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How to use get_test_times method in stestr

Best Python code snippet using stestr_python

Trainer.py

Source:Trainer.py

...36        self.eval_data = AerialDataset(dataset=self.dataset,data_path=self.data_path,mode='val')37        self.eval_loader = DataLoader(self.eval_data,batch_size=1,shuffle=False,num_workers=2)38        if self.dataset=='Potsdam':39            self.num_of_class=640            self.epoch_repeat = get_test_times(6000,6000,self.train_crop_size,self.train_crop_size)41        elif self.dataset=='UDD5':42            self.num_of_class=543            self.epoch_repeat = get_test_times(4000,3000,self.train_crop_size,self.train_crop_size)44        elif self.dataset=='UDD6':45            self.num_of_class=646            self.epoch_repeat = get_test_times(4000,3000,self.train_crop_size,self.train_crop_size)47        else:48            raise NotImplementedError49        if args.model == 'FCN':50            self.model = models.FCN8(num_classes=self.num_of_class)51        elif args.model == 'DeepLabV3+':52            self.model = models.DeepLab(num_classes=self.num_of_class,backbone='resnet')53        elif args.model == 'GCN':54            self.model = models.GCN(num_classes=self.num_of_class)55        elif args.model == 'UNet':56            self.model = models.UNet(num_classes=self.num_of_class)57        elif args.model == 'ENet':58            self.model = models.ENet(num_classes=self.num_of_class)59        elif args.model == 'D-LinkNet':60            self.model = models.DinkNet34(num_classes=self.num_of_class)61        else:62            raise NotImplementedError63        if args.loss == 'CE':64            self.criterion = CrossEntropyLoss2d()65        elif args.loss == 'LS':66            self.criterion = LovaszSoftmax()67        elif args.loss == 'F':68            self.criterion = FocalLoss()69        elif args.loss == 'CE+D':70            self.criterion = CE_DiceLoss()71        else:72            raise NotImplementedError73        74        self.schedule_mode = args.schedule_mode75        self.optimizer = opt.AdamW(self.model.parameters(),lr=args.lr)76        if self.schedule_mode == 'step':77            self.scheduler = opt.lr_scheduler.StepLR(self.optimizer, step_size=30, gamma=0.1)78        elif self.schedule_mode == 'miou' or self.schedule_mode == 'acc':79            self.scheduler = opt.lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='max', patience=10, factor=0.1)80        elif self.schedule_mode == 'poly':81            iters_per_epoch=len(self.train_loader)82            self.scheduler = Poly(self.optimizer,num_epochs=args.epochs,iters_per_epoch=iters_per_epoch)83        else:84            raise NotImplementedError85        self.evaluator = Evaluator(self.num_of_class)86        self.model = nn.DataParallel(self.model)87        88        self.cuda = args.cuda89        if self.cuda is True:90            self.model = self.model.cuda()91        self.resume = args.resume92        self.finetune = args.finetune93        assert not (self.resume != None and self.finetune != None)94        if self.resume != None:95            print("Loading existing model...")96            if self.cuda:97                checkpoint = torch.load(args.resume)98            else:99                checkpoint = torch.load(args.resume, map_location='cpu') 100            self.model.load_state_dict(checkpoint['parameters'])101            self.optimizer.load_state_dict(checkpoint['optimizer'])102            self.scheduler.load_state_dict(checkpoint['scheduler'])103            self.start_epoch = checkpoint['epoch'] + 1104            #start from next epoch105        elif self.finetune != None:106            print("Loading existing model...")107            if self.cuda:108                checkpoint = torch.load(args.finetune)109            else:110                checkpoint = torch.load(args.finetune, map_location='cpu')111            self.model.load_state_dict(checkpoint['parameters'])112            self.start_epoch = checkpoint['epoch'] + 1113        else:114            self.start_epoch = 1115        if self.mode=='train':    116            self.writer = SummaryWriter(comment='-'+self.dataset+'_'+self.model.__class__.__name__+'_'+args.loss)117        self.init_eval = args.init_eval118        119    #Note: self.start_epoch and self.epochs are only used in run() to schedule training & validation120    def run(self):121        if self.init_eval: #init with an evaluation122            init_test_epoch = self.start_epoch - 1123            Acc,_,mIoU,_ = self.validate(init_test_epoch,save=True)124            self.writer.add_scalar('eval/Acc', Acc, init_test_epoch)125            self.writer.add_scalar('eval/mIoU', mIoU, init_test_epoch)126            self.writer.flush()127        end_epoch = self.start_epoch + self.epochs128        for epoch in range(self.start_epoch,end_epoch):  129            loss = self.train(epoch)130            self.writer.add_scalar('train/lr',self.optimizer.state_dict()['param_groups'][0]['lr'],epoch)131            self.writer.add_scalar('train/loss',loss,epoch)132            self.writer.flush()133            saved_dict = {134                'model': self.model.__class__.__name__,135                'epoch': epoch,136                'dataset': self.dataset,137                'parameters': self.model.state_dict(),138                'optimizer': self.optimizer.state_dict(),139                'scheduler': self.scheduler.state_dict()140            }141            torch.save(saved_dict, f'./{self.model.__class__.__name__}_{self.dataset}_epoch{epoch}.pth.tar')142            143            Acc,_,mIoU,_ = self.validate(epoch,save=True)144            self.writer.add_scalar('eval/Acc',Acc,epoch)145            self.writer.add_scalar('eval/mIoU',mIoU,epoch)146            self.writer.flush()147            if self.schedule_mode == 'step' or self.schedule_mode == 'poly':148                self.scheduler.step()149            elif self.schedule_mode == 'miou':150                self.scheduler.step(mIoU)151            elif self.schedule_mode == 'acc':152                self.scheduler.step(Acc)153            else:154                raise NotImplementedError155        self.writer.close()156    def train(self,epoch):157        self.model.train()158        print(f"----------epoch {epoch}----------")159        print("lr:",self.optimizer.state_dict()['param_groups'][0]['lr'])160        total_loss = 0161        num_of_batches = len(self.train_loader) * self.epoch_repeat162        for itr in range(100):163            for i,[img,gt] in enumerate(self.train_loader):164                print(f"epoch: {epoch} batch: {i+1+itr*len(self.train_loader)}/{num_of_batches}")165                print("img:",img.shape)166                print("gt:",gt.shape)167                self.optimizer.zero_grad()168                if self.cuda:169                    img,gt = img.cuda(),gt.cuda()170                pred = self.model(img)171                print("pred:",pred.shape)172                loss = self.criterion(pred,gt.long())173                print("loss:",loss)174                total_loss += loss.data175                loss.backward()176                self.optimizer.step()177        return total_loss178    def validate(self,epoch,save):179        self.model.eval()180        print(f"----------validate epoch {epoch}----------")181        if save and not os.path.exists("epoch_"+str(epoch)):182            os.mkdir("epoch"+str(epoch))183        num_of_imgs = len(self.eval_loader)184        for i,sample in enumerate(self.eval_loader):185            img_name,gt_name = sample['img'][0],sample['gt'][0]186            print(f"{i+1}/{num_of_imgs}:")187            img = Image.open(img_name).convert('RGB')188            gt = np.array(Image.open(gt_name))189            times, points = self.get_pointset(img)190            print(f'{times} tests will be carried out on {img_name}...')191            W,H = img.size #TODO: check numpy & PIL dimensions192            label_map = np.zeros([H,W],dtype=np.uint8)193            score_map = np.zeros([H,W],dtype=np.uint8)194            #score_map not necessarily to be uint8 but uint8 gets better result...195            tbar = tqdm(points)196            for i,j in tbar:197                tbar.set_description(f"{i},{j}")198                label_map,score_map = self.test_patch(i,j,img,label_map,score_map)199            #finish a large200            self.evaluator.add_batch(label_map,gt)201            if save:   202                mask = ret2mask(label_map,dataset=self.dataset)203                png_name = os.path.join("epoch"+str(epoch),os.path.basename(img_name).split('.')[0]+'.png')204                Image.fromarray(mask).save(png_name)205        Acc = self.evaluator.Pixel_Accuracy()206        Acc_class = self.evaluator.Pixel_Accuracy_Class()207        mIoU = self.evaluator.Mean_Intersection_over_Union()208        FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union() 209        print("Acc:",Acc)210        print("Acc_class:",Acc_class)211        print("mIoU:",mIoU)212        print("FWIoU:",FWIoU)213        self.evaluator.reset()214        return Acc,Acc_class,mIoU,FWIoU215    def test_patch(self,i,j,img,label_map,score_map):216        tr = EvaluationTransform(mean = [0.485, 0.456, 0.406], 217                                    std = [0.229, 0.224, 0.225])218        #print(img.size)219        cropped = img.crop((i,j,i+self.eval_crop_size,j+self.eval_crop_size))220        cropped = tr(cropped).unsqueeze(0)221        if self.cuda:222            cropped = cropped.cuda()223        out = self.model(cropped)224        #out = torch.nn.functional.softmax(out, dim=1)225        ret = torch.max(out.squeeze(),dim=0)226        score = ret[0].data.detach().cpu().numpy()227        label = ret[1].data.detach().cpu().numpy()228        #numpy array's shape is [H,W] while PIL.Image is [W,H]229        score_temp = score_map[j:j+self.eval_crop_size,i:i+self.eval_crop_size]230        label_temp = label_map[j:j+self.eval_crop_size,i:i+self.eval_crop_size]231        index = score > score_temp232        score_temp[index] = score[index]233        label_temp[index] = label[index]234        label_map[j:j+self.eval_crop_size,i:i+self.eval_crop_size] = label_temp235        score_map[j:j+self.eval_crop_size,i:i+self.eval_crop_size] = score_temp236        return label_map,score_map237    def get_pointset(self,img):238        W, H = img.size239        pointset = []240        count=0241        i = 0242        while i<W:243            break_flag_i = False244            if i+self.eval_crop_size >= W:245                i = W - self.eval_crop_size246                break_flag_i = True247            j = 0248            while j<H:249                break_flag_j = False250                if j + self.eval_crop_size >= H:251                    j = H - self.eval_crop_size252                    break_flag_j = True253                count+=1254                pointset.append((i,j))255                if break_flag_j:256                    break257                j+=self.stride258            if break_flag_i:259                break260            i+=self.stride261        value = get_test_times(W,H,self.eval_crop_size,self.stride)262        assert count==value,f'count={count} while get_test_times returns {value}'263        return count, pointset     264if __name__ == "__main__":265   print("--Trainer.py--")...

om-cp_data_logger_software_class.py

Source:om-cp_data_logger_software_class.py

...24        self.device_info.drop(range(2, len(self.device_info.index), 1), axis=0, inplace=True)25        self.device_info.columns = self.device_info.iloc[0]26        self.device_info = self.device_info.drop([0], axis=0).reset_index(drop=True)27        return self.device_info28    def get_test_times(self):29        test_times = pd.read_json(f'../json_tables/times_{self.name}.json',30                                  convert_dates=(self.start_col, self.end_col))31        return test_times32    def get_split_data(self):33        dfs = {}34        for x in range(len(self.get_test_times())):35            dfs[x] = self.data[self.get_test_times()[self.start_col][x]:self.get_test_times()[self.end_col][x]]36        return dfs37    @staticmethod38    def resample(df, col_name, order, cut_off, b_type):39        new_dfs = {}40        return_dfs = {}41        if type(df) == dict:42            for x in range(len(df)):43                b, a = signal.butter(order, cut_off, btype=b_type, analog=False)44                new_dfs[x] = pd.DataFrame(signal.filtfilt(b, a, df[x][col_name]))45                new_dfs[x].index = df[x].index46                return_dfs[x] = pd.concat([df[x], new_dfs[x]], axis=1)47                return_dfs[x].rename(columns={0: f"{col_name}_resample"}, inplace=True)48            return return_dfs49        else:50            b, a = signal.butter(order, cut_off, btype=b_type, analog=False)51            new_dfs = pd.DataFrame(signal.filtfilt(b, a, df[col_name]))52            new_dfs.index = df.index53            return_dfs = pd.concat([df, new_dfs], axis=1)54            return_dfs.rename(columns={0: f"{col_name}_resample"}, inplace=True)55        return return_dfs56    def graphing(self, df, name_addon=""):57        p = Path(f'../graphs/{self.name}')58        if type(df) == dict:59            for x in range(len(df)):60                fig, ax = plt.subplots(figsize=(10, 6))61                ax.plot(df[x]['Shock - Z Axis (g)'], color='#ffb300', marker='o', markersize=1, label='Test 5')62                ax.set_xlabel('Time (Hours:Minutes:Seconds.Milliseconds)', fontsize=15)63                ax.set_ylabel('Acceleration (g)', fontsize=15)64                plt.xticks(rotation=15)65                plt.grid()66                date_form = DateFormatter("%H:%M:%S.%f")67                ax.xaxis.set_major_formatter(date_form)68                self.annot_max(df[x].index, df[x]['Shock - Z Axis (g)'])69                self.annot_min(df[x].index, df[x]['Shock - Z Axis (g)'])70                p.mkdir(exist_ok=True)71                # plt.tight_layout()72                fig.set_size_inches(12, 8)73                fig.savefig(f'../graphs/{self.name}/impact_{self.name}_test_{x}{name_addon}.pdf')74                plt.close(fig)75        else:76            fig, ax = plt.subplots(figsize=(10, 6))77            ax.plot(df['Shock - Z Axis (g)'], color='#ffb300', marker='o', markersize=1, label='Test 5')78            ax.set_xlabel('Time (Hours:Minutes:Seconds)', fontsize=15)79            ax.set_ylabel('Acceleration (g)', fontsize=15)80            plt.xticks(rotation=15)81            plt.grid()82            date_form = DateFormatter("%H:%M:%S")83            ax.xaxis.set_major_formatter(date_form)84            self.annot_max(df.index, df['Shock - Z Axis (g)'])85            self.annot_min(df.index, df['Shock - Z Axis (g)'])86            p.mkdir(exist_ok=True)87            # plt.tight_layout()88            fig.set_size_inches(12, 8)89            fig.savefig(f'../graphs/{self.name}/impact_{self.name}_test{name_addon}.pdf')90            plt.close(fig)91    def graphing_2_dataframes(self, df1, df2, name_addon=""):92        p = Path(f'../graphs/{self.name}')93        if type(df1) == dict:94            for x in range(len(df1)):95                fig, ax = plt.subplots(figsize=(10, 6))96                ax.plot(df1[x]['Shock - Z Axis (g)'], color='#ffb300', marker='o', markersize=1, label='Original')97                ax.plot(df2[x]['Shock - Z Axis (g)_resample'], color='#00c8ff', marker='o', markersize=1,98                        label='Resample')99                ax.set_xlabel('Time (Hours:Minutes:Seconds.Milliseconds)', fontsize=15)100                ax.set_ylabel('Acceleration (g)', fontsize=15)101                plt.xticks(rotation=15)102                plt.grid()103                ax.legend()104                date_form = DateFormatter("%H:%M:%S.%f")105                ax.xaxis.set_major_formatter(date_form)106                self.annot_max(df1[x].index, df1[x]['Shock - Z Axis (g)'])107                self.annot_min(df1[x].index, df1[x]['Shock - Z Axis (g)'])108                p.mkdir(exist_ok=True)109                # plt.tight_layout()110                fig.set_size_inches(12, 8)111                fig.savefig(f'../graphs/{self.name}/impact_{self.name}_test_{x}{name_addon}.pdf')112                plt.close(fig)113        else:114            fig, ax = plt.subplots(figsize=(10, 6))115            ax.plot(df1['Shock - Z Axis (g)'], color='#ffb300', marker='o', markersize=1, label='Original')116            ax.plot(df2['Shock - Z Axis (g)_resample'], color='#00c8ff', marker='o', markersize=1, label='Resample')117            ax.set_xlabel('Time (Hours:Minutes:Seconds)', fontsize=15)118            ax.set_ylabel('Acceleration (g)', fontsize=15)119            plt.xticks(rotation=15)120            plt.grid()121            ax.legend()122            date_form = DateFormatter("%H:%M:%S")123            ax.xaxis.set_major_formatter(date_form)124            self.annot_max(df1.index, df1['Shock - Z Axis (g)'])125            self.annot_min(df1.index, df1['Shock - Z Axis (g)'])126            p.mkdir(exist_ok=True)127            # plt.tight_layout()128            fig.set_size_inches(12, 8)129            fig.savefig(f'../graphs/{self.name}/impact_{self.name}_test{name_addon}.pdf')130            plt.close(fig)131    @staticmethod132    def annot_max(x, y, ax=None):133        x_max = x[np.argmax(y)]134        y_max = y.max()135        if not ax:136            ax = plt.gca()137        bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)138        arrow_props = dict(arrowstyle="->")139        kw = dict(xycoords='data', textcoords="axes fraction",140                  arrowprops=arrow_props, bbox=bbox_props, ha="left", va="top")141        ax.annotate(f"Max={y_max}", xy=(x_max, y_max), xytext=(0.04, 0.94), **kw)142    @staticmethod143    def annot_min(x, y, ax=None):144        x_min = x[np.argmin(y)]145        y_min = y.min()146        if not ax:147            ax = plt.gca()148        bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)149        arrow_props = dict(arrowstyle="->")150        kw = dict(xycoords='data', textcoords="axes fraction",151                  arrowprops=arrow_props, bbox=bbox_props, ha="left", va="top")152        ax.annotate(f"Min={y_min}", xy=(x_min, y_min), xytext=(0.80, 0.10), **kw)153    @staticmethod154    def find_max_and_min(df, col_name_x, col_name_y):155        if type(df) == dict:156            min = {}157            max = {}158            for k in range(len(df)):159                tmp = df[k].reset_index()160                x = tmp[col_name_x]161                y = tmp[col_name_y]162                min[k] = (x[np.argmin(y)], y.min())163                max[k] = (x[np.argmax(y)], y.max())164            return min, max165        else:166            tmp = df.reset_index()167            x = tmp[col_name_x]168            y = tmp[col_name_y]169            return (x[np.argmin(y)], y.min()), (x[np.argmax(y)], y.max())170    def auto_split_data(self, df, time):171        dfs = {}172        min = {}173        max = {}174        x_start = {}175        x_end = {}176        dfu = {}177        for k in range(len(self.get_test_times())):178            dfs[k] = df[self.get_test_times()[self.start_col][k]:self.get_test_times()[self.end_col][k]]179            min[k], max[k] = self.find_max_and_min(dfs[k], "Time", "Shock - Z Axis (g)")180            x_start[k] = min[k][0] - timedelta(milliseconds=time)181            x_end[k] = min[k][0] + timedelta(milliseconds=time)182            dfu[k] = df[x_start[k]:x_end[k]]183        return dfu184    @staticmethod185    def intergrate(x_array1, y_array2):186        tmp = float(np.trapz(abs(x_array1), y_array2))187        return tmp * (10 ** -9)188    @staticmethod189    def to_latex(df, filename, caption, label):190        df.to_latex(f'../tex_files/{filename}.tex', longtable=True,191                    index=False, caption=caption, label=label)192        df.to_latex(f'../tex_files/{filename}_escape.tex', longtable=True,...

test_util_functions.py

Source:test_util_functions.py

...8        """Test `num_or_str_to_date()`."""9        self.assertIsInstance(util.num_or_str_to_date(1), datetime.datetime)10        parsed_date = util.num_or_str_to_date("2016-07-14T03:25:00.000+0000")11        self.assertIsInstance(parsed_date, datetime.datetime)12    def test_get_test_times(self):13        """Test `get_test_times()`."""14        def assert_instancesof_datetime(tuples):15            """16            Assert that all of the items in a list of tuples are instanced of17            `datetime.datetime`.18            """19            for pair in tuples:20                for item in pair:21                    self.assertIsInstance(item, datetime.datetime)22        perf_json = {23            "results": [],24            "start": "2016-07-14T03:25:00.000+0000",25            "end": "2016-07-14T03:24:00.000+0000",26        }27        times = util.get_test_times(perf_json)28        self.assertEqual(len(times), 1)29        assert_instancesof_datetime(times)30        perf_json = {31            "results": [32                {"start": 1, "end": 2},33                {"start": 3, "end": 4},34                {"start": 5, "end": "2016-07-14T03:24:00.000+0000"},35            ]36        }37        times = util.get_test_times(perf_json)38        self.assertEqual(len(times), 3)...

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