



import numpy as np
from matplotlib import pyplot as plt





def     __lowerCamelCase							(   __magic_name__						:	List[Any]    ):
							return 1 / (1 + np.exp(-1 * x    ))


class 						lowerCamelCase__			:


						def __init__(			self    :	List[Any]   ,							_a    :	int   ,							_a    :	int=None   ,							_a    :	List[Any]=None   ,							_a    :	Optional[Any]=False      ):
													a__:							Any									=units
													a__:							List[Any]									=None
													a__:							str									=None
													a__:							Any									=activation
													if learning_rate is None:
																				a__:							Union[str, Any]									=0.3
													a__:							Dict									=learning_rate
													a__:							Optional[Any]									=is_input_layer


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	Optional[int]      ):
													a__:							List[Any]									=np.asmatrix(np.random.normal(0   ,							0.5   ,							(self.units, back_units)      )      )
													a__:							List[str]									=np.asmatrix(np.random.normal(0   ,							0.5   ,							self.units      )      ).T
													if self.activation is None:
																				a__:							Union[str, Any]									=sigmoid


						def 	_lowerCamelCase      (			self    :	str      ):
													# activation function may be sigmoid or linear
													if self.activation == sigmoid:
																				a__:							Union[str, Any]									=np.dot(self.output   ,							(1 - self.output).T      )
																				a__:							List[str]									=np.diag(np.diag(_a      )      )
													else:
																				a__:							Union[str, Any]									=1
													return gradient_activation


						def 	_lowerCamelCase      (			self    :	Optional[int]   ,							_a    :	List[str]      ):
													a__:							List[Any]									=xdata
													if self.is_input_layer:
																				# input layer
																				a__:							int									=xdata
																				a__:							Optional[Any]									=xdata
																				return xdata
													else:
																				a__:							str									=np.dot(self.weight   ,							self.xdata      ) - self.bias
																				a__:							int									=self.activation(self.wx_plus_b      )
																				return self.output







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	List[str]      ):
													a__:							Optional[int]									=self.cal_gradient()  # i * i 维
													a__:							str									=np.asmatrix(np.dot(gradient.T   ,							_a      )      )

													a__:							Optional[Any]									=np.asmatrix(self.xdata      )
													a__:							List[Any]									=-1
													a__:							Dict									=self.weight

													a__:							Any									=np.dot(gradient.T   ,							self._gradient_weight.T      )
													a__:							Union[str, Any]									=gradient * self._gradient_bias
													a__:							Optional[int]									=np.dot(_a   ,							self._gradient_x      ).T
													# upgrade: the Negative gradient direction
													a__:							str									=self.weight - self.learn_rate * self.gradient_weight
													a__:							Tuple									=self.bias - self.learn_rate * self.gradient_bias.T
													# updates the weights and bias according to learning rate (0.3 if undefined)
													return self.gradient


class 						lowerCamelCase__			:


						def __init__(			self    :	List[Any]      ):
													a__:							List[str]									=[]
													a__:							Any									=[]
													a__:							Optional[Any]									=plt.figure()
													a__:							List[str]									=self.fig_loss.add_subplot(1   ,							1   ,							1      )


						def 	_lowerCamelCase      (			self    :	Optional[Any]   ,							_a    :	int      ):
													self.layers.append(_a      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													for i, layer in enumerate(self.layers[:]      ):
																				if i < 1:
																											a__:							Optional[int]									=True
																				else:
																											layer.initializer(self.layers[i - 1].units      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													for i, layer in enumerate(self.layers[:]      ):
																				print(F"------- layer {i} -------"      )
																				print("weight.shape "   ,							np.shape(layer.weight      )      )
																				print("bias.shape "   ,							np.shape(layer.bias      )      )


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	str   ,							_a    :	Union[str, Any]   ,							_a    :	Tuple   ,							_a    :	int      ):
													a__:							List[Any]									=train_round
													a__:							Union[str, Any]									=accuracy

													self.ax_loss.hlines(self.accuracy   ,							0   ,							self.train_round * 1.1      )

													a__:							int									=np.shape(_a      )
													for _ in range(_a      ):
																				a__:							Optional[Any]									=0
																				for row in range(x_shape[0]      ):
																											a__:							List[str]									=np.asmatrix(xdata[row, :]      ).T
																											a__:							Dict									=np.asmatrix(ydata[row, :]      ).T

																											# forward propagation
																											for layer in self.layers:
																																		a__:							Union[str, Any]									=layer.forward_propagation(_xdata      )

																											a__				,      a__:							Dict									=self.cal_loss(_ydata   ,							_xdata      )
																											a__:							List[str]									=all_loss + loss

																											# back propagation: the input_layer does not upgrade
																											for layer in self.layers[:0:-1]:
																																		a__:							Union[str, Any]									=layer.back_propagation(_a      )

																				a__:							Tuple									=all_loss / x_shape[0]
																				self.train_mse.append(_a      )

																				self.plot_loss()

																				if mse < self.accuracy:
																											print("----达到精度----"      )
																											return mse
													return None


						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	int   ,							_a    :	Any      ):
													a__:							Any									=np.sum(np.power((ydata - ydata_)   ,							2      )      )
													a__:							Union[str, Any]									=2 * (ydata_ - ydata)
													# vector (shape is the same as _ydata.shape)
													return self.loss, self.loss_gradient







						def 	_lowerCamelCase      (			self    :	Tuple      ):
													if self.ax_loss.lines:
																				self.ax_loss.lines.remove(self.ax_loss.lines[0]      )
													self.ax_loss.plot(self.train_mse   ,							"r-"      )
													plt.ion()
													plt.xlabel("step"      )
													plt.ylabel("loss"      )
													plt.show()
													plt.pause(0.1      )





def     __lowerCamelCase							(   ):
							a__:							List[Any]									=np.random.randn(10  ,			10    )
							a__:							Optional[Any]									=np.asarray(
							    [
							        [0.8, 0.4],
							        [0.4, 0.3],
							        [0.34, 0.45],
							        [0.67, 0.32],
							        [0.88, 0.67],
							        [0.78, 0.77],
							        [0.55, 0.66],
							        [0.55, 0.43],
							        [0.54, 0.1],
							        [0.1, 0.5],
							    ]    )
							a__:							Dict									=BPNN()
							for i in (10, 20, 30, 2):
														model.add_layer(DenseLayer(__magic_name__    )    )
							model.build()
							model.summary()
							model.train(xdata=__magic_name__  ,			ydata=__magic_name__  ,			train_round=100  ,			accuracy=0.01    )


if __name__ == "__main__":
		example()










import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__UpperCAmelCase    	=  False
__UpperCAmelCase    	=  Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
__UpperCAmelCase    	=  datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)


class 						lowerCamelCase__			:


						def __init__(			self    :	int   ,							_a    :	List[str]=sys.argv[1:]      ):
													a__:							List[Any]									=None
													a__:							int									=None
													a__:							int									=None
													a__:							List[str]									=1
													a__				,      a__:							List[Any]									=getopt.getopt(_a   ,							"i:o:b:s"   ,							["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
													for opt, arg in opts:
																				if opt in ("-i", "--inputdir"):
																											a__:							Union[str, Any]									=arg
																				elif opt in ("-o", "--outfile"):
																											a__:							List[str]									=arg
																				elif opt in ("-b", "--batch_size"):
																											a__:							List[str]									=int(_a      )
																				elif opt in ("-s", "--subset_list"):
																											a__:							str									=arg

													assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
													assert outputfile is not None and not os.path.isfile(_a      ), F"{outputfile}"
													if subset_list is not None:
																				with open(os.path.realpath(_a      )      ) as f:
																											a__:							Any									={self._vqa_file_split()[0] for x in tryload(_a      )}
													else:
																				a__:							Optional[Any]									=None

													a__:							List[Any]									=CONFIG
													if torch.cuda.is_available():
																				a__:							Dict									="cuda"
													a__:							Any									=os.path.realpath(_a      )
													a__:							Optional[int]									=os.path.realpath(_a      )
													a__:							Union[str, Any]									=Preprocess(self.config      )
													a__:							str									=GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"   ,							config=self.config      )
													a__:							List[Any]									=batch_size if batch_size != 0 else 1
													a__:							Union[str, Any]									=DEFAULT_SCHEMA


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Dict      ):
													a__:							Tuple									=int(file.split("."      )[0].split("_"      )[-1]      )
													a__:							int									=os.path.join(self.inputdir   ,							_a      )
													return (img_id, filepath)


						@property
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							List[Any]									=[]
													for i, file in enumerate(os.listdir(self.inputdir      )      ):
																				if self.subset_list is not None and i not in self.subset_list:
																											continue
																				batch.append(self._vqa_file_split(_a      )      )
																				if len(_a      ) == self.batch:
																											a__:							List[Any]									=batch
																											a__:							Union[str, Any]									=[]
																											yield list(map(_a   ,							zip(*_a      )      )      )

													for i in range(1      ):
																				yield list(map(_a   ,							zip(*_a      )      )      )







						def __call__(			self    :	List[str]      ):
													# make writer
													if not TEST:
																				a__:							int									=datasets.ArrowWriter(features=self.schema   ,							path=self.outputfile      )
													# do file generator
													for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
																				a__				,      a__				,      a__:							Dict									=self.preprocess(_a      )
																				a__:							Optional[Any]									=self.model(
																				    _a   ,							_a   ,							scales_yx=_a   ,							padding="max_detections"   ,							max_detections=self.config.MAX_DETECTIONS   ,							pad_value=0   ,							return_tensors="np"   ,							location="cpu"   ,							)
																				a__:							Dict									=output_dict.pop("normalized_boxes"      )
																				if not TEST:
																											a__:							Tuple									=np.array(_a      )
																											a__:							Union[str, Any]									=self.schema.encode_batch(_a      )
																											writer.write_batch(_a      )
																				if TEST:
																											break
																				# finalizer the writer
													if not TEST:
																				a__				,      a__:							Any									=writer.finalize()
																				print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"      )





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]    ):
							try:
														a__:							Any									=json.load(__magic_name__    )
														try:
																					a__:							List[str]									=list(data.keys()    )
														except Exception:
																					a__:							Union[str, Any]									=[d["img_id"] for d in data]
							except Exception:
														try:
																					a__:							Dict									=eval(stream.read()    )
														except Exception:
																					a__:							int									=stream.read().split("\n"    )
							return data


if __name__ == "__main__":
		__UpperCAmelCase    	=  Extract(sys.argv[1:])
		extract()
		if not TEST:
				__UpperCAmelCase    	=  datasets.Dataset.from_file(extract.outputfile)
				# wala!
				# print(np.array(dataset[0:2]["roi_features"]).shape)





