a d0@sddlZddlmZmZddlZddlmZddlmmZ ddl m Z m Z gdZ ejdddZGdd d ejZGd d d ejZGd d d ejZdejeeeejdddZdejeeejdddZdejeeeeejdddZdS)N)ListTuple)filter2dgaussian_blur2d)PyrDownPyrUp ScalePyramidpyrdownpyrup build_pyramidreturncCs.tgdgdgdgdgdggdS)z>> input = torch.rand(1, 2, 4, 4) >>> output = PyrDown()(input) # 1x2x2x2 reflectF@N) border_type align_cornersfactorr cs t||_||_||_dSN)super__init__rrr)selfrrr __class__rrr5s zPyrDown.__init__inputr cCst||j|j|jSr)r rrrrr#rrrforward;szPyrDown.forward)rFr) __name__ __module__ __qualname____doc__strboolfloatrrTensorr% __classcell__rrr rrsrcs>eZdZdZd eedfdd Zejejddd Z Z S) raUpsample a tensor and then blurs it. Args: borde_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. align_corners: interpolation flag. Return: the upsampled tensor. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H * 2, W * 2)` Examples: >>> input = torch.rand(1, 2, 4, 4) >>> output = PyrUp()(input) # 1x2x8x8 rF)rrcst||_||_dSr)rrrr)rrrr rrrTs zPyrUp.__init__r"cCst||j|jSr)r rrr$rrrr%Ysz PyrUp.forward)rF) r&r'r(r)r*r+rrr-r%r.rrr rr?srcsneZdZdZdeeeedfdd Zed d d Z ed d dZ ddZ e j eeeefdddZZS)raCreate an scale pyramid of image, usually used for local feature detection. Images are consequently smoothed with Gaussian blur and downscaled. Args: n_levels: number of the levels in octave. init_sigma: initial blur level. min_size: the minimum size of the octave in pixels. double_image: add 2x upscaled image as 1st level of pyramid. OpenCV SIFT does this. Returns: 1st output: images 2nd output: sigmas (coefficients for scale conversion) 3rd output: pixelDists (coefficients for coordinate conversion) Shape: - Input: :math:`(B, C, H, W)` - Output 1st: :math:`[(B, C, NL, H, W), (B, C, NL, H/2, W/2), ...]` - Output 2nd: :math:`[(B, NL), (B, NL), (B, NL), ...]` - Output 3rd: :math:`[(B, NL), (B, NL), (B, NL), ...]` Examples: >>> input = torch.rand(2, 4, 100, 100) >>> sp, sigmas, pds = ScalePyramid(3, 15)(input) 皙?F)n_levels init_sigmamin_size double_imagecsNt||_d|_||_||_|dd|_ddt|j|_||_ dS)Nr/r) rrr2 extra_levelsr3r4borderr, sigma_stepr5)rr2r3r4r5r rrrxs zScalePyramid.__init__r cCs|jjdt|jddt|jddt|jddt|jddt|jddt|jddt|j d S) Nz (n_levels=z, z init_sigma=z min_size=z extra_levels=zborder=z sigma_step=z double_image=)) r!r&r*r2r3r4r8r9r:r5)rrrr__repr__sX     zScalePyramid.__repr__)sigmacCs(td|d}|ddkr$|d7}|S)Ng @rr6rr7)int)rr=ksizerrrget_kernel_sizes zScalePyramid.get_kernel_sizecCsd}d}|jr.tj|dddd}d}|d9}n|}|j|krtt|jd|dd}||}t|||f||f}|j}n|}|||fS) Nrg?rbilinearF)Z scale_factormoderr6g{Gz?) r5F interpolater3maxmathsqrtr@r)rr#pixel_distance cur_sigmaxr=r? cur_levelrrrget_first_levels   zScalePyramid.get_first_level)rJr cCsH|\}}}}||\}}}|t||j|j|j|jg}|t||j|j|j|jg}|gg} d} | dd}t d|j|jD]} |t |j dd} | | } t| t|d|d} | ddkr| d7} t|| | f| | f}||j 9}| d|||ddd| f<||ddd| f<q| d|j }tj||dd|ddfdd }|d 9}|j}t|d|d|jkrq| |g||t||j|j|j||t||j|j|j| d7} qt t| D]}tj| |dd | |<q | ||fS) Nrr7r6rr/Znearest)sizerBr)Zdim)rOrLrZonesr2r8toZdeviceZdtyperangerFrGr:r@minrappendrCrDr3r4lenstack)rrJbs_rKrIrHZsigmasZ pixel_distsZpyrZoct_idxZ level_idxr=r?Z_pyrZnextOctaveFirstLevelirrrr%sB**     && zScalePyramid.forward)r/r0r1F)r&r'r(r)r>r,r+rr*r<r@rLrr-rrr%r.rrr rr]s   rrFr)r#rrrr c Cspt|jdkstd|jt}|j\}}}}t|||}tj|tt||tt||fd|d} | S)aBlur a tensor and downsamples it. .. image:: _static/img/pyrdown.png Args: input: the tensor to be downsampled. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. align_corners: interpolation flag. factor: the downsampling factor Return: the downsampled tensor. Examples: >>> input = torch.arange(16, dtype=torch.float32).reshape(1, 1, 4, 4) >>> pyrdown(input, align_corners=True) tensor([[[[ 3.7500, 5.2500], [ 9.7500, 11.2500]]]]) -Invalid input shape, we expect BxCxHxW. Got: rArOrBr) rTshape ValueErrorrrrCrDr>r,) r#rrrkernelrWheightwidthx_bluroutrrrr s r )r#rrr c Cs`t|jdkstd|jt}|j\}}}}tj||d|dfd|d}t|||}|S)aUpsample a tensor and then blurs it. .. image:: _static/img/pyrup.png Args: input: the tensor to be downsampled. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. align_corners: interpolation flag. Return: the downsampled tensor. Examples: >>> input = torch.arange(4, dtype=torch.float32).reshape(1, 1, 2, 2) >>> pyrup(input, align_corners=True) tensor([[[[0.7500, 0.8750, 1.1250, 1.2500], [1.0000, 1.1250, 1.3750, 1.5000], [1.5000, 1.6250, 1.8750, 2.0000], [1.7500, 1.8750, 2.1250, 2.2500]]]]) rYrZr6rAr[)rTr\r]rrCrDr) r#rrr^rWr_r`Zx_uprarrrr s r )r# max_levelrrr cCst|tjstdt|t|jdksrSrQr )r#rcrrZpyramidrWZimg_currZimg_downrrrr 5s    r )rFr)rF)rF)rFtypingrrrZtorch.nnnnZtorch.nn.functionalZ functionalrCZkornia.filtersrr__all__r-rModulerrrr*r+r,r r r>r rrrrs,     *&