code stringlengths 42 43.2k | apis sequence | extract_api stringlengths 115 61.9k |
|---|---|---|
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from network import NN
from evaluate import accuracy
def read_data(fpath):
iris = pd.read_csv(fpath)
iris.loc[iris['species'] == 'virginica', 'species'] = 0
iris.loc[iris['species'] == 'versicolor', 'species'] = 1
iris.loc[iris['sp... | [
"evaluate.accuracy"
] | [((159, 177), 'pandas.read_csv', 'pd.read_csv', (['fpath'], {}), '(fpath)\n', (170, 177), True, 'import pandas as pd\n'), ((519, 587), 'matplotlib.pyplot.scatter', 'plt.scatter', (['X[:, 0]', 'X[:, 1]'], {'c': 'y[:, 0]', 's': '(40)', 'cmap': 'plt.cm.Spectral'}), '(X[:, 0], X[:, 1], c=y[:, 0], s=40, cmap=plt.cm.Spectral... |
import datetime
import os
import copy
import json
import numpy as np
from pytz import timezone
from gamified_squad import GamifiedSquad
from agent import CustomAgent
import generic
import evaluate
SAVE_CHECKPOINT = 100000
def train():
time_1 = datetime.datetime.now()
config = generic.load_config()
env =... | [
"evaluate.evaluate"
] | [((252, 275), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (273, 275), False, 'import datetime\n'), ((289, 310), 'generic.load_config', 'generic.load_config', ([], {}), '()\n', (308, 310), False, 'import generic\n'), ((321, 342), 'gamified_squad.GamifiedSquad', 'GamifiedSquad', (['config'], {}), ... |
#!/usr/bin/env python
# coding: utf-8
from __future__ import division, print_function, unicode_literals
import argparse
import json
import os
import shutil
import time
import torch
from utils import util
from evaluate import MultiWozEvaluator
from model.model import Model
parser = argparse.ArgumentParser(descriptio... | [
"evaluate.MultiWozEvaluator"
] | [((286, 328), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""S2S"""'}), "(description='S2S')\n", (309, 328), False, 'import argparse\n'), ((1679, 1707), 'torch.manual_seed', 'torch.manual_seed', (['args.seed'], {}), '(args.seed)\n', (1696, 1707), False, 'import torch\n'), ((1718, 1762), ... |
import argparse
import os
import torch
import yaml
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from utils.model import get_vocoder, get_param_num
from utils.tools import to_device, log, synth_one_sample
from model im... | [
"evaluate.evaluate"
] | [((740, 825), 'dataset.Dataset', 'Dataset', (['"""train.txt"""', 'preprocess_config', 'train_config'], {'sort': '(True)', 'drop_last': '(True)'}), "('train.txt', preprocess_config, train_config, sort=True, drop_last=True\n )\n", (747, 825), False, 'from dataset import Dataset\n'), ((1141, 1245), 'torch.utils.data.Da... |
from i3Deep import utils
import os
from evaluate import evaluate
import numpy as np
from skimage.segmentation.random_walker_segmentation import random_walker
from tqdm import tqdm
import torchio
import torch
def compute_predictions(image_path, mask_path, gt_path, save_path, nr_modalities, class_labels, resize... | [
"evaluate.evaluate"
] | [((432, 463), 'i3Deep.utils.load_filenames', 'utils.load_filenames', (['mask_path'], {}), '(mask_path)\n', (452, 463), False, 'from i3Deep import utils\n'), ((1785, 1827), 'evaluate.evaluate', 'evaluate', (['gt_path', 'save_path', 'class_labels'], {}), '(gt_path, save_path, class_labels)\n', (1793, 1827), False, 'from ... |
import random
import os
import sys
from models.bert import BERT_Model
from models.bilstm_crf_ import BiLSTM_CRF_Model
from data import build_corpus
from config import ModelPathConfig,ResultPathConfig
from datetime import datetime
from utils import extend_map_bert,save_model,load_model,extend_map,add_label_for_lstmcrf
... | [
"evaluate.unitstopd",
"evaluate.evaluate_single_label",
"evaluate.evaluate_entity_label"
] | [((504, 540), 'os.path.exists', 'os.path.exists', (['ModelPathConfig.bert'], {}), '(ModelPathConfig.bert)\n', (518, 540), False, 'import os\n'), ((609, 630), 'data.build_corpus', 'build_corpus', (['"""train"""'], {}), "('train')\n", (621, 630), False, 'from data import build_corpus\n'), ((670, 690), 'data.build_corpus'... |
from __future__ import print_function, division
import sys
sys.path.append('core')
import argparse
import os
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader... | [
"evaluate.validate_chairs",
"evaluate.validate_kitti",
"evaluate.validate_sintel"
] | [((59, 82), 'sys.path.append', 'sys.path.append', (['"""core"""'], {}), "('core')\n", (74, 82), False, 'import sys\n'), ((14695, 14726), 'datasets.fetch_dataloader', 'datasets.fetch_dataloader', (['args'], {}), '(args)\n', (14720, 14726), False, 'import datasets\n'), ((17465, 17490), 'argparse.ArgumentParser', 'argpars... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import numpy as np
import tensorflow as tf
from evaluate import evaluate
from utils import get_data, tf_melspectogram
from shallow_nn import shallow_nn
from deep_nn import deep_nn
from sh... | [
"evaluate.evaluate"
] | [((461, 569), 'tensorflow.app.flags.DEFINE_integer', 'tf.app.flags.DEFINE_integer', (['"""epochs"""', '(100)', '"""Number of mini-batches to train on. (default: %(default)d)"""'], {}), "('epochs', 100,\n 'Number of mini-batches to train on. (default: %(default)d)')\n", (488, 569), True, 'import tensorflow as tf\n'),... |
from implicit_neural_networks import IMLP
import torch
import torch.optim as optim
import numpy as np
from evaluate import evaluate_model
from datetime import datetime
from loss_utils import get_gradient_loss, get_rigidity_loss, \
get_optical_flow_loss, get_optical_flow_alpha_loss
from unwrap_utils import get_tupl... | [
"evaluate.evaluate_model"
] | [((659, 683), 'numpy.int64', 'np.int64', (["config['resx']"], {}), "(config['resx'])\n", (667, 683), True, 'import numpy as np\n'), ((695, 719), 'numpy.int64', 'np.int64', (["config['resy']"], {}), "(config['resy'])\n", (703, 719), True, 'import numpy as np\n'), ((893, 927), 'numpy.int64', 'np.int64', (["config['evalua... |
'''Train CIFAR10/100 with PyTorch using standard Contrastive Learning. This script tunes the L2 reg weight of the
final classifier.'''
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import math
import os
import argparse
from models import *
from configs import get_datasets
from evaluate impor... | [
"evaluate.encode_feature_averaging",
"evaluate.train_clf"
] | [((368, 447), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Final evaluation with feature averaging."""'}), "(description='Final evaluation with feature averaging.')\n", (391, 447), False, 'import argparse\n'), ((1181, 1208), 'os.path.isdir', 'os.path.isdir', (['"""checkpoint"""'], {}),... |
import os, sys
root_path = os.path.realpath(__file__).split('/evaluate/multipose_coco_eval.py')[0]
os.chdir(root_path)
sys.path.append(root_path)
from network.posenet import poseNet
from evaluate.tester import Tester
backbone = 'resnet101'
# Set Training parameters
params = Tester.TestParams()
params.subnet_name = '... | [
"evaluate.tester.Tester",
"evaluate.tester.Tester.TestParams"
] | [((99, 118), 'os.chdir', 'os.chdir', (['root_path'], {}), '(root_path)\n', (107, 118), False, 'import os, sys\n'), ((119, 145), 'sys.path.append', 'sys.path.append', (['root_path'], {}), '(root_path)\n', (134, 145), False, 'import os, sys\n'), ((278, 297), 'evaluate.tester.Tester.TestParams', 'Tester.TestParams', ([], ... |
import torch
from torch import nn
from Fashion_Mnist import load_data_fashion_mnist
from evaluate import Accumulator, accurate_num, evaluate_accuracy
net = nn.Sequential(nn.Flatten(),
nn.Linear(784,512), nn.BatchNorm1d(512),
nn.ReLU(),
nn.Linear(512, ... | [
"evaluate.Accumulator",
"evaluate.evaluate_accuracy",
"evaluate.accurate_num"
] | [((874, 895), 'torch.nn.CrossEntropyLoss', 'nn.CrossEntropyLoss', ([], {}), '()\n', (893, 895), False, 'from torch import nn\n'), ((988, 1023), 'Fashion_Mnist.load_data_fashion_mnist', 'load_data_fashion_mnist', (['batch_size'], {}), '(batch_size)\n', (1011, 1023), False, 'from Fashion_Mnist import load_data_fashion_mn... |
import argparse, os
import matplotlib
matplotlib.use('Agg')
import torch
from evaluate import evaluate_synthesis, evaluate_projection
import numpy as np
from synth.synthesize import create_synth
from utils.data import get_external_sounds
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=st... | [
"evaluate.evaluate_projection",
"evaluate.evaluate_synthesis"
] | [((38, 59), 'matplotlib.use', 'matplotlib.use', (['"""Agg"""'], {}), "('Agg')\n", (52, 59), False, 'import matplotlib\n'), ((247, 272), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (270, 272), False, 'import argparse, os\n'), ((2635, 2688), 'torch.load', 'torch.load', (['args.model_path'], {'... |
import evaluate
import pandas as pd
import sys
import glob
sys.path.append('../gopher')
import utils
import numpy as np
import json
def get_runs(glob_pattern):
bin_run = {}
for run_dir in glob.glob(glob_pattern):
config = utils.get_config(run_dir)
if config['loss_fn']['value'] == 'poisson':
... | [
"evaluate.change_resolution",
"evaluate.get_performance"
] | [((59, 87), 'sys.path.append', 'sys.path.append', (['"""../gopher"""'], {}), "('../gopher')\n", (74, 87), False, 'import sys\n'), ((415, 446), 'utils.make_dir', 'utils.make_dir', (['"""inter_results"""'], {}), "('inter_results')\n", (429, 446), False, 'import utils\n'), ((481, 550), 'utils.collect_whole_testset', 'util... |
import logging
import numpy as np
import torch
from torch import nn
from anchor_based import anchor_helper
from anchor_based.dsnet import DSNet
from anchor_based.losses import calc_cls_loss, calc_loc_loss
from evaluate import evaluate
from helpers import data_helper, vsumm_helper, bbox_helper
logger = logging.getLog... | [
"evaluate.evaluate"
] | [((306, 325), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (323, 325), False, 'import logging\n'), ((644, 798), 'anchor_based.dsnet.DSNet', 'DSNet', ([], {'base_model': 'args.base_model', 'num_feature': 'args.num_feature', 'num_hidden': 'args.num_hidden', 'anchor_scales': 'args.anchor_scales', 'num_head'... |
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