def eval_on_ILSVRC12(model, sess_init, data_dir):
ds = dataset.ILSVRC12(data_dir, 'val', shuffle=False)
def resize_func(im):
h, w = im.shape[:2]
scale = 256.0 / min(h, w)
desSize = map(int, (max(224, min(w, scale * w)),\
max(224, min(h, scale * h))))
im = cv2.resize(im, tuple(desSize), interpolation=cv2.INTER_CUBIC)
return im
transformers = [
imgaug.MapImage(resize_func),
imgaug.CenterCrop((224, 224)),
]
ds = AugmentImageComponent(ds, transformers)
ds = BatchData(ds, 128, remainder=True)
ds = PrefetchData(ds, 10, 1)
cfg = PredictConfig(
model=model,
session_init=sess_init,
session_config=get_default_sess_config(0.99),
output_var_names=['prob:0', 'wrong-top1:0', 'wrong-top5:0']
)
pred = SimpleDatasetPredictor(cfg, ds)
acc1, acc5 = RatioCounter(), RatioCounter()
for idx, o in enumerate(pred.get_result()):
output, w1, w5 = o
batch_size = output.shape[0]
acc1.feed(w1, batch_size)
acc5.feed(w5, batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio))
def eval_on_ILSVRC12(model, sess_init, data_dir):
ds = dataset.ILSVRC12(data_dir, 'val', shuffle=False)
def resize_func(im):
h, w = im.shape[:2]
scale = 256.0 / min(h, w)
desSize = map(int, (max(224, min(w, scale * w)),\
max(224, min(h, scale * h))))
im = cv2.resize(im, tuple(desSize), interpolation=cv2.INTER_CUBIC)
return im
transformers = [
imgaug.MapImage(resize_func),
imgaug.CenterCrop((224, 224)),
]
ds = AugmentImageComponent(ds, transformers)
ds = BatchData(ds, 128, remainder=True)
ds = PrefetchData(ds, 10, 1)
cfg = PredictConfig(
model=model,
session_init=sess_init,
session_config=get_default_sess_config(0.99),
output_var_names=['prob:0', 'wrong-top1:0', 'wrong-top5:0'])
pred = SimpleDatasetPredictor(cfg, ds)
acc1, acc5 = RatioCounter(), RatioCounter()
for idx, o in enumerate(pred.get_result()):
output, w1, w5 = o
batch_size = output.shape[0]
acc1.feed(w1, batch_size)
acc5.feed(w5, batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio))
def eval_on_ILSVRC12(model_file, data_dir):
ds = get_data('val')
pred_config = PredictConfig(model=Model(),
input_var_names=['input', 'label'],
session_init=get_model_loader(model_file),
output_var_names=['wrong-top1', 'wrong-top5'])
pred = SimpleDatasetPredictor(pred_config, ds)
acc1, acc5 = RatioCounter(), RatioCounter()
for o in pred.get_result():
batch_size = o[0].shape[0]
acc1.feed(o[0].sum(), batch_size)
acc5.feed(o[1].sum(), batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio))
def eval_on_ILSVRC12(params, data_dir):
ds = dataset.ILSVRC12(data_dir, 'val', shuffle=False, dir_structure='train')
ds = AugmentImageComponent(ds, get_inference_augmentor())
ds = BatchData(ds, 128, remainder=True)
pred_config = PredictConfig(
model=Model(),
input_var_names=['input', 'label'],
session_init=ParamRestore(params),
output_var_names=['prob', 'wrong-top1', 'wrong-top5']
)
pred = SimpleDatasetPredictor(pred_config, ds)
acc1, acc5 = RatioCounter(), RatioCounter()
for o in pred.get_result():
batch_size = o[0].shape[0]
acc1.feed(o[1], batch_size)
acc5.feed(o[2], batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio))