Python torch.isclose方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_torch_ref_match():

    # Verify if the torch implementation values match the original Numpy implementation.

    num_teachers, num_examples, num_labels = (100, 50, 10)
    preds = (np.random.rand(num_teachers, num_examples) * num_labels).astype(int)  # fake preds

    indices = (np.random.rand(num_examples) * num_labels).astype(int)  # true answers

    preds[:, 0:10] *= 0

    data_dep_eps, data_ind_eps = pate.perform_analysis_torch(
        preds, indices, noise_eps=0.1, delta=1e-5
    )

    data_dep_eps_ref, data_ind_eps_ref = pate.perform_analysis(
        preds, indices, noise_eps=0.1, delta=1e-5
    )

    assert torch.isclose(data_dep_eps, torch.tensor(data_dep_eps_ref, dtype=torch.float32))
    assert torch.isclose(data_ind_eps, torch.tensor(data_ind_eps_ref, dtype=torch.float32)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_hinge_loss():
    true_value = torch.Tensor([[1.2], [1], [1], [1]])
    pred_value = torch.Tensor([[1.2], [0.1], [0], [-0.3]])
    expected_loss = torch.Tensor([(0 + 1 - 0.3 + 0) / 2.0])
    loss = losses.RankHingeLoss()(pred_value, true_value)
    assert torch.isclose(expected_loss, loss)
    expected_loss = torch.Tensor(
        [(2 + 0.1 - 1.2 + 2 - 0.3 + 0) / 2.0])
    loss = losses.RankHingeLoss(margin=2)(pred_value, true_value)
    assert torch.isclose(expected_loss, loss)
    true_value = torch.Tensor(
        [[1.2], [1], [0.8], [1], [1], [0.8]])
    pred_value = torch.Tensor(
        [[1.2], [0.1], [-0.5], [0], [0], [-0.3]])
    expected_loss = torch.Tensor(
        [(0 + 1 - 0.15) / 2.0])
    loss = losses.RankHingeLoss(num_neg=2, margin=1)(
        pred_value, true_value)
    assert torch.isclose(expected_loss, loss) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_rank_crossentropy_loss():
    losses.neg_num = 1

    def softmax(x):
        return np.exp(x) / np.sum(np.exp(x), axis=0)

    true_value = torch.Tensor([[1.], [0.], [0.], [1.]])
    pred_value = torch.Tensor([[0.8], [0.1], [0.8], [0.1]])
    expected_loss = torch.Tensor(
        [(-np.log(softmax([0.8, 0.1])[0]) - np.log(
            softmax([0.8, 0.1])[1])) / 2])
    loss = losses.RankCrossEntropyLoss()(pred_value, true_value)
    assert torch.isclose(expected_loss, loss)
    true_value = torch.Tensor([[1.], [0.], [0.], [0.], [1.], [0.]])
    pred_value = torch.Tensor([[0.8], [0.1], [0.1], [0.8], [0.1], [0.1]])
    expected_loss = torch.Tensor(
        [(-np.log(softmax([0.8, 0.1, 0.1])[0]) - np.log(
            softmax([0.8, 0.1, 0.1])[1])) / 2])
    loss = losses.RankCrossEntropyLoss(num_neg=2)(
        pred_value, true_value)
    assert torch.isclose(expected_loss, loss) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def _components(self) -> Dict[Tuple[str, str, str], Tuple[Tensor, Tensor]]:
        states_per_measure = defaultdict(list)
        for state_belief in self.state_beliefs:
            for m, measure in enumerate(self.design.measures):
                H = state_belief.H[:, m, :].data
                m = H * state_belief.means.data
                std = H * torch.diagonal(state_belief.covs.data, dim1=-2, dim2=-1).sqrt()
                states_per_measure[measure].append((m, std))

        out = {}
        for measure, means_and_stds in states_per_measure.items():
            means, stds = zip(*means_and_stds)
            means = torch.stack(means).permute(1, 0, 2)
            stds = torch.stack(stds).permute(1, 0, 2)
            for s, (process_name, state_element) in enumerate(self.design.state_elements):
                if ~torch.isclose(means[:, :, s].abs().max(), torch.zeros(1)):
                    out[(measure, process_name, state_element)] = (means[:, :, s], stds[:, :, s])
        return out 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_rand(self):
        """Tests uniform random variable generation on [0, 1)"""
        for size in [(10,), (10, 10), (10, 10, 10)]:
            randvec = crypten.rand(*size)
            self.assertTrue(randvec.size() == size, "Incorrect size")
            tensor = randvec.get_plain_text()
            self.assertTrue(
                (tensor >= 0).all() and (tensor < 1).all(), "Invalid values"
            )

        randvec = crypten.rand(int(1e6)).get_plain_text()
        mean = torch.mean(randvec)
        var = torch.var(randvec)
        self.assertTrue(torch.isclose(mean, torch.Tensor([0.5]), rtol=1e-3, atol=1e-3))
        self.assertTrue(
            torch.isclose(var, torch.Tensor([1.0 / 12]), rtol=1e-3, atol=1e-3)
        ) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def _run_act_layer_grad(act_type):
    x = torch.rand(10, 1000) * 10
    m = MLP(act_layer=act_type)

    def _run(x, act_layer=''):
        if act_layer:
            # replace act layer if set
            m.act = create_act_layer(act_layer, inplace=True)
        out = m(x)
        l = (out - 0).pow(2).sum()
        return l

    out_me = _run(x)

    with set_layer_config(scriptable=True):
        out_jit = _run(x, act_type)

    assert torch.isclose(out_jit, out_me)

    with set_layer_config(no_jit=True):
        out_basic = _run(x, act_type)

    assert torch.isclose(out_basic, out_jit) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_arcface_loss(self):
        margin = 30
        scale = 64
        loss_func = ArcFaceLoss(margin=margin, scale=scale, num_classes=10, embedding_size=2)

        embedding_angles = torch.arange(0, 180)
        embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=torch.float) #2D embeddings
        labels = torch.randint(low=0, high=10, size=(180,))

        loss = loss_func(embeddings, labels)
        loss.backward()

        weights = torch.nn.functional.normalize(loss_func.W, p=2, dim=0)
        logits = torch.matmul(embeddings, weights)
        for i, c in enumerate(labels):
            logits[i, c] = torch.cos(torch.acos(logits[i, c]) + torch.tensor(np.radians(margin)))
        
        correct_loss = torch.nn.functional.cross_entropy(logits*scale, labels)
        self.assertTrue(torch.isclose(loss, correct_loss)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_triplet_margin_loss(self):
        margin = 0.2
        loss_funcA = TripletMarginLoss(margin=margin)
        loss_funcB = TripletMarginLoss(margin=margin, reducer=MeanReducer())
        embedding_angles = [0, 20, 40, 60, 80]
        embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=torch.float) #2D embeddings
        labels = torch.LongTensor([0, 0, 1, 1, 2])

        lossA = loss_funcA(embeddings, labels)
        lossB = loss_funcB(embeddings, labels)
        lossA.backward()
        lossB.backward()
        
        triplets = [(0,1,2), (0,1,3), (0,1,4), (1,0,2), (1,0,3), (1,0,4), (2,3,0), (2,3,1), (2,3,4), (3,2,0), (3,2,1), (3,2,4)]

        correct_loss = 0
        num_non_zero_triplets = 0
        for a, p, n in triplets:
            anchor, positive, negative = embeddings[a], embeddings[p], embeddings[n]
            curr_loss = torch.relu(torch.sqrt(torch.sum((anchor-positive)**2)) - torch.sqrt(torch.sum((anchor-negative)**2)) + margin)
            if curr_loss > 0:
                num_non_zero_triplets += 1
            correct_loss += curr_loss
        self.assertTrue(torch.isclose(lossA, correct_loss/num_non_zero_triplets))
        self.assertTrue(torch.isclose(lossB, correct_loss/len(triplets))) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_angular_loss(self):
        loss_func = AngularLoss(alpha=40)
        embedding_angles = [0, 20, 40, 60, 80]
        embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=torch.float) #2D embeddings
        labels = torch.LongTensor([0, 0, 1, 1, 2])

        loss = loss_func(embeddings, labels)
        loss.backward()
        sq_tan_alpha = torch.tan(torch.tensor(np.radians(40)))**2
        triplets = [(0,1,2), (0,1,3), (0,1,4), (1,0,2), (1,0,3), (1,0,4), (2,3,0), (2,3,1), (2,3,4), (3,2,0), (3,2,1), (3,2,4)]

        correct_losses = [0,0,0,0]
        for a, p, n in triplets:
            anchor, positive, negative = embeddings[a], embeddings[p], embeddings[n]
            exponent = 4*sq_tan_alpha*torch.matmul(anchor+positive,negative) - 2*(1+sq_tan_alpha)*torch.matmul(anchor, positive)
            correct_losses[a] += torch.exp(exponent)
        total_loss = 0
        for c in correct_losses:
            total_loss += torch.log(1+c)
        total_loss /= len(correct_losses)
        self.assertTrue(torch.isclose(loss, total_loss.to(torch.float32))) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_cosface_loss(self):
        margin = 0.5
        scale = 64
        loss_func = CosFaceLoss(margin=margin, scale=scale, num_classes=10, embedding_size=2)

        embedding_angles = torch.arange(0, 180)
        embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=torch.float) #2D embeddings
        labels = torch.randint(low=0, high=10, size=(180,))

        loss = loss_func(embeddings, labels)
        loss.backward()

        weights = torch.nn.functional.normalize(loss_func.W, p=2, dim=0)
        logits = torch.matmul(embeddings, weights)
        for i, c in enumerate(labels):
            logits[i, c] -= margin
        
        correct_loss = torch.nn.functional.cross_entropy(logits*scale, labels)
        self.assertTrue(torch.isclose(loss, correct_loss)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_proxy_nca_loss(self):
        softmax_scale = 10
        loss_func = ProxyNCALoss(softmax_scale=softmax_scale, num_classes=10, embedding_size=2)

        embedding_angles = torch.arange(0, 180)
        embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=torch.float) #2D embeddings
        labels = torch.randint(low=0, high=10, size=(180,))

        loss = loss_func(embeddings, labels)
        loss.backward()

        proxies = torch.nn.functional.normalize(loss_func.proxies, p=2, dim=1)
        correct_loss = 0
        for i in range(len(embeddings)):
            curr_emb, curr_label = embeddings[i], labels[i]
            curr_proxy = proxies[curr_label]
            denominator = torch.sum((curr_emb-proxies)**2, dim=1)
            denominator = torch.sum(torch.exp(-denominator*softmax_scale))
            numerator = torch.sum((curr_emb-curr_proxy)**2)
            numerator = torch.exp(-numerator*softmax_scale)
            correct_loss += -torch.log(numerator/denominator)
        
        correct_loss /= len(embeddings)
        self.assertTrue(torch.isclose(loss, correct_loss)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_add_infinity_and_beyond(a, b, c, negative, manifold, dtype):
    _a = a
    if torch.isclose(c, c.new_zeros(())).any():
        pytest.skip("zero not checked")
    infty = b * 10000000
    for i in range(100):
        z = manifold.expmap(a, infty, project=False)
        z = manifold.projx(z)
        assert not torch.isnan(z).any(), ("Found nans", i, z)
        assert torch.isfinite(z).all(), ("Found Infs", i, z)
        z = manifold.mobius_scalar_mul(
            torch.tensor(1000.0, dtype=z.dtype), z, project=False
        )
        z = manifold.projx(z)
        assert not torch.isnan(z).any(), ("Found nans", i, z)
        assert torch.isfinite(z).all(), ("Found Infs", i, z)

        infty = manifold.transp(a, z, infty)
        assert torch.isfinite(infty).all(), (i, infty)
        a = z
    z = manifold.expmap(a, -infty)
    # they just need to be very far, exact answer is not supposed
    tolerance = {
        torch.float32: dict(rtol=3e-1, atol=2e-1),
        torch.float64: dict(rtol=1e-1, atol=1e-3),
    }
    if negative:
        np.testing.assert_allclose(z.detach(), -a.detach(), **tolerance[dtype])
    else:
        assert not torch.isnan(z).any(), "Found nans"
        assert not torch.isnan(a).any(), "Found nans" 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_weighted_midpoint(_k, lincomb):
    manifold = stereographic.Stereographic(_k, learnable=True)
    a = manifold.random(2, 3, 10).requires_grad_(True)
    mid = manifold.weighted_midpoint(a, lincomb=lincomb)
    assert torch.isfinite(mid).all()
    assert mid.shape == (a.shape[-1],)
    mid.sum().backward()
    assert torch.isfinite(a.grad).all()
    assert not torch.isclose(manifold.k.grad, manifold.k.new_zeros(())) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_weighted_midpoint_reduce_dim(_k, lincomb):
    manifold = stereographic.Stereographic(_k, learnable=True)
    a = manifold.random(2, 3, 10).requires_grad_(True)
    mid = manifold.weighted_midpoint(a, reducedim=[0], lincomb=lincomb)
    assert mid.shape == a.shape[-2:]
    assert torch.isfinite(mid).all()
    mid.sum().backward()
    assert torch.isfinite(a.grad).all()
    assert not torch.isclose(manifold.k.grad, manifold.k.new_zeros(())) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import isclose [as 别名]
def test_weighted_midpoint_weighted(_k, lincomb):
    manifold = stereographic.Stereographic(_k, learnable=True)
    a = manifold.random(2, 3, 10).requires_grad_(True)
    mid = manifold.weighted_midpoint(
        a, reducedim=[0], lincomb=lincomb, weights=torch.rand_like(a[..., 0])
    )
    assert mid.shape == a.shape[-2:]
    assert torch.isfinite(mid).all()
    mid.sum().backward()
    assert torch.isfinite(a.grad).all()
    assert not torch.isclose(manifold.k.grad, manifold.k.new_zeros(()))