Changes to mxnet.metric

benchmark/python/sparse/sparse_end2end.py

@@ -225,7 +225,7 @@ def row_sparse_pull(kv, key, data, slices, weight_array, priority):
                            learning_rate=0.1, rescale_grad=1.0/batch_size/num_worker)
     mod.init_optimizer(optimizer=sgd, kvstore=kv)
     # use accuracy as the metric
-    metric = mx.metric.create('acc')
+    metric = mx.gluon.metric.create('acc')
 
     index = mod._exec_group.param_names.index('w')
     # weight_array bound to executors of the contexts

example/adversary/adversary_generation.ipynb

@@ -168,7 +168,7 @@
     "epoch = 3\n",
     "for e in range(epoch):\n",
     "    train_loss = 0.\n",
-    "    acc = mx.metric.Accuracy()\n",
+    "    acc = mx.gluon.metric.Accuracy()\n",
     "    for i, (data, label) in enumerate(train_data):\n",
     "        data = data.as_in_context(ctx)\n",
     "        label = label.as_in_context(ctx)\n",
@@ -223,7 +223,7 @@
     "    l = loss(output, label)\n",
     "l.backward()\n",
     "\n",
-    "acc = mx.metric.Accuracy()\n",
+    "acc = mx.gluon.metric.Accuracy()\n",
     "acc.update(label, output)\n",
     "\n",
     "print(\"Validation batch accuracy {}\".format(acc.get()[1]))"
@@ -256,7 +256,7 @@
     "\n",
     "output = net(data_perturbated)    \n",
     "\n",
-    "acc = mx.metric.Accuracy()\n",
+    "acc = mx.gluon.metric.Accuracy()\n",
     "acc.update(label, output)\n",
     "\n",
     "print(\"Validation batch accuracy after perturbation {}\".format(acc.get()[1]))"

example/autoencoder/variational_autoencoder/VAE_example.ipynb

@@ -610,7 +610,7 @@
    ],
    "source": [
     "# calculate the ELBO which is minus the loss for test set\n",
-    "metric = mx.metric.Loss()\n",
+    "metric = mx.gluon.metric.Loss()\n",
     "model.score(nd_iter_test, metric)"
    ]
   },

example/caffe/caffe_net.py

@@ -140,6 +140,6 @@ def parse_args():
 
     # train
     if use_caffe_loss:
-        train_model.fit(args, net, get_iterator(data_shape, use_caffe_data), mx.metric.Caffe())
+        train_model.fit(args, net, get_iterator(data_shape, use_caffe_data), mx.gluon.metric.Caffe())
     else:
         train_model.fit(args, net, get_iterator(data_shape, use_caffe_data))

example/caffe/train_model.py

@@ -93,7 +93,7 @@ def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
         eval_metrics = ['accuracy']
         # TopKAccuracy only allows top_k > 1
         for top_k in [5, 10, 20]:
-            eval_metrics.append(mx.metric.create('top_k_accuracy', top_k=top_k))
+            eval_metrics.append(mx.gluon.metric.create('top_k_accuracy', top_k=top_k))
 
     if batch_end_callback is not None:
         if not isinstance(batch_end_callback, list):

example/capsnet/capsulenet.py

@@ -122,7 +122,7 @@ def to4d(img):
     return img.reshape(img.shape[0], 1, 28, 28).astype(np.float32)/255
 
 
-class LossMetric(mx.metric.EvalMetric):
+class LossMetric(mx.gluon.metric.EvalMetric):
     """Evaluate the loss function"""
     def __init__(self, batch_size, num_gpus):
         super(LossMetric, self).__init__('LossMetric')

example/ctc/lstm_ocr_train.py

@@ -103,7 +103,7 @@ def main():
         module.fit(train_data=data_train,
                    eval_data=data_val,
                    # use metrics.accuracy or metrics.accuracy_lcs
-                   eval_metric=mx.metric.np(metrics.accuracy, allow_extra_outputs=True),
+                   eval_metric=mx.gluon.metric.np(metrics.accuracy, allow_extra_outputs=True),
                    optimizer='sgd',
                    optimizer_params={'learning_rate': hp.learning_rate,
                                      'momentum': hp.momentum,

example/deep-embedded-clustering/autoencoder.py

@@ -165,7 +165,7 @@ def l2_norm(label, pred):
             return np.mean(np.square(label-pred))/2.0
         solver = Solver(optimizer, momentum=0.9, wd=decay, learning_rate=l_rate,
                         lr_scheduler=lr_scheduler)
-        solver.set_metric(mx.metric.CustomMetric(l2_norm))
+        solver.set_metric(mx.gluon.metric.CustomMetric(l2_norm))
         solver.set_monitor(Monitor(print_every))
         data_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=True,
                                       last_batch_handle='roll_over')
@@ -188,7 +188,7 @@ def l2_norm(label, pred):
             return np.mean(np.square(label-pred))/2.0
         solver = Solver(optimizer, momentum=0.9, wd=decay, learning_rate=l_rate,
                         lr_scheduler=lr_scheduler)
-        solver.set_metric(mx.metric.CustomMetric(l2_norm))
+        solver.set_metric(mx.gluon.metric.CustomMetric(l2_norm))
         solver.set_monitor(Monitor(print_every))
         data_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=True,
                                       last_batch_handle='roll_over')

example/deep-embedded-clustering/dec.py

@@ -122,7 +122,7 @@ def cluster(self, X, y=None, update_interval=None):
 
         def ce(label, pred):
             return np.sum(label*np.log(label/(pred+0.000001)))/label.shape[0]
-        solver.set_metric(mx.metric.CustomMetric(ce))
+        solver.set_metric(mx.gluon.metric.CustomMetric(ce))
 
         label_buff = np.zeros((X.shape[0], self.num_centers))
         train_iter = mx.io.NDArrayIter({'data': X}, {'label': label_buff}, batch_size=batch_size,

example/distributed_training-horovod/gluon_mnist.py

@@ -104,7 +104,7 @@ def conv_nets():
 # Function to evaluate accuracy for a model
 def evaluate(model, data_iter, context):
     data_iter.reset()
-    metric = mx.metric.Accuracy()
+    metric = mx.gluon.metric.Accuracy()
     for _, batch in enumerate(data_iter):
         data = batch.data[0].as_in_context(context)
         label = batch.label[0].as_in_context(context)
@@ -149,7 +149,7 @@ def evaluate(model, data_iter, context):
 
 # Create loss function and train metric
 loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
-metric = mx.metric.Accuracy()
+metric = mx.gluon.metric.Accuracy()
 
 # Train model
 for epoch in range(args.epochs):

example/distributed_training-horovod/module_mnist.py

@@ -157,7 +157,7 @@ def conv_net():
           num_epoch=args.epochs)  # train for at most 10 dataset passes
 
 # Step 7: evaluate model accuracy
-acc = mx.metric.Accuracy()
+acc = mx.gluon.metric.Accuracy()
 model.score(val_iter, acc)
 
 if hvd.rank() == 0:

example/distributed_training-horovod/resnet50_imagenet.py

@@ -286,8 +286,8 @@ def evaluate(epoch):
             return
 
         val_data.reset()
-        acc_top1 = mx.metric.Accuracy()
-        acc_top5 = mx.metric.TopKAccuracy(5)
+        acc_top1 = mx.gluon.metric.Accuracy()
+        acc_top5 = mx.gluon.metric.TopKAccuracy(5)
         for _, batch in enumerate(val_data):
             data, label = batch_fn(batch, context)
             output = net(data.astype(args.dtype, copy=False))
@@ -321,7 +321,7 @@ def evaluate(epoch):
 
     # Create loss function and train metric
     loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
-    metric = mx.metric.Accuracy()
+    metric = mx.gluon.metric.Accuracy()
 
     # Train model
     for epoch in range(args.num_epochs):
@@ -450,8 +450,8 @@ def train_module():
 
     # Evaluate performance if not using synthetic data
     if args.use_rec:
-        acc_top1 = mx.metric.Accuracy()
-        acc_top5 = mx.metric.TopKAccuracy(5)
+        acc_top1 = mx.gluon.metric.Accuracy()
+        acc_top5 = mx.gluon.metric.TopKAccuracy(5)
         res = mod.score(val_data, [acc_top1, acc_top5])
         for name, val in res:
             logging.info('Epoch[%d] Rank[%d] Validation-%s=%f',

example/distributed_training/cifar10_dist.py

@@ -121,7 +121,7 @@ def evaluate_accuracy(data_iterator, network):
     ----------
     tuple of array element
     """
-    acc = mx.metric.Accuracy()
+    acc = mx.gluon.metric.Accuracy()
 
     # Iterate through data and label
     for i, (data, label) in enumerate(data_iterator):

example/distributed_training/cifar10_kvstore_hvd.py

@@ -123,7 +123,7 @@ def evaluate(data_iterator, network, context):
     ----------
     tuple of array element
     """
-    acc = mx.metric.Accuracy()
+    acc = mx.gluon.metric.Accuracy()
 
     # Iterate through data and label
     for i, (data, label) in enumerate(data_iterator):
@@ -208,7 +208,7 @@ def __len__(self):
                         optimizer_params={'learning_rate': args.lr},
                         kvstore=store)
 
-train_metric = mx.metric.Accuracy()
+train_metric = mx.gluon.metric.Accuracy()
 
 # Run as many epochs as required
 for epoch in range(args.epochs):

example/fcn-xs/solver.py

@@ -23,7 +23,7 @@
 from collections import namedtuple
 from mxnet import optimizer as opt
 from mxnet.optimizer import get_updater
-from mxnet import metric
+from mxnet.gluon import metric
 
 # Parameter to pass to batch_end_callback
 BatchEndParam = namedtuple('BatchEndParams', ['epoch', 'nbatch', 'eval_metric'])

example/gluon/audio/urban_sounds/train.py

@@ -28,7 +28,7 @@
 
 def evaluate_accuracy(data_iterator, net):
     """Function to evaluate accuracy of any data iterator passed to it as an argument"""
-    acc = mx.metric.Accuracy()
+    acc = mx.gluon.metric.Accuracy()
     for data, label in data_iterator:
         output = net(data)
         predictions = nd.argmax(output, axis=1)

example/gluon/dc_gan/dcgan.py

@@ -259,7 +259,7 @@ def main():
     real_label = mx.nd.ones((opt.batch_size,), ctx=ctx)
     fake_label = mx.nd.zeros((opt.batch_size,), ctx=ctx)
 
-    metric = mx.metric.Accuracy()
+    metric = mx.gluon.metric.Accuracy()
     print('Training... ')
     stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
 

example/gluon/image_classification.py

@@ -27,7 +27,7 @@
 from mxnet.gluon.model_zoo import vision as models
 from mxnet import autograd as ag
 from mxnet.test_utils import get_mnist_iterator
-from mxnet.metric import Accuracy, TopKAccuracy, CompositeEvalMetric
+from mxnet.gluon.metric import Accuracy, TopKAccuracy, CompositeEvalMetric
 import numpy as np
 
 from data import (get_cifar10_iterator, get_imagenet_iterator,

example/gluon/mnist/mnist.py

@@ -70,7 +70,7 @@ def transformer(data, label):
 # train
 
 def test(ctx):
-    metric = mx.metric.Accuracy()
+    metric = mx.gluon.metric.Accuracy()
     for data, label in val_data:
         data = data.as_in_context(ctx)
         label = label.as_in_context(ctx)
@@ -86,7 +86,7 @@ def train(epochs, ctx):
     # Trainer is for updating parameters with gradient.
     trainer = gluon.Trainer(net.collect_params(), 'sgd',
                             {'learning_rate': opt.lr, 'momentum': opt.momentum})
-    metric = mx.metric.Accuracy()
+    metric = mx.gluon.metric.Accuracy()
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
 
     for epoch in range(epochs):

example/gluon/sn_gan/train.py

@@ -102,7 +102,7 @@ def facc(label, pred):
 g_net.collect_params().zero_grad()
 d_net.collect_params().zero_grad()
 # define evaluation metric
-metric = mx.metric.CustomMetric(facc)
+metric = mx.gluon.metric.CustomMetric(facc)
 # initialize labels
 real_label = nd.ones(BATCH_SIZE, CTX)
 fake_label = nd.zeros(BATCH_SIZE, CTX)

example/gluon/super_resolution/super_resolution.py

@@ -156,7 +156,7 @@ def hybrid_forward(self, F, x):
         return x
 
 net = SuperResolutionNet(upscale_factor)
-metric = mx.metric.MSE()
+metric = mx.gluon.metric.MSE()
 
 def test(ctx):
     val_data.reset()

example/gluon/tree_lstm/main.py

@@ -96,7 +96,7 @@
 net = SimilarityTreeLSTM(sim_hidden_size, rnn_hidden_size, vocab.size, vocab.embed.shape[1], num_classes)
 
 # use pearson correlation and mean-square error for evaluation
-metric = mx.metric.create(['pearsonr', 'mse'])
+metric = mx.gluon.metric.create(['pearsonr', 'mse'])
 
 def to_target(x):
     target = np.zeros((1, num_classes))

example/image-classification/common/fit.py

@@ -290,7 +290,7 @@ def fit(args, network, data_loader, **kwargs):
     # evaluation metrices
     eval_metrics = ['accuracy']
     if args.top_k > 0:
-        eval_metrics.append(mx.metric.create(
+        eval_metrics.append(mx.gluon.metric.create(
             'top_k_accuracy', top_k=args.top_k))
 
     supported_loss = ['ce', 'nll_loss']
@@ -306,7 +306,7 @@ def fit(args, network, data_loader, **kwargs):
                     logging.warning(loss_type + ' is not an valid loss type, only cross-entropy or ' \
                                     'negative likelihood loss is supported!')
                 else:
-                    eval_metrics.append(mx.metric.create(loss_type))
+                    eval_metrics.append(mx.gluon.metric.create(loss_type))
         else:
             logging.warning("The output is not softmax_output, loss argument will be skipped!")
 

example/image-classification/score.py

@@ -97,8 +97,8 @@ def score(model, data_val, metrics, gpus, batch_size, rgb_mean=None, mean_img=No
     logger = logging.getLogger()
     logger.setLevel(logging.DEBUG)
 
-    metrics = [mx.metric.create('acc'),
-               mx.metric.create('top_k_accuracy', top_k = 5)]
+    metrics = [mx.gluon.metric.create('acc'),
+               mx.gluon.metric.create('top_k_accuracy', top_k = 5)]
 
     (speed,) = score(metrics = metrics, **vars(args))
     logging.info('Finished with %f images per second', speed)

example/image-classification/test_score.py

@@ -43,7 +43,7 @@ def test_imagenet1k_resnet(imagenet_val_5k_settings):
     models = ['imagenet1k-resnet-50', 'imagenet1k-resnet-152']
     accs = [.77, .78]
     for (m, g) in zip(models, accs):
-        acc = mx.metric.create('acc')
+        acc = mx.gluon.metric.create('acc')
         (speed,) = score(model=m, data_val=imagenet_val_5k,
                          rgb_mean='0,0,0', metrics=acc, **kwargs)
         r = acc.get()[1]
@@ -52,7 +52,7 @@ def test_imagenet1k_resnet(imagenet_val_5k_settings):
 
 def test_imagenet1k_inception_bn(imagenet_val_5k_settings):
     imagenet_val_5k, kwargs = imagenet_val_5k_settings
-    acc = mx.metric.create('acc')
+    acc = mx.gluon.metric.create('acc')
     m = 'imagenet1k-inception-bn'
     g = 0.75
     (speed,) = score(model=m,

example/kaggle-ndsb2/Train.py

@@ -111,7 +111,7 @@ def encode_csv(label_csv, systole_csv, diastole_csv):
         wd                 = 0.00001,
         momentum           = 0.9)
 
-systole_model.fit(X=data_train, eval_metric = mx.metric.np(CRPS))
+systole_model.fit(X=data_train, eval_metric = mx.gluon.metric.np(CRPS))
 
 
 # # Predict systole
@@ -139,7 +139,7 @@ def encode_csv(label_csv, systole_csv, diastole_csv):
         wd                 = 0.00001,
         momentum           = 0.9)
 
-diastole_model.fit(X=data_train, eval_metric = mx.metric.np(CRPS))
+diastole_model.fit(X=data_train, eval_metric = mx.gluon.metric.np(CRPS))
 
 
 # # Predict diastole

example/model-parallel/matrix_factorization/train.py

@@ -94,7 +94,7 @@
         'rescale_grad': 1.0/batch_size}
 
     # use MSE as the metric
-    metric = mx.metric.create(['MSE'])
+    metric = mx.gluon.metric.create(['MSE'])
 
     speedometer = mx.callback.Speedometer(batch_size, print_every)
 

example/module/mnist_mlp.py

@@ -55,7 +55,7 @@
 mod.init_params()
 
 mod.init_optimizer(optimizer_params={'learning_rate':0.01, 'momentum': 0.9})
-metric = mx.metric.create('acc')
+metric = mx.gluon.metric.create('acc')
 
 for i_epoch in range(n_epoch):
     for i_iter, batch in enumerate(train_dataiter):

example/multi-task/multi-task-learning.ipynb

@@ -267,8 +267,8 @@
    "outputs": [],
    "source": [
     "def evaluate_accuracy(net, data_iterator):\n",
-    "    acc_digits = mx.metric.Accuracy(name='digits')\n",
-    "    acc_odd_even = mx.metric.Accuracy(name='odd_even')\n",
+    "    acc_digits = mx.gluon.metric.Accuracy(name='digits')\n",
+    "    acc_odd_even = mx.gluon.metric.Accuracy(name='odd_even')\n",
     "    \n",
     "    for i, (data, label_digit, label_odd_even) in enumerate(data_iterator):\n",
     "        data = data.as_in_context(ctx)\n",
@@ -335,8 +335,8 @@
    "source": [
     "for e in range(epochs):\n",
     "    # Accuracies for each task\n",
-    "    acc_digits = mx.metric.Accuracy(name='digits')\n",
-    "    acc_odd_even = mx.metric.Accuracy(name='odd_even')\n",
+    "    acc_digits = mx.gluon.metric.Accuracy(name='digits')\n",
+    "    acc_odd_even = mx.gluon.metric.Accuracy(name='odd_even')\n",
     "    # Accumulative losses\n",
     "    l_digits_ = 0.\n",
     "    l_odd_even_ = 0. \n",

example/multivariate_time_series/src/metrics.py

@@ -46,10 +46,10 @@ def get_custom_metrics():
     """
     :return: mxnet metric object
     """
-    _rse = mx.metric.create(rse)
-    _rae = mx.metric.create(rae)
-    _corr = mx.metric.create(corr)
-    return mx.metric.create([_rae, _rse, _corr])
+    _rse = mx.gluon.metric.create(rse)
+    _rae = mx.gluon.metric.create(rae)
+    _corr = mx.gluon.metric.create(corr)
+    return mx.gluon.metric.create([_rae, _rse, _corr])
 
 def evaluate(pred, label):
     return {"RAE":rae(label, pred), "RSE":rse(label,pred),"CORR": corr(label,pred)}