How to use test_interval method in localstack

Best Python code snippet using localstack_python

do_solve.py

Source:do_solve.py

1def do_solve(niter, solver, disp_interval, test_interval, test_iters, training_id, batch_size):2    """Run solvers for niter iterations,3       returning the loss and recorded each iteration.4       `solvers` is a list of (name, solver) tuples."""5    import tempfile6    import numpy as np7    import os8    from pylab import zeros, arange, subplots, plt, savefig9    import glob10    import time11    # SET PLOTS DATA12    # train_loss = zeros(niter/disp_interval)13    train_loss_r = zeros(niter/disp_interval)14    train_correct_pairs = zeros(niter/disp_interval)15    # train_acc = zeros(niter/disp_interval)16    # val_loss = zeros(niter/test_interval)17    val_loss_r = zeros(niter/test_interval)18    val_correct_pairs = zeros(niter/test_interval)19    # val_acc = zeros(niter/test_interval)20    it_axes = (arange(niter) * disp_interval) + disp_interval21    it_val_axes = (arange(niter) * test_interval) + test_interval22    _, ax1 = subplots()23    ax2 = ax1.twinx()24    ax1.set_xlabel('iteration')25    ax1.set_ylabel('train loss (r), val loss (g),')# train loss_r (c), val loss_r (o)')26    ax2.set_ylabel('train correct pairs (b) val correct pairs (m)')# train top1 (y) val top1 (bk)')27    ax2.set_autoscaley_on(False)28    ax2.set_ylim([0, batch_size])29    # loss = {name: np.zeros(niter) for name, _ in solvers}30    loss_r = np.zeros(niter)31    correct_pairs = np.zeros(niter)32    # acc = {name: np.zeros(niter) for name, _ in solvers}33    lowest_val_loss = 100034    best_it = 035    #RUN TRAINING36    for it in range(niter):37        # start = time.time()38        solver.step(1)  # run a single SGD step in Caffe39        # end = time.time()40        # print "Time step: " + str((end - start))41        # print "Max before ReLU: " + str(np.max(s.net.blobs['inception_5b/pool_proj'].data))42        # print "Max last FC: " + str(np.max(s.net.blobs['loss3/classifierCustom'].data))43        #loss[name][it] = s.net.blobs['loss3/loss3/classification'].data.copy()44        loss_r[it] = solver.net.blobs['loss3/loss3/ranking'].data.copy()45        correct_pairs[it] = solver.net.blobs['correct_pairs'].data.copy()46        # acc[name][it] = s.net.blobs['loss3/top-1'].data.copy()47        #PLOT48        if it % disp_interval == 0 or it + 1 == niter:49            # loss_disp = 'loss=' + str(loss['my_solver'][it]) + ' correct_pairs=' + str(correct_pairs['my_solver'][it]) + ' loss ranking=' + str(loss_r['my_solver'][it])50            loss_disp = ' correct_pairs=' + str(correct_pairs[it]) + ' loss ranking=' + str(loss_r[it])51            print '%3d) %s' % (it, loss_disp)52            # train_loss[it/disp_interval] = loss[it]53            train_loss_r[it/disp_interval] = loss_r[it]54            train_correct_pairs[it/disp_interval] = correct_pairs[it]55            # train_acc[it/disp_interval] = acc[it] *12056            # ax1.plot(it_axes[0:it/disp_interval], train_loss[0:it/disp_interval], 'r')57            ax1.plot(it_axes[0:it/disp_interval], train_loss_r[0:it/disp_interval], 'c')58            ax2.plot(it_axes[0:it/disp_interval], train_correct_pairs[0:it/disp_interval], 'b')59            # ax2.plot(it_axes[0:it/disp_interval], train_acc[0:it/disp_interval], 'gold')60            # if it > test_interval:61            #     ax1.plot(it_val_axes[0:it/test_interval], val_loss[0:it/test_interval], 'g') #Val always on top62            ax1.set_ylim([0,2])63            plt.title(training_id)64            plt.ion()65            plt.grid(True)66            plt.show()67            plt.pause(0.001)68            # title = '../training/numbers/training-' + str(it) + '.png'  # Save graph to disk69            # savefig(title, bbox_inches='tight')70        #VALIDATE71        if it % test_interval == 0 and it > 0:72            # loss_val = 073            loss_val_r = 074            cur_correct_pairs = 075            # cur_acc = 076            for i in range(test_iters):77                solver.test_nets[0].forward()78                # loss_val += solver.test_nets[0].blobs['loss3/loss3/classification'].data79                loss_val_r += solver.test_nets[0].blobs['loss3/loss3/ranking'].data80                cur_correct_pairs += solver.test_nets[0].blobs['correct_pairs'].data81                # cur_acc += solvers[0][1].test_nets[0].blobs['loss3/top-1'].data82            # loss_val /= test_iters83            loss_val_r /= test_iters84            cur_correct_pairs /= test_iters85            # cur_acc /= test_iters86            # cur_acc *= 12087            # print("Val loss: " + str(loss_val) + " Val correct pairs: " + str(cur_correct_pairs) + " Val loss ranking: " + str(loss_val_r) + "Val acc: "+ str(cur_acc))88            print(" Val correct pairs: " + str(cur_correct_pairs) + " Val loss ranking: " + str(loss_val_r))89            # val_loss[it/test_interval - 1] = loss_val90            val_loss_r[it/test_interval - 1] = loss_val_r91            val_correct_pairs[it/test_interval - 1] = cur_correct_pairs92            # val_acc[it/test_interval - 1] = cur_acc93            # ax1.plot(it_val_axes[0:it/test_interval], val_loss[0:it/test_interval], 'g')94            ax1.plot(it_val_axes[0:it/test_interval], val_loss_r[0:it/test_interval], 'orange')95            ax2.plot(it_val_axes[0:it/test_interval], val_correct_pairs[0:it/test_interval], 'm')96            # ax2.plot(it_val_axes[0:it/test_interval], val_acc[0:it/test_interval], 'k')97            ax1.set_ylim([0,2])98            ax1.set_xlabel('iteration ' + 'Best it: ' + str(best_it) + ' Best Val Loss: ' + str(int(lowest_val_loss)))99            plt.title(training_id)100            plt.ion()101            plt.grid(True)102            plt.show()103            plt.pause(0.001)104            title = '../../../hd/datasets/instaFashion/models/training/' + training_id + str(it) + '.png'  # Save graph to disk105            savefig(title, bbox_inches='tight')106            if loss_val_r < lowest_val_loss:107                print("Best Val loss!")108                lowest_val_loss = loss_val_r109                best_it = it110                filename = '../../../hd/datasets/instaFashion/models/CNNContrastive/' + training_id + 'best_valLoss_' + str(111                    int(loss_val_r)) + '_it_' + str(it) + '.caffemodel'112                prefix = 30113                for cur_filename in glob.glob(filename[:-prefix] + '*'):114                    print(cur_filename)115                    os.remove(cur_filename)...

__init__.py

Source:__init__.py

1# import numpy as np2# import PIL.Image3# from dream_utils import *4# resize_in = (224, 224)5# resize_out = (700, 700)6# Athena squared7# image = np.float32(PIL.Image.open('images/athena_louvre_700px.jpg'))8# image_mask = PIL.Image.open('images/athena_louvre_700px_face_mask.png')9# Louise10# image = np.float32(PIL.Image.open('images/louise.jpg'))11# image_mask = PIL.Image.open('images/louise_crop_mask.png')12TIME_FORMAT = "%Y-%m-%d_%H:%M:%S.%f"13SOLVERS = [14    # {'name': '0010', 'snapshot':  1,  'max_iter':   10, 'base_lr': 0.0001, 'test_interval': 10},  # 015    {'snapshot':   1, 'max_iter':   20, 'base_lr': 0.0001, 'test_interval': 20},  # 116    {'snapshot':   1, 'max_iter':   40, 'base_lr': 0.001,  'test_interval': 20},  # 217    {'snapshot':   1, 'max_iter':   80, 'base_lr': 0.01,   'test_interval': 40},  # 318    {'snapshot':   1, 'max_iter':  120, 'base_lr': 0.01,   'test_interval': 20},  # 419    {'snapshot':   1, 'max_iter':  160, 'base_lr': 0.01,   'test_interval': 20},  # 520    {'snapshot':   1, 'max_iter':  200, 'base_lr': 0.01,   'test_interval': 20},  # 621    {'snapshot':   1, 'max_iter':  220, 'base_lr': 0.02,   'test_interval': 10},  # 722    {'snapshot':   1, 'max_iter':  240, 'base_lr': 0.02,   'test_interval': 10},  # 823    {'snapshot':   1, 'max_iter':  250, 'base_lr': 0.03,   'test_interval': 10},  # 924    {'snapshot':   1, 'max_iter':  260, 'base_lr': 0.01,   'test_interval': 10},  # 1025    {'snapshot':   1, 'max_iter':  270, 'base_lr': 0.0005, 'test_interval': 10},  # 1126    {'snapshot':   1, 'max_iter':  290, 'base_lr': 0.001,  'test_interval': 10},  # 1227    {'snapshot':   5, 'max_iter':  340, 'base_lr': 0.01,   'test_interval': 10},  # 1328    {'snapshot':  20, 'max_iter':  480, 'base_lr': 0.01,   'test_interval': 100}, # 1429    {'snapshot':  20, 'max_iter':  680, 'base_lr': 0.01,   'test_interval': 100}, # 1530    {'snapshot':  20, 'max_iter':  880, 'base_lr': 0.01,   'test_interval': 100}, # 1631    {'snapshot':  20, 'max_iter': 1080, 'base_lr': 0.01,   'test_interval': 100}, # 1732    {'snapshot':  40, 'max_iter': 1200, 'base_lr': 0.01,   'test_interval': 100}, # 1833    {'snapshot':  40, 'max_iter': 1400, 'base_lr': 0.005,  'test_interval': 100}, # 1934    {'snapshot': 100, 'max_iter': 2200, 'base_lr': 0.005,  'test_interval': 200}, # 2035    {'snapshot': 100, 'max_iter': 3000, 'base_lr': 0.005,  'test_interval': 200}, # 2136    {'snapshot':   1, 'max_iter': 3020, 'base_lr': 0.001,  'test_interval': 20, 'gamma': 1.0}, # 2237    {'snapshot':   1, 'max_iter': 3040, 'base_lr': 0.01,   'test_interval': 20, 'gamma': 1.0}, # 2338    {'snapshot':   1, 'max_iter': 3060, 'base_lr': 0.02,   'test_interval': 20, 'gamma': 1.0}, # 2439    {'snapshot':   1, 'max_iter': 3080, 'base_lr': 0.015,  'test_interval': 20, 'gamma': 1.0}, # 2540    {'snapshot':   1, 'max_iter': 3100, 'base_lr': 0.01,   'test_interval': 20, 'gamma': 1.0}, # 2641    {'snapshot':   1, 'max_iter': 3150, 'base_lr': 0.005,  'test_interval': 25, 'gamma': 1.0}, # 2742    {'snapshot':   1, 'max_iter': 3170, 'base_lr': 0.005,  'test_interval': 20, 'gamma': 1.0}, # 2843    {'snapshot':   1, 'max_iter': 3200, 'base_lr': 0.005,  'test_interval': 20, 'gamma': 1.0}, # 2944    {'snapshot':  10, 'max_iter': 3300, 'base_lr': 0.001,  'test_interval': 20, 'gamma': 1.0}, # 3045    {'snapshot':  25, 'max_iter': 3500, 'base_lr': 0.0005, 'test_interval': 100, 'gamma': 1.0}, # 3146]47# default solver name equals to max_iter48for solver in SOLVERS:49    if not solver.has_key('name'):50        solver['name'] = '%s' % solver['max_iter']51RIA_MODEL_DIR = 'models/Ria_Gurtow/'52RIA_MODEL_SNAPSHOTS_PREFIX = 'models/Ria_Gurtow/generations/ria_gurtow_iter_'53EMOTIONS_MODEL = 'models/VGG_S_rgb/EmotiW_VGG_S.caffemodel'...

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