From 67b8406e0d694003a3dc68ce6bd727aeae3b5603 Mon Sep 17 00:00:00 2001 From: Chris Lamb Date: Sun, 25 Feb 2018 22:40:51 +0000 Subject: [PATCH] Cleanup/remove some unnecessary control-flow statement around unconditional control-flow. --- .../algorithms/classify/src/gtr123_model.py | 3 +- .../src/algorithms/evaluation/metrics.py | 4 +-- .../algorithms/identify/src/gtr123_model.py | 3 +- .../segment/src/models/unet_3d_model.py | 28 +++++++++---------- prediction/src/preprocess/load_ct.py | 4 +-- prediction/src/tests/__init__.py | 4 +-- .../src/tests/test_grt123_preprocess.py | 7 +++-- 7 files changed, 26 insertions(+), 27 deletions(-) diff --git a/prediction/src/algorithms/classify/src/gtr123_model.py b/prediction/src/algorithms/classify/src/gtr123_model.py index d45e2783..a8a21808 100644 --- a/prediction/src/algorithms/classify/src/gtr123_model.py +++ b/prediction/src/algorithms/classify/src/gtr123_model.py @@ -46,12 +46,11 @@ def __init__(self, n_in, n_out, stride=1): self.conv2 = nn.Conv3d(n_out, n_out, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm3d(n_out) + self.shortcut = None if stride != 1 or n_out != n_in: self.shortcut = nn.Sequential( nn.Conv3d(n_in, n_out, kernel_size=1, stride=stride), nn.BatchNorm3d(n_out)) - else: - self.shortcut = None def forward(self, x): residual = x diff --git a/prediction/src/algorithms/evaluation/metrics.py b/prediction/src/algorithms/evaluation/metrics.py index 3161c119..a6695824 100644 --- a/prediction/src/algorithms/evaluation/metrics.py +++ b/prediction/src/algorithms/evaluation/metrics.py @@ -23,5 +23,5 @@ def logloss(true_label, predicted, eps=1e-15): if true_label == 1: return -np.log(p) - else: - return -np.log(1 - p) + + return -np.log(1 - p) diff --git a/prediction/src/algorithms/identify/src/gtr123_model.py b/prediction/src/algorithms/identify/src/gtr123_model.py index 47cd0031..8ba35052 100644 --- a/prediction/src/algorithms/identify/src/gtr123_model.py +++ b/prediction/src/algorithms/identify/src/gtr123_model.py @@ -49,12 +49,11 @@ def __init__(self, n_in, n_out, stride=1): self.conv2 = nn.Conv3d(n_out, n_out, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm3d(n_out) + self.shortcut = None if stride != 1 or n_out != n_in: self.shortcut = nn.Sequential( nn.Conv3d(n_in, n_out, kernel_size=1, stride=stride), nn.BatchNorm3d(n_out)) - else: - self.shortcut = None def forward(self, x): """ diff --git a/prediction/src/algorithms/segment/src/models/unet_3d_model.py b/prediction/src/algorithms/segment/src/models/unet_3d_model.py index 438aafd6..5fb9f08a 100644 --- a/prediction/src/algorithms/segment/src/models/unet_3d_model.py +++ b/prediction/src/algorithms/segment/src/models/unet_3d_model.py @@ -111,18 +111,18 @@ def compute_level_output_shape(filters, depth, pool_size, image_shape): def get_upconv(depth, nb_filters, pool_size, image_shape, kernel_size=(2, 2, 2), strides=(2, 2, 2), deconvolution=False): - if deconvolution: - try: - from keras_contrib.layers import Deconvolution3D - except ImportError: - raise ImportError("Install keras_contrib in order to use deconvolution. Otherwise set deconvolution=False.") - - return Deconvolution3D(filters=nb_filters, kernel_size=kernel_size, - output_shape=compute_level_output_shape(filters=nb_filters, depth=depth, - pool_size=pool_size, image_shape=image_shape), - strides=strides, input_shape=compute_level_output_shape(filters=nb_filters, - depth=depth + 1, - pool_size=pool_size, - image_shape=image_shape)) - else: + if not deconvolution: return UpSampling3D(size=pool_size) + + try: + from keras_contrib.layers import Deconvolution3D + except ImportError: + raise ImportError("Install keras_contrib in order to use deconvolution. Otherwise set deconvolution=False.") + + return Deconvolution3D(filters=nb_filters, kernel_size=kernel_size, + output_shape=compute_level_output_shape(filters=nb_filters, depth=depth, + pool_size=pool_size, image_shape=image_shape), + strides=strides, input_shape=compute_level_output_shape(filters=nb_filters, + depth=depth + 1, + pool_size=pool_size, + image_shape=image_shape)) diff --git a/prediction/src/preprocess/load_ct.py b/prediction/src/preprocess/load_ct.py index c665993a..90c88284 100644 --- a/prediction/src/preprocess/load_ct.py +++ b/prediction/src/preprocess/load_ct.py @@ -198,5 +198,5 @@ def __init__(self, meta): elif isinstance(self.meta, MetaData): self.non_copy_constructor(meta) - else: - raise ValueError('The meta should be either list[dicom.dataset.FileDataset] or SimpleITK.SimpleITK.Image') + + raise ValueError('The meta should be either list[dicom.dataset.FileDataset] or SimpleITK.SimpleITK.Image') diff --git a/prediction/src/tests/__init__.py b/prediction/src/tests/__init__.py index ec77e5a6..ef1cc845 100644 --- a/prediction/src/tests/__init__.py +++ b/prediction/src/tests/__init__.py @@ -23,5 +23,5 @@ def get_timeout(): if run_slow_tests: return 0 - else: - return int(os.environ.get('TESTS_TIMEOUT', DEFAULT_TIMEOUT)) + + return int(os.environ.get('TESTS_TIMEOUT', DEFAULT_TIMEOUT)) diff --git a/prediction/src/tests/test_grt123_preprocess.py b/prediction/src/tests/test_grt123_preprocess.py index 9a176830..b88caa1e 100644 --- a/prediction/src/tests/test_grt123_preprocess.py +++ b/prediction/src/tests/test_grt123_preprocess.py @@ -94,7 +94,8 @@ def resample(imgs, spacing, new_spacing, order=2): imgs = zoom(imgs, resize_factor, mode='nearest', order=order) return imgs, true_spacing - elif len(imgs.shape) == 4: + + if len(imgs.shape) == 4: n = imgs.shape[-1] newimg = [] @@ -106,8 +107,8 @@ def resample(imgs, spacing, new_spacing, order=2): newimg = np.transpose(np.array(newimg), [1, 2, 3, 0]) return newimg, true_spacing - else: - raise ValueError('wrong shape') + + raise ValueError('wrong shape') def test_lum_trans(metaimage_path):