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@ -35,6 +35,22 @@ class TestLookupTableOp(OpTest):
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self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
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class TestLookupTableOpWithTensorIds(OpTest):
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def setUp(self):
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self.op_type = "lookup_table"
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table = np.random.random((17, 31)).astype("float32")
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ids = np.random.randint(
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low=0, high=17, size=(2, 4, 5, 1)).astype("int64")
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self.inputs = {'W': table, 'Ids': ids}
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self.outputs = {'Out': table[ids.flatten()].reshape((2, 4, 5, 31))}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
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class TestLookupTableOpWithPadding(TestLookupTableOp):
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def test_check_output(self):
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ids = np.squeeze(self.inputs['Ids'])
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@ -44,21 +60,34 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
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self.check_output()
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def test_check_grad(self):
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# Since paddings are not trainable and fixed in forward, the gradient of
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# Since paddings are not trainable and fixed in forward, the gradient of
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# paddings makes no sense and we don't test the gradient here.
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pass
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class TestLookupTableWIsSelectedRows(OpTest):
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def check_with_place(self, place):
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scope = core.Scope()
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class TestLookupTableOpWithTensorIdsAndPadding(TestLookupTableOpWithTensorIds):
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def test_check_output(self):
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ids = self.inputs['Ids']
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flatten_idx = ids.flatten()
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padding_idx = np.random.choice(flatten_idx, 1)[0]
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self.outputs['Out'][np.squeeze(ids == padding_idx)] = np.zeros(31)
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self.attrs = {'padding_idx': long(padding_idx)}
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self.check_output()
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def test_check_grad(self):
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# Since paddings are not trainable and fixed in forward, the gradient of
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# paddings makes no sense and we don't test the gradient here.
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pass
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# create and initialize Id Variable
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class TestLookupTableWIsSelectedRows(OpTest):
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def prepare_ids(self, scope, place):
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ids_tensor = scope.var('Ids').get_tensor()
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ids_array = np.array([[0], [4], [3], [5]]).astype("int64")
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ids_tensor.set(ids_array, place)
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return ids_array
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# create and initialize W Variable
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def prepare_w(self, scope, place):
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rows = [0, 1, 2, 3, 4, 5, 6]
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row_numel = 12
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@ -71,8 +100,22 @@ class TestLookupTableWIsSelectedRows(OpTest):
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w_tensor = w_selected_rows.get_tensor()
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w_tensor.set(w_array, place)
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# create Out Variable
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out_tensor = scope.var('Out').get_tensor()
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def create_out_tensor(self, scope, place):
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return scope.var('Out').get_tensor()
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def check_result(self, ids_array, result_array):
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# all(): return True if all elements of the iterable are true (or if the iterable is empty)
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for idx, row in enumerate(ids_array):
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assert (row[0] == result_array[idx]).all()
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def check_with_place(self, place):
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scope = core.Scope()
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ids_array = self.prepare_ids(scope, place)
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self.prepare_w(scope, place)
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out_tensor = self.create_out_tensor(scope, place)
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# create and run lookup_table operator
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lookup_table = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
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@ -80,9 +123,8 @@ class TestLookupTableWIsSelectedRows(OpTest):
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# get result from Out
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result_array = np.array(out_tensor)
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# all(): return True if all elements of the iterable are true (or if the iterable is empty)
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for idx, row in enumerate(ids_array):
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assert (row[0] == result_array[idx]).all()
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self.check_result(ids_array, result_array)
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def test_w_is_selected_rows(self):
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places = [core.CPUPlace()]
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@ -91,5 +133,19 @@ class TestLookupTableWIsSelectedRows(OpTest):
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self.check_with_place(place)
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class TestLookupTableWithTensorIdsWIsSelectedRows(
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TestLookupTableWIsSelectedRows):
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def prepare_ids(self, scope, place):
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ids_tensor = scope.var('Ids').get_tensor()
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ids_array = np.random.randint(
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low=0, high=6, size=(2, 4, 3, 1)).astype("int64")
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ids_tensor.set(ids_array, place)
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return ids_array
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def check_result(self, ids_array, result_array):
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for idx, row in np.ndenumerate(ids_array):
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assert (row == result_array[idx]).all()
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if __name__ == "__main__":
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unittest.main()
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