style(imperative): remove absolute imports in megengine

GitOrigin-RevId: 700492906963551974b40f694949a1f80b67ab95

imperative/python/megengine/core/autodiff/grad.py

@@ -14,8 +14,6 @@ import weakref
 
 import numpy as np
 
-import megengine as mge
-
 from .._imperative_rt import core2, ops
 from ..ops.builtin import Elemwise, OpDef, RemoteSend
 from ..ops.special import Const

imperative/python/megengine/data/sampler.py

@@ -13,7 +13,7 @@ from typing import Any, Generator, Iterator, List, Union
 
 import numpy as np
 
-import megengine.distributed as dist
+from .. import distributed as dist
 
 
 class Sampler(ABC):

imperative/python/megengine/experimental/weight_scaler.py

 import types
 from functools import partial
 
-import megengine.functional as F
-import megengine.module as M
-from megengine.functional.tensor import zeros
-from megengine.utils.module_utils import set_module_mode_safe
+from .. import functional as F
+from .. import module as M
+from ..utils.module_utils import set_module_mode_safe
 
 
 def get_norm_mod_value(weight, norm_value):
@@ -81,7 +80,7 @@ def get_scaled_model(model, scale_submodel, input_shape=None):
     if input_shape is None:
         raise ValueError("input_shape is required for calculating scale value")
 
-    input = zeros(input_shape)
+    input = F.zeros(input_shape)
 
     hooks = []
     for modelname, submodel in model.named_modules():

imperative/python/megengine/utils/module_stats.py

@@ -13,24 +13,20 @@ from typing import Iterable
 import numpy as np
 import tabulate
 
-import megengine as mge
-import megengine.module as m
-import megengine.module.qat as qatm
-import megengine.module.quantized as qm
-from megengine import Tensor
-from megengine import functional as F
-from megengine.core.tensor.dtype import get_dtype_bit
-from megengine.functional.tensor import zeros
-from megengine.tensor import Tensor
-
+from .. import Tensor
+from .. import functional as F
+from .. import get_logger
+from .. import module as M
+from ..core.tensor.dtype import get_dtype_bit
+from ..logger import MegEngineLogFormatter
 from .module_utils import set_module_mode_safe
 
 try:
-    mge.logger.MegEngineLogFormatter.max_lines = float("inf")
+    MegEngineLogFormatter.max_lines = float("inf")
 except AttributeError as e:
     raise ValueError("set logger max lines failed")
 
-logger = mge.get_logger(__name__)
+logger = get_logger(__name__)
 logger.setLevel("INFO")
 
 
@@ -95,9 +91,9 @@ def disable_receptive_field():
 
 
 @register_flops(
-    m.Conv1d, m.Conv2d, m.Conv3d, m.ConvTranspose2d, m.LocalConv2d, m.DeformableConv2d
+    M.Conv1d, M.Conv2d, M.Conv3d, M.ConvTranspose2d, M.LocalConv2d, M.DeformableConv2d
 )
-def flops_convNd(module: m.Conv2d, inputs, outputs):
+def flops_convNd(module: M.Conv2d, inputs, outputs):
     bias = 1 if module.bias is not None else 0
     # N x Cout x H x W x  (Cin x Kw x Kh + bias)
     return np.prod(outputs[0].shape) * (
@@ -106,14 +102,14 @@ def flops_convNd(module: m.Conv2d, inputs, outputs):
 
 
 @register_flops(
-    m.batchnorm._BatchNorm, m.SyncBatchNorm, m.GroupNorm, m.LayerNorm, m.InstanceNorm,
+    M.batchnorm._BatchNorm, M.SyncBatchNorm, M.GroupNorm, M.LayerNorm, M.InstanceNorm,
 )
-def flops_norm(module: m.Linear, inputs, outputs):
+def flops_norm(module: M.Linear, inputs, outputs):
     return np.prod(inputs[0].shape) * 7
 
 
-@register_flops(m.AvgPool2d, m.MaxPool2d)
-def flops_pool(module: m.AvgPool2d, inputs, outputs):
+@register_flops(M.AvgPool2d, M.MaxPool2d)
+def flops_pool(module: M.AvgPool2d, inputs, outputs):
     kernel_sum = 0
     if isinstance(module.kernel_size, tuple) and len(module.kernel_size) == 2:
         kernel_sum = np.prod(module.kernel_size)
@@ -122,8 +118,8 @@ def flops_pool(module: m.AvgPool2d, inputs, outputs):
     return np.prod(outputs[0].shape) * kernel_sum
 
 
-@register_flops(m.AdaptiveAvgPool2d, m.AdaptiveMaxPool2d)
-def flops_adaptivePool(module: m.AdaptiveAvgPool2d, inputs, outputs):
+@register_flops(M.AdaptiveAvgPool2d, M.AdaptiveMaxPool2d)
+def flops_adaptivePool(module: M.AdaptiveAvgPool2d, inputs, outputs):
     stride_h = np.floor(inputs[0].shape[2] / (inputs[0].shape[2] - 1))
     kernel_h = inputs[0].shape[2] - (inputs[0].shape[2] - 1) * stride_h
     stride_w = np.floor(inputs[0].shape[3] / (inputs[0].shape[3] - 1))
@@ -131,14 +127,14 @@ def flops_adaptivePool(module: m.AdaptiveAvgPool2d, inputs, outputs):
     return np.prod(outputs[0].shape) * kernel_h * kernel_w
 
 
-@register_flops(m.Linear)
-def flops_linear(module: m.Linear, inputs, outputs):
+@register_flops(M.Linear)
+def flops_linear(module: M.Linear, inputs, outputs):
     bias = module.out_features if module.bias is not None else 0
     return np.prod(outputs[0].shape) * module.in_features + bias
 
 
-@register_flops(m.BatchMatMulActivation)
-def flops_batchmatmul(module: m.BatchMatMulActivation, inputs, outputs):
+@register_flops(M.BatchMatMulActivation)
+def flops_batchmatmul(module: M.BatchMatMulActivation, inputs, outputs):
     bias = 1 if module.bias is not None else 0
     x = inputs[0]
     w = module.weight
@@ -150,25 +146,25 @@ def flops_batchmatmul(module: m.BatchMatMulActivation, inputs, outputs):
 
 # does not need import qat and quantized module since they inherit from float module.
 hook_modules = (
-    m.conv._ConvNd,
-    m.Linear,
-    m.BatchMatMulActivation,
-    m.batchnorm._BatchNorm,
-    m.LayerNorm,
-    m.GroupNorm,
-    m.InstanceNorm,
-    m.pooling._PoolNd,
-    m.adaptive_pooling._AdaptivePoolNd,
+    M.conv._ConvNd,
+    M.Linear,
+    M.BatchMatMulActivation,
+    M.batchnorm._BatchNorm,
+    M.LayerNorm,
+    M.GroupNorm,
+    M.InstanceNorm,
+    M.pooling._PoolNd,
+    M.adaptive_pooling._AdaptivePoolNd,
 )
 
 
 def _mean(inp):
-    inp = mge.tensor(inp).astype(np.float32)
+    inp = Tensor(inp).astype(np.float32)
     return F.mean(inp).numpy()
 
 
 def _std(inp):
-    inp = mge.tensor(inp).astype(np.float32)
+    inp = Tensor(inp).astype(np.float32)
     return F.std(inp).numpy()
 
 
@@ -412,7 +408,7 @@ def print_summary(**kwargs):
 
 
 def module_stats(
-    model: m.Module,
+    model: M.Module,
     inputs: Iterable[np.ndarray] = None,
     input_shapes: list = None,
     cal_params: bool = True,
@@ -457,7 +453,7 @@ def module_stats(
         if input_shapes:
             if not isinstance(input_shapes[0], tuple):
                 input_shapes = [input_shapes]
-            inputs = [zeros(in_size, dtype=np.float32) for in_size in input_shapes]
+            inputs = [F.zeros(in_size, dtype=np.float32) for in_size in input_shapes]
         else:
             logger.error(
                 "Inputs or input_shapes is required for running model and calculating stats.",