&`ip- >dZddlmZmZmZmZddlZddlm Z ddl m Z ddl m Z ddlmZe \ZZe dd ed efd Ze d eeefd eeeeeffdeeeeeffd eeeeeffeeefffdZe deeefdeeefdeeeeeffd eeeeeffeeefffdZe d eeefd eeeeeffdeeeeeffd eeeffdZe GddejZe GddejZe GddejZe GddejZdS)z< Code adapted from https://github.com/ikostrikov/pytorch-a3c)AnyListTupleUnionN)get_activation_fn) DeveloperAPI)try_import_torch) TensorType?stdreturncfd}|S)Nc |jdd|xjt|jdddz zc_dS)NrT)keepdim)datanormal_torchsqrtpowsum)tensorr s o/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/models/torch/misc.py initializerz&normc_initializer..initializersa Aq!!! sUZZ (:(:(>(>q$(>(O(OPPPP )r rs` rnormc_initializerrs)QQQQQ rin_size filter_size stride_sizec|\}}t|tr||}}n|\}}t|ttfrt|t|}}n*t|dt|d}}ttjt|t|z } ttjt|t|z } t| dz |z|z|z } t| dz |z|z|z } | dz} | | z }| dz}| |z }||| |f}| | f}||fS)a Note: Padding is added to match TF conv2d `same` padding. See www.tensorflow.org/versions/r0.12/api_docs/python/nn/convolution Args: in_size: Rows (Height), Column (Width) for input stride_size (Union[int,Tuple[int, int]]): Rows (Height), column (Width) for stride. If int, height == width. filter_size: Rows (Height), column (Width) for filter Returns: padding: For input into torch.nn.ZeroPad2d. output: Output shape after padding and convolution. rrr isinstanceintfloatnpceil)rr r! in_heightin_width filter_height filter_width stride_height stride_width out_height out_widthpad_along_heightpad_along_widthpad_top pad_bottompad_left pad_rightpaddingoutputs r same_paddingr9si("Ix+s##2&1;| &1# |+U|,,O&)+&6&6K8H8H| &)+a.&9&93{1~;N;N| RWU9--m0D0DDEEFFJBGE(OOeL.A.AABBCCIJNm;mKiWXX9q=L8<G(RSSO!#G!G+J!#H(*IGZ8G) $F F?r strided_sizekernelstridecNt|tr||fn|\}}t|tr||fn|\}}|dz |zdz |dz |zdz }}|dz} | |dzdkrdndz} |dz} | |dzdkrdndz} |d|z|z dz|d|z|z dzf} | | | | f| fS)aComputes padding and output size such that TF Conv2DTranspose `same` is matched. Note that when padding="same", TensorFlow's Conv2DTranspose makes sure that 0-padding is added to the already strided image in such a way that the output image has the same size as the input image times the stride (and no matter the kernel size). For example: Input image is (4, 4, 24) (not yet strided), padding is "same", stride=2, kernel=5. First, the input image is strided (with stride=2): Input image (4x4): A B C D E F G H I J K L M N O P Stride with stride=2 -> (7x7) A 0 B 0 C 0 D 0 0 0 0 0 0 0 E 0 F 0 G 0 H 0 0 0 0 0 0 0 I 0 J 0 K 0 L 0 0 0 0 0 0 0 M 0 N 0 O 0 P Then this strided image (strided_size=7x7) is padded (exact padding values will be output by this function): padding -> (left=3, right=2, top=3, bottom=2) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 A 0 B 0 C 0 D 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 E 0 F 0 G 0 H 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 I 0 J 0 K 0 L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 M 0 N 0 O 0 P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Then deconvolution with kernel=5 yields an output image of 8x8 (x num output filters). Args: strided_size: The size (width x height) of the already strided image. kernel: Either width x height (tuple of ints) or - if a square kernel is used - a single int for both width and height. stride: Either stride width x stride height (tuple of ints) or - if square striding is used - a single int for both width- and height striding. Returns: Tuple consisting of 1) `padding`: A 4-tuple to pad the input after(!) striding. The values are for left, right, top, and bottom padding, individually. This 4-tuple can be used in a torch.nn.ZeroPad2d layer, and 2) the output shape after striding, padding, and the conv transpose layer. rrr)r$r%)r:r;r<k_wk_hs_ws_h pad_total_w pad_total_hr6r5r4r3 output_shapes r#same_padding_transpose_after_striderECsJ$.fc#:#:FHC#-fc#:#:FHC #Qw}q0#'C-!2CKq IqA!5!5AA1=H!JqA!5!5AA1=G Q+%+a/Q+%+a/L i* 5| CCrc|\}}t|tr||}}n|\}}t|ttfrt|t|}}n*t|dt|d}}ttj||z dzt|z } ttj||z dzt|z } | | fS)auEmulates TF Conv2DLayer "valid" padding (no padding) and computes output dims. This method, analogous to its "same" counterpart, but it only computes the output image size, since valid padding means (0, 0, 0, 0). See www.tensorflow.org/versions/r0.12/api_docs/python/nn/convolution Args: in_size: Rows (Height), Column (Width) for input stride_size (Union[int,Tuple[int, int]]): Rows (Height), column (Width) for stride. If int, height == width. filter_size: Rows (Height), column (Width) for filter Returns: The output shape after padding and convolution. rrr#) rr r!r)r*r+r,r-r.r/r0s r valid_paddingrGs,"Ix+s##2&1;| &1# |+U|,,O&)+&6&6K8H8H| &)+a.&9&93{1~;N;N| RWi-7!;u]?S?SSTTUUJBGX 4q8E,rsSBB************444444444444666666------     r53( 38_(sE#s(O+,(sE#s(O+,( CsCx !5c?3 ((((VWDS/WD #s(OWD #uS#X& 'WD CsCx !5c?3 WDWDWDWDt"# 38_"#sE#s(O+,"#sE#s(O+,"# 38_ "#"#"#"#J88888888v)))))RY)))X     bi   #####bi#####r