Python nd.argmax方法代码示例

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def evaluate_accuracy(data_iterator, net):

    acc = mx.metric.Accuracy()

    # Iterate through data and label
    for i, (data, label) in enumerate(data_iterator):

        # Get the data and label into the GPU
        data = data.as_in_context(ctx[0])
        label = label.as_in_context(ctx[0])

        # Get network's output which is a probability distribution
        # Apply argmax on the probability distribution to get network's classification.
        output = net(data)
        predictions = nd.argmax(output, axis=1)

        # Give network's prediction and the correct label to update the metric
        acc.update(preds=predictions, labels=label)

    # Return the accuracy
    return acc.get()[1]

# We'll use cross entropy loss since we are doing multiclass classification 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def eval_net(args, net, dataloader, criterion):
    total = 0
    total_loss = 0
    total_correct = 0

    for data in dataloader:
        graphs, labels = data
        labels = labels.as_in_context(args.device)
        feat = graphs.ndata['attr'].astype('float32').as_in_context(args.device)

        total += len(labels)

        outputs = net(graphs, feat)
        predicted = nd.argmax(outputs, axis=1)

        total_correct += (predicted == labels).sum().asscalar()
        loss = criterion(outputs, labels)
        # crossentropy(reduce=True) for default
        total_loss += loss.sum().asscalar()

    loss, acc = 1.0 * total_loss / total, 1.0*total_correct / total

    return loss, acc 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def get_max_pred(batch_heatmaps):
    batch_size = batch_heatmaps.shape[0]
    num_joints = batch_heatmaps.shape[1]
    width = batch_heatmaps.shape[3]
    heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1))
    idx = nd.argmax(heatmaps_reshaped, 2)
    maxvals = nd.max(heatmaps_reshaped, 2)

    maxvals = maxvals.reshape((batch_size, num_joints, 1))
    idx = idx.reshape((batch_size, num_joints, 1))

    preds = nd.tile(idx, (1, 1, 2)).astype(np.float32)

    preds[:, :, 0] = (preds[:, :, 0]) % width
    preds[:, :, 1] = nd.floor((preds[:, :, 1]) / width)

    pred_mask = nd.tile(nd.greater(maxvals, 0.0), (1, 1, 2))
    pred_mask = pred_mask.astype(np.float32)

    preds *= pred_mask
    return preds, maxvals 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def hybrid_forward(self, F, X, y=None):
        # import pdb; pdb.set_trace()
        X = self.net[0](X) # Conv1
        X = self.net[1](X) # Primary Capsule
        X = self.net[2](X) # Digital Capsule
        # import pdb ; pdb.set_trace()
        X = X.reshape((X.shape[0],X.shape[2], X.shape[4]))
        # get length of vector for margin loss calculation
        X_l2norm = nd.sqrt((X**2).sum(axis=-1))
        # import pdb ; pdb.set_trace()
        prob = nd.softmax(X_l2norm, axis=-1)

        if y is not None:
            max_len_indices = y
        else:
            
            max_len_indices = nd.argmax(prob,axis=-1)


        y_tile = nd.tile(y.expand_dims(axis=1), reps=(1, X.shape[-1]))
        batch_activated_capsules = nd.pick(X, y_tile, axis=1, keepdims=True)

        reconstrcutions = self.net[3](batch_activated_capsules)

        return  prob, X_l2norm, reconstrcutions 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs):
    """Train and evaluate a model."""
    print('training on', ctx)
    if isinstance(ctx, mx.Context):
        ctx = [ctx]
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n, m, start = 0.0, 0.0, 0, 0, time.time()
        for i, batch in enumerate(train_iter):
            Xs, ys, batch_size = _get_batch(batch, ctx)   
            with autograd.record():
                y_hats = [net(X) for X in Xs]
                ls = [loss(y_hat, y) for y_hat, y in zip(y_hats, ys)]
            for l in ls:
                l.backward()
            trainer.step(batch_size)
            train_l_sum += sum([l.sum().asscalar() for l in ls])
            n += sum([l.size for l in ls])
            train_acc_sum += sum([(y_hat.argmax(axis=1) == y).sum().asscalar()
                                 for y_hat, y in zip(y_hats, ys)])
            m += sum([y.size for y in ys])
        test_acc = evaluate_accuracy(test_iter, net, ctx)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, '
              'time %.1f sec'
              % (epoch + 1, train_l_sum / n, train_acc_sum / m, test_acc,
                 time.time() - start)) 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx,
              num_epochs):
    """Train and evaluate a model with CPU or GPU."""
    print('training on', ctx)
    loss = gloss.SoftmaxCrossEntropyLoss()
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()
        for X, y in train_iter:
            X, y = X.as_in_context(ctx), y.as_in_context(ctx)
            with autograd.record():
                y_hat = net(X)
                l = loss(y_hat, y).sum()
            l.backward()
            trainer.step(batch_size)
            y = y.astype('float32')
            train_l_sum += l.asscalar()
            train_acc_sum += (y_hat.argmax(axis=1) == y).sum().asscalar()
            n += y.size
        test_acc = evaluate_accuracy(test_iter, net, ctx)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, '
              'time %.1f sec'
              % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc,
                 time.time() - start)) 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
              params=None, lr=None, trainer=None):
    """Train and evaluate a model with CPU."""
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
        for X, y in train_iter:
            with autograd.record():
                y_hat = net(X)
                l = loss(y_hat, y).sum()
            l.backward()
            if trainer is None:
                sgd(params, lr, batch_size)
            else:
                trainer.step(batch_size)
            y = y.astype('float32')
            train_l_sum += l.asscalar()
            train_acc_sum += (y_hat.argmax(axis=1) == y).sum().asscalar()
            n += y.size
        test_acc = evaluate_accuracy(test_iter, net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
              % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc)) 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def _evaluate_accuracy(self, X, Y, batch_size=64):
        data_loader = self.generate_batch(X, Y, batch_size, shuffled=False)

        softmax_loss = gluon.loss.SoftmaxCrossEntropyLoss()

        num_batches = len(X) // batch_size

        metric = mx.metric.Accuracy()
        loss_avg = 0.
        for i, (data, label) in enumerate(data_loader):
            data = data.as_in_context(self.model_ctx)
            label = label.as_in_context(self.model_ctx)
            output = self.model(data)
            predictions = nd.argmax(output, axis=1)
            loss = softmax_loss(output, label)
            metric.update(preds=predictions, labels=label)
            loss_avg = loss_avg * i / (i + 1) + nd.mean(loss).asscalar() / (i + 1)

            if i + 1 == num_batches:
                break
        return metric.get()[1], loss_avg 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def predict(yolo:Yolo,x,threshold=0.5):
    """
    return label ,C,location
    :param yolo:
    :return:
    """
    assert  len(x)==1,"Only One image for now"
    ypre = yolo(x)
    label, preds, location = deal_output(ypre, yolo.s, b=yolo.b, c=yolo.class_num)
    indexs = []
    for i,c in enumerate(preds[0]):
        if c > threshold:
            indexs.append(i)
    class_names = []
    C_list  =[]
    bos_list = []
    for index in indexs:
        label_index = int(index / 2)
        location_offect = int(index % 2)
        class_index = nd.argmax(label[0][label_index], axis=0)
        C = preds[0][index]
        locat = location[0][label_index][location_offect]
        C_list.append(C.asscalar())
        #######traslate the name
        label_name = yolo.class_names
        text = label_name[int(class_index.asscalar()) ]
        class_names.append(text)
        ###traslate the locat
        x, y, w, h = locat
        w, h = nd.power(w, 2), nd.power(h, 2)
        ceil = 1 / 4
        row = int(label_index / 4)
        columns = label_index % 4
        x_center = columns * ceil + x
        y_center = row * ceil + y
        x_min, y_min, x_max, y_max = x_center - 0.5 * w, y_center - 0.5 * h, x_center + 0.5 * w, y_center + 0.5 * h
        box = nd.concatenate([x_min, y_min, x_max, y_max], axis=0) * 256
        bos_list.append(box.asnumpy())
        return class_names,C_list,bos_list 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def pseudo_labeling(self, logits, confidence=0.):
        softmax = nd.softmax(logits, axis=1)
        prob = nd.max(softmax, axis=1)
        p_label = nd.argmax(softmax, axis=1)
        mask = prob > confidence
        return p_label, mask

    # def update_beta(self):
    #     return self.args.beta 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def evaluate_accuracy(data_iterator, net):
    acc = mx.metric.Accuracy()
    for i, (data, label) in enumerate(data_iterator):
        data = data.as_in_context(model_ctx).reshape((-1, 784))
        label = label.as_in_context(model_ctx)
        output = net(data)
        predictions = nd.argmax(output, axis=1)
        acc.update(preds=predictions, labels=label)
    return acc.get()[1] 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def test(data_iterator, net, ctx):
    acc = mx.metric.Accuracy()
    for i, (data, label) in tqdm(enumerate(data_iterator),total=len(data_iterator), ncols=70, leave=False, unit='b'):
        data = data.as_in_context(ctx)
        label = label.as_in_context(ctx)
        prob,_,_ = net(data,label)
        predictions = nd.argmax(prob, axis=1)
        acc.update(preds=predictions, labels=label)
    return acc.get()[1] 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def evaluate_accuracy(data_iter, net, ctx=[mx.cpu()]):
    """Evaluate accuracy of a model on the given data set."""
    if isinstance(ctx, mx.Context):
        ctx = [ctx]
    acc_sum, n = nd.array([0]), 0
    for batch in data_iter:
        features, labels, _ = _get_batch(batch, ctx)
        for X, y in zip(features, labels):
            y = y.astype('float32')
            acc_sum += (net(X).argmax(axis=1) == y).sum().copyto(mx.cpu())
            n += y.size
        acc_sum.wait_to_read()
    return acc_sum.asscalar() / n 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def predict_rnn(prefix, num_chars, rnn, params, init_rnn_state,
                num_hiddens, vocab_size, ctx, idx_to_char, char_to_idx):
    """Predict next chars with a RNN model"""
    state = init_rnn_state(1, num_hiddens, ctx)
    output = [char_to_idx[prefix[0]]]
    for t in range(num_chars + len(prefix) - 1):
        X = to_onehot(nd.array([output[-1]], ctx=ctx), vocab_size)
        (Y, state) = rnn(X, state, params)
        if t < len(prefix) - 1:
            output.append(char_to_idx[prefix[t + 1]])
        else:
            output.append(int(Y[0].argmax(axis=1).asscalar()))
    return ''.join([idx_to_char[i] for i in output]) 

# 需要导入模块: from mxnet import nd [as 别名]
# 或者: from mxnet.nd import argmax [as 别名]
def predict_rnn_gluon(prefix, num_chars, model, vocab_size, ctx, idx_to_char,
                      char_to_idx):
    """Precit next chars with a Gluon RNN model"""
    state = model.begin_state(batch_size=1, ctx=ctx)
    output = [char_to_idx[prefix[0]]]
    for t in range(num_chars + len(prefix) - 1):
        X = nd.array([output[-1]], ctx=ctx).reshape((1, 1))
        (Y, state) = model(X, state)
        if t < len(prefix) - 1:
            output.append(char_to_idx[prefix[t + 1]])
        else:
            output.append(int(Y.argmax(axis=1).asscalar()))
    return ''.join([idx_to_char[i] for i in output])