Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/utils/conv_utils.py
2023-06-19 00:49:18 +02:00

582 lines
20 KiB
Python

# Copyright 2015 The TensorFlow 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.
# ==============================================================================
"""Utilities used by convolution layers."""
import itertools
import numpy as np
import tensorflow.compat.v2 as tf
from keras import backend
def convert_data_format(data_format, ndim):
if data_format == "channels_last":
if ndim == 3:
return "NWC"
elif ndim == 4:
return "NHWC"
elif ndim == 5:
return "NDHWC"
else:
raise ValueError(
f"Input rank not supported: {ndim}. "
"Expected values are [3, 4, 5]"
)
elif data_format == "channels_first":
if ndim == 3:
return "NCW"
elif ndim == 4:
return "NCHW"
elif ndim == 5:
return "NCDHW"
else:
raise ValueError(
f"Input rank not supported: {ndim}. "
"Expected values are [3, 4, 5]"
)
else:
raise ValueError(
f"Invalid data_format: {data_format}. "
'Expected values are ["channels_first", "channels_last"]'
)
def normalize_tuple(value, n, name, allow_zero=False):
"""Transforms non-negative/positive integer/integers into an integer tuple.
Args:
value: The value to validate and convert. Could an int, or any iterable of
ints.
n: The size of the tuple to be returned.
name: The name of the argument being validated, e.g. "strides" or
"kernel_size". This is only used to format error messages.
allow_zero: Default to False. A ValueError will raised if zero is received
and this param is False.
Returns:
A tuple of n integers.
Raises:
ValueError: If something else than an int/long or iterable thereof or a
negative value is
passed.
"""
error_msg = (
f"The `{name}` argument must be a tuple of {n} "
f"integers. Received: {value}"
)
if isinstance(value, int):
value_tuple = (value,) * n
else:
try:
value_tuple = tuple(value)
except TypeError:
raise ValueError(error_msg)
if len(value_tuple) != n:
raise ValueError(error_msg)
for single_value in value_tuple:
try:
int(single_value)
except (ValueError, TypeError):
error_msg += (
f"including element {single_value} of "
f"type {type(single_value)}"
)
raise ValueError(error_msg)
if allow_zero:
unqualified_values = {v for v in value_tuple if v < 0}
req_msg = ">= 0"
else:
unqualified_values = {v for v in value_tuple if v <= 0}
req_msg = "> 0"
if unqualified_values:
error_msg += (
f" including {unqualified_values}"
f" that does not satisfy the requirement `{req_msg}`."
)
raise ValueError(error_msg)
return value_tuple
def conv_output_length(input_length, filter_size, padding, stride, dilation=1):
"""Determines output length of a convolution given input length.
Args:
input_length: integer.
filter_size: integer.
padding: one of "same", "valid", "full", "causal"
stride: integer.
dilation: dilation rate, integer.
Returns:
The output length (integer).
"""
if input_length is None:
return None
assert padding in {"same", "valid", "full", "causal"}
dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
if padding in ["same", "causal"]:
output_length = input_length
elif padding == "valid":
output_length = input_length - dilated_filter_size + 1
elif padding == "full":
output_length = input_length + dilated_filter_size - 1
return (output_length + stride - 1) // stride
def conv_input_length(output_length, filter_size, padding, stride):
"""Determines input length of a convolution given output length.
Args:
output_length: integer.
filter_size: integer.
padding: one of "same", "valid", "full".
stride: integer.
Returns:
The input length (integer).
"""
if output_length is None:
return None
assert padding in {"same", "valid", "full"}
if padding == "same":
pad = filter_size // 2
elif padding == "valid":
pad = 0
elif padding == "full":
pad = filter_size - 1
return (output_length - 1) * stride - 2 * pad + filter_size
def deconv_output_length(
input_length,
filter_size,
padding,
output_padding=None,
stride=0,
dilation=1,
):
"""Determines output length of a transposed convolution given input length.
Args:
input_length: Integer.
filter_size: Integer.
padding: one of `"same"`, `"valid"`, `"full"`.
output_padding: Integer, amount of padding along the output dimension.
Can be set to `None` in which case the output length is inferred.
stride: Integer.
dilation: Integer.
Returns:
The output length (integer).
"""
assert padding in {"same", "valid", "full"}
if input_length is None:
return None
# Get the dilated kernel size
filter_size = filter_size + (filter_size - 1) * (dilation - 1)
# Infer length if output padding is None, else compute the exact length
if output_padding is None:
if padding == "valid":
length = input_length * stride + max(filter_size - stride, 0)
elif padding == "full":
length = input_length * stride - (stride + filter_size - 2)
elif padding == "same":
length = input_length * stride
else:
if padding == "same":
pad = filter_size // 2
elif padding == "valid":
pad = 0
elif padding == "full":
pad = filter_size - 1
length = (
(input_length - 1) * stride + filter_size - 2 * pad + output_padding
)
return length
def normalize_data_format(value):
if value is None:
value = backend.image_data_format()
data_format = value.lower()
if data_format not in {"channels_first", "channels_last"}:
raise ValueError(
"The `data_format` argument must be one of "
f'"channels_first", "channels_last". Received: {value}'
)
return data_format
def normalize_padding(value):
if isinstance(value, (list, tuple)):
return value
padding = value.lower()
if padding not in {"valid", "same", "causal"}:
raise ValueError(
"The `padding` argument must be a list/tuple or one of "
'"valid", "same" (or "causal", only for `Conv1D). '
f"Received: {padding}"
)
return padding
def conv_kernel_mask(input_shape, kernel_shape, strides, padding):
"""Compute a mask representing the connectivity of a convolution operation.
Assume a convolution with given parameters is applied to an input having N
spatial dimensions with `input_shape = (d_in1, ..., d_inN)` to produce an
output with shape `(d_out1, ..., d_outN)`. This method returns a boolean
array of shape `(d_in1, ..., d_inN, d_out1, ..., d_outN)` with `True`
entries indicating pairs of input and output locations that are connected by
a weight.
Example:
>>> input_shape = (4,)
>>> kernel_shape = (2,)
>>> strides = (1,)
>>> padding = "valid"
>>> conv_kernel_mask(input_shape, kernel_shape, strides, padding)
array([[ True, False, False],
[ True, True, False],
[False, True, True],
[False, False, True]])
where rows and columns correspond to inputs and outputs respectively.
Args:
input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the
input.
kernel_shape: tuple of size N, spatial shape of the convolutional kernel /
receptive field.
strides: tuple of size N, strides along each spatial dimension.
padding: type of padding, string `"same"` or `"valid"`.
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
Returns:
A boolean 2N-D `np.ndarray` of shape
`(d_in1, ..., d_inN, d_out1, ..., d_outN)`, where `(d_out1, ..., d_outN)`
is the spatial shape of the output. `True` entries in the mask represent
pairs of input-output locations that are connected by a weight.
Raises:
ValueError: if `input_shape`, `kernel_shape` and `strides` don't have the
same number of dimensions.
NotImplementedError: if `padding` is not in {`"same"`, `"valid"`}.
"""
if padding not in {"same", "valid"}:
raise NotImplementedError(
f"Padding type {padding} not supported. "
'Only "valid" and "same" are implemented.'
)
in_dims = len(input_shape)
if isinstance(kernel_shape, int):
kernel_shape = (kernel_shape,) * in_dims
if isinstance(strides, int):
strides = (strides,) * in_dims
kernel_dims = len(kernel_shape)
stride_dims = len(strides)
if kernel_dims != in_dims or stride_dims != in_dims:
raise ValueError(
"Number of strides, input and kernel dimensions must all "
f"match. Received: stride_dims={stride_dims}, "
f"in_dims={in_dims}, kernel_dims={kernel_dims}"
)
output_shape = conv_output_shape(
input_shape, kernel_shape, strides, padding
)
mask_shape = input_shape + output_shape
mask = np.zeros(mask_shape, bool)
output_axes_ticks = [range(dim) for dim in output_shape]
for output_position in itertools.product(*output_axes_ticks):
input_axes_ticks = conv_connected_inputs(
input_shape, kernel_shape, output_position, strides, padding
)
for input_position in itertools.product(*input_axes_ticks):
mask[input_position + output_position] = True
return mask
def conv_kernel_idxs(
input_shape,
kernel_shape,
strides,
padding,
filters_in,
filters_out,
data_format,
):
"""Yields output-input tuples of indices in a CNN layer.
The generator iterates over all `(output_idx, input_idx)` tuples, where
`output_idx` is an integer index in a flattened tensor representing a single
output image of a convolutional layer that is connected (via the layer
weights) to the respective single input image at `input_idx`
Example:
>>> input_shape = (2, 2)
>>> kernel_shape = (2, 1)
>>> strides = (1, 1)
>>> padding = "valid"
>>> filters_in = 1
>>> filters_out = 1
>>> data_format = "channels_last"
>>> list(conv_kernel_idxs(input_shape, kernel_shape, strides, padding,
... filters_in, filters_out, data_format))
[(0, 0), (0, 2), (1, 1), (1, 3)]
Args:
input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the
input.
kernel_shape: tuple of size N, spatial shape of the convolutional kernel /
receptive field.
strides: tuple of size N, strides along each spatial dimension.
padding: type of padding, string `"same"` or `"valid"`.
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
filters_in: `int`, number if filters in the input to the layer.
filters_out: `int', number if filters in the output of the layer.
data_format: string, "channels_first" or "channels_last".
Yields:
The next tuple `(output_idx, input_idx)`, where `output_idx` is an integer
index in a flattened tensor representing a single output image of a
convolutional layer that is connected (via the layer weights) to the
respective single input image at `input_idx`.
Raises:
ValueError: if `data_format` is neither `"channels_last"` nor
`"channels_first"`, or if number of strides, input, and kernel number
of dimensions do not match.
NotImplementedError: if `padding` is neither `"same"` nor `"valid"`.
"""
if padding not in ("same", "valid"):
raise NotImplementedError(
f"Padding type {padding} not supported. "
'Only "valid" and "same" are implemented.'
)
in_dims = len(input_shape)
if isinstance(kernel_shape, int):
kernel_shape = (kernel_shape,) * in_dims
if isinstance(strides, int):
strides = (strides,) * in_dims
kernel_dims = len(kernel_shape)
stride_dims = len(strides)
if kernel_dims != in_dims or stride_dims != in_dims:
raise ValueError(
"Number of strides, input and kernel dimensions must all "
f"match. Received: stride_dims={stride_dims}, "
f"in_dims={in_dims}, kernel_dims={kernel_dims}"
)
output_shape = conv_output_shape(
input_shape, kernel_shape, strides, padding
)
output_axes_ticks = [range(dim) for dim in output_shape]
if data_format == "channels_first":
concat_idxs = (
lambda spatial_idx, filter_idx: (filter_idx,) + spatial_idx
)
elif data_format == "channels_last":
concat_idxs = lambda spatial_idx, filter_idx: spatial_idx + (
filter_idx,
)
else:
raise ValueError(
f"Data format `{data_format}` not recognized."
'`data_format` must be "channels_first" or "channels_last".'
)
for output_position in itertools.product(*output_axes_ticks):
input_axes_ticks = conv_connected_inputs(
input_shape, kernel_shape, output_position, strides, padding
)
for input_position in itertools.product(*input_axes_ticks):
for f_in in range(filters_in):
for f_out in range(filters_out):
out_idx = np.ravel_multi_index(
multi_index=concat_idxs(output_position, f_out),
dims=concat_idxs(output_shape, filters_out),
)
in_idx = np.ravel_multi_index(
multi_index=concat_idxs(input_position, f_in),
dims=concat_idxs(input_shape, filters_in),
)
yield (out_idx, in_idx)
def conv_connected_inputs(
input_shape, kernel_shape, output_position, strides, padding
):
"""Return locations of the input connected to an output position.
Assume a convolution with given parameters is applied to an input having N
spatial dimensions with `input_shape = (d_in1, ..., d_inN)`. This method
returns N ranges specifying the input region that was convolved with the
kernel to produce the output at position
`output_position = (p_out1, ..., p_outN)`.
Example:
>>> input_shape = (4, 4)
>>> kernel_shape = (2, 1)
>>> output_position = (1, 1)
>>> strides = (1, 1)
>>> padding = "valid"
>>> conv_connected_inputs(input_shape, kernel_shape, output_position,
... strides, padding)
[range(1, 3), range(1, 2)]
Args:
input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the
input.
kernel_shape: tuple of size N, spatial shape of the convolutional kernel /
receptive field.
output_position: tuple of size N: `(p_out1, ..., p_outN)`, a single
position in the output of the convolution.
strides: tuple of size N, strides along each spatial dimension.
padding: type of padding, string `"same"` or `"valid"`.
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
Returns:
N ranges `[[p_in_left1, ..., p_in_right1], ...,
[p_in_leftN, ..., p_in_rightN]]` specifying the region in the
input connected to output_position.
"""
ranges = []
ndims = len(input_shape)
for d in range(ndims):
left_shift = int(kernel_shape[d] / 2)
right_shift = kernel_shape[d] - left_shift
center = output_position[d] * strides[d]
if padding == "valid":
center += left_shift
start = max(0, center - left_shift)
end = min(input_shape[d], center + right_shift)
ranges.append(range(start, end))
return ranges
def conv_output_shape(input_shape, kernel_shape, strides, padding):
"""Return the output shape of an N-D convolution.
Forces dimensions where input is empty (size 0) to remain empty.
Args:
input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the
input.
kernel_shape: tuple of size N, spatial shape of the convolutional kernel /
receptive field.
strides: tuple of size N, strides along each spatial dimension.
padding: type of padding, string `"same"` or `"valid"`.
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
Returns:
tuple of size N: `(d_out1, ..., d_outN)`, spatial shape of the output.
"""
dims = range(len(kernel_shape))
output_shape = [
conv_output_length(input_shape[d], kernel_shape[d], padding, strides[d])
for d in dims
]
output_shape = tuple(
[0 if input_shape[d] == 0 else output_shape[d] for d in dims]
)
return output_shape
def squeeze_batch_dims(inp, op, inner_rank):
"""Returns `unsqueeze_batch(op(squeeze_batch(inp)))`.
Where `squeeze_batch` reshapes `inp` to shape
`[prod(inp.shape[:-inner_rank])] + inp.shape[-inner_rank:]`
and `unsqueeze_batch` does the reverse reshape but on the output.
Args:
inp: A tensor with dims `batch_shape + inner_shape` where `inner_shape`
is length `inner_rank`.
op: A callable that takes a single input tensor and returns a single.
output tensor.
inner_rank: A python integer.
Returns:
`unsqueeze_batch_op(squeeze_batch(inp))`.
"""
with tf.name_scope("squeeze_batch_dims"):
shape = inp.shape
inner_shape = shape[-inner_rank:]
if not inner_shape.is_fully_defined():
inner_shape = tf.shape(inp)[-inner_rank:]
batch_shape = shape[:-inner_rank]
if not batch_shape.is_fully_defined():
batch_shape = tf.shape(inp)[:-inner_rank]
if isinstance(inner_shape, tf.TensorShape):
inp_reshaped = tf.reshape(inp, [-1] + inner_shape.as_list())
else:
inp_reshaped = tf.reshape(
inp, tf.concat(([-1], inner_shape), axis=-1)
)
out_reshaped = op(inp_reshaped)
out_inner_shape = out_reshaped.shape[-inner_rank:]
if not out_inner_shape.is_fully_defined():
out_inner_shape = tf.shape(out_reshaped)[-inner_rank:]
out = tf.reshape(
out_reshaped, tf.concat((batch_shape, out_inner_shape), axis=-1)
)
out.set_shape(inp.shape[:-inner_rank] + out.shape[-inner_rank:])
return out