(self, name, dir_structure='original'):
"""
Args:
name (str): 'train' or 'val' or 'test'
dir_structure (str): same as in :meth:`ILSVRC12.__init__()`.
Returns:
list: list of (image filename, label)
"""
assert name in ['train', ... | def get_image_list(self, name, dir_structure='original'):
"""
Args:
name (str): 'train' or 'val' or 'test'
dir_structure (str): same as in :meth:`ILSVRC12.__init__()`.
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list: list of (image filename, label)
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train | ILSVRCMeta.get_per_pixel_mean | Args:
size (tuple): image size in (h, w). Defaults to (256, 256).
Returns:
np.ndarray: per-pixel mean of shape (h, w, 3 (BGR)) in range [0, 255]. | tensorpack/dataflow/dataset/ilsvrc.py | def get_per_pixel_mean(self, size=None):
"""
Args:
size (tuple): image size in (h, w). Defaults to (256, 256).
Returns:
np.ndarray: per-pixel mean of shape (h, w, 3 (BGR)) in range [0, 255].
"""
if self.caffepb is None:
self.caffepb = get_caffe... | def get_per_pixel_mean(self, size=None):
"""
Args:
size (tuple): image size in (h, w). Defaults to (256, 256).
Returns:
np.ndarray: per-pixel mean of shape (h, w, 3 (BGR)) in range [0, 255].
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if self.caffepb is None:
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train | ILSVRCMeta.guess_dir_structure | Return the directory structure of "dir".
Args:
dir(str): something like '/path/to/imagenet/val'
Returns:
either 'train' or 'original' | tensorpack/dataflow/dataset/ilsvrc.py | def guess_dir_structure(dir):
"""
Return the directory structure of "dir".
Args:
dir(str): something like '/path/to/imagenet/val'
Returns:
either 'train' or 'original'
"""
subdir = os.listdir(dir)[0]
# find a subdir starting with 'n'
... | def guess_dir_structure(dir):
"""
Return the directory structure of "dir".
Args:
dir(str): something like '/path/to/imagenet/val'
Returns:
either 'train' or 'original'
"""
subdir = os.listdir(dir)[0]
# find a subdir starting with 'n'
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train | COCODetection.print_coco_metrics | Args:
json_file (str): path to the results json file in coco format
Returns:
dict: the evaluation metrics | examples/FasterRCNN/dataset.py | def print_coco_metrics(self, json_file):
"""
Args:
json_file (str): path to the results json file in coco format
Returns:
dict: the evaluation metrics
"""
from pycocotools.cocoeval import COCOeval
ret = {}
cocoDt = self.coco.loadRes(json_fi... | def print_coco_metrics(self, json_file):
"""
Args:
json_file (str): path to the results json file in coco format
Returns:
dict: the evaluation metrics
"""
from pycocotools.cocoeval import COCOeval
ret = {}
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"COCOeval"... | d7a13cb74c9066bc791d7aafc3b744b60ee79a9f |
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