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How to use map_method method in avocado

Best Python code snippet using avocado_python

gen_layer.py

Source:gen_layer.py

1from sklearn.utils import shuffle2from glob import glob3import os4import numpy as np5import makiflow6from makiflow.generators.pipeline.gen_base import PathGenerator7from makiflow.generators.segmentator.map_methods import SegmentIterator8from makiflow.generators.segmentator.map_methods import LoadDataMethod, ComputePositivesPostMethod, ResizePostMethod9from makiflow.generators.segmentator.map_methods import NormalizePostMethod, RGB2BGRPostMethod, SqueezeMaskPostMethod10from makiflow.generators.segmentator.gen_layers import InputGenLayer11import tensorflow as tf12from makiflow.generators.segmentator import (InputGenNumpyGetterLayer, data_reader_wrapper, data_resize_wrapper, 13                                             data_elastic_wrapper, AugmentationPostMethod)14class Generator(PathGenerator):15    def __init__(self, path_images, path_masks):16        self.images = glob(os.path.join(path_images, '*.bmp'))17        self.masks = glob(os.path.join(path_masks, '*.bmp'))18        for _ in range(50):19            self.images, self.masks = shuffle(self.images, self.masks)20    def next_element(self):21        index = 022        while True:23            index = np.random.randint(low=0, high=len(self.images))24            25            el = {26                SegmentIterator.IMAGE: self.images[index],27                SegmentIterator.MASK: self.masks[index]28            }29            30            yield el31def create_generator(path_images, path_masks):32    gen = Generator(path_images, path_masks).next_element()33    gen = data_reader_wrapper(gen, use_bgr2rgb=False)34    gen = data_resize_wrapper(gen, resize_to=(1024,1024))35    gen = data_elastic_wrapper(36        gen, alpha=500, std=5, num_maps=20, noise_invert_scale=15, seed=None,37        img_inter='linear', mask_inter='nearest', border_mode='reflect_101',38        keep_old_data=False, prob=0.939    )40    41    return gen42def get_gen_layer(data_gen_path, im_hw, batch_sz, prefetch_sz):43    path_images, path_masks = data_gen_path44    map_method = AugmentationPostMethod(45        use_rotation=True,46        angle_min=-60.0,47        angle_max=60.0,48        use_shift=False,49        dx_min=None,50        dx_max=None,51        dy_min=None,52        dy_max=None,53        use_zoom=False,54        zoom_min=0.9,55        zoom_max=1.156    )57    map_method = SqueezeMaskPostMethod()(map_method)58    #map_method = RGB2BGRPostMethod()(map_method)59    map_method = NormalizePostMethod(use_float64=True)(map_method)60    map_method = ComputePositivesPostMethod()(map_method)61    return InputGenNumpyGetterLayer(62        prefetch_size=prefetch_sz,63        batch_size=batch_sz,64        path_generator=create_generator(path_images, path_masks),65        name='Input',66        map_operation=map_method,67        num_parallel_calls=5,68        mask_shape=im_hw,69        image_shape=(im_hw[0], im_hw[1], 3)70    )71def main():72    makiflow.set_main_gpu(1)73    sess = tf.Session()74    75    import matplotlib.pyplot as plt76    #gen = create_generator(f'/raid/rustam/med_data/aspai_{1}/images', f'/raid/rustam/med_data/aspai_{1}/masks')77    #gen = Generator(f'/raid/rustam/med_data/aspai_{1}/images', f'/raid/rustam/med_data/aspai_{1}/masks')78    gen = get_generator(f'/raid/rustam/med_data/aspai_{1}/images', f'/raid/rustam/med_data/aspai_{1}/masks')79    t = gen.get_data_tensor()80    gen.get_iterator()81    plt.figure(figsize=(10, 10))82    plt.imshow(sess.run(t)[0].astype(np.uint8))83    plt.figure(figsize=(10, 10))...

get_init_map.py

Source:get_init_map.py

1# -*- coding: utf-8 -*-2"""3Created on Fri Jul 23 16:04:46 202145@author: zhoux6"""78legal_methods = {'naive', 'simulated_annealing'}910def get_init_map(DG, AG, map_method='naive'):11    '''12    Return13        map_list: represents a mapping in which indices and values stand for 14                  logical and physical qubits.15    '''16    if not map_method in legal_methods:17        raise(Exception("Unsupported method {} for initial mapping".format(map_method)))18    if map_method == 'naive':19        return list(range(len(AG)))20    if map_method == 'simulated_annealing':21        from .sa_mapping import InitialMapSimulatedAnnealingWeighted22        return InitialMapSimulatedAnnealingWeighted(DG, AG)
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