If variable is not used within the loop body, start the name with an underscore

python/mxnet/gluon/contrib/estimator/estimator.py

@@ -392,7 +392,7 @@ def fit(self, train_data,
             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)

python/mxnet/operator.py

@@ -824,7 +824,7 @@ def infer_storage_type_backward_entry(num_tensor, tensor_stypes, tags, _):
                         "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):

python/mxnet/test_utils.py

@@ -1584,7 +1584,7 @@ def smaller_dtype(dt1, dt2):
             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:

tests/nightly/dist_device_sync_kvstore_byteps.py

@@ -65,7 +65,7 @@ def check_default_keys(nrepeat=3):
         # 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
 

tests/nightly/dist_device_sync_kvstore_custom.py

@@ -54,7 +54,7 @@ def check_default_keys(nrepeat=3):
         # 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
 

tests/nightly/dist_sync_kvstore.py

@@ -153,7 +153,7 @@ def check_row_sparse_keys_with_zeros(dtype, 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 check_compr_random(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 check_compr_random(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)
 

tests/nightly/test_kvstore.py

@@ -150,7 +150,7 @@ def pull_init_test(kv):
             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 verify_residual_1bit(kv, threshold, rate):
                 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 verify_residual_2bit(kv, threshold, rate):
         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)])

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)

tests/nightly/test_server_profiling.py

@@ -35,7 +35,7 @@ def check_default_keys(kv, my_rank):
         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)

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 get_num_devices():
         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)
 

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)

tests/python/gpu/test_kvstore_gpu.py

@@ -65,7 +65,7 @@ def check_rsp_pull(kv, ctxs, sparse_pull, is_same_rowid=False, use_slice=False):
                 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

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 _flatten_list(nested_list):
                                     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':

tests/python/mkl/subgraphs/test_conv_subgraph.py

@@ -254,7 +254,7 @@ class SingleConcat(nn.HybridBlock):
     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):

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)

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)

tests/python/unittest/test_autograd.py

@@ -387,7 +387,7 @@ def backward(self, dY):
     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 backward(self, dY):
     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()

tests/python/unittest/test_contrib_control_flow.py

@@ -990,7 +990,7 @@ def verify_foreach(step, in_syms, state_syms, free_syms,
             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 verify_foreach(step, in_syms, state_syms, free_syms,
 
         # 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()

tests/python/unittest/test_contrib_gluon_data_vision.py

@@ -92,7 +92,7 @@ def test_imageiter(self):
                         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 @@ def test_bbox_augmenters(self):
             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()

tests/python/unittest/test_contrib_io.py

@@ -33,7 +33,7 @@ def test_mnist_batches(batch_size, expected, last_batch='discard'):
         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)
 

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]
 

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()
 

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),
@@ -107,15 +107,15 @@ 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)),
                                 2,
                                 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),

tests/python/unittest/test_gluon.py

@@ -692,7 +692,7 @@ def _syncParameters(bn1, bn2, ctx):
         # 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 forward(self, in1):
     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 forward(self, in1):
     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 __init__(self,
                      **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)):