diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py index 64f7aa7070bf81..dc79776afe90d6 100755 --- a/python/paddle/nn/functional/pooling.py +++ b/python/paddle/nn/functional/pooling.py @@ -14,7 +14,7 @@ import numpy as np -from paddle import _C_ops, _legacy_C_ops, in_dynamic_mode +from paddle import _C_ops, in_dynamic_mode from paddle.base.framework import ( Variable, in_dygraph_mode, @@ -830,24 +830,6 @@ def max_unpool1d( x, indices, kernel_size, stride, padding, output_size, data_format ) return squeeze(output, [2]) - elif in_dynamic_mode(): - output = _legacy_C_ops.unpool( - x, - indices, - 'unpooling_type', - 'max', - 'ksize', - kernel_size, - 'strides', - stride, - 'paddings', - padding, - "output_size", - output_size, - "data_format", - data_format, - ) - return squeeze(output, [2]) op_type = "unpool" helper = LayerHelper(op_type, **locals()) @@ -980,24 +962,6 @@ def max_unpool2d( x, indices, kernel_size, stride, padding, output_size, data_format ) return output - elif in_dynamic_mode(): - output = _legacy_C_ops.unpool( - x, - indices, - 'unpooling_type', - 'max', - 'ksize', - kernel_size, - 'strides', - stride, - 'paddings', - padding, - "output_size", - output_size, - "data_format", - data_format, - ) - return output op_type = "unpool" helper = LayerHelper(op_type, **locals()) @@ -1127,24 +1091,6 @@ def max_unpool3d( x, indices, kernel_size, stride, padding, output_size, data_format ) return output - elif in_dynamic_mode(): - output = _legacy_C_ops.unpool3d( - x, - indices, - 'unpooling_type', - 'max', - 'ksize', - kernel_size, - 'strides', - stride, - 'paddings', - padding, - "output_size", - output_size, - "data_format", - data_format, - ) - return output op_type = "unpool3d" helper = LayerHelper(op_type, **locals()) diff --git a/python/paddle/nn/functional/vision.py b/python/paddle/nn/functional/vision.py index 3d66e03275c415..7a76f35b9589c5 100644 --- a/python/paddle/nn/functional/vision.py +++ b/python/paddle/nn/functional/vision.py @@ -88,25 +88,12 @@ def affine_grid(theta, out_shape, align_corners=True, name=None): False # ROCM platform do not have MIOPEN kernel for affine_grid ) - if in_dygraph_mode(): + if in_dynamic_mode(): _out_shape = ( out_shape.tolist() if isinstance(out_shape, Variable) else out_shape ) theta = theta._use_gpudnn(use_cudnn) return _C_ops.affine_grid(theta, _out_shape, align_corners) - elif in_dynamic_mode(): - _out_shape = ( - out_shape.tolist() if isinstance(out_shape, Variable) else out_shape - ) - return _legacy_C_ops.affine_grid( - theta, - "output_shape", - _out_shape, - "align_corners", - align_corners, - "use_cudnn", - use_cudnn, - ) elif in_pir_mode(): return _C_ops.affine_grid( theta, @@ -311,18 +298,6 @@ def grid_sample( if in_dynamic_or_pir_mode(): return _C_ops.grid_sample(x, grid, mode, padding_mode, align_corners) - elif in_dynamic_mode(): - attrs = ( - 'mode', - mode, - 'padding_mode', - padding_mode, - 'align_corners', - align_corners, - 'use_cudnn', - use_cudnn, - ) - out = _legacy_C_ops.grid_sampler(x, grid, *attrs) else: helper = LayerHelper("grid_sample", **locals()) check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'grid_sample')