[LINT][PY] Fixes for pylint==2.4.4 (#4849)

Makefile

@@ -94,10 +94,7 @@ javadoc:
 
 # Cython build
 cython:
-	cd python; python setup.py build_ext --inplace
-
-cython2:
-	cd python; python2 setup.py build_ext --inplace
+	cd python; python3 setup.py build_ext --inplace
 
 cython3:
 	cd python; python3 setup.py build_ext --inplace

python/tvm/_ffi/base.py

@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 # coding: utf-8
-# pylint: disable=invalid-name
+# pylint: disable=invalid-name, import-outside-toplevel
 """Base library for TVM FFI."""
 import sys
 import os
@@ -204,14 +204,14 @@ def _find_error_type(line):
         if _valid_error_name(err_name):
             return err_name
         return None
-    else:
-        end_pos = line.find(":")
-        if end_pos == -1:
-            return None
-        err_name = line[:end_pos]
-        if _valid_error_name(err_name):
-            return err_name
+
+    end_pos = line.find(":")
+    if end_pos == -1:
         return None
+    err_name = line[:end_pos]
+    if _valid_error_name(err_name):
+        return err_name
+    return None
 
 
 def c2pyerror(err_msg):

python/tvm/autotvm/database.py

@@ -104,6 +104,7 @@ class RedisDatabase(Database):
     MAGIC_SPLIT = "$"
 
     def __init__(self, db_index=REDIS_PROD):
+        # pylint: disable=import-outside-toplevel
         import redis
 
         if db_index == RedisDatabase.REDIS_TEST:

python/tvm/autotvm/feature.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name
+# pylint: disable=invalid-name,
 """Extract feature of iter vars
 
 There are two types of feature
@@ -148,6 +148,7 @@ def get_flatten_name(fea):
     }
 
     if isinstance(fea, str):
+        # pylint: disable=import-outside-toplevel
         from .record import decode
         # flatten line to feature
         line = fea

python/tvm/autotvm/graph_tuner/base_graph_tuner.py

@@ -539,4 +539,3 @@ class BaseGraphTuner(object):
     @abstractmethod
     def run(self, **kwargs):
         """Run graph tuning."""
-        pass

python/tvm/autotvm/graph_tuner/utils/traverse_graph.py

@@ -65,6 +65,7 @@ def expr2graph(expr, target_ops, node_dict, node_list):
                                % op_name)
         topi_funcs += OP2COMPUTE[op_name]
     env.reset(topi_funcs)
+    # pylint: disable=not-context-manager
     with env:
         _expr2graph_impl(expr, target_ops, node_dict, node_list)
         task_pos = 0

python/tvm/autotvm/measure/measure.py

@@ -208,6 +208,7 @@ def measure_option(builder, runner):
     Using `min_repeat_ms` can dynamically adjusts `number`, so it is recommended.
     The typical value for NVIDIA GPU is 150 ms.
     """
+    # pylint: disable=import-outside-toplevel
     from .measure_methods import LocalBuilder, LocalRunner
 
     if isinstance(builder, str):

python/tvm/autotvm/measure/measure_methods.py

@@ -324,11 +324,11 @@ class LocalRunner(RPCRunner):
         self.server = None
 
     def set_task(self, task):
-        self.task = task
-
+        # pylint: disable=import-outside-toplevel
         from ...rpc.tracker import Tracker
         from ...rpc.server import Server
 
+        self.task = task
         tracker = Tracker('0.0.0.0', port=9000, port_end=10000, silent=True)
         device_key = '$local$device$%d' % tracker.port
         server = Server('0.0.0.0', port=9000, port_end=10000,
@@ -362,6 +362,7 @@ def _build_func_common(measure_input, check_gpu=None, cuda_arch=None, build_opti
         # if target is vta, we need to use vta build
         if hasattr(measure_input.target, 'device_name') and \
             measure_input.target.device_name == 'vta':
+            # pylint: disable=import-outside-toplevel
             import vta
             func = vta.build(s, args, target_host=task.target_host)
         else:
@@ -460,6 +461,7 @@ def run_through_rpc(measure_input, build_result,
         # Program the FPGA every single time when targeting VTA
         if hasattr(measure_input.target, 'device_name') and \
             measure_input.target.device_name == 'vta':
+            # pylint: disable=import-outside-toplevel
             from vta import program_fpga, reconfig_runtime
             program_fpga(remote, None)
             reconfig_runtime(remote)

python/tvm/autotvm/task/dispatcher.py

@@ -282,6 +282,7 @@ class ApplyHistoryBest(DispatchContext):
                        Each row of this file is an encoded record pair.
             Otherwise, it is an iterator.
         """
+        # pylint: disable=import-outside-toplevel
         from pathlib import Path
         from ..record import load_from_file
 
@@ -454,6 +455,7 @@ class ApplyGraphBest(DispatchContext):
                    Each row of this file is an encoded record pair.
             Otherwise, it is an iterator.
         """
+        # pylint: disable=import-outside-toplevel
         from ..record import load_from_file
 
         super(ApplyGraphBest, self).__init__()

python/tvm/autotvm/task/relay_integration.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=unused-variable,invalid-name
+# pylint: disable=unused-variable,invalid-name, not-context-manager
 """
 Decorator and utilities for the integration with TOPI and Relay
 99.9% copy-paste of implementation by @MerryMercy
@@ -37,7 +37,7 @@ def _lower(mod,
            params):
     """ Helper to lower VTA properly.
     """
-
+    # pylint: disable=import-outside-toplevel
     from tvm import relay
     from tvm.relay.backend import graph_runtime_codegen
 
@@ -114,6 +114,7 @@ def extract_from_multiple_program(mods, params, ops, target, target_host=None,
     task: Array of autotvm.task.Task
         collected tasks
     """
+    # pylint: disable=import-outside-toplevel
     import tvm.relay.op
     from tvm import relay
     import topi

python/tvm/autotvm/task/topi_integration.py

@@ -76,6 +76,7 @@ class TaskExtractEnv:
     registered = None
 
     def __init__(self, allow_duplicate=False):
+        # pylint: disable=import-outside-toplevel
         import topi
 
         # topi compute -> autotvm task name
@@ -168,6 +169,7 @@ class TaskExtractEnv:
 
     def _register_topi_task(self):
         """register tuning wrapper for topi function"""
+        # pylint: disable=import-outside-toplevel
         import topi
 
         # Avoid double registration for certain targets

python/tvm/autotvm/tophub.py

@@ -147,6 +147,7 @@ def check_backend(tophub_location, backend):
     if os.path.isfile(os.path.join(AUTOTVM_TOPHUB_ROOT_PATH, package_name)):
         return True
 
+    # pylint: disable=import-outside-toplevel
     if sys.version_info >= (3,):
         import urllib.request as urllib2
     else:

python/tvm/autotvm/tuner/callback.py

@@ -53,6 +53,7 @@ def log_to_file(file_out, protocol='json'):
             for inp, result in zip(inputs, results):
                 file_out.write(record.encode(inp, result, protocol) + "\n")
 
+    # pylint: disable=import-outside-toplevel
     from pathlib import Path
     if isinstance(file_out, Path):
         file_out = str(file_out)

python/tvm/autotvm/tuner/sa_model_optimizer.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=consider-using-enumerate, invalid-name
+# pylint: disable=consider-using-enumerate, invalid-name, invalid-sequence-index
 """
 Cost model optimizer based on simulated annealing
 """

python/tvm/autotvm/tuner/xgboost_cost_model.py

@@ -420,6 +420,7 @@ def _extract_curve_feature_log(arg):
 def custom_callback(stopping_rounds, metric, fevals, evals=(), log_file=None,
                     maximize=False, verbose_eval=True):
     """callback function for xgboost to support multiple custom evaluation functions"""
+    # pylint: disable=import-outside-toplevel
     from xgboost.core import EarlyStopException
     from xgboost.callback import _fmt_metric
     from xgboost.training import aggcv

python/tvm/build_module.py

@@ -467,7 +467,7 @@ def _build_for_device(flist, target, target_host):
             func = ir_pass.InferFragment(func)
             warp_size = target.thread_warp_size
             func = ir_pass.LowerThreadAllreduce(func, warp_size)
-            fsplits = [s for s in ir_pass.SplitHostDevice(func)]
+            fsplits = list(ir_pass.SplitHostDevice(func))
             fhost.append(fsplits[0])
             for x in fsplits[1:]:
                 fdevice.append(x)

python/tvm/contrib/cc.py

@@ -76,8 +76,7 @@ def get_target_by_dump_machine(compiler):
                 msg += py_str(out)
                 return None
             return py_str(out)
-        else:
-            return None
+        return None
 
     return get_target_triple
 

python/tvm/contrib/dlpack.py

@@ -54,6 +54,7 @@ def to_pytorch_func(tvm_func):
     wrapped_func: Function
         Wrapped tvm function that operates on PyTorch tensors
     """
+    # pylint: disable=import-outside-toplevel
     import torch
     import torch.utils.dlpack
     return convert_func(tvm_func, torch.Tensor, torch.utils.dlpack.to_dlpack)

python/tvm/contrib/download.py

@@ -15,9 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 """Helper utility for downloading"""
-from __future__ import print_function
-from __future__ import absolute_import as _abs
-
 import os
 import sys
 import time
@@ -48,10 +45,8 @@ def download(url, path, overwrite=False, size_compare=False, verbose=1, retries=
     retries: int, optional
         Number of time to retry download, default at 3.
     """
-    if sys.version_info >= (3,):
-        import urllib.request as urllib2
-    else:
-        import urllib2
+    # pylint: disable=import-outside-toplevel
+    import urllib.request as urllib2
 
     if os.path.isfile(path) and not overwrite:
         if size_compare:
@@ -114,9 +109,8 @@ def download(url, path, overwrite=False, size_compare=False, verbose=1, retries=
                 if os.path.exists(tempfile):
                     os.remove(tempfile)
                 raise err
-            else:
-                print("download failed due to {}, retrying, {} attempt{} left"
-                      .format(repr(err), retries, 's' if retries > 1 else ''))
+            print("download failed due to {}, retrying, {} attempt{} left"
+                  .format(repr(err), retries, 's' if retries > 1 else ''))
 
 
 if "TEST_DATA_ROOT_PATH" in os.environ:

python/tvm/contrib/mxnet.py

@@ -49,7 +49,7 @@ def to_mxnet_func(func, const_loc=None):
         Run asynchrously in MXNet's async engine.
     """
     # only import mxnet when wrap get called.
-    # pylint: disable=import-self
+    # pylint: disable=import-self, import-outside-toplevel
     import mxnet
     if isinstance(func, Module):
         func = func.entry_func

python/tvm/contrib/util.py

@@ -15,7 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 """Common system utilities"""
-from __future__ import absolute_import as _abs
 import os
 import tempfile
 import shutil
@@ -167,35 +166,3 @@ def which(exec_name):
         if os.path.isfile(full_path) and os.access(full_path, os.X_OK):
             return full_path
     return None
-
-def get_lower_ir(s):
-    """Get lower ir code of a schedule.
-    This is useful for debug, since you don't have to find all inputs/outputs
-    for a schedule in a fused subgraph.
-
-    Parameters
-    ----------
-    s: Schedule
-
-    Returns
-    -------
-    ir: str
-        The lower ir
-    """
-    from .. import tensor
-    from ..build_module import lower
-
-    outputs = s.outputs
-
-    inputs = []
-    def find_all(op):
-        if isinstance(op, tensor.PlaceholderOp):
-            inputs.append(op.output(0))
-        else:
-            for x in op.input_tensors:
-                find_all(x.op)
-
-    for out in outputs:
-        find_all(out)
-
-    return lower(s, inputs, simple_mode=True)

python/tvm/hybrid/__init__.py

@@ -50,7 +50,8 @@ def script(pyfunc):
     hybrid_func : function
         A decorated hybrid script function.
     """
-    def wrapped_func(func, *args, **kwargs): #pylint: disable=missing-docstring
+    # pylint: disable=import-outside-toplevel, missing-docstring
+    def wrapped_func(func, *args, **kwargs):
         from .util import _is_tvm_arg_types
         if _is_tvm_arg_types(args):
             src = _pruned_source(func)

python/tvm/hybrid/calls.py

@@ -69,6 +69,7 @@ def bind(func_id, args):
 
 
 def _math_intrin(func_id, args):
+    # pylint: disable=import-outside-toplevel
     from .. import intrin
     return getattr(intrin, func_id)(*args)
 

python/tvm/hybrid/parser.py

@@ -198,7 +198,7 @@ class HybridParser(ast.NodeVisitor):
             ty, entry = self.symbols[key] #pylint: disable=invalid-name
             if ty in [Symbol.Input, Symbol.OutputBuffer]:
                 continue
-            elif 'Buffer' in ty.name:
+            if 'Buffer' in ty.name:
                 _buf = entry
                 _scope = 'global' if ty is Symbol.BufferVar else ty.name[:-6].lower()
                 to_pop.append(key)

python/tvm/hybrid/util.py

@@ -70,6 +70,7 @@ def _pruned_source(func):
 
 def replace_io(body, rmap):
     """Replacing tensors usage according to the dict given"""
+    # pylint: disable=import-outside-toplevel
     from .. import ir_pass
 
     def replace(op):

python/tvm/relay/_parser.py

@@ -78,7 +78,7 @@ class ParseError(Exception):
 
 class OpWrapper:
     """Overload the __call__ for op."""
-    pass
+
 
 class ExprOp(OpWrapper):
     """Call an expr. The default, but does not handle attrs well."""
@@ -273,7 +273,7 @@ class ParseTreeToRelayIR(RelayVisitor):
     def _type_expr_name(self, e):
         if isinstance(e, adt.Constructor):
             return "`{0}` ADT constructor".format(e.belong_to.name_hint)
-        elif isinstance(e, ty.GlobalTypeVar):
+        if isinstance(e, ty.GlobalTypeVar):
             if e.kind == ty.Kind.AdtHandle:
                 return "ADT definition"
         return "function definition"
@@ -623,7 +623,7 @@ class ParseTreeToRelayIR(RelayVisitor):
     def call(self, func, args, attrs, type_args):
         if isinstance(func, OpWrapper):
             return func(args, attrs, type_args)
-        elif isinstance(func, adt.Constructor):
+        if isinstance(func, adt.Constructor):
             return func(*args)
         return expr.Call(func, args, attrs, type_args)
 

python/tvm/relay/analysis.py

@@ -384,7 +384,7 @@ def detect_feature(a, b=None):
     """
     if isinstance(a, Module):
         a, b = b, a
-    return set([Feature(int(x)) for x in _analysis.detect_feature(a, b)])
+    return {Feature(int(x)) for x in _analysis.detect_feature(a, b)}
 
 
 def structural_hash(value):

python/tvm/relay/backend/_backend.py

@@ -44,8 +44,9 @@ def lower(sch, inputs, func_name, source_func):
     lowered_funcs : List[tvm.LoweredFunc]
         The result of lowering.
     """
+    # pylint: disable=broad-except, import-outside-toplevel
     import traceback
-    # pylint: disable=broad-except
+
     try:
         f = _build.lower(sch, inputs, name=func_name)
         # logging.debug("lower function %s", func_name)

python/tvm/relay/backend/compile_engine.py

@@ -86,7 +86,7 @@ class CompileEngine(Object):
         cached_func: CachedFunc
             The result of lowering.
         """
-        # pylint: disable=broad-except
+        # pylint: disable=broad-except, import-outside-toplevel
         try:
             key = _get_cache_key(source_func, target)
             return _backend._CompileEngineLower(self, key)

python/tvm/relay/build_module.py

@@ -407,7 +407,6 @@ def create_executor(kind="debug",
         return _interpreter.Interpreter(mod, ctx, target)
     if kind == "graph":
         return GraphExecutor(mod, ctx, target)
-    elif kind == "vm":
+    if kind == "vm":
         return VMExecutor(mod, ctx, target)
-    else:
-        raise RuntimeError("unknown execution strategy: {0}".format(kind))
+    raise RuntimeError("unknown execution strategy: {0}".format(kind))

python/tvm/relay/debug.py

@@ -20,7 +20,7 @@ from __future__ import absolute_import
 from ..api import register_func
 
 
-# pylint: disable=unused-argument
+# pylint: disable=unused-argument, import-outside-toplevel
 def _debugger_init(expr, stack):
     import pdb
     pdb.set_trace()

python/tvm/relay/expr.py

@@ -125,8 +125,7 @@ class Expr(RelayNode):
     def __rsub__(self, other):
         if isinstance(other, _Number):
             raise TypeError('convert "%s" with `const` first' % str(other))
-        else:
-            raise TypeError("type %s not supported" % str(type(other)))
+        raise TypeError("type %s not supported" % str(type(other)))
 
     def __mul__(self, other):
         if isinstance(other, Expr):
@@ -150,8 +149,7 @@ class Expr(RelayNode):
     def __rdiv__(self, other):
         if isinstance(other, _Number):
             raise TypeError('convert "%s" with `const` first' % str(other))
-        else:
-            raise TypeError("type %s not supported" % str(type(other)))
+        raise TypeError("type %s not supported" % str(type(other)))
 
     def __truediv__(self, other):
         return self.__div__(other)

python/tvm/relay/frontend/caffe2.py

@@ -401,6 +401,7 @@ class Caffe2NetDef(object):
         params : dict
             A dict of name: tvm.nd.array pairs, used as pretrained weights
         """
+        # pylint: disable=import-outside-toplevel
         from caffe2.python import workspace
         workspace.RunNetOnce(init_net)
 

python/tvm/relay/frontend/common.py

@@ -302,7 +302,7 @@ class ExprTable(object):
             self.exprs[name] = expr
 
     def has_expr(self, name):
-        return True if name in self.exprs else False
+        return name in self.exprs
 
     def set_padding(self, paddings):
         self.paddings = paddings
@@ -391,7 +391,7 @@ class AttrCvt(object):
             if k in self._excludes:
                 raise NotImplementedError('Attribute %s in operator %s is not' +
                                           ' supported.', k, op_name)
-            elif k in self._disables:
+            if k in self._disables:
                 logging.warning("Attribute %s is disabled in relay.sym.%s", k, op_name)
             elif k in self._ignores:
                 if k != 'tvm_custom':
@@ -485,6 +485,7 @@ def infer_value(input_val, params):
     portion of the relay graph. This is often needed for functions that
     whose output shape depends on the value of a tensor.
     """
+    # pylint: disable=import-outside-toplevel
     from tvm.contrib import graph_runtime
     # Check that all free variables have associated parameters.
     assert all(var.name_hint in params.keys() for var in analysis.free_vars(

python/tvm/relay/frontend/coreml.py

@@ -14,7 +14,8 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, import-self, unused-argument, unused-variable, inconsistent-return-statements
+# pylint: disable=invalid-name, import-self, unused-argument, unused-variable
+# pylint: disable=inconsistent-return-statements, import-outside-toplevel
 """CoreML frontend."""
 from __future__ import absolute_import as _abs
 import math
@@ -111,14 +112,13 @@ def _BatchnormLayerParams(op, inexpr, etab):
     if op.instanceNormalization:
         raise tvm.error.OpNotImplemented(
             'Operator "instance normalization" is not supported in frontend CoreML.')
-    else:
-        params = {'gamma':etab.new_const(list(op.gamma.floatValue)),
-                  'beta':etab.new_const(list(op.beta.floatValue)),
-                  'moving_mean':etab.new_const(list(op.mean.floatValue)),
-                  'moving_var': etab.new_const(list(op.variance.floatValue)),
-                  'epsilon': op.epsilon}
-        result, moving_mean, moving_var = _op.nn.batch_norm(data=inexpr, **params)
-        return result
+    params = {'gamma':etab.new_const(list(op.gamma.floatValue)),
+              'beta':etab.new_const(list(op.beta.floatValue)),
+              'moving_mean':etab.new_const(list(op.mean.floatValue)),
+              'moving_var': etab.new_const(list(op.variance.floatValue)),
+              'epsilon': op.epsilon}
+    result, moving_mean, moving_var = _op.nn.batch_norm(data=inexpr, **params)
+    return result
 
 
 def _ActivationParams(op, inexpr, etab):
@@ -197,37 +197,36 @@ def _PoolingLayerParams(op, inexpr, etab):
         raise tvm.error.OpNotImplemented(
             'Only Max and Average Pooling are supported in frontend CoreML.')
 
-    else:
-        params = {'pool_size':list(op.kernelSize),
-                  'strides':list(op.stride)}
-
-        if op.WhichOneof('PoolingPaddingType') == 'valid':
-            valid = op.valid
-            if valid.paddingAmounts.borderAmounts:
-                assert len(valid.paddingAmounts.borderAmounts) == 2
-                pad_t = valid.paddingAmounts.borderAmounts[0].startEdgeSize
-                pad_l = valid.paddingAmounts.borderAmounts[1].startEdgeSize
-                pad_b = valid.paddingAmounts.borderAmounts[0].endEdgeSize
-                pad_r = valid.paddingAmounts.borderAmounts[1].endEdgeSize
-                if not all(v == 0 for v in (pad_t, pad_l, pad_b, pad_r)):
-                    params['padding'] = [pad_t, pad_l, pad_b, pad_r]
-        elif op.WhichOneof('PoolingPaddingType') == 'includeLastPixel':
-            # I don't know if this is correct
-            valid = op.includeLastPixel
-            padding = list(valid.paddingAmounts)
-            params['padding'] = padding
-            params['ceil_mode'] = True
-        else:
-            msg = 'PoolingPaddingType {} is not supported in operator Pooling.'
-            op_name = op.WhichOneof('PoolingPaddingType')
-            raise tvm.error.OpAttributeUnImplemented(msg.format(op_name))
+    params = {'pool_size':list(op.kernelSize),
+              'strides':list(op.stride)}
 
-        if op.type == 0:
-            return _op.nn.max_pool2d(inexpr, **params)
-        if op.type == 1:
-            return _op.nn.avg_pool2d(inexpr, **params)
-        raise tvm.error.OpNotImplemented(
-            'Only Max and Average Pooling are supported in CoreML.')
+    if op.WhichOneof('PoolingPaddingType') == 'valid':
+        valid = op.valid
+        if valid.paddingAmounts.borderAmounts:
+            assert len(valid.paddingAmounts.borderAmounts) == 2
+            pad_t = valid.paddingAmounts.borderAmounts[0].startEdgeSize
+            pad_l = valid.paddingAmounts.borderAmounts[1].startEdgeSize
+            pad_b = valid.paddingAmounts.borderAmounts[0].endEdgeSize
+            pad_r = valid.paddingAmounts.borderAmounts[1].endEdgeSize
+            if not all(v == 0 for v in (pad_t, pad_l, pad_b, pad_r)):
+                params['padding'] = [pad_t, pad_l, pad_b, pad_r]
+    elif op.WhichOneof('PoolingPaddingType') == 'includeLastPixel':
+        # I don't know if this is correct
+        valid = op.includeLastPixel
+        padding = list(valid.paddingAmounts)
+        params['padding'] = padding
+        params['ceil_mode'] = True
+    else:
+        msg = 'PoolingPaddingType {} is not supported in operator Pooling.'
+        op_name = op.WhichOneof('PoolingPaddingType')
+        raise tvm.error.OpAttributeUnImplemented(msg.format(op_name))
+
+    if op.type == 0:
+        return _op.nn.max_pool2d(inexpr, **params)
+    if op.type == 1:
+        return _op.nn.avg_pool2d(inexpr, **params)
+    raise tvm.error.OpNotImplemented(
+        'Only Max and Average Pooling are supported in CoreML.')
 
 
 def _SoftmaxLayerParams(op, inexpr, etab):
@@ -297,10 +296,8 @@ def _PaddingLayerParams(op, inexpr, etab):
                                                   (0, 0),
                                                   (pad_t, pad_b),
                                                   (pad_l, pad_r)))
-
-    else:
-        raise tvm.error.OpNotImplemented(
-            'Non-constant padding is not supported in frontend CoreML.')
+    raise tvm.error.OpNotImplemented(
+        'Non-constant padding is not supported in frontend CoreML.')
 
 
 def _PermuteLayerParams(op, inexpr, etab):

python/tvm/relay/frontend/keras.py

@@ -14,9 +14,8 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, import-self
+# pylint: disable=invalid-name, import-self, import-outside-toplevel
 """Keras frontend."""
-from __future__ import absolute_import as _abs
 import sys
 import numpy as np
 import tvm
@@ -133,7 +132,7 @@ def _convert_advanced_activation(inexpr, keras_layer, etab):
             # f(x) = max_value, for x >= max_value
             # f(x) = x,         for threshold <= x < max_value
             return _op.clip(inexpr, a_min=0., a_max=float(keras_layer.max_value))
-        elif keras_layer.max_value and _op.greater(threshold, inexpr).astype('float32'):
+        if keras_layer.max_value and _op.greater(threshold, inexpr).astype('float32'):
             # f(x) = negative_slope * (inexpr - threshold)
             negative_slope = _expr.const(keras_layer.negative_slope, dtype='float32')
             return _op.multiply(negative_slope, _op.subtract(inexpr, threshold))

python/tvm/relay/frontend/nnvm_common.py

@@ -16,15 +16,13 @@
 # under the License.
 # pylint: disable=invalid-name, import-self, len-as-condition
 """Utility functions common to NNVM and MxNet conversion."""
-from __future__ import absolute_import as _abs
-
+import warnings
 from .. import expr as _expr
 from .. import op as _op
 from .common import get_relay_op
 from .common import infer_type as _infer_type
 
 def _warn_not_used(attr, op='nnvm'):
-    import warnings
     err = "{} is ignored in {}.".format(attr, op)
     warnings.warn(err)
 

python/tvm/relay/frontend/onnx.py

@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 # pylint: disable=invalid-name, import-self, len-as-condition, unused-argument, too-many-lines
+# pylint: disable=import-outside-toplevel
 """ONNX: Open Neural Network Exchange frontend for Relay."""
 from __future__ import absolute_import as _abs
 

python/tvm/relay/frontend/tensorflow.py

@@ -16,6 +16,7 @@
 # specific language governing permissions and limitations
 # under the License.
 # pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel
 """TF: Tensorflow frontend."""
 from __future__ import absolute_import as _abs
 from __future__ import print_function

python/tvm/relay/frontend/tensorflow_parser.py

@@ -15,8 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 """TF: Tensorflow parser"""
-from __future__ import absolute_import as _abs
-from __future__ import print_function
+# pylint: disable=import-outside-toplevel, assignment-from-no-return
+
 import os
 from tvm.contrib import util
 

python/tvm/relay/frontend/tflite.py

@@ -14,7 +14,8 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, unused-argument, too-many-lines
+# pylint: disable=invalid-name, unused-argument, too-many-lines, import-outside-toplevel
+
 """Tensorflow lite frontend."""
 from __future__ import absolute_import as _abs
 import math
@@ -1458,8 +1459,7 @@ class OperatorConverter(object):
                 raise tvm.error.OpNotImplemented(
                     'Operator {} with fused activation is not supported yet.'
                     .format('qnn.op.pool2d'))
-            else:
-                out = self.convert_fused_activation_function(out, fused_activation_fn)
+            out = self.convert_fused_activation_function(out, fused_activation_fn)
         return out
 
     def convert_pad(self, op):

python/tvm/relay/op/__init__.py

@@ -46,6 +46,7 @@ from ..base import register_relay_node
 
 
 def _register_op_make():
+    # pylint: disable=import-outside-toplevel
     from . import _make
     from .. import expr
     expr._op_make = _make

python/tvm/relay/op/_transform.py

@@ -200,13 +200,12 @@ def take_shape_func(attrs, inputs, out_ndims):
     """
     if attrs.axis is None:
         return [_take_no_axis_shape_func(inputs[1], out_ndims[0])]
-    else:
-        axis = get_const_int(attrs.axis)
-        data_ndim = int(inputs[0].shape[0])
-        if axis < 0:
-            axis += data_ndim
-        assert 0 <= axis < data_ndim
-        return [_take_with_axis_shape_func(*inputs, convert(axis), out_ndims[0])]
+    axis = get_const_int(attrs.axis)
+    data_ndim = int(inputs[0].shape[0])
+    if axis < 0:
+        axis += data_ndim
+    assert 0 <= axis < data_ndim
+    return [_take_with_axis_shape_func(*inputs, convert(axis), out_ndims[0])]
 
 @script
 def _argwhere_shape_func_1d(condition):
@@ -275,13 +274,13 @@ def argwhere_shape_func(attrs, inputs, out_ndims):
     """
     if len(inputs[0].shape) == 1:
         return [_argwhere_shape_func_1d(inputs[0])]
-    elif len(inputs[0].shape) == 2:
+    if len(inputs[0].shape) == 2:
         return [_argwhere_shape_func_2d(inputs[0])]
-    elif len(inputs[0].shape) == 3:
+    if len(inputs[0].shape) == 3:
         return [_argwhere_shape_func_3d(inputs[0])]
-    elif len(inputs[0].shape) == 4:
+    if len(inputs[0].shape) == 4:
         return [_argwhere_shape_func_4d(inputs[0])]
-    elif len(inputs[0].shape) == 5:
+    if len(inputs[0].shape) == 5:
         return [_argwhere_shape_func_5d(inputs[0])]
     return ValueError("Does not support rank higher than 5 in argwhere")
 

python/tvm/relay/op/nn/_nn.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=no-else-return, invalid-name, unused-argument, too-many-arguments
+# pylint: disable=no-else-return, invalid-name, unused-argument, too-many-arguments, consider-using-in
 """Backend compiler related feature registration"""
 from __future__ import absolute_import
 
@@ -265,6 +265,7 @@ def schedule_conv2d(attrs, outs, target):
 @reg.register_alter_op_layout("nn.conv2d")
 def alter_op_layout_conv2d(attrs, inputs, tinfos):
     """Alternate the layout of conv2d"""
+    # pylint: disable=import-outside-toplevel
     from ... import op
     return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, op)
 
@@ -309,7 +310,7 @@ def convert_conv2d(attrs, inputs, tinfos, desired_layout):
     result : tvm.relay.Expr
         The transformed expr
     """
-
+    # pylint: disable=import-outside-toplevel
     from tvm import relay
     data_layout = attrs['data_layout']
     kernel_layout = attrs['kernel_layout']

python/tvm/relay/op/transform.py

@@ -14,6 +14,8 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+
+# pylint: disable=import-outside-toplevel
 """Transform operators."""
 
 from . import _make

python/tvm/relay/parser.py

@@ -22,6 +22,7 @@ from .. import register_func
 @register_func("relay.fromtext")
 def fromtext(data, source_name=None):
     """Parse a Relay program."""
+    # pylint: disable=import-outside-toplevel
     from tvm.relay import _parser
     x = _parser.fromtext(data + "\n", source_name)
     if x is None:

python/tvm/relay/qnn/op/op.py

@@ -16,7 +16,7 @@
 # under the License.
 #pylint: disable=unused-argument
 """The register functions for the QNN dialect."""
-from tvm.relay.op.op import register as register
+from tvm.relay.op.op import register
 
 def register_qnn_legalize(op_name, legal_op=None, level=10):
     """Register legal transformation function for a QNN op

python/tvm/relay/quantize/_partition.py

@@ -88,7 +88,7 @@ def add_partition_generic(ref_call, new_args, ctx):
         lhs = new_args[0].realize()
         rhs = new_args[1].realize()
         return _forward_op(ref_call, [lhs, rhs])
-    elif not lhs_cond and rhs_cond:
+    if not lhs_cond and rhs_cond:
         # - introduced by residual connection in ResNet
         #     ...
         #     %13 = nn.conv2d(%12, %meta[relay.Constant])
@@ -104,7 +104,7 @@ def add_partition_generic(ref_call, new_args, ctx):
         #     ...
         rhs = new_args[1].realize()
         return _forward_op(ref_call, [lhs, rhs])
-    elif lhs_cond and not rhs_cond:
+    if lhs_cond and not rhs_cond:
         if _analysis.check_constant(rhs):
             # - introduced by batch_norm: add(out, bias)
             return QPartitionExpr(_forward_op(ref_call, [lhs, rhs]))
@@ -121,11 +121,11 @@ def add_partition_generic(ref_call, new_args, ctx):
         #     ...
         lhs = new_args[0].realize()
         return _forward_op(ref_call, [lhs, rhs])
-    elif not lhs_cond and not rhs_cond:
+    if not lhs_cond and not rhs_cond:
         # trivial case
         return None
-    else:
-        raise ValueError
+
+    raise ValueError
 
 
 # TODO(ziheng) enhance `register_partition_function` to dispatch

python/tvm/relay/quantize/quantize.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-#pylint: disable=unused-argument
+#pylint: disable=unused-argument, not-context-manager
 """Automatic quantization toolkit."""
 from __future__ import absolute_import
 from . import _quantize

python/tvm/relay/scope_builder.py

@@ -41,8 +41,7 @@ class WithScope(object):
     def __exit__(self, ptype, value, trace):
         if value:
             raise value
-        else:
-            self._exit_cb()
+        self._exit_cb()
 
 def _make_lets(bindings, ret_value):
     """Make a nested let expressions.

python/tvm/relay/testing/darknet.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, unused-variable, unused-argument, no-init
+# pylint: disable=invalid-name, unused-variable, unused-argument, no-init, unpacking-non-sequence
 """
 Compile DarkNet Models
 ====================

python/tvm/relay/testing/resnet.py

@@ -85,24 +85,25 @@ def residual_unit(data,
                 data=act1, channels=num_filter, kernel_size=(1, 1),
                 strides=stride, name=name+'_sc')
         return relay.add(conv3, shortcut)
+
+    bn1 = layers.batch_norm_infer(data=data, epsilon=2e-5, name=name + '_bn1')
+    act1 = relay.nn.relu(data=bn1)
+    conv1 = layers.conv2d(
+        data=act1, channels=num_filter, kernel_size=(3, 3),
+        strides=stride, padding=(1, 1), name=name + '_conv1')
+    bn2 = layers.batch_norm_infer(data=conv1, epsilon=2e-5, name=name + '_bn2')
+    act2 = relay.nn.relu(data=bn2)
+    conv2 = layers.conv2d(
+        data=act2, channels=num_filter, kernel_size=(3, 3),
+        strides=(1, 1), padding=(1, 1), name=name + '_conv2')
+
+    if dim_match:
+        shortcut = data
     else:
-        bn1 = layers.batch_norm_infer(data=data, epsilon=2e-5, name=name + '_bn1')
-        act1 = relay.nn.relu(data=bn1)
-        conv1 = layers.conv2d(
-            data=act1, channels=num_filter, kernel_size=(3, 3),
-            strides=stride, padding=(1, 1), name=name + '_conv1')
-        bn2 = layers.batch_norm_infer(data=conv1, epsilon=2e-5, name=name + '_bn2')
-        act2 = relay.nn.relu(data=bn2)
-        conv2 = layers.conv2d(
-            data=act2, channels=num_filter, kernel_size=(3, 3),
-            strides=(1, 1), padding=(1, 1), name=name + '_conv2')
-        if dim_match:
-            shortcut = data
-        else:
-            shortcut = layers.conv2d(
-                data=act1, channels=num_filter, kernel_size=(1, 1),
-                strides=stride, name=name+'_sc')
-        return relay.add(conv2, shortcut)
+        shortcut = layers.conv2d(
+            data=act1, channels=num_filter, kernel_size=(1, 1),
+            strides=stride, name=name+'_sc')
+    return relay.add(conv2, shortcut)
 
 
 def resnet(units,

python/tvm/relay/testing/tf.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, unused-variable, unused-argument, no-init
+# pylint: disable=invalid-name, unused-variable, unused-argument, no-init, import-outside-toplevel
 """
 Tensorflow Model Helpers
 ========================
@@ -346,7 +346,7 @@ def get_workload_ptb():
     sample_data_file = 'simple-examples.tgz'
     sample_url = sample_repo+sample_data_file
     ptb_model_file = 'RNN/ptb/ptb_model_with_lstmblockcell.pb'
-
+    # pylint: disable=import-outside-toplevel
     import tarfile
     file_path = download_testdata(sample_url, sample_data_file, module=['data', 'ptb_data'])
     dir_path = os.path.dirname(file_path)

python/tvm/relay/testing/yolo_detection.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, unused-variable, unused-argument, no-init
+# pylint: disable=invalid-name, unused-variable, unused-argument, no-init,
 """
 Yolo detection boxes helper functions
 ====================
@@ -224,6 +224,7 @@ def _draw_label(im, r, c, label, rgb):
                         _set_pixel(im, i+c, j+r, k, val)#rgb[k] * val)
 
 def _get_label(font_path, labelstr, rgb):
+    # pylint: disable=import-outside-toplevel
     from PIL import Image
     from PIL import ImageDraw
     from PIL import ImageFont

python/tvm/rpc/proxy.py

@@ -508,8 +508,7 @@ class Proxy(object):
             except socket.error as sock_err:
                 if sock_err.errno in [98, 48]:
                     continue
-                else:
-                    raise sock_err
+                raise sock_err
         if not self.port:
             raise ValueError("cannot bind to any port in [%d, %d)" % (port, port_end))
         logging.info("RPCProxy: client port bind to %s:%d", host, self.port)
@@ -569,7 +568,7 @@ def websocket_proxy_server(url, key=""):
         magic = struct.unpack('<i', msg[:4])[0]
         if magic == base.RPC_CODE_DUPLICATE:
             raise RuntimeError("key: %s has already been used in proxy" % key)
-        elif magic == base.RPC_CODE_MISMATCH:
+        if magic == base.RPC_CODE_MISMATCH:
             logging.info("RPCProxy do not have matching client key %s", key)
         elif magic != base.RPC_CODE_SUCCESS:
             raise RuntimeError("%s is not RPC Proxy" % url)

python/tvm/rpc/server.py

@@ -161,11 +161,10 @@ def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr):
                 conn.close()
                 logger.warning("mismatch key from %s", addr)
                 continue
-            else:
-                conn.sendall(struct.pack("<i", base.RPC_CODE_SUCCESS))
-                conn.sendall(struct.pack("<i", len(server_key)))
-                conn.sendall(server_key.encode("utf-8"))
-                return conn, addr, _parse_server_opt(arr[1:])
+            conn.sendall(struct.pack("<i", base.RPC_CODE_SUCCESS))
+            conn.sendall(struct.pack("<i", len(server_key)))
+            conn.sendall(server_key.encode("utf-8"))
+            return conn, addr, _parse_server_opt(arr[1:])
 
     # Server logic
     tracker_conn = None
@@ -208,6 +207,7 @@ def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr):
         server_proc.join(opts.get("timeout", None))
         if server_proc.is_alive():
             logger.info("Timeout in RPC session, kill..")
+            # pylint: disable=import-outside-toplevel
             import psutil
             parent = psutil.Process(server_proc.pid)
             # terminate worker childs
@@ -233,7 +233,8 @@ def _connect_proxy_loop(addr, key, load_library):
             magic = struct.unpack("<i", base.recvall(sock, 4))[0]
             if magic == base.RPC_CODE_DUPLICATE:
                 raise RuntimeError("key: %s has already been used in proxy" % key)
-            elif magic == base.RPC_CODE_MISMATCH:
+
+            if magic == base.RPC_CODE_MISMATCH:
                 logger.warning("RPCProxy do not have matching client key %s", key)
             elif magic != base.RPC_CODE_SUCCESS:
                 raise RuntimeError("%s is not RPC Proxy" % str(addr))
@@ -380,8 +381,7 @@ class Server(object):
                 except socket.error as sock_err:
                     if sock_err.errno in [98, 48]:
                         continue
-                    else:
-                        raise sock_err
+                    raise sock_err
             if not self.port:
                 raise ValueError("cannot bind to any port in [%d, %d)" % (port, port_end))
             logger.info("bind to %s:%d", host, self.port)

python/tvm/rpc/tornado_util.py

@@ -92,8 +92,8 @@ class TCPHandler(object):
             except socket.error as err:
                 if err.args[0] in (errno.EAGAIN, errno.EWOULDBLOCK):
                     break
-                else:
-                    self.on_error(err)
+                self.on_error(err)
+
         if self._pending_write:
             self._ioloop.update_handler(
                 self._sock.fileno(), self._ioloop.READ | self._ioloop.ERROR | self._ioloop.WRITE)

python/tvm/rpc/tracker.py

@@ -393,8 +393,7 @@ class Tracker(object):
             except socket.error as sock_err:
                 if sock_err.errno in [98, 48]:
                     continue
-                else:
-                    raise sock_err
+                raise sock_err
         if not self.port:
             raise ValueError("cannot bind to any port in [%d, %d)" % (port, port_end))
         logger.info("bind to %s:%d", host, self.port)

python/tvm/runtime/module.py

@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# pylint: disable=invalid-name, unused-import
+# pylint: disable=invalid-name, unused-import, import-outside-toplevel
 """Runtime Module namespace."""
 import ctypes
 import struct

python/tvm/runtime/ndarray.py

@@ -184,7 +184,7 @@ class NDArray(NDArrayBase):
         """
         if isinstance(target, NDArrayBase):
             return self._copyto(target)
-        elif isinstance(target, TVMContext):
+        if isinstance(target, TVMContext):
             res = empty(self.shape, self.dtype, target)
             return self._copyto(res)
         raise ValueError("Unsupported target type %s" % str(type(target)))

python/tvm/tensor.py

@@ -179,7 +179,6 @@ class BaseComputeOp(Operation):
 @tvm._ffi.register_object
 class ComputeOp(BaseComputeOp):
     """Scalar operation."""
-    pass
 
 
 @tvm._ffi.register_object

python/tvm/tensor_intrin.py

@@ -112,7 +112,7 @@ def decl_tensor_intrin(op,
         raise TypeError("expect Operation")
     inputs = op.input_tensors
     binds = binds if binds else {}
-    tensors = [x for x in inputs]
+    tensors = list(inputs)
     for i in range(op.num_outputs):
         tensors.append(op.output(i))
 

topi/python/topi/arm_cpu/conv2d.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, unused-variable, no-else-return, unused-argument
+# pylint: disable=invalid-name, unused-variable, no-else-return, unused-argument, import-outside-toplevel
 """Conv2D schedule for ARM CPU"""
 from __future__ import absolute_import as _abs
 
@@ -528,8 +528,7 @@ def _alter_conv2d_layout_arm(attrs, inputs, tinfos, F):
     Unlike other TOPI functions, this function operates on both graph level and operator level,
     so we have to pass 'F' to make it support our two versions of graph IR,  Relay.
     """
-    copy_inputs = [s for s in inputs]
-
+    copy_inputs = list(inputs)
     new_attrs = {k: attrs[k] for k in attrs.keys()}
 
     if F.__name__ == 'tvm.relay.op':

topi/python/topi/bifrost/conv2d.py

@@ -74,8 +74,7 @@ def conv2d_bifrost(cfg, data, kernel, strides, padding, dilation, layout, out_dt
     if layout == 'NCHW':
         return conv2d_spatial_pack_nchw(cfg, data, kernel, strides, padding,
                                         dilation, out_dtype, num_tile=3)
-    else:
-        raise ValueError("Unsupported layout {}".format(layout))
+    raise ValueError("Unsupported layout {}".format(layout))
 
 
 @autotvm.register_topi_schedule(schedule_conv2d_nchw, 'bifrost', ['direct', 'winograd'])

topi/python/topi/cuda/conv2d_winograd.py

@@ -328,7 +328,7 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, F):
     if 'cudnn' in tvm.target.current_target().libs or 'miopen' in tvm.target.current_target().libs:
         return None
 
-    copy_inputs = [s for s in inputs]
+    copy_inputs = list(inputs)
     new_attrs = {k: attrs[k] for k in attrs.keys()}
 
 

topi/python/topi/cuda/nms.py

@@ -14,7 +14,8 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, no-member, too-many-locals, too-many-arguments, too-many-statements, singleton-comparison, unused-argument
+# pylint: disable=invalid-name, no-member, too-many-locals, too-many-arguments, too-many-statements, singleton-comparison
+# pylint: disable=bad-continuation, unused-argument
 """Non-maximum suppression operator"""
 import math
 import tvm
@@ -397,7 +398,7 @@ def get_valid_counts_gpu(data, score_threshold=0, id_index=0, score_index=1):
                    name="get_valid_counts_phase_four")
     valid_count, out_tensor = \
 	tvm.extern([(batch_size,), data.shape], [data, temp_flag, temp_idx_final],
-            lambda ins, outs: get_valid_counts_ir(
+               lambda ins, outs: get_valid_counts_ir(
                 ins[0], ins[1], ins[2], outs[0], outs[1]),
             dtype=["int32", data.dtype],
             in_buffers=[data_buf, temp_flag_buf, temp_idx_buf],

topi/python/topi/cuda/rcnn/proposal.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, singleton-comparison
+# pylint: disable=invalid-name, singleton-comparison, bad-continuation
 """Proposal operator"""
 import math
 import tvm
@@ -177,7 +177,7 @@ def argsort_ir(data_buf, out_index_buf):
         with ib.for_range(0, num_bbox) as k:
             offset = start + 2 * tid + idxm(k, 2)
             with ib.if_scope(
-                tvm.all(offset + 1 < num_bbox, p_data[offset] < p_data[offset + 1])):
+                    tvm.all(offset + 1 < num_bbox, p_data[offset] < p_data[offset + 1])):
                 temp_data[0] = p_data[offset]
                 p_data[offset] = p_data[offset + 1]
                 p_data[offset + 1] = temp_data[0]

topi/python/topi/cuda/softmax.py

@@ -54,9 +54,9 @@ def schedule_softmax(outs):
 
     if len(softmax.shape) > 2:
         ops = [max_elem.op, expsum.op, softmax.op]
-        if exp != None:
+        if exp is not None:
             ops.append(exp.op)
-            
+
         for op in ops:
             s = schedule_injective_from_existing(s, op.output(0))
     else:
@@ -64,7 +64,7 @@ def schedule_softmax(outs):
         block_x = tvm.thread_axis("blockIdx.x")
         thread_x = tvm.thread_axis((0, num_thread), "threadIdx.x")
 
-        if exp != None:
+        if exp is not None:
             s[exp].bind(exp.op.axis[0], block_x)
 
         s[max_elem].bind(max_elem.op.axis[0], block_x)

topi/python/topi/cuda/sort.py

@@ -42,6 +42,7 @@ def _schedule_sort(outs):
     outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
     s = tvm.create_schedule([x.op for x in outs])
     scheduled_ops = []
+    # pylint: disable=import-outside-toplevel
     from .injective import schedule_injective_from_existing
     def traverse(op):
         if tag.is_injective(op.tag):

topi/python/topi/cuda/vision.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, unused-variable, unused-argument, no-member
+# pylint: disable=invalid-name, unused-variable, unused-argument, no-member, import-outside-toplevel
 """Schedule for vision operators"""
 from __future__ import absolute_import as _abs
 import tvm

topi/python/topi/hls/nn.py

@@ -275,7 +275,7 @@ def schedule_softmax(outs):
         raise ValueError('Tag is expected to be softmax_output or log_softmax_output. \
                          Got {0}'.format(op_tag))
 
-    if exp != None:
+    if exp is not None:
         s[exp].compute_at(s[softmax], s[softmax].op.axis[1])
 
     s[expsum].compute_at(s[softmax], s[softmax].op.axis[1])

topi/python/topi/intel_graphics/conv2d.py

@@ -38,17 +38,17 @@ from ..util import simplify, get_const_tuple
 def _get_default_config(cfg, data, kernel, strides, padding, out_dtype, is_depthwise=False):
     if is_depthwise:
         raise RuntimeError("Depthwise not supported for intel graphics.")
-    else:
-        batch_size, in_channel, height, width = get_const_tuple(data.shape)
-        out_channel, _, hkernel, _ = get_const_tuple(kernel.shape)
-        HSTR, _ = strides
 
-        ic_bn = 1
-        oc_bn, oc_bn_upper = 16, 16
-        for i in range(oc_bn_upper, 0, -1):
-            if out_channel % i == 0:
-                oc_bn = i
-                break
+    batch_size, in_channel, height, width = get_const_tuple(data.shape)
+    out_channel, _, hkernel, _ = get_const_tuple(kernel.shape)
+    HSTR, _ = strides
+
+    ic_bn = 1
+    oc_bn, oc_bn_upper = 16, 16
+    for i in range(oc_bn_upper, 0, -1):
+        if out_channel % i == 0:
+            oc_bn = i
+            break
 
         if HSTR == 2:
             if out_channel + hkernel == 515:
@@ -189,7 +189,7 @@ def __topi_nn_conv2d_NCHWc(*args, **kwargs):
 
 @conv2d_alter_layout.register(["intel_graphics"])
 def _alter_conv2d_layout(attrs, inputs, tinfo, F):
-    copy_inputs = [s for s in inputs]
+    copy_inputs = list(inputs)
     new_attrs = {k : attrs[k] for k in attrs.keys()}
 
     if F.__name__ == 'tvm.relay.op':

topi/python/topi/nn/bitserial_util.py

@@ -60,7 +60,7 @@ def bitpack(data, bits, pack_axis, bit_axis, pack_type, name="QuantizeInput"):
             for i in range(n+1):
                 if i == bit_axis:
                     continue
-                elif i == pack_axis:
+                if i == pack_axis:
                     idx[j] = indices[i] * data_width + k
                 else:
                     idx[j] = indices[i]
@@ -88,4 +88,3 @@ def binary_op_multiplier(pack_dtype):
     pack_dtype: string
         pack type for the operator (must be a uint)"""
     return int(pack_dtype[4:])
-    
\ No newline at end of file

topi/python/topi/nn/conv2d.py

@@ -66,9 +66,9 @@ def conv2d(input, filter, strides, padding, dilation, layout='NCHW', out_dtype=N
     # default declaration
     if layout == 'NCHW':
         return conv2d_nchw(input, filter, strides, padding, dilation, out_dtype)
-    elif layout == 'HWCN':
+    if layout == 'HWCN':
         return conv2d_hwcn(input, filter, strides, padding, dilation, out_dtype)
-    elif layout == 'NHWC':
+    if layout == 'NHWC':
         return conv2d_nhwc(input, filter, strides, padding, dilation, out_dtype)
     raise ValueError("not support this layout {} yet".format(layout))
 
@@ -764,6 +764,7 @@ def conv2d_winograd_nnpack_weight_transform(kernel, convolution_algorithm, out_d
     output : tvm.Tensor
         4-D with shape [alpha, alpha, CO, CI]
     """
+    # pylint: disable=import-outside-toplevel
     from tvm.contrib import nnpack
     return nnpack.convolution_inference_weight_transform(
         kernel, algorithm=convolution_algorithm, dtype=out_dtype)

topi/python/topi/nn/fifo_buffer.py

@@ -76,7 +76,7 @@ def fifo_buffer(data, buffer, axis):
                                             buffer[i + data_size],
                                             data[i - buflen + data_size]),
                            name='new_buffer')
-    elif len(buffer.shape) == 2:
+    if len(buffer.shape) == 2:
         if axis == 0:
             return tvm.compute(buffer.shape,
                                lambda i, j:

topi/python/topi/opengl/softmax.py

@@ -51,7 +51,7 @@ def schedule_softmax(outs):
         raise ValueError('Tag is expected to be softmax_output or log_softmax_output. \
                          Got {0}'.format(op_tag))
 
-    if exp != None:
+    if exp is not None:
         s[exp].opengl()
 
     s[max_elem].opengl()

topi/python/topi/testing/one_hot.py

@@ -62,7 +62,7 @@ def one_hot(indices, on_value, off_value, depth, axis, dtype):
             indices_index += 1
 
     out = np.empty(oshape)
-    output_indices = [index for index in np.ndindex(out.shape)]
+    output_indices = list(np.ndindex(out.shape))
     for output_index in output_indices:
         indices_indices = []
         for i, out_idx in enumerate(output_index):

topi/python/topi/transform.py

@@ -238,13 +238,10 @@ def strided_set(a, v, begin, end, strides=None):
         from_val = []
         index_tuple = []
         for i in range(n):
-            from_val.append(
-       	        within_index(begin[i], end[i], strides[i], indices[i]))
+            from_val.append(within_index(begin[i], end[i], strides[i], indices[i]))
             index_tuple.append(
                 make_idx(begin[i], end[i], strides[i], a.shape[i], indices[i]))
-        return tvm.if_then_else(tvm.all(*from_val),
-       	       	                v(*index_tuple),
-                                a(*indices))
+        return tvm.if_then_else(tvm.all(*from_val), v(*index_tuple), a(*indices))
 
     return tvm.compute(a.shape, _select, name="strided_set")
 
@@ -568,7 +565,7 @@ def sequence_mask(data, valid_length, mask_value=0, axis=0):
 
     assert len(data.shape) >= 2,\
         "only support data.ndim >= 2, received data.shape = {}".format(data.shape)
-    assert axis == 0 or axis == 1, "only support axis = 0, 1, received axis = {}".format(axis)
+    assert axis in (0, 1), "only support axis = 0, 1, received axis = {}".format(axis)
     return cpp.sequence_mask(data, valid_length, mask_value, axis)
 
 

topi/python/topi/util.py

@@ -25,7 +25,6 @@ from . import tag, cpp
 
 class InvalidShapeError(ValueError):
     """Invalid shape for a topi function. i.e. call winograd template for non-3x3 kernel)"""
-    pass
 
 def nchw_pack_layout(layout_info):
     """Check whether the layout type is NCHWinic"""
@@ -350,7 +349,7 @@ def get_shape(src_shape, src_layout, dst_layout):
 
     layout_mapping = bijective_layout(src_layout, dst_layout)
     dst_indices = layout_mapping.forward_index(
-        tvm.convert([i for i in range(len(src_layout))]))
+        tvm.convert(list(range(len(src_layout)))))
 
     return get_const_tuple(tuple([src_shape[i.value] for i in dst_indices]))
 

topi/python/topi/vision/rcnn/proposal.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name, singleton-comparison
+# pylint: disable=invalid-name, singleton-comparison, bad-continuation
 """Proposal operator"""
 import math
 import tvm
@@ -303,7 +303,7 @@ def prepare_output_ir(sorted_bbox_buf, remove_mask_buf, out_buf):
     with ib.for_range(0, batch) as b:
         with ib.if_scope(nkeep[b] > 0):
             with ib.for_range(0, tvm.ceil(
-                tvm.const(rpn_post_nms_top_n, 'float32') / nkeep[b]).astype('int32')):
+                    tvm.const(rpn_post_nms_top_n, 'float32') / nkeep[b]).astype('int32')):
                 with ib.for_range(0, num_bbox) as j:
                     offset_j = (b * num_bbox + j) * 5
                     offset_i = (b * rpn_post_nms_top_n + i[b]) * 5

topi/python/topi/x86/conv2d.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name,unused-variable,unused-argument,no-member
+# pylint: disable=invalid-name,unused-variable,unused-argument,no-member,import-outside-toplevel
 """Conv2D schedule on x86"""
 
 import logging
@@ -126,7 +126,7 @@ def _declaration_conv(cfg, data, kernel, strides, padding, dilation, layout, out
     #     # specialize for INT8 1X1 conv on X86
     #     return conv2d_avx_1x1._declaration_conv_nhwc_pack(cfg, data, kernel, strides,
     #                                                       padding, dilation, out_dtype)
-    elif layout == 'NHWC':
+    if layout == 'NHWC':
         return nn.conv2d_nhwc(data, kernel, strides, padding, dilation, out_dtype)
     raise ValueError("not support this layout {} yet".format(layout))
 

topi/python/topi/x86/conv2d_alter_op.py

@@ -63,7 +63,7 @@ def _alter_conv2d_layout(attrs, inputs, tinfo, F):
     is_depthwise = groups == kshape[0] and kshape[1] == 1
 
     # Save the input exprs.
-    copy_inputs = [s for s in inputs]
+    copy_inputs = list(inputs)
 
     # Set the new attrs
     new_attrs = {k : attrs[k] for k in attrs.keys()}

topi/python/topi/x86/conv2d_int8.py

@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=invalid-name,unused-variable,unused-argument,no-member
+# pylint: disable=invalid-name,unused-variable,unused-argument,no-member, import-outside-toplevel
 """Conv2D int8 schedule on x86"""
 
 import re
@@ -70,7 +70,7 @@ def _is_int8_hw_support(data_dtype, kernel_dtype):
     # 3) Check target
     mcpu = tvm.target.current_target().mcpu
     is_target_support = False
-    if mcpu == 'skylake-avx512' or mcpu == 'cascadelake':
+    if mcpu in ('skylake-avx512', 'cascadelake'):
         is_target_support = True
 
     return is_dtype_support and is_llvm_support and is_target_support

topi/python/topi/x86/nn.py

@@ -63,7 +63,7 @@ def schedule_softmax(outs):
     s[max_elem].compute_at(s[softmax], fused_outer_axes)
     s[expsum].compute_at(s[softmax], fused_outer_axes)
 
-    if exp != None:
+    if exp is not None:
         s[exp].compute_at(s[softmax], fused_outer_axes)
 
     return s

topi/python/topi/x86/util.py

@@ -21,6 +21,6 @@ import tvm
 def get_fp32_len():
     mcpu = tvm.target.current_target().mcpu
     fp32_vec_len = 8
-    if mcpu == 'skylake-avx512' or mcpu == 'cascadelake':
+    if mcpu in ('skylake-avx512', 'cascadelake'):
         fp32_vec_len = 16
     return fp32_vec_len

vta/python/vta/bitstream.py

@@ -79,11 +79,10 @@ compilation guide to get Xilinx toolchains setup) and add it to your \
 $VTA_CACHE_PATH. Alternatively edit your config.json back to its default \
 settings. You can see the list of available bitstreams under {}"
                 .format(url, BITSTREAM_URL))
-        else:
-            raise RuntimeError(
-                # This could happen when trying to access the URL behind a proxy
-                "Something went wrong when trying to access {}. Check your \
+        raise RuntimeError(
+            # This could happen when trying to access the URL behind a proxy
+            "Something went wrong when trying to access {}. Check your \
 internet connection or proxy settings."
-                .format(url))
+            .format(url))
 
     return success

vta/python/vta/environment.py

@@ -231,9 +231,9 @@ class Environment(object):
         """The target host"""
         if self.TARGET in ["pynq", "de10nano"]:
             return "llvm -target=armv7-none-linux-gnueabihf"
-        elif self.TARGET == "ultra96":
+        if self.TARGET == "ultra96":
             return "llvm -target=aarch64-linux-gnu"
-        elif self.TARGET in ["sim", "tsim"]:
+        if self.TARGET in ["sim", "tsim"]:
             return "llvm"
         raise ValueError("Unknown target %s" % self.TARGET)
 

vta/python/vta/exec/rpc_server.py

@@ -66,6 +66,7 @@ def server_start():
 
     @tvm.register_func("tvm.contrib.vta.init", override=True)
     def program_fpga(file_name):
+        # pylint: disable=import-outside-toplevel
         from pynq import xlnk
         # Reset xilinx driver
         xlnk.Xlnk().xlnk_reset()

vta/python/vta/ir_pass.py

@@ -15,9 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 """Additional IR Pass for VTA"""
-# pylint: disable=len-as-condition
-from __future__ import absolute_import as _abs
-
+# pylint: disable=len-as-condition, no-else-return
 import tvm
 from topi import util
 

vta/python/vta/program_bitstream.py

@@ -43,6 +43,7 @@ def main():
     bitstream_program(args.target, args.bitstream)
 
 def pynq_bitstream_program(bitstream_path):
+    # pylint: disable=import-outside-toplevel
     from pynq import Bitstream
     bitstream = Bitstream(bitstream_path)
     bitstream.download()

vta/python/vta/top/graphpack.py

@@ -151,14 +151,12 @@ class ExprPack(ExprMutator):
             assert not self.start_pack
             self.start_pack = True
             return _pack_batch_channel(args[0], oshape, self.bfactor, self.cfactor)
-        elif call.op == self.bitpack_end:
+        if call.op == self.bitpack_end:
             if self.start_pack:
                 self.start_pack = False
                 data = args[0]
                 data_shape = _get_shape(call.args[0])
                 return _unpack_batch_channel(data, data_shape)
-            else:
-                pass
         if self.start_pack:
             # Operator cases
             if call.op == self.conv2d and odtype == 'int32':
@@ -188,7 +186,8 @@ class ExprPack(ExprMutator):
                     kernel_layout=kernel_layout,
                     out_dtype=call.attrs.out_dtype)
                 return conv2d
-            elif call.op == self.conv2d_transpose and odtype == 'int32':
+
+            if call.op == self.conv2d_transpose and odtype == 'int32':
                 self.number_of_conv2d += 1
                 assert 8 % self.weight_bits == 0
                 w_lanes = 8 // self.weight_bits
@@ -213,7 +212,7 @@ class ExprPack(ExprMutator):
                         output_padding=call.attrs.output_padding,
                         out_dtype=call.attrs.out_dtype)
                 return conv2d
-            elif call.op == self.add and \
+            if call.op == self.add and \
                     tuple(input_types[0].shape) == tuple(input_types[1].shape):
                 pass
             elif call.op == self.add and len(input_types[1].shape) == 3:
@@ -272,7 +271,7 @@ def get_subgraph(expr, start_name, stop_name, start_name_idx, stop_name_idx, cou
                                        _recursion(anf.body, start_found, stop_found,
                                                   operator_current_idx),
                                        anf.ret_type, anf.type_params, anf.attrs)
-        elif isinstance(anf, relay.expr.Let):
+        if isinstance(anf, relay.expr.Let):
             value = anf.value
             if isinstance(value, relay.expr.Call):
                 if isinstance(value.op, relay.op.Op):

vta/python/vta/top/op.py

@@ -127,10 +127,9 @@ def compute_conv2d_transpose(attrs, inputs, output_type, target):
         if is_packed_layout(layout):
             return [topi.nn.conv2d_transpose_nchw(
                 inputs[0], inputs[1], strides, padding, out_dtype)]
-        else:
-            # If it's not packed, run on ARM CPU
-            with tvm.target.arm_cpu(tvm.target.current_target().model):
-                return _nn.compute_conv2d_transpose(attrs, inputs, output_type, target)
+        # If it's not packed, run on ARM CPU
+        with tvm.target.arm_cpu(tvm.target.current_target().model):
+            return _nn.compute_conv2d_transpose(attrs, inputs, output_type, target)
 
     # If VTA is not the target, default to _nn def
     return _nn.compute_conv2d_transpose(attrs, inputs, output_type, target)
@@ -145,10 +144,9 @@ def schedule_conv2d_transpose(attrs, outputs, target):
     if target.device_name == "vta":
         if is_packed_layout(layout):
             return topi.nn.schedule_conv2d_transpose_nchw(outputs)
-        else:
-            # If it's not packed, run on ARM CPU
-            with tvm.target.arm_cpu(tvm.target.current_target().model):
-                return _nn.schedule_conv2d_transpose(attrs, outputs, tvm.target.current_target())
+        # If it's not packed, run on ARM CPU
+        with tvm.target.arm_cpu(tvm.target.current_target().model):
+            return _nn.schedule_conv2d_transpose(attrs, outputs, tvm.target.current_target())
 
     # If VTA is not the target, default to _nn def
     return _nn.schedule_conv2d_transpose(attrs, outputs, tvm.target.current_target())

vta/scripts/tune_conv2d.py

@@ -23,7 +23,6 @@ import os
 
 import tvm
 from tvm import autotvm
-from tvm.contrib.util import get_lower_ir
 import topi
 import vta
 import vta.testing

vta/scripts/tune_conv2d_transpose.py

@@ -23,7 +23,6 @@ import os
 
 import tvm
 from tvm import autotvm
-from tvm.contrib.util import get_lower_ir
 import topi
 import vta
 import vta.testing

vta/scripts/tune_dense.py

@@ -23,7 +23,6 @@ import os
 
 import tvm
 from tvm import autotvm
-from tvm.contrib.util import get_lower_ir
 import topi
 import vta
 import vta.testing

vta/scripts/tune_group_conv2d.py

@@ -23,7 +23,6 @@ import os
 
 import tvm
 from tvm import autotvm
-from tvm.contrib.util import get_lower_ir
 import topi
 import vta
 import vta.testing