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Posted to commits@mxnet.apache.org by zh...@apache.org on 2021/09/05 03:54:30 UTC
[incubator-mxnet] branch master updated: If variable is not used
within the loop body, start the name with an underscore (#20505)
This is an automated email from the ASF dual-hosted git repository.
zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 8c7d5c6 If variable is not used within the loop body, start the name with an underscore (#20505)
8c7d5c6 is described below
commit 8c7d5c618d55467793503506a3b5cea9c7913025
Author: mozga <ma...@intel.com>
AuthorDate: Sun Sep 5 05:52:38 2021 +0200
If variable is not used within the loop body, start the name with an underscore (#20505)
* Loop control variable 'op' not used within the loop body. If this is intended, start the name with an underscore
* Unittest, undo
* Set axis on a value between the range ndim - 1
* The loop covers up data type
* Conflicts, and CR: changes
* Master chanhes
* Align function
* Code-review
* Hybrydize is removed
---
python/mxnet/gluon/contrib/estimator/estimator.py | 2 +-
python/mxnet/operator.py | 2 +-
python/mxnet/test_utils.py | 2 +-
tests/nightly/dist_device_sync_kvstore_byteps.py | 2 +-
tests/nightly/dist_device_sync_kvstore_custom.py | 2 +-
tests/nightly/dist_sync_kvstore.py | 6 +-
tests/nightly/test_kvstore.py | 6 +-
tests/nightly/test_np_random.py | 2 +-
tests/nightly/test_server_profiling.py | 2 +-
tests/python/gpu/test_gluon_gpu.py | 4 +-
tests/python/gpu/test_gluon_model_zoo_gpu.py | 2 +-
tests/python/gpu/test_kvstore_gpu.py | 2 +-
tests/python/gpu/test_operator_gpu.py | 10 +-
tests/python/mkl/subgraphs/test_conv_subgraph.py | 2 +-
tests/python/mkl/test_mkldnn.py | 2 +-
tests/python/quantization/test_quantization.py | 2 +-
tests/python/unittest/test_autograd.py | 4 +-
tests/python/unittest/test_contrib_control_flow.py | 4 +-
.../unittest/test_contrib_gluon_data_vision.py | 4 +-
tests/python/unittest/test_contrib_io.py | 2 +-
tests/python/unittest/test_dgl_graph.py | 2 +-
tests/python/unittest/test_engine.py | 2 +-
tests/python/unittest/test_executor.py | 6 +-
tests/python/unittest/test_gluon.py | 10 +-
tests/python/unittest/test_gluon_data.py | 30 +++---
tests/python/unittest/test_gluon_probability_v2.py | 2 +-
tests/python/unittest/test_gluon_rnn.py | 8 +-
tests/python/unittest/test_gluon_trainer.py | 4 +-
tests/python/unittest/test_image.py | 6 +-
tests/python/unittest/test_io.py | 18 ++--
tests/python/unittest/test_kvstore.py | 4 +-
tests/python/unittest/test_loss.py | 2 +-
tests/python/unittest/test_ndarray.py | 24 ++---
.../test_numpy_contrib_gluon_data_vision.py | 2 +-
tests/python/unittest/test_numpy_gluon.py | 4 +-
.../python/unittest/test_numpy_interoperability.py | 4 +-
tests/python/unittest/test_numpy_loss.py | 2 +-
tests/python/unittest/test_numpy_ndarray.py | 2 +-
tests/python/unittest/test_numpy_op.py | 23 +++--
tests/python/unittest/test_operator.py | 112 ++++++++++-----------
tests/python/unittest/test_profiler.py | 4 +-
tests/python/unittest/test_random.py | 6 +-
tests/python/unittest/test_sparse_ndarray.py | 6 +-
tests/python/unittest/test_sparse_operator.py | 4 +-
tests/python/unittest/test_symbol.py | 2 +-
45 files changed, 177 insertions(+), 176 deletions(-)
diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
index bd1e166..73ddcbc 100644
--- a/python/mxnet/gluon/contrib/estimator/estimator.py
+++ b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -392,7 +392,7 @@ class Estimator(object):
for handler in epoch_begin:
handler.epoch_begin(estimator_ref)
- for i, batch in enumerate(train_data):
+ for batch in train_data:
# batch begin
for handler in batch_begin:
handler.batch_begin(estimator_ref, batch=batch)
diff --git a/python/mxnet/operator.py b/python/mxnet/operator.py
index b34fe0d..a40c94f 100644
--- a/python/mxnet/operator.py
+++ b/python/mxnet/operator.py
@@ -824,7 +824,7 @@ def register(reg_name):
"entries in returned aux stypes, " \
"got %d."%(len(tensors[4]), len(ret[4]))
rstype = []
- for i, ret_list in enumerate(ret):
+ for ret_list in ret:
rstype.extend(ret_list)
for i, stype in enumerate(rstype):
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index bd0e73c..2fdb913 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1584,7 +1584,7 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
else:
arg_params[n] = np.random.normal(size=arr.shape,
scale=scale).astype(rand_type)
- for n, arr in exe_list[0].aux_dict.items():
+ for n in exe_list[0].aux_dict:
if n not in aux_params:
aux_params[n] = 0
for exe in exe_list:
diff --git a/tests/nightly/dist_device_sync_kvstore_byteps.py b/tests/nightly/dist_device_sync_kvstore_byteps.py
index 676be92..4ddde57 100644
--- a/tests/nightly/dist_device_sync_kvstore_byteps.py
+++ b/tests/nightly/dist_device_sync_kvstore_byteps.py
@@ -65,7 +65,7 @@ def test_pushpull():
# init kv dns keys
kv.broadcast('3', mx.nd.ones(shape, ctx=get_current_context(device=True)), mx.nd.ones(shape, ctx=get_current_context(device=True)))
kv.broadcast('99', mx.nd.ones(big_shape, ctx=get_current_context(device=True)), mx.nd.ones(big_shape, ctx=get_current_context(device=True)))
- for i in range(nrepeat):
+ for _ in range(nrepeat):
scale = my_rank + 1
num = (my_num_workers + 1) * my_num_workers / 2
diff --git a/tests/nightly/dist_device_sync_kvstore_custom.py b/tests/nightly/dist_device_sync_kvstore_custom.py
index 6d3608c..17e0d32 100644
--- a/tests/nightly/dist_device_sync_kvstore_custom.py
+++ b/tests/nightly/dist_device_sync_kvstore_custom.py
@@ -54,7 +54,7 @@ def test_pushpull():
# init kv dns keys
kv.broadcast('3', mx.nd.ones(shape, ctx=mx.gpu()), mx.nd.ones(shape, ctx=mx.gpu()))
kv.broadcast('99', mx.nd.ones(big_shape, ctx=mx.gpu()), mx.nd.ones(big_shape, ctx=mx.gpu()))
- for i in range(nrepeat):
+ for _ in range(nrepeat):
scale = my_rank + 1
num = (my_num_workers + 1) * my_num_workers * num_gpus / 2
diff --git a/tests/nightly/dist_sync_kvstore.py b/tests/nightly/dist_sync_kvstore.py
index 1a89f5c..28e4af8 100644
--- a/tests/nightly/dist_sync_kvstore.py
+++ b/tests/nightly/dist_sync_kvstore.py
@@ -153,7 +153,7 @@ def test_sync_push_pull(nrepeat):
v = mx.nd.sparse.zeros('row_sparse', shape, dtype=dtype)
big_v = mx.nd.sparse.zeros('row_sparse', big_shape, dtype=dtype)
# push
- for i in range(nrepeat):
+ for _ in range(nrepeat):
kv.push(k1, v)
kv.push(k2, big_v)
# pull a subset of rows this worker is interested in
@@ -301,7 +301,7 @@ def test_sync_1bit_compression(threshold, nrepeat):
kv.init(k, mx.nd.zeros(s))
for k,s in compr_random_keys_shapes:
curr_residual = np.zeros(s)
- for l in range(nrepeat):
+ for _ in range(nrepeat):
orig_val = mx.nd.zeros(s)
kv.pull(k, orig_val)
@@ -399,7 +399,7 @@ def test_sync_2bit_compression(threshold, nrepeat):
kv.init(k, mx.nd.zeros(s))
for k,s in compr_random_keys_shapes:
curr_residual = np.zeros(s)
- for l in range(nrepeat):
+ for _ in range(nrepeat):
orig_val = mx.nd.zeros(s)
kv.pull(k, orig_val)
diff --git a/tests/nightly/test_kvstore.py b/tests/nightly/test_kvstore.py
index 65f6c4a..54801c2 100755
--- a/tests/nightly/test_kvstore.py
+++ b/tests/nightly/test_kvstore.py
@@ -150,7 +150,7 @@ def test_compress_kvstore(kv_type, compression='2bit', threshold=0.5):
assert_almost_equal(o.asnumpy(), exp)
def pull_before_push(kv):
- for i in range(nrepeat):
+ for _ in range(nrepeat):
for j in range(len(keys)):
out = [mx.nd.ones(shapes[j], mx.gpu(g)) for g in range(nworker)]
kv.pull(keys[j], out=out)
@@ -209,7 +209,7 @@ def test_compress_kvstore(kv_type, compression='2bit', threshold=0.5):
check_diff_to_scalar(o, curr_val)
def push_zeros(kv):
- for i in range(nrepeat):
+ for _ in range(nrepeat):
for j in range(len(keys)):
kv.push(keys[j], [mx.nd.zeros(shapes[j], mx.gpu(g)) for g in range(nworker)])
out = [mx.nd.ones(shapes[j], mx.gpu(g)) for g in range(nworker)]
@@ -249,7 +249,7 @@ def test_compress_kvstore(kv_type, compression='2bit', threshold=0.5):
return curval
def check_neg(kv, neg, rate, curval):
- for r in range(nrepeat):
+ for _ in range(nrepeat):
curval = curval + rate*nworker*neg
for j in range(len(keys)):
kv.push(keys[j], [mx.nd.ones(shapes[j], mx.gpu(g))*neg for g in range(nworker)])
diff --git a/tests/nightly/test_np_random.py b/tests/nightly/test_np_random.py
index ccb74e1..753a232 100644
--- a/tests/nightly/test_np_random.py
+++ b/tests/nightly/test_np_random.py
@@ -130,7 +130,7 @@ def test_np_gamma():
# Generation test
trials = 8
num_buckets = 5
- for dtype in types:
+ for _ in types:
for alpha, beta in [(2.0, 3.0), (0.5, 1.0)]:
buckets, probs = gen_buckets_probs_with_ppf(
lambda x: ss.gamma.ppf(x, a=alpha, loc=0, scale=beta), num_buckets)
diff --git a/tests/nightly/test_server_profiling.py b/tests/nightly/test_server_profiling.py
index 7d157a3..7024acf 100644
--- a/tests/nightly/test_server_profiling.py
+++ b/tests/nightly/test_server_profiling.py
@@ -35,7 +35,7 @@ def test_sync_push_pull():
nrepeat = 10
# checks pull after push in loop, because behavior during
# consecutive pushes doesn't offer any guarantees
- for i in range(nrepeat):
+ for _ in range(nrepeat):
kv.push(key, mx.nd.ones(shape, dtype='float32') * (my_rank+1))
val = mx.nd.zeros(shape, dtype='float32')
kv.pull(key, out=val)
diff --git a/tests/python/gpu/test_gluon_gpu.py b/tests/python/gpu/test_gluon_gpu.py
index 9b42e74..134eab3 100644
--- a/tests/python/gpu/test_gluon_gpu.py
+++ b/tests/python/gpu/test_gluon_gpu.py
@@ -108,7 +108,7 @@ def test_lstmp():
assert_almost_equal(layer_output, cell_output, rtol=rtol, atol=atol)
layer_output.backward()
cell_output.backward()
- for k, v in weights.items():
+ for k, _ in weights.items():
layer_grad = layer_params['l0_' + k].grad()
cell_grad = cell_params[k].grad()
print('checking gradient for {}'.format('lstm0_l0_' + k))
@@ -414,7 +414,7 @@ def test_sync_batchnorm():
return
ndev = 2
# check with unsync version
- for i in range(10):
+ for _ in range(10):
_check_batchnorm_result(mx.np.random.uniform(size=(4, 1, 4, 4)),
num_devices=ndev, cuda=True)
diff --git a/tests/python/gpu/test_gluon_model_zoo_gpu.py b/tests/python/gpu/test_gluon_model_zoo_gpu.py
index c4cde1d..4d3ba3c 100644
--- a/tests/python/gpu/test_gluon_model_zoo_gpu.py
+++ b/tests/python/gpu/test_gluon_model_zoo_gpu.py
@@ -79,7 +79,7 @@ def test_inference(model_name):
gpu_param.set_data(cpu_param.data().as_in_context(mx.gpu()))
cpu_data = mx.np.array(data, ctx=mx.cpu())
- for i in range(5):
+ for _ in range(5):
# Run inference.
with autograd.record(train_mode=False):
cpu_out = cpu_model(cpu_data)
diff --git a/tests/python/gpu/test_kvstore_gpu.py b/tests/python/gpu/test_kvstore_gpu.py
index 4a2530a..a756296 100644
--- a/tests/python/gpu/test_kvstore_gpu.py
+++ b/tests/python/gpu/test_kvstore_gpu.py
@@ -65,7 +65,7 @@ def test_rsp_push_pull():
total_row_ids = mx.nd.array(np.random.randint(num_rows, size=count*num_rows))
row_ids = [total_row_ids[i*num_rows : (i+1)*num_rows] for i in range(count)]
else:
- for i in range(count):
+ for _ in range(count):
row_id = np.random.randint(num_rows, size=num_rows)
row_ids.append(mx.nd.array(row_id))
row_ids_to_pull = row_ids[0] if (len(row_ids) == 1 or is_same_rowid) else row_ids
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index 03aef70..9ce005b 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -180,7 +180,7 @@ def check_fft(shape):
def test_fft():
nrepeat = 2
maxdim = 10
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
for order in [2,4]:
shape = tuple(np.random.randint(1, maxdim, size=order))
check_fft(shape)
@@ -339,7 +339,7 @@ def check_preloaded_multi_sgd(dtype, shapes, momentum, use_master_weights):
rescale_grad=0.95, momentum=momentum, out=mx_p_w)
def _assert_all_almost_equal(lhs_list, rhs_list, rtol, atol):
- for i, (lhs, rhs) in enumerate(zip(lhs_list, rhs_list)):
+ for _, (lhs, rhs) in enumerate(zip(lhs_list, rhs_list)):
assert_almost_equal(lhs.asnumpy(), rhs.asnumpy(), rtol=rtol, atol=atol)
if dtype == 'float16':
rtol = 1e-3
@@ -1788,7 +1788,7 @@ def test_autograd_save_memory():
x.attach_grad()
with mx.autograd.record():
- for i in range(200):
+ for _ in range(200):
x = x + 1
x.wait_to_read()
x.backward()
@@ -1848,7 +1848,7 @@ def test_cross_device_autograd():
with mx.autograd.record():
y = x
- for i in range(3):
+ for _ in range(3):
y = mx.nd.tanh(y)
y.backward()
@@ -2224,7 +2224,7 @@ def math_square(shape, dtype, check_value):
def run_math(op, shape, dtype="float32", check_value=True):
run_num = 10
- for i in range(run_num):
+ for _ in range(run_num):
if op == 'log':
math_log(shape=shape, dtype=dtype, check_value=check_value)
elif op == 'erf':
diff --git a/tests/python/mkl/subgraphs/test_conv_subgraph.py b/tests/python/mkl/subgraphs/test_conv_subgraph.py
index a4efab4..e965fab 100644
--- a/tests/python/mkl/subgraphs/test_conv_subgraph.py
+++ b/tests/python/mkl/subgraphs/test_conv_subgraph.py
@@ -254,7 +254,7 @@ def test_pos_single_concat(data_shape, input_num, dim, out_type):
def __init__(self, input_num, dim, **kwargs):
super(SingleConcat, self).__init__(**kwargs)
self.concat = nn.HybridConcatenate(axis=dim)
- for i in range(input_num):
+ for _ in range(input_num):
self.concat.add(nn.Identity())
def forward(self, x):
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index aa7a8f4..52de29c 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -235,7 +235,7 @@ def test_mkldnn_sum_with_mkldnn_layout():
num_inputs = [2, 3, 4, 5]
for i in num_inputs:
inputs = []
- for n in range(i):
+ for _ in range(i):
inputs.append(z)
y = mx.sym.add_n(*inputs) # (only MKLDNN data input)
exe = y._simple_bind(ctx=mx.cpu(), x=x_shape, w=w_shape)
diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 640469e..60a11d6 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -1312,7 +1312,7 @@ def test_optimal_threshold_adversarial_case():
hist_edges = []
min_val = -2
max_val = 2
- for i in range(0, 998):
+ for _ in range(0, 998):
hist.append(0)
for i in range(0, 999):
hist_edges.append((max_val - min_val) / 999 * i + min_val)
diff --git a/tests/python/unittest/test_autograd.py b/tests/python/unittest/test_autograd.py
index b9d11f5..554d830 100644
--- a/tests/python/unittest/test_autograd.py
+++ b/tests/python/unittest/test_autograd.py
@@ -387,7 +387,7 @@ def test_function1():
with mx.autograd.record():
X = mx.nd.zeros((3, 4))
#X.attach_grad() # uncommenting this line works
- for i in range(5):
+ for _ in range(5):
f = Foo()
X = f(X)
X.wait_to_read()
@@ -446,7 +446,7 @@ def test_np_function1():
with mx.autograd.record():
X = mx.np.zeros((3, 4))
#X.attach_grad() # uncommenting this line works
- for i in range(5):
+ for _ in range(5):
f = Foo()
X = f(X)
X.wait_to_read()
diff --git a/tests/python/unittest/test_contrib_control_flow.py b/tests/python/unittest/test_contrib_control_flow.py
index 1a2a6cc..20833dc 100644
--- a/tests/python/unittest/test_contrib_control_flow.py
+++ b/tests/python/unittest/test_contrib_control_flow.py
@@ -990,7 +990,7 @@ def test_foreach():
e = out._bind(ctx=default_context(), args=arg_dict)
# the inputs to forward and backward are the same so forward and backward
# should always return the same outputs.
- for i in range(num_iters):
+ for _ in range(num_iters):
e.forward(is_train=is_train)
if (is_train):
# backward
@@ -1000,7 +1000,7 @@ def test_foreach():
# Below we use imperative to reimplement foreach and compute its gradients.
res = []
- for i in range(len(_as_list(out_grads[0]))):
+ for _ in range(len(_as_list(out_grads[0]))):
res.append([])
for arr in _as_list(in_arrs):
arr.attach_grad()
diff --git a/tests/python/unittest/test_contrib_gluon_data_vision.py b/tests/python/unittest/test_contrib_gluon_data_vision.py
index 83b1447..2ed11e5 100644
--- a/tests/python/unittest/test_contrib_gluon_data_vision.py
+++ b/tests/python/unittest/test_contrib_gluon_data_vision.py
@@ -92,7 +92,7 @@ class TestImage(unittest.TestCase):
rand_crop=1, rand_gray=0.1, rand_mirror=True)
]
for it in imageiter_list:
- for batch in it:
+ for _ in it:
pass
def test_image_bbox_iter(self):
@@ -141,6 +141,6 @@ class TestImage(unittest.TestCase):
pca_noise=0.1, hue=0.1, inter_method=10,
max_aspect_ratio=5, area_range=(0.1, 4.0),
max_attempts=50)
- for batch in det_iter:
+ for _ in det_iter:
pass
mx.npx.waitall()
diff --git a/tests/python/unittest/test_contrib_io.py b/tests/python/unittest/test_contrib_io.py
index ada65ae..5adff93 100644
--- a/tests/python/unittest/test_contrib_io.py
+++ b/tests/python/unittest/test_contrib_io.py
@@ -33,7 +33,7 @@ def test_contrib_DataLoaderIter():
assert batch.label[0].shape == (batch_size,)
count = 0
test_iter.reset()
- for batch in test_iter:
+ for _ in test_iter:
count += 1
assert count == expected, "expected {} batches, given {}".format(expected, count)
diff --git a/tests/python/unittest/test_dgl_graph.py b/tests/python/unittest/test_dgl_graph.py
index 89533fb..5337f90 100644
--- a/tests/python/unittest/test_dgl_graph.py
+++ b/tests/python/unittest/test_dgl_graph.py
@@ -227,7 +227,7 @@ def test_subgraph():
assert np.sum(remain == row_start) == len(remain)
break
row = subgs[0].indices[row_start:row_end]
- for j, subv2 in enumerate(row.asnumpy()):
+ for _, subv2 in enumerate(row.asnumpy()):
v2 = vertices[subv2]
assert sp_g[v1, v2] == sp_subg[subv1, subv2]
diff --git a/tests/python/unittest/test_engine.py b/tests/python/unittest/test_engine.py
index 642d9e1..296bc2b 100644
--- a/tests/python/unittest/test_engine.py
+++ b/tests/python/unittest/test_engine.py
@@ -28,7 +28,7 @@ def test_bulk():
x.wait_to_read()
x += 1
assert (x.asnumpy() == 4).all()
- for i in range(100):
+ for _ in range(100):
x += 1
assert (x.asnumpy() == 104).all()
diff --git a/tests/python/unittest/test_executor.py b/tests/python/unittest/test_executor.py
index fe30b3b..b735c83 100644
--- a/tests/python/unittest/test_executor.py
+++ b/tests/python/unittest/test_executor.py
@@ -77,7 +77,7 @@ def test_bind():
'MXNET_EXEC_BULK_EXEC_TRAIN': enable_bulking}):
nrepeat = 10
maxdim = 4
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
for dim in range(1, maxdim):
check_bind_with_uniform(lambda x, y: x + y,
lambda g, x, y: (g, g),
@@ -107,7 +107,7 @@ def test_bind():
def test_dot():
nrepeat = 10
maxdim = 4
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
s =tuple(np.random.randint(1, 200, size=3))
check_bind_with_uniform(lambda x, y: np.dot(x, y),
lambda g, x, y: (np.dot(g, y.T), np.dot(x.T, g)),
@@ -115,7 +115,7 @@ def test_dot():
lshape=(s[0], s[1]),
rshape=(s[1], s[2]),
sf = mx.symbol.dot)
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
s =tuple(np.random.randint(1, 200, size=1))
check_bind_with_uniform(lambda x, y: np.dot(x, y),
lambda g, x, y: (g * y, g * x),
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index 6d5e40c..791ecef 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -692,7 +692,7 @@ def test_sync_batchnorm():
# check with unsync version
for shape in [(batch_size, 2), (batch_size, 3, 4), (batch_size, 4, 4, 4), (batch_size, 5, 6, 4, 4)]:
print(str((ndev, cuda, shape)))
- for i in range(10):
+ for _ in range(10):
_check_batchnorm_result(mx.np.random.uniform(size=shape,
ctx=mx.cpu(0)),
num_devices=ndev, cuda=cuda)
@@ -1741,7 +1741,7 @@ def test_share_inputs_outputs():
for param in params:
t = TestIOForward()
t.hybridize(**param)
- for i in range(5):
+ for _ in range(5):
d1.attach_grad()
out_grad = mx.np.random.uniform(size=(10))
res = t(d1)
@@ -1751,7 +1751,7 @@ def test_share_inputs_outputs():
for param in params:
t = TestIOBackward()
t.hybridize(**param)
- for i in range(5):
+ for _ in range(5):
d1.attach_grad()
d2.attach_grad()
out_grad = mx.np.random.uniform(size=(10))
@@ -1946,13 +1946,13 @@ def test_concat():
**kwargs):
super(Net, self).__init__(**kwargs)
self.concat = nn.HybridConcatenate(axis=check_dim)
- for i in range(input_num):
+ for _ in range(input_num):
self.concat.add(gluon.nn.Conv2D(chn_num, (kernel, kernel)))
def forward(self, x):
return self.concat(x)
- for s in range(len(shape_list)):
+ for _ in range(len(shape_list)):
shape = (batch_size,) + (3,) + shape_list[i]
x = mx.np.random.uniform(-1.0, 1.0, size=shape)
for i in range(len(chn_list)):
diff --git a/tests/python/unittest/test_gluon_data.py b/tests/python/unittest/test_gluon_data.py
index fe0cf7d..aadc372 100644
--- a/tests/python/unittest/test_gluon_data.py
+++ b/tests/python/unittest/test_gluon_data.py
@@ -218,7 +218,7 @@ def test_image_list_dataset_handle(prepare_record):
def test_list_dataset():
for num_worker in range(0, 3):
data = mx.gluon.data.DataLoader([([1,2], 0), ([3, 4], 1)], batch_size=1, num_workers=num_worker)
- for d, l in data:
+ for _ in data:
pass
@@ -325,14 +325,14 @@ def _batchify(data):
def test_multi_worker_forked_data_loader():
data = _Dummy(False)
loader = DataLoader(data, batch_size=40, batchify_fn=_batchify, num_workers=2)
- for epoch in range(1):
- for i, data in enumerate(loader):
+ for _ in range(1):
+ for _ in loader:
pass
data = _Dummy(True)
loader = DataLoader(data, batch_size=40, batchify_fn=_batchify_list, num_workers=2)
- for epoch in range(1):
- for i, data in enumerate(loader):
+ for _ in range(1):
+ for _ in loader:
pass
def test_multi_worker_dataloader_release_pool():
@@ -382,12 +382,12 @@ def test_dataset_filter():
a = mx.gluon.data.SimpleDataset([i for i in range(length)])
a_filtered = a.filter(lambda x: x % 10 == 0)
assert(len(a_filtered) == 10)
- for idx, sample in enumerate(a_filtered):
+ for sample in a_filtered:
assert sample % 10 == 0
a_xform_filtered = a.transform(lambda x: x + 1).filter(lambda x: x % 10 == 0)
assert(len(a_xform_filtered) == 10)
# the filtered data is already transformed
- for idx, sample in enumerate(a_xform_filtered):
+ for sample in a_xform_filtered:
assert sample % 10 == 0
def test_dataset_filter_handle():
@@ -395,12 +395,12 @@ def test_dataset_filter_handle():
a = mx.gluon.data.SimpleDataset(np.arange(length))
a_filtered = a.filter(lambda x: x % 10 == 0).__mx_handle__()
assert(len(a_filtered) == 10)
- for idx, sample in enumerate(a_filtered):
+ for sample in a_filtered:
assert sample % 10 == 0
a_xform_filtered = a.transform(lambda x: x + 1).filter(lambda x: x % 10 == 0)
assert(len(a_xform_filtered) == 10)
# the filtered data is already transformed
- for idx, sample in enumerate(a_xform_filtered):
+ for sample in a_xform_filtered:
assert sample % 10 == 0
def test_dataset_shard():
@@ -417,7 +417,7 @@ def test_dataset_shard():
assert len(shard_3) == 2
total = 0
for shard in [shard_0, shard_1, shard_2, shard_3]:
- for idx, sample in enumerate(shard):
+ for sample in shard:
total += sample
assert total == sum(a)
@@ -435,7 +435,7 @@ def test_dataset_shard_handle():
assert len(shard_3) == 2
total = 0
for shard in [shard_0, shard_1, shard_2, shard_3]:
- for idx, sample in enumerate(shard):
+ for sample in shard:
total += sample
assert total == sum(a)
@@ -451,7 +451,7 @@ def test_dataset_take():
assert len(a_take_10) == count
expected_total = sum([i for i in range(count)])
total = 0
- for idx, sample in enumerate(a_take_10):
+ for sample in a_take_10:
assert sample < count
total += sample
assert total == expected_total
@@ -460,7 +460,7 @@ def test_dataset_take():
assert len(a_xform_take_10) == count
expected_total = sum([i * 10 for i in range(count)])
total = 0
- for idx, sample in enumerate(a_xform_take_10):
+ for sample in a_xform_take_10:
assert sample < count * 10
total += sample
assert total == expected_total
@@ -477,7 +477,7 @@ def test_dataset_take_handle():
assert len(a_take_10) == count
expected_total = sum([i for i in range(count)])
total = 0
- for idx, sample in enumerate(a_take_10):
+ for sample in a_take_10:
assert sample < count
total += sample
assert total == expected_total
@@ -486,7 +486,7 @@ def test_dataset_take_handle():
assert len(a_xform_take_10) == count
expected_total = sum([i for i in range(count)])
total = 0
- for idx, sample in enumerate(a_xform_take_10):
+ for sample in a_xform_take_10:
assert sample < count
total += sample
assert total == expected_total
diff --git a/tests/python/unittest/test_gluon_probability_v2.py b/tests/python/unittest/test_gluon_probability_v2.py
index 33f8e27..c25fa71 100644
--- a/tests/python/unittest/test_gluon_probability_v2.py
+++ b/tests/python/unittest/test_gluon_probability_v2.py
@@ -77,7 +77,7 @@ def test_gluon_uniform():
net = TestUniform("log_prob")
if hybridize:
net.hybridize()
- for i in range(2):
+ for _ in range(2):
mx_out = net(low, high, samples).asnumpy()
np_out = ss.uniform(low.asnumpy(),
(high - low).asnumpy()).logpdf(samples.asnumpy())
diff --git a/tests/python/unittest/test_gluon_rnn.py b/tests/python/unittest/test_gluon_rnn.py
index 5a2661d..6af6209 100644
--- a/tests/python/unittest/test_gluon_rnn.py
+++ b/tests/python/unittest/test_gluon_rnn.py
@@ -86,7 +86,7 @@ def test_lstmp():
num_layers=num_layers, layout='TNC', bidirectional=False)
stack_layer = mx.gluon.rnn.HybridSequentialRNNCell()
- for i in range(num_layers):
+ for _ in range(num_layers):
stack_layer.add(gluon.rnn.LSTMPCell(hidden_size,
projection_size=projection_size))
fused_layer.initialize()
@@ -118,7 +118,7 @@ def test_lstmp():
num_layers=num_layers, layout='TNC', bidirectional=True)
stack_layer = mx.gluon.rnn.HybridSequentialRNNCell()
- for i in range(num_layers):
+ for _ in range(num_layers):
stack_layer.add(
gluon.rnn.BidirectionalCell(gluon.rnn.LSTMPCell(hidden_size,
projection_size=projection_size),
@@ -738,7 +738,7 @@ def check_rnn_unidir_layer_gradients(mode, input_size, hidden_size, num_layers,
fused_layer.initialize()
stack_layer = mx.gluon.rnn.HybridSequentialRNNCell()
- for n in range(num_layers):
+ for _ in range(num_layers):
stack_layer.add(stack_op(hidden_size))
stack_layer.initialize()
check_rnn_consistency(fused_layer, stack_layer, loss, input_size, hidden_size)
@@ -751,7 +751,7 @@ def check_rnn_bidir_layer_gradients(mode, input_size, hidden_size, num_layers, l
fused_layer.initialize()
stack_layer = mx.gluon.rnn.HybridSequentialRNNCell()
- for n in range(num_layers):
+ for _ in range(num_layers):
stack_layer.add(gluon.rnn.BidirectionalCell(stack_op(hidden_size),
stack_op(hidden_size)))
stack_layer.initialize()
diff --git a/tests/python/unittest/test_gluon_trainer.py b/tests/python/unittest/test_gluon_trainer.py
index e454be8..e256856 100644
--- a/tests/python/unittest/test_gluon_trainer.py
+++ b/tests/python/unittest/test_gluon_trainer.py
@@ -285,13 +285,13 @@ def test_gluon_trainer_param_order():
net = mx.gluon.nn.Sequential()
# layers may be added in a random order for all workers
layers = {'ones_': 1, 'zeros_': 0}
- for name, init in layers.items():
+ for _, init in layers.items():
net.add(mx.gluon.nn.Dense(10, in_units=10, weight_initializer=mx.init.Constant(init),
use_bias=False))
net.initialize()
params = net.collect_params()
trainer = gluon.Trainer(params, 'sgd')
- for name, init in layers.items():
+ for name, _ in layers.items():
expected_idx = 0 if name == 'ones_' else 1
expected_name = '{}.weight'.format(expected_idx)
assert trainer._params[expected_idx].name == params[expected_name].name
diff --git a/tests/python/unittest/test_image.py b/tests/python/unittest/test_image.py
index 7757219..bbc689b 100644
--- a/tests/python/unittest/test_image.py
+++ b/tests/python/unittest/test_image.py
@@ -63,7 +63,7 @@ def _test_imageiter_last_batch(imageiter_list, assert_data_shape):
# test last batch handle(discard)
test_iter = imageiter_list[1]
i = 0
- for batch in test_iter:
+ for _ in test_iter:
i += 1
assert i == 5
# test last_batch_handle(pad)
@@ -294,7 +294,7 @@ class TestImage(unittest.TestCase):
resize=640, rand_crop=True, rand_resize=True, rand_mirror=True, mean=True,
std=np.array([1.1, 1.03, 1.05]), brightness=0.1, contrast=0.1, saturation=0.1,
hue=0.1, pca_noise=0.1, rand_gray=0.2, inter_method=10, path_root=self.IMAGES_DIR, shuffle=True)
- for batch in test_iter:
+ for _ in test_iter:
pass
def test_image_detiter(self):
@@ -346,7 +346,7 @@ class TestImage(unittest.TestCase):
pca_noise=0.1, hue=0.1, inter_method=10, min_object_covered=0.5,
aspect_ratio_range=(0.2, 5), area_range=(0.1, 4.0), min_eject_coverage=0.5,
max_attempts=50)
- for batch in det_iter:
+ for _ in det_iter:
pass
def test_random_size_crop(self):
diff --git a/tests/python/unittest/test_io.py b/tests/python/unittest/test_io.py
index 72da325..c16c7e6 100644
--- a/tests/python/unittest/test_io.py
+++ b/tests/python/unittest/test_io.py
@@ -55,7 +55,7 @@ def test_MNISTIter(tmpdir):
# test_loop
nbatch = 60000 / batch_size
batch_count = 0
- for batch in train_dataiter:
+ for _ in train_dataiter:
batch_count += 1
assert(nbatch == batch_count)
# test_reset
@@ -104,7 +104,7 @@ def test_inter_methods_in_augmenter(inter_method, cifar10):
data_shape=(3, 28, 28),
batch_size=100,
inter_method=inter_method)
- for batch in dataiter:
+ for _ in dataiter:
pass
def test_image_iter_exception(cifar10):
@@ -121,7 +121,7 @@ def test_image_iter_exception(cifar10):
prefetch_buffer=1)
labelcount = [0 for i in range(10)]
batchcount = 0
- for batch in dataiter:
+ for _ in dataiter:
pass
def _init_NDArrayIter_data(data_type, is_image=False):
@@ -345,7 +345,7 @@ def test_LibSVMIter(tmpdir):
first = mx.nd.array([[0.5, 0., 1.2], [0., 0., 0.], [0.6, 2.4, 1.2]])
second = mx.nd.array([[0., 0., -1.2], [0.5, 0., 1.2], [0., 0., 0.]])
i = 0
- for batch in iter(data_train):
+ for _ in iter(data_train):
expected = first.asnumpy() if i == 0 else second.asnumpy()
data = data_train.getdata()
data.check_format(True)
@@ -369,7 +369,7 @@ def test_LibSVMIter(tmpdir):
path = os.path.join(data_dir, news_metadata['name'])
data_train = mx.io.LibSVMIter(data_libsvm=path, data_shape=(news_metadata['feature_dim'],),
batch_size=batch_size)
- for epoch in range(2):
+ for _ in range(2):
num_batches = 0
for batch in data_train:
# check the range of labels
@@ -401,7 +401,7 @@ def test_LibSVMIter(tmpdir):
data_dir = os.path.join(str(tmpdir), 'data')
data_train = mx.io.LibSVMIter(data_libsvm=data_path, label_libsvm=label_path,
data_shape=(3, ), label_shape=(3, ), batch_size=3)
- for batch in iter(data_train):
+ for _ in iter(data_train):
data_train.get_data().asnumpy()
check_libSVMIter_synthetic()
@@ -432,16 +432,16 @@ def test_CSVIter(tmpdir):
if dtype is 'int64':
entry_str = '2147483648'
with open(data_path, 'w') as fout:
- for i in range(1000):
+ for _ in range(1000):
fout.write(','.join([entry_str for _ in range(8*8)]) + '\n')
with open(label_path, 'w') as fout:
- for i in range(1000):
+ for _ in range(1000):
fout.write('0\n')
data_train = mx.io.CSVIter(data_csv=data_path, data_shape=(8, 8),
label_csv=label_path, batch_size=100, dtype=dtype)
expected = mx.nd.ones((100, 8, 8), dtype=dtype) * int(entry_str)
- for batch in iter(data_train):
+ for _ in iter(data_train):
data_batch = data_train.getdata()
assert_almost_equal(data_batch.asnumpy(), expected.asnumpy())
assert data_batch.asnumpy().dtype == expected.asnumpy().dtype
diff --git a/tests/python/unittest/test_kvstore.py b/tests/python/unittest/test_kvstore.py
index 61ae409..3fcd15b 100644
--- a/tests/python/unittest/test_kvstore.py
+++ b/tests/python/unittest/test_kvstore.py
@@ -74,7 +74,7 @@ def test_row_sparse_pull():
vals = []
row_ids = []
all_row_ids = np.arange(num_rows)
- for i in range(count):
+ for _ in range(count):
vals.append(mx.nd.zeros(shape).tostype('row_sparse'))
row_id = np.random.randint(num_rows, size=num_rows)
row_ids.append(mx.nd.array(row_id).reshape((2, num_rows//2)))
@@ -254,7 +254,7 @@ def test_updater(dev='cpu'):
outs = [[mx.nd.empty(shape, d) for d in devs]] * len(key_list)
num_push = 4
- for i in range(num_push):
+ for _ in range(num_push):
kv.push(key_list, vals)
kv.pull(key_list, out=outs)
diff --git a/tests/python/unittest/test_loss.py b/tests/python/unittest/test_loss.py
index 103ccc6..8c28691 100644
--- a/tests/python/unittest/test_loss.py
+++ b/tests/python/unittest/test_loss.py
@@ -126,7 +126,7 @@ def test_sdml_loss():
model.initialize(mx.init.Xavier(), ctx=mx.current_context())
trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate' : 0.1})
- for i in range(EPOCHS): # Training loop
+ for _ in range(EPOCHS): # Training loop
data_iter.reset()
for iter_batch in data_iter:
batch = [datum.as_in_context(mx.current_context()) for datum in iter_batch.data]
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index b935290..a2aae07 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -175,7 +175,7 @@ def test_ndarray_elementwise():
maxdim = 4
all_type = [np.float32, np.float64, np.float16, np.uint8, np.int8, np.int32, np.int64]
real_type = [np.float32, np.float64, np.float16]
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
for dim in range(1, maxdim):
check_with_uniform(lambda x, y: x + y, 2, dim, type_list=all_type)
check_with_uniform(lambda x, y: x - y, 2, dim, type_list=all_type)
@@ -295,7 +295,7 @@ def test_ndarray_choose():
npy = np.arange(np.prod(shape)).reshape(shape)
arr = mx.nd.array(npy)
nrepeat = 3
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
indices = np.random.randint(shape[1], size=shape[0])
assert same(npy[np.arange(shape[0]), indices],
mx.nd.choose_element_0index(arr, mx.nd.array(indices)).asnumpy())
@@ -307,7 +307,7 @@ def test_ndarray_fill():
arr = mx.nd.array(npy)
new_npy = npy.copy()
nrepeat = 3
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
indices = np.random.randint(shape[1], size=shape[0])
val = np.random.randint(shape[1], size=shape[0])
new_npy[:] = npy
@@ -321,7 +321,7 @@ def test_ndarray_onehot():
npy = np.arange(np.prod(shape)).reshape(shape)
arr = mx.nd.array(npy)
nrepeat = 3
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
indices = np.random.randint(shape[1], size=shape[0])
npy[:] = 0.0
npy[np.arange(shape[0]), indices] = 1.0
@@ -374,10 +374,10 @@ def test_ndarray_pickle():
def test_ndarray_saveload(save_fn):
nrepeat = 10
fname = 'tmp_list'
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
data = []
# test save/load as list
- for i in range(10):
+ for _ in range(10):
data.append(random_ndarray(np.random.randint(1, 5)))
if save_fn is mx.nd.save:
save_fn(fname, data)
@@ -437,7 +437,7 @@ def test_ndarray_load_fortran_order(tmp_path):
def test_ndarray_legacy_load():
data = []
- for i in range(6):
+ for _ in range(6):
data.append(mx.nd.arange(128))
path = os.path.dirname(os.path.realpath(__file__))
legacy_data = mx.nd.load(os.path.join(path, 'legacy_ndarray.v0'))
@@ -452,7 +452,7 @@ def test_buffer_load():
for repeat in range(nrepeat):
# test load_buffer as list
data = []
- for i in range(10):
+ for _ in range(10):
data.append(random_ndarray(np.random.randint(1, 5)))
fname = os.path.join(tmpdir, 'list_{0}.param'.format(repeat))
mx.nd.save(fname, data)
@@ -616,7 +616,7 @@ def test_reduce():
dtypes = [(np.float16, 1),
(np.float32, 4),
(np.double, 6)]
- for i in range(sample_num):
+ for _ in range(sample_num):
dtype, decimal = random.choice(dtypes)
ndim = np.random.randint(1, 6)
shape = np.random.randint(1, 11, size=ndim)
@@ -857,7 +857,7 @@ def test_moveaxis():
def test_arange():
- for i in range(5):
+ for _ in range(5):
start = np.random.rand() * 10
stop = start + np.random.rand() * 100
step = np.random.rand() * 4
@@ -873,7 +873,7 @@ def test_arange():
def test_linspace():
- for i in range(5):
+ for _ in range(5):
start = np.random.rand() * 100
stop = np.random.rand() * 100
num = np.random.randint(20)
@@ -1791,7 +1791,7 @@ def test_ndarray_cpu_shared_ctx():
@pytest.mark.serial
def test_dlpack():
- for dtype in [np.float32, np.int32]:
+ for _ in [np.float32, np.int32]:
for shape in [(3, 4, 5, 6), (2, 10), (15,)]:
a = mx.nd.random.uniform(shape = shape)
a_np = a.copy()
diff --git a/tests/python/unittest/test_numpy_contrib_gluon_data_vision.py b/tests/python/unittest/test_numpy_contrib_gluon_data_vision.py
index 1722a5f..feaf17e 100644
--- a/tests/python/unittest/test_numpy_contrib_gluon_data_vision.py
+++ b/tests/python/unittest/test_numpy_contrib_gluon_data_vision.py
@@ -92,7 +92,7 @@ class TestImage(unittest.TestCase):
rand_crop=1, rand_gray=0.1, rand_mirror=True)
]
for it in imageiter_list:
- for batch in it:
+ for _ in it:
pass
@use_np
diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
index 6c1fa67..c79b163 100644
--- a/tests/python/unittest/test_numpy_gluon.py
+++ b/tests/python/unittest/test_numpy_gluon.py
@@ -41,7 +41,7 @@ def test_create_np_param():
net.hybridize()
net(x)
params = net.collect_params()
- for k, v in params.items():
+ for _, v in params.items():
assert type(v.data()) is expected_type
@use_np
@@ -101,7 +101,7 @@ def test_optimizer_with_np_ndarrays():
'sgd',
{'learning_rate': 1e-3, 'momentum': 0.9})
- for t in range(2):
+ for _ in range(2):
with autograd.record():
output = regressor(x) # output is a type of np.ndarray because np.dot is the last op in the network
loss = total_loss(output, y) # loss is a scalar np.ndarray
diff --git a/tests/python/unittest/test_numpy_interoperability.py b/tests/python/unittest/test_numpy_interoperability.py
index 1b8fe4d..db643d1 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -421,7 +421,7 @@ def _add_workload_swapaxes():
# assertRaises(np.AxisError, np.swapaxes, -5, 0)
for i in range(-4, 4):
for j in range(-4, 4):
- for k, src in enumerate((a, b)):
+ for src in (a, b):
OpArgMngr.add_workload('swapaxes', src, i, j)
@@ -448,7 +448,7 @@ def _add_workload_tile():
shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
for s in shape:
b = np.random.randint(0, 10, size=s)
- for r in reps:
+ for _ in reps:
# RuntimeError to be tracked
# where s = (3, 4, 3), r = (2, 3, 2)
# OpArgMngr.add_workload('tile', b, r)
diff --git a/tests/python/unittest/test_numpy_loss.py b/tests/python/unittest/test_numpy_loss.py
index d452566..93fd2f9 100644
--- a/tests/python/unittest/test_numpy_loss.py
+++ b/tests/python/unittest/test_numpy_loss.py
@@ -220,7 +220,7 @@ def test_sdml_loss():
model.initialize(mx.init.Xavier(), ctx=mx.current_context())
trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate' : 0.1})
- for i in range(EPOCHS): # Training loop
+ for _ in range(EPOCHS): # Training loop
data_iter.reset()
for iter_batch in data_iter:
batch = [datum.as_in_ctx(mx.current_context()).as_np_ndarray() for datum in iter_batch.data]
diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py
index c6221c5..559b8a5 100644
--- a/tests/python/unittest/test_numpy_ndarray.py
+++ b/tests/python/unittest/test_numpy_ndarray.py
@@ -1129,7 +1129,7 @@ def test_np_multinomial():
if pvals_mx_np_array:
pvals = mx.np.array(pvals)
x = np.random.multinomial(small_exp, pvals)
- for i in range(total_exp // small_exp):
+ for _ in range(total_exp // small_exp):
x = x + np.random.multinomial(20, pvals)
freq = (x.asnumpy() / _np.float32(total_exp)).reshape((-1, len(pvals)))
for i in range(freq.shape[0]):
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 053fa9d..e21e8fd 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -1437,7 +1437,7 @@ def test_npx_index_add():
ind = onp.array(ind).astype(onp.int32)
# case: val is scalar
configs.append(tuple([shape, ind, (), ind_ndim, ind_num]))
- for val_ndim in range(1, 5 - ind_ndim):
+ for _ in range(1, 5 - ind_ndim):
val_shape = [1 if onp.random.randint(0, 5)==0 else ind_num]
for val_dim in range(ind_ndim, 4):
val_shape.append(1 if onp.random.randint(0, 5)==0 else shape[val_dim])
@@ -1600,7 +1600,7 @@ def test_npx_index_update():
ind = onp.array(ind).astype(onp.int32)
# case: val is scalar
configs.append(tuple([shape, ind, (), ind_ndim, ind_num]))
- for val_ndim in range(1, 5 - ind_ndim):
+ for _ in range(1, 5 - ind_ndim):
val_shape = [1 if onp.random.randint(0, 5)==0 else ind_num]
for val_dim in range(ind_ndim, 4):
val_shape.append(1 if onp.random.randint(0, 5)==0 else shape[val_dim])
@@ -4831,15 +4831,16 @@ def test_npx_sample_n():
return (s,)
class TestSampleN(HybridBlock):
- def __init__(self, shape, op_name):
+ def __init__(self, shape, op_name, dtype):
super(TestSampleN, self).__init__()
self._shape = shape
self._op_name = op_name
+ self._dtype = dtype
def forward(self, param1, param2):
op = getattr(npx.random, self._op_name, None)
assert op is not None
- return op(param1, param2, batch_shape=self._shape)
+ return op(param1, param2, batch_shape=self._shape, dtype=self._dtype)
batch_shapes = [(10,), (2, 3), 6, ()]
event_shapes = [(), (2,), (2,2)]
@@ -4848,7 +4849,7 @@ def test_npx_sample_n():
for bshape, eshape, dtype, op in itertools.product(batch_shapes, event_shapes, dtypes, op_names):
for hybridize in [True, False]:
- net = TestSampleN(bshape, op)
+ net = TestSampleN(bshape, op, dtype)
if hybridize:
net.hybridize()
expected_shape = (shape_formatter(bshape) +
@@ -5498,7 +5499,7 @@ def test_np_choice():
bins = onp.zeros((num_classes))
expected_freq = (weight.asnumpy() if weight is not None else
onp.array([1 / num_classes] * num_classes))
- for i in range(num_trials):
+ for _ in range(num_trials):
out = sampler(num_classes, 1, replace=False, p=weight).item()
bins[out] += 1
bins /= num_trials
@@ -8396,7 +8397,7 @@ def test_np_einsum():
dtype=dtype))
for optimize in [False, True]:
x = []
- for (iop, op) in enumerate(operands):
+ for iop in range(len(operands)):
x.append(np.array(x_np[iop], dtype=dtype))
x[-1].attach_grad()
test_einsum = TestEinsum(subscripts, optimize)
@@ -8410,10 +8411,10 @@ def test_np_einsum():
assert_almost_equal(out_mx.asnumpy(), expected_np, rtol=rtol, atol=atol)
out_mx.backward()
cur_grad = []
- for (iop, op) in enumerate(x):
+ for op in x:
cur_grad.append(op.grad.asnumpy())
grad.append(cur_grad)
- for (iop, op) in enumerate(grad[0]):
+ for iop in range(len(grad[0])):
assert_almost_equal(grad[0][iop], grad[1][iop], rtol=rtol, atol=atol)
@@ -8482,7 +8483,7 @@ def test_np_pad():
if (type(shape) == int):
pw += (2,3)
else:
- for i in range(len(shape)):
+ for _ in range(len(shape)):
pw += ((2,3),)
test_pad = TestPad(pw, m)
if hybridize:
@@ -8934,7 +8935,7 @@ def test_np_percentile():
def test_np_diff():
def np_diff_backward(ograd, n, axis):
res = ograd
- for i in range(n):
+ for _ in range(n):
res = onp.negative(onp.diff(res, n=1, axis=axis, prepend=0, append=0))
return res
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index cbae11e..610b700 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -193,7 +193,7 @@ def check_elementwise_sum_with_shape(shape, n):
def test_elementwise_sum():
nrepeat = 2
maxdim = 4
- for repeat in range(nrepeat):
+ for _ in range(nrepeat):
for dim in range(1, maxdim):
shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
check_elementwise_sum_with_shape(shape, np.random.randint(1, 8))
@@ -2518,7 +2518,7 @@ def test_reduce():
def test_broadcast():
sample_num = 200
- for i in range(sample_num):
+ for _ in range(sample_num):
# Generate random data that has ndim between 1-7 and all the shape dims between 1-5
ndim = np.random.randint(1, 6)
target_shape = np.random.randint(1, 6, size=(ndim,))
@@ -2561,7 +2561,7 @@ def test_broadcast():
def test_transpose():
for ndim in range(1, 10):
- for t in range(5):
+ for _ in range(5):
dims = list(np.random.randint(1, 5, size=ndim))
axes = list(range(ndim))
random.shuffle(axes)
@@ -2634,12 +2634,12 @@ def test_expand_dims():
def test_crop():
for ndim in range(1, 6):
- for t in range(5):
+ for _ in range(5):
dims = []
begin = []
end = []
idx = []
- for i in range(ndim):
+ for _ in range(ndim):
d = random.randint(1, 5)
b = random.randint(0, d-1)
e = random.randint(b+1, d)
@@ -2779,7 +2779,7 @@ def test_broadcast_like_different_types():
def test_flip():
for ndim in range(1, 6):
- for t in range(5):
+ for _ in range(5):
dims = [random.randint(1,10) for i in range(ndim)]
axis = random.randint(0, ndim-1)
idx = [slice(None, None, -1) if i == axis else slice(None, None) for i in range(ndim)]
@@ -4179,56 +4179,56 @@ def test_take(mode, out_of_range, data_ndim, idx_ndim):
grad_in[:, :, :, :, idx] += 1.0
else:
raise ValueError("axis %d is not supported..." % axis)
-
+
for axis in range(-data_ndim, data_ndim):
- data_shape = ()
- for _ in range(data_ndim):
- data_shape += (np.random.randint(low=1, high=5), )
- idx_shape = ()
- for _ in range(idx_ndim):
- idx_shape += (np.random.randint(low=1, high=5), )
-
- data = mx.sym.Variable('a')
- idx = mx.sym.Variable('indices')
- idx = mx.sym.BlockGrad(idx)
- result = mx.sym.take(a=data, indices=idx, axis=axis, mode=mode)
- exe = result._simple_bind(default_context(), a=data_shape,
- indices=idx_shape)
- data_real = np.random.normal(size=data_shape).astype('float32')
- if out_of_range:
- idx_real = np.random.randint(low=-data_shape[axis], high=data_shape[axis], size=idx_shape)
- if mode == 'raise':
- idx_real[idx_real == 0] = 1
- idx_real *= data_shape[axis]
- else:
- idx_real = np.random.randint(low=0, high=data_shape[axis], size=idx_shape)
- if axis < 0:
- axis += len(data_shape)
+ data_shape = ()
+ for _ in range(data_ndim):
+ data_shape += (np.random.randint(low=1, high=5), )
+ idx_shape = ()
+ for _ in range(idx_ndim):
+ idx_shape += (np.random.randint(low=1, high=5), )
+
+ data = mx.sym.Variable('a')
+ idx = mx.sym.Variable('indices')
+ idx = mx.sym.BlockGrad(idx)
+ result = mx.sym.take(a=data, indices=idx, axis=axis, mode=mode)
+ exe = result._simple_bind(default_context(), a=data_shape,
+ indices=idx_shape)
+ data_real = np.random.normal(size=data_shape).astype('float32')
+ if out_of_range:
+ idx_real = np.random.randint(low=-data_shape[axis], high=data_shape[axis], size=idx_shape)
+ if mode == 'raise':
+ idx_real[idx_real == 0] = 1
+ idx_real *= data_shape[axis]
+ else:
+ idx_real = np.random.randint(low=0, high=data_shape[axis], size=idx_shape)
+ if axis < 0:
+ axis += len(data_shape)
- grad_out = np.ones((data_shape[0:axis] if axis > 0 else ()) + idx_shape + (data_shape[axis+1:] if axis < len(data_shape) - 1 else ()), dtype='float32')
- grad_in = np.zeros(data_shape, dtype='float32')
+ grad_out = np.ones((data_shape[0:axis] if axis > 0 else ()) + idx_shape + (data_shape[axis+1:] if axis < len(data_shape) - 1 else ()), dtype='float32')
+ grad_in = np.zeros(data_shape, dtype='float32')
- exe.arg_dict['a'][:] = mx.nd.array(data_real)
- exe.arg_dict['indices'][:] = mx.nd.array(idx_real)
- exe.forward(is_train=True)
- if out_of_range and mode == 'raise':
- try:
- mx_out = exe.outputs[0].asnumpy()
- except MXNetError as e:
- return
- else:
- # Did not raise exception
- assert False, "did not raise %s" % MXNetError.__name__
+ exe.arg_dict['a'][:] = mx.nd.array(data_real)
+ exe.arg_dict['indices'][:] = mx.nd.array(idx_real)
+ exe.forward(is_train=True)
+ if out_of_range and mode == 'raise':
+ try:
+ mx_out = exe.outputs[0].asnumpy()
+ except MXNetError as e:
+ return
+ else:
+ # Did not raise exception
+ assert False, "did not raise %s" % MXNetError.__name__
- assert_almost_equal(exe.outputs[0], np.take(data_real, idx_real, axis=axis, mode=mode))
+ assert_almost_equal(exe.outputs[0], np.take(data_real, idx_real, axis=axis, mode=mode))
- for i in np.nditer(idx_real):
- if mode == 'clip':
- i = np.clip(i, 0, data_shape[axis])
- grad_helper(grad_in, axis, i)
+ for i in np.nditer(idx_real):
+ if mode == 'clip':
+ i = np.clip(i, 0, data_shape[axis])
+ grad_helper(grad_in, axis, i)
- exe.backward([mx.nd.array(grad_out)])
- assert_almost_equal(exe.grad_dict['a'], grad_in)
+ exe.backward([mx.nd.array(grad_out)])
+ assert_almost_equal(exe.grad_dict['a'], grad_in)
def test_grid_generator():
@@ -4443,7 +4443,7 @@ def test_repeat():
repeats = 3
for ndim in range(1, ndim_max+1):
shape = ()
- for i in range(0, ndim):
+ for _ in range(0, ndim):
shape += (np.random.randint(1, size_max+1), )
a = np.random.random_sample(size=shape)
aa = np.repeat(a, repeats)
@@ -4530,7 +4530,7 @@ def test_tile():
rep_max = 10 # max number of tiling in each dim
for ndim in range(ndim_min, ndim_max+1):
shape = []
- for i in range(1, ndim+1):
+ for _ in range(1, ndim+1):
shape.append(np.random.randint(1, size_max+1))
shape = tuple(shape)
a = np.random.randint(0, 100, shape)
@@ -4538,7 +4538,7 @@ def test_tile():
reps_len = np.random.randint(1, length_max+1)
reps_tuple = ()
- for i in range(1, reps_len):
+ for _ in range(1, reps_len):
reps_tuple += (np.random.randint(1, rep_max), )
reps_array = np.asarray(reps_tuple)
@@ -4623,7 +4623,7 @@ def test_one_hot():
off_value = 0
for ndim in range(1, ndim_max+1):
shape = ()
- for i in range(1, ndim+1):
+ for _ in range(1, ndim+1):
shape += (np.random.randint(1, dim_size_max+1), )
indices = np.random.randint(-dim_size_max, dim_size_max+1,
size=np.prod(shape)).reshape(shape)
@@ -8703,7 +8703,7 @@ def test_np_shape_decorator():
check_concat((0, 3, 4), (5, 3, 4), 0)
check_concat((8, 0, 5), (8, 7, 5), 1)
check_concat((8, 0, 0), (8, 0, 0), 2)
- for active in [True, False]:
+ for _ in [True, False]:
check_concat((0, 3, 4), (5, 3, 4), 0)
check_concat((8, 0, 5), (8, 7, 5), 1)
check_concat((8, 0, 0), (8, 0, 0), 2)
@@ -9523,7 +9523,7 @@ def test_take_grads():
def run_model(model, loss, X, Y, num_iters=5):
grads = []
- for i in range(num_iters):
+ for _ in range(num_iters):
with autograd.record():
Y_hat = model(X)
ll = loss(Y_hat, Y)
diff --git a/tests/python/unittest/test_profiler.py b/tests/python/unittest/test_profiler.py
index f284c7a..d3d3eaf 100644
--- a/tests/python/unittest/test_profiler.py
+++ b/tests/python/unittest/test_profiler.py
@@ -262,12 +262,12 @@ def test_aggregate_stats_sorting():
def check_sorting(debug_str, sort_by, ascending):
target_dict = json.loads(debug_str, object_pairs_hook=OrderedDict)
lst = []
- for domain_name, domain in target_dict['Time'].items():
+ for _, domain in target_dict['Time'].items():
lst = [item[sort_by_options[sort_by]] for item_name, item in domain.items()]
check_ascending(lst, ascending)
# Memory items do not have stat 'Total'
if sort_by != 'total':
- for domain_name, domain in target_dict['Memory'].items():
+ for _, domain in target_dict['Memory'].items():
lst = [item[sort_by_options[sort_by]] for item_name, item in domain.items()]
check_ascending(lst, ascending)
diff --git a/tests/python/unittest/test_random.py b/tests/python/unittest/test_random.py
index 9cd935d..e03d6d0 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -917,7 +917,7 @@ def test_shuffle():
# Check that the shuffling is along the first axis.
# The order of the elements in each subarray must not change.
# This takes long time so `repeat1` need to be small.
- for i in range(repeat1):
+ for _ in range(repeat1):
ret = mx.nd.random.shuffle(data)
check_first_axis_shuffle(ret)
# Count the number of each different outcome.
@@ -925,7 +925,7 @@ def test_shuffle():
# the outcomes as long as the order of the elements in each subarray does not change.
count = {}
stride = int(data.size / data.shape[0])
- for i in range(repeat2):
+ for _ in range(repeat2):
ret = mx.nd.random.shuffle(data)
h = str(ret.reshape((ret.size,))[::stride])
c = count.get(h, 0)
@@ -953,7 +953,7 @@ def test_shuffle():
# and count the number of different outcomes.
stride = int(data.size / data.shape[0])
count = {}
- for i in range(repeat):
+ for _ in range(repeat):
ret = mx.nd.random.shuffle(data)
check_first_axis_shuffle(ret)
h = str(ret.reshape((ret.size,))[::stride])
diff --git a/tests/python/unittest/test_sparse_ndarray.py b/tests/python/unittest/test_sparse_ndarray.py
index e427377..160cb60 100644
--- a/tests/python/unittest/test_sparse_ndarray.py
+++ b/tests/python/unittest/test_sparse_ndarray.py
@@ -57,7 +57,7 @@ def test_sparse_nd_elemwise_add():
num_repeats = 3
g = lambda x,y: x + y
op = mx.nd.elemwise_add
- for i in range(num_repeats):
+ for _ in range(num_repeats):
shape = [rand_shape_2d()] * 2
check_sparse_nd_elemwise_binary(shape, ['default'] * 2, op, g)
check_sparse_nd_elemwise_binary(shape, ['row_sparse', 'row_sparse'], op, g)
@@ -163,7 +163,7 @@ def test_sparse_nd_concat():
nds = []
zero_nds = []
ncols = rnd.randint(2, 10)
- for i in range(3):
+ for _ in range(3):
shape = (rnd.randint(2, 10), ncols)
A, _ = rand_sparse_ndarray(shape, 'csr')
nds.append(A)
@@ -548,7 +548,7 @@ def test_sparse_nd_save_load(save_fn):
densities = [0, 0.5]
fname = 'tmp_list.npz'
data_list1 = []
- for i in range(num_data):
+ for _ in range(num_data):
stype = stypes[np.random.randint(0, len(stypes))]
shape = rand_shape_2d(dim0=40, dim1=40)
density = densities[np.random.randint(0, len(densities))]
diff --git a/tests/python/unittest/test_sparse_operator.py b/tests/python/unittest/test_sparse_operator.py
index 731f908..10bfb65 100644
--- a/tests/python/unittest/test_sparse_operator.py
+++ b/tests/python/unittest/test_sparse_operator.py
@@ -490,7 +490,7 @@ def test_elemwise_binary_ops():
with warnings.catch_warnings():
warnings.simplefilter("ignore")
- for ii in range(1):
+ for _ in range(1):
# Run defaults
check_elemwise_binary_ops('default', 'default', rand_shape_2d(5, 5))
@@ -1733,7 +1733,7 @@ def test_sparse_elementwise_sum():
test_len = np.random.randint(5, 10)
# at least one default type
stypes = ['default']
- for i in range(test_len):
+ for _ in range(test_len):
pick_side = np.random.randint(2)
pick_type = np.random.randint(3)
stypes = ([all_stypes[pick_type]] if pick_side is 0 else []) + stypes + ([all_stypes[pick_type]] if pick_side is 1 else [])
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index f93649a..c10a5a0 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -313,7 +313,7 @@ def test_simple_bind_gradient_graph_possible_with_cycle():
def test_children_same_name():
a = mx.sym.Variable('data')
b = a + a
- for c in b.get_children():
+ for _ in b.get_children():
pass
def test_transpose_nullop():