conv.py 65.9 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13
#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
14
from __future__ import print_function
15

W
whs 已提交
16 17
__all__ = [
    'conv1d',
18
    'conv1d_transpose',
W
whs 已提交
19
    'conv2d',
20
    'conv2d_transpose',
W
whs 已提交
21
    'conv3d',
22
    'conv3d_transpose',
W
whs 已提交
23
]
24

25
import numpy as np
L
LielinJiang 已提交
26
from ...device import get_cudnn_version
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
from ...fluid.framework import Variable, in_dygraph_mode
from ...fluid import core, dygraph_utils
from ...fluid.layers import nn, utils
from ...fluid.data_feeder import check_variable_and_dtype
from ...fluid.param_attr import ParamAttr
from ...fluid.layer_helper import LayerHelper


def _is_list_or_tuple(input):
    return isinstance(input, (list, tuple))


def _zero_padding_in_batch_and_channel(padding, channel_last):
    if channel_last:
        return list(padding[0]) == [0, 0] and list(padding[-1]) == [0, 0]
    else:
        return list(padding[0]) == [0, 0] and list(padding[1]) == [0, 0]


def _exclude_padding_in_batch_and_channel(padding, channel_last):
    padding_ = padding[1:-1] if channel_last else padding[2:]
    padding_ = [elem for pad_a_dim in padding_ for elem in pad_a_dim]
    return padding_


def _update_padding_nd(padding, channel_last, num_dims):
    if isinstance(padding, str):
        padding = padding.upper()
        if padding not in ["SAME", "VALID"]:
            raise ValueError(
                "Unknown padding: '{}'. It can only be 'SAME' or 'VALID'.".
                format(padding))
        if padding == "VALID":
            padding_algorithm = "VALID"
            padding = [0] * num_dims
        else:
            padding_algorithm = "SAME"
            padding = [0] * num_dims
    elif _is_list_or_tuple(padding):
        # for padding like
        # [(pad_before, pad_after), (pad_before, pad_after), ...]
        # padding for batch_dim and channel_dim included
        if len(padding) == 2 + num_dims and _is_list_or_tuple(padding[0]):
            if not _zero_padding_in_batch_and_channel(padding, channel_last):
                raise ValueError(
                    "Non-zero padding({}) in the batch or channel dimensions "
                    "is not supported.".format(padding))
            padding_algorithm = "EXPLICIT"
            padding = _exclude_padding_in_batch_and_channel(padding,
                                                            channel_last)
            if utils._is_symmetric_padding(padding, num_dims):
                padding = padding[0::2]
        # for padding like [pad_before, pad_after, pad_before, pad_after, ...]
        elif len(padding) == 2 * num_dims and isinstance(padding[0], int):
            padding_algorithm = "EXPLICIT"
            padding = utils.convert_to_list(padding, 2 * num_dims, 'padding')
            if utils._is_symmetric_padding(padding, num_dims):
                padding = padding[0::2]
        # for padding like [pad_d1, pad_d2, ...]
        elif len(padding) == num_dims and isinstance(padding[0], int):
            padding_algorithm = "EXPLICIT"
            padding = utils.convert_to_list(padding, num_dims, 'padding')
        else:
            raise ValueError("In valid padding: {}".format(padding))
    # for integer padding
    else:
        padding_algorithm = "EXPLICIT"
        padding = utils.convert_to_list(padding, num_dims, 'padding')
    return padding, padding_algorithm


L
LielinJiang 已提交
98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
def _conv_nd(x,
             weight,
             bias=None,
             stride=1,
             padding=0,
             padding_algorithm=None,
             dilation=1,
             groups=1,
             data_format="NCHW",
             channel_dim=1,
             op_type="conv2d",
             use_cudnn=True,
             use_mkldnn=False,
             name=None):

113 114
    # Due to the poor performance of NHWC, we transpose the input to NCHW.
    origin_format = data_format
L
LielinJiang 已提交
115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
    if in_dygraph_mode():
        attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
                 'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn',
                 use_mkldnn, 'fuse_relu_before_depthwise_conv', False,
                 "padding_algorithm", padding_algorithm, "data_format",
                 data_format)
        pre_bias = getattr(core.ops, op_type)(x, weight, *attrs)
        if bias is not None:
            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
        else:
            out = pre_bias
    else:
        inputs = {'Input': [x], 'Filter': [weight]}
        attrs = {
            'strides': stride,
            'paddings': padding,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn,
            'use_mkldnn': use_mkldnn,
            'fuse_relu_before_depthwise_conv': False,
            "padding_algorithm": padding_algorithm,
            "data_format": data_format
        }
        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                                 op_type)
        helper = LayerHelper(op_type, **locals())
        dtype = helper.input_dtype(input_param_name='x')
        pre_bias = helper.create_variable_for_type_inference(dtype)
        outputs = {"Output": [pre_bias]}
        helper.append_op(
            type=op_type, inputs=inputs, outputs=outputs, attrs=attrs)
        if bias is not None:
            out = helper.create_variable_for_type_inference(dtype)
            helper.append_op(
                type='elementwise_add',
                inputs={'X': [pre_bias],
                        'Y': [bias]},
                outputs={'Out': [out]},
                attrs={'axis': channel_dim,
                       'use_mkldnn': use_mkldnn})
        else:
            out = pre_bias
    return out


W
whs 已提交
161 162 163 164 165 166 167 168 169
def conv1d(x,
           weight,
           bias=None,
           stride=1,
           padding=0,
           dilation=1,
           groups=1,
           data_format='NCL',
           name=None):
170
    r"""
W
whs 已提交
171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
    The convolution1D layer calculates the output based on the input, filter
    and strides, paddings, dilations, groups parameters. Input and
    Output are in NCL format, where N is batch size, C is the number of
    channels, L is the length of the feature.
    Filter is in MCK format, where M is the number of output image channels,
    C is the number of input image channels, K is the size of the kernel.
    If the groups is greater than 1, C will equal the number of input image
    channels divided by the groups. If bias attribution and activation type
    are provided, bias is added to the output of the convolution, and the
    corresponding activation function is applied to the final result.

    For each input :math:`X`, the equation is:

    .. math::

W
whs 已提交
186
        Out = \sigma (W \ast X + b)
W
whs 已提交
187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212

    Where:

    * :math:`X`: Input value, a tensor with NCL format.
    * :math:`W`: Kernel value, a tensor with MCK format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, L_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, L_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, L_{out})`

        Where

        .. math::

W
whs 已提交
213
            L_{out} = \frac{(L_{in} + 2 * padding - (dilation * (L_f - 1) + 1))}{stride} + 1
W
whs 已提交
214 215 216 217 218 219 220 221 222

    Args:
        x (Tensor): The input is 3-D Tensor with shape [N, C, L], the data type 
            of input is float16 or float32 or float64.
        weight (Tensor): The convolution kernel with shape [M, C/g, K], where M is
            the number of output channels, g is the number of groups, K is the kernel's size. 
        bias (Tensor, optional): The bias with shape [M,]. Default: None.
        stride (int or tuple, optional): The stride size. If stride is a tuple, it must
            contain one integers, (stride_size). Default: 1.
223
        padding(int|str|tuple|list, optional): The padding size. Padding could be in one of the following forms.
W
whs 已提交
224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249
            1. a string in ['valid', 'same'].
            2. an int, which means the feature map is zero paded by size of `padding` on both sides.
            3. a list[int] or tuple[int] whose length is 1, which means the feature map is zero paded by size of `padding[0]` on both sides.
            4. a list[int] or tuple[int] whose length is 2. It has the form  [pad_before, pad_after].
            5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0).
            The default value is 0.
        dilation (int or tuple, optional): The dilation size. If dilation is a tuple, it must
            contain one integer, (dilation_size). Default: 1.
        groups (int, optional): The groups number of the conv1d function. According to grouped
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
            connected to the second half of the input channels. Default: 1.
        data_format (str, optional): Specify the data format of the input, and the data format of the output 
            will be consistent with that of the input. An optional string from: `"NCL"`, `"NLC"`.
            The default is `"NCL"`. When it is `"NCL"`, the data is stored in the order of:
            `[batch_size, input_channels, feature_length]`.
        name(str, optional): For detailed information, please refer 
           to :ref:`api_guide_Name`. Usually name is no need to set and 
           None by default.

    Returns:
        A tensor representing the conv1d, whose data type is the 
        same with input.

    Raises:
250
        ValueError: If the channel dimension of the input is less than or equal to zero.
W
whs 已提交
251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276
        ValueError: If `data_format` is not "NCL" or "NLC".
        ValueError: If `padding` is a string, but not "SAME" or "VALID".
        ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
            or the element corresponding to the input's channel is not 0.
        ShapeError: If the input is not 3-D Tensor.
        ShapeError: If the input's dimension size and filter's dimension size not equal.
        ShapeError: If the dimension size of input minus the size of `stride` is not 1.
        ShapeError: If the number of input channels is not equal to filter's channels * groups.
        ShapeError: If the number of output channels is not be divided by groups.

    Examples:
        .. code-block:: python

          import paddle
          import paddle.nn.functional as F
          import numpy as np
          x = np.array([[[4, 8, 1, 9],
            [7, 2, 0, 9],
            [6, 9, 2, 6]]]).astype(np.float32)
          w=np.array(
          [[[9, 3, 4],
            [0, 0, 7],
            [2, 5, 6]],
           [[0, 3, 4],
            [2, 9, 7],
            [5, 6, 8]]]).astype(np.float32)
L
LielinJiang 已提交
277
          
W
whs 已提交
278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296
          x_var = paddle.to_tensor(x)
          w_var = paddle.to_tensor(w)
          y_var = F.conv1d(x_var, w_var)
          y_np = y_var.numpy()
          print(y_np)
          
          # [[[133. 238.]
          #   [160. 211.]]]
    """
    cudnn_version = get_cudnn_version()
    if cudnn_version is not None:
        use_cudnn = True
    else:
        use_cudnn = False

    if data_format not in ["NCL", "NLC"]:
        raise ValueError("Attr(data_format) should be 'NCL' or 'NLC'. "
                         "Received Attr(data_format): {}.".format(data_format))

L
LielinJiang 已提交
297
    channel_last = (data_format == "NLC")
W
whs 已提交
298 299 300 301 302
    channel_dim = -1 if channel_last else 1
    conv2d_data_format = "NHWC" if channel_last else "NCHW"
    num_channels = x.shape[channel_dim]
    num_filters = weight.shape[0]
    if num_channels < 0:
303
        raise ValueError("The channel dimension of the input({}) "
W
whs 已提交
304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324
                         "should be defined. Received: {}.".format(
                             x.shape, num_channels))
    if num_channels % groups != 0:
        raise ValueError(
            "the channel of input must be divisible by groups,"
            "received: the channel of input is {}, the shape of input is {}"
            ", the groups is {}".format(num_channels, x.shape, groups))
    if num_filters % groups != 0:
        raise ValueError(
            "the number of filters must be divisible by groups,"
            "received: the number of filters is {}, the shape of weight is {}"
            ", the groups is {}".format(num_filters, weight.shape, groups))

    # update attrs
    padding, padding_algorithm = _update_padding_nd(padding, channel_last, 1)
    if len(padding) == 2:
        padding = padding + [0] * 2
    elif len(padding) == 1:
        padding = padding + [0]
    else:
        raise ValueError(
325
            "The size of padding's dimension should be 1 or 2. But got padding={}".
W
whs 已提交
326 327 328 329 330 331
            format(padding))

    stride = utils.convert_to_list(stride, 1, 'stride') + [1]
    dilation = utils.convert_to_list(dilation, 1, 'dilation') + [1]

    l_type = "conv2d"
L
LielinJiang 已提交
332 333
    if (num_channels == groups and num_channels != 1 and
            num_filters % num_channels == 0 and not use_cudnn):
W
whs 已提交
334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375
        l_type = 'depthwise_conv2d'
        use_cudnn = False

    inputs = {'Input': [x], 'Filter': [weight]}
    attrs = {
        'strides': stride,
        'paddings': padding,
        'dilations': dilation,
        'groups': groups,
        'use_cudnn': use_cudnn,
        'use_mkldnn': False,
        'fuse_relu_before_depthwise_conv': False,
        "padding_algorithm": padding_algorithm,
        "data_format": conv2d_data_format
    }
    squeeze_aixs = -2 if channel_last else -1
    x = nn.unsqueeze(input=x, axes=[squeeze_aixs])
    weight = nn.unsqueeze(input=weight, axes=[-1])
    if in_dygraph_mode():
        attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
                 'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn', False,
                 'fuse_relu_before_depthwise_conv', False, "padding_algorithm",
                 padding_algorithm, "data_format", conv2d_data_format)
        out = getattr(core.ops, l_type)(x, weight, *attrs)
        if bias is not None:
            out = nn.elementwise_add(out, bias, axis=channel_dim)
    else:
        inputs = {'Input': [x], 'Filter': [weight]}
        attrs = {
            'strides': stride,
            'paddings': padding,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn,
            'use_mkldnn': False,
            'fuse_relu_before_depthwise_conv': False,
            "padding_algorithm": padding_algorithm,
            "data_format": conv2d_data_format
        }
        check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'],
                                 'conv2d')
        helper = LayerHelper(l_type, **locals())
376
        dtype = helper.input_dtype(input_param_name='x')
W
whs 已提交
377 378 379 380 381 382 383 384 385 386
        out = helper.create_variable_for_type_inference(dtype)
        outputs = {"Output": [out]}
        helper.append_op(
            type=l_type, inputs=inputs, outputs=outputs, attrs=attrs)
        if bias is not None:
            out = nn.elementwise_add(out, bias, axis=channel_dim)
    out = nn.squeeze(input=out, axes=[squeeze_aixs])
    return out


387
def conv2d(x,
388 389 390
           weight,
           bias=None,
           stride=1,
391
           padding=0,
392 393 394 395
           dilation=1,
           groups=1,
           data_format="NCHW",
           name=None):
396
    r"""
S
swtkiwi 已提交
397

398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414
    The convolution2D layer calculates the output based on the input, filter
    and strides, paddings, dilations, groups parameters. Input and
    Output are in NCHW or NHWC format, where N is batch size, C is the number of
    channels, H is the height of the feature, and W is the width of the feature.
    Filter is in MCHW format, where M is the number of output image channels,
    C is the number of input image channels, H is the height of the filter,
    and W is the width of the filter. If the groups is greater than 1,
    C will equal the number of input image channels divided by the groups.
    Please refer to UFLDL's `convolution
    <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
    for more details.
    If bias attribution and activation type are provided, bias is added to the
    output of the convolution, and the corresponding activation function is
    applied to the final result.

    For each input :math:`X`, the equation is:

415
    ..  math::
416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441

        Out = \sigma (W \\ast X + b)

    Where:

    * :math:`X`: Input value, a tensor with NCHW or NHWC format.
    * :math:`W`: Filter value, a tensor with MCHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`

        Where

442
        ..  math::
443 444 445 446 447

            H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
            W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

    Args:
448
        x (Tensor): The input is 4-D Tensor with shape [N, C, H, W], the data type 
449
            of input is float16 or float32 or float64.
450
        weight (Tensor): The convolution kernel with shape [M, C/g, kH, kW], where M is
451 452
            the number of output channels, g is the number of groups, kH is the filter's
            height, kW is the filter's width. 
453 454 455 456
        bias (Tensor, optional): The bias with shape [M,].
        stride (int|tuple): The stride size. It means the stride in convolution. 
            If stride is a tuple, it must contain two integers, (stride_height, stride_width). 
            Otherwise, stride_height = stride_width = stride. Default: stride = 1.
457 458 459 460 461 462 463
        padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
            on both sides for each dimension.If `padding` is a string, either 'VALID' or
            'SAME' which is the padding algorithm. If padding size is a tuple or list,
            it could be in three forms: `[pad_height, pad_width]` or
            `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when 
            `data_format` is `"NCHW"`, `padding` can be in the form `[[0,0], [0,0], 
            [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
L
LielinJiang 已提交
464
            when `data_format` is `"NHWC"`, `padding` can be in the form
465 466 467 468 469 470
            `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
            Default: padding = 0.
        dilation (int|tuple): The dilation size. It means the spacing between the kernel
            points. If dilation is a tuple, it must contain two integers, (dilation_height, 
            dilation_width). Otherwise, dilation_height = dilation_width = dilation. 
            Default: dilation = 1.
C
cnn 已提交
471
        groups (int): The groups number of the Conv2D Layer. According to grouped
472 473 474 475 476 477 478 479 480 481 482 483 484
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
            connected to the second half of the input channels. Default: groups=1.
        data_format (str, optional): Specify the data format of the input, and the data format of the output 
            will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`.
        name(str, optional): For detailed information, please refer 
           to :ref:`api_guide_Name`. Usually name is no need to set and 
           None by default.

    Returns:
485
        A Tensor representing the conv2d result, whose data type is the same with input. 
486 487 488

    Raises:
        ValueError: If `data_format` is not "NCHW" or "NHWC".
489
        ValueError: If the channel dimension of the input is less than or equal to zero.
490 491 492 493 494 495 496 497 498 499 500 501
        ValueError: If `padding` is a string, but not "SAME" or "VALID".
        ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
            or the element corresponding to the input's channel is not 0.
        ShapeError: If the input is not 4-D Tensor.
        ShapeError: If the input's dimension size and filter's dimension size not equal.
        ShapeError: If the dimension size of input minus the size of `stride` is not 2.
        ShapeError: If the number of input channels is not equal to filter's channels * groups.
        ShapeError: If the number of output channels is not be divided by groups.

    Examples:
        .. code-block:: python

502
          import paddle
503 504
          import paddle.nn.functional as F

505 506
          x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
          w_var = paddle.randn((6, 3, 3, 3), dtype='float32')
507 508 509 510

          y_var = F.conv2d(x_var, w_var)
          y_np = y_var.numpy()

511 512 513 514 515 516 517 518 519 520
          print(y_np.shape)
          # (2, 6, 6, 6)
    """
    # entry checks
    if data_format not in ["NCHW", "NHWC"]:
        raise ValueError("Attr(data_format) should be 'NCHW' or 'NHWC'. "
                         "Received Attr(data_format): {}.".format(data_format))

    channel_last = (data_format == "NHWC")
    channel_dim = -1 if channel_last else 1
521
    num_channels = x.shape[channel_dim]
522 523
    num_filters = weight.shape[0]
    if num_channels < 0:
524
        raise ValueError("The channel dimension of the input({}) "
525
                         "should be defined. Received: {}.".format(
526
                             x.shape, num_channels))
527 528 529 530
    if num_channels % groups != 0:
        raise ValueError(
            "the channel of input must be divisible by groups,"
            "received: the channel of input is {}, the shape of input is {}"
531
            ", the groups is {}".format(num_channels, x.shape, groups))
532 533 534 535 536 537
    if num_filters % groups != 0:
        raise ValueError(
            "the number of filters must be divisible by groups,"
            "received: the number of filters is {}, the shape of weight is {}"
            ", the groups is {}".format(num_filters, weight.shape, groups))

538 539 540 541 542
    cudnn_version = get_cudnn_version()

    use_cudnn = True if (core.is_compiled_with_cuda() and
                         cudnn_version is not None) else False

L
LielinJiang 已提交
543 544
    use_mkldnn = core.globals()["FLAGS_use_mkldnn"]

545 546 547 548 549 550
    # update attrs
    padding, padding_algorithm = _update_padding_nd(padding, channel_last, 2)
    stride = utils.convert_to_list(stride, 2, 'stride')
    dilation = utils.convert_to_list(dilation, 2, 'dilation')

    l_type = "conv2d"
L
LielinJiang 已提交
551 552
    if (num_channels == groups and num_channels != 1 and
            num_filters % num_channels == 0):
553
        l_type = 'depthwise_conv2d'
554
        use_cudnn = False
555

L
LielinJiang 已提交
556 557 558
    return _conv_nd(x, weight, bias, stride, padding, padding_algorithm,
                    dilation, groups, data_format, channel_dim, l_type,
                    use_cudnn, use_mkldnn, name)
559 560


561
def conv1d_transpose(x,
562 563 564 565 566 567 568 569 570 571
                     weight,
                     bias=None,
                     stride=1,
                     padding=0,
                     output_padding=0,
                     groups=1,
                     dilation=1,
                     output_size=None,
                     data_format="NCL",
                     name=None):
572
    r"""
573 574 575 576 577 578 579 580 581 582 583 584 585 586
    The 1-D convolution transpose layer calculates the output based on the input,
    filter, and dilation, stride, padding. Input(Input) and output(Output)
    are in 'NCL' format or 'NLC' where N is batch size, C is the number of channels,
    L is the length of the feature. The details of convolution transpose
    layer, please refer to the following explanation and references
    `therein <https://arxiv.org/pdf/1603.07285.pdf>`_.
    If bias attribution and activation type are provided, bias is added to
    the output of the convolution, and the corresponding activation function
    is applied to the final result.

    For each input :math:`X`, the equation is:

    .. math::

W
whs 已提交
587
        Out = \sigma (W \ast X + b)
588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696

    Where:

    * :math:`X`: Input value, a 3-D Tensor with 'NCL' format or 'NLC' format.
    * :math:`W`: Filter value, a 3-D Tensor with 'MCK' format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D Tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, a 3-D Tensor with data format 'NCL' or 'NLC', the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, L_{in})`

          Filter shape: :math:`(C_{in}, C_{out}, L_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, L_{out})`

        Where

        .. math::

           L^\prime_{out} &= (L_{in} - 1) * stride - pad_top - pad_bottom + dilation * (L_f - 1) + 1 + output_padding \\\\
           L_{out} &\in [ L^\prime_{out}, L^\prime_{out} + stride ]

    Note:
          The conv1d_transpose can be seen as the backward of the conv1d. For conv1d,
          when stride > 1, conv1d maps multiple input shape to the same output shape,
          so for conv1d_transpose, when stride > 1, input shape maps multiple output shape.
          If output_size is None, :math:`L_{out} = L^\prime_{out}`;
          else, the :math:`L_{out}` of the output size must between :math:`L^\prime_{out}`
          and :math:`L^\prime_{out} + stride`. conv1d_transpose can compute the kernel size automatically.

    Args:
        x(Tensor): 3-D tensor with [N, C, L] or [N, L, C] format,
                         its data type is float32 or float64.
        weight(Tensor): The convolution kernel, a Tensor with shape [C, M/g, K],
            where M is the number of output channels(filters), g is the number of groups,
            K is the size of the kernel.
        bias(Tensor, optional): The bias, a Tensor with shape [M, ].
        stride(int|tuple|list, optional): The stride size. It means the stride in transposed convolution.
            If stride is a tuple, it must contain one integer, `(stride_size)`.
            Default: stride = 1.
        padding(int|list|str|tuple, optional): The padding size. The padding argument effectively adds
             `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a
             string, either 'VALID' or 'SAME' supported, which is the padding algorithm.
             If `padding` is a tuple or list, it could be in two forms:
             `[pad]` or `[pad_left, pad_right]`. Default: padding = 0.
        output_padding(int|list|tuple, optional): The count of zeros to be added to tail of each dimension.
             If it is a tuple, it must contain one integer. Default: 0.
        groups(int, optional): The groups number of the conv1d transpose function. Inspired by
            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
            when group=2, the first half of the filters is only connected to the
            first half of the input channels, while the second half of the
            filters is only connected to the second half of the input channels.
            Default: groups = 1.
        dilation(int|tuple|list, optional): The dilation size. It means the spacing between the kernel points.
            If dilation is a tuple, it must contain one integer, `(dilation_size)`.
            Default: dilation = 1.
        output_size(int|tuple|list, optional): The output image size. If output size is a
            tuple, it must contain one integer, `(feature_length)`. None if use
            filter_size, padding, and stride to calculate output_size.
            If output_size and filter_size are specified at the same time, They
            should follow the formula above. Default: None. output_size and filter_size
            should not be None at the same time.
        data_format (str, optional): Specify the data format of the input, and the data format of the output 
            will be consistent with that of the input. An optional string from: `"NCL"`, `"NLC"`.
            The default is `"NCL"`. When it is `"NCL"`, the data is stored in the order of:
            `[batch_size, input_channels, input_length]`.
        name(str, optional): For detailed information, please refer 
           to :ref:`api_guide_Name`. Usually name is no need to set and 
           None by default.

    Returns:
        A  tensor representing the result of 1-D transpose convolution, whose
        data type is the same with input. And its shape is (num_batches, channels, length)
        when data_format is `"NCL"` and (num_batches, length, channels) when data_format is
        `"NLC"`.

    Raises:
        ValueError: If `data_format` is a string, but not "NCL" or "NLC".
        ValueError: If `padding` is a string, but not "SAME" or "VALID".
        ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
            or the element corresponding to the input's channel is not 0.
        ValueError: If `output_size` and filter_size are None at the same time.
        ValueError: If `output_padding` is greater than `stride`.
        ShapeError: If the input is not 3-D Tensor.
        ShapeError: If the input's dimension size and filter's dimension size not equal.
        ShapeError: If the dimension size of input minus the size of `stride` is not 1.
        ShapeError: If the number of input channels is not equal to filter's channels.
        ShapeError: If the size of `output_size` is not equal to that of `stride`.

    Examples:
        .. code-block:: python



          import paddle
          import paddle.nn.functional as F
          import numpy as np
          
          # shape: (1, 2, 4)
          x=np.array([[[4, 0, 9, 7],
                       [8, 0, 9, 2,]]]).astype(np.float32)
          # shape: (2, 1, 2)
W
whs 已提交
697
          w=np.array([[[7, 0]],
698 699 700
                      [[4, 2]]]).astype(np.float32)
          x_var = paddle.to_tensor(x)
          w_var = paddle.to_tensor(w)
701
          y_var = F.conv1d_transpose(x_var, w_var)
W
whs 已提交
702
          print(y_var)
703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721
          
          # [[[60. 16. 99. 75.  4.]]]
    """
    cudnn_version = get_cudnn_version()
    if cudnn_version is not None:
        use_cudnn = True
    else:
        use_cudnn = False

    if data_format not in ['NCL', 'NLC']:
        raise ValueError(
            "Attr(data_format) of conv2d_transpose got wrong value: "
            "received {}, but only 'NCL' or 'NLC' are supported.".format(
                data_format))
    channel_last = (data_format == "NLC")
    channel_dim = -1 if channel_last else 1

    num_channels = x.shape[channel_dim]
    if num_channels < 0:
722
        raise ValueError("The channel dimension of the input({}) "
723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739
                         "should be defined. Received: {}.".format(
                             x.shape, num_channels))
    if num_channels % groups != 0:
        raise ValueError(
            "the channel of input must be divisible by groups,"
            "received: the channel of input is {}, the shape of input is {}"
            ", the groups is {}".format(num_channels, x.shape, groups))

    # update attrs
    padding, padding_algorithm = _update_padding_nd(padding, channel_last, 1)

    if len(padding) == 2:
        padding = padding + [0] * 2
    elif len(padding) == 1:
        padding = padding + [0]
    else:
        raise ValueError(
740
            "The size of padding's dimension should 1 or 2. But got padding={}".
741 742 743 744 745 746 747 748
            format(padding))

    stride = utils.convert_to_list(stride, 1, 'stride') + [1]
    dilation = utils.convert_to_list(dilation, 1, 'dilation') + [1]

    if output_size is None:
        output_size = []
    else:
L
LielinJiang 已提交
749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769
        if output_padding != 0:
            raise ValueError('output_padding option is mutually exclusive with '
                             'output_size')
        if isinstance(output_size, (list, tuple, int)):
            output_size = utils.convert_to_list(output_size, 1,
                                                'output_size') + [1]
        else:
            raise ValueError(
                "output_size should be int, or list, tuple of ints")

    if output_padding == 0:
        output_padding = []
    else:
        output_padding = utils.convert_to_list(output_padding, 1,
                                               'output_padding') + [0]

    if len(output_padding) > 0 and output_padding[0] > stride[0]:
        raise ValueError(
            "The size of output_padding should not be greater than stride."
            "But got output_padding={} and stride={}".format(output_padding[0],
                                                             stride[0]))
770 771 772

    op_type = 'conv2d_transpose'
    num_filters = weight.shape[1]
L
LielinJiang 已提交
773 774
    if (num_channels == groups and num_channels != 1 and num_filters == 1 and
            not use_cudnn):
775 776 777 778 779 780 781 782 783 784
        op_type = 'depthwise_conv2d_transpose'
        use_cudnn = False

    squeeze_axis = -2 if channel_last else -1
    conv2d_data_format = "NHWC" if channel_last else "NCHW"

    x = nn.unsqueeze(input=x, axes=[squeeze_axis])
    weight = nn.unsqueeze(input=weight, axes=[-1])

    if in_dygraph_mode():
L
LielinJiang 已提交
785 786 787 788
        attrs = ('output_padding', output_padding, 'output_size', output_size,
                 'strides', stride, 'paddings', padding, 'padding_algorithm',
                 padding_algorithm, 'dilations', dilation, 'groups', groups,
                 'use_cudnn', use_cudnn, 'data_format', conv2d_data_format)
789 790 791 792 793 794
        out = getattr(core.ops, op_type)(x, weight, *attrs)
        if bias is not None:
            out = nn.elementwise_add(out, bias, axis=channel_dim)
    else:
        inputs = {'Input': [x], 'Filter': [weight]}
        attrs = {
L
LielinJiang 已提交
795
            'output_padding': output_padding,
796 797 798 799 800 801 802 803 804 805 806 807
            'output_size': output_size,
            'strides': stride,
            'paddings': padding,
            'padding_algorithm': padding_algorithm,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn,
            'data_format': conv2d_data_format
        }
        check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'],
                                 'conv2d_transpose')
        helper = LayerHelper(op_type, **locals())
808
        dtype = helper.input_dtype(input_param_name='x')
809 810 811 812 813 814 815 816 817 818 819
        out = helper.create_variable_for_type_inference(dtype)
        outputs = {"Output": [out]}
        helper.append_op(
            type=op_type, inputs=inputs, outputs=outputs, attrs=attrs)
        if bias is not None:
            out = nn.elementwise_add(out, bias, axis=channel_dim)

    out = nn.squeeze(input=out, axes=[squeeze_axis])
    return out


820
def conv2d_transpose(x,
821 822 823
                     weight,
                     bias=None,
                     stride=1,
L
LielinJiang 已提交
824 825 826
                     padding=0,
                     output_padding=0,
                     dilation=1,
827
                     groups=1,
L
LielinJiang 已提交
828
                     output_size=None,
829
                     data_format='NCHW',
830
                     name=None):
831
    r"""
S
swtkiwi 已提交
832

833 834 835 836 837 838 839 840 841 842 843
    The convolution2D transpose layer calculates the output based on the input,
    filter, and dilations, strides, paddings. Input(Input) and output(Output)
    are in NCHW or NHWC format. Where N is batch size, C is the number of channels,
    H is the height of the feature, and W is the width of the feature.
    Parameters(dilations, strides, paddings) are two elements. These two elements
    represent height and width, respectively. The details of convolution transpose
    layer, please refer to the following explanation and references
    `therein <https://arxiv.org/pdf/1603.07285.pdf>`_.
    If bias attribution and activation type are provided, bias is added to
    the output of the convolution, and the corresponding activation function
    is applied to the final result.
L
LielinJiang 已提交
844
    See more detail in :ref:`api_nn_conv_ConvTranspose2d` .
845 846 847

    For each input :math:`X`, the equation is:

848
    ..  math::
849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874

        Out = \sigma (W \\ast X + b)

    Where:

    * :math:`X`: Input value, a 4-D Tensor with NCHW or NHWC format.
    * :math:`W`: Filter value, a 4-D Tensor with MCHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D Tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, a 4-D Tensor with data format 'NCHW' or 'NHWC', the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{in}, C_{out}, H_f, W_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`

        Where

875
        ..  math::
876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892

           H^\prime_{out} &= (H_{in} - 1) * strides[0] - pad_height_top - pad_height_bottom + dilations[0] * (H_f - 1) + 1 \\\\
           W^\prime_{out} &= (W_{in} - 1) * strides[1] - pad_width_left - pad_width_right + dilations[1] * (W_f - 1) + 1 \\\\
           H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[0] ] \\\\
           W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] ]

    Note:
          The conv2d_transpose can be seen as the backward of the conv2d. For conv2d, 
          when stride > 1, conv2d maps multiple input shape to the same output shape, 
          so for conv2d_transpose, when stride > 1, input shape maps multiple output shape.
          If output_size is None, :math:`H_{out} = H^\prime_{out}, W_{out} = W^\prime_{out}`; 
          else, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` 
          and :math:`H^\prime_{out} + strides[0]`, and the :math:`W_{out}` of the output size must 
          between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[1]`, 
          conv2d_transpose can compute the kernel size automatically.

    Args:
L
LielinJiang 已提交
893
        x(Tensor): 4-D Tensor with [N, C, H, W] or [N, H, W, C] format,
894
            whose data type is float32 or float64.
L
LielinJiang 已提交
895
        weight(Tensor): The convolution kernel, a Tensor with shape [C, M/g, kH, kW],
896 897
            where M is the number of output channels(filters), g is the number of groups,
            kH is the height of the kernel, and kW is the width of the kernel.
L
LielinJiang 已提交
898 899 900 901
        bias(Tensor, optional): The bias, a Tensor with shape [M, ].
        stride(int|list|tuple, optional): The stride size. It means the stride in transposed convolution. 
            If stride is a tuple, it must contain two integers, (stride_height, stride_width). 
            Otherwise, stride_height = stride_width = stride. Default: stride = 1.
902 903 904 905 906
        padding(str|int|list|tuple, optional): The padding size. It means the number of zero-paddings 
            on both sides for each dimension. If `padding` is a string, either 'VALID' or 
            'SAME' which is the padding algorithm. If padding size is a tuple or list,
            it could be in three forms: `[pad_height, pad_width]` or 
            `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
L
LielinJiang 已提交
907
            and when `data_format` is `"NCHW"`, `padding` can be in the form 
908
            `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
L
LielinJiang 已提交
909
            when `data_format` is `"NHWC"`, `padding` can be in the form 
910 911
            `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
            Default: padding = 0.
L
LielinJiang 已提交
912 913
        output_padding(int|list|tuple, optional): Additional size added to one side
            of each dimension in the output shape. Default: 0.
C
cnn 已提交
914
        groups(int, optional): The groups number of the Conv2D transpose layer. Inspired by
915 916 917 918 919
            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
            when group=2, the first half of the filters is only connected to the
            first half of the input channels, while the second half of the
            filters is only connected to the second half of the input channels.
            Default: groups = 1.
920 921 922
        dilation(int|list|tuple, optional): The dilation size. It means the spacing between the kernel points. 
            If dilation is a tuple, it must contain two integers, (dilation_height, dilation_width). 
            Otherwise, dilation_height = dilation_width = dilation. Default: dilation = 1.
L
LielinJiang 已提交
923 924 925 926 927 928
        output_size(int|tuple|list, optional): The output image size. If output size is a
            tuple, it must contain two integers, (image_height, image_width). None if use
            filter_size, padding, and stride to calculate output_size.
            If output_size is specified, output_size and filter_size (weight)'s shape 
            should follow the formula above. Default: None. output_size and filter_size 
            should not be None at the same time.
929 930 931 932 933 934 935 936 937
        data_format (str, optional): Specify the data format of the input, and the data format of the output 
            will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`.
        name(str, optional): For detailed information, please refer 
           to :ref:`api_guide_Name`. Usually name is no need to set and 
           None by default.

    Returns:
938
        A Tensor representing the conv2d_transpose, whose
939
        data type is the same with input and shape is (num_batches, channels, out_h, 
L
LielinJiang 已提交
940 941
        out_w) or (num_batches, out_h, out_w, channels). The tensor variable storing 
        transposed convolution result.
942 943 944 945 946 947

    Raises:
        ValueError: If `data_format` is not "NCHW" or "NHWC".
        ValueError: If `padding` is a string, but not "SAME" or "VALID".
        ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
            or the element corresponding to the input's channel is not 0.
L
LielinJiang 已提交
948
        ValueError: If `output_size` and kernel_size are None at the same time.
949 950 951 952 953 954 955 956 957
        ShapeError: If the input is not 4-D Tensor.
        ShapeError: If the input's dimension size and filter's dimension size not equal.
        ShapeError: If the dimension size of input minus the size of `stride` is not 2.
        ShapeError: If the number of input channels is not equal to filter's channels.
        ShapeError: If the size of `output_size` is not equal to that of `stride`.

    Examples:
        .. code-block:: python

L
LielinJiang 已提交
958 959
          import paddle
          import paddle.nn.functional as F
960

961 962
          x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
          w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
963

964
          y_var = F.conv2d_transpose(x_var, w_var)
L
LielinJiang 已提交
965
          y_np = y_var.numpy()
966

967
          print(y_np.shape)
968 969 970 971 972 973 974 975 976 977
          # (2, 6, 10, 10)
    """

    if data_format not in ['NCHW', 'NHWC']:
        raise ValueError(
            "Attr(data_format) of conv2d_transpose got wrong value: "
            "received {}, but only 'NCHW' or 'NHWC' are supported.".format(
                data_format))
    channel_last = (data_format == "NHWC")
    channel_dim = -1 if channel_last else 1
L
LielinJiang 已提交
978
    num_channels = x.shape[channel_dim]
979
    if num_channels < 0:
980
        raise ValueError("The channel dimension of the input({}) "
981
                         "should be defined. Received: {}.".format(
L
LielinJiang 已提交
982
                             x.shape, num_channels))
983 984 985 986
    if num_channels % groups != 0:
        raise ValueError(
            "the channel of input must be divisible by groups,"
            "received: the channel of input is {}, the shape of input is {}"
L
LielinJiang 已提交
987 988 989 990 991 992
            ", the groups is {}".format(num_channels, x.shape, groups))

    cudnn_version = get_cudnn_version()

    use_cudnn = True if (core.is_compiled_with_cuda() and
                         cudnn_version is not None) else False
993 994 995 996 997

    # update attrs
    padding, padding_algorithm = _update_padding_nd(padding, channel_last, 2)
    stride = utils.convert_to_list(stride, 2, 'stride')
    dilation = utils.convert_to_list(dilation, 2, 'dilation')
L
LielinJiang 已提交
998

999 1000 1001
    if output_size is None:
        output_size = []
    else:
L
LielinJiang 已提交
1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015
        if output_padding != 0:
            raise ValueError('output_padding option is mutually exclusive with '
                             'output_size')
        if isinstance(output_size, (list, tuple, int)):
            output_size = utils.convert_to_list(output_size, 2, 'output_size')
        else:
            raise ValueError(
                "output_size should be int, or list, tuple of ints")

    if output_padding == 0:
        output_padding = []
    else:
        output_padding = utils.convert_to_list(output_padding, 2,
                                               'output_padding')
1016 1017 1018

    op_type = 'conv2d_transpose'
    num_filters = weight.shape[1]
L
LielinJiang 已提交
1019
    if (num_channels == groups and num_channels != 1 and num_filters == 1):
1020
        op_type = 'depthwise_conv2d_transpose'
L
LielinJiang 已提交
1021
        use_cudnn = False
1022 1023

    if in_dygraph_mode():
L
LielinJiang 已提交
1024 1025 1026 1027 1028
        attrs = ('output_padding', output_padding, 'output_size', output_size,
                 'strides', stride, 'paddings', padding, 'padding_algorithm',
                 padding_algorithm, 'dilations', dilation, 'groups', groups,
                 'use_cudnn', use_cudnn, 'data_format', data_format)
        pre_bias = getattr(core.ops, op_type)(x, weight, *attrs)
1029
        if bias is not None:
L
LielinJiang 已提交
1030
            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
1031
        else:
L
LielinJiang 已提交
1032
            out = pre_bias
1033
    else:
L
LielinJiang 已提交
1034
        inputs = {'Input': [x], 'Filter': [weight]}
1035
        attrs = {
L
LielinJiang 已提交
1036
            'output_padding': output_padding,
1037 1038 1039 1040 1041 1042 1043 1044 1045
            'output_size': output_size,
            'strides': stride,
            'paddings': padding,
            'padding_algorithm': padding_algorithm,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn,
            'data_format': data_format
        }
L
LielinJiang 已提交
1046
        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
1047 1048
                                 'conv2d_transpose')
        helper = LayerHelper(op_type, **locals())
L
LielinJiang 已提交
1049
        pre_bias = helper.create_variable_for_type_inference(x.dtype)
1050 1051 1052
        outputs = {"Output": [pre_bias]}
        helper.append_op(
            type=op_type, inputs=inputs, outputs=outputs, attrs=attrs)
L
LielinJiang 已提交
1053

1054
        if bias is not None:
L
LielinJiang 已提交
1055
            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
1056
        else:
L
LielinJiang 已提交
1057 1058
            out = pre_bias

1059 1060 1061
    return out


1062
def conv3d(x,
1063 1064 1065
           weight,
           bias=None,
           stride=1,
1066
           padding=0,
1067 1068 1069 1070
           dilation=1,
           groups=1,
           data_format="NCDHW",
           name=None):
1071
    r"""
S
swtkiwi 已提交
1072

1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083
    The convolution3D layer calculates the output based on the input, filter
    and strides, paddings, dilations, groups parameters. Input(Input) and
    Output(Output) are in NCDHW or NDHWC format. Where N is batch size C is the number of
    channels, D is the depth of the feature, H is the height of the feature,
    and W is the width of the feature. Convlution3D is similar with Convlution2D
    but adds one dimension(depth). If bias attribution and activation type are
    provided, bias is added to the output of the convolution, and the
    corresponding activation function is applied to the final result.

    For each input :math:`X`, the equation is:

1084
    ..  math::
1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109

        Out = \sigma (W \\ast X + b)

    In the above equation:

    * :math:`X`: Input value, a tensor with NCDHW or NDHWC format.
    * :math:`W`: Filter value, a tensor with MCDHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`

        - Output:
          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`

        Where

1110
        ..  math::
1111 1112 1113 1114 1115 1116

            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1

    Args:
1117
        x (Tensor): The input is 5-D Tensor with shape [N, C, D, H, W], the data 
1118
            type of input is float16 or float32 or float64.
1119
        weight (Tensor): The convolution kernel, a Tensor with shape [M, C/g, kD, kH, kW],
1120 1121
            where M is the number of filters(output channels), g is the number of groups,
            kD, kH, kW are the filter's depth, height and width respectively.
1122 1123 1124 1125
        bias (Tensor, optional): The bias, a Tensor of shape [M, ].
        stride (int|tuple): The stride size. It means the stride in convolution. If stride is a 
            tuple, it must contain three integers, (stride_depth, stride_height, stride_width). 
            Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
1126 1127 1128 1129 1130
        padding (string|int|list|tuple): The padding size. It means the number of zero-paddings 
            on both sides for each dimension. If `padding` is a string, either 'VALID' or
            'SAME' which is the padding algorithm. If padding size is a tuple or list,
            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
L
LielinJiang 已提交
1131
            and when `data_format` is `"NCDHW"`, `padding` can be in the form
1132
            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
L
LielinJiang 已提交
1133
            when `data_format` is `"NDHWC"`, `padding` can be in the form
1134 1135 1136 1137 1138 1139
            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
            Default: padding = 0.
        dilation (int|tuple): The dilation size. It means the spacing between the kernel points. 
            If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height,
            dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation. 
            Default: dilation = 1.
C
cnn 已提交
1140
        groups (int): The groups number of the Conv3D Layer. According to grouped
1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
            connected to the second half of the input channels. Default: groups=1
        data_format (str, optional): Specify the data format of the input, and the data format of the output 
            will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`.
        name(str|None): For detailed information, please refer 
           to :ref:`api_guide_Name`. Usually name is no need to set and 
           None by default.

    Returns:
1154
        A Tensor representing the conv3d, whose data type is 
1155 1156
        the same with input. If act is None, the tensor storing the 
        convolution result, and if act is not None, the tensor storing 
1157 1158 1159 1160 1161
        convolution and non-linearity activation result.

    Examples:
        .. code-block:: python

1162 1163
            import paddle
            import paddle.nn.functional as F
1164

1165 1166
            x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
            w_var = paddle.randn((6, 3, 3, 3, 3), dtype='float32')
1167

1168 1169
            y_var = F.conv3d(x_var, w_var)
            y_np = y_var.numpy()
1170

1171
            print(y_np.shape)
1172 1173 1174 1175 1176 1177 1178 1179 1180 1181
            # (2, 6, 6, 6, 6)
    """
    # entry check
    if data_format not in ["NCDHW", "NDHWC"]:
        raise ValueError(
            "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
            "Attr(data_format): {}.".format(data_format))

    channel_last = (data_format == "NDHWC")
    channel_dim = -1 if channel_last else 1
1182
    num_channels = x.shape[channel_dim]
1183 1184 1185
    num_filters = weight.shape[0]
    if num_channels < 0:
        raise ValueError(
1186
            "The channel dimension of the input({}) should be defined. "
1187
            "Received: {}.".format(x.shape, num_channels))
1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198
    if num_channels % groups != 0:
        raise ValueError(
            "The number of input channels must be divisible by Attr(groups). "
            "Received: number of channels({}), groups({}).".format(num_channels,
                                                                   groups))
    if num_filters % groups != 0:
        raise ValueError(
            "The number of filters must be divisible by Attr(groups). "
            "Received: number of filters({}), groups({}).".format(num_filters,
                                                                  groups))

1199 1200 1201 1202
    cudnn_version = get_cudnn_version()
    use_cudnn = True if (core.is_compiled_with_cuda() and
                         cudnn_version is not None) else False

1203 1204 1205 1206 1207
    padding, padding_algorithm = _update_padding_nd(padding, channel_last, 3)
    stride = utils.convert_to_list(stride, 3, 'stride')
    dilation = utils.convert_to_list(dilation, 3, 'dilation')
    op_type = "conv3d"

L
LielinJiang 已提交
1208 1209 1210
    return _conv_nd(x, weight, bias, stride, padding, padding_algorithm,
                    dilation, groups, data_format, channel_dim, op_type,
                    use_cudnn, False, name)
1211 1212


1213
def conv3d_transpose(x,
1214 1215 1216
                     weight,
                     bias=None,
                     stride=1,
L
LielinJiang 已提交
1217 1218
                     padding=0,
                     output_padding=0,
1219
                     groups=1,
L
LielinJiang 已提交
1220 1221
                     dilation=1,
                     output_size=None,
1222
                     data_format='NCDHW',
1223
                     name=None):
1224
    r"""
L
LielinJiang 已提交
1225
    The convolution3d transpose layer calculates the output based on the input,
1226 1227 1228 1229 1230 1231 1232 1233 1234 1235
    filter, and dilations, strides, paddings. Input(Input) and output(Output)
    are in NCDHW or NDHWC format. Where N is batch size, C is the number of channels,
    D is the depth of the feature, H is the height of the feature, and W
    is the width of the feature. Parameters(dilations, strides, paddings) are
    two elements. These two elements represent height and width, respectively.
    The details of convolution transpose layer, please refer to the following
    explanation and references `therein <https://arxiv.org/pdf/1603.07285.pdf>`_.
    If bias attribution and activation type are provided, bias is added to
    the output of the convolution, and the corresponding activation function
    is applied to the final result.
L
LielinJiang 已提交
1236
    See more detail in :ref:`api_nn_conv_ConvTranspose3d` .
1237 1238 1239

    For each input :math:`X`, the equation is:

1240
    ..  math::
1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266

        Out = \sigma (W \\ast X + b)

    In the above equation:

    * :math:`X`: Input value, a Tensor with NCDHW or NDHWC format.
    * :math:`W`: Filter value, a Tensor with MCDHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D Tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.

    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`

        - Output:

          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`

        Where

1267
        ..  math::
1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288

           D^\prime_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
           H^\prime_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
           W^\prime_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1 \\\\
           D_{out} &\in [ D^\prime_{out}, D^\prime_{out} + strides[0] ] \\\\
           H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[1] ] \\\\
           W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[2] ]

    Note:
          The conv3d_transpose can be seen as the backward of the conv3d. For conv3d, 
          when stride > 1, conv3d maps multiple input shape to the same output shape, 
          so for conv3d_transpose, when stride > 1, input shape maps multiple output shape.
          If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \
          H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output 
          size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`, 
          the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` 
          and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must 
          between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`, 
          conv3d_transpose can compute the kernel size automatically.

    Args:
L
LielinJiang 已提交
1289
        x(Tensor): The input is 5-D Tensor with shape [N, C, D, H, W] or [N, D, H, W, C], the data type 
1290
            of input is float32 or float64.
L
LielinJiang 已提交
1291
        weight (Tensor): The convolution kernel, a Tensor with shape [C, M/g, kD, kH, kW],
1292 1293
            where M is the number of filters(output channels), g is the number of groups,
            kD, kH, kW are the filter's depth, height and width respectively.
L
LielinJiang 已提交
1294 1295 1296 1297 1298
        bias (Tensor, optional): The bias, a Tensor of shape [M, ].
        stride(int|list|tuple, optional): The stride size. It means the stride in transposed convolution. 
            If stride is a tuple, it must contain three integers, (stride_depth, stride_height, 
            stride_width). Otherwise, stride_depth = stride_height = stride_width = stride. 
            Default: stride = 1.
1299 1300 1301 1302
        padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings 
            on both sides for each dimension. If `padding` is a string, either 'VALID' or
            'SAME' which is the padding algorithm. If padding size is a tuple or list,
            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
1303
            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
L
LielinJiang 已提交
1304
            and when `data_format` is `"NCDHW"`, `padding` can be in the form
1305
            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
L
LielinJiang 已提交
1306
            when `data_format` is `"NDHWC"`, `padding` can be in the form
1307 1308
            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
            Default: padding = 0.
L
LielinJiang 已提交
1309 1310
        output_padding(int|list|tuple, optional): Additional size added to one side
            of each dimension in the output shape. Default: 0.
C
cnn 已提交
1311
        groups(int, optional): The groups number of the Conv3D transpose layer. Inspired by
1312 1313 1314 1315 1316
            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
            when group=2, the first half of the filters is only connected to the
            first half of the input channels, while the second half of the
            filters is only connected to the second half of the input channels.
            Default: groups=1
1317 1318 1319 1320
        dilation(int|list|tuple, optional): The dilation size. It means the spacing between the kernel points. 
            If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height, 
            dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation. 
            Default: dilation = 1.
L
LielinJiang 已提交
1321 1322 1323 1324 1325
        output_size(int|list|tuple, optional): The output image size. If output size is a
            tuple, it must contain three integers, (image_depth, image_height, image_width). This
            parameter only works when filter_size is None. If output_size and filter_size are 
            specified at the same time, They should follow the formula above. Default: None. 
            Output_size and filter_size should not be None at the same time.
1326 1327 1328 1329
        data_format (str, optional): Specify the data format of the input, and the data format of the output 
            will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`.
1330 1331 1332 1333 1334
        name(str, optional): For detailed information, please refer 
           to :ref:`api_guide_Name`. Usually name is no need to set and 
           None by default.

    Returns:
1335
        A Tensor representing the conv3d_transpose, whose data
1336 1337 1338 1339 1340 1341 1342 1343 1344 1345
        type is the same with input and shape is (num_batches, channels, out_d, out_h, 
        out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor 
        variable storing the transposed convolution result, and if act is not None, the tensor 
        variable storing transposed convolution and non-linearity activation result.

    Raises:
        ValueError: If `data_format` is not "NCDHW" or "NDHWC".
        ValueError: If `padding` is a string, but not "SAME" or "VALID".
        ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 
            or the element corresponding to the input's channel is not 0.
L
LielinJiang 已提交
1346
        ValueError: If `output_size` and kernel_size are None at the same time.
1347 1348 1349 1350 1351 1352 1353 1354
        ShapeError: If the input is not 5-D Tensor.
        ShapeError: If the input's dimension size and filter's dimension size not equal.
        ShapeError: If the dimension size of input minus the size of `stride` is not 2.
        ShapeError: If the number of input channels is not equal to filter's channels.
        ShapeError: If the size of `output_size` is not equal to that of `stride`.

    Examples:
       .. code-block:: python
L
LielinJiang 已提交
1355 1356
          
          import paddle
1357 1358
          import paddle.nn.functional as F

1359 1360
          x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
          w_var = paddle.randn((3, 6, 3, 3, 3), dtype='float32')
1361

1362
          y_var = F.conv3d_transpose(x_var, w_var)
L
LielinJiang 已提交
1363
          y_np = y_var.numpy()
1364

1365
          print(y_np.shape)
1366 1367 1368 1369 1370 1371 1372 1373 1374 1375
          # (2, 6, 10, 10, 10)
    """
    # entry checks
    if data_format not in ["NCDHW", "NDHWC"]:
        raise ValueError(
            "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
            "Attr(data_format): {}.".format(data_format))

    channel_last = (data_format == "NDHWC")
    channel_dim = -1 if channel_last else 1
L
LielinJiang 已提交
1376
    num_channels = x.shape[channel_dim]
1377 1378 1379
    num_filters = weight.shape[1]
    if num_channels < 0:
        raise ValueError(
1380
            "The channel dimension of the input({}) should be defined. "
L
LielinJiang 已提交
1381
            "Received: {}.".format(x.shape, num_channels))
1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393
    if num_channels % groups != 0:
        raise ValueError(
            "The number of input channels must be divisible by Attr(groups). "
            "Received: number of channels({}), groups({}).".format(num_channels,
                                                                   groups))

    padding, padding_algorithm = _update_padding_nd(padding, channel_last, 3)
    stride = utils.convert_to_list(stride, 3, 'stride')
    dilation = utils.convert_to_list(dilation, 3, 'dilation')
    if output_size is None:
        output_size = []
    else:
L
LielinJiang 已提交
1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413
        if output_padding != 0:
            raise ValueError('output_padding option is mutually exclusive with '
                             'output_size')
        if isinstance(output_size, (list, tuple, int)):
            output_size = utils.convert_to_list(output_size, 3, 'output_size')
        else:
            raise ValueError(
                "output_size should be int, or list, tuple of ints")

    if output_padding == 0:
        output_padding = []
    else:
        output_padding = utils.convert_to_list(output_padding, 3,
                                               'output_padding')

    cudnn_version = get_cudnn_version()

    #TODO(LielinJiang): whether to use cudnn according to the version of cudnn
    use_cudnn = True if (core.is_compiled_with_cuda() and
                         cudnn_version is not None) else False
1414 1415 1416 1417 1418

    op_type = 'conv3d_transpose'
    data_format_ = "NHWC" if channel_last else "NCHW"

    if in_dygraph_mode():
L
LielinJiang 已提交
1419 1420 1421 1422 1423
        attrs = ('output_padding', output_padding, 'output_size', output_size,
                 'paddings', padding, "padding_algorithm", padding_algorithm,
                 'strides', stride, 'dilations', dilation, 'groups', groups,
                 'use_cudnn', use_cudnn, "data_format", data_format_)
        pre_bias = getattr(core.ops, op_type)(x, weight, *attrs)
1424
        if bias is not None:
L
LielinJiang 已提交
1425
            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
1426
        else:
L
LielinJiang 已提交
1427
            out = pre_bias
1428
    else:
L
LielinJiang 已提交
1429
        inputs = {'Input': [x], 'Filter': [weight]}
1430
        attrs = {
L
LielinJiang 已提交
1431
            'output_padding': output_padding,
1432 1433 1434 1435 1436 1437 1438 1439 1440 1441
            'output_size': output_size,
            'paddings': padding,
            "padding_algorithm": padding_algorithm,
            'strides': stride,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn,
            "data_format": data_format_
        }
        helper = LayerHelper(op_type, **locals())
L
LielinJiang 已提交
1442 1443
        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                                 'conv3d')
1444

L
LielinJiang 已提交
1445
        pre_bias = helper.create_variable_for_type_inference(x.dtype)
1446 1447 1448 1449 1450
        outputs = {"Output": [pre_bias]}

        helper.append_op(
            type=op_type, inputs=inputs, outputs=outputs, attrs=attrs)
        if bias is not None:
L
LielinJiang 已提交
1451
            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
1452
        else:
L
LielinJiang 已提交
1453
            out = pre_bias
1454 1455

    return out