a  d2 @s|ddlmZddlmZmZmZddlmZmZm Z m Z m Z m Z m Z mZmZddlZddlZddlmZddlmZmZmZerddlZddlZddlmZe ejeejjej ffZ!ejjZ"ej#j$Z%n2e e&e&fZ!e&Z"ddl'm(Z(e(eee dd d Z%erddl)Z)d d gZ*er$er$d gnd gZ*ddddZ+e%e*d!eee eee,efe ee,effe e"e e-e e.e e.e e,e e,e!dddZ/e0e e0e0fdddZ1ee"ej fe0eej e"fdddZ2e%e*ed"ee e"e-e e ee0e,fe!dddZ3e%e*d#ee eeefe e ee ee effe ee e"e e e,e,fe e,e e,e!ddd Z4dS)$)product)ceilfloorsqrt) AnyDict GeneratorListOptionalSequenceTupleUnion no_type_checkN)Tensor)_LATEX_AVAILABLE_MATPLOTLIB_AVAILABLE_SCIENCEPLOT_AVAILABLE)contextmanager)argskwargsreturncos dVdS)z0No-ops decorator if matplotlib is not installed.N)rrrrd/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/utilities/plot.py style_change'srZsciencezno-latexdefault)rcCsts tddS)z+Raise error if matplotlib is not installed.z`Plot function expects `matplotlib` to be installed. Please install with `pip install matplotlib`N)rModuleNotFoundErrorrrrr_error_on_missing_matplotlib5sr)valaxhigher_is_better lower_bound upper_bound legend_namenamerc s4t|durtnd|f\}}|dttrdkrb|j gdddnHt D]>\}} |r|d|n|} |j|| ddd| d qjntt rLt  D]\}\} } | dkr*|j| ddd | d |d |d |tt| q|j|| dd| d qƐn ttrdttdt rfdddD D]&\} } |j| ddd | d qntdjdk} | r܈jndt D]J\}} | r|r|d|n|nd} |j| ddd | d q|d |d |tntd|\} }| r|r|j| |dddd d d|}|dur|durd||}nd|d|d}|j|dur||n |d||dur ||n |d|d|d ||dur8|nd|}d|d|d}g}|durr| ||dur| ||j!||d|dddd|dur,|dur|s|"|d||d|j#|d|dddd|dur,|r,|"|d||d|j#|d|dddd||fS)aPlot a single metric value or multiple, including bounds of value if existing. Args: val: A single tensor with one or multiple values (multiclass/label/output format) or a list of such tensors. If a list is provided the values are interpreted as a time series of evolving values. ax: Axis from a figure. higher_is_better: Indicates if a label indicating where the optimal value it should be added to the figure lower_bound: lower value that the metric can take upper_bound: upper value that the metric can take legend_name: for class based metrics specify the legend prefix e.g. Class or Label to use when multiple values are provided name: Name of the metric to use for the y-axis label Returns: A tuple consisting of the figure and respective ax objects of the generated figure Raises: ModuleNotFoundError: If `matplotlib` is not installed NFo )marker markersize None)r'r( linestylelabel-TZStep)r'r(r,rcs,i|]$tfddtDqS)csg|]}|qSrr).0i)krrr sz7plot_single_or_multi_val...)torchstackrange)r.Zn_stepsr)r0r sr2z,plot_single_or_multi_val..z&Got unknown format for argument `val`.z upper center)g?gffffff?)locZbbox_to_anchorZncolZfancyboxZshadowg?)ZbottomtopZdashedr0)Z linestylescolorszOptimal valuecenter)sZhorizontalalignmentZverticalalignment)$rpltsubplotsZ get_xaxisZ set_visible isinstancerZnumelplotdetachcpu enumeratedictitems set_xlabel set_xticksr3arangelenr r4ndimTZ unsqueeze ValueErrorZget_legend_handles_labelslegendZget_ylimZset_ylimgrid set_ylabelZget_xlimappendZhlinesZset_xlimtext)rrrr r!r"r#figr/vr,r0Z multi_seriesZhandleslabelsZylimZfactorZxlimZy_linesrr6rplot_single_or_multi_val=s  $  " "  $          rW)nrcCsXt|}|||kr$t|t|fSt|t||krHt|t|fSt|t|fS)z/Split `n` figures into `rows` x `cols` figures.)rintrr)rXZnsqrrr_get_col_row_splits  rZ)axsnbrcCs:t|tr|S|j}||dD] }|q |d|S)zSReduce `axs` to `nb` Axes. All further Axes are removed from the figure. N)rA_AX_TYPEZflatremove)r[r\rrrrtrim_axss   r_T)confmatradd_textrVrc Cs>t|jdkr.|jdd}}t|\}}nd|jdddf\}}}}|dur~|jdkr~t||kr~tdt|d||jdkr|pt|}tt t t |}nd}|pt| }|durt j||dn ||f\} } t| |} t |D]:} |dkr|dkr| | n| }|dur@|jd || d d ||jdkr`|| n ||jd d d |jd d d |tt ||tt ||j|ddd|j|ddd|rtt |t |D]N\} } |jdkr|| | | fn || | f}|j| | t |ddd dqq| | fS)a?Plot an confusion matrix. Inspired by: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/_plot/confusion_matrix.py. Works for both binary, multiclass and multilabel confusion matrices. Args: confmat: the confusion matrix. Either should be an [N,N] matrix in the binary and multiclass cases or an [N, 2, 2] matrix for multilabel classification ax: Axis from a figure. If not provided, a new figure and axis will be created add_text: if text should be added to each cell with the given value labels: labels to add the x- and y-axis Returns: A tuple consisting of the figure and respective ax objects (or array of ax objects) of the generated figure Raises: ModuleNotFoundError: If `matplotlib` is not installed r9rr$NzYExpected number of elements in arg `labels` to match number of labels in confmat but got  and )ZnrowsZncolszLabel )fontsizezPredicted classz True class-r&)Zrotationrer=)Zhavare)rrLshaperZrKrNnprJlistmapstrr5tolistr?r@Z get_figurer_ set_titleZimshowrDrCrHrQrIZ set_yticksZset_xticklabelsZset_yticklabelsrrSitem)r`rrarVr\Z n_classesrowscolsZ fig_labelrTr[r/iiZjjrrrrplot_confusion_matrixsH  &   .&$rt)curvescorer label_namesr"r#rc Cst|dkrtd|dd\}}t|dur:tnd|f\}}t|trt|tr|jdkr|jdkr|durd|dnd} |j | | dd| d|dur| |d | |d| dur|nt|trt|ts*t|trt|tr|jdkr|jdkrtt||D]|\} \} } |dur\|d | nt| } | |durd || dnd 7} |j | | | d |q8ntdt|dt|d|d||||fS)awInspired by: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/_plot/roc_curve.py. Plots a curve object Args: curve: a tuple of (x, y, t) where x and y are the coordinates of the curve and t are the thresholds used to compute the curve score: optional area under the curve added as label to the plot ax: Axis from a figure label_names: Tuple containing the names of the x and y axis legend_name: Name of the curve to be used in the legend name: Custom name to describe the metric Returns: A tuple consisting of the figure and respective ax objects (or array of ax objects) of the generated figure Raises: ModuleNotFoundError: If `matplotlib` is not installed ValueError: If `curve` does not have 3 elements, being in the wrong format rbz6Expected 2 or 3 elements in curve but got {len(curve)}Nr$zAUC=z0.3fr-)r+Z linewidthr,r_z AUC=r8)r,zQUnknown format for argument `x` and `y`. 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