o "jVT@sddlZddlZddlZddlZddlmmZddl mZddl m Z ddl m Z ddlmZddlmZddlmZdd lmZmZmZmZgZGd d d Zdd dZdddZddZddZddZddZ ddZ!ddZ"ddZ#dS) N)core) convert_dtype)switch_to_static_graph)TranslatedLayer)layers) logging_utils)func_to_source_codeparse_arg_and_kwargsparse_varargs_name type_namec@seZdZdZdddZddZddZed d Zed d Z d dZ ddZ e ddZ e ddZe ddZe ddZe ddZdS) FunctionSpecz8 Wrapper class for a function for class method. NcCs||_|durd|_d|_n|||_tj|j|_t|\|_|_ t ||_ |j durBt t |ddtrD|j|jj7_dSdSdS)N__self__)_dygraph_function _input_spec_flat_input_spec_verify_input_specpaddleutilsflattenr _arg_names_default_kwargsr Z varargs_name isinstancegetattrrrZ_input_args_names)selffunction input_specrc/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/jit/dy2static/function_spec.py__init__,s    zFunctionSpec.__init__cCst|jt|kr/d|jjt|j|jt||}|r+t|dr+|d7}t|t|t |}t t|t|jD]/}|j|}||vrS| ||||=q=||j vrdtd|jj|||| |j |q=t ||fS)a Moves kwargs with default value into arguments list to keep `args` contain the same length value as function definition. For example: Given function definition: `def foo(x, a=1, b=2)`, when calling it by `foo(23)`, the args is `[23]`, kwargs is `{a=1, b=2}`. In this function, it will return args with `[23, 1, 2]`, kwargs with `{}` Args: args(tuple): tuple of input arguments value of decorated function. kwargs(dict): dict of input keyword arguments value of decorated function. Return: New arguments tuple containing default kwargs value. zOThe decorated function `{}` requires {} arguments: {}, but received {} with {}.rzP Maybe the function has more than one decorator, we don't support this for now.zS`{}()` requires `{}` arguments, but not found in input `args`: {} and `kwargs`: {}.)lenrformatr__name__inspectisclassNotImplementedError ValueErrorlistrangeappendrtuple)rargskwargs error_msgiarg_namerrrunified_args_and_kwargs>s:   z$FunctionSpec.unified_args_and_kwargscsg}g}|jdur2|rtd|jj|t|t|jkr+tdt|t|jt||j}n#t||j}dd| D}tt | |fddt |D}t |j|}||fS)a Converts input arguments into InputSpec. 1. If specific input_spec, use them to construct feed layers. 2. If input_spec is None, consider all Tensor and Numpy.ndarray as feed layers Args: args(tuple): tuple of input arguments value of function containing default kwargs value. kwargs(dict): kwargs arguments received by **kwargs. Return: Same nest structure with args and kwargs by replacing value with InputSpec. Nzc{} got unexpected keyword arguments: {}. Cannot trace the function when `input_spec` is specificed.zZRequires len(arguments) >= len(input_spec), but received len(args):{} < len(InputSpec): {}cSsg|]}d|qS)zkwargs.r).0keyrrr z3FunctionSpec.args_to_input_spec..csi|] \}}||qSrr)r1idxr2Zkwargs_list_with_specrr sz3FunctionSpec.args_to_input_spec..)rr&r!rr"r convert_to_input_spec$_replace_to_input_spec_with_new_namerkeysr'values enumeratereplace_spec_empty_name)rr+r,args_with_specZkwargs_with_specZ kwarg_namesrr6rargs_to_input_specss<    zFunctionSpec.args_to_input_specc Cstj|}g}t|<t|D]/\}}t|tjjr:t |dd}tjj j |j p-d||j t|jd}||_n|}||qWdn1sLwYtj||S) Constructs feed layer by inputs with InputSpec information for main program. Args: input_with_spec(tuple): input arguments by replacing argument with InputSpec. main_program(Program): main program for inserting feed layer. stop_gradientFfeed_%s)nameshapedtypeN)rrr ir_staticZ program_guardr<rstatic InputSpecrinputdatarCrDrrErAr)pack_sequence_as) rinput_with_spec main_programflat_input_specinputsr.var_specrAZ feed_valuerrrpir_to_static_inputs_with_specs"     z+FunctionSpec.pir_to_static_inputs_with_specc Cstj|}g}|}t|D]+\}}t|tjjr4t|dd}|j |j p)d||j |j dd|d} n|} | | qtj||S)r@rAFrBT)rCrDrEZis_dataZneed_check_feedrA)rrrZ global_blockr<rrGrHrZ create_varrCrDrEr)rK) rrLrMrNrOblockr.rPrAZ feed_layerrrrto_static_inputs_with_specs"    z'FunctionSpec.to_static_inputs_with_speccCs(t|ttfstdt|t|S)zJ Verifies the `input_spec` and its element type is valid. zEThe type(input_spec) should be one of (tuple, list), but received {}.)rr*r' TypeErrorr!r )rrrrrrszFunctionSpec._verify_input_speccCsd|jjd|j|jS)Nz function: {}({}), input_spec: {},)r!rr"joinrrrrrr__repr__s  zFunctionSpec.__repr__cC|jSN)rrWrrrdygraph_functionzFunctionSpec.dygraph_functioncCrYrZ)rrWrrr args_namer\zFunctionSpec.args_namecCrYrZ)rrWrrrrr\zFunctionSpec.input_speccCrYrZ)rrWrrrrN r\zFunctionSpec.flat_input_speccCs t|jSrZ)r rrWrrrcodes zFunctionSpec.coderZ)r" __module__ __qualname____doc__rr0r?rrQrSrrXpropertyr[r]rrNr^rrrrr 's* 5<       r TcCjt}|dur3t|tjr*|r%|}dd|D}tt||}|S|j}|Std t ||S)z Returns parameters of decorated layers. If set `include_sublayer` True, the parameters created in sub layers will be added. NcSg|]}|jqSrrC)r1prrrr3z"get_parameters...rh) rirjrrrkbuffersrm_buffersrTr!r )rorprurrrrr get_buffers+s   rwcCsg}ttj|D]L\}}t|tjrtjj |}d|_ n2t|t j j r4|j }tjj|}||_ nt|tjjjtjjfrQ|j }tj|j|j|j}||_ ||q tj||}|S)NT)r<rrrrnpndarrayrGrH from_numpyrAreagerTensor from_tensorbase frameworkVariablepirValuerDrErCr)rK)r+r>r5 input_varrArrr_replace_value_with_input_specBs(   rc CsPt|t|ks Jttt|d}g}tt||D]\}\}}d}ttj|D]p\}} t| tj rBtj j | } d| _ n1t| tjjrW| j } tj j | } | | _ nt| tjjjrq| j } tj | j| j| j} | | _ n| } t| tj tjjtjjjfrdt||d|dt|| _|d7}|| q-qtj||}|S)NrrTz_jst..)r strr<rmrrrrrxryrGrHrzrArr{r|r}r~rrrDrErCzfillr)rK) r+ arg_namesZ order_digitr>orderargZ name_prefixindexr5Z origin_inputrrArrrr9ZsB  & r9c sd fdd }tttfr`g}|dtD]\}}t||}||qttkrStdD]}t|tjj t j frRt dt|q<|td|Sttri}|dD]\}} |vrt| |||<qq| ||<qq|Sttjjr tgd} t| tjjstdd j| _t| rj| _| St d d | d | SS)aD Replaces tensor in structured `inputs` by InputSpec in `input_spec`. Args: inputs(list|dict): nested structure list or dict. input_spec(list|dict): same nested structure list or dict as inputs. Return: Same structure with inputs by replacing the element with specified InputSpec. Fcsbt|t|urtdt|dt|d|r-t|t|kr/tdttdSdS)Nztype(input) should be z, but received rzYRequires len(inputs) >= len(input_spec), but received len(inputs):{} < len(input_spec):{})typerTr r&r!)rIspec check_lengthrrOrrcheck_type_and_lens z1convert_to_input_spec..check_type_and_lenTNzThe inputs constain `{}` without specificing InputSpec, its shape and dtype will be treated immutable. Please specific InputSpec information in `@to_static` if you expect them as mutable inputs.rz1Give input spec into a non-tensorable arguments `z`.z=input spec is not compatitable with real inputs. input_spec: z , real_spec:  )F)rr*r'r<r8r)r rr{r|rxryrwarnr!r extenddictitemsrrGrHr RuntimeErrorrC spec_greaterrD) rOrrrLr.rZout_specZ rest_inputrCrIZ real_specrrrr8sZ         r8cCsDt|}|dt|}t|D]\}}||}t||||<q|S)ap Adds default name according to argument name from decorated function if without specificing InputSpec.name The naming rule are as followed: 1. If InputSpec.name is not None, do nothing. 2. If each argument `x` corresponds to an InputSpec, using the argument name like `x` 3. If the arguments `inputs` corresponds to a list(InputSpec), using name like `inputs_0`, `inputs_1` 4. If the arguments `input_dic` corresponds to a dict(InputSpec), using key as name. For example: # case 1: foo(x, y) foo = to_static(foo, input_spec=[InputSpec([None, 10]), InputSpec([None])]) print([in_var.name for in_var in foo.inputs]) # [x, y] # case 2: foo(inputs) where inputs is a list foo = to_static(foo, input_spec=[[InputSpec([None, 10]), InputSpec([None])]]) print([in_var.name for in_var in foo.inputs]) # [inputs_0, inputs_1] # case 3: foo(inputs) where inputs is a dict foo = to_static(foo, input_spec=[{'x': InputSpec([None, 10]), 'y': InputSpec([None])}]) print([in_var.name for in_var in foo.inputs]) # [x, y] N)r'r r<_replace_spec_name)r]rLZcandidate_arg_namesr.r/rrrrr=s r=cCst|tjjr|jdur||_|St|ttfr4g}t|D]\}}|d|}|t ||q|St|t rMi}| D] \}}t ||||<q?|S|S)zM Replaces InputSpec.name with given `name` while not specificing it. N_) rrrGrHrCr'r*r<r)rrr)rCrZprocessed_specsr.rnew_namer2rrrrs   rcsbddtj|D}|ddtj|D7}difddfdd|D}t|S)a Generater hash spec with args/kwargs InputSpec names. Consider the following InputSpecs with same shape/dtype except for name: 1. [InputSpec([3,3], 'float32', 'x'), InputSpec([3,3], 'float32', 'x')] 2. [InputSpec([3,3], 'float32', 'x'), InputSpec([3,3], 'float32', 'y')] Under @to_static, we should generate two different program not just one, because the former has one input ('x'), but the latter has two input ('x', 'y'). cS g|] }t|tjjr|jqSrrrrGrHrCr1rrrrr3  z$_hash_spec_names..cSrrrrrrrr3rrcs |vr |<d7|S)Nrrre)r.name_idsrrto_idxsz _hash_spec_names..to_idxcsg|]}|qSrr)r1rC)rrrr3%r4)rrrr*)Z args_specsZ kwargs_specsZ spec_namesvaluer)r.rrr_hash_spec_names s   rcCsdd}||j|jS)NcSsBt|t|kr dSt||D]\}}|dkr||krdSqdS)NFT)r rm)Z first_shapeZ second_shapeZfirst_nZsecond_nrrr_shape_greater+sz$spec_greater.._shape_greater)rD)firstotherrrrrr*sr)T)$rir#numpyrxrZpaddle.pir.corerrrFZ paddle.baseZpaddle.base.data_feederrZpaddle.base.dygraph.baserZpaddle.jit.translated_layerrZpaddle.nn.layerrrrr r r r __all__r rsrwrr9r8r=rrrrrrrs0       m &O# !