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How to use norm_position method in ATX

Best Python code snippet using ATX

ns_modules.py

Source:ns_modules.py

1import torch2import torch.nn as nn3from .base_model import BaseModel4from solvers import metrics5import torch.nn.functional as F6class MlpLayer(nn.Module):7    def __init__(self, input_d, output_d, feature_num=64, layer_norm=True):8        super(MlpLayer, self).__init__()9        if not layer_norm:10            self.mini_version = True11        else:12            self.mini_version = False13        self.fc1 = nn.Linear(input_d, feature_num)14        self.fc2 = nn.Linear(feature_num, feature_num)15        self.fc3 = nn.Linear(feature_num, feature_num)16        self.fc4 = nn.Linear(feature_num, feature_num)17        self.fc5 = nn.Linear(feature_num, feature_num)18        self.bn1 = nn.BatchNorm1d(num_features=feature_num) if layer_norm else nn.Identity()19        self.bn2 = nn.BatchNorm1d(num_features=feature_num) if layer_norm else nn.Identity()20        self.bn3 = nn.BatchNorm1d(num_features=feature_num) if layer_norm else nn.Identity()21        self.bn4 = nn.BatchNorm1d(num_features=feature_num) if layer_norm else nn.Identity()22        self.bn5 = nn.BatchNorm1d(num_features=feature_num) if layer_norm else nn.Identity()23        self.output_fc = nn.Linear(feature_num, output_d)24    def forward(self, x):25        x = F.relu(self.bn1(self.fc1(x)))26        x = F.relu(self.bn2(self.fc2(x)))27        if not self.mini_version:28            x = F.relu(self.bn3(self.fc3(x)))29            x = F.relu(self.bn4(self.fc4(x)))30            x = F.relu(self.bn5(self.fc5(x)))31        x = self.output_fc(x)32        return x33class MLPNS(nn.Module):34    def __init__(self, hidden_size=64, layer_norm=True):35        super(MLPNS, self).__init__()36        self.force_feature_size, self.state_feature_size, self.cp_feature_size = hidden_size, hidden_size, hidden_size37        self.state_tensor_dim, self.force_tensor_dim, self.cp_tensor_dim = 14, 15, 1538        self.state_encoder = MlpLayer(input_d=self.state_tensor_dim, output_d=self.state_feature_size, layer_norm=layer_norm)39        self.force_encoder = MlpLayer(input_d=self.force_tensor_dim, output_d=self.force_feature_size, layer_norm=layer_norm)40        self.cp_encoder = MlpLayer(input_d=self.cp_tensor_dim, output_d=self.cp_feature_size, layer_norm=layer_norm)41        self.force_decoder = MlpLayer(input_d=self.state_feature_size + self.force_feature_size + self.cp_feature_size,42                                      output_d=self.state_tensor_dim, layer_norm=layer_norm)43    def forward(self, state_tensor, force_tensor, contact_points):44        batch_size = force_tensor.shape[0]45        force_tensor, contact_points, state_tensor = force_tensor.reshape(batch_size, -1), contact_points.reshape(batch_size, -1), \46                                                        state_tensor.reshape(batch_size, -1)47        force_feature = self.force_encoder(force_tensor)48        state_feature = self.state_encoder(state_tensor)49        cp_feature = self.cp_encoder(contact_points)50        fused_feature = torch.cat([force_feature, state_feature, cp_feature], dim=-1)51        predict_residual_state = self.force_decoder(fused_feature)52        # predict_residual_state[:, :3] /= 1053        return state_tensor + predict_residual_state54class NSWithImageFeature(nn.Module):55    def __init__(self, hidden_size=64, layer_norm=True, image_feature_dim=225, norm_position=True):56        super(NSWithImageFeature, self).__init__()57        self.force_feature_size, self.state_feature_size, self.cp_feature_size, self.img_feature_size = \58            hidden_size, hidden_size, hidden_size, hidden_size59        self.state_tensor_dim, self.force_tensor_dim, self.cp_tensor_dim, self.img_feature_dim = \60            7, 15, 15, image_feature_dim61        self.state_encoder = MlpLayer(input_d=self.state_tensor_dim, output_d=self.state_feature_size,62                                      layer_norm=layer_norm)63        self.force_encoder = MlpLayer(input_d=self.force_tensor_dim, output_d=self.force_feature_size,64                                      layer_norm=layer_norm)65        self.cp_encoder = MlpLayer(input_d=self.cp_tensor_dim, output_d=self.cp_feature_size, layer_norm=layer_norm)66        self.image_encoder = MlpLayer(input_d=self.img_feature_dim, output_d=self.img_feature_size,67                                      layer_norm=layer_norm)68        total_dim_before_decode = self.state_feature_size + self.force_feature_size + self.cp_feature_size + \69                                  self.img_feature_size70        self.force_decoder = MlpLayer(input_d=total_dim_before_decode, output_d=self.state_tensor_dim,71                                      layer_norm=layer_norm)72        self.norm_position = norm_position73    def forward(self, state_tensor, force_tensor, contact_points, image_feature):74        batch_size = force_tensor.shape[0]75        force_tensor, contact_points, state_tensor = force_tensor.reshape(batch_size, -1), contact_points.reshape(batch_size, -1), \76                                                     state_tensor.reshape(batch_size, -1)77        force_feature = self.force_encoder(force_tensor)78        state_feature = self.state_encoder(state_tensor)79        cp_feature = self.cp_encoder(contact_points)80        img_feature = self.image_encoder(image_feature)81        fused_feature = torch.cat([force_feature, state_feature, cp_feature, img_feature], dim=-1)82        predict_residual_state = self.force_decoder(fused_feature)83        if self.norm_position:84            predict_residual_state[:, :3] /= 1085        return state_tensor + predict_residual_state86class NSLSTM(nn.Module):87    def __init__(self, hidden_size=64, layer_norm=True, image_feature_dim=225, norm_position=True):88        super(NSLSTM, self).__init__()89        self.force_feature_size, self.state_feature_size, self.cp_feature_size, self.img_feature_size = \90            hidden_size, hidden_size, hidden_size, hidden_size91        self.state_tensor_dim, self.force_tensor_dim, self.cp_tensor_dim, self.img_feature_dim = \92            7, 15, 15, image_feature_dim93        self.num_layers = 394        self.state_encoder = MlpLayer(input_d=self.state_tensor_dim, output_d=self.state_feature_size,95                                      layer_norm=layer_norm)96        self.force_encoder = MlpLayer(input_d=self.force_tensor_dim, output_d=self.force_feature_size,97                                      layer_norm=layer_norm)98        self.cp_encoder = MlpLayer(input_d=self.cp_tensor_dim, output_d=self.cp_feature_size, layer_norm=layer_norm)99        self.image_encoder = MlpLayer(input_d=self.img_feature_dim, output_d=self.img_feature_size,100                                      layer_norm=layer_norm)101        lstm_io_size = self.state_feature_size + self.force_feature_size + self.cp_feature_size + self.img_feature_size102        self.state_lstm = nn.LSTM(input_size=lstm_io_size,103                                  hidden_size=hidden_size, batch_first=True, num_layers=self.num_layers)104        self.state_decoder = MlpLayer(input_d=hidden_size, output_d=self.state_tensor_dim, layer_norm=layer_norm)105        self.norm_position = norm_position106    def forward(self, state_tensor, force_tensor, contact_points, image_feature, last_hidden, last_cell):107        batch_size = force_tensor.shape[0]108        force_tensor, contact_points, state_tensor = force_tensor.reshape(batch_size, -1), \109                                                     contact_points.reshape(batch_size, -1), \110                                                     state_tensor.reshape(batch_size, -1)111        force_feature = self.force_encoder(force_tensor)112        state_feature = self.state_encoder(state_tensor)113        cp_feature = self.cp_encoder(contact_points)114        img_feature = self.image_encoder(image_feature)115        fused_input_feature = torch.cat([force_feature, state_feature, cp_feature, img_feature], dim=-1).unsqueeze(0)116        if last_hidden is None or last_cell is None:117            output_state_feature, (last_hidden, last_cell) = self.state_lstm(fused_input_feature)118        else:119            output_state_feature, (last_hidden, last_cell) = self.state_lstm(fused_input_feature, (last_hidden, last_cell))120        output_state_feature = output_state_feature.squeeze(1)121        predicted_state = self.state_decoder(output_state_feature)122        if self.norm_position:123            predicted_state[:, :3] /= 10124        return state_tensor + predicted_state, last_hidden, last_cell125def get_denorm_state_tensor(state_ten, stat):126    pos_mean, pos_std, rot_mean, rot_std,  = \127        stat['position_mean'], stat['position_std'], stat['rotation_mean'], stat['rotation_std']128    state_ten[:, :3] = state_ten[:, :3] * pos_std + pos_mean129    if 'velocity_mean' in stat.keys():130        vel_mean, vel_std, omg_mean, omg_std = stat['velocity_mean'], stat['velocity_std'], stat['omega_mean'], stat['omega_std']131        state_ten[:, 7:10] = state_ten[:, 7:10] * vel_std + vel_mean132        state_ten[:, 10:] = state_ten[:, 10:] * omg_std + omg_mean133    return state_ten134class NSBaseModel(BaseModel):135    # deprecating136    metric = [metrics.StateMetric]137    def __init__(self, args, ):138        super(NSBaseModel, self).__init__(args)139        self.loss_function = args.loss140        self.force_feature_size, self.state_feature_size, self.cp_feature_size = 64, 64, 64141        self.state_tensor_dim, self.force_tensor_dim, self.cp_tensor_dim = 7, 15, 15142        self.state_encoder = MlpLayer(input_d=self.state_tensor_dim, output_d=self.state_feature_size)143        self.force_encoder = MlpLayer(input_d=self.force_tensor_dim, output_d=self.force_feature_size)144        self.cp_encoder = MlpLayer(input_d=self.cp_tensor_dim, output_d=self.cp_feature_size)145        self.force_decoder = MlpLayer(input_d=self.state_feature_size + self.force_feature_size + self.cp_feature_size,146                                      output_d=self.state_tensor_dim)147        self.number_of_cp = args.number_of_cp148        self.sequence_length = args.sequence_length149        self.gpu_ids = args.gpu_ids150        self.residual = args.residual151    def loss(self, args):152        return self.loss_function(args)153    def forward(self, input_d, target_d):154        forces, contact_points, state_tensor = input_d['norm_force'], input_d['norm_contact_points'], \155                                               input_d['norm_state_tensor']156        batch_size = forces.shape[0]157        forces, contact_points, state_tensor = forces.reshape(batch_size, -1), contact_points.reshape(batch_size, -1), \158                                               state_tensor.reshape(batch_size, -1)159        force_feature = self.force_encoder(forces)160        state_feature = self.state_encoder(state_tensor)161        cp_feature = self.cp_encoder(contact_points)162        fused_feature = torch.cat([force_feature, state_feature, cp_feature], dim=-1)163        predict_state = self.force_decoder(fused_feature)164        if self.residual:165            predict_state = state_tensor + predict_state166        sta = {one_key: target_d['statistics'][one_key].squeeze() for one_key in target_d['statistics']}167        target_d['norm_state_tensor'] = target_d['norm_state_tensor'].reshape(batch_size, -1)168        target_d['denorm_state_tensor'] = get_denorm_state_tensor(state_ten=target_d['norm_state_tensor'].clone(), stat=sta)169        target_d['denorm_input_state'] = get_denorm_state_tensor(state_ten=state_tensor.clone().detach(), stat=sta)170        denorm_predict_state = get_denorm_state_tensor(state_ten=predict_state.clone(), stat=sta)171        output_d = {172            'norm_state_tensor': predict_state,173            'denorm_state_tensor': denorm_predict_state174        }175        return output_d, target_d176    def optimizer(self):...

encoder.py

Source:encoder.py

1#!/usr/bin/env python2# coding: utf-83# Author: Vladimir M. Zaytsev <zaytsev@usc.edu>4"""5This module contains implementations of various routines for6encoding and decoding raw data into feature vectors.7"""8S_BEGIN = "<<BEGIN>>" #chr(244)9S_END   = "<<END>>"   #chr(243)10U_WORD  = "<<UWORD>>" #chr(242)11WORD_TAG_SEP = "_"12class Token(object):13    def __init__(self, tag_prev, tag, word, sent_length, position, token_prev, unknown=False):14        self.tag = tag15        self.tag_prev = tag_prev16        self.raw = word17        self.token_prev = token_prev18        if word is not None:19            self.title = word.istitle()20            self.upper = word.isupper()21            self.form = word.lower()22            self.lemma = word.lower()23            self.word_len = len(word)24        else:25            self.title = False26            self.upper = False27            self.form = None28            self.lemma = None29            self.word_len = 030        if unknown:31            self.form = U_WORD32            self.lemma = U_WORD33        self.position = position34        self.sent_len = sent_length35        self.norm_position = position36    def __repr__(self):37        return "<Token(%s, %s, %s, pos=%.3f)>" % (self.tag_prev,38                                                  self.tag,39                                                  self.form,40                                                  self.norm_position)41def tag(tagged_word):42    return tagged_word[1]43def word(tagged_word):44    return tagged_word[0]45def bigrams(sequence):46    seq_bigrams = []47    for i in xrange(len(sequence) - 1):48        seq_bigrams.append((sequence[i], sequence[i + 1]))49    return seq_bigrams50def words(tagged_words):51    return [tw[0] for tw in tagged_words]52def tags(tagged_words):53    return [tw[1] for tw in tagged_words]54class InputTransformer(object):55    def __init__(self):56        self.words = set()57        self.tags = set()58    def annotate(self, input_stream, tagged=True):59        lineCtr=060        annotated = []61        for line in input_stream:62            lineCtr = lineCtr+163            print lineCtr64            tagged_words = line.split()65            sent_len = len(tagged_words)66            tag_prev = S_BEGIN67            token_prev = None68            tokens = []69            for i, word_tag in enumerate(tagged_words):70                if tagged:71                    word, tag = word_tag.split(WORD_TAG_SEP)72                    token = Token(tag_prev, tag, word, sent_len, i, token_prev)73                    tag_prev = tag74                    self.words.add(token.form)75                    self.tags.add(token.tag)76                else:77                    word = word_tag78                    if word.lower() in self.words:79                        token = Token(tag_prev, None, word, sent_len, i, token_prev)80                    else:81                        token = Token(tag_prev, None, word, sent_len, i, token_prev, unknown=True)82                83                #print token84                tokens.append(token)85                token_prev = token86            tokens.append(Token(tag_prev, S_END, None, sent_len, sent_len, token_prev))87            annotated.append(tokens)...

path_weighting.py

Source:path_weighting.py

1#!/usr/bin/env python32"""Nicolas Gampierakis (2019). Scintillometer path weighting and3effective height calculator from path position and normalised height.4"""5import math6import numpy as np7import pandas as pd8import scipy as sp9from scipy import integrate, special10def bessel_second(x):11    """Calculates the bessel function for the specific path position.12    Args:13        x (float): path position, m14    Returns:15        y (float): bessel function of path point16    """17    bessel_variable = 2.283 * math.pi * (x - 0.5)18    if bessel_variable == 0:19        y = 120    else:21        y = 2 * (sp.special.jv(1, bessel_variable)) / bessel_variable22    return y23def pwf(path_position):24    """Path weighting function for effective path height25    Args:26        path_position (Series): input data array27    Returns:28        weight (float): weight29    """30    weight = []31    for i in path_position:32        weight.append(2.163 * bessel_second(i))33    return weight34def effective_z(path_height, path_position, b):35    """Calculates the effective path height across the entire36    scintillometer path based on actual path height and position.37    Args:38        path_height (Series): actual path height, m39        path_position (Series): the position along the40            scintillometer path41        b (str) = stability conditions, either stable, unstable, or no42            height dependency43    Returns:44        z_eff (float): effective path height, m45    """46    if b.lower() in ["stable", "s"]:47        b = -2 / 348    elif b.lower() in ["unstable", "u"]:49        b = -4 / 350    else:51        b = 152        print("No height dependency selected")53    ph_list = np.multiply(path_height ** b, pwf(path_position))54    # For path-building intersections55    ph_list[np.isnan(ph_list)] = 056    z_eff = (57        (sp.integrate.trapz(ph_list)) / (sp.integrate.trapz(pwf(path_position)))58    ) ** (1 / b)59    return z_eff60def return_z_effective(location_name):61    if location_name.lower() == "h":62        location_name = "hungerburg"63    elif location_name.lower() in ["s", "schieÃstand"]:64        location_name = "schiessstand"65    path_string = "../MATLAB/path_height_" + location_name.lower() + ".csv"66    path_height_data = pd.read_csv(67        path_string, header=None, names=["path_height", "norm_position"]68    )69    stability_factor = input(70        "Please enter the stability conditions (stable, " "unstable, or other):\n"71    )72    effective_path_height = effective_z(73        path_height_data["path_height"],74        path_height_data["norm_position"],75        stability_factor,76    )77    mean_path_height = np.mean(path_height_data["path_height"])78    path_weight = pwf(path_height_data["norm_position"])79    path_height_data["path_weight"] = path_weight80    print("Mean path height: " + str(mean_path_height) + "m")81    print("Effective path height: " + str(effective_path_height) + "m")82    return effective_path_height83    # For Matlab export84    # path_height_data.to_csv(path_or_buf="../MATLAB/hungerburg_sim.csv",...

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