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How to use test_tds method in Molotov
Best Python code snippet using molotov_python
main_cifar.py
Source:main_cifar.py 
1# -*- coding: utf-8 -*-2""" Code for training and evaluating Self-Explaining Neural Networks.3Copyright (C) 2018 David Alvarez-Melis <dalvmel@mit.edu>4This program is free software: you can redistribute it and/or modify5it under the terms of the GNU General Public License as published by6the Free Software Foundation, either version 3 of the License,7(at your option) any later version.8This program is distributed in the hope that it will be useful,9but WITHOUT ANY WARRANTY; without even the implied warranty of10MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the11GNU General Public License for more details.12You should have received a copy of the GNU General Public License13along with this program. If not, see <https://www.gnu.org/licenses/>.14"""15# Standard Imports16import sys, os17import numpy as np18import pdb19import pickle20import argparse21import operator22import matplotlib23import matplotlib.pyplot as plt24# Torch Imports25import torch26from torch.utils.data import TensorDataset27from torch.autograd import Variable28import torchvision29from torchvision import transforms30from torchvision.datasets import CIFAR1031from torch.utils.data.sampler import SubsetRandomSampler32import torch.utils.data.dataloader as dataloader33# Imports from my other repos34from robust_interpret.explainers import gsenn_wrapper35from robust_interpret.utils import lipschitz_boxplot, lipschitz_argmax_plot36# Local imports37from SENN.arglist import get_senn_parser #parse_args as parse_senn_args38from SENN.models import GSENN39from SENN.conceptizers import image_fcc_conceptizer, image_cnn_conceptizer, input_conceptizer40from SENN.parametrizers import image_parametrizer, torchvision_parametrizer, vgg_parametrizer41from SENN.aggregators import linear_scalar_aggregator, additive_scalar_aggregator42from SENN.trainers import HLearningClassTrainer, VanillaClassTrainer, GradPenaltyTrainer43from SENN.utils import plot_theta_stability, generate_dir_names, noise_stability_plots, concept_grid44from SENN.eval_utils import estimate_dataset_lipschitz45def load_cifar_data(valid_size=0.1, shuffle=True, resize = None, random_seed=2008, batch_size = 64,46 num_workers = 1):47 """48 We return train and test for plots and post-training experiments49 """50 transf_seq = [51 transforms.ToTensor(),52 transforms.Normalize(mean=[0.485, 0.456, 0.406],53 std=[0.229, 0.224, 0.225])54 ]55 if resize and (resize[0] != 32 or resize[1] != 32):56 transf_seq.insert(0, transforms.Resize(resize))57 transform = transforms.Compose(transf_seq)58 # normalized according to pytorch torchvision guidelines https://chsasank.github.io/vision/models.html59 train = CIFAR10('data/CIFAR', train=True, download=True, transform=transform)60 test = CIFAR10('data/CIFAR', train=False, download=True, transform=transform)61 num_train = len(train)62 indices = list(range(num_train))63 split = int(np.floor(valid_size * num_train))64 train_idx, valid_idx = indices[split:], indices[:split]65 train_sampler = SubsetRandomSampler(train_idx)66 valid_sampler = SubsetRandomSampler(valid_idx)67 if shuffle == True:68 np.random.seed(random_seed)69 np.random.shuffle(indices)70 # Create DataLoader71 dataloader_args = dict(batch_size=batch_size,num_workers=num_workers)72 train_loader = dataloader.DataLoader(train, sampler=train_sampler, **dataloader_args)73 valid_loader = dataloader.DataLoader(train, sampler=valid_sampler, **dataloader_args)74 dataloader_args['shuffle'] = False75 test_loader = dataloader.DataLoader(test, **dataloader_args)76 return train_loader, valid_loader, test_loader, train, test77def parse_args():78 senn_parser = get_senn_parser()79 ### Local ones80 parser = argparse.ArgumentParser(parents =[senn_parser],add_help=False,81 description='Interpteratbility robustness evaluation on MNIST')82 # #setup83 parser.add_argument('-d','--datasets', nargs='+',84 default = ['heart', 'ionosphere', 'breast-cancer','wine','heart',85 'glass','diabetes','yeast','leukemia','abalone'], help='<Required> Set flag')86 parser.add_argument('--lip_calls', type=int, default=10,87 help='ncalls for bayes opt gp method in Lipschitz estimation')88 parser.add_argument('--lip_eps', type=float, default=0.01,89 help='eps for Lipschitz estimation')90 parser.add_argument('--lip_points', type=int, default=100,91 help='sample size for dataset Lipschitz estimation')92 parser.add_argument('--optim', type=str, default='gp',93 help='black-box optimization method')94 #####95 args = parser.parse_args()96 print("\nParameters:")97 for attr, value in sorted(args.__dict__.items()):98 print("\t{}={}".format(attr.upper(), value))99 return args100def main():101 args = parse_args()102 np.random.seed(args.seed)103 torch.manual_seed(args.seed)104 args.nclasses = 10105 args.theta_dim = args.nclasses106 if (args.theta_arch == 'simple') or ('vgg' in args.theta_arch):107 H, W = 32, 32108 else:109 # Need to resize to have access to torchvision's models110 H, W = 224, 224111 args.input_dim = H*W112 model_path, log_path, results_path = generate_dir_names('cifar', args)113 train_loader, valid_loader, test_loader, train_tds, test_tds = load_cifar_data(114 batch_size=args.batch_size,num_workers=args.num_workers,115 resize=(H,W)116 )117 if args.h_type == 'input':118 conceptizer = input_conceptizer()119 args.nconcepts = args.input_dim + int(not args.nobias)120 elif args.h_type == 'cnn':121 122 # biase. They treat it like any other concept.123 #args.nconcepts += int(not args.nobias)124 conceptizer = image_cnn_conceptizer(args.input_dim, args.nconcepts, args.concept_dim, nchannel = 3) #, sparsity = sparsity_l)125 else:126 #args.nconcepts += int(not args.nobias)127 conceptizer = image_fcc_conceptizer(args.input_dim, args.nconcepts, args.concept_dim, nchannel = 3) #, sparsity = sparsity_l)128 if args.theta_arch == 'simple':129 parametrizer = image_parametrizer(args.input_dim, args.nconcepts, args.theta_dim, nchannel = 3, only_positive = args.positive_theta)130 elif 'vgg' in args.theta_arch:131 parametrizer = vgg_parametrizer(args.input_dim, args.nconcepts, args.theta_dim, arch = args.theta_arch, nchannel = 3, only_positive = args.positive_theta) #torchvision.models.alexnet(num_classes = args.nconcepts*args.theta_dim)132 else:133 parametrizer = torchvision_parametrizer(args.input_dim, args.nconcepts, args.theta_dim, arch = args.theta_arch, nchannel = 3, only_positive = args.positive_theta) #torchvision.models.alexnet(num_classes = args.nconcepts*args.theta_dim)134 aggregator = additive_scalar_aggregator(args.concept_dim, args.nclasses)135 model = GSENN(conceptizer, parametrizer, aggregator) #, learn_h = args.train_h)136 # if not args.train and args.load_model:137 # checkpoint = torch.load(os.path.join(model_path,'model_best.pth.tar'), map_location=lambda storage, loc: storage)138 # checkpoint.keys()139 # model = checkpoint['model']140 #141 #142 if args.theta_reg_type in ['unreg','none', None]:143 trainer = VanillaClassTrainer(model, args)144 elif args.theta_reg_type == 'grad1':145 trainer = GradPenaltyTrainer(model, args, typ = 1)146 elif args.theta_reg_type == 'grad2':147 trainer = GradPenaltyTrainer(model, args, typ = 2)148 elif args.theta_reg_type == 'grad3':149 trainer = GradPenaltyTrainer(model, args, typ = 3)150 elif args.theta_reg_type == 'crosslip':151 trainer = CLPenaltyTrainer(model, args)152 else:153 raise ValueError('Unrecoginzed theta_reg_type')154 if args.train or not args.load_model or (not os.path.isfile(os.path.join(model_path,'model_best.pth.tar'))):155 trainer.train(train_loader, valid_loader, epochs = args.epochs, save_path = model_path)156 trainer.plot_losses(save_path=results_path)157 else:158 checkpoint = torch.load(os.path.join(model_path,'model_best.pth.tar'), map_location=lambda storage, loc: storage)159 checkpoint.keys()160 model = checkpoint['model']161 trainer = VanillaClassTrainer(model, args) # arbtrary trained, only need to compuyte val acc162 #trainer.validate(test_loader, fold = 'test')163 model.eval()164 All_Results = {}165 ### 0. Concept Grid for Visualization166 #concept_grid(model, test_loader, top_k = 10, cuda = args.cuda, save_path = results_path + '/concept_grid.pdf')167 ### 1. Single point lipshiz estimate via black box optim (for fair comparison)168 # with other methods in which we have to use BB optimization.169 features = None170 classes = [str(i) for i in range(10)]171 expl = gsenn_wrapper(model,172 mode = 'classification',173 input_type = 'image',174 multiclass=True,175 feature_names = features,176 class_names = classes,177 train_data = train_loader,178 skip_bias = True,179 verbose = False)180 ### Debug single input181 # x = next(iter(train_tds))[0]182 # attr = expl(x, show_plot = False)183 # pdb.set_trace()184 # #### Debug multi input185 # x = next(iter(test_loader))[0] # Transformed186 # x_raw = test_loader.dataset.test_data[:args.batch_size,:,:]187 # attr = expl(x, x_raw = x_raw, show_plot = True)188 # #pdb.set_trace()189 # #### Debug argmaz plot_theta_stability190 if args.h_type == 'input':191 x = next(iter(test_tds))[0].numpy()192 y = next(iter(test_tds))[0].numpy()193 x_raw = (test_tds.test_data[0].float()/255).numpy()194 y_raw = revert_to_raw(x)195 att_x = expl(x, show_plot = False)196 att_y = expl(y, show_plot = False)197 lip = 1198 lipschitz_argmax_plot(x_raw, y_raw, att_x,att_y, lip)# save_path=fpath)199 #pdb.set_trace()200 ### 2. Single example lipschitz estimate with Black Box201 do_bb_stability_example = True202 if do_bb_stability_example:203 print('**** Performing lipschitz estimation for a single point ****')204 idx = 0205 print('Example index: {}'.format(idx))206 #x = train_tds[idx][0].view(1,28,28).numpy()207 x = next(iter(test_tds))[0].numpy()208 #x_raw = (test_tds.test_data[0].float()/255).numpy()209 x_raw = (test_tds.test_data[0]/255)210 #x_raw = next(iter(train_tds))[0]211 # args.optim = 'gp'212 # args.lip_eps = 0.1213 # args.lip_calls = 10214 Results = {}215 lip, argmax = expl.local_lipschitz_estimate(x, bound_type='box_std',216 optim=args.optim,217 eps=args.lip_eps,218 n_calls=4*args.lip_calls,219 njobs = 1,220 verbose=2)221 #pdb.set_trace()222 Results['lip_argmax'] = (x, argmax, lip)223 # .reshape(inputs.shape[0], inputs.shape[1], -1)224 att = expl(x, None, show_plot=False)#.squeeze()225 # .reshape(inputs.shape[0], inputs.shape[1], -1)226 att_argmax = expl(argmax, None, show_plot=False)#.squeeze()227 #pdb.set_trace()228 Argmax_dict = {'lip': lip, 'argmax': argmax, 'x': x}229 fpath = os.path.join(results_path, 'argmax_lip_gp_senn.pdf')230 if args.h_type == 'input':231 lipschitz_argmax_plot(x_raw, revert_to_raw(argmax), att, att_argmax, lip, save_path=fpath)232 pickle.dump(Argmax_dict, open(233 results_path + '/argmax_lip_gp_senn.pkl', "wb"))234 #noise_stability_plots(model, test_tds, cuda = args.cuda, save_path = results_path)235 ### 3. Local lipschitz estimate over multiple samples with Black BOx Optim236 do_bb_stability = True237 if do_bb_stability:238 print('**** Performing black-box lipschitz estimation over subset of dataset ****')239 maxpoints = 20240 #valid_loader 0 it's shuffled, so it's like doing random choice241 mini_test = next(iter(valid_loader))[0][:maxpoints].numpy()242 lips = expl.estimate_dataset_lipschitz(mini_test,243 n_jobs=-1, bound_type='box_std',244 eps=args.lip_eps, optim=args.optim,245 n_calls=args.lip_calls, verbose=2)246 Stability_dict = {'lips': lips}247 pickle.dump(Stability_dict, open(results_path + '_stability_blackbox.pkl', "wb"))248 All_Results['stability_blackbox'] = lips249 pickle.dump(All_Results, open(results_path + '_combined_metrics.pkl'.format(dataname), "wb"))250 251 # args.epoch_stats = epoch_stats252 # save_path = args.results_path253 # print("Save train/dev results to", save_path)254 # args_dict = vars(args)255 # pickle.dump(args_dict, open(save_path,'wb') )256if __name__ == '__main__':...
main_mnist.py
Source:main_mnist.py
1# -*- coding: utf-8 -*-2""" Code for training and evaluating Self-Explaining Neural Networks.3Copyright (C) 2018 David Alvarez-Melis <dalvmel@mit.edu>4This program is free software: you can redistribute it and/or modify5it under the terms of the GNU General Public License as published by6the Free Software Foundation, either version 3 of the License,7(at your option) any later version.8This program is distributed in the hope that it will be useful,9but WITHOUT ANY WARRANTY; without even the implied warranty of10MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the11GNU General Public License for more details.12You should have received a copy of the GNU General Public License13along with this program. If not, see <https://www.gnu.org/licenses/>.14"""15# Standard Imports16import sys, os17import numpy as np18import pdb19import pickle20import argparse21import operator22import matplotlib23import matplotlib.pyplot as plt24# Torch-related25import torch26from torch.utils.data import TensorDataset27from torch.autograd import Variable28import torchvision29from torchvision import transforms30from torchvision.datasets import MNIST31from torch.utils.data.sampler import SubsetRandomSampler32import torch.utils.data.dataloader as dataloader33# Local imports34from SENN.arglist import get_senn_parser #parse_args as parse_senn_args35from SENN.models import GSENN36from SENN.conceptizers import image_fcc_conceptizer, image_cnn_conceptizer, input_conceptizer37from SENN.parametrizers import image_parametrizer38from SENN.aggregators import linear_scalar_aggregator, additive_scalar_aggregator39from SENN.trainers import HLearningClassTrainer, VanillaClassTrainer, GradPenaltyTrainer40from SENN.utils import plot_theta_stability, generate_dir_names, noise_stability_plots, concept_grid, plot_digit, plot_concept_vis41from SENN.eval_utils import estimate_dataset_lipschitz42from robust_interpret.explainers import gsenn_wrapper43from robust_interpret.utils import lipschitz_boxplot, lipschitz_argmax_plot44def revert_to_raw(t):45 return ((t*.3081) + .1307)46def load_mnist_data(valid_size=0.1, shuffle=True, random_seed=2008, batch_size = 64,47 num_workers = 1):48 """49 We return train and test for plots and post-training experiments50 """51 transform = transforms.Compose([52 transforms.ToTensor(),53 transforms.Normalize((0.1307,), (0.3081,))54 ])55 train = MNIST('data/MNIST', train=True, download=True, transform=transform)56 test = MNIST('data/MNIST', train=False, download=True, transform=transform)57 num_train = len(train)58 indices = list(range(num_train))59 split = int(np.floor(valid_size * num_train))60 train_idx, valid_idx = indices[split:], indices[:split]61 train_sampler = SubsetRandomSampler(train_idx)62 valid_sampler = SubsetRandomSampler(valid_idx)63 if shuffle == True:64 np.random.seed(random_seed)65 np.random.shuffle(indices)66 # Create DataLoader67 dataloader_args = dict(batch_size=batch_size,num_workers=num_workers)68 train_loader = dataloader.DataLoader(train, sampler=train_sampler, **dataloader_args)69 valid_loader = dataloader.DataLoader(train, sampler=valid_sampler, **dataloader_args)70 dataloader_args['shuffle'] = False71 test_loader = dataloader.DataLoader(test, **dataloader_args)72 return train_loader, valid_loader, test_loader, train, test73def parse_args():74 senn_parser = get_senn_parser()75 ### Local ones76 parser = argparse.ArgumentParser(parents =[senn_parser],add_help=False,77 description='Interpteratbility robustness evaluation on MNIST')78 # #setup79 parser.add_argument('-d','--datasets', nargs='+',80 default = ['heart', 'ionosphere', 'breast-cancer','wine','heart',81 'glass','diabetes','yeast','leukemia','abalone'], help='<Required> Set flag')82 parser.add_argument('--lip_calls', type=int, default=10,83 help='ncalls for bayes opt gp method in Lipschitz estimation')84 parser.add_argument('--lip_eps', type=float, default=0.01,85 help='eps for Lipschitz estimation')86 parser.add_argument('--lip_points', type=int, default=100,87 help='sample size for dataset Lipschitz estimation')88 parser.add_argument('--optim', type=str, default='gp',89 help='black-box optimization method')90 parser.add_argument('--autovis', action='store_true', default=False, help='Visualize the autoencoder / activations' )91 #####92 args = parser.parse_args()93 print("\nParameters:")94 for attr, value in sorted(args.__dict__.items()):95 print("\t{}={}".format(attr.upper(), value))96 return args97def main():98 args = parse_args()99 args.nclasses = 10100 args.theta_dim = args.nclasses101 model_path, log_path, results_path = generate_dir_names('mnist', args)102 train_loader, valid_loader, test_loader, train_tds, test_tds = load_mnist_data(103 batch_size=args.batch_size,num_workers=args.num_workers104 )105 if args.autovis:106 checkpoint = torch.load(os.path.join(model_path,'model_best.pth.tar'), map_location=lambda storage, loc: storage)107 checkpoint.keys()108 model = checkpoint['model']109 model.eval()110 with torch.no_grad():111 plot_concept_vis(model, args.nconcepts, 5, 4)112 exit()113 # print(next(iter(test_tds))[0].shape)114 # exit()115 if args.h_type == 'input':116 conceptizer = input_conceptizer()117 args.nconcepts = 28*28 + int(not args.nobias)118 elif args.h_type == 'cnn':119 #args.nconcepts += int(not args.nobias)120 conceptizer = image_cnn_conceptizer(28*28, args.nconcepts, args.concept_dim) #, sparsity = sparsity_l)121 else:122 #args.nconcepts += int(not args.nobias)123 conceptizer = image_fcc_conceptizer(28*28, args.nconcepts, args.concept_dim) #, sparsity = sparsity_l)124 parametrizer = image_parametrizer(28*28, args.nconcepts, args.theta_dim, only_positive = args.positive_theta)125 aggregator = additive_scalar_aggregator(args.concept_dim, args.nclasses)126 model = GSENN(conceptizer, parametrizer, aggregator) #, learn_h = args.train_h)127 # if args.load_model:128 # checkpoint = torch.load(os.path.join(model_path,'model_best.pth.tar'), map_location=lambda storage, loc: storage)129 # checkpoint.keys()130 # model = checkpoint['model']131 # model.eval()132 #133 # accum = get_concept_vis(model, args.nconcepts)134 # print(accum)135 # print(model)136 # x = next(iter(test_tds))[0]137 # x = test_tds[1][0]138 # print(x.shape)139 # plot_digit()140 # plot_digit(x)141 # x = x[None, :, :, :]142 # print(x.size())143 # with torch.no_grad():144 # enc = model.conceptizer.encode(x).squeeze()[:, None]145 # print(enc.shape)146 # plot_digit(enc)147 # plt.show()148 #149 # exit()150 if args.theta_reg_type in ['unreg','none', None]:151 trainer = VanillaClassTrainer(model, args)152 elif args.theta_reg_type == 'grad1':153 trainer = GradPenaltyTrainer(model, args, typ = 1)154 elif args.theta_reg_type == 'grad2':155 trainer = GradPenaltyTrainer(model, args, typ = 2)156 elif args.theta_reg_type == 'grad3':157 trainer = GradPenaltyTrainer(model, args, typ = 3)158 elif args.theta_reg_type == 'crosslip':159 trainer = CLPenaltyTrainer(model, args)160 else:161 raise ValueError('Unrecoginzed theta_reg_type')162 if not args.load_model and args.train:163 trainer.train(train_loader, valid_loader, epochs = args.epochs, save_path = model_path)164 trainer.plot_losses(save_path=results_path)165 else:166 checkpoint = torch.load(os.path.join(model_path,'model_best.pth.tar'), map_location=lambda storage, loc: storage)167 checkpoint.keys()168 model = checkpoint['model']169 trainer = VanillaClassTrainer(model, args)170 trainer.validate(test_loader, fold = 'test')171 All_Results = {}172 ### 1. Single point lipshiz estimate via black box optim173 # All methods tested with BB optim for fair comparison)174 features = None175 classes = [str(i) for i in range(10)]176 model.eval()177 if args.cuda:178 model.cpu()179 expl = gsenn_wrapper(model,180 mode = 'classification',181 input_type = 'image',182 multiclass=True,183 feature_names = features,184 class_names = classes,185 train_data = train_loader,186 skip_bias = True,187 verbose = False)188 #### Debug single input189 # x = next(iter(train_tds))[0]190 # attr = expl(x, show_plot = False)191 # pdb.set_trace()192 # #### Debug multi input193 # x = next(iter(test_loader))[0] # Transformed194 # x_raw = test_loader.dataset.test_data[:args.batch_size,:,:]195 # attr = expl(x, x_raw = x_raw, show_plot = True)196 # #pdb.set_trace()197 # #### Debug argmax plot_theta_stability198 if args.h_type == 'input':199 x = next(iter(test_tds))[0].numpy()200 y = next(iter(test_tds))[0].numpy()201 x_raw = (test_tds.test_data[0].float()/255).numpy()202 y_raw = revert_to_raw(x)203 att_x = expl(x, show_plot = False)204 att_y = expl(y, show_plot = False)205 lip = 1206 lipschitz_argmax_plot(x_raw, y_raw, att_x,att_y, lip)# save_path=fpath)207 #pdb.set_trace()208 ### 2. Single example lipschitz estimate with Black Box209 do_bb_stability_example = True210 if do_bb_stability_example:211 print('**** Performing lipschitz estimation for a single point ****')212 idx = 0213 print('Example index: {}'.format(idx))214 #x = train_tds[idx][0].view(1,28,28).numpy()215 x = next(iter(test_tds))[0].numpy()216 x_raw = (test_tds.test_data[0].float()/255).numpy()217 #x_raw = next(iter(train_tds))[0]218 # args.optim = 'gp'219 # args.lip_eps = 0.1220 # args.lip_calls = 10221 Results = {}222 lip, argmax = expl.local_lipschitz_estimate(x, bound_type='box_std',223 optim=args.optim,224 eps=args.lip_eps,225 n_calls=4*args.lip_calls,226 njobs = 1,227 verbose=2)228 #pdb.set_trace()229 Results['lip_argmax'] = (x, argmax, lip)230 # .reshape(inputs.shape[0], inputs.shape[1], -1)231 att = expl(x, None, show_plot=False)#.squeeze()232 # .reshape(inputs.shape[0], inputs.shape[1], -1)233 att_argmax = expl(argmax, None, show_plot=False)#.squeeze()234 #pdb.set_trace()235 Argmax_dict = {'lip': lip, 'argmax': argmax, 'x': x}236 fpath = os.path.join(results_path, 'argmax_lip_gp_senn.pdf')237 if args.h_type == 'input':238 lipschitz_argmax_plot(x_raw, revert_to_raw(argmax), att, att_argmax, lip, save_path=fpath)239 pickle.dump(Argmax_dict, open(240 results_path + '/argmax_lip_gp_senn.pkl', "wb"))241 pdb.set_trace()242 # print(asd.asd)243 #noise_stability_plots(model, test_tds, cuda = args.cuda, save_path = results_path)244 ### 3. Local lipschitz estimate over multiple samples with Black BOx Optim245 do_bb_stability = False246 if do_bb_stability:247 print('**** Performing black-box lipschitz estimation over subset of dataset ****')248 maxpoints = 20249 #valid_loader 0 it's shuffled, so it's like doing random choice250 mini_test = next(iter(valid_loader))[0][:maxpoints].numpy()251 lips = expl.estimate_dataset_lipschitz(mini_test,252 n_jobs=-1, bound_type='box_std',253 eps=args.lip_eps, optim=args.optim,254 n_calls=args.lip_calls, verbose=2)255 pdb.set_trace()256 Stability_dict = {'lips': lips}257 pickle.dump(Stability_dict, open(results_path + '_stability_blackbox.pkl', "wb"))258 All_Results['stability_blackbox'] = lips259 # add concept plot260 concept_grid(model, test_loader, top_k = 10, save_path = results_path + '/concept_grid.pdf')261 pickle.dump(All_Results, open(results_path + '_combined_metrics.pkl', "wb"))262 # args.epoch_stats = epoch_stats263 # save_path = args.results_path264 # print("Save train/dev results to", save_path)265 # args_dict = vars(args)266 # pickle.dump(args_dict, open(save_path,'wb') )267if __name__ == '__main__':...
tests.py
Source:tests.py
1from django.core.exceptions import ValidationError2from django.test import TestCase3from django.utils import six4from .models import (5 TestModel,6 TestNullableModel,7 TestDefaultModel,8 DEFAULT_DURATION9)10from durationfield.utils import timestring11from datetime import timedelta12import unittest13class DurationFieldTests(TestCase):14 def setUp(self):15 self.test_tds = [16 timedelta(hours=1), # No days, no micro, single-digit hour17 timedelta(hours=10), # double-digit hour18 timedelta(hours=10, minutes=35, seconds=1),19 timedelta(days=1), # Day, no micro20 timedelta(days=1, microseconds=1), # Day, with micro21 timedelta(days=10), # Days, no micro22 timedelta(days=10, microseconds=1), # Days, with micro23 ]24 return super(DurationFieldTests, self).setUp()25 def _delta_to_microseconds(self, td):26 """27 Get the total number of microseconds in a timedelta, normalizing days and28 seconds to microseconds.29 """30 SECONDS_TO_US = 1000 * 100031 MINUTES_TO_US = SECONDS_TO_US * 6032 HOURS_TO_US = MINUTES_TO_US * 6033 DAYS_TO_US = HOURS_TO_US * 2434 td_in_ms = td.days * DAYS_TO_US + td.seconds * SECONDS_TO_US + td.microseconds35 self.assertEqual(timedelta(microseconds=td_in_ms), td)36 return td_in_ms37 def testTimedeltaStrRoundtrip(self):38 for td in self.test_tds:39 td_str = str(td)40 td_from_str = timestring.str_to_timedelta(td_str)41 self.assertEqual(td_from_str, td)42 def testTimedeltaStrInvalid(self):43 self.assertRaises(ValidationError, timestring.str_to_timedelta, 'fake')44 def testDbRoundTrip(self):45 """46 Data should remain the same when taking a round trip to and from the db47 """48 models = [TestModel, TestNullableModel, TestDefaultModel]49 for td in self.test_tds:50 for ModelClass in models:51 tm = ModelClass()52 tm.duration_field = td53 tm.save()54 tm_saved = ModelClass.objects.get(pk=tm.pk)55 self.assertEqual(tm_saved.duration_field, tm.duration_field)56 def testInvalidSaveAttempt(self):57 self.assertRaises(ValidationError, TestModel, duration_field='invalid')58 # not saved in DB59 self.assertEqual(TestModel.objects.count(), 0)60 def testDefaultValue(self):61 """62 Default value should be empty and fetchable63 """64 model_test = TestNullableModel()65 model_test.save()66 model_test_saved = TestNullableModel.objects.get(pk=model_test.pk)67 self.assertEqual(model_test.duration_field, None)68 self.assertEqual(model_test_saved.duration_field, None)69 def testDefaultGiven(self):70 """71 Default value should use the default argument72 """73 model_test = TestDefaultModel()74 model_test.save()75 model_test_saved = TestDefaultModel.objects.get(pk=model_test.pk)76 self.assertEqual(model_test.duration_field, DEFAULT_DURATION)77 self.assertEqual(model_test_saved.duration_field, DEFAULT_DURATION)78 def testApplicationType(self):79 """80 Timedeltas should be returned to the applciation81 """82 for td in self.test_tds:83 model_test = TestModel()84 model_test.duration_field = td85 model_test.save()86 model_test = TestModel.objects.get(pk=model_test.pk)87 self.assertEqual(td, model_test.duration_field)88 # Test with strings89 model_test = TestModel()90 model_test.duration_field = str(td)91 model_test.save()92 model_test = TestModel.objects.get(pk=model_test.pk)93 self.assertEqual(td, model_test.duration_field)94 # Test with int95 model_test = TestModel()96 model_test.duration_field = self._delta_to_microseconds(td)97 model_test.save()98 model_test = TestModel.objects.get(pk=model_test.pk)99 self.assertEqual(td, model_test.duration_field)100 @unittest.skipIf(six.PY3, 'long not present in Python 3')101 def testLongInPython2(self):102 for td in self.test_tds:103 # Test with long104 model_test = TestModel()105 model_test.duration_field = long(self._delta_to_microseconds(td))106 model_test.save()107 model_test = TestModel.objects.get(pk=model_test.pk)108 self.assertEqual(td, model_test.duration_field)109 def testInputTime(self):110 delta = timestring.str_to_timedelta("10:23")111 seconds = (10 * 60 * 60) + (23 * 60)112 self.assertEqual(seconds, delta.seconds)113 def testInputTimeSeonds(self):114 delta = timestring.str_to_timedelta("12:21:24")115 seconds = (12 * 60 * 60) + (21 * 60) + 24116 self.assertEqual(seconds, delta.seconds)117 def testInputTimeSecondsMicroseconds(self):118 delta = timestring.str_to_timedelta("11:20:22.000098")119 seconds = (11 * 60 * 60) + (20 * 60) + 22120 self.assertEqual(seconds, delta.seconds)121 self.assertEqual(98, delta.microseconds)122 def testInputTimeMicrosecondsRightPadZeros(self):123 delta = timestring.str_to_timedelta("11:20:22.160")124 self.assertEqual(160000, delta.microseconds)125 def testInputTimeMicrosecondsLeftPadZeros(self):126 delta = timestring.str_to_timedelta("11:20:22.016")127 self.assertEqual(16000, delta.microseconds)128 def testInputTimeMicrosecondsBothPadZeros(self):129 delta = timestring.str_to_timedelta("11:20:22.0160")130 self.assertEqual(16000, delta.microseconds)131 def testInputAll(self):132 delta = timestring.str_to_timedelta("1 year, 10 months, 3 weeks, 2 days, 3:40:50")133 days = (134 (1 * 365) +135 (10 * 30) +136 (3 * 7) +137 2138 )139 seconds = (140 (3 * 60 * 60) +141 (40 * 60) +142 50143 )144 self.assertEqual(145 days, delta.days146 )147 self.assertEqual(148 seconds, delta.seconds149 )150 def testInputAllAbbreviated(self):151 delta = timestring.str_to_timedelta("2y 9m 1w 20d 0:10:39")152 days = (153 (2 * 365) +154 (9 * 30) +155 (1 * 7) +156 20157 )158 seconds = (159 (0 * 60 * 60) +160 (10 * 60) +161 39162 )163 self.assertEqual(164 days, delta.days165 )166 self.assertEqual(167 seconds, delta.seconds168 )169 def testInputDaysOnly(self):170 delta = timestring.str_to_timedelta("24 days")171 self.assertEqual(172 24, delta.days...
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