本文整理汇总了Python中tensorpack.predict.FeedfreePredictor方法的典型用法代码示例。如果您正苦于以下问题：Python predict.FeedfreePredictor方法的具体用法？Python predict.FeedfreePredictor怎么用？Python predict.FeedfreePredictor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tensorpack.predict的用法示例。
在下文中一共展示了predict.FeedfreePredictor方法的4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: eval_classification
# 需要导入模块: from tensorpack import predict [as 别名]
# 或者: from tensorpack.predict import FeedfreePredictor [as 别名]
def eval_classification(model, sessinit, dataflow):
"""
Eval a classification model on the dataset. It assumes the model inputs are
named "input" and "label", and contains "wrong-top1" and "wrong-top5" in the graph.
"""
pred_config = PredictConfig(
model=model,
session_init=sessinit,
input_names=['input', 'label'],
output_names=['wrong-top1', 'wrong-top5']
)
acc1, acc5 = RatioCounter(), RatioCounter()
# This does not have a visible improvement over naive predictor,
# but will have an improvement if image_dtype is set to float32.
pred = FeedfreePredictor(pred_config, StagingInput(QueueInput(dataflow), device='/gpu:0'))
for _ in tqdm.trange(dataflow.size()):
top1, top5 = pred()
batch_size = top1.shape[0]
acc1.feed(top1.sum(), batch_size)
acc5.feed(top5.sum(), batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio)) 示例2: eval_on_ILSVRC12
# 需要导入模块: from tensorpack import predict [as 别名]
# 或者: from tensorpack.predict import FeedfreePredictor [as 别名]
def eval_on_ILSVRC12(model, sessinit, dataflow):
pred_config = PredictConfig(
model=model,
session_init=sessinit,
input_names=['input', 'label'],
output_names=['wrong-top1', 'wrong-top5']
)
acc1, acc5 = RatioCounter(), RatioCounter()
# This does not have a visible improvement over naive predictor,
# but will have an improvement if image_dtype is set to float32.
pred = FeedfreePredictor(pred_config, StagingInput(QueueInput(dataflow), device='/gpu:0'))
for _ in tqdm.trange(dataflow.size()):
top1, top5 = pred()
batch_size = top1.shape[0]
acc1.feed(top1.sum(), batch_size)
acc5.feed(top5.sum(), batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio)) 示例3: eval_on_ILSVRC12
# 需要导入模块: from tensorpack import predict [as 别名]
# 或者: from tensorpack.predict import FeedfreePredictor [as 别名]
def eval_on_ILSVRC12(model, sessinit, dataflow):
pred_config = PredictConfig(
model=model,
session_init=sessinit,
input_names=['input', 'label', 'input2', 'label2'],
output_names=['wrong-top1', 'wrong-top5']
)
acc1, acc5 = RatioCounter(), RatioCounter()
# This does not have a visible improvement over naive predictor,
# but will have an improvement if image_dtype is set to float32.
pred = FeedfreePredictor(pred_config, StagingInput(QueueInput(dataflow), device='/gpu:0'))
for _ in tqdm.trange(dataflow.size()):
top1, top5 = pred()
batch_size = top1.shape[0]
acc1.feed(top1.sum(), batch_size)
acc5.feed(top5.sum(), batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio)) 示例4: test
# 需要导入模块: from tensorpack import predict [as 别名]
# 或者: from tensorpack.predict import FeedfreePredictor [as 别名]
def test(net,
session_init,
val_dataflow,
do_calc_flops=False,
extended_log=False):
"""
Main test routine.
Parameters:
----------
net : obj
Model.
session_init : SessionInit
Session initializer.
do_calc_flops : bool, default False
Whether to calculate count of weights.
extended_log : bool, default False
Whether to log more precise accuracy values.
"""
pred_config = PredictConfig(
model=net,
session_init=session_init,
input_names=["input", "label"],
output_names=["wrong-top1", "wrong-top5"]
)
err_top1 = RatioCounter()
err_top5 = RatioCounter()
tic = time.time()
pred = FeedfreePredictor(pred_config, StagingInput(QueueInput(val_dataflow), device="/gpu:0"))
for _ in tqdm.trange(val_dataflow.size()):
err_top1_val, err_top5_val = pred()
batch_size = err_top1_val.shape[0]
err_top1.feed(err_top1_val.sum(), batch_size)
err_top5.feed(err_top5_val.sum(), batch_size)
err_top1_val = err_top1.ratio
err_top5_val = err_top5.ratio
if extended_log:
logging.info("Test: err-top1={top1:.4f} ({top1})\terr-top5={top5:.4f} ({top5})".format(
top1=err_top1_val, top5=err_top5_val))
else:
logging.info("Test: err-top1={top1:.4f}\terr-top5={top5:.4f}".format(
top1=err_top1_val, top5=err_top5_val))
logging.info("Time cost: {:.4f} sec".format(
time.time() - tic))
if do_calc_flops:
calc_flops(model=net)