Nahrát soubory do „pages/students/2016/lukas_pokryvka/dp2021/lungCancer/util“

pages/students/2016/lukas_pokryvka/dp2021/lungCancer/util/augmentation.py

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+import math
+import random
+import warnings
+
+import numpy as np
+import scipy.ndimage
+
+import torch
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+import torch.backends.cudnn as cudnn
+
+from util.logconf import logging
+log = logging.getLogger(__name__)
+# log.setLevel(logging.WARN)
+# log.setLevel(logging.INFO)
+log.setLevel(logging.DEBUG)
+
+def cropToShape(image, new_shape, center_list=None, fill=0.0):
+    # log.debug([image.shape, new_shape, center_list])
+    # assert len(image.shape) == 3, repr(image.shape)
+
+    if center_list is None:
+        center_list = [int(image.shape[i] / 2) for i in range(3)]
+
+    crop_list = []
+    for i in range(0, 3):
+        crop_int = center_list[i]
+        if image.shape[i] > new_shape[i] and crop_int is not None:
+
+            # We can't just do crop_int +/- shape/2 since shape might be odd
+            # and ints round down.
+            start_int = crop_int - int(new_shape[i]/2)
+            end_int = start_int + new_shape[i]
+            crop_list.append(slice(max(0, start_int), end_int))
+        else:
+            crop_list.append(slice(0, image.shape[i]))
+
+    # log.debug([image.shape, crop_list])
+    image = image[crop_list]
+
+    crop_list = []
+    for i in range(0, 3):
+        if image.shape[i] < new_shape[i]:
+            crop_int = int((new_shape[i] - image.shape[i]) / 2)
+            crop_list.append(slice(crop_int, crop_int + image.shape[i]))
+        else:
+            crop_list.append(slice(0, image.shape[i]))
+
+    # log.debug([image.shape, crop_list])
+    new_image = np.zeros(new_shape, dtype=image.dtype)
+    new_image[:] = fill
+    new_image[crop_list] = image
+
+    return new_image
+
+
+def zoomToShape(image, new_shape, square=True):
+    # assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
+
+    if square and image.shape[0] != image.shape[1]:
+        crop_int = min(image.shape[0], image.shape[1])
+        new_shape = [crop_int, crop_int, image.shape[2]]
+        image = cropToShape(image, new_shape)
+
+    zoom_shape = [new_shape[i] / image.shape[i] for i in range(3)]
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        image = scipy.ndimage.interpolation.zoom(
+            image, zoom_shape,
+            output=None, order=0, mode='nearest', cval=0.0, prefilter=True)
+
+    return image
+
+def randomOffset(image_list, offset_rows=0.125, offset_cols=0.125):
+
+    center_list = [int(image_list[0].shape[i] / 2) for i in range(3)]
+    center_list[0] += int(offset_rows * (random.random() - 0.5) * 2)
+    center_list[1] += int(offset_cols * (random.random() - 0.5) * 2)
+    center_list[2] = None
+
+    new_list = []
+    for image in image_list:
+        new_image = cropToShape(image, image.shape, center_list)
+        new_list.append(new_image)
+
+    return new_list
+
+
+def randomZoom(image_list, scale=None, scale_min=0.8, scale_max=1.3):
+    if scale is None:
+        scale = scale_min + (scale_max - scale_min) * random.random()
+
+    new_list = []
+    for image in image_list:
+        # assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            # log.info([image.shape])
+            zimage = scipy.ndimage.interpolation.zoom(
+                image, [scale, scale, 1.0],
+                output=None, order=0, mode='nearest', cval=0.0, prefilter=True)
+        image = cropToShape(zimage, image.shape)
+
+        new_list.append(image)
+
+    return new_list
+
+
+_randomFlip_transform_list = [
+    # lambda a: np.rot90(a, axes=(0, 1)),
+    # lambda a: np.flip(a, 0),
+    lambda a: np.flip(a, 1),
+]
+
+def randomFlip(image_list, transform_bits=None):
+    if transform_bits is None:
+        transform_bits = random.randrange(0, 2 ** len(_randomFlip_transform_list))
+
+    new_list = []
+    for image in image_list:
+        # assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
+
+        for n in range(len(_randomFlip_transform_list)):
+            if transform_bits & 2**n:
+                # prhist(image, 'before')
+                image = _randomFlip_transform_list[n](image)
+                # prhist(image, 'after ')
+
+        new_list.append(image)
+
+    return new_list
+
+
+def randomSpin(image_list, angle=None, range_tup=None, axes=(0, 1)):
+    if range_tup is None:
+        range_tup = (0, 360)
+
+    if angle is None:
+        angle = range_tup[0] + (range_tup[1] - range_tup[0]) * random.random()
+
+    new_list = []
+    for image in image_list:
+        # assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
+
+        image = scipy.ndimage.interpolation.rotate(
+                image, angle, axes=axes, reshape=False,
+                output=None, order=0, mode='nearest', cval=0.0, prefilter=True)
+
+        new_list.append(image)
+
+    return new_list
+
+
+def randomNoise(image_list, noise_min=-0.1, noise_max=0.1):
+    noise = np.zeros_like(image_list[0])
+    noise += (noise_max - noise_min) * np.random.random_sample(image_list[0].shape) + noise_min
+    noise *= 5
+    noise = scipy.ndimage.filters.gaussian_filter(noise, 3)
+    # noise += (noise_max - noise_min) * np.random.random_sample(image_hsv.shape) + noise_min
+
+    new_list = []
+    for image_hsv in image_list:
+        image_hsv = image_hsv + noise
+
+        new_list.append(image_hsv)
+
+    return new_list
+
+
+def randomHsvShift(image_list, h=None, s=None, v=None,
+                   h_min=-0.1, h_max=0.1,
+                   s_min=0.5, s_max=2.0,
+                   v_min=0.5, v_max=2.0):
+    if h is None:
+        h = h_min + (h_max - h_min) * random.random()
+    if s is None:
+        s = s_min + (s_max - s_min) * random.random()
+    if v is None:
+        v = v_min + (v_max - v_min) * random.random()
+
+    new_list = []
+    for image_hsv in image_list:
+        # assert image_hsv.shape[-1] == 3, repr(image_hsv.shape)
+
+        image_hsv[:,:,0::3] += h
+        image_hsv[:,:,1::3] = image_hsv[:,:,1::3] ** s
+        image_hsv[:,:,2::3] = image_hsv[:,:,2::3] ** v
+
+        new_list.append(image_hsv)
+
+    return clampHsv(new_list)
+
+
+def clampHsv(image_list):
+    new_list = []
+    for image_hsv in image_list:
+        image_hsv = image_hsv.clone()
+
+        # Hue wraps around
+        image_hsv[:,:,0][image_hsv[:,:,0] > 1] -= 1
+        image_hsv[:,:,0][image_hsv[:,:,0] < 0] += 1
+
+        # Everything else clamps between 0 and 1
+        image_hsv[image_hsv > 1] = 1
+        image_hsv[image_hsv < 0] = 0
+
+        new_list.append(image_hsv)
+
+    return new_list
+
+
+# def torch_augment(input):
+#     theta = random.random() * math.pi * 2
+#     s = math.sin(theta)
+#     c = math.cos(theta)
+#     c1 = 1 - c
+#     axis_vector = torch.rand(3, device='cpu', dtype=torch.float64)
+#     axis_vector -= 0.5
+#     axis_vector /= axis_vector.abs().sum()
+#     l, m, n = axis_vector
+#
+#     matrix = torch.tensor([
+#         [l*l*c1 +   c, m*l*c1 - n*s, n*l*c1 + m*s, 0],
+#         [l*m*c1 + n*s, m*m*c1 +   c, n*m*c1 - l*s, 0],
+#         [l*n*c1 - m*s, m*n*c1 + l*s, n*n*c1 +   c, 0],
+#         [0, 0, 0, 1],
+#     ], device=input.device, dtype=torch.float32)
+#
+#     return th_affine3d(input, matrix)
+
+
+
+
+# following from https://github.com/ncullen93/torchsample/blob/master/torchsample/utils.py
+# MIT licensed
+
+# def th_affine3d(input, matrix):
+#     """
+#     3D Affine image transform on torch.Tensor
+#     """
+#     A = matrix[:3,:3]
+#     b = matrix[:3,3]
+#
+#     # make a meshgrid of normal coordinates
+#     coords = th_iterproduct(input.size(-3), input.size(-2), input.size(-1), dtype=torch.float32)
+#
+#     # shift the coordinates so center is the origin
+#     coords[:,0] = coords[:,0] - (input.size(-3) / 2. - 0.5)
+#     coords[:,1] = coords[:,1] - (input.size(-2) / 2. - 0.5)
+#     coords[:,2] = coords[:,2] - (input.size(-1) / 2. - 0.5)
+#
+#     # apply the coordinate transformation
+#     new_coords = coords.mm(A.t().contiguous()) + b.expand_as(coords)
+#
+#     # shift the coordinates back so origin is origin
+#     new_coords[:,0] = new_coords[:,0] + (input.size(-3) / 2. - 0.5)
+#     new_coords[:,1] = new_coords[:,1] + (input.size(-2) / 2. - 0.5)
+#     new_coords[:,2] = new_coords[:,2] + (input.size(-1) / 2. - 0.5)
+#
+#     # map new coordinates using bilinear interpolation
+#     input_transformed = th_trilinear_interp3d(input, new_coords)
+#
+#     return input_transformed
+#
+#
+# def th_trilinear_interp3d(input, coords):
+#     """
+#     trilinear interpolation of 3D torch.Tensor image
+#     """
+#     # take clamp then floor/ceil of x coords
+#     x = torch.clamp(coords[:,0], 0, input.size(-3)-2)
+#     x0 = x.floor()
+#     x1 = x0 + 1
+#     # take clamp then floor/ceil of y coords
+#     y = torch.clamp(coords[:,1], 0, input.size(-2)-2)
+#     y0 = y.floor()
+#     y1 = y0 + 1
+#     # take clamp then floor/ceil of z coords
+#     z = torch.clamp(coords[:,2], 0, input.size(-1)-2)
+#     z0 = z.floor()
+#     z1 = z0 + 1
+#
+#     stride = torch.tensor(input.stride()[-3:], dtype=torch.int64, device=input.device)
+#     x0_ix = x0.mul(stride[0]).long()
+#     x1_ix = x1.mul(stride[0]).long()
+#     y0_ix = y0.mul(stride[1]).long()
+#     y1_ix = y1.mul(stride[1]).long()
+#     z0_ix = z0.mul(stride[2]).long()
+#     z1_ix = z1.mul(stride[2]).long()
+#
+#     # input_flat = th_flatten(input)
+#     input_flat = x.contiguous().view(x[0], x[1], -1)
+#
+#     vals_000 = input_flat[:, :, x0_ix+y0_ix+z0_ix]
+#     vals_001 = input_flat[:, :, x0_ix+y0_ix+z1_ix]
+#     vals_010 = input_flat[:, :, x0_ix+y1_ix+z0_ix]
+#     vals_011 = input_flat[:, :, x0_ix+y1_ix+z1_ix]
+#     vals_100 = input_flat[:, :, x1_ix+y0_ix+z0_ix]
+#     vals_101 = input_flat[:, :, x1_ix+y0_ix+z1_ix]
+#     vals_110 = input_flat[:, :, x1_ix+y1_ix+z0_ix]
+#     vals_111 = input_flat[:, :, x1_ix+y1_ix+z1_ix]
+#
+#     xd = x - x0
+#     yd = y - y0
+#     zd = z - z0
+#     xm1 = 1 - xd
+#     ym1 = 1 - yd
+#     zm1 = 1 - zd
+#
+#     x_mapped = (
+#             vals_000.mul(xm1).mul(ym1).mul(zm1) +
+#             vals_001.mul(xm1).mul(ym1).mul(zd) +
+#             vals_010.mul(xm1).mul(yd).mul(zm1) +
+#             vals_011.mul(xm1).mul(yd).mul(zd) +
+#             vals_100.mul(xd).mul(ym1).mul(zm1) +
+#             vals_101.mul(xd).mul(ym1).mul(zd) +
+#             vals_110.mul(xd).mul(yd).mul(zm1) +
+#             vals_111.mul(xd).mul(yd).mul(zd)
+#     )
+#
+#     return x_mapped.view_as(input)
+#
+# def th_iterproduct(*args, dtype=None):
+#     return torch.from_numpy(np.indices(args).reshape((len(args),-1)).T)
+#
+# def th_flatten(x):
+#     """Flatten tensor"""
+#     return x.contiguous().view(x[0], x[1], -1)

pages/students/2016/lukas_pokryvka/dp2021/lungCancer/util/disk.py

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+import gzip
+
+from diskcache import FanoutCache, Disk
+from diskcache.core import BytesType, MODE_BINARY, BytesIO
+
+from util.logconf import logging
+log = logging.getLogger(__name__)
+# log.setLevel(logging.WARN)
+log.setLevel(logging.INFO)
+# log.setLevel(logging.DEBUG)
+
+
+class GzipDisk(Disk):
+    def store(self, value, read, key=None):
+        """
+        Override from base class diskcache.Disk.
+
+        Chunking is due to needing to work on pythons < 2.7.13:
+        - Issue #27130: In the "zlib" module, fix handling of large buffers
+          (typically 2 or 4 GiB).  Previously, inputs were limited to 2 GiB, and
+          compression and decompression operations did not properly handle results of
+          2 or 4 GiB.
+
+        :param value: value to convert
+        :param bool read: True when value is file-like object
+        :return: (size, mode, filename, value) tuple for Cache table
+        """
+        # pylint: disable=unidiomatic-typecheck
+        if type(value) is BytesType:
+            if read:
+                value = value.read()
+                read = False
+
+            str_io = BytesIO()
+            gz_file = gzip.GzipFile(mode='wb', compresslevel=1, fileobj=str_io)
+
+            for offset in range(0, len(value), 2**30):
+                gz_file.write(value[offset:offset+2**30])
+            gz_file.close()
+
+            value = str_io.getvalue()
+
+        return super(GzipDisk, self).store(value, read)
+
+
+    def fetch(self, mode, filename, value, read):
+        """
+        Override from base class diskcache.Disk.
+
+        Chunking is due to needing to work on pythons < 2.7.13:
+        - Issue #27130: In the "zlib" module, fix handling of large buffers
+          (typically 2 or 4 GiB).  Previously, inputs were limited to 2 GiB, and
+          compression and decompression operations did not properly handle results of
+          2 or 4 GiB.
+
+        :param int mode: value mode raw, binary, text, or pickle
+        :param str filename: filename of corresponding value
+        :param value: database value
+        :param bool read: when True, return an open file handle
+        :return: corresponding Python value
+        """
+        value = super(GzipDisk, self).fetch(mode, filename, value, read)
+
+        if mode == MODE_BINARY:
+            str_io = BytesIO(value)
+            gz_file = gzip.GzipFile(mode='rb', fileobj=str_io)
+            read_csio = BytesIO()
+
+            while True:
+                uncompressed_data = gz_file.read(2**30)
+                if uncompressed_data:
+                    read_csio.write(uncompressed_data)
+                else:
+                    break
+
+            value = read_csio.getvalue()
+
+        return value
+
+def getCache(scope_str):
+    return FanoutCache('data-unversioned/cache/' + scope_str,
+                       disk=GzipDisk,
+                       shards=64,
+                       timeout=1,
+                       size_limit=3e11,
+                       # disk_min_file_size=2**20,
+                       )
+
+# def disk_cache(base_path, memsize=2):
+#     def disk_cache_decorator(f):
+#         @functools.wraps(f)
+#         def wrapper(*args, **kwargs):
+#             args_str = repr(args) + repr(sorted(kwargs.items()))
+#             file_str = hashlib.md5(args_str.encode('utf8')).hexdigest()
+#
+#             cache_path = os.path.join(base_path, f.__name__, file_str + '.pkl.gz')
+#
+#             if not os.path.exists(os.path.dirname(cache_path)):
+#                 os.makedirs(os.path.dirname(cache_path), exist_ok=True)
+#
+#             if os.path.exists(cache_path):
+#                 return pickle_loadgz(cache_path)
+#             else:
+#                 ret = f(*args, **kwargs)
+#                 pickle_dumpgz(cache_path, ret)
+#                 return ret
+#
+#         return wrapper
+#
+#     return disk_cache_decorator
+#
+#
+# def pickle_dumpgz(file_path, obj):
+#     log.debug("Writing {}".format(file_path))
+#     with open(file_path, 'wb') as file_obj:
+#         with gzip.GzipFile(mode='wb', compresslevel=1, fileobj=file_obj) as gz_file:
+#             pickle.dump(obj, gz_file, pickle.HIGHEST_PROTOCOL)
+#
+#
+# def pickle_loadgz(file_path):
+#     log.debug("Reading {}".format(file_path))
+#     with open(file_path, 'rb') as file_obj:
+#         with gzip.GzipFile(mode='rb', fileobj=file_obj) as gz_file:
+#             return pickle.load(gz_file)
+#
+#
+# def dtpath(dt=None):
+#     if dt is None:
+#         dt = datetime.datetime.now()
+#
+#     return str(dt).rsplit('.', 1)[0].replace(' ', '--').replace(':', '.')
+#
+#
+# def safepath(s):
+#     s = s.replace(' ', '_')
+#     return re.sub('[^A-Za-z0-9_.-]', '', s)

pages/students/2016/lukas_pokryvka/dp2021/lungCancer/util/logconf.py

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+import logging
+import logging.handlers
+
+root_logger = logging.getLogger()
+root_logger.setLevel(logging.INFO)
+
+# Some libraries attempt to add their own root logger handlers. This is
+# annoying and so we get rid of them.
+for handler in list(root_logger.handlers):
+    root_logger.removeHandler(handler)
+
+logfmt_str = "%(asctime)s %(levelname)-8s pid:%(process)d %(name)s:%(lineno)03d:%(funcName)s %(message)s"
+formatter = logging.Formatter(logfmt_str)
+
+streamHandler = logging.StreamHandler()
+streamHandler.setFormatter(formatter)
+streamHandler.setLevel(logging.DEBUG)
+
+root_logger.addHandler(streamHandler)

pages/students/2016/lukas_pokryvka/dp2021/lungCancer/util/unet.py

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+# From https://github.com/jvanvugt/pytorch-unet
+# https://raw.githubusercontent.com/jvanvugt/pytorch-unet/master/unet.py
+
+# MIT License
+#
+# Copyright (c) 2018 Joris
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# Adapted from https://discuss.pytorch.org/t/unet-implementation/426
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class UNet(nn.Module):
+    def __init__(self, in_channels=1, n_classes=2, depth=5, wf=6, padding=False,
+                 batch_norm=False, up_mode='upconv'):
+        """
+        Implementation of
+        U-Net: Convolutional Networks for Biomedical Image Segmentation
+        (Ronneberger et al., 2015)
+        https://arxiv.org/abs/1505.04597
+
+        Using the default arguments will yield the exact version used
+        in the original paper
+
+        Args:
+            in_channels (int): number of input channels
+            n_classes (int): number of output channels
+            depth (int): depth of the network
+            wf (int): number of filters in the first layer is 2**wf
+            padding (bool): if True, apply padding such that the input shape
+                            is the same as the output.
+                            This may introduce artifacts
+            batch_norm (bool): Use BatchNorm after layers with an
+                               activation function
+            up_mode (str): one of 'upconv' or 'upsample'.
+                           'upconv' will use transposed convolutions for
+                           learned upsampling.
+                           'upsample' will use bilinear upsampling.
+        """
+        super(UNet, self).__init__()
+        assert up_mode in ('upconv', 'upsample')
+        self.padding = padding
+        self.depth = depth
+        prev_channels = in_channels
+        self.down_path = nn.ModuleList()
+        for i in range(depth):
+            self.down_path.append(UNetConvBlock(prev_channels, 2**(wf+i),
+                                                padding, batch_norm))
+            prev_channels = 2**(wf+i)
+
+        self.up_path = nn.ModuleList()
+        for i in reversed(range(depth - 1)):
+            self.up_path.append(UNetUpBlock(prev_channels, 2**(wf+i), up_mode,
+                                            padding, batch_norm))
+            prev_channels = 2**(wf+i)
+
+        self.last = nn.Conv2d(prev_channels, n_classes, kernel_size=1)
+
+    def forward(self, x):
+        blocks = []
+        for i, down in enumerate(self.down_path):
+            x = down(x)
+            if i != len(self.down_path)-1:
+                blocks.append(x)
+                x = F.avg_pool2d(x, 2)
+
+        for i, up in enumerate(self.up_path):
+            x = up(x, blocks[-i-1])
+
+        return self.last(x)
+
+
+class UNetConvBlock(nn.Module):
+    def __init__(self, in_size, out_size, padding, batch_norm):
+        super(UNetConvBlock, self).__init__()
+        block = []
+
+        block.append(nn.Conv2d(in_size, out_size, kernel_size=3,
+                               padding=int(padding)))
+        block.append(nn.ReLU())
+        # block.append(nn.LeakyReLU())
+        if batch_norm:
+            block.append(nn.BatchNorm2d(out_size))
+
+        block.append(nn.Conv2d(out_size, out_size, kernel_size=3,
+                               padding=int(padding)))
+        block.append(nn.ReLU())
+        # block.append(nn.LeakyReLU())
+        if batch_norm:
+            block.append(nn.BatchNorm2d(out_size))
+
+        self.block = nn.Sequential(*block)
+
+    def forward(self, x):
+        out = self.block(x)
+        return out
+
+
+class UNetUpBlock(nn.Module):
+    def __init__(self, in_size, out_size, up_mode, padding, batch_norm):
+        super(UNetUpBlock, self).__init__()
+        if up_mode == 'upconv':
+            self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2,
+                                         stride=2)
+        elif up_mode == 'upsample':
+            self.up = nn.Sequential(nn.Upsample(mode='bilinear', scale_factor=2),
+                                    nn.Conv2d(in_size, out_size, kernel_size=1))
+
+        self.conv_block = UNetConvBlock(in_size, out_size, padding, batch_norm)
+
+    def center_crop(self, layer, target_size):
+        _, _, layer_height, layer_width = layer.size()
+        diff_y = (layer_height - target_size[0]) // 2
+        diff_x = (layer_width - target_size[1]) // 2
+        return layer[:, :, diff_y:(diff_y + target_size[0]), diff_x:(diff_x + target_size[1])]
+
+    def forward(self, x, bridge):
+        up = self.up(x)
+        crop1 = self.center_crop(bridge, up.shape[2:])
+        out = torch.cat([up, crop1], 1)
+        out = self.conv_block(out)
+
+        return out