--- theano/tensor/nnet/tests/test_blocksparse.py.orig 2020-07-27 10:09:29.000000000 -0600
+++ theano/tensor/nnet/tests/test_blocksparse.py 2020-08-07 08:05:19.415353280 -0600
@@ -42,11 +42,11 @@ class BlockSparse_Gemv_and_Outer(utt.Inf
input = randn(batchSize, inputWindowSize, inputSize).astype('float32')
permutation = np.random.permutation
- inputIndice = np.vstack(permutation(nInputBlock)[:inputWindowSize]
- for _ in range(batchSize)).astype('int32')
+ inputIndice = np.vstack(list(permutation(nInputBlock)[:inputWindowSize]
+ for _ in range(batchSize))).astype('int32')
outputIndice = np.vstack(
- permutation(nOutputBlock)[:outputWindowSize]
- for _ in range(batchSize)).astype('int32')
+ list(permutation(nOutputBlock)[:outputWindowSize]
+ for _ in range(batchSize))).astype('int32')
weight = randn(nInputBlock, nOutputBlock,
inputSize, outputSize).astype('float32')
bias = randn(nOutputBlock, outputSize).astype('float32')
@@ -67,10 +67,10 @@ class BlockSparse_Gemv_and_Outer(utt.Inf
x = randn(batchSize, xWindowSize, xSize).astype('float32')
y = randn(batchSize, yWindowSize, ySize).astype('float32')
randint = np.random.randint
- xIdx = np.vstack(randint(0, nInputBlock, size=xWindowSize)
- for _ in range(batchSize)).astype('int32')
- yIdx = np.vstack(randint(0, nOutputBlock, size=yWindowSize)
- for _ in range(batchSize)).astype('int32')
+ xIdx = np.vstack(list(randint(0, nInputBlock, size=xWindowSize)
+ for _ in range(batchSize))).astype('int32')
+ yIdx = np.vstack(list(randint(0, nOutputBlock, size=yWindowSize)
+ for _ in range(batchSize))).astype('int32')
return o, x, y, xIdx, yIdx
--- theano/tensor/signal/tests/test_pool.py.orig 2020-07-27 10:09:29.000000000 -0600
+++ theano/tensor/signal/tests/test_pool.py 2020-08-07 08:05:19.416353279 -0600
@@ -196,7 +196,7 @@ class TestDownsampleFactorMax(utt.InferS
r_stride = builtins.max(r_stride, pad[i])
r_end = builtins.min(r_end, input.shape[-nd + i] + pad[i])
region.append(slice(r_stride, r_end))
- patch = padded_input[l][region]
+ patch = padded_input[l][tuple(region)]
output_val[l][r] = func(patch)
return output_val
@@ -303,7 +303,7 @@ class TestDownsampleFactorMax(utt.InferS
r_stride = r[i] * stride[i]
r_end = builtins.min(r_stride + ws[i], input.shape[-nd + i])
region.append(slice(r_stride, r_end))
- patch = input[l][region]
+ patch = input[l][tuple(region)]
output_val[l][r] = func(patch)
return output_val
--- theano/tensor/sort.py.orig 2020-07-27 10:09:29.000000000 -0600
+++ theano/tensor/sort.py 2020-08-07 08:05:19.417353279 -0600
@@ -279,10 +279,10 @@ def _topk_py_impl(op, x, k, axis, idx_dt
idx[axis] = (slice(-k, None) if k > 0 else slice(-k))
if not op.return_indices:
- zv = np.partition(x, -k, axis=axis)[idx]
+ zv = np.partition(x, -k, axis=axis)[tuple(idx)]
return zv
elif op.return_values:
- zi = np.argpartition(x, -k, axis=axis)[idx]
+ zi = np.argpartition(x, -k, axis=axis)[tuple(idx)]
idx2 = tuple(
np.arange(s).reshape(
(s,) + (1,) * (ndim - i - 1)
@@ -290,7 +290,7 @@ def _topk_py_impl(op, x, k, axis, idx_dt
zv = x[idx2]
return zv, zi.astype(idx_dtype)
else:
- zi = np.argpartition(x, -k, axis=axis)[idx]
+ zi = np.argpartition(x, -k, axis=axis)[tuple(idx)]
return zi.astype(idx_dtype)
--- theano/tensor/tests/test_subtensor.py.orig 2020-07-27 10:09:29.000000000 -0600
+++ theano/tensor/tests/test_subtensor.py 2020-08-07 13:00:03.256695604 -0600
@@ -320,7 +320,7 @@ class T_subtensor(unittest.TestCase, utt
x = theano.tensor.arange(100).reshape((5, 5, 4))
res = x[[slice(1, -1)] * x.ndim].eval()
x = np.arange(100).reshape((5, 5, 4))
- np.allclose(res, x[[slice(1, -1)] * x.ndim])
+ np.allclose(res, x[tuple([slice(1, -1)] * x.ndim)])
def test_slice_symbol(self):
x = self.shared(np.random.rand(5, 4).astype(self.dtype))
@@ -360,7 +360,7 @@ class T_subtensor(unittest.TestCase, utt
def test_boolean(self):
def numpy_inc_subtensor(x, idx, a):
x = x.copy()
- x[idx] += a
+ x[tuple(idx)] += a
return x
numpy_n = np.arange(6, dtype=self.dtype).reshape((2, 3))