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How to use patch_config method in yandex-tank
Best Python code snippet using yandex-tank
xception.py
Source:xception.py 
...8 aligned_xception.preprocess_input)9def AlignedXception41Stride16(*args, **kwargs):10 base = AlignedXception41(*args, **kwargs)11 conf = base.get_config()12 conf = patch_config(conf, ['exit_flow/block1/unit1/sepconv3_depthwise'], 'strides', (1, 1))13 conf = patch_config(conf, ['exit_flow/block1/unit1/shortcut'], 'strides', (1, 1))14 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv1_depthwise'], 'dilation_rate', (2, 2))15 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv2_depthwise'], 'dilation_rate', (2, 2))16 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv3_depthwise'], 'dilation_rate', (2, 2))17 patch = models.Model.from_config(conf)18 patch.set_weights(base.get_weights())19 return patch20def AlignedXception41Stride8(*args, **kwargs):21 base = AlignedXception41(*args, **kwargs)22 conf = base.get_config()23 conf = patch_config(conf, ['entry_flow/block3/unit1/sepconv3_depthwise'], 'strides', (1, 1))24 conf = patch_config(conf, ['entry_flow/block3/unit1/shortcut'], 'strides', (1, 1))25 for i in range(8):26 conf = patch_config(conf, [27 'middle_flow/block1/unit{}/sepconv1_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))28 conf = patch_config(conf, [29 'middle_flow/block1/unit{}/sepconv2_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))30 conf = patch_config(conf, [31 'middle_flow/block1/unit{}/sepconv3_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))32 conf = patch_config(conf, ['exit_flow/block1/unit1/sepconv3_depthwise'], 'strides', (1, 1))33 conf = patch_config(conf, ['exit_flow/block1/unit1/shortcut'], 'strides', (1, 1))34 # Output shape is different when using exit dilation rates like in35 # https://github.com/bonlime/keras-deeplab-v3-plus/blob/master/model.py#L26536 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv1_depthwise'], 'dilation_rate', (2, 2))37 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv2_depthwise'], 'dilation_rate', (2, 2))38 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv3_depthwise'], 'dilation_rate', (2, 2))39 patch = models.Model.from_config(conf)40 patch.set_weights(base.get_weights())41 return patch42AlignedXception65 = partial(43 wrap_bone,44 aligned_xception.Xception65,45 aligned_xception.preprocess_input)46def AlignedXception65Stride16(*args, **kwargs):47 base = AlignedXception65(*args, **kwargs)48 conf = base.get_config()49 conf = patch_config(conf, ['exit_flow/block1/unit1/sepconv3_depthwise'], 'strides', (1, 1))50 conf = patch_config(conf, ['exit_flow/block1/unit1/shortcut'], 'strides', (1, 1))51 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv1_depthwise'], 'dilation_rate', (2, 2))52 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv2_depthwise'], 'dilation_rate', (2, 2))53 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv3_depthwise'], 'dilation_rate', (2, 2))54 patch = models.Model.from_config(conf)55 patch.set_weights(base.get_weights())56 return patch57def AlignedXception65Stride8(*args, **kwargs):58 base = AlignedXception65(*args, **kwargs)59 conf = base.get_config()60 conf = patch_config(conf, ['entry_flow/block3/unit1/sepconv3_depthwise'], 'strides', (1, 1))61 conf = patch_config(conf, ['entry_flow/block3/unit1/shortcut'], 'strides', (1, 1))62 for i in range(16):63 conf = patch_config(conf, [64 'middle_flow/block1/unit{}/sepconv1_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))65 conf = patch_config(conf, [66 'middle_flow/block1/unit{}/sepconv2_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))67 conf = patch_config(conf, [68 'middle_flow/block1/unit{}/sepconv3_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))69 conf = patch_config(conf, ['exit_flow/block1/unit1/sepconv3_depthwise'], 'strides', (1, 1))70 conf = patch_config(conf, ['exit_flow/block1/unit1/shortcut'], 'strides', (1, 1))71 # Output shape is different when using exit dilation rates like in72 # https://github.com/bonlime/keras-deeplab-v3-plus/blob/master/model.py#L26573 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv1_depthwise'], 'dilation_rate', (2, 2))74 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv2_depthwise'], 'dilation_rate', (2, 2))75 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv3_depthwise'], 'dilation_rate', (2, 2))76 patch = models.Model.from_config(conf)77 patch.set_weights(base.get_weights())78 return patch79AlignedXception71 = partial(80 wrap_bone,81 aligned_xception.Xception71,82 aligned_xception.preprocess_input)83def AlignedXception71Stride16(*args, **kwargs):84 base = AlignedXception71(*args, **kwargs)85 conf = base.get_config()86 conf = patch_config(conf, ['exit_flow/block1/unit1/sepconv3_depthwise'], 'strides', (1, 1))87 conf = patch_config(conf, ['exit_flow/block1/unit1/shortcut'], 'strides', (1, 1))88 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv1_depthwise'], 'dilation_rate', (2, 2))89 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv2_depthwise'], 'dilation_rate', (2, 2))90 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv3_depthwise'], 'dilation_rate', (2, 2))91 patch = models.Model.from_config(conf)92 patch.set_weights(base.get_weights())93 return patch94def AlignedXception71Stride8(*args, **kwargs):95 base = AlignedXception71(*args, **kwargs)96 conf = base.get_config()97 conf = patch_config(conf, ['entry_flow/block5/unit1/sepconv3_depthwise'], 'strides', (1, 1))98 conf = patch_config(conf, ['entry_flow/block5/unit1/shortcut'], 'strides', (1, 1))99 for i in range(16):100 conf = patch_config(conf, [101 'middle_flow/block1/unit{}/sepconv1_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))102 conf = patch_config(conf, [103 'middle_flow/block1/unit{}/sepconv2_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))104 conf = patch_config(conf, [105 'middle_flow/block1/unit{}/sepconv3_depthwise'.format(i + 1)], 'dilation_rate', (2, 2))106 conf = patch_config(conf, ['exit_flow/block1/unit1/sepconv3_depthwise'], 'strides', (1, 1))107 conf = patch_config(conf, ['exit_flow/block1/unit1/shortcut'], 'strides', (1, 1))108 # Output shape is different when using exit dilation rates like in109 # https://github.com/bonlime/keras-deeplab-v3-plus/blob/master/model.py#L265110 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv1_depthwise'], 'dilation_rate', (2, 2))111 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv2_depthwise'], 'dilation_rate', (2, 2))112 conf = patch_config(conf, ['exit_flow/block2/unit1/sepconv3_depthwise'], 'dilation_rate', (2, 2))113 patch = models.Model.from_config(conf)114 patch.set_weights(base.get_weights())...
luna_s3_p8.py
Source:luna_s3_p8.py
1import data_transforms2import data_iterators3import pathfinder4import utils5import string6import numpy as np7import lasagne as nn8# IMPORT A CORRECT PATCH MODEL HERE9import lung_segmentation10import configs_seg_patch.luna_p8 as patch_config11# print utils.get_script_name(__file__).split('_')[-1]12# print patch_config.__name__.split('.')[-1]13# assert utils.get_script_name(__file__).replace('_s*_', '_') == patch_config.__name__.split('.')[-1]14rng = patch_config.rng15# calculate the following things correctly!16p_transform = {'patch_size': (416, 416, 416),17 'mm_patch_size': (416, 416, 416),18 'pixel_spacing': patch_config.p_transform['pixel_spacing']19 }20window_size = 16021stride = 12822n_windows = (p_transform['patch_size'][0] - window_size) / stride + 123valid_pids = patch_config.valid_pids24def data_prep_function(data, luna_annotations, pixel_spacing, luna_origin,25 p_transform=p_transform,26 p_transform_augment=None):27 # make sure the data is processed the same way28 lung_mask = lung_segmentation.segment_HU_scan(data)29 x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=data,30 pixel_spacing=pixel_spacing,31 p_transform=p_transform,32 luna_annotations=luna_annotations,33 p_transform_augment=None,34 luna_origin=luna_origin,35 lung_mask=lung_mask)36 x = data_transforms.pixelnormHU(x)37 y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')38 return x, y, lung_mask_out, annotations_tf, tf_matrix39valid_data_iterator = data_iterators.LunaScanPositiveLungMaskDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,40 transform_params=p_transform,41 data_prep_fun=data_prep_function,42 rng=rng,43 batch_size=1,44 patient_ids=valid_pids,45 full_batch=True,46 random=False, infinite=False)47def build_model():48 metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)49 metadata_path = utils.find_model_metadata(metadata_dir, patch_config.__name__.split('.')[-1])50 metadata = utils.load_pkl(metadata_path)51 print 'Build model'52 model = patch_config.build_model(patch_size=(window_size, window_size, window_size))53 all_layers = nn.layers.get_all_layers(model.l_out)54 num_params = nn.layers.count_params(model.l_out)55 print ' number of parameters: %d' % num_params56 print string.ljust(' layer output shapes:', 36),57 print string.ljust('#params:', 10),58 print 'output shape:'59 for layer in all_layers:60 name = string.ljust(layer.__class__.__name__, 32)61 num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])62 num_param = string.ljust(num_param.__str__(), 10)63 print ' %s %s %s' % (name, num_param, layer.output_shape)64 nn.layers.set_all_param_values(model.l_out, metadata['param_values'])...
luna_s2_p8.py
Source:luna_s2_p8.py
1import data_transforms2import data_iterators3import pathfinder4import utils5import string6import numpy as np7import lasagne as nn8# IMPORT A CORRECT PATCH MODEL HERE9import configs_seg_patch.luna_p8 as patch_config10# print utils.get_script_name(__file__).split('_')[-1]11# print patch_config.__name__.split('.')[-1]12# assert utils.get_script_name(__file__).replace('_s*_', '_') == patch_config.__name__.split('.')[-1]13rng = patch_config.rng14# calculate the following things correctly!15p_transform = {'patch_size': (416, 416, 416),16 'mm_patch_size': (416, 416, 416),17 'pixel_spacing': patch_config.p_transform['pixel_spacing']18 }19window_size = 16020stride = 12821n_windows = (p_transform['patch_size'][0] - window_size) / stride + 122valid_pids = patch_config.valid_pids23def data_prep_function(data, luna_annotations, pixel_spacing, luna_origin,24 p_transform=p_transform,25 p_transform_augment=None):26 # make sure the data is processed the same way 27 x, annotations_tf, tf_matrix = data_transforms.transform_scan3d(data=data,28 pixel_spacing=pixel_spacing,29 p_transform=p_transform,30 luna_annotations=luna_annotations,31 p_transform_augment=None,32 luna_origin=luna_origin)33 x = data_transforms.pixelnormHU(x)34 y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')35 return x, y, annotations_tf, tf_matrix36valid_data_iterator = data_iterators.LunaScanPositiveDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,37 transform_params=p_transform,38 data_prep_fun=data_prep_function,39 rng=rng,40 patient_ids=valid_pids,41 random=False, infinite=False)42def build_model():43 metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)44 metadata_path = utils.find_model_metadata(metadata_dir, patch_config.__name__.split('.')[-1])45 metadata = utils.load_pkl(metadata_path)46 print 'Build model'47 model = patch_config.build_model(patch_size=(window_size, window_size, window_size))48 all_layers = nn.layers.get_all_layers(model.l_out)49 num_params = nn.layers.count_params(model.l_out)50 print ' number of parameters: %d' % num_params51 print string.ljust(' layer output shapes:', 36),52 print string.ljust('#params:', 10),53 print 'output shape:'54 for layer in all_layers:55 name = string.ljust(layer.__class__.__name__, 32)56 num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])57 num_param = string.ljust(num_param.__str__(), 10)58 print ' %s %s %s' % (name, num_param, layer.output_shape)59 nn.layers.set_all_param_values(model.l_out, metadata['param_values'])...
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