abhinavs95 commented on a change in pull request #14587: [MXNET-1344, 1346][FIT
API] Retrieve Batch size and Logging verbose support for Gluon fit() API
URL: https://github.com/apache/incubator-mxnet/pull/14587#discussion_r271964229
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File path: tests/python/unittest/test_gluon_estimator.py
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@@ -275,3 +278,44 @@ def test_context():
loss=loss,
metrics=metrics,
context='cpu')
+
+
+def test_batch_size():
+ '''Test batch size'''
+ num_samples = 32
+
+ # No Data Loader
+ data = mx.nd.random.uniform(shape=(num_samples, 3, 28, 28))
+ label = mx.nd.random.randint(low=0, high=2, shape=(num_samples,))
+ data_iter = mx.io.NDArrayIter(data=data, label=label, batch_size=16)
+ net = get_model()
+ loss = mx.gluon.loss.L2Loss()
+ ctx = mx.cpu()
+ est = estimator.Estimator(net=net, loss=loss, context=ctx)
+ with assert_raises(ValueError):
+ est.fit(train_data=data_iter)
+
+ # Empty data loader
+ data = mx.nd.random.uniform(shape=(0,))
+ label = mx.nd.random.randint(low=0, high=2, shape=(0,))
+ batch_size = 2
+ data_arr = mx.gluon.data.dataset.ArrayDataset(data, label)
+ data_loader = mx.gluon.data.DataLoader(data_arr, batch_size=batch_size)
+ est = estimator.Estimator(net=net, loss=loss, context=ctx)
+ with assert_raises(ValueError):
+ est.fit(train_data=data_loader)
+
+ # Batch size less than context
+ ctx = [mx.gpu(i) for i in range(4)]
+ data = mx.nd.random.uniform(shape=(num_samples, 3, 28, 28))
+ label = mx.nd.random.randint(low=0, high=2, shape=(num_samples,))
+ batch_size = 2
+ data_arr = mx.gluon.data.dataset.ArrayDataset(data, label)
+ data_loader = mx.gluon.data.DataLoader(data_arr, batch_size=batch_size)
+ est = estimator.Estimator(net=net, loss=loss, context=ctx)
Review comment:
A new net needs to be created here, otherwise it will use the one created
before and the test won't be accurate.
The same problem is also present in the pre-existing test_context() test
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