python类moveaxis()的实例源码

fit_unet_d8g_222_swrap_10.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 39 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_02.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_07.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_11.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_09.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_05.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 41 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_08.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 40 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_01.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_04.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_04.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_07.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 44 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_10.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 40 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_06.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_06.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
refit_unet_d8g_222_swrap_03.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_05.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_01.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_08.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
fit_unet_d8g_222_swrap_03.py 文件源码 项目:kaggle_dsb2017 作者: astoc 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def data_from_grid (cells, gridwidth, gridheight, grid=32):

    width = cells.shape[4]
    crop = (width - grid ) // 2 ## for simplicity we are assuming the same crop (and grid) in x & y directions 

    if crop > 0:  # do NOT crop with 0 as we get empty cells ...
        cells = cells[:,:,:,crop:-crop,crop:-crop]     

    shape = cells.shape
    new_shape_1_dim = shape[0]// (gridwidth * gridheight)  # ws // 36 -- Improved on 20170306
    new_shape = (gridwidth * gridheight, new_shape_1_dim, ) +  tuple([x for x in shape][1:])   # was 36,  Improved on 20170306
    cells = np.reshape(cells, new_shape)  
    cells = np.moveaxis(cells, 0, -3)

    shape = cells.shape
    new_shape2 = tuple([x for x in shape[0:3]]) + (gridheight, gridwidth,) + tuple([x for x in shape[4:]])
    cells = np.reshape(cells, new_shape2)
    cells = cells.swapaxes(-2, -3)
    shape = cells.shape
    combine_shape =tuple([x for x in shape[0:3]]) + (shape[-4]*shape[-3], shape[-2]*shape[-1],)
    cells = np.reshape(cells, combine_shape)

    return cells
inception_model.py 文件源码 项目:sea-lion-counter 作者: rdinse 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def generateCountMaps(self, coords):
    '''Generates a count map for the provided list of coordinates.
    '''

    s = self.config['projective_field_size']
    unpadded_size = self.config['output_size']
    target_size = 3 + unpadded_size + 2 * s 
    countMaps = np.zeros((self.config['cls_nb'], target_size, target_size), dtype=np.int16)

    for coord in coords:
      y = coord[1] - self.config['contextual_pad']
      x = coord[2] - self.config['contextual_pad']
      if y >= 0 and y < self.config['tile_size'] and \
         x >= 0 and x < self.config['tile_size']:

        self.inc_region(countMaps[coord[0]], *self.target_sizes[y, x])

    return np.moveaxis(countMaps, 0, -1).astype(np.float32)
basic_model.py 文件源码 项目:sea-lion-counter 作者: rdinse 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def generateCountMaps(self, coords):
    '''Generates a count map for the provided list of coordinates.  It can
    count at most 256 object within the receptive field.  Beyond that it
    overflows.
    '''

    s = self.config['receptive_field_size']
    pad = s // 2
    unpadded_size = self.config['tile_size']
    target_size = 1 + unpadded_size + 2 * pad
    countMaps = np.zeros((self.config['cls_nb'], target_size, target_size), dtype=np.int16)

    y_min = 0
    y_max = unpadded_size
    x_min = 0
    x_max = unpadded_size
    for coord in coords:
      if coord[1] >= y_min and coord[1] < y_max and coord[2] >= x_min and coord[2] < x_max:
        self.inc_region(countMaps[coord[0]], coord[1] + pad, coord[2] + pad, s, s)

    return np.moveaxis(countMaps, 0, -1).astype(np.float32)
contextual_inception_model.py 文件源码 项目:sea-lion-counter 作者: rdinse 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def preprocessExample(self, image, coords, angle, shear_x, shear_y, scale):
    size_in = image.shape[0]
    size_out = self.config['tile_size'] + 2 * self.config['contextual_pad']

    # h = base64.b64encode(struct.pack(">q", hash(image.tostring()))).decode()

    # data_preparation.imshow(image, coords=coords, save=True, title='%s_preprocessExampleA' %h)

    image = self.applyLinearTransformToImage(image, angle, shear_x, shear_y, scale, size_out)
    image = self.applyColorAugmentation(image, self.config['aug_color_std'], \
                                        self.config['aug_gamma_factor'])
    coords[:, 1:] = self.applyLinearTransformToCoords(coords[:, 1:], angle, shear_x,
                                                      shear_y, scale, size_in, size_out)
    target = self.generateCountMaps(coords)
    large_target = self.generateLargeCountMaps(coords)

    if self.config['draw_border'] and self.config['contextual_pad'] > 0:
      image = self.draw_border(image, self.config['contextual_pad'], self.config['tile_size'])

    # data_preparation.imshow(image, coords=coords, save=True, title='%s_preprocessExampleB' % h)
    # t = np.concatenate(np.moveaxis(target, -1, 0))
    # data_preparation.imshow(t, normalize=True, save=True, title='%s_preprocessExampleC' % h)

    return image.astype(np.float32), target, large_target
test_numeric.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def test_errors(self):
        x = np.random.randn(1, 2, 3)
        assert_raises_regex(ValueError, 'invalid axis .* `source`',
                            np.moveaxis, x, 3, 0)
        assert_raises_regex(ValueError, 'invalid axis .* `source`',
                            np.moveaxis, x, -4, 0)
        assert_raises_regex(ValueError, 'invalid axis .* `destination`',
                            np.moveaxis, x, 0, 5)
        assert_raises_regex(ValueError, 'repeated axis in `source`',
                            np.moveaxis, x, [0, 0], [0, 1])
        assert_raises_regex(ValueError, 'repeated axis in `destination`',
                            np.moveaxis, x, [0, 1], [1, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, 0, [0, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, [0, 1], [0])
tools.py 文件源码 项目:quadpy 作者: nschloe 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def show(
        scheme,
        tet=numpy.array([
            [+1, 0, -1.0/numpy.sqrt(2.0)],
            [-1, 0, -1.0/numpy.sqrt(2.0)],
            [0, +1, +1.0/numpy.sqrt(2.0)],
            [0, -1, +1.0/numpy.sqrt(2.0)],
            ]),
        backend='mpl'
        ):
    edges = numpy.array([[tet[i], tet[j]] for i in range(4) for j in range(i)])
    edges = numpy.moveaxis(edges, 1, 2)
    helpers.backend_to_function[backend](
            transform(scheme.points.T, tet.T).T,
            scheme.weights,
            get_vol(tet),
            edges
            )
    return
tools.py 文件源码 项目:quadpy 作者: nschloe 项目源码 文件源码 阅读 43 收藏 0 点赞 0 评论 0
def get_vol(simplex):
    # Compute the volume via the Cayley-Menger determinant
    # <http://mathworld.wolfram.com/Cayley-MengerDeterminant.html>. One
    # advantage is that it can compute the volume of the simplex indenpendent
    # of the dimension of the space where it's embedded.

    # compute all edge lengths
    edges = numpy.subtract(simplex[:, None], simplex[None, :])
    ei_dot_ej = numpy.einsum('...k,...k->...', edges, edges)

    j = simplex.shape[0] - 1
    a = numpy.empty((j+2, j+2) + ei_dot_ej.shape[2:])
    a[1:, 1:] = ei_dot_ej
    a[0, 1:] = 1.0
    a[1:, 0] = 1.0
    a[0, 0] = 0.0

    a = numpy.moveaxis(a, (0, 1), (-2, -1))
    det = numpy.linalg.det(a)

    vol = numpy.sqrt((-1.0)**(j+1) / 2**j / math.factorial(j)**2 * det)
    return vol
data_augmentation.py 文件源码 项目:pl-cnn 作者: oval-group 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def test_horizontal_flip_with_swap(self):
        """
        Test horizontal flip with swapping axes
        NB: horizontal flip <=> reverse order on columns
        """

        np.random.seed(0)

        # 5 images of size 10 x 10 x 3
        shape = [5, 10, 10, 3]
        batch = np.random.random(size=shape).astype(np.float32)

        # move axis and then flip horizontally
        flipped_1 = np.moveaxis(batch, 3, 1)[:, :, :, ::-1]

        # flip horizontally without moving axis
        flipped_2 = batch[:, :, ::-1, :]

        for i in range(shape[0]):
            for j in range(shape[3]):
                assert np.all(np.isclose(flipped_1[i, j],
                                         flipped_2[i, :, :, j]))
test_numeric.py 文件源码 项目:krpcScripts 作者: jwvanderbeck 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def test_errors(self):
        x = np.random.randn(1, 2, 3)
        assert_raises_regex(ValueError, 'invalid axis .* `source`',
                            np.moveaxis, x, 3, 0)
        assert_raises_regex(ValueError, 'invalid axis .* `source`',
                            np.moveaxis, x, -4, 0)
        assert_raises_regex(ValueError, 'invalid axis .* `destination`',
                            np.moveaxis, x, 0, 5)
        assert_raises_regex(ValueError, 'repeated axis in `source`',
                            np.moveaxis, x, [0, 0], [0, 1])
        assert_raises_regex(ValueError, 'repeated axis in `destination`',
                            np.moveaxis, x, [0, 1], [1, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, 0, [0, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, [0, 1], [0])
load.py 文件源码 项目:carvana-challenge 作者: chplushsieh 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def load_train_image(data_dir, img_name,
                     is_hflip=False, hshift=0, vshift=0, rotate=0, scale_size=0,
                     is_color_trans=False, is_fancy_pca_trans=False, is_edge_enh_trans=False,
                     test_time_aug=None, paddings=None, tile_size=None):
    '''
    load a train image
    '''
    img_file_name = tile.get_img_name(img_name)
    img_ext = 'jpg'
    img = load_image_file(data_dir, img_file_name, img_ext, rotate)
    # img.shape: (height, width, 3)

    if is_color_trans :
        img = color.transform(img)
    if is_fancy_pca_trans:
        img = fancy_pca.rgb_shift(img)
    if is_edge_enh_trans:
        img = cv2.detailEnhance(img, sigma_s=5, sigma_r=0.1)

    img = np.moveaxis(img, 2, 0)
    # img.shape: (3, height, width)

    return preprocess(img, img_name, is_hflip, hshift, vshift, scale_size, paddings, tile_size, test_time_aug)
index.py 文件源码 项目:Kaleido 作者: vacancy 项目源码 文件源码 阅读 37 收藏 0 点赞 0 评论 0
def _do_bprop(self, env, idx):
        if idx == 0:
            x = self.inputs[0].get_value()
            i = self.inputs[1].get_value()

            g = self.outputs[0].get_grad()

            xshape = x.shape
            axis, ndim, alen = self._axis, len(xshape), xshape[self._axis]

            i_hat = i.reshpae(-1)
            g_hat = g.reshape(-1)
            r_hat = np.zeros((i.shape[0], alen), dtype=x.dtype)
            r_hat[np.arange(r_hat.shape[0]), i_hat] = g_hat

            r = r_hat.reshape(xshape[:axis] + xshape[axis+1:] + (alen, ))
            r = np.moveaxis(r, -1, axis)
            return r
        else:
            return 0
tensor_utils.py 文件源码 项目:tensortools 作者: ahwillia 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def fold(unfolded_tensor, mode, shape):
    """Refolds the mode-`mode` unfolding into a tensor of shape `shape`
        In other words, refolds the n-mode unfolded tensor
        into the original tensor of the specified shape.
    Parameters
    ----------
    unfolded_tensor : ndarray
        unfolded tensor of shape ``(shape[mode], -1)``
    mode : int
        the mode of the unfolding
    shape : tuple
        shape of the original tensor before unfolding
    Returns
    -------
    ndarray
        folded_tensor of shape `shape`
    """
    full_shape = list(shape)
    mode_dim = full_shape.pop(mode)
    full_shape.insert(0, mode_dim)
    return np.moveaxis(unfolded_tensor.reshape(full_shape), 0, mode)


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