Python torch.max方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def greedy_decode(self, latent, max_len, start_id):
        '''
        latent: (batch_size, max_src_seq, d_model)
        src_mask: (batch_size, 1, max_src_len)
        '''
        batch_size = latent.size(0)
        ys = get_cuda(torch.ones(batch_size, 1).fill_(start_id).long())  # (batch_size, 1)
        for i in range(max_len - 1):
            # input("==========")
            # print("="*10, i)
            # print("ys", ys.size())  # (batch_size, i)
            # print("tgt_mask", subsequent_mask(ys.size(1)).size())  # (1, i, i)
            out = self.decode(latent.unsqueeze(1), to_var(ys), to_var(subsequent_mask(ys.size(1)).long()))
            prob = self.generator(out[:, -1])
            # print("prob", prob.size())  # (batch_size, vocab_size)
            _, next_word = torch.max(prob, dim=1)
            # print("next_word", next_word.size())  # (batch_size)

            # print("next_word.unsqueeze(1)", next_word.unsqueeze(1).size())

            ys = torch.cat([ys, next_word.unsqueeze(1)], dim=1)
            # print("ys", ys.size())
        return ys[:, 1:] 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def crop_images_random(path='../images/', scale=0.50):  # from utils.utils import *; crop_images_random()
    # crops images into random squares up to scale fraction
    # WARNING: overwrites images!
    for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
        img = cv2.imread(file)  # BGR
        if img is not None:
            h, w = img.shape[:2]

            # create random mask
            a = 30  # minimum size (pixels)
            mask_h = random.randint(a, int(max(a, h * scale)))  # mask height
            mask_w = mask_h  # mask width

            # box
            xmin = max(0, random.randint(0, w) - mask_w // 2)
            ymin = max(0, random.randint(0, h) - mask_h // 2)
            xmax = min(w, xmin + mask_w)
            ymax = min(h, ymin + mask_h)

            # apply random color mask
            cv2.imwrite(file, img[ymin:ymax, xmin:xmax]) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_results(hyp)
    # Plot hyperparameter evolution results in evolve.txt
    x = np.loadtxt('evolve.txt', ndmin=2)
    f = fitness(x)
    weights = (f - f.min()) ** 2  # for weighted results
    fig = plt.figure(figsize=(12, 10))
    matplotlib.rc('font', **{'size': 8})
    for i, (k, v) in enumerate(hyp.items()):
        y = x[:, i + 5]
        # mu = (y * weights).sum() / weights.sum()  # best weighted result
        mu = y[f.argmax()]  # best single result
        plt.subplot(4, 5, i + 1)
        plt.plot(mu, f.max(), 'o', markersize=10)
        plt.plot(y, f, '.')
        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
        print('%15s: %.3g' % (k, mu))
    fig.tight_layout()
    plt.savefig('evolve.png', dpi=200) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def record_output(self, output, output_indices, target, prev_absolutes,
                      next_absolutes, batch_size=1):
        assert output.dim() == 4
        assert target.dim() == 3

        _, predictions = output.max(3)

        # Compute per class accuracy for unbalanced data.
        sequence_length = output.size(1)
        num_label = output.size(2)
        num_class = output.size(3)
        correct_alljoint = (target == predictions).float().sum(2)
        sum_of_corrects = correct_alljoint.sum(1)
        max_value = num_label * sequence_length
        count_correct = (sum_of_corrects == max_value).float().mean()
        correct_per_seq = ((correct_alljoint == num_label - 1).sum(1).float() /
                           sequence_length).mean()
        self.meter.update(
            torch.Tensor([count_correct * 100, correct_per_seq * 100]),
            batch_size) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def extract_feature(model, dataloader, save_path, load_from_disk=True, model_path=''):
    if load_from_disk:
        model = models.Network(base_net=args.model_name,
                               n_class=args.num_class)
        model.load_state_dict(torch.load(model_path))
        model = model.to(DEVICE)
    model.eval()
    correct = 0
    fea_all = torch.zeros(1,1+model.base_network.output_num()).to(DEVICE)
    with torch.no_grad():
        for inputs, labels in dataloader:
            inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
            feas = model.get_features(inputs)
            labels = labels.view(labels.size(0), 1).float()
            x = torch.cat((feas, labels), dim=1)
            fea_all = torch.cat((fea_all, x), dim=0)
            outputs = model(inputs)
            preds = torch.max(outputs, 1)[1]
            correct += torch.sum(preds == labels.data.long())
        test_acc = correct.double() / len(dataloader.dataset)
    fea_numpy = fea_all.cpu().numpy()
    np.savetxt(save_path, fea_numpy[1:], fmt='%.6f', delimiter=',')
    print('Test acc: %f' % test_acc)

# You may want to classify with 1nn after getting features 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def test(model, target_test_loader):
    model.eval()
    test_loss = utils.AverageMeter()
    correct = 0
    criterion = torch.nn.CrossEntropyLoss()
    len_target_dataset = len(target_test_loader.dataset)
    with torch.no_grad():
        for data, target in target_test_loader:
            data, target = data.to(DEVICE), target.to(DEVICE)
            s_output = model.predict(data)
            loss = criterion(s_output, target)
            test_loss.update(loss.item())
            pred = torch.max(s_output, 1)[1]
            correct += torch.sum(pred == target)

    print('{} --> {}: max correct: {}, accuracy{: .2f}%\n'.format(
        source_name, target_name, correct, 100. * correct / len_target_dataset)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def get(self):
        """Get top cost recorded.

        You should call step before get at least once.

        Returns:
            A float number of cost.

        """
        assert len(self.coll) != 0, 'Please call step before get'
        if self.type == 'mean':
            ret = np.mean(self.coll)
        elif self.type == 'max':
            ret = np.max(self.coll)
        else:
            ret = np.min(self.coll)
        return ret 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def evo_norm(x, prefix, running_var, v, weight, bias,
             training, momentum, eps=0.1, groups=32):
    if prefix == 'b0':
        if training:
            var = torch.var(x, dim=(0, 2, 3), keepdim=True)
            running_var.mul_(momentum)
            running_var.add_((1 - momentum) * var)
        else:
            var = running_var
        if v is not None:
            den = torch.max((var + eps).sqrt(), v * x + instance_std(x, eps))
            x = x / den * weight + bias
        else:
            x = x * weight + bias
    else:
        if v is not None:
            x = x * torch.sigmoid(v * x) / group_std(x,
                                                     groups, eps) * weight + bias
        else:
            x = x * weight + bias

    return x 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def forward(self,x):
        input_size = x.size()[2]
        self.interp1 = nn.UpsamplingBilinear2d(size = (  int(input_size*0.75)+1,  int(input_size*0.75)+1  ))
        self.interp2 = nn.UpsamplingBilinear2d(size = (  int(input_size*0.5)+1,   int(input_size*0.5)+1   ))
        self.interp3 = nn.UpsamplingBilinear2d(size = (  outS(input_size),   outS(input_size)   ))

        out = []
        x2 = self.interp1(x)        
        x3 = self.interp2(x)

        out.append(self.Scale(x))                   #1.0x
        out.append(self.interp3(self.Scale(x2)))    #0.75x
        out.append(self.interp3(self.Scale(x3)))    #0.5x
        #out.append(self.Scale(x3))  # for 0.5x scale
        
        x2Out_interp = out[1]
        x3Out_interp = out[2]
        temp1 = torch.max(out[0],x2Out_interp)
        out.append(torch.max(temp1,x3Out_interp))
        return out 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def do_eval(model, test_loader, cuda):
    model.is_training = False
    predicted = []
    true_label = []
    for X, y in test_loader:
        X = Variable(X)
        if cuda:
            X = X.cuda()
        output = model(X)
        output = output.squeeze(0)
        _, output = torch.max(output, 1)
        if cuda:
            output = output.cpu()
        predicted.extend(output.data.numpy().tolist())
        y = y.squeeze(0)
        true_label.extend(y.numpy().tolist()) 
    print("Acc: %.3f" % accuracy(predicted, true_label))
    return predicted 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def shem(roi_probs_neg, negative_count, ohem_poolsize):
    """
    stochastic hard example mining: from a list of indices (referring to non-matched predictions),
    determine a pool of highest scoring (worst false positives) of size negative_count*ohem_poolsize.
    Then, sample n (= negative_count) predictions of this pool as negative examples for loss.
    :param roi_probs_neg: tensor of shape (n_predictions, n_classes).
    :param negative_count: int.
    :param ohem_poolsize: int.
    :return: (negative_count).  indices refer to the positions in roi_probs_neg. If pool smaller than expected due to
    limited negative proposals availabel, this function will return sampled indices of number < negative_count without
    throwing an error.
    """
    # sort according to higehst foreground score.
    probs, order = roi_probs_neg[:, 1:].max(1)[0].sort(descending=True)
    select = torch.tensor((ohem_poolsize * int(negative_count), order.size()[0])).min().int()
    pool_indices = order[:select]
    rand_idx = torch.randperm(pool_indices.size()[0])
    return pool_indices[rand_idx[:negative_count].cuda()] 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def iou(source: Tensor, other: Tensor) -> Tensor:
        source, other = source.unsqueeze(dim=-2).repeat(1, 1, other.shape[-2], 1), \
                        other.unsqueeze(dim=-3).repeat(1, source.shape[-2], 1, 1)

        source_area = (source[..., 2] - source[..., 0]) * (source[..., 3] - source[..., 1])
        other_area = (other[..., 2] - other[..., 0]) * (other[..., 3] - other[..., 1])

        intersection_left = torch.max(source[..., 0], other[..., 0])
        intersection_top = torch.max(source[..., 1], other[..., 1])
        intersection_right = torch.min(source[..., 2], other[..., 2])
        intersection_bottom = torch.min(source[..., 3], other[..., 3])
        intersection_width = torch.clamp(intersection_right - intersection_left, min=0)
        intersection_height = torch.clamp(intersection_bottom - intersection_top, min=0)
        intersection_area = intersection_width * intersection_height

        return intersection_area / (source_area + other_area - intersection_area) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def _get_waveform_and_window_properties(waveform: Tensor,
                                        channel: int,
                                        sample_frequency: float,
                                        frame_shift: float,
                                        frame_length: float,
                                        round_to_power_of_two: bool,
                                        preemphasis_coefficient: float) -> Tuple[Tensor, int, int, int]:
    r"""Gets the waveform and window properties
    """
    channel = max(channel, 0)
    assert channel < waveform.size(0), ('Invalid channel %d for size %d' % (channel, waveform.size(0)))
    waveform = waveform[channel, :]  # size (n)
    window_shift = int(sample_frequency * frame_shift * MILLISECONDS_TO_SECONDS)
    window_size = int(sample_frequency * frame_length * MILLISECONDS_TO_SECONDS)
    padded_window_size = _next_power_of_2(window_size) if round_to_power_of_two else window_size

    assert 2 <= window_size <= len(waveform), ('choose a window size %d that is [2, %d]' % (window_size, len(waveform)))
    assert 0 < window_shift, '`window_shift` must be greater than 0'
    assert padded_window_size % 2 == 0, 'the padded `window_size` must be divisible by two.' \
                                        ' use `round_to_power_of_two` or change `frame_length`'
    assert 0. <= preemphasis_coefficient <= 1.0, '`preemphasis_coefficient` must be between [0,1]'
    assert sample_frequency > 0, '`sample_frequency` must be greater than zero'
    return waveform, window_shift, window_size, padded_window_size 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def decode_greedy(self, logits, seq_lens):
        decoded = []
        tlogits = logits.transpose(0, 1)
        _, tokens = torch.max(tlogits, 2)
        for i in range(tlogits.size(0)):
            output_str = self.convert_to_string(tokens[i], seq_lens[i])
            decoded.append(output_str)
        return decoded 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import max [as 别名]
def _region_classification(self, fc7): 
    cls_score = self.cls_score_net(fc7)
    cls_pred = torch.max(cls_score, 1)[1]  # the prediction class of each bbox
    cls_prob = F.softmax(cls_score)
    bbox_pred = self.bbox_pred_net(fc7)
    bbox_prob = torch.stack([F.softmax(bbox_pred[:,i]) for i in range(bbox_pred.size(1))], 1)
    fuse_prob = cls_prob.mul(bbox_prob)
    image_prob = fuse_prob.sum(0,keepdim=True)
    
    self._predictions["cls_pred"] = cls_pred
    self._predictions["cls_prob"] = cls_prob
    self._predictions["bbox_prob"] = bbox_prob
    self._predictions["fuse_prob"] = fuse_prob
    self._predictions["image_prob"] = image_prob

    return cls_prob, bbox_prob, fuse_prob, image_prob