def test_eval_logic(self, dataloader, param: FixMatchParams):
from thexp.calculate import accuracy as acc
param.topk = param.default([1, 5])
with torch.no_grad():
noisy_mem = torch.zeros(50000,
device=self.device,
dtype=torch.long)
count_dict = Meter()
for batch_data in dataloader:
ids, xs, labels = batch_data
preds = self.predict(xs)
noisy_ys = preds.argmax(dim=1)
noisy_mem[ids] = noisy_ys
total, topk_res = acc.classify(preds, labels, topk=param.topk)
count_dict["total"] += total
for i, topi_res in zip(param.topk, topk_res):
count_dict["top{}".format(i)] += topi_res
import numpy as np
noisy_mem = noisy_mem.detach().cpu().numpy()
np.save('noisy_{}.npy'.format(count_dict['top1']), noisy_mem)
self.logger.info()
return count_dict
def test_classify():
labels = torch.tensor([0, 1, 2, 3])
preds = torch.tensor(
[[5, 4, 3, 2], [5, 4, 3, 2], [5, 4, 3, 2], [5, 4, 3, 2]],
dtype=torch.float)
total, res = acc.classify(preds, labels, topk=(1, 2, 3, 4))
assert total == 4
assert res[0] == 1 and res[1] == 2 and res[2] == 3 and res[3] == 4, str(
res)
def test_eval_logic(self, dataloader, param: Params):
from thexp.calculate import accuracy as acc
with torch.no_grad():
count_dict = Meter()
for xs, labels in dataloader:
xs, labels = xs.to(self.device), labels.to(self.device)
preds = self.predict(xs)
total, topk_res = acc.classify(preds, labels, topk=param.topk)
count_dict["total"] += total
for i, topi_res in zip(param.topk, topk_res):
count_dict[i] += topi_res
return count_dict
def test_eval_logic(self, dataloader, param: Params):
from thexp.calculate import accuracy as acc
param.topk = param.default([1, 5])
with torch.no_grad():
count_dict = Meter()
for xs, labels in dataloader:
preds = self.predict(xs)
total, topk_res = acc.classify(preds, labels, topk=param.topk)
count_dict["total"] += total
for i, topi_res in zip(param.topk, topk_res):
count_dict["top{}".format(i)] += topi_res
return count_dict