def eval_func(self, label):
# chainer.functionのtype_checkを通過させるため、[]のwrapperをつける
xp = cuda.get_array_module(self.pool.variable.data)
mean_activations = Variable(xp.array(
[xp.mean(self.pool.variable.data, axis=0)],
dtype=xp.float32))
label = Variable(xp.array(label, dtype=xp.int32))
# cpuに移す
mean_activations.to_cpu()
label.to_cpu()
mean_activations = mean_activations.data
label = label.data
# top1, top5の算出
res_top1 = mean_activations.argmax()
res_top5 = mean_activations.argsort()[0][-5:]
top1 = 1.0 if label[0] == res_top1 else 0.0
top5 = 1.0 if label[0] in res_top5 else 0.0
report({'top1': top1, 'top5': top5})
def eval_func(self, label):
# chainer.functionのtype_checkを通過させるため、[]のwrapperをつける
xp = cuda.get_array_module(self.pool.variable.data)
mean_activations = Variable(xp.array(
[xp.mean(self.pool.variable.data, axis=0)],
dtype=xp.float32))
label = Variable(xp.array(label, dtype=xp.int32))
# cpuに移す
mean_activations.to_cpu()
label.to_cpu()
mean_activations = mean_activations.data
label = label.data
# top1, top5の算出
res_top1 = mean_activations.argmax()
res_top5 = mean_activations.argsort()[0][-5:]
# ndarray(numpy) -> list(pure python)
label.tolist()
res_top1 = int(res_top1)
res_top5.tolist()
# label[0] は imageに対応する元々のlabel
# label はcommon_root_labels
ref_top1 = 1.0 if res_top1 in label else 0.0
ref_top5 = 1.0 if set(res_top5) & set(label) else 0.0
org_top1 = 1.0 if label[0] == res_top1 else 0.0
org_top5 = 1.0 if label[0] in res_top5 else 0.0
report({
'ref/top1': ref_top1,
'ref/top5': ref_top5,
'org/top1': org_top1,
'org/top5': org_top5 })
