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Changes to mxnet.metric (#18083)
* finish 5 changes * move metric.py to gluon, replace mx.metric with mx.gluon.metric in python/mxnet/ * fix importError * replace mx.metric with mx.gluon.metric in tests/python * remove global support * remove macro support * rewrite BinaryAccuracy * extend F1 to multiclass/multilabel * add tests for new F1, remove global tests * use mxnet.numpy instead of numpy * fix sanity * rewrite ce and ppl, improve some details * use mxnet.numpy.float64 * remove sklearn * remove reset_local() and get_global in other files * fix test_mlp * replace mx.metric with mx.gluon.metric in example * fix context difference * Disable -DUSE_TVM_OP on GPU builds * Fix disable tvm op for gpu runs * use label.ctx in metric.py; remove gluoncv dependency in test_cvnets * fix sanity * fix importError * remove nose Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Leonard Lausen <[email protected]>
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‎benchmark/python/sparse/sparse_end2end.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -225,7 +225,7 @@ def row_sparse_pull(kv, key, data, slices, weight_array, priority):
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learning_rate=0.1, rescale_grad=1.0/batch_size/num_worker)
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mod.init_optimizer(optimizer=sgd, kvstore=kv)
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# use accuracy as the metric
228-
metric = mx.metric.create('acc')
228+
metric = mx.gluon.metric.create('acc')
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index = mod._exec_group.param_names.index('w')
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# weight_array bound to executors of the contexts

‎example/adversary/adversary_generation.ipynb‎

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

‎example/autoencoder/variational_autoencoder/VAE_example.ipynb‎

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

‎example/caffe/caffe_net.py‎

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

‎example/caffe/train_model.py‎

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

‎example/capsnet/capsulenet.py‎

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Original file line numberDiff line numberDiff line change
@@ -122,7 +122,7 @@ def to4d(img):
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return img.reshape(img.shape[0], 1, 28, 28).astype(np.float32)/255
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125-
class LossMetric(mx.metric.EvalMetric):
125+
class LossMetric(mx.gluon.metric.EvalMetric):
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"""Evaluate the loss function"""
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def __init__(self, batch_size, num_gpus):
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super(LossMetric, self).__init__('LossMetric')

‎example/ctc/lstm_ocr_train.py‎

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@@ -103,7 +103,7 @@ def main():
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module.fit(train_data=data_train,
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eval_data=data_val,
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# use metrics.accuracy or metrics.accuracy_lcs
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eval_metric=mx.metric.np(metrics.accuracy, allow_extra_outputs=True),
106+
eval_metric=mx.gluon.metric.np(metrics.accuracy, allow_extra_outputs=True),
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optimizer='sgd',
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optimizer_params={'learning_rate': hp.learning_rate,
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'momentum': hp.momentum,

‎example/deep-embedded-clustering/autoencoder.py‎

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Original file line numberDiff line numberDiff line change
@@ -165,7 +165,7 @@ def l2_norm(label, pred):
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return np.mean(np.square(label-pred))/2.0
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solver = Solver(optimizer, momentum=0.9, wd=decay, learning_rate=l_rate,
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lr_scheduler=lr_scheduler)
168-
solver.set_metric(mx.metric.CustomMetric(l2_norm))
168+
solver.set_metric(mx.gluon.metric.CustomMetric(l2_norm))
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solver.set_monitor(Monitor(print_every))
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data_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=True,
171171
last_batch_handle='roll_over')
@@ -188,7 +188,7 @@ def l2_norm(label, pred):
188188
return np.mean(np.square(label-pred))/2.0
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solver = Solver(optimizer, momentum=0.9, wd=decay, learning_rate=l_rate,
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lr_scheduler=lr_scheduler)
191-
solver.set_metric(mx.metric.CustomMetric(l2_norm))
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solver.set_metric(mx.gluon.metric.CustomMetric(l2_norm))
192192
solver.set_monitor(Monitor(print_every))
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data_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=True,
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last_batch_handle='roll_over')

‎example/deep-embedded-clustering/dec.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -122,7 +122,7 @@ def cluster(self, X, y=None, update_interval=None):
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def ce(label, pred):
124124
return np.sum(label*np.log(label/(pred+0.000001)))/label.shape[0]
125-
solver.set_metric(mx.metric.CustomMetric(ce))
125+
solver.set_metric(mx.gluon.metric.CustomMetric(ce))
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127127
label_buff = np.zeros((X.shape[0], self.num_centers))
128128
train_iter = mx.io.NDArrayIter({'data': X}, {'label': label_buff}, batch_size=batch_size,

‎example/distributed_training-horovod/gluon_mnist.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -104,7 +104,7 @@ def conv_nets():
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# Function to evaluate accuracy for a model
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def evaluate(model, data_iter, context):
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data_iter.reset()
107-
metric = mx.metric.Accuracy()
107+
metric = mx.gluon.metric.Accuracy()
108108
for _, batch in enumerate(data_iter):
109109
data = batch.data[0].as_in_context(context)
110110
label = batch.label[0].as_in_context(context)
@@ -149,7 +149,7 @@ def evaluate(model, data_iter, context):
149149

150150
# Create loss function and train metric
151151
loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
152-
metric = mx.metric.Accuracy()
152+
metric = mx.gluon.metric.Accuracy()
153153

154154
# Train model
155155
for epoch in range(args.epochs):

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