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+2025-02-24 01:13:34,680 INFO    MainThread:144 [wandb_setup.py:_flush():67] Loading settings from /app/pytorch-image-models/wandb/settings
+2025-02-24 01:13:34,680 INFO    MainThread:144 [wandb_setup.py:_flush():67] Loading settings from environment variables
+2025-02-24 01:13:34,680 INFO    MainThread:144 [wandb_init.py:setup_run_log_directory():647] Logging user logs to /app/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug.log
+2025-02-24 01:13:34,680 INFO    MainThread:144 [wandb_init.py:setup_run_log_directory():648] Logging internal logs to /app/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-internal.log
+2025-02-24 01:13:34,680 INFO    MainThread:144 [wandb_init.py:init():761] calling init triggers
+2025-02-24 01:13:34,681 INFO    MainThread:144 [wandb_init.py:init():766] wandb.init called with sweep_config: {}
+config: {'data': None, 'data_dir': None, 'dataset': 'hfds/datacomp/imagenet-1k-random-20.0-frac-1over4', 'train_split': 'train', 'val_split': 'validation', 'train_num_samples': None, 'val_num_samples': None, 'dataset_download': False, 'class_map': '', 'input_img_mode': None, 'input_key': None, 'target_key': None, 'dataset_trust_remote_code': False, 'model': 'seresnet34', 'pretrained': False, 'pretrained_path': None, 'initial_checkpoint': '', 'resume': '', 'no_resume_opt': False, 'num_classes': 1000, 'gp': None, 'img_size': None, 'in_chans': None, 'input_size': None, 'crop_pct': None, 'mean': None, 'std': None, 'interpolation': '', 'batch_size': 256, 'validation_batch_size': None, 'channels_last': False, 'fuser': '', 'grad_accum_steps': 1, 'grad_checkpointing': False, 'fast_norm': False, 'model_kwargs': {}, 'head_init_scale': None, 'head_init_bias': None, 'torchcompile_mode': None, 'torchscript': False, 'torchcompile': None, 'device': 'cuda:0', 'amp': True, 'amp_dtype': 'float16', 'amp_impl': 'native', 'model_dtype': None, 'no_ddp_bb': False, 'synchronize_step': False, 'local_rank': 0, 'device_modules': None, 'opt': 'sgd', 'opt_eps': None, 'opt_betas': None, 'momentum': 0.9, 'weight_decay': 2e-05, 'clip_grad': None, 'clip_mode': 'norm', 'layer_decay': None, 'opt_kwargs': {}, 'sched': 'cosine', 'sched_on_updates': False, 'lr': 0.4, 'lr_base': 0.1, 'lr_base_size': 256, 'lr_base_scale': '', 'lr_noise': None, 'lr_noise_pct': 0.67, 'lr_noise_std': 1.0, 'lr_cycle_mul': 1.0, 'lr_cycle_decay': 0.5, 'lr_cycle_limit': 1, 'lr_k_decay': 1.0, 'warmup_lr': 1e-05, 'min_lr': 0, 'epochs': 150, 'epoch_repeats': 0.0, 'start_epoch': None, 'decay_milestones': [90, 180, 270], 'decay_epochs': 90, 'warmup_epochs': 5, 'warmup_prefix': False, 'cooldown_epochs': 0, 'patience_epochs': 10, 'decay_rate': 0.1, 'no_aug': False, 'train_crop_mode': None, 'scale': [0.08, 1.0], 'ratio': [0.75, 1.3333333333333333], 'hflip': 0.5, 'vflip': 0.0, 'color_jitter': 0.4, 'color_jitter_prob': None, 'grayscale_prob': None, 'gaussian_blur_prob': None, 'aa': None, 'aug_repeats': 0, 'aug_splits': 0, 'jsd_loss': False, 'bce_loss': False, 'bce_sum': False, 'bce_target_thresh': None, 'bce_pos_weight': None, 'reprob': 0.5, 'remode': 'pixel', 'recount': 1, 'resplit': False, 'mixup': 0.0, 'cutmix': 0.0, 'cutmix_minmax': None, 'mixup_prob': 1.0, 'mixup_switch_prob': 0.5, 'mixup_mode': 'batch', 'mixup_off_epoch': 0, 'smoothing': 0.1, 'train_interpolation': 'random', 'drop': 0.0, 'drop_connect': None, 'drop_path': None, 'drop_block': None, 'bn_momentum': None, 'bn_eps': None, 'sync_bn': False, 'dist_bn': 'reduce', 'split_bn': False, 'model_ema': False, 'model_ema_force_cpu': False, 'model_ema_decay': 0.9998, 'model_ema_warmup': False, 'seed': 42, 'worker_seeding': 'all', 'log_interval': 50, 'recovery_interval': 0, 'checkpoint_hist': 10, 'workers': 4, 'save_images': False, 'pin_mem': False, 'no_prefetcher': False, 'output': '', 'experiment': 'ImageNetTraining20.0-frac-1over4', 'eval_metric': 'top1', 'tta': 0, 'use_multi_epochs_loader': False, 'log_wandb': True, 'wandb_project': 'ImageNetTraining20.0-frac-1over4', 'wandb_tags': [], 'wandb_resume_id': '', 'prefetcher': True, 'distributed': True, 'world_size': 4, 'rank': 0, '_wandb': {}}
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diff --git a/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/output.log b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..31a4003c8fa4b2728bc8c71e0dcdea28473336fa
--- /dev/null
+++ b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/output.log
@@ -0,0 +1,2506 @@
+Scheduled epochs: 150 (epochs + cooldown_epochs). Warmup within epochs when warmup_prefix=False. LR stepped per epoch.
+Train: 0 [   0/312 (  0%)]  Loss: 6.95 (6.95)  Time: 4.079s,  251.05/s  (4.079s,  251.05/s)  LR: 1.000e-05  Data: 1.481 (1.481)
+Train: 0 [  50/312 ( 16%)]  Loss: 6.94 (6.94)  Time: 0.391s, 2616.31/s  (0.464s, 2208.95/s)  LR: 1.000e-05  Data: 0.027 (0.056)
+Train: 0 [ 100/312 ( 32%)]  Loss: 6.94 (6.94)  Time: 0.395s, 2590.44/s  (0.429s, 2386.31/s)  LR: 1.000e-05  Data: 0.029 (0.042)
+Train: 0 [ 150/312 ( 48%)]  Loss: 6.94 (6.94)  Time: 0.395s, 2592.85/s  (0.418s, 2448.53/s)  LR: 1.000e-05  Data: 0.027 (0.037)
+Train: 0 [ 200/312 ( 64%)]  Loss: 6.94 (6.94)  Time: 0.398s, 2570.92/s  (0.413s, 2478.66/s)  LR: 1.000e-05  Data: 0.027 (0.035)
+Train: 0 [ 250/312 ( 80%)]  Loss: 6.95 (6.94)  Time: 0.400s, 2560.06/s  (0.410s, 2494.97/s)  LR: 1.000e-05  Data: 0.029 (0.034)
+Train: 0 [ 300/312 ( 96%)]  Loss: 6.94 (6.94)  Time: 0.401s, 2553.32/s  (0.409s, 2505.34/s)  LR: 1.000e-05  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.591 (1.591)  Loss:   6.945 ( 6.945)  Acc@1:   0.098 (  0.098)  Acc@5:   0.391 (  0.391)
+Test: [  48/48]  Time: 0.692 (0.350)  Loss:   6.940 ( 6.939)  Acc@1:   0.118 (  0.088)  Acc@5:   0.354 (  0.504)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
+
+Train: 1 [   0/312 (  0%)]  Loss: 6.94 (6.94)  Time: 1.743s,  587.60/s  (1.743s,  587.60/s)  LR: 8.001e-02  Data: 1.375 (1.375)
+Train: 1 [  50/312 ( 16%)]  Loss: 6.90 (6.91)  Time: 0.412s, 2483.58/s  (0.437s, 2343.95/s)  LR: 8.001e-02  Data: 0.028 (0.054)
+Train: 1 [ 100/312 ( 32%)]  Loss: 6.80 (6.87)  Time: 0.414s, 2471.45/s  (0.424s, 2414.94/s)  LR: 8.001e-02  Data: 0.027 (0.041)
+Train: 1 [ 150/312 ( 48%)]  Loss: 6.73 (6.84)  Time: 0.411s, 2492.05/s  (0.420s, 2437.43/s)  LR: 8.001e-02  Data: 0.023 (0.037)
+Train: 1 [ 200/312 ( 64%)]  Loss: 6.72 (6.82)  Time: 0.415s, 2466.90/s  (0.418s, 2448.82/s)  LR: 8.001e-02  Data: 0.028 (0.034)
+Train: 1 [ 250/312 ( 80%)]  Loss: 6.69 (6.80)  Time: 0.413s, 2477.39/s  (0.417s, 2456.15/s)  LR: 8.001e-02  Data: 0.028 (0.033)
+Train: 1 [ 300/312 ( 96%)]  Loss: 6.69 (6.78)  Time: 0.409s, 2501.62/s  (0.416s, 2461.93/s)  LR: 8.001e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   6.543 ( 6.543)  Acc@1:   1.270 (  1.270)  Acc@5:   4.004 (  4.004)
+Test: [  48/48]  Time: 0.090 (0.326)  Loss:   6.472 ( 6.522)  Acc@1:   1.297 (  1.210)  Acc@5:   4.717 (  4.160)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
+
+Train: 2 [   0/312 (  0%)]  Loss: 6.69 (6.69)  Time: 1.864s,  549.24/s  (1.864s,  549.24/s)  LR: 1.600e-01  Data: 1.124 (1.124)
+Train: 2 [  50/312 ( 16%)]  Loss: 6.67 (6.68)  Time: 0.418s, 2447.12/s  (0.438s, 2336.64/s)  LR: 1.600e-01  Data: 0.025 (0.049)
+Train: 2 [ 100/312 ( 32%)]  Loss: 6.67 (6.67)  Time: 0.414s, 2474.64/s  (0.426s, 2404.65/s)  LR: 1.600e-01  Data: 0.027 (0.038)
+Train: 2 [ 150/312 ( 48%)]  Loss: 6.62 (6.66)  Time: 0.406s, 2521.15/s  (0.420s, 2435.56/s)  LR: 1.600e-01  Data: 0.027 (0.035)
+Train: 2 [ 200/312 ( 64%)]  Loss: 6.56 (6.65)  Time: 0.404s, 2536.92/s  (0.417s, 2457.88/s)  LR: 1.600e-01  Data: 0.028 (0.033)
+Train: 2 [ 250/312 ( 80%)]  Loss: 6.57 (6.63)  Time: 0.404s, 2532.28/s  (0.414s, 2471.09/s)  LR: 1.600e-01  Data: 0.027 (0.032)
+Train: 2 [ 300/312 ( 96%)]  Loss: 6.52 (6.62)  Time: 0.407s, 2513.62/s  (0.413s, 2479.67/s)  LR: 1.600e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   6.168 ( 6.168)  Acc@1:   1.953 (  1.953)  Acc@5:   7.031 (  7.031)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   6.080 ( 6.129)  Acc@1:   2.830 (  2.332)  Acc@5:   9.552 (  7.886)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
+
+Train: 3 [   0/312 (  0%)]  Loss: 6.51 (6.51)  Time: 1.626s,  629.64/s  (1.626s,  629.64/s)  LR: 2.400e-01  Data: 1.254 (1.254)
+Train: 3 [  50/312 ( 16%)]  Loss: 6.51 (6.51)  Time: 0.417s, 2456.95/s  (0.437s, 2345.17/s)  LR: 2.400e-01  Data: 0.033 (0.055)
+Train: 3 [ 100/312 ( 32%)]  Loss: 6.48 (6.50)  Time: 0.417s, 2453.47/s  (0.426s, 2406.14/s)  LR: 2.400e-01  Data: 0.028 (0.042)
+Train: 3 [ 150/312 ( 48%)]  Loss: 6.41 (6.48)  Time: 0.413s, 2479.22/s  (0.422s, 2426.23/s)  LR: 2.400e-01  Data: 0.028 (0.037)
+Train: 3 [ 200/312 ( 64%)]  Loss: 6.39 (6.46)  Time: 0.414s, 2475.62/s  (0.421s, 2434.90/s)  LR: 2.400e-01  Data: 0.027 (0.035)
+Train: 3 [ 250/312 ( 80%)]  Loss: 6.38 (6.44)  Time: 0.412s, 2486.54/s  (0.419s, 2445.46/s)  LR: 2.400e-01  Data: 0.027 (0.033)
+Train: 3 [ 300/312 ( 96%)]  Loss: 6.39 (6.43)  Time: 0.408s, 2511.55/s  (0.417s, 2455.04/s)  LR: 2.400e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   5.748 ( 5.748)  Acc@1:   4.492 (  4.492)  Acc@5:  14.355 ( 14.355)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   5.695 ( 5.742)  Acc@1:   5.542 (  4.724)  Acc@5:  15.330 ( 14.202)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
+
+Train: 4 [   0/312 (  0%)]  Loss: 6.29 (6.29)  Time: 1.858s,  551.01/s  (1.858s,  551.01/s)  LR: 3.200e-01  Data: 1.484 (1.484)
+Train: 4 [  50/312 ( 16%)]  Loss: 6.27 (6.30)  Time: 0.416s, 2462.13/s  (0.440s, 2329.00/s)  LR: 3.200e-01  Data: 0.032 (0.056)
+Train: 4 [ 100/312 ( 32%)]  Loss: 6.23 (6.28)  Time: 0.415s, 2467.43/s  (0.427s, 2395.81/s)  LR: 3.200e-01  Data: 0.027 (0.042)
+Train: 4 [ 150/312 ( 48%)]  Loss: 6.21 (6.26)  Time: 0.416s, 2462.90/s  (0.424s, 2417.18/s)  LR: 3.200e-01  Data: 0.028 (0.037)
+Train: 4 [ 200/312 ( 64%)]  Loss: 6.18 (6.25)  Time: 0.410s, 2500.49/s  (0.421s, 2430.87/s)  LR: 3.200e-01  Data: 0.027 (0.035)
+Train: 4 [ 250/312 ( 80%)]  Loss: 6.09 (6.23)  Time: 0.409s, 2506.51/s  (0.419s, 2443.88/s)  LR: 3.200e-01  Data: 0.028 (0.034)
+Train: 4 [ 300/312 ( 96%)]  Loss: 6.16 (6.21)  Time: 0.409s, 2500.68/s  (0.417s, 2454.32/s)  LR: 3.200e-01  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.413 (1.413)  Loss:   5.680 ( 5.680)  Acc@1:   5.371 (  5.371)  Acc@5:  14.258 ( 14.258)
+Test: [  48/48]  Time: 0.091 (0.318)  Loss:   5.528 ( 5.608)  Acc@1:   4.953 (  5.764)  Acc@5:  17.807 ( 15.870)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
+
+Train: 5 [   0/312 (  0%)]  Loss: 6.08 (6.08)  Time: 1.555s,  658.59/s  (1.555s,  658.59/s)  LR: 3.989e-01  Data: 1.181 (1.181)
+Train: 5 [  50/312 ( 16%)]  Loss: 5.97 (6.09)  Time: 0.414s, 2475.95/s  (0.434s, 2359.19/s)  LR: 3.989e-01  Data: 0.026 (0.050)
+Train: 5 [ 100/312 ( 32%)]  Loss: 5.95 (6.07)  Time: 0.415s, 2468.27/s  (0.424s, 2412.66/s)  LR: 3.989e-01  Data: 0.028 (0.039)
+Train: 5 [ 150/312 ( 48%)]  Loss: 5.94 (6.05)  Time: 0.409s, 2502.53/s  (0.420s, 2438.08/s)  LR: 3.989e-01  Data: 0.029 (0.035)
+Train: 5 [ 200/312 ( 64%)]  Loss: 5.94 (6.03)  Time: 0.413s, 2482.03/s  (0.418s, 2451.65/s)  LR: 3.989e-01  Data: 0.029 (0.033)
+Train: 5 [ 250/312 ( 80%)]  Loss: 5.89 (6.01)  Time: 0.414s, 2474.83/s  (0.417s, 2458.04/s)  LR: 3.989e-01  Data: 0.027 (0.032)
+Train: 5 [ 300/312 ( 96%)]  Loss: 5.95 (6.00)  Time: 0.412s, 2484.91/s  (0.416s, 2459.84/s)  LR: 3.989e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.433 (1.433)  Loss:   4.997 ( 4.997)  Acc@1:  10.449 ( 10.449)  Acc@5:  25.488 ( 25.488)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   4.894 ( 4.967)  Acc@1:  11.439 ( 10.860)  Acc@5:  28.774 ( 26.684)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
+
+Train: 6 [   0/312 (  0%)]  Loss: 5.85 (5.85)  Time: 1.792s,  571.28/s  (1.792s,  571.28/s)  LR: 3.984e-01  Data: 1.369 (1.369)
+Train: 6 [  50/312 ( 16%)]  Loss: 5.82 (5.85)  Time: 0.412s, 2486.59/s  (0.437s, 2340.98/s)  LR: 3.984e-01  Data: 0.030 (0.054)
+Train: 6 [ 100/312 ( 32%)]  Loss: 5.75 (5.83)  Time: 0.414s, 2474.43/s  (0.426s, 2406.35/s)  LR: 3.984e-01  Data: 0.027 (0.041)
+Train: 6 [ 150/312 ( 48%)]  Loss: 5.76 (5.82)  Time: 0.410s, 2497.24/s  (0.421s, 2429.86/s)  LR: 3.984e-01  Data: 0.026 (0.036)
+Train: 6 [ 200/312 ( 64%)]  Loss: 5.85 (5.80)  Time: 0.415s, 2468.75/s  (0.419s, 2441.69/s)  LR: 3.984e-01  Data: 0.027 (0.034)
+Train: 6 [ 250/312 ( 80%)]  Loss: 5.73 (5.79)  Time: 0.418s, 2450.04/s  (0.418s, 2447.00/s)  LR: 3.984e-01  Data: 0.027 (0.033)
+Train: 6 [ 300/312 ( 96%)]  Loss: 5.70 (5.78)  Time: 0.414s, 2472.19/s  (0.418s, 2450.68/s)  LR: 3.984e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.440 (1.440)  Loss:   4.962 ( 4.962)  Acc@1:  10.156 ( 10.156)  Acc@5:  25.684 ( 25.684)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   4.890 ( 4.991)  Acc@1:  10.495 ( 10.894)  Acc@5:  27.476 ( 26.240)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
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+Train: 7 [   0/312 (  0%)]  Loss: 5.69 (5.69)  Time: 1.801s,  568.48/s  (1.801s,  568.48/s)  LR: 3.979e-01  Data: 1.253 (1.253)
+Train: 7 [  50/312 ( 16%)]  Loss: 5.65 (5.64)  Time: 0.411s, 2489.95/s  (0.441s, 2321.38/s)  LR: 3.979e-01  Data: 0.028 (0.051)
+Train: 7 [ 100/312 ( 32%)]  Loss: 5.61 (5.64)  Time: 0.415s, 2470.09/s  (0.428s, 2394.27/s)  LR: 3.979e-01  Data: 0.024 (0.040)
+Train: 7 [ 150/312 ( 48%)]  Loss: 5.73 (5.62)  Time: 0.414s, 2475.78/s  (0.423s, 2420.08/s)  LR: 3.979e-01  Data: 0.028 (0.036)
+Train: 7 [ 200/312 ( 64%)]  Loss: 5.65 (5.61)  Time: 0.415s, 2465.10/s  (0.421s, 2434.38/s)  LR: 3.979e-01  Data: 0.028 (0.033)
+Train: 7 [ 250/312 ( 80%)]  Loss: 5.56 (5.60)  Time: 0.412s, 2483.38/s  (0.419s, 2443.36/s)  LR: 3.979e-01  Data: 0.027 (0.032)
+Train: 7 [ 300/312 ( 96%)]  Loss: 5.52 (5.59)  Time: 0.417s, 2457.97/s  (0.418s, 2449.54/s)  LR: 3.979e-01  Data: 0.033 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.442 (1.442)  Loss:   4.422 ( 4.422)  Acc@1:  16.113 ( 16.113)  Acc@5:  37.305 ( 37.305)
+Test: [  48/48]  Time: 0.092 (0.321)  Loss:   4.218 ( 4.399)  Acc@1:  19.340 ( 16.618)  Acc@5:  42.099 ( 37.170)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
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+Train: 8 [  50/312 ( 16%)]  Loss: 5.49 (5.45)  Time: 0.413s, 2479.61/s  (0.435s, 2352.27/s)  LR: 3.972e-01  Data: 0.029 (0.050)
+Train: 8 [ 100/312 ( 32%)]  Loss: 5.47 (5.46)  Time: 0.412s, 2485.08/s  (0.424s, 2416.66/s)  LR: 3.972e-01  Data: 0.027 (0.039)
+Train: 8 [ 150/312 ( 48%)]  Loss: 5.43 (5.45)  Time: 0.411s, 2492.49/s  (0.420s, 2437.93/s)  LR: 3.972e-01  Data: 0.028 (0.035)
+Train: 8 [ 200/312 ( 64%)]  Loss: 5.50 (5.45)  Time: 0.414s, 2475.92/s  (0.418s, 2447.84/s)  LR: 3.972e-01  Data: 0.027 (0.034)
+Train: 8 [ 250/312 ( 80%)]  Loss: 5.37 (5.44)  Time: 0.413s, 2480.33/s  (0.417s, 2453.79/s)  LR: 3.972e-01  Data: 0.029 (0.032)
+Train: 8 [ 300/312 ( 96%)]  Loss: 5.50 (5.44)  Time: 0.412s, 2486.87/s  (0.417s, 2457.18/s)  LR: 3.972e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   4.332 ( 4.332)  Acc@1:  18.262 ( 18.262)  Acc@5:  38.672 ( 38.672)
+Test: [  48/48]  Time: 0.091 (0.322)  Loss:   4.163 ( 4.297)  Acc@1:  20.047 ( 18.760)  Acc@5:  43.868 ( 40.012)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
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+Train: 9 [   0/312 (  0%)]  Loss: 5.21 (5.21)  Time: 1.709s,  599.11/s  (1.709s,  599.11/s)  LR: 3.965e-01  Data: 1.334 (1.334)
+Train: 9 [  50/312 ( 16%)]  Loss: 5.21 (5.33)  Time: 0.413s, 2480.42/s  (0.443s, 2311.78/s)  LR: 3.965e-01  Data: 0.025 (0.053)
+Train: 9 [ 100/312 ( 32%)]  Loss: 5.40 (5.32)  Time: 0.413s, 2481.57/s  (0.427s, 2395.70/s)  LR: 3.965e-01  Data: 0.028 (0.040)
+Train: 9 [ 150/312 ( 48%)]  Loss: 5.37 (5.32)  Time: 0.414s, 2471.20/s  (0.422s, 2424.77/s)  LR: 3.965e-01  Data: 0.032 (0.036)
+Train: 9 [ 200/312 ( 64%)]  Loss: 5.31 (5.32)  Time: 0.412s, 2488.27/s  (0.420s, 2439.40/s)  LR: 3.965e-01  Data: 0.027 (0.034)
+Train: 9 [ 250/312 ( 80%)]  Loss: 5.27 (5.31)  Time: 0.409s, 2505.31/s  (0.418s, 2449.20/s)  LR: 3.965e-01  Data: 0.028 (0.033)
+Train: 9 [ 300/312 ( 96%)]  Loss: 5.19 (5.30)  Time: 0.406s, 2521.01/s  (0.416s, 2459.20/s)  LR: 3.965e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   4.056 ( 4.056)  Acc@1:  22.656 ( 22.656)  Acc@5:  44.922 ( 44.922)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   3.951 ( 4.083)  Acc@1:  25.472 ( 22.084)  Acc@5:  48.939 ( 45.206)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-0.pth.tar', 0.0879999999666214)
+
+Train: 10 [   0/312 (  0%)]  Loss: 5.25 (5.25)  Time: 1.581s,  647.77/s  (1.581s,  647.77/s)  LR: 3.956e-01  Data: 1.047 (1.047)
+Train: 10 [  50/312 ( 16%)]  Loss: 5.13 (5.20)  Time: 0.407s, 2513.17/s  (0.430s, 2383.41/s)  LR: 3.956e-01  Data: 0.028 (0.047)
+Train: 10 [ 100/312 ( 32%)]  Loss: 5.14 (5.20)  Time: 0.412s, 2486.61/s  (0.420s, 2437.13/s)  LR: 3.956e-01  Data: 0.030 (0.037)
+Train: 10 [ 150/312 ( 48%)]  Loss: 5.16 (5.20)  Time: 0.414s, 2473.33/s  (0.418s, 2451.70/s)  LR: 3.956e-01  Data: 0.028 (0.034)
+Train: 10 [ 200/312 ( 64%)]  Loss: 5.19 (5.20)  Time: 0.409s, 2501.62/s  (0.415s, 2464.80/s)  LR: 3.956e-01  Data: 0.028 (0.033)
+Train: 10 [ 250/312 ( 80%)]  Loss: 5.18 (5.20)  Time: 0.414s, 2472.33/s  (0.414s, 2473.03/s)  LR: 3.956e-01  Data: 0.028 (0.032)
+Train: 10 [ 300/312 ( 96%)]  Loss: 5.20 (5.19)  Time: 0.412s, 2484.39/s  (0.413s, 2477.47/s)  LR: 3.956e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.427 (1.427)  Loss:   3.989 ( 3.989)  Acc@1:  21.484 ( 21.484)  Acc@5:  44.922 ( 44.922)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   3.786 ( 3.958)  Acc@1:  23.939 ( 22.954)  Acc@5:  50.354 ( 46.528)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-1.pth.tar', 1.2099999978637694)
+
+Train: 11 [   0/312 (  0%)]  Loss: 5.10 (5.10)  Time: 1.816s,  563.86/s  (1.816s,  563.86/s)  LR: 3.947e-01  Data: 1.439 (1.439)
+Train: 11 [  50/312 ( 16%)]  Loss: 5.20 (5.09)  Time: 0.409s, 2506.58/s  (0.439s, 2330.27/s)  LR: 3.947e-01  Data: 0.027 (0.055)
+Train: 11 [ 100/312 ( 32%)]  Loss: 5.03 (5.10)  Time: 0.408s, 2510.86/s  (0.424s, 2412.54/s)  LR: 3.947e-01  Data: 0.028 (0.041)
+Train: 11 [ 150/312 ( 48%)]  Loss: 5.10 (5.09)  Time: 0.409s, 2501.06/s  (0.419s, 2441.17/s)  LR: 3.947e-01  Data: 0.028 (0.037)
+Train: 11 [ 200/312 ( 64%)]  Loss: 5.03 (5.09)  Time: 0.414s, 2471.71/s  (0.418s, 2452.01/s)  LR: 3.947e-01  Data: 0.026 (0.034)
+Train: 11 [ 250/312 ( 80%)]  Loss: 5.12 (5.09)  Time: 0.413s, 2477.71/s  (0.417s, 2456.75/s)  LR: 3.947e-01  Data: 0.028 (0.033)
+Train: 11 [ 300/312 ( 96%)]  Loss: 5.02 (5.08)  Time: 0.411s, 2488.99/s  (0.416s, 2460.66/s)  LR: 3.947e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.411 (1.411)  Loss:   3.796 ( 3.796)  Acc@1:  26.660 ( 26.660)  Acc@5:  51.074 ( 51.074)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   3.564 ( 3.777)  Acc@1:  28.774 ( 25.922)  Acc@5:  56.840 ( 50.240)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-2.pth.tar', 2.331999997177124)
+
+Train: 12 [   0/312 (  0%)]  Loss: 4.95 (4.95)  Time: 1.668s,  613.74/s  (1.668s,  613.74/s)  LR: 3.937e-01  Data: 1.294 (1.294)
+Train: 12 [  50/312 ( 16%)]  Loss: 5.08 (5.00)  Time: 0.410s, 2497.22/s  (0.434s, 2361.64/s)  LR: 3.937e-01  Data: 0.027 (0.052)
+Train: 12 [ 100/312 ( 32%)]  Loss: 5.02 (5.00)  Time: 0.414s, 2474.02/s  (0.422s, 2426.47/s)  LR: 3.937e-01  Data: 0.028 (0.040)
+Train: 12 [ 150/312 ( 48%)]  Loss: 5.12 (4.99)  Time: 0.411s, 2491.63/s  (0.419s, 2446.15/s)  LR: 3.937e-01  Data: 0.027 (0.036)
+Train: 12 [ 200/312 ( 64%)]  Loss: 4.97 (4.99)  Time: 0.412s, 2484.86/s  (0.417s, 2454.76/s)  LR: 3.937e-01  Data: 0.026 (0.034)
+Train: 12 [ 250/312 ( 80%)]  Loss: 4.99 (4.99)  Time: 0.410s, 2496.03/s  (0.416s, 2460.57/s)  LR: 3.937e-01  Data: 0.027 (0.033)
+Train: 12 [ 300/312 ( 96%)]  Loss: 4.86 (4.99)  Time: 0.413s, 2479.00/s  (0.416s, 2464.14/s)  LR: 3.937e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   3.660 ( 3.660)  Acc@1:  27.832 ( 27.832)  Acc@5:  52.148 ( 52.148)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   3.500 ( 3.622)  Acc@1:  30.071 ( 28.166)  Acc@5:  56.958 ( 53.358)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-3.pth.tar', 4.723999999694824)
+
+Train: 13 [   0/312 (  0%)]  Loss: 4.84 (4.84)  Time: 2.338s,  437.89/s  (2.338s,  437.89/s)  LR: 3.926e-01  Data: 1.239 (1.239)
+Train: 13 [  50/312 ( 16%)]  Loss: 4.90 (4.91)  Time: 0.409s, 2501.27/s  (0.447s, 2291.61/s)  LR: 3.926e-01  Data: 0.027 (0.052)
+Train: 13 [ 100/312 ( 32%)]  Loss: 4.84 (4.91)  Time: 0.410s, 2494.79/s  (0.430s, 2383.59/s)  LR: 3.926e-01  Data: 0.027 (0.040)
+Train: 13 [ 150/312 ( 48%)]  Loss: 4.94 (4.91)  Time: 0.411s, 2494.07/s  (0.424s, 2416.87/s)  LR: 3.926e-01  Data: 0.028 (0.036)
+Train: 13 [ 200/312 ( 64%)]  Loss: 4.94 (4.92)  Time: 0.409s, 2506.43/s  (0.420s, 2436.37/s)  LR: 3.926e-01  Data: 0.027 (0.034)
+Train: 13 [ 250/312 ( 80%)]  Loss: 4.97 (4.92)  Time: 0.409s, 2501.89/s  (0.419s, 2446.58/s)  LR: 3.926e-01  Data: 0.028 (0.033)
+Train: 13 [ 300/312 ( 96%)]  Loss: 4.92 (4.92)  Time: 0.408s, 2511.07/s  (0.417s, 2453.95/s)  LR: 3.926e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.395 (1.395)  Loss:   3.656 ( 3.656)  Acc@1:  27.637 ( 27.637)  Acc@5:  52.246 ( 52.246)
+Test: [  48/48]  Time: 0.091 (0.318)  Loss:   3.443 ( 3.638)  Acc@1:  31.722 ( 28.358)  Acc@5:  57.783 ( 53.424)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-4.pth.tar', 5.76400000213623)
+
+Train: 14 [   0/312 (  0%)]  Loss: 4.89 (4.89)  Time: 1.711s,  598.56/s  (1.711s,  598.56/s)  LR: 3.915e-01  Data: 1.337 (1.337)
+Train: 14 [  50/312 ( 16%)]  Loss: 4.90 (4.81)  Time: 0.411s, 2490.27/s  (0.437s, 2345.12/s)  LR: 3.915e-01  Data: 0.026 (0.053)
+Train: 14 [ 100/312 ( 32%)]  Loss: 4.84 (4.83)  Time: 0.409s, 2501.85/s  (0.424s, 2413.39/s)  LR: 3.915e-01  Data: 0.026 (0.040)
+Train: 14 [ 150/312 ( 48%)]  Loss: 4.83 (4.83)  Time: 0.410s, 2499.70/s  (0.420s, 2440.50/s)  LR: 3.915e-01  Data: 0.028 (0.036)
+Train: 14 [ 200/312 ( 64%)]  Loss: 4.86 (4.84)  Time: 0.410s, 2496.05/s  (0.417s, 2452.84/s)  LR: 3.915e-01  Data: 0.025 (0.034)
+Train: 14 [ 250/312 ( 80%)]  Loss: 4.94 (4.85)  Time: 0.416s, 2462.81/s  (0.416s, 2460.00/s)  LR: 3.915e-01  Data: 0.026 (0.033)
+Train: 14 [ 300/312 ( 96%)]  Loss: 4.80 (4.85)  Time: 0.411s, 2494.42/s  (0.415s, 2464.90/s)  LR: 3.915e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.447 (1.447)  Loss:   3.467 ( 3.467)  Acc@1:  31.934 ( 31.934)  Acc@5:  56.738 ( 56.738)
+Test: [  48/48]  Time: 0.091 (0.319)  Loss:   3.319 ( 3.491)  Acc@1:  33.962 ( 30.984)  Acc@5:  61.439 ( 56.644)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-5.pth.tar', 10.860000007629395)
+
+Train: 15 [   0/312 (  0%)]  Loss: 4.74 (4.74)  Time: 1.483s,  690.44/s  (1.483s,  690.44/s)  LR: 3.902e-01  Data: 1.109 (1.109)
+Train: 15 [  50/312 ( 16%)]  Loss: 4.85 (4.77)  Time: 0.411s, 2491.67/s  (0.432s, 2368.88/s)  LR: 3.902e-01  Data: 0.027 (0.049)
+Train: 15 [ 100/312 ( 32%)]  Loss: 4.80 (4.78)  Time: 0.410s, 2499.80/s  (0.422s, 2425.30/s)  LR: 3.902e-01  Data: 0.027 (0.039)
+Train: 15 [ 150/312 ( 48%)]  Loss: 4.78 (4.78)  Time: 0.410s, 2500.39/s  (0.419s, 2445.99/s)  LR: 3.902e-01  Data: 0.027 (0.035)
+Train: 15 [ 200/312 ( 64%)]  Loss: 4.61 (4.77)  Time: 0.409s, 2505.59/s  (0.417s, 2457.58/s)  LR: 3.902e-01  Data: 0.027 (0.033)
+Train: 15 [ 250/312 ( 80%)]  Loss: 4.87 (4.78)  Time: 0.411s, 2488.76/s  (0.416s, 2464.18/s)  LR: 3.902e-01  Data: 0.027 (0.032)
+Train: 15 [ 300/312 ( 96%)]  Loss: 4.78 (4.78)  Time: 0.415s, 2468.54/s  (0.415s, 2468.27/s)  LR: 3.902e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.412 (1.412)  Loss:   3.346 ( 3.346)  Acc@1:  33.008 ( 33.008)  Acc@5:  57.715 ( 57.715)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   3.167 ( 3.406)  Acc@1:  36.557 ( 31.740)  Acc@5:  63.090 ( 57.648)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-6.pth.tar', 10.894000001831055)
+
+Train: 16 [   0/312 (  0%)]  Loss: 4.67 (4.67)  Time: 1.670s,  613.14/s  (1.670s,  613.14/s)  LR: 3.889e-01  Data: 1.295 (1.295)
+Train: 16 [  50/312 ( 16%)]  Loss: 4.75 (4.70)  Time: 0.410s, 2498.53/s  (0.435s, 2352.08/s)  LR: 3.889e-01  Data: 0.027 (0.052)
+Train: 16 [ 100/312 ( 32%)]  Loss: 4.87 (4.70)  Time: 0.413s, 2479.88/s  (0.423s, 2419.53/s)  LR: 3.889e-01  Data: 0.030 (0.040)
+Train: 16 [ 150/312 ( 48%)]  Loss: 4.86 (4.71)  Time: 0.412s, 2482.82/s  (0.419s, 2441.45/s)  LR: 3.889e-01  Data: 0.029 (0.036)
+Train: 16 [ 200/312 ( 64%)]  Loss: 4.79 (4.71)  Time: 0.412s, 2482.70/s  (0.417s, 2453.19/s)  LR: 3.889e-01  Data: 0.028 (0.034)
+Train: 16 [ 250/312 ( 80%)]  Loss: 4.75 (4.71)  Time: 0.412s, 2486.28/s  (0.416s, 2460.36/s)  LR: 3.889e-01  Data: 0.027 (0.033)
+Train: 16 [ 300/312 ( 96%)]  Loss: 4.88 (4.72)  Time: 0.414s, 2474.63/s  (0.415s, 2465.43/s)  LR: 3.889e-01  Data: 0.032 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.695 (1.695)  Loss:   3.259 ( 3.259)  Acc@1:  34.375 ( 34.375)  Acc@5:  60.449 ( 60.449)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   3.095 ( 3.265)  Acc@1:  35.495 ( 34.784)  Acc@5:  62.972 ( 60.850)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-7.pth.tar', 16.617999997558595)
+
+Train: 17 [   0/312 (  0%)]  Loss: 4.62 (4.62)  Time: 1.512s,  677.21/s  (1.512s,  677.21/s)  LR: 3.875e-01  Data: 1.091 (1.091)
+Train: 17 [  50/312 ( 16%)]  Loss: 4.68 (4.65)  Time: 0.414s, 2472.79/s  (0.432s, 2370.12/s)  LR: 3.875e-01  Data: 0.028 (0.048)
+Train: 17 [ 100/312 ( 32%)]  Loss: 4.65 (4.65)  Time: 0.409s, 2500.93/s  (0.422s, 2427.02/s)  LR: 3.875e-01  Data: 0.027 (0.039)
+Train: 17 [ 150/312 ( 48%)]  Loss: 4.54 (4.65)  Time: 0.409s, 2502.67/s  (0.418s, 2447.69/s)  LR: 3.875e-01  Data: 0.026 (0.035)
+Train: 17 [ 200/312 ( 64%)]  Loss: 4.66 (4.66)  Time: 0.409s, 2502.94/s  (0.417s, 2457.51/s)  LR: 3.875e-01  Data: 0.026 (0.033)
+Train: 17 [ 250/312 ( 80%)]  Loss: 4.65 (4.66)  Time: 0.409s, 2503.51/s  (0.415s, 2465.78/s)  LR: 3.875e-01  Data: 0.027 (0.032)
+Train: 17 [ 300/312 ( 96%)]  Loss: 4.62 (4.66)  Time: 0.408s, 2508.10/s  (0.414s, 2471.19/s)  LR: 3.875e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.389 ( 3.389)  Acc@1:  34.766 ( 34.766)  Acc@5:  60.254 ( 60.254)
+Test: [  48/48]  Time: 0.092 (0.322)  Loss:   3.221 ( 3.407)  Acc@1:  36.321 ( 33.772)  Acc@5:  63.561 ( 59.466)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-8.pth.tar', 18.759999981689454)
+
+Train: 18 [   0/312 (  0%)]  Loss: 4.51 (4.51)  Time: 1.591s,  643.61/s  (1.591s,  643.61/s)  LR: 3.860e-01  Data: 1.216 (1.216)
+Train: 18 [  50/312 ( 16%)]  Loss: 4.64 (4.59)  Time: 0.408s, 2508.36/s  (0.433s, 2363.25/s)  LR: 3.860e-01  Data: 0.026 (0.050)
+Train: 18 [ 100/312 ( 32%)]  Loss: 4.51 (4.60)  Time: 0.411s, 2491.98/s  (0.422s, 2425.00/s)  LR: 3.860e-01  Data: 0.028 (0.039)
+Train: 18 [ 150/312 ( 48%)]  Loss: 4.63 (4.60)  Time: 0.411s, 2491.94/s  (0.419s, 2445.43/s)  LR: 3.860e-01  Data: 0.028 (0.036)
+Train: 18 [ 200/312 ( 64%)]  Loss: 4.58 (4.60)  Time: 0.411s, 2488.72/s  (0.417s, 2456.33/s)  LR: 3.860e-01  Data: 0.028 (0.034)
+Train: 18 [ 250/312 ( 80%)]  Loss: 4.54 (4.61)  Time: 0.409s, 2500.84/s  (0.416s, 2464.02/s)  LR: 3.860e-01  Data: 0.027 (0.032)
+Train: 18 [ 300/312 ( 96%)]  Loss: 4.66 (4.61)  Time: 0.410s, 2494.88/s  (0.415s, 2470.23/s)  LR: 3.860e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.444 (1.444)  Loss:   3.298 ( 3.298)  Acc@1:  33.398 ( 33.398)  Acc@5:  59.570 ( 59.570)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   3.095 ( 3.278)  Acc@1:  37.736 ( 34.292)  Acc@5:  63.797 ( 60.662)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-9.pth.tar', 22.08400001098633)
+
+Train: 19 [   0/312 (  0%)]  Loss: 4.52 (4.52)  Time: 2.163s,  473.45/s  (2.163s,  473.45/s)  LR: 3.844e-01  Data: 1.788 (1.788)
+Train: 19 [  50/312 ( 16%)]  Loss: 4.47 (4.52)  Time: 0.409s, 2502.34/s  (0.445s, 2301.48/s)  LR: 3.844e-01  Data: 0.027 (0.062)
+Train: 19 [ 100/312 ( 32%)]  Loss: 4.47 (4.53)  Time: 0.410s, 2497.16/s  (0.428s, 2392.40/s)  LR: 3.844e-01  Data: 0.027 (0.045)
+Train: 19 [ 150/312 ( 48%)]  Loss: 4.50 (4.54)  Time: 0.416s, 2464.32/s  (0.422s, 2423.72/s)  LR: 3.844e-01  Data: 0.028 (0.039)
+Train: 19 [ 200/312 ( 64%)]  Loss: 4.59 (4.55)  Time: 0.409s, 2505.17/s  (0.420s, 2440.20/s)  LR: 3.844e-01  Data: 0.027 (0.036)
+Train: 19 [ 250/312 ( 80%)]  Loss: 4.72 (4.55)  Time: 0.411s, 2494.23/s  (0.418s, 2451.78/s)  LR: 3.844e-01  Data: 0.028 (0.035)
+Train: 19 [ 300/312 ( 96%)]  Loss: 4.60 (4.56)  Time: 0.410s, 2494.92/s  (0.416s, 2458.82/s)  LR: 3.844e-01  Data: 0.028 (0.034)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.418 (1.418)  Loss:   3.060 ( 3.060)  Acc@1:  37.793 ( 37.793)  Acc@5:  63.477 ( 63.477)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.837 ( 3.048)  Acc@1:  42.217 ( 38.808)  Acc@5:  69.575 ( 64.960)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-10.pth.tar', 22.953999991455078)
+
+Train: 20 [   0/312 (  0%)]  Loss: 4.54 (4.54)  Time: 1.464s,  699.62/s  (1.464s,  699.62/s)  LR: 3.827e-01  Data: 1.064 (1.064)
+Train: 20 [  50/312 ( 16%)]  Loss: 4.42 (4.48)  Time: 0.410s, 2498.86/s  (0.431s, 2373.44/s)  LR: 3.827e-01  Data: 0.029 (0.049)
+Train: 20 [ 100/312 ( 32%)]  Loss: 4.58 (4.49)  Time: 0.413s, 2481.75/s  (0.421s, 2432.01/s)  LR: 3.827e-01  Data: 0.031 (0.039)
+Train: 20 [ 150/312 ( 48%)]  Loss: 4.54 (4.49)  Time: 0.410s, 2497.19/s  (0.418s, 2451.74/s)  LR: 3.827e-01  Data: 0.028 (0.035)
+Train: 20 [ 200/312 ( 64%)]  Loss: 4.54 (4.50)  Time: 0.410s, 2494.74/s  (0.416s, 2462.13/s)  LR: 3.827e-01  Data: 0.028 (0.033)
+Train: 20 [ 250/312 ( 80%)]  Loss: 4.53 (4.51)  Time: 0.412s, 2483.40/s  (0.415s, 2468.10/s)  LR: 3.827e-01  Data: 0.030 (0.032)
+Train: 20 [ 300/312 ( 96%)]  Loss: 4.40 (4.51)  Time: 0.410s, 2495.53/s  (0.414s, 2471.82/s)  LR: 3.827e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.406 (1.406)  Loss:   3.292 ( 3.292)  Acc@1:  35.156 ( 35.156)  Acc@5:  60.547 ( 60.547)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   3.070 ( 3.277)  Acc@1:  40.684 ( 35.368)  Acc@5:  63.679 ( 60.594)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-11.pth.tar', 25.92200002319336)
+
+Train: 21 [   0/312 (  0%)]  Loss: 4.37 (4.37)  Time: 1.726s,  593.20/s  (1.726s,  593.20/s)  LR: 3.810e-01  Data: 1.352 (1.352)
+Train: 21 [  50/312 ( 16%)]  Loss: 4.49 (4.44)  Time: 0.411s, 2490.42/s  (0.437s, 2345.69/s)  LR: 3.810e-01  Data: 0.028 (0.054)
+Train: 21 [ 100/312 ( 32%)]  Loss: 4.47 (4.44)  Time: 0.411s, 2490.19/s  (0.424s, 2415.85/s)  LR: 3.810e-01  Data: 0.029 (0.041)
+Train: 21 [ 150/312 ( 48%)]  Loss: 4.45 (4.46)  Time: 0.410s, 2496.51/s  (0.420s, 2440.03/s)  LR: 3.810e-01  Data: 0.026 (0.037)
+Train: 21 [ 200/312 ( 64%)]  Loss: 4.47 (4.46)  Time: 0.410s, 2495.02/s  (0.417s, 2453.06/s)  LR: 3.810e-01  Data: 0.027 (0.034)
+Train: 21 [ 250/312 ( 80%)]  Loss: 4.55 (4.47)  Time: 0.412s, 2485.59/s  (0.416s, 2460.51/s)  LR: 3.810e-01  Data: 0.027 (0.033)
+Train: 21 [ 300/312 ( 96%)]  Loss: 4.50 (4.47)  Time: 0.412s, 2484.73/s  (0.415s, 2465.26/s)  LR: 3.810e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.434 (1.434)  Loss:   3.072 ( 3.072)  Acc@1:  39.551 ( 39.551)  Acc@5:  64.746 ( 64.746)
+Test: [  48/48]  Time: 0.090 (0.318)  Loss:   2.854 ( 3.091)  Acc@1:  43.750 ( 38.280)  Acc@5:  69.340 ( 64.412)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-12.pth.tar', 28.16600000488281)
+
+Train: 22 [   0/312 (  0%)]  Loss: 4.59 (4.59)  Time: 1.737s,  589.64/s  (1.737s,  589.64/s)  LR: 3.791e-01  Data: 1.108 (1.108)
+Train: 22 [  50/312 ( 16%)]  Loss: 4.37 (4.40)  Time: 0.412s, 2484.14/s  (0.438s, 2335.96/s)  LR: 3.791e-01  Data: 0.029 (0.050)
+Train: 22 [ 100/312 ( 32%)]  Loss: 4.34 (4.41)  Time: 0.412s, 2483.45/s  (0.426s, 2406.12/s)  LR: 3.791e-01  Data: 0.027 (0.039)
+Train: 22 [ 150/312 ( 48%)]  Loss: 4.38 (4.41)  Time: 0.412s, 2487.71/s  (0.421s, 2431.02/s)  LR: 3.791e-01  Data: 0.027 (0.035)
+Train: 22 [ 200/312 ( 64%)]  Loss: 4.41 (4.42)  Time: 0.409s, 2502.44/s  (0.419s, 2443.52/s)  LR: 3.791e-01  Data: 0.028 (0.033)
+Train: 22 [ 250/312 ( 80%)]  Loss: 4.57 (4.42)  Time: 0.411s, 2494.24/s  (0.418s, 2451.05/s)  LR: 3.791e-01  Data: 0.027 (0.032)
+Train: 22 [ 300/312 ( 96%)]  Loss: 4.42 (4.43)  Time: 0.413s, 2480.11/s  (0.417s, 2456.55/s)  LR: 3.791e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.445 (1.445)  Loss:   2.926 ( 2.926)  Acc@1:  40.234 ( 40.234)  Acc@5:  66.504 ( 66.504)
+Test: [  48/48]  Time: 0.091 (0.322)  Loss:   2.764 ( 2.979)  Acc@1:  43.396 ( 39.758)  Acc@5:  69.811 ( 65.878)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-13.pth.tar', 28.358000010986327)
+
+Train: 23 [   0/312 (  0%)]  Loss: 4.27 (4.27)  Time: 1.792s,  571.58/s  (1.792s,  571.58/s)  LR: 3.772e-01  Data: 1.115 (1.115)
+Train: 23 [  50/312 ( 16%)]  Loss: 4.38 (4.36)  Time: 0.413s, 2481.03/s  (0.439s, 2333.47/s)  LR: 3.772e-01  Data: 0.029 (0.049)
+Train: 23 [ 100/312 ( 32%)]  Loss: 4.34 (4.37)  Time: 0.410s, 2499.95/s  (0.425s, 2406.94/s)  LR: 3.772e-01  Data: 0.027 (0.038)
+Train: 23 [ 150/312 ( 48%)]  Loss: 4.42 (4.38)  Time: 0.412s, 2486.95/s  (0.421s, 2430.62/s)  LR: 3.772e-01  Data: 0.028 (0.035)
+Train: 23 [ 200/312 ( 64%)]  Loss: 4.40 (4.39)  Time: 0.409s, 2504.71/s  (0.419s, 2446.30/s)  LR: 3.772e-01  Data: 0.027 (0.033)
+Train: 23 [ 250/312 ( 80%)]  Loss: 4.31 (4.39)  Time: 0.411s, 2490.20/s  (0.417s, 2455.52/s)  LR: 3.772e-01  Data: 0.027 (0.032)
+Train: 23 [ 300/312 ( 96%)]  Loss: 4.47 (4.39)  Time: 0.412s, 2485.10/s  (0.416s, 2460.30/s)  LR: 3.772e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.443 (1.443)  Loss:   2.922 ( 2.922)  Acc@1:  40.332 ( 40.332)  Acc@5:  66.797 ( 66.797)
+Test: [  48/48]  Time: 0.091 (0.319)  Loss:   2.795 ( 2.950)  Acc@1:  43.632 ( 40.762)  Acc@5:  70.401 ( 67.086)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-14.pth.tar', 30.984000014648437)
+
+Train: 24 [   0/312 (  0%)]  Loss: 4.23 (4.23)  Time: 1.560s,  656.50/s  (1.560s,  656.50/s)  LR: 3.753e-01  Data: 1.097 (1.097)
+Train: 24 [  50/312 ( 16%)]  Loss: 4.16 (4.32)  Time: 0.417s, 2457.99/s  (0.435s, 2356.59/s)  LR: 3.753e-01  Data: 0.028 (0.049)
+Train: 24 [ 100/312 ( 32%)]  Loss: 4.35 (4.33)  Time: 0.413s, 2482.13/s  (0.424s, 2417.80/s)  LR: 3.753e-01  Data: 0.029 (0.039)
+Train: 24 [ 150/312 ( 48%)]  Loss: 4.38 (4.34)  Time: 0.417s, 2457.55/s  (0.420s, 2438.62/s)  LR: 3.753e-01  Data: 0.028 (0.035)
+Train: 24 [ 200/312 ( 64%)]  Loss: 4.32 (4.34)  Time: 0.410s, 2498.02/s  (0.418s, 2448.95/s)  LR: 3.753e-01  Data: 0.026 (0.033)
+Train: 24 [ 250/312 ( 80%)]  Loss: 4.35 (4.35)  Time: 0.416s, 2462.11/s  (0.417s, 2455.52/s)  LR: 3.753e-01  Data: 0.029 (0.032)
+Train: 24 [ 300/312 ( 96%)]  Loss: 4.37 (4.36)  Time: 0.412s, 2488.04/s  (0.416s, 2460.13/s)  LR: 3.753e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.575 (1.575)  Loss:   2.917 ( 2.917)  Acc@1:  41.113 ( 41.113)  Acc@5:  66.699 ( 66.699)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.679 ( 2.905)  Acc@1:  44.575 ( 41.180)  Acc@5:  71.580 ( 67.612)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-15.pth.tar', 31.739999978027345)
+
+Train: 25 [   0/312 (  0%)]  Loss: 4.29 (4.29)  Time: 1.673s,  611.96/s  (1.673s,  611.96/s)  LR: 3.732e-01  Data: 1.298 (1.298)
+Train: 25 [  50/312 ( 16%)]  Loss: 4.29 (4.28)  Time: 0.413s, 2482.18/s  (0.434s, 2361.61/s)  LR: 3.732e-01  Data: 0.029 (0.055)
+Train: 25 [ 100/312 ( 32%)]  Loss: 4.25 (4.29)  Time: 0.408s, 2510.99/s  (0.420s, 2436.26/s)  LR: 3.732e-01  Data: 0.027 (0.042)
+Train: 25 [ 150/312 ( 48%)]  Loss: 4.34 (4.30)  Time: 0.415s, 2468.57/s  (0.417s, 2458.31/s)  LR: 3.732e-01  Data: 0.027 (0.037)
+Train: 25 [ 200/312 ( 64%)]  Loss: 4.21 (4.31)  Time: 0.417s, 2457.93/s  (0.416s, 2463.58/s)  LR: 3.732e-01  Data: 0.033 (0.035)
+Train: 25 [ 250/312 ( 80%)]  Loss: 4.24 (4.32)  Time: 0.409s, 2503.77/s  (0.415s, 2469.44/s)  LR: 3.732e-01  Data: 0.030 (0.033)
+Train: 25 [ 300/312 ( 96%)]  Loss: 4.32 (4.32)  Time: 0.409s, 2501.35/s  (0.414s, 2474.35/s)  LR: 3.732e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.414 (1.414)  Loss:   2.920 ( 2.920)  Acc@1:  42.773 ( 42.773)  Acc@5:  66.797 ( 66.797)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.714 ( 2.935)  Acc@1:  45.165 ( 41.360)  Acc@5:  71.698 ( 67.070)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-17.pth.tar', 33.77200000488281)
+
+Train: 26 [   0/312 (  0%)]  Loss: 4.26 (4.26)  Time: 1.583s,  646.93/s  (1.583s,  646.93/s)  LR: 3.711e-01  Data: 1.092 (1.092)
+Train: 26 [  50/312 ( 16%)]  Loss: 4.22 (4.25)  Time: 0.413s, 2477.69/s  (0.437s, 2344.28/s)  LR: 3.711e-01  Data: 0.027 (0.049)
+Train: 26 [ 100/312 ( 32%)]  Loss: 4.30 (4.26)  Time: 0.412s, 2483.09/s  (0.425s, 2408.46/s)  LR: 3.711e-01  Data: 0.033 (0.039)
+Train: 26 [ 150/312 ( 48%)]  Loss: 4.20 (4.27)  Time: 0.411s, 2494.36/s  (0.420s, 2439.63/s)  LR: 3.711e-01  Data: 0.028 (0.035)
+Train: 26 [ 200/312 ( 64%)]  Loss: 4.24 (4.28)  Time: 0.408s, 2510.81/s  (0.417s, 2455.87/s)  LR: 3.711e-01  Data: 0.026 (0.033)
+Train: 26 [ 250/312 ( 80%)]  Loss: 4.35 (4.28)  Time: 0.410s, 2498.27/s  (0.416s, 2464.14/s)  LR: 3.711e-01  Data: 0.027 (0.032)
+Train: 26 [ 300/312 ( 96%)]  Loss: 4.25 (4.29)  Time: 0.411s, 2489.58/s  (0.415s, 2467.21/s)  LR: 3.711e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   2.802 ( 2.802)  Acc@1:  43.359 ( 43.359)  Acc@5:  69.336 ( 69.336)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.614 ( 2.805)  Acc@1:  46.462 ( 43.398)  Acc@5:  71.934 ( 69.298)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-18.pth.tar', 34.291999973144534)
+
+Train: 27 [   0/312 (  0%)]  Loss: 4.18 (4.18)  Time: 1.773s,  577.67/s  (1.773s,  577.67/s)  LR: 3.689e-01  Data: 1.182 (1.182)
+Train: 27 [  50/312 ( 16%)]  Loss: 4.22 (4.22)  Time: 0.412s, 2486.55/s  (0.439s, 2332.57/s)  LR: 3.689e-01  Data: 0.026 (0.051)
+Train: 27 [ 100/312 ( 32%)]  Loss: 4.20 (4.24)  Time: 0.410s, 2495.36/s  (0.426s, 2406.15/s)  LR: 3.689e-01  Data: 0.027 (0.039)
+Train: 27 [ 150/312 ( 48%)]  Loss: 4.26 (4.25)  Time: 0.411s, 2488.98/s  (0.421s, 2432.04/s)  LR: 3.689e-01  Data: 0.027 (0.036)
+Train: 27 [ 200/312 ( 64%)]  Loss: 4.38 (4.25)  Time: 0.410s, 2495.78/s  (0.419s, 2443.24/s)  LR: 3.689e-01  Data: 0.028 (0.034)
+Train: 27 [ 250/312 ( 80%)]  Loss: 4.30 (4.26)  Time: 0.409s, 2503.96/s  (0.417s, 2454.38/s)  LR: 3.689e-01  Data: 0.029 (0.033)
+Train: 27 [ 300/312 ( 96%)]  Loss: 4.28 (4.26)  Time: 0.410s, 2496.35/s  (0.416s, 2461.73/s)  LR: 3.689e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.414 (1.414)  Loss:   3.092 ( 3.092)  Acc@1:  40.625 ( 40.625)  Acc@5:  63.477 ( 63.477)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.839 ( 3.097)  Acc@1:  42.453 ( 38.488)  Acc@5:  68.986 ( 64.136)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-27.pth.tar', 38.488000018310544)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-16.pth.tar', 34.78400003417969)
+
+Train: 28 [   0/312 (  0%)]  Loss: 4.26 (4.26)  Time: 1.600s,  640.04/s  (1.600s,  640.04/s)  LR: 3.666e-01  Data: 1.224 (1.224)
+Train: 28 [  50/312 ( 16%)]  Loss: 4.06 (4.17)  Time: 0.408s, 2510.96/s  (0.435s, 2352.44/s)  LR: 3.666e-01  Data: 0.028 (0.051)
+Train: 28 [ 100/312 ( 32%)]  Loss: 4.19 (4.19)  Time: 0.409s, 2504.32/s  (0.422s, 2425.73/s)  LR: 3.666e-01  Data: 0.025 (0.040)
+Train: 28 [ 150/312 ( 48%)]  Loss: 4.28 (4.20)  Time: 0.412s, 2487.27/s  (0.418s, 2452.41/s)  LR: 3.666e-01  Data: 0.027 (0.036)
+Train: 28 [ 200/312 ( 64%)]  Loss: 4.28 (4.21)  Time: 0.412s, 2486.42/s  (0.416s, 2463.07/s)  LR: 3.666e-01  Data: 0.028 (0.034)
+Train: 28 [ 250/312 ( 80%)]  Loss: 4.13 (4.22)  Time: 0.412s, 2486.09/s  (0.415s, 2467.66/s)  LR: 3.666e-01  Data: 0.028 (0.033)
+Train: 28 [ 300/312 ( 96%)]  Loss: 4.24 (4.23)  Time: 0.411s, 2489.34/s  (0.414s, 2471.40/s)  LR: 3.666e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.427 (1.427)  Loss:   2.852 ( 2.852)  Acc@1:  43.848 ( 43.848)  Acc@5:  67.188 ( 67.188)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.700 ( 2.852)  Acc@1:  47.877 ( 42.772)  Acc@5:  70.637 ( 68.720)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-27.pth.tar', 38.488000018310544)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-20.pth.tar', 35.3679999609375)
+
+Train: 29 [   0/312 (  0%)]  Loss: 4.17 (4.17)  Time: 1.764s,  580.42/s  (1.764s,  580.42/s)  LR: 3.642e-01  Data: 1.392 (1.392)
+Train: 29 [  50/312 ( 16%)]  Loss: 4.12 (4.16)  Time: 0.406s, 2519.13/s  (0.433s, 2362.89/s)  LR: 3.642e-01  Data: 0.027 (0.055)
+Train: 29 [ 100/312 ( 32%)]  Loss: 4.19 (4.16)  Time: 0.409s, 2504.68/s  (0.421s, 2431.57/s)  LR: 3.642e-01  Data: 0.029 (0.041)
+Train: 29 [ 150/312 ( 48%)]  Loss: 4.17 (4.17)  Time: 0.412s, 2488.35/s  (0.418s, 2449.38/s)  LR: 3.642e-01  Data: 0.028 (0.037)
+Train: 29 [ 200/312 ( 64%)]  Loss: 4.30 (4.18)  Time: 0.407s, 2516.14/s  (0.416s, 2460.33/s)  LR: 3.642e-01  Data: 0.027 (0.035)
+Train: 29 [ 250/312 ( 80%)]  Loss: 4.26 (4.19)  Time: 0.407s, 2517.57/s  (0.415s, 2470.33/s)  LR: 3.642e-01  Data: 0.025 (0.033)
+Train: 29 [ 300/312 ( 96%)]  Loss: 4.24 (4.20)  Time: 0.408s, 2507.39/s  (0.413s, 2476.59/s)  LR: 3.642e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.411 (1.411)  Loss:   2.748 ( 2.748)  Acc@1:  45.410 ( 45.410)  Acc@5:  69.727 ( 69.727)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   2.591 ( 2.753)  Acc@1:  47.877 ( 43.896)  Acc@5:  72.524 ( 69.670)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-27.pth.tar', 38.488000018310544)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-21.pth.tar', 38.28)
+
+Train: 30 [   0/312 (  0%)]  Loss: 4.15 (4.15)  Time: 1.756s,  583.05/s  (1.756s,  583.05/s)  LR: 3.618e-01  Data: 1.076 (1.076)
+Train: 30 [  50/312 ( 16%)]  Loss: 4.08 (4.11)  Time: 0.410s, 2499.98/s  (0.440s, 2329.22/s)  LR: 3.618e-01  Data: 0.027 (0.048)
+Train: 30 [ 100/312 ( 32%)]  Loss: 4.25 (4.14)  Time: 0.409s, 2505.21/s  (0.425s, 2409.58/s)  LR: 3.618e-01  Data: 0.028 (0.038)
+Train: 30 [ 150/312 ( 48%)]  Loss: 4.26 (4.14)  Time: 0.412s, 2484.40/s  (0.420s, 2439.20/s)  LR: 3.618e-01  Data: 0.033 (0.035)
+Train: 30 [ 200/312 ( 64%)]  Loss: 4.08 (4.15)  Time: 0.413s, 2479.47/s  (0.418s, 2451.14/s)  LR: 3.618e-01  Data: 0.027 (0.033)
+Train: 30 [ 250/312 ( 80%)]  Loss: 4.30 (4.16)  Time: 0.413s, 2481.12/s  (0.417s, 2455.06/s)  LR: 3.618e-01  Data: 0.028 (0.032)
+Train: 30 [ 300/312 ( 96%)]  Loss: 4.29 (4.17)  Time: 0.410s, 2497.91/s  (0.416s, 2460.71/s)  LR: 3.618e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.449 (1.449)  Loss:   2.762 ( 2.762)  Acc@1:  43.652 ( 43.652)  Acc@5:  69.336 ( 69.336)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.542 ( 2.772)  Acc@1:  47.877 ( 43.404)  Acc@5:  72.170 ( 69.692)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-27.pth.tar', 38.488000018310544)
+
+Train: 31 [   0/312 (  0%)]  Loss: 4.24 (4.24)  Time: 1.581s,  647.80/s  (1.581s,  647.80/s)  LR: 3.593e-01  Data: 1.088 (1.088)
+Train: 31 [  50/312 ( 16%)]  Loss: 4.13 (4.09)  Time: 0.409s, 2504.56/s  (0.432s, 2371.01/s)  LR: 3.593e-01  Data: 0.027 (0.049)
+Train: 31 [ 100/312 ( 32%)]  Loss: 4.26 (4.11)  Time: 0.415s, 2466.92/s  (0.421s, 2429.74/s)  LR: 3.593e-01  Data: 0.029 (0.038)
+Train: 31 [ 150/312 ( 48%)]  Loss: 4.01 (4.12)  Time: 0.411s, 2489.81/s  (0.418s, 2447.61/s)  LR: 3.593e-01  Data: 0.032 (0.035)
+Train: 31 [ 200/312 ( 64%)]  Loss: 4.19 (4.13)  Time: 0.405s, 2525.93/s  (0.416s, 2463.18/s)  LR: 3.593e-01  Data: 0.028 (0.033)
+Train: 31 [ 250/312 ( 80%)]  Loss: 3.98 (4.13)  Time: 0.406s, 2520.35/s  (0.414s, 2473.71/s)  LR: 3.593e-01  Data: 0.028 (0.032)
+Train: 31 [ 300/312 ( 96%)]  Loss: 4.12 (4.14)  Time: 0.410s, 2497.70/s  (0.413s, 2479.77/s)  LR: 3.593e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.426 (1.426)  Loss:   2.728 ( 2.728)  Acc@1:  44.141 ( 44.141)  Acc@5:  69.531 ( 69.531)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.527 ( 2.736)  Acc@1:  49.646 ( 45.114)  Acc@5:  73.585 ( 70.878)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-19.pth.tar', 38.80800004516602)
+
+Train: 32 [   0/312 (  0%)]  Loss: 4.07 (4.07)  Time: 1.639s,  624.68/s  (1.639s,  624.68/s)  LR: 3.567e-01  Data: 1.098 (1.098)
+Train: 32 [  50/312 ( 16%)]  Loss: 4.03 (4.06)  Time: 0.410s, 2494.59/s  (0.437s, 2344.72/s)  LR: 3.567e-01  Data: 0.027 (0.049)
+Train: 32 [ 100/312 ( 32%)]  Loss: 4.08 (4.08)  Time: 0.406s, 2519.11/s  (0.423s, 2421.00/s)  LR: 3.567e-01  Data: 0.028 (0.038)
+Train: 32 [ 150/312 ( 48%)]  Loss: 4.02 (4.08)  Time: 0.406s, 2523.22/s  (0.418s, 2451.87/s)  LR: 3.567e-01  Data: 0.027 (0.035)
+Train: 32 [ 200/312 ( 64%)]  Loss: 4.06 (4.09)  Time: 0.409s, 2505.92/s  (0.415s, 2466.65/s)  LR: 3.567e-01  Data: 0.030 (0.033)
+Train: 32 [ 250/312 ( 80%)]  Loss: 4.09 (4.11)  Time: 0.411s, 2489.68/s  (0.414s, 2473.24/s)  LR: 3.567e-01  Data: 0.028 (0.032)
+Train: 32 [ 300/312 ( 96%)]  Loss: 4.15 (4.12)  Time: 0.413s, 2481.24/s  (0.414s, 2474.35/s)  LR: 3.567e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.439 (1.439)  Loss:   2.638 ( 2.638)  Acc@1:  45.996 ( 45.996)  Acc@5:  70.312 ( 70.312)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.481 ( 2.669)  Acc@1:  49.057 ( 45.128)  Acc@5:  73.939 ( 70.936)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-22.pth.tar', 39.7580000402832)
+
+Train: 33 [   0/312 (  0%)]  Loss: 4.00 (4.00)  Time: 1.576s,  649.94/s  (1.576s,  649.94/s)  LR: 3.541e-01  Data: 1.182 (1.182)
+Train: 33 [  50/312 ( 16%)]  Loss: 4.06 (4.05)  Time: 0.407s, 2518.33/s  (0.430s, 2381.81/s)  LR: 3.541e-01  Data: 0.027 (0.051)
+Train: 33 [ 100/312 ( 32%)]  Loss: 3.97 (4.06)  Time: 0.408s, 2512.20/s  (0.419s, 2445.83/s)  LR: 3.541e-01  Data: 0.028 (0.040)
+Train: 33 [ 150/312 ( 48%)]  Loss: 4.23 (4.07)  Time: 0.412s, 2486.45/s  (0.416s, 2463.97/s)  LR: 3.541e-01  Data: 0.027 (0.036)
+Train: 33 [ 200/312 ( 64%)]  Loss: 4.04 (4.08)  Time: 0.411s, 2492.24/s  (0.414s, 2470.60/s)  LR: 3.541e-01  Data: 0.028 (0.034)
+Train: 33 [ 250/312 ( 80%)]  Loss: 4.07 (4.09)  Time: 0.414s, 2471.83/s  (0.414s, 2475.24/s)  LR: 3.541e-01  Data: 0.028 (0.033)
+Train: 33 [ 300/312 ( 96%)]  Loss: 4.12 (4.09)  Time: 0.415s, 2468.37/s  (0.413s, 2478.74/s)  LR: 3.541e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   2.753 ( 2.753)  Acc@1:  45.117 ( 45.117)  Acc@5:  69.629 ( 69.629)
+Test: [  48/48]  Time: 0.089 (0.323)  Loss:   2.569 ( 2.751)  Acc@1:  48.939 ( 44.468)  Acc@5:  72.995 ( 70.128)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-23.pth.tar', 40.76200001342774)
+
+Train: 34 [   0/312 (  0%)]  Loss: 4.01 (4.01)  Time: 1.650s,  620.64/s  (1.650s,  620.64/s)  LR: 3.514e-01  Data: 1.275 (1.275)
+Train: 34 [  50/312 ( 16%)]  Loss: 3.89 (3.99)  Time: 0.412s, 2484.91/s  (0.437s, 2345.13/s)  LR: 3.514e-01  Data: 0.028 (0.052)
+Train: 34 [ 100/312 ( 32%)]  Loss: 4.07 (4.02)  Time: 0.414s, 2473.32/s  (0.425s, 2411.73/s)  LR: 3.514e-01  Data: 0.028 (0.040)
+Train: 34 [ 150/312 ( 48%)]  Loss: 4.01 (4.04)  Time: 0.410s, 2496.49/s  (0.421s, 2434.89/s)  LR: 3.514e-01  Data: 0.027 (0.036)
+Train: 34 [ 200/312 ( 64%)]  Loss: 4.05 (4.05)  Time: 0.413s, 2482.22/s  (0.419s, 2445.53/s)  LR: 3.514e-01  Data: 0.026 (0.034)
+Train: 34 [ 250/312 ( 80%)]  Loss: 4.25 (4.06)  Time: 0.408s, 2509.79/s  (0.417s, 2454.89/s)  LR: 3.514e-01  Data: 0.028 (0.033)
+Train: 34 [ 300/312 ( 96%)]  Loss: 4.12 (4.07)  Time: 0.408s, 2509.05/s  (0.416s, 2464.22/s)  LR: 3.514e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   2.729 ( 2.729)  Acc@1:  46.191 ( 46.191)  Acc@5:  71.289 ( 71.289)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.619 ( 2.796)  Acc@1:  48.467 ( 45.380)  Acc@5:  73.585 ( 70.524)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-24.pth.tar', 41.18000003540039)
+
+Train: 35 [   0/312 (  0%)]  Loss: 4.02 (4.02)  Time: 1.814s,  564.56/s  (1.814s,  564.56/s)  LR: 3.486e-01  Data: 1.091 (1.091)
+Train: 35 [  50/312 ( 16%)]  Loss: 3.97 (3.99)  Time: 0.411s, 2491.45/s  (0.436s, 2348.63/s)  LR: 3.486e-01  Data: 0.025 (0.048)
+Train: 35 [ 100/312 ( 32%)]  Loss: 4.10 (3.99)  Time: 0.413s, 2477.35/s  (0.424s, 2413.75/s)  LR: 3.486e-01  Data: 0.029 (0.038)
+Train: 35 [ 150/312 ( 48%)]  Loss: 4.04 (4.01)  Time: 0.412s, 2486.22/s  (0.420s, 2436.01/s)  LR: 3.486e-01  Data: 0.029 (0.035)
+Train: 35 [ 200/312 ( 64%)]  Loss: 4.05 (4.01)  Time: 0.413s, 2476.79/s  (0.418s, 2447.36/s)  LR: 3.486e-01  Data: 0.027 (0.033)
+Train: 35 [ 250/312 ( 80%)]  Loss: 4.07 (4.03)  Time: 0.411s, 2490.25/s  (0.417s, 2453.99/s)  LR: 3.486e-01  Data: 0.028 (0.032)
+Train: 35 [ 300/312 ( 96%)]  Loss: 4.02 (4.04)  Time: 0.415s, 2470.32/s  (0.417s, 2458.28/s)  LR: 3.486e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.431 (1.431)  Loss:   2.711 ( 2.711)  Acc@1:  44.043 ( 44.043)  Acc@5:  70.996 ( 70.996)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.615 ( 2.772)  Acc@1:  47.995 ( 44.398)  Acc@5:  72.524 ( 70.320)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-35.pth.tar', 44.39800003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-25.pth.tar', 41.360000032958986)
+
+Train: 36 [   0/312 (  0%)]  Loss: 4.08 (4.08)  Time: 1.823s,  561.72/s  (1.823s,  561.72/s)  LR: 3.458e-01  Data: 1.448 (1.448)
+Train: 36 [  50/312 ( 16%)]  Loss: 4.07 (3.98)  Time: 0.413s, 2476.46/s  (0.440s, 2325.32/s)  LR: 3.458e-01  Data: 0.032 (0.056)
+Train: 36 [ 100/312 ( 32%)]  Loss: 4.04 (3.98)  Time: 0.411s, 2492.73/s  (0.426s, 2404.54/s)  LR: 3.458e-01  Data: 0.029 (0.042)
+Train: 36 [ 150/312 ( 48%)]  Loss: 4.07 (3.98)  Time: 0.411s, 2494.15/s  (0.421s, 2433.18/s)  LR: 3.458e-01  Data: 0.028 (0.037)
+Train: 36 [ 200/312 ( 64%)]  Loss: 4.06 (3.99)  Time: 0.413s, 2481.70/s  (0.419s, 2445.56/s)  LR: 3.458e-01  Data: 0.028 (0.035)
+Train: 36 [ 250/312 ( 80%)]  Loss: 4.03 (4.00)  Time: 0.413s, 2480.20/s  (0.417s, 2453.66/s)  LR: 3.458e-01  Data: 0.029 (0.034)
+Train: 36 [ 300/312 ( 96%)]  Loss: 4.12 (4.02)  Time: 0.410s, 2496.43/s  (0.416s, 2459.56/s)  LR: 3.458e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.410 (1.410)  Loss:   2.669 ( 2.669)  Acc@1:  48.535 ( 48.535)  Acc@5:  70.508 ( 70.508)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.492 ( 2.686)  Acc@1:  50.825 ( 46.244)  Acc@5:  75.590 ( 71.672)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-35.pth.tar', 44.39800003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-28.pth.tar', 42.77200004760742)
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+Train: 37 [  50/312 ( 16%)]  Loss: 3.85 (3.94)  Time: 0.411s, 2491.72/s  (0.441s, 2323.67/s)  LR: 3.429e-01  Data: 0.028 (0.058)
+Train: 37 [ 100/312 ( 32%)]  Loss: 3.99 (3.96)  Time: 0.409s, 2502.94/s  (0.425s, 2407.57/s)  LR: 3.429e-01  Data: 0.027 (0.043)
+Train: 37 [ 150/312 ( 48%)]  Loss: 4.00 (3.97)  Time: 0.409s, 2501.95/s  (0.420s, 2436.90/s)  LR: 3.429e-01  Data: 0.026 (0.038)
+Train: 37 [ 200/312 ( 64%)]  Loss: 3.99 (3.98)  Time: 0.407s, 2513.59/s  (0.418s, 2450.79/s)  LR: 3.429e-01  Data: 0.027 (0.036)
+Train: 37 [ 250/312 ( 80%)]  Loss: 4.09 (3.99)  Time: 0.410s, 2495.48/s  (0.416s, 2459.07/s)  LR: 3.429e-01  Data: 0.029 (0.034)
+Train: 37 [ 300/312 ( 96%)]  Loss: 4.07 (4.00)  Time: 0.411s, 2488.60/s  (0.416s, 2464.47/s)  LR: 3.429e-01  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
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+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.422 ( 2.663)  Acc@1:  50.943 ( 46.290)  Acc@5:  77.476 ( 71.956)
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+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-35.pth.tar', 44.39800003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-26.pth.tar', 43.39800001464844)
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+Train: 38 [  50/312 ( 16%)]  Loss: 3.81 (3.90)  Time: 0.410s, 2497.82/s  (0.433s, 2364.16/s)  LR: 3.399e-01  Data: 0.028 (0.053)
+Train: 38 [ 100/312 ( 32%)]  Loss: 4.01 (3.92)  Time: 0.410s, 2499.82/s  (0.422s, 2429.31/s)  LR: 3.399e-01  Data: 0.027 (0.041)
+Train: 38 [ 150/312 ( 48%)]  Loss: 4.07 (3.94)  Time: 0.410s, 2497.53/s  (0.418s, 2449.14/s)  LR: 3.399e-01  Data: 0.028 (0.037)
+Train: 38 [ 200/312 ( 64%)]  Loss: 4.03 (3.95)  Time: 0.412s, 2485.38/s  (0.416s, 2459.89/s)  LR: 3.399e-01  Data: 0.029 (0.034)
+Train: 38 [ 250/312 ( 80%)]  Loss: 4.01 (3.97)  Time: 0.411s, 2493.47/s  (0.415s, 2468.05/s)  LR: 3.399e-01  Data: 0.028 (0.033)
+Train: 38 [ 300/312 ( 96%)]  Loss: 4.01 (3.97)  Time: 0.412s, 2487.89/s  (0.414s, 2471.32/s)  LR: 3.399e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   2.660 ( 2.660)  Acc@1:  47.266 ( 47.266)  Acc@5:  71.777 ( 71.777)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.496 ( 2.679)  Acc@1:  50.825 ( 46.268)  Acc@5:  75.354 ( 71.664)
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+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-35.pth.tar', 44.39800003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-30.pth.tar', 43.40400004760742)
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+Train: 39 [  50/312 ( 16%)]  Loss: 3.95 (3.87)  Time: 0.412s, 2485.17/s  (0.438s, 2337.02/s)  LR: 3.369e-01  Data: 0.029 (0.051)
+Train: 39 [ 100/312 ( 32%)]  Loss: 3.91 (3.90)  Time: 0.411s, 2494.50/s  (0.424s, 2413.76/s)  LR: 3.369e-01  Data: 0.028 (0.040)
+Train: 39 [ 150/312 ( 48%)]  Loss: 3.90 (3.92)  Time: 0.407s, 2518.38/s  (0.419s, 2445.01/s)  LR: 3.369e-01  Data: 0.027 (0.036)
+Train: 39 [ 200/312 ( 64%)]  Loss: 3.93 (3.93)  Time: 0.411s, 2488.79/s  (0.416s, 2459.83/s)  LR: 3.369e-01  Data: 0.028 (0.034)
+Train: 39 [ 250/312 ( 80%)]  Loss: 3.99 (3.94)  Time: 0.411s, 2489.85/s  (0.415s, 2466.57/s)  LR: 3.369e-01  Data: 0.027 (0.032)
+Train: 39 [ 300/312 ( 96%)]  Loss: 3.95 (3.95)  Time: 0.413s, 2476.72/s  (0.415s, 2468.56/s)  LR: 3.369e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.421 (1.421)  Loss:   2.735 ( 2.735)  Acc@1:  47.852 ( 47.852)  Acc@5:  72.461 ( 72.461)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.579 ( 2.744)  Acc@1:  49.057 ( 46.688)  Acc@5:  74.057 ( 71.814)
+Current checkpoints:
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+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-35.pth.tar', 44.39800003417969)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-29.pth.tar', 43.895999982910155)
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+Train: 40 [   0/312 (  0%)]  Loss: 3.96 (3.96)  Time: 1.676s,  610.98/s  (1.676s,  610.98/s)  LR: 3.338e-01  Data: 1.073 (1.073)
+Train: 40 [  50/312 ( 16%)]  Loss: 3.93 (3.86)  Time: 0.412s, 2487.34/s  (0.436s, 2349.60/s)  LR: 3.338e-01  Data: 0.027 (0.049)
+Train: 40 [ 100/312 ( 32%)]  Loss: 3.81 (3.88)  Time: 0.411s, 2488.78/s  (0.423s, 2418.85/s)  LR: 3.338e-01  Data: 0.027 (0.039)
+Train: 40 [ 150/312 ( 48%)]  Loss: 4.00 (3.90)  Time: 0.409s, 2503.62/s  (0.419s, 2442.65/s)  LR: 3.338e-01  Data: 0.027 (0.035)
+Train: 40 [ 200/312 ( 64%)]  Loss: 3.98 (3.91)  Time: 0.412s, 2488.11/s  (0.417s, 2454.47/s)  LR: 3.338e-01  Data: 0.029 (0.033)
+Train: 40 [ 250/312 ( 80%)]  Loss: 4.00 (3.92)  Time: 0.412s, 2486.21/s  (0.416s, 2462.03/s)  LR: 3.338e-01  Data: 0.027 (0.032)
+Train: 40 [ 300/312 ( 96%)]  Loss: 4.05 (3.93)  Time: 0.417s, 2455.26/s  (0.415s, 2466.41/s)  LR: 3.338e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.404 (1.404)  Loss:   2.640 ( 2.640)  Acc@1:  47.266 ( 47.266)  Acc@5:  72.070 ( 72.070)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.451 ( 2.655)  Acc@1:  48.703 ( 46.290)  Acc@5:  75.825 ( 71.594)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-35.pth.tar', 44.39800003417969)
+
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+Train: 41 [  50/312 ( 16%)]  Loss: 3.87 (3.84)  Time: 0.408s, 2508.13/s  (0.434s, 2361.86/s)  LR: 3.307e-01  Data: 0.028 (0.050)
+Train: 41 [ 100/312 ( 32%)]  Loss: 3.88 (3.86)  Time: 0.409s, 2503.81/s  (0.422s, 2424.22/s)  LR: 3.307e-01  Data: 0.027 (0.039)
+Train: 41 [ 150/312 ( 48%)]  Loss: 3.88 (3.87)  Time: 0.416s, 2464.22/s  (0.418s, 2448.03/s)  LR: 3.307e-01  Data: 0.027 (0.035)
+Train: 41 [ 200/312 ( 64%)]  Loss: 3.87 (3.89)  Time: 0.413s, 2479.05/s  (0.417s, 2458.21/s)  LR: 3.307e-01  Data: 0.033 (0.034)
+Train: 41 [ 250/312 ( 80%)]  Loss: 4.02 (3.90)  Time: 0.408s, 2508.87/s  (0.415s, 2466.01/s)  LR: 3.307e-01  Data: 0.029 (0.033)
+Train: 41 [ 300/312 ( 96%)]  Loss: 3.99 (3.91)  Time: 0.411s, 2490.86/s  (0.414s, 2471.57/s)  LR: 3.307e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   2.597 ( 2.597)  Acc@1:  47.070 ( 47.070)  Acc@5:  71.777 ( 71.777)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.414 ( 2.612)  Acc@1:  51.415 ( 47.432)  Acc@5:  75.236 ( 72.428)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-33.pth.tar', 44.46800005615234)
+
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+Train: 42 [  50/312 ( 16%)]  Loss: 3.87 (3.83)  Time: 0.410s, 2495.34/s  (0.439s, 2333.17/s)  LR: 3.275e-01  Data: 0.028 (0.056)
+Train: 42 [ 100/312 ( 32%)]  Loss: 4.03 (3.84)  Time: 0.407s, 2513.62/s  (0.424s, 2413.28/s)  LR: 3.275e-01  Data: 0.028 (0.042)
+Train: 42 [ 150/312 ( 48%)]  Loss: 3.97 (3.86)  Time: 0.407s, 2518.25/s  (0.419s, 2446.60/s)  LR: 3.275e-01  Data: 0.029 (0.037)
+Train: 42 [ 200/312 ( 64%)]  Loss: 3.88 (3.86)  Time: 0.408s, 2507.65/s  (0.416s, 2462.91/s)  LR: 3.275e-01  Data: 0.030 (0.035)
+Train: 42 [ 250/312 ( 80%)]  Loss: 4.04 (3.88)  Time: 0.408s, 2507.28/s  (0.414s, 2470.81/s)  LR: 3.275e-01  Data: 0.027 (0.034)
+Train: 42 [ 300/312 ( 96%)]  Loss: 4.00 (3.89)  Time: 0.412s, 2483.51/s  (0.414s, 2474.46/s)  LR: 3.275e-01  Data: 0.029 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.435 (1.435)  Loss:   2.631 ( 2.631)  Acc@1:  46.973 ( 46.973)  Acc@5:  71.094 ( 71.094)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.499 ( 2.691)  Acc@1:  48.467 ( 45.870)  Acc@5:  72.759 ( 70.918)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-42.pth.tar', 45.86999998046875)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-31.pth.tar', 45.1140000402832)
+
+Train: 43 [   0/312 (  0%)]  Loss: 3.78 (3.78)  Time: 1.413s,  724.63/s  (1.413s,  724.63/s)  LR: 3.242e-01  Data: 1.042 (1.042)
+Train: 43 [  50/312 ( 16%)]  Loss: 3.80 (3.79)  Time: 0.406s, 2523.42/s  (0.426s, 2401.12/s)  LR: 3.242e-01  Data: 0.029 (0.048)
+Train: 43 [ 100/312 ( 32%)]  Loss: 3.76 (3.82)  Time: 0.410s, 2498.38/s  (0.417s, 2454.34/s)  LR: 3.242e-01  Data: 0.027 (0.038)
+Train: 43 [ 150/312 ( 48%)]  Loss: 3.88 (3.83)  Time: 0.410s, 2496.57/s  (0.415s, 2466.42/s)  LR: 3.242e-01  Data: 0.027 (0.035)
+Train: 43 [ 200/312 ( 64%)]  Loss: 3.92 (3.85)  Time: 0.411s, 2488.93/s  (0.414s, 2472.29/s)  LR: 3.242e-01  Data: 0.027 (0.033)
+Train: 43 [ 250/312 ( 80%)]  Loss: 3.91 (3.86)  Time: 0.411s, 2492.53/s  (0.413s, 2476.44/s)  LR: 3.242e-01  Data: 0.028 (0.032)
+Train: 43 [ 300/312 ( 96%)]  Loss: 3.97 (3.87)  Time: 0.409s, 2501.26/s  (0.413s, 2478.71/s)  LR: 3.242e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.409 (1.409)  Loss:   2.657 ( 2.657)  Acc@1:  45.215 ( 45.215)  Acc@5:  72.070 ( 72.070)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.446 ( 2.674)  Acc@1:  51.297 ( 46.008)  Acc@5:  74.764 ( 71.326)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-43.pth.tar', 46.00800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-42.pth.tar', 45.86999998046875)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-32.pth.tar', 45.12799997802735)
+
+Train: 44 [   0/312 (  0%)]  Loss: 3.73 (3.73)  Time: 1.519s,  674.24/s  (1.519s,  674.24/s)  LR: 3.209e-01  Data: 1.111 (1.111)
+Train: 44 [  50/312 ( 16%)]  Loss: 3.80 (3.78)  Time: 0.411s, 2494.29/s  (0.432s, 2372.10/s)  LR: 3.209e-01  Data: 0.025 (0.049)
+Train: 44 [ 100/312 ( 32%)]  Loss: 3.75 (3.79)  Time: 0.408s, 2510.93/s  (0.421s, 2432.99/s)  LR: 3.209e-01  Data: 0.028 (0.038)
+Train: 44 [ 150/312 ( 48%)]  Loss: 3.90 (3.82)  Time: 0.408s, 2512.71/s  (0.417s, 2457.46/s)  LR: 3.209e-01  Data: 0.027 (0.035)
+Train: 44 [ 200/312 ( 64%)]  Loss: 3.97 (3.83)  Time: 0.410s, 2500.27/s  (0.415s, 2469.14/s)  LR: 3.209e-01  Data: 0.024 (0.033)
+Train: 44 [ 250/312 ( 80%)]  Loss: 3.95 (3.84)  Time: 0.411s, 2489.78/s  (0.414s, 2473.62/s)  LR: 3.209e-01  Data: 0.028 (0.032)
+Train: 44 [ 300/312 ( 96%)]  Loss: 3.97 (3.85)  Time: 0.415s, 2467.87/s  (0.414s, 2476.09/s)  LR: 3.209e-01  Data: 0.030 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.438 (1.438)  Loss:   2.602 ( 2.602)  Acc@1:  47.949 ( 47.949)  Acc@5:  71.387 ( 71.387)
+Test: [  48/48]  Time: 0.090 (0.324)  Loss:   2.433 ( 2.648)  Acc@1:  49.764 ( 46.636)  Acc@5:  76.533 ( 71.872)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-43.pth.tar', 46.00800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-42.pth.tar', 45.86999998046875)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-34.pth.tar', 45.380000045166014)
+
+Train: 45 [   0/312 (  0%)]  Loss: 3.74 (3.74)  Time: 1.631s,  627.75/s  (1.631s,  627.75/s)  LR: 3.176e-01  Data: 1.257 (1.257)
+Train: 45 [  50/312 ( 16%)]  Loss: 3.72 (3.76)  Time: 0.411s, 2489.21/s  (0.435s, 2355.87/s)  LR: 3.176e-01  Data: 0.027 (0.051)
+Train: 45 [ 100/312 ( 32%)]  Loss: 3.75 (3.77)  Time: 0.410s, 2499.95/s  (0.423s, 2422.13/s)  LR: 3.176e-01  Data: 0.028 (0.040)
+Train: 45 [ 150/312 ( 48%)]  Loss: 3.82 (3.79)  Time: 0.410s, 2499.26/s  (0.418s, 2447.66/s)  LR: 3.176e-01  Data: 0.028 (0.036)
+Train: 45 [ 200/312 ( 64%)]  Loss: 3.87 (3.80)  Time: 0.408s, 2508.18/s  (0.416s, 2459.72/s)  LR: 3.176e-01  Data: 0.028 (0.034)
+Train: 45 [ 250/312 ( 80%)]  Loss: 3.93 (3.81)  Time: 0.408s, 2509.16/s  (0.415s, 2469.43/s)  LR: 3.176e-01  Data: 0.028 (0.033)
+Train: 45 [ 300/312 ( 96%)]  Loss: 3.86 (3.83)  Time: 0.412s, 2484.80/s  (0.413s, 2477.29/s)  LR: 3.176e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.462 (1.462)  Loss:   2.596 ( 2.596)  Acc@1:  46.680 ( 46.680)  Acc@5:  72.168 ( 72.168)
+Test: [  48/48]  Time: 0.089 (0.324)  Loss:   2.381 ( 2.615)  Acc@1:  51.533 ( 47.118)  Acc@5:  76.533 ( 72.314)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-43.pth.tar', 46.00800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-42.pth.tar', 45.86999998046875)
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+Train: 46 [   0/312 (  0%)]  Loss: 3.69 (3.69)  Time: 1.631s,  627.80/s  (1.631s,  627.80/s)  LR: 3.141e-01  Data: 1.089 (1.089)
+Train: 46 [  50/312 ( 16%)]  Loss: 3.80 (3.73)  Time: 0.408s, 2512.51/s  (0.432s, 2370.95/s)  LR: 3.141e-01  Data: 0.026 (0.048)
+Train: 46 [ 100/312 ( 32%)]  Loss: 3.85 (3.75)  Time: 0.409s, 2502.00/s  (0.422s, 2429.11/s)  LR: 3.141e-01  Data: 0.028 (0.038)
+Train: 46 [ 150/312 ( 48%)]  Loss: 3.82 (3.77)  Time: 0.416s, 2458.88/s  (0.418s, 2447.34/s)  LR: 3.141e-01  Data: 0.030 (0.035)
+Train: 46 [ 200/312 ( 64%)]  Loss: 3.88 (3.79)  Time: 0.405s, 2528.28/s  (0.416s, 2459.35/s)  LR: 3.141e-01  Data: 0.028 (0.033)
+Train: 46 [ 250/312 ( 80%)]  Loss: 3.90 (3.80)  Time: 0.405s, 2526.24/s  (0.414s, 2470.72/s)  LR: 3.141e-01  Data: 0.027 (0.032)
+Train: 46 [ 300/312 ( 96%)]  Loss: 3.73 (3.81)  Time: 0.411s, 2493.91/s  (0.413s, 2478.40/s)  LR: 3.141e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.411 (1.411)  Loss:   2.510 ( 2.510)  Acc@1:  49.219 ( 49.219)  Acc@5:  74.902 ( 74.902)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.418 ( 2.616)  Acc@1:  51.533 ( 47.556)  Acc@5:  75.354 ( 72.216)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-43.pth.tar', 46.00800004638672)
+
+Train: 47 [   0/312 (  0%)]  Loss: 3.60 (3.60)  Time: 1.559s,  656.91/s  (1.559s,  656.91/s)  LR: 3.107e-01  Data: 1.185 (1.185)
+Train: 47 [  50/312 ( 16%)]  Loss: 3.79 (3.71)  Time: 0.412s, 2488.05/s  (0.432s, 2371.76/s)  LR: 3.107e-01  Data: 0.028 (0.050)
+Train: 47 [ 100/312 ( 32%)]  Loss: 3.73 (3.73)  Time: 0.411s, 2489.61/s  (0.421s, 2432.34/s)  LR: 3.107e-01  Data: 0.032 (0.039)
+Train: 47 [ 150/312 ( 48%)]  Loss: 3.81 (3.75)  Time: 0.411s, 2493.10/s  (0.417s, 2454.98/s)  LR: 3.107e-01  Data: 0.028 (0.035)
+Train: 47 [ 200/312 ( 64%)]  Loss: 3.82 (3.76)  Time: 0.409s, 2505.32/s  (0.416s, 2462.46/s)  LR: 3.107e-01  Data: 0.027 (0.033)
+Train: 47 [ 250/312 ( 80%)]  Loss: 3.79 (3.77)  Time: 0.407s, 2514.07/s  (0.414s, 2470.81/s)  LR: 3.107e-01  Data: 0.027 (0.032)
+Train: 47 [ 300/312 ( 96%)]  Loss: 3.93 (3.78)  Time: 0.407s, 2514.90/s  (0.413s, 2477.25/s)  LR: 3.107e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.471 (1.471)  Loss:   2.584 ( 2.584)  Acc@1:  50.098 ( 50.098)  Acc@5:  73.730 ( 73.730)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   2.492 ( 2.639)  Acc@1:  49.528 ( 47.580)  Acc@5:  74.646 ( 72.340)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-36.pth.tar', 46.24399997070312)
+
+Train: 48 [   0/312 (  0%)]  Loss: 3.78 (3.78)  Time: 1.492s,  686.32/s  (1.492s,  686.32/s)  LR: 3.072e-01  Data: 1.067 (1.067)
+Train: 48 [  50/312 ( 16%)]  Loss: 3.61 (3.71)  Time: 0.412s, 2482.46/s  (0.432s, 2370.71/s)  LR: 3.072e-01  Data: 0.026 (0.048)
+Train: 48 [ 100/312 ( 32%)]  Loss: 3.76 (3.73)  Time: 0.407s, 2516.87/s  (0.421s, 2431.58/s)  LR: 3.072e-01  Data: 0.026 (0.038)
+Train: 48 [ 150/312 ( 48%)]  Loss: 3.71 (3.74)  Time: 0.410s, 2499.57/s  (0.417s, 2457.28/s)  LR: 3.072e-01  Data: 0.026 (0.035)
+Train: 48 [ 200/312 ( 64%)]  Loss: 3.88 (3.76)  Time: 0.409s, 2504.95/s  (0.415s, 2469.47/s)  LR: 3.072e-01  Data: 0.027 (0.033)
+Train: 48 [ 250/312 ( 80%)]  Loss: 3.82 (3.77)  Time: 0.411s, 2492.62/s  (0.414s, 2474.42/s)  LR: 3.072e-01  Data: 0.028 (0.032)
+Train: 48 [ 300/312 ( 96%)]  Loss: 3.92 (3.78)  Time: 0.411s, 2491.22/s  (0.414s, 2476.07/s)  LR: 3.072e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.419 (1.419)  Loss:   2.541 ( 2.541)  Acc@1:  49.121 ( 49.121)  Acc@5:  73.242 ( 73.242)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.382 ( 2.591)  Acc@1:  50.000 ( 47.950)  Acc@5:  77.123 ( 72.852)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-38.pth.tar', 46.26800003540039)
+
+Train: 49 [   0/312 (  0%)]  Loss: 3.64 (3.64)  Time: 1.670s,  613.12/s  (1.670s,  613.12/s)  LR: 3.036e-01  Data: 1.298 (1.298)
+Train: 49 [  50/312 ( 16%)]  Loss: 3.71 (3.67)  Time: 0.407s, 2516.16/s  (0.431s, 2374.52/s)  LR: 3.036e-01  Data: 0.028 (0.053)
+Train: 49 [ 100/312 ( 32%)]  Loss: 3.71 (3.70)  Time: 0.413s, 2479.19/s  (0.420s, 2440.71/s)  LR: 3.036e-01  Data: 0.027 (0.041)
+Train: 49 [ 150/312 ( 48%)]  Loss: 3.83 (3.71)  Time: 0.409s, 2505.23/s  (0.416s, 2461.56/s)  LR: 3.036e-01  Data: 0.026 (0.036)
+Train: 49 [ 200/312 ( 64%)]  Loss: 3.79 (3.73)  Time: 0.414s, 2474.46/s  (0.415s, 2466.80/s)  LR: 3.036e-01  Data: 0.029 (0.034)
+Train: 49 [ 250/312 ( 80%)]  Loss: 3.83 (3.74)  Time: 0.410s, 2494.68/s  (0.415s, 2469.81/s)  LR: 3.036e-01  Data: 0.028 (0.033)
+Train: 49 [ 300/312 ( 96%)]  Loss: 3.67 (3.75)  Time: 0.407s, 2514.33/s  (0.414s, 2475.01/s)  LR: 3.036e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   2.512 ( 2.512)  Acc@1:  49.219 ( 49.219)  Acc@5:  72.754 ( 72.754)
+Test: [  48/48]  Time: 0.090 (0.318)  Loss:   2.335 ( 2.565)  Acc@1:  52.005 ( 48.288)  Acc@5:  76.533 ( 72.792)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-40.pth.tar', 46.29000001831054)
+
+Train: 50 [   0/312 (  0%)]  Loss: 3.59 (3.59)  Time: 1.603s,  638.78/s  (1.603s,  638.78/s)  LR: 3.000e-01  Data: 1.231 (1.231)
+Train: 50 [  50/312 ( 16%)]  Loss: 3.73 (3.66)  Time: 0.411s, 2491.60/s  (0.432s, 2370.01/s)  LR: 3.000e-01  Data: 0.026 (0.052)
+Train: 50 [ 100/312 ( 32%)]  Loss: 3.60 (3.69)  Time: 0.416s, 2463.44/s  (0.423s, 2423.33/s)  LR: 3.000e-01  Data: 0.028 (0.040)
+Train: 50 [ 150/312 ( 48%)]  Loss: 3.70 (3.69)  Time: 0.409s, 2505.93/s  (0.419s, 2444.21/s)  LR: 3.000e-01  Data: 0.027 (0.036)
+Train: 50 [ 200/312 ( 64%)]  Loss: 3.75 (3.70)  Time: 0.415s, 2469.54/s  (0.417s, 2453.37/s)  LR: 3.000e-01  Data: 0.027 (0.034)
+Train: 50 [ 250/312 ( 80%)]  Loss: 3.87 (3.72)  Time: 0.413s, 2479.73/s  (0.416s, 2459.69/s)  LR: 3.000e-01  Data: 0.026 (0.033)
+Train: 50 [ 300/312 ( 96%)]  Loss: 3.75 (3.73)  Time: 0.407s, 2514.98/s  (0.415s, 2466.71/s)  LR: 3.000e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.431 (1.431)  Loss:   2.628 ( 2.628)  Acc@1:  47.949 ( 47.949)  Acc@5:  71.582 ( 71.582)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.462 ( 2.671)  Acc@1:  52.594 ( 47.498)  Acc@5:  74.528 ( 71.696)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-50.pth.tar', 47.49799996337891)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-37.pth.tar', 46.29000002197266)
+
+Train: 51 [   0/312 (  0%)]  Loss: 3.73 (3.73)  Time: 1.560s,  656.55/s  (1.560s,  656.55/s)  LR: 2.964e-01  Data: 1.181 (1.181)
+Train: 51 [  50/312 ( 16%)]  Loss: 3.51 (3.64)  Time: 0.411s, 2489.40/s  (0.439s, 2334.71/s)  LR: 2.964e-01  Data: 0.030 (0.050)
+Train: 51 [ 100/312 ( 32%)]  Loss: 3.61 (3.66)  Time: 0.412s, 2485.20/s  (0.426s, 2403.08/s)  LR: 2.964e-01  Data: 0.026 (0.039)
+Train: 51 [ 150/312 ( 48%)]  Loss: 3.75 (3.68)  Time: 0.412s, 2484.11/s  (0.421s, 2431.01/s)  LR: 2.964e-01  Data: 0.032 (0.036)
+Train: 51 [ 200/312 ( 64%)]  Loss: 3.85 (3.69)  Time: 0.405s, 2526.36/s  (0.418s, 2450.59/s)  LR: 2.964e-01  Data: 0.028 (0.033)
+Train: 51 [ 250/312 ( 80%)]  Loss: 3.75 (3.70)  Time: 0.409s, 2501.83/s  (0.416s, 2461.04/s)  LR: 2.964e-01  Data: 0.028 (0.032)
+Train: 51 [ 300/312 ( 96%)]  Loss: 3.76 (3.71)  Time: 0.414s, 2473.91/s  (0.415s, 2465.16/s)  LR: 2.964e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.436 (1.436)  Loss:   2.646 ( 2.646)  Acc@1:  48.340 ( 48.340)  Acc@5:  71.582 ( 71.582)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.605 ( 2.718)  Acc@1:  48.113 ( 45.896)  Acc@5:  72.877 ( 70.836)
+Train: 52 [   0/312 (  0%)]  Loss: 3.64 (3.64)  Time: 1.535s,  666.89/s  (1.535s,  666.89/s)  LR: 2.927e-01  Data: 1.162 (1.162)
+Train: 52 [  50/312 ( 16%)]  Loss: 3.66 (3.63)  Time: 0.407s, 2515.27/s  (0.430s, 2381.99/s)  LR: 2.927e-01  Data: 0.027 (0.050)
+Train: 52 [ 100/312 ( 32%)]  Loss: 3.59 (3.65)  Time: 0.408s, 2509.55/s  (0.419s, 2441.78/s)  LR: 2.927e-01  Data: 0.027 (0.039)
+Train: 52 [ 150/312 ( 48%)]  Loss: 3.61 (3.66)  Time: 0.412s, 2487.08/s  (0.417s, 2457.31/s)  LR: 2.927e-01  Data: 0.028 (0.035)
+Train: 52 [ 200/312 ( 64%)]  Loss: 3.66 (3.67)  Time: 0.412s, 2484.25/s  (0.416s, 2462.38/s)  LR: 2.927e-01  Data: 0.029 (0.033)
+Train: 52 [ 250/312 ( 80%)]  Loss: 3.62 (3.69)  Time: 0.409s, 2506.20/s  (0.415s, 2468.98/s)  LR: 2.927e-01  Data: 0.028 (0.032)
+Train: 52 [ 300/312 ( 96%)]  Loss: 3.69 (3.70)  Time: 0.408s, 2511.84/s  (0.414s, 2474.86/s)  LR: 2.927e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.436 (1.436)  Loss:   2.596 ( 2.596)  Acc@1:  48.828 ( 48.828)  Acc@5:  72.266 ( 72.266)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.330 ( 2.620)  Acc@1:  53.066 ( 47.450)  Acc@5:  77.476 ( 71.934)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-50.pth.tar', 47.49799996337891)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-52.pth.tar', 47.4500000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-44.pth.tar', 46.63600002685547)
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+Train: 53 [   0/312 (  0%)]  Loss: 3.57 (3.57)  Time: 1.683s,  608.58/s  (1.683s,  608.58/s)  LR: 2.889e-01  Data: 1.310 (1.310)
+Train: 53 [  50/312 ( 16%)]  Loss: 3.57 (3.59)  Time: 0.410s, 2496.53/s  (0.435s, 2351.70/s)  LR: 2.889e-01  Data: 0.028 (0.053)
+Train: 53 [ 100/312 ( 32%)]  Loss: 3.72 (3.63)  Time: 0.409s, 2502.36/s  (0.424s, 2414.10/s)  LR: 2.889e-01  Data: 0.029 (0.041)
+Train: 53 [ 150/312 ( 48%)]  Loss: 3.93 (3.64)  Time: 0.407s, 2514.95/s  (0.419s, 2442.18/s)  LR: 2.889e-01  Data: 0.026 (0.036)
+Train: 53 [ 200/312 ( 64%)]  Loss: 3.68 (3.65)  Time: 0.411s, 2494.35/s  (0.417s, 2456.05/s)  LR: 2.889e-01  Data: 0.029 (0.034)
+Train: 53 [ 250/312 ( 80%)]  Loss: 3.84 (3.67)  Time: 0.413s, 2476.83/s  (0.416s, 2462.13/s)  LR: 2.889e-01  Data: 0.029 (0.033)
+Train: 53 [ 300/312 ( 96%)]  Loss: 3.81 (3.68)  Time: 0.410s, 2499.85/s  (0.415s, 2466.22/s)  LR: 2.889e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.440 (1.440)  Loss:   2.546 ( 2.546)  Acc@1:  49.512 ( 49.512)  Acc@5:  72.363 ( 72.363)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.394 ( 2.589)  Acc@1:  49.646 ( 48.098)  Acc@5:  76.415 ( 72.700)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-50.pth.tar', 47.49799996337891)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-52.pth.tar', 47.4500000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-39.pth.tar', 46.68799997802734)
+
+Train: 54 [   0/312 (  0%)]  Loss: 3.54 (3.54)  Time: 1.668s,  613.75/s  (1.668s,  613.75/s)  LR: 2.852e-01  Data: 1.040 (1.040)
+Train: 54 [  50/312 ( 16%)]  Loss: 3.63 (3.60)  Time: 0.411s, 2490.69/s  (0.435s, 2355.41/s)  LR: 2.852e-01  Data: 0.028 (0.048)
+Train: 54 [ 100/312 ( 32%)]  Loss: 3.70 (3.62)  Time: 0.414s, 2472.68/s  (0.424s, 2417.73/s)  LR: 2.852e-01  Data: 0.032 (0.038)
+Train: 54 [ 150/312 ( 48%)]  Loss: 3.71 (3.63)  Time: 0.408s, 2512.39/s  (0.419s, 2441.49/s)  LR: 2.852e-01  Data: 0.027 (0.035)
+Train: 54 [ 200/312 ( 64%)]  Loss: 3.65 (3.64)  Time: 0.409s, 2505.92/s  (0.417s, 2456.86/s)  LR: 2.852e-01  Data: 0.028 (0.033)
+Train: 54 [ 250/312 ( 80%)]  Loss: 3.80 (3.65)  Time: 0.413s, 2478.60/s  (0.416s, 2463.24/s)  LR: 2.852e-01  Data: 0.027 (0.032)
+Train: 54 [ 300/312 ( 96%)]  Loss: 3.77 (3.66)  Time: 0.415s, 2465.62/s  (0.415s, 2465.72/s)  LR: 2.852e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.495 (1.495)  Loss:   2.627 ( 2.627)  Acc@1:  47.656 ( 47.656)  Acc@5:  71.777 ( 71.777)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.426 ( 2.645)  Acc@1:  50.472 ( 47.818)  Acc@5:  75.825 ( 71.948)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-50.pth.tar', 47.49799996337891)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-52.pth.tar', 47.4500000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-45.pth.tar', 47.11800001953125)
+
+Train: 55 [   0/312 (  0%)]  Loss: 3.48 (3.48)  Time: 1.685s,  607.76/s  (1.685s,  607.76/s)  LR: 2.813e-01  Data: 1.309 (1.309)
+Train: 55 [  50/312 ( 16%)]  Loss: 3.52 (3.59)  Time: 0.411s, 2492.78/s  (0.437s, 2343.78/s)  LR: 2.813e-01  Data: 0.027 (0.053)
+Train: 55 [ 100/312 ( 32%)]  Loss: 3.62 (3.60)  Time: 0.409s, 2501.76/s  (0.425s, 2411.18/s)  LR: 2.813e-01  Data: 0.027 (0.041)
+Train: 55 [ 150/312 ( 48%)]  Loss: 3.63 (3.61)  Time: 0.412s, 2486.26/s  (0.421s, 2433.50/s)  LR: 2.813e-01  Data: 0.026 (0.036)
+Train: 55 [ 200/312 ( 64%)]  Loss: 3.82 (3.63)  Time: 0.410s, 2498.00/s  (0.418s, 2447.33/s)  LR: 2.813e-01  Data: 0.027 (0.034)
+Train: 55 [ 250/312 ( 80%)]  Loss: 3.63 (3.64)  Time: 0.412s, 2485.32/s  (0.417s, 2454.48/s)  LR: 2.813e-01  Data: 0.026 (0.033)
+Train: 55 [ 300/312 ( 96%)]  Loss: 3.89 (3.65)  Time: 0.414s, 2471.36/s  (0.416s, 2459.22/s)  LR: 2.813e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.441 (1.441)  Loss:   2.553 ( 2.553)  Acc@1:  47.266 ( 47.266)  Acc@5:  72.656 ( 72.656)
+Test: [  48/48]  Time: 0.091 (0.322)  Loss:   2.363 ( 2.573)  Acc@1:  52.241 ( 48.338)  Acc@5:  76.297 ( 72.622)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-50.pth.tar', 47.49799996337891)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-52.pth.tar', 47.4500000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-41.pth.tar', 47.43199996826172)
+
+Train: 56 [   0/312 (  0%)]  Loss: 3.48 (3.48)  Time: 1.624s,  630.63/s  (1.624s,  630.63/s)  LR: 2.775e-01  Data: 1.251 (1.251)
+Train: 56 [  50/312 ( 16%)]  Loss: 3.69 (3.56)  Time: 0.414s, 2475.74/s  (0.435s, 2355.25/s)  LR: 2.775e-01  Data: 0.027 (0.052)
+Train: 56 [ 100/312 ( 32%)]  Loss: 3.61 (3.58)  Time: 0.410s, 2499.62/s  (0.423s, 2422.55/s)  LR: 2.775e-01  Data: 0.028 (0.040)
+Train: 56 [ 150/312 ( 48%)]  Loss: 3.69 (3.59)  Time: 0.410s, 2495.92/s  (0.419s, 2444.92/s)  LR: 2.775e-01  Data: 0.028 (0.036)
+Train: 56 [ 200/312 ( 64%)]  Loss: 3.76 (3.61)  Time: 0.410s, 2497.18/s  (0.417s, 2456.71/s)  LR: 2.775e-01  Data: 0.027 (0.034)
+Train: 56 [ 250/312 ( 80%)]  Loss: 3.60 (3.62)  Time: 0.408s, 2508.93/s  (0.416s, 2464.14/s)  LR: 2.775e-01  Data: 0.028 (0.033)
+Train: 56 [ 300/312 ( 96%)]  Loss: 3.71 (3.63)  Time: 0.412s, 2486.52/s  (0.415s, 2470.32/s)  LR: 2.775e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.404 (1.404)  Loss:   2.653 ( 2.653)  Acc@1:  46.191 ( 46.191)  Acc@5:  70.312 ( 70.312)
+Test: [  48/48]  Time: 0.091 (0.325)  Loss:   2.448 ( 2.658)  Acc@1:  52.241 ( 47.346)  Acc@5:  74.528 ( 71.718)
+Train: 57 [   0/312 (  0%)]  Loss: 3.49 (3.49)  Time: 1.428s,  717.09/s  (1.428s,  717.09/s)  LR: 2.736e-01  Data: 1.046 (1.046)
+Train: 57 [  50/312 ( 16%)]  Loss: 3.45 (3.54)  Time: 0.411s, 2491.96/s  (0.430s, 2383.61/s)  LR: 2.736e-01  Data: 0.028 (0.047)
+Train: 57 [ 100/312 ( 32%)]  Loss: 3.54 (3.56)  Time: 0.409s, 2504.97/s  (0.420s, 2437.55/s)  LR: 2.736e-01  Data: 0.028 (0.038)
+Train: 57 [ 150/312 ( 48%)]  Loss: 3.67 (3.57)  Time: 0.409s, 2502.78/s  (0.417s, 2456.09/s)  LR: 2.736e-01  Data: 0.025 (0.034)
+Train: 57 [ 200/312 ( 64%)]  Loss: 3.53 (3.59)  Time: 0.409s, 2502.63/s  (0.415s, 2466.10/s)  LR: 2.736e-01  Data: 0.027 (0.033)
+Train: 57 [ 250/312 ( 80%)]  Loss: 3.67 (3.60)  Time: 0.410s, 2497.76/s  (0.414s, 2471.56/s)  LR: 2.736e-01  Data: 0.029 (0.032)
+Train: 57 [ 300/312 ( 96%)]  Loss: 3.52 (3.61)  Time: 0.418s, 2450.97/s  (0.414s, 2475.13/s)  LR: 2.736e-01  Data: 0.037 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.405 (1.405)  Loss:   2.733 ( 2.733)  Acc@1:  45.312 ( 45.312)  Acc@5:  70.410 ( 70.410)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.489 ( 2.670)  Acc@1:  53.538 ( 47.834)  Acc@5:  74.528 ( 71.938)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-57.pth.tar', 47.83400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-50.pth.tar', 47.49799996337891)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-52.pth.tar', 47.4500000390625)
+
+Train: 58 [   0/312 (  0%)]  Loss: 3.49 (3.49)  Time: 1.919s,  533.65/s  (1.919s,  533.65/s)  LR: 2.697e-01  Data: 1.371 (1.371)
+Train: 58 [  50/312 ( 16%)]  Loss: 3.55 (3.51)  Time: 0.412s, 2482.90/s  (0.441s, 2321.01/s)  LR: 2.697e-01  Data: 0.029 (0.054)
+Train: 58 [ 100/312 ( 32%)]  Loss: 3.64 (3.53)  Time: 0.411s, 2489.36/s  (0.427s, 2400.00/s)  LR: 2.697e-01  Data: 0.028 (0.041)
+Train: 58 [ 150/312 ( 48%)]  Loss: 3.73 (3.55)  Time: 0.409s, 2502.65/s  (0.422s, 2428.64/s)  LR: 2.697e-01  Data: 0.028 (0.037)
+Train: 58 [ 200/312 ( 64%)]  Loss: 3.61 (3.57)  Time: 0.411s, 2493.27/s  (0.419s, 2442.68/s)  LR: 2.697e-01  Data: 0.029 (0.034)
+Train: 58 [ 250/312 ( 80%)]  Loss: 3.69 (3.58)  Time: 0.412s, 2487.16/s  (0.418s, 2450.36/s)  LR: 2.697e-01  Data: 0.028 (0.033)
+Train: 58 [ 300/312 ( 96%)]  Loss: 3.61 (3.59)  Time: 0.411s, 2494.08/s  (0.417s, 2455.86/s)  LR: 2.697e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   2.633 ( 2.633)  Acc@1:  49.121 ( 49.121)  Acc@5:  72.461 ( 72.461)
+Test: [  48/48]  Time: 0.089 (0.320)  Loss:   2.433 ( 2.627)  Acc@1:  51.297 ( 48.128)  Acc@5:  76.651 ( 72.432)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-57.pth.tar', 47.83400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-50.pth.tar', 47.49799996337891)
+
+Train: 59 [   0/312 (  0%)]  Loss: 3.54 (3.54)  Time: 1.554s,  658.84/s  (1.554s,  658.84/s)  LR: 2.658e-01  Data: 1.104 (1.104)
+Train: 59 [  50/312 ( 16%)]  Loss: 3.44 (3.53)  Time: 0.409s, 2503.34/s  (0.432s, 2372.82/s)  LR: 2.658e-01  Data: 0.028 (0.049)
+Train: 59 [ 100/312 ( 32%)]  Loss: 3.52 (3.53)  Time: 0.410s, 2496.42/s  (0.421s, 2429.84/s)  LR: 2.658e-01  Data: 0.028 (0.038)
+Train: 59 [ 150/312 ( 48%)]  Loss: 3.52 (3.54)  Time: 0.412s, 2486.38/s  (0.418s, 2450.71/s)  LR: 2.658e-01  Data: 0.027 (0.035)
+Train: 59 [ 200/312 ( 64%)]  Loss: 3.63 (3.55)  Time: 0.411s, 2491.68/s  (0.416s, 2460.43/s)  LR: 2.658e-01  Data: 0.028 (0.033)
+Train: 59 [ 250/312 ( 80%)]  Loss: 3.58 (3.56)  Time: 0.413s, 2481.44/s  (0.415s, 2464.72/s)  LR: 2.658e-01  Data: 0.030 (0.032)
+Train: 59 [ 300/312 ( 96%)]  Loss: 3.78 (3.58)  Time: 0.411s, 2490.09/s  (0.415s, 2467.34/s)  LR: 2.658e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   2.625 ( 2.625)  Acc@1:  47.656 ( 47.656)  Acc@5:  71.777 ( 71.777)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.422 ( 2.642)  Acc@1:  50.590 ( 47.584)  Acc@5:  75.943 ( 71.700)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-57.pth.tar', 47.83400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-59.pth.tar', 47.58400006225586)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-46.pth.tar', 47.55600001953125)
+
+Train: 60 [   0/312 (  0%)]  Loss: 3.51 (3.51)  Time: 2.171s,  471.70/s  (2.171s,  471.70/s)  LR: 2.618e-01  Data: 1.797 (1.797)
+Train: 60 [  50/312 ( 16%)]  Loss: 3.47 (3.50)  Time: 0.410s, 2497.62/s  (0.444s, 2305.24/s)  LR: 2.618e-01  Data: 0.027 (0.063)
+Train: 60 [ 100/312 ( 32%)]  Loss: 3.60 (3.51)  Time: 0.409s, 2504.70/s  (0.428s, 2394.71/s)  LR: 2.618e-01  Data: 0.028 (0.046)
+Train: 60 [ 150/312 ( 48%)]  Loss: 3.64 (3.53)  Time: 0.411s, 2494.47/s  (0.421s, 2432.17/s)  LR: 2.618e-01  Data: 0.031 (0.040)
+Train: 60 [ 200/312 ( 64%)]  Loss: 3.66 (3.54)  Time: 0.409s, 2503.78/s  (0.418s, 2451.14/s)  LR: 2.618e-01  Data: 0.027 (0.037)
+Train: 60 [ 250/312 ( 80%)]  Loss: 3.62 (3.55)  Time: 0.411s, 2490.47/s  (0.416s, 2459.35/s)  LR: 2.618e-01  Data: 0.027 (0.035)
+Train: 60 [ 300/312 ( 96%)]  Loss: 3.54 (3.56)  Time: 0.410s, 2500.43/s  (0.415s, 2464.89/s)  LR: 2.618e-01  Data: 0.028 (0.034)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.415 (1.415)  Loss:   2.575 ( 2.575)  Acc@1:  52.051 ( 52.051)  Acc@5:  73.242 ( 73.242)
+Test: [  48/48]  Time: 0.089 (0.321)  Loss:   2.375 ( 2.631)  Acc@1:  54.717 ( 48.734)  Acc@5:  77.241 ( 72.434)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-57.pth.tar', 47.83400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-59.pth.tar', 47.58400006225586)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-47.pth.tar', 47.57999998901367)
+
+Train: 61 [   0/312 (  0%)]  Loss: 3.55 (3.55)  Time: 1.725s,  593.56/s  (1.725s,  593.56/s)  LR: 2.578e-01  Data: 1.131 (1.131)
+Train: 61 [  50/312 ( 16%)]  Loss: 3.47 (3.46)  Time: 0.405s, 2528.33/s  (0.432s, 2372.26/s)  LR: 2.578e-01  Data: 0.028 (0.050)
+Train: 61 [ 100/312 ( 32%)]  Loss: 3.52 (3.48)  Time: 0.409s, 2506.29/s  (0.420s, 2438.01/s)  LR: 2.578e-01  Data: 0.028 (0.039)
+Train: 61 [ 150/312 ( 48%)]  Loss: 3.46 (3.50)  Time: 0.411s, 2489.78/s  (0.417s, 2456.44/s)  LR: 2.578e-01  Data: 0.028 (0.035)
+Train: 61 [ 200/312 ( 64%)]  Loss: 3.60 (3.51)  Time: 0.411s, 2492.36/s  (0.416s, 2463.50/s)  LR: 2.578e-01  Data: 0.027 (0.033)
+Train: 61 [ 250/312 ( 80%)]  Loss: 3.71 (3.53)  Time: 0.407s, 2517.55/s  (0.414s, 2471.48/s)  LR: 2.578e-01  Data: 0.028 (0.032)
+Train: 61 [ 300/312 ( 96%)]  Loss: 3.71 (3.54)  Time: 0.405s, 2527.35/s  (0.413s, 2478.40/s)  LR: 2.578e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.426 (1.426)  Loss:   2.635 ( 2.635)  Acc@1:  48.438 ( 48.438)  Acc@5:  71.582 ( 71.582)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.394 ( 2.639)  Acc@1:  52.005 ( 47.548)  Acc@5:  76.415 ( 71.676)
+Train: 62 [   0/312 (  0%)]  Loss: 3.57 (3.57)  Time: 1.769s,  578.90/s  (1.769s,  578.90/s)  LR: 2.538e-01  Data: 1.395 (1.395)
+Train: 62 [  50/312 ( 16%)]  Loss: 3.47 (3.45)  Time: 0.411s, 2494.41/s  (0.436s, 2350.38/s)  LR: 2.538e-01  Data: 0.025 (0.055)
+Train: 62 [ 100/312 ( 32%)]  Loss: 3.41 (3.47)  Time: 0.410s, 2495.47/s  (0.424s, 2417.37/s)  LR: 2.538e-01  Data: 0.028 (0.041)
+Train: 62 [ 150/312 ( 48%)]  Loss: 3.53 (3.48)  Time: 0.408s, 2506.93/s  (0.418s, 2446.84/s)  LR: 2.538e-01  Data: 0.029 (0.037)
+Train: 62 [ 200/312 ( 64%)]  Loss: 3.51 (3.50)  Time: 0.407s, 2517.62/s  (0.416s, 2462.66/s)  LR: 2.538e-01  Data: 0.028 (0.035)
+Train: 62 [ 250/312 ( 80%)]  Loss: 3.55 (3.51)  Time: 0.410s, 2494.66/s  (0.415s, 2470.11/s)  LR: 2.538e-01  Data: 0.029 (0.033)
+Train: 62 [ 300/312 ( 96%)]  Loss: 3.64 (3.52)  Time: 0.410s, 2496.21/s  (0.414s, 2473.34/s)  LR: 2.538e-01  Data: 0.025 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.412 (1.412)  Loss:   2.589 ( 2.589)  Acc@1:  49.023 ( 49.023)  Acc@5:  72.656 ( 72.656)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.402 ( 2.631)  Acc@1:  53.656 ( 48.390)  Acc@5:  76.297 ( 71.932)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-57.pth.tar', 47.83400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-59.pth.tar', 47.58400006225586)
+
+Train: 63 [   0/312 (  0%)]  Loss: 3.50 (3.50)  Time: 1.765s,  580.27/s  (1.765s,  580.27/s)  LR: 2.497e-01  Data: 1.392 (1.392)
+Train: 63 [  50/312 ( 16%)]  Loss: 3.41 (3.42)  Time: 0.408s, 2509.91/s  (0.435s, 2355.89/s)  LR: 2.497e-01  Data: 0.027 (0.055)
+Train: 63 [ 100/312 ( 32%)]  Loss: 3.50 (3.45)  Time: 0.409s, 2506.29/s  (0.422s, 2423.92/s)  LR: 2.497e-01  Data: 0.027 (0.041)
+Train: 63 [ 150/312 ( 48%)]  Loss: 3.56 (3.47)  Time: 0.411s, 2493.61/s  (0.418s, 2447.68/s)  LR: 2.497e-01  Data: 0.028 (0.037)
+Train: 63 [ 200/312 ( 64%)]  Loss: 3.54 (3.48)  Time: 0.411s, 2494.43/s  (0.417s, 2458.41/s)  LR: 2.497e-01  Data: 0.028 (0.035)
+Train: 63 [ 250/312 ( 80%)]  Loss: 3.51 (3.50)  Time: 0.408s, 2507.62/s  (0.415s, 2466.63/s)  LR: 2.497e-01  Data: 0.027 (0.033)
+Train: 63 [ 300/312 ( 96%)]  Loss: 3.65 (3.51)  Time: 0.407s, 2517.30/s  (0.414s, 2474.10/s)  LR: 2.497e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.415 (1.415)  Loss:   2.613 ( 2.613)  Acc@1:  48.828 ( 48.828)  Acc@5:  72.363 ( 72.363)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.513 ( 2.654)  Acc@1:  50.943 ( 48.352)  Acc@5:  72.877 ( 71.882)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-57.pth.tar', 47.83400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-54.pth.tar', 47.81800001098633)
+
+Train: 64 [   0/312 (  0%)]  Loss: 3.43 (3.43)  Time: 1.859s,  550.70/s  (1.859s,  550.70/s)  LR: 2.457e-01  Data: 1.485 (1.485)
+Train: 64 [  50/312 ( 16%)]  Loss: 3.47 (3.42)  Time: 0.410s, 2496.29/s  (0.439s, 2332.45/s)  LR: 2.457e-01  Data: 0.028 (0.057)
+Train: 64 [ 100/312 ( 32%)]  Loss: 3.45 (3.43)  Time: 0.413s, 2480.67/s  (0.425s, 2406.69/s)  LR: 2.457e-01  Data: 0.034 (0.043)
+Train: 64 [ 150/312 ( 48%)]  Loss: 3.49 (3.45)  Time: 0.406s, 2519.55/s  (0.420s, 2439.59/s)  LR: 2.457e-01  Data: 0.028 (0.038)
+Train: 64 [ 200/312 ( 64%)]  Loss: 3.49 (3.46)  Time: 0.409s, 2503.08/s  (0.417s, 2456.94/s)  LR: 2.457e-01  Data: 0.027 (0.035)
+Train: 64 [ 250/312 ( 80%)]  Loss: 3.49 (3.48)  Time: 0.409s, 2502.55/s  (0.415s, 2465.97/s)  LR: 2.457e-01  Data: 0.027 (0.034)
+Train: 64 [ 300/312 ( 96%)]  Loss: 3.52 (3.49)  Time: 0.409s, 2506.62/s  (0.415s, 2470.08/s)  LR: 2.457e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   2.533 ( 2.533)  Acc@1:  49.512 ( 49.512)  Acc@5:  74.219 ( 74.219)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.369 ( 2.609)  Acc@1:  52.005 ( 48.104)  Acc@5:  75.354 ( 72.218)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-64.pth.tar', 48.104000030517575)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-57.pth.tar', 47.83400005004883)
+
+Train: 65 [   0/312 (  0%)]  Loss: 3.38 (3.38)  Time: 1.706s,  600.18/s  (1.706s,  600.18/s)  LR: 2.416e-01  Data: 1.332 (1.332)
+Train: 65 [  50/312 ( 16%)]  Loss: 3.34 (3.40)  Time: 0.412s, 2482.74/s  (0.436s, 2348.74/s)  LR: 2.416e-01  Data: 0.028 (0.053)
+Train: 65 [ 100/312 ( 32%)]  Loss: 3.55 (3.43)  Time: 0.408s, 2510.87/s  (0.424s, 2417.17/s)  LR: 2.416e-01  Data: 0.028 (0.041)
+Train: 65 [ 150/312 ( 48%)]  Loss: 3.49 (3.43)  Time: 0.415s, 2469.98/s  (0.419s, 2446.13/s)  LR: 2.416e-01  Data: 0.031 (0.037)
+Train: 65 [ 200/312 ( 64%)]  Loss: 3.49 (3.45)  Time: 0.409s, 2500.87/s  (0.416s, 2461.35/s)  LR: 2.416e-01  Data: 0.027 (0.034)
+Train: 65 [ 250/312 ( 80%)]  Loss: 3.43 (3.46)  Time: 0.411s, 2493.42/s  (0.415s, 2467.39/s)  LR: 2.416e-01  Data: 0.027 (0.033)
+Train: 65 [ 300/312 ( 96%)]  Loss: 3.53 (3.47)  Time: 0.409s, 2503.06/s  (0.414s, 2470.96/s)  LR: 2.416e-01  Data: 0.025 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.404 (1.404)  Loss:   2.718 ( 2.718)  Acc@1:  46.973 ( 46.973)  Acc@5:  71.582 ( 71.582)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.499 ( 2.684)  Acc@1:  52.123 ( 47.312)  Acc@5:  75.472 ( 71.366)
+Train: 66 [   0/312 (  0%)]  Loss: 3.40 (3.40)  Time: 1.671s,  612.70/s  (1.671s,  612.70/s)  LR: 2.375e-01  Data: 1.297 (1.297)
+Train: 66 [  50/312 ( 16%)]  Loss: 3.39 (3.38)  Time: 0.411s, 2492.49/s  (0.435s, 2351.40/s)  LR: 2.375e-01  Data: 0.028 (0.053)
+Train: 66 [ 100/312 ( 32%)]  Loss: 3.33 (3.40)  Time: 0.407s, 2515.09/s  (0.423s, 2423.37/s)  LR: 2.375e-01  Data: 0.028 (0.040)
+Train: 66 [ 150/312 ( 48%)]  Loss: 3.37 (3.42)  Time: 0.409s, 2506.25/s  (0.418s, 2452.27/s)  LR: 2.375e-01  Data: 0.030 (0.036)
+Train: 66 [ 200/312 ( 64%)]  Loss: 3.41 (3.43)  Time: 0.409s, 2504.32/s  (0.415s, 2466.21/s)  LR: 2.375e-01  Data: 0.028 (0.034)
+Train: 66 [ 250/312 ( 80%)]  Loss: 3.48 (3.44)  Time: 0.412s, 2483.56/s  (0.414s, 2471.23/s)  LR: 2.375e-01  Data: 0.028 (0.033)
+Train: 66 [ 300/312 ( 96%)]  Loss: 3.54 (3.46)  Time: 0.415s, 2468.65/s  (0.414s, 2473.74/s)  LR: 2.375e-01  Data: 0.033 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.438 (1.438)  Loss:   2.658 ( 2.658)  Acc@1:  47.949 ( 47.949)  Acc@5:  73.047 ( 73.047)
+Test: [  48/48]  Time: 0.089 (0.319)  Loss:   2.485 ( 2.693)  Acc@1:  50.708 ( 47.642)  Acc@5:  73.467 ( 71.290)
+Train: 67 [   0/312 (  0%)]  Loss: 3.35 (3.35)  Time: 1.586s,  645.85/s  (1.586s,  645.85/s)  LR: 2.334e-01  Data: 1.213 (1.213)
+Train: 67 [  50/312 ( 16%)]  Loss: 3.20 (3.37)  Time: 0.407s, 2514.95/s  (0.429s, 2386.24/s)  LR: 2.334e-01  Data: 0.026 (0.051)
+Train: 67 [ 100/312 ( 32%)]  Loss: 3.54 (3.38)  Time: 0.407s, 2517.81/s  (0.418s, 2448.24/s)  LR: 2.334e-01  Data: 0.028 (0.040)
+Train: 67 [ 150/312 ( 48%)]  Loss: 3.52 (3.40)  Time: 0.409s, 2505.14/s  (0.415s, 2466.01/s)  LR: 2.334e-01  Data: 0.025 (0.036)
+Train: 67 [ 200/312 ( 64%)]  Loss: 3.46 (3.41)  Time: 0.411s, 2492.04/s  (0.414s, 2472.37/s)  LR: 2.334e-01  Data: 0.027 (0.034)
+Train: 67 [ 250/312 ( 80%)]  Loss: 3.50 (3.42)  Time: 0.409s, 2501.17/s  (0.414s, 2475.77/s)  LR: 2.334e-01  Data: 0.028 (0.033)
+Train: 67 [ 300/312 ( 96%)]  Loss: 3.46 (3.44)  Time: 0.413s, 2480.87/s  (0.413s, 2477.99/s)  LR: 2.334e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   2.580 ( 2.580)  Acc@1:  49.805 ( 49.805)  Acc@5:  72.754 ( 72.754)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.392 ( 2.641)  Acc@1:  51.415 ( 48.150)  Acc@5:  75.354 ( 71.490)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-67.pth.tar', 48.150000032958985)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-64.pth.tar', 48.104000030517575)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-48.pth.tar', 47.95)
+
+Train: 68 [   0/312 (  0%)]  Loss: 3.37 (3.37)  Time: 1.738s,  589.03/s  (1.738s,  589.03/s)  LR: 2.292e-01  Data: 1.366 (1.366)
+Train: 68 [  50/312 ( 16%)]  Loss: 3.36 (3.35)  Time: 0.406s, 2523.13/s  (0.432s, 2372.36/s)  LR: 2.292e-01  Data: 0.027 (0.054)
+Train: 68 [ 100/312 ( 32%)]  Loss: 3.38 (3.37)  Time: 0.408s, 2509.91/s  (0.419s, 2446.09/s)  LR: 2.292e-01  Data: 0.033 (0.041)
+Train: 68 [ 150/312 ( 48%)]  Loss: 3.44 (3.38)  Time: 0.408s, 2508.42/s  (0.415s, 2469.81/s)  LR: 2.292e-01  Data: 0.028 (0.037)
+Train: 68 [ 200/312 ( 64%)]  Loss: 3.48 (3.40)  Time: 0.410s, 2500.07/s  (0.413s, 2477.97/s)  LR: 2.292e-01  Data: 0.028 (0.035)
+Train: 68 [ 250/312 ( 80%)]  Loss: 3.56 (3.41)  Time: 0.410s, 2499.23/s  (0.413s, 2480.71/s)  LR: 2.292e-01  Data: 0.027 (0.033)
+Train: 68 [ 300/312 ( 96%)]  Loss: 3.52 (3.42)  Time: 0.414s, 2472.40/s  (0.413s, 2482.07/s)  LR: 2.292e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.447 (1.447)  Loss:   2.588 ( 2.588)  Acc@1:  48.926 ( 48.926)  Acc@5:  71.875 ( 71.875)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.371 ( 2.648)  Acc@1:  52.241 ( 48.344)  Acc@5:  76.533 ( 71.778)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-67.pth.tar', 48.150000032958985)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-64.pth.tar', 48.104000030517575)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-53.pth.tar', 48.097999975585935)
+
+Train: 69 [   0/312 (  0%)]  Loss: 3.34 (3.34)  Time: 2.004s,  511.07/s  (2.004s,  511.07/s)  LR: 2.251e-01  Data: 1.628 (1.628)
+Train: 69 [  50/312 ( 16%)]  Loss: 3.27 (3.33)  Time: 0.412s, 2483.79/s  (0.443s, 2309.71/s)  LR: 2.251e-01  Data: 0.028 (0.059)
+Train: 69 [ 100/312 ( 32%)]  Loss: 3.42 (3.35)  Time: 0.408s, 2507.25/s  (0.428s, 2393.12/s)  LR: 2.251e-01  Data: 0.026 (0.044)
+Train: 69 [ 150/312 ( 48%)]  Loss: 3.39 (3.35)  Time: 0.412s, 2484.02/s  (0.423s, 2422.71/s)  LR: 2.251e-01  Data: 0.028 (0.038)
+Train: 69 [ 200/312 ( 64%)]  Loss: 3.38 (3.37)  Time: 0.407s, 2513.98/s  (0.420s, 2440.13/s)  LR: 2.251e-01  Data: 0.028 (0.036)
+Train: 69 [ 250/312 ( 80%)]  Loss: 3.46 (3.39)  Time: 0.407s, 2515.55/s  (0.417s, 2454.42/s)  LR: 2.251e-01  Data: 0.028 (0.034)
+Train: 69 [ 300/312 ( 96%)]  Loss: 3.50 (3.40)  Time: 0.406s, 2522.89/s  (0.415s, 2464.94/s)  LR: 2.251e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.436 (1.436)  Loss:   2.636 ( 2.636)  Acc@1:  47.949 ( 47.949)  Acc@5:  72.070 ( 72.070)
+Test: [  48/48]  Time: 0.090 (0.318)  Loss:   2.439 ( 2.665)  Acc@1:  52.948 ( 48.180)  Acc@5:  74.057 ( 71.676)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-69.pth.tar', 48.180000052490236)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-67.pth.tar', 48.150000032958985)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-64.pth.tar', 48.104000030517575)
+
+Train: 70 [   0/312 (  0%)]  Loss: 3.31 (3.31)  Time: 1.655s,  618.89/s  (1.655s,  618.89/s)  LR: 2.209e-01  Data: 1.178 (1.178)
+Train: 70 [  50/312 ( 16%)]  Loss: 3.34 (3.31)  Time: 0.410s, 2499.83/s  (0.433s, 2362.26/s)  LR: 2.209e-01  Data: 0.027 (0.050)
+Train: 70 [ 100/312 ( 32%)]  Loss: 3.45 (3.34)  Time: 0.413s, 2481.47/s  (0.423s, 2421.07/s)  LR: 2.209e-01  Data: 0.028 (0.039)
+Train: 70 [ 150/312 ( 48%)]  Loss: 3.33 (3.35)  Time: 0.413s, 2481.55/s  (0.420s, 2440.18/s)  LR: 2.209e-01  Data: 0.027 (0.036)
+Train: 70 [ 200/312 ( 64%)]  Loss: 3.41 (3.36)  Time: 0.414s, 2472.64/s  (0.418s, 2450.19/s)  LR: 2.209e-01  Data: 0.028 (0.034)
+Train: 70 [ 250/312 ( 80%)]  Loss: 3.35 (3.38)  Time: 0.412s, 2484.97/s  (0.417s, 2457.47/s)  LR: 2.209e-01  Data: 0.028 (0.033)
+Train: 70 [ 300/312 ( 96%)]  Loss: 3.43 (3.39)  Time: 0.418s, 2447.66/s  (0.416s, 2461.16/s)  LR: 2.209e-01  Data: 0.032 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.437 (1.437)  Loss:   2.600 ( 2.600)  Acc@1:  48.926 ( 48.926)  Acc@5:  71.973 ( 71.973)
+Test: [  48/48]  Time: 0.089 (0.320)  Loss:   2.488 ( 2.681)  Acc@1:  52.476 ( 47.888)  Acc@5:  73.703 ( 70.950)
+Train: 71 [   0/312 (  0%)]  Loss: 3.16 (3.16)  Time: 1.706s,  600.20/s  (1.706s,  600.20/s)  LR: 2.167e-01  Data: 1.100 (1.100)
+Train: 71 [  50/312 ( 16%)]  Loss: 3.37 (3.29)  Time: 0.405s, 2525.91/s  (0.432s, 2372.66/s)  LR: 2.167e-01  Data: 0.027 (0.049)
+Train: 71 [ 100/312 ( 32%)]  Loss: 3.33 (3.31)  Time: 0.407s, 2514.52/s  (0.419s, 2442.47/s)  LR: 2.167e-01  Data: 0.028 (0.038)
+Train: 71 [ 150/312 ( 48%)]  Loss: 3.34 (3.33)  Time: 0.410s, 2495.35/s  (0.416s, 2462.38/s)  LR: 2.167e-01  Data: 0.028 (0.035)
+Train: 71 [ 200/312 ( 64%)]  Loss: 3.33 (3.34)  Time: 0.410s, 2495.22/s  (0.415s, 2467.94/s)  LR: 2.167e-01  Data: 0.028 (0.033)
+Train: 71 [ 250/312 ( 80%)]  Loss: 3.44 (3.35)  Time: 0.408s, 2511.28/s  (0.414s, 2473.79/s)  LR: 2.167e-01  Data: 0.028 (0.032)
+Train: 71 [ 300/312 ( 96%)]  Loss: 3.39 (3.37)  Time: 0.408s, 2508.24/s  (0.413s, 2478.74/s)  LR: 2.167e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.436 (1.436)  Loss:   2.556 ( 2.556)  Acc@1:  50.195 ( 50.195)  Acc@5:  73.730 ( 73.730)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.474 ( 2.678)  Acc@1:  50.708 ( 47.976)  Acc@5:  76.651 ( 71.458)
+Train: 72 [   0/312 (  0%)]  Loss: 3.32 (3.32)  Time: 1.624s,  630.73/s  (1.624s,  630.73/s)  LR: 2.126e-01  Data: 1.202 (1.202)
+Train: 72 [  50/312 ( 16%)]  Loss: 3.40 (3.29)  Time: 0.414s, 2474.92/s  (0.436s, 2350.80/s)  LR: 2.126e-01  Data: 0.028 (0.051)
+Train: 72 [ 100/312 ( 32%)]  Loss: 3.30 (3.30)  Time: 0.409s, 2501.08/s  (0.424s, 2413.37/s)  LR: 2.126e-01  Data: 0.026 (0.039)
+Train: 72 [ 150/312 ( 48%)]  Loss: 3.33 (3.32)  Time: 0.408s, 2512.11/s  (0.419s, 2443.20/s)  LR: 2.126e-01  Data: 0.026 (0.036)
+Train: 72 [ 200/312 ( 64%)]  Loss: 3.32 (3.33)  Time: 0.408s, 2507.50/s  (0.416s, 2459.02/s)  LR: 2.126e-01  Data: 0.028 (0.034)
+Train: 72 [ 250/312 ( 80%)]  Loss: 3.42 (3.34)  Time: 0.411s, 2490.42/s  (0.415s, 2467.14/s)  LR: 2.126e-01  Data: 0.028 (0.033)
+Train: 72 [ 300/312 ( 96%)]  Loss: 3.38 (3.35)  Time: 0.414s, 2476.20/s  (0.415s, 2469.65/s)  LR: 2.126e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.416 (1.416)  Loss:   2.676 ( 2.676)  Acc@1:  47.754 ( 47.754)  Acc@5:  71.191 ( 71.191)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.454 ( 2.670)  Acc@1:  51.533 ( 48.082)  Acc@5:  73.585 ( 71.074)
+Train: 73 [   0/312 (  0%)]  Loss: 3.36 (3.36)  Time: 1.628s,  628.83/s  (1.628s,  628.83/s)  LR: 2.084e-01  Data: 1.205 (1.205)
+Train: 73 [  50/312 ( 16%)]  Loss: 3.35 (3.26)  Time: 0.407s, 2513.14/s  (0.434s, 2360.31/s)  LR: 2.084e-01  Data: 0.028 (0.051)
+Train: 73 [ 100/312 ( 32%)]  Loss: 3.42 (3.29)  Time: 0.409s, 2505.06/s  (0.421s, 2430.03/s)  LR: 2.084e-01  Data: 0.029 (0.039)
+Train: 73 [ 150/312 ( 48%)]  Loss: 3.45 (3.30)  Time: 0.413s, 2479.77/s  (0.418s, 2449.19/s)  LR: 2.084e-01  Data: 0.028 (0.036)
+Train: 73 [ 200/312 ( 64%)]  Loss: 3.33 (3.31)  Time: 0.411s, 2492.38/s  (0.416s, 2459.08/s)  LR: 2.084e-01  Data: 0.025 (0.034)
+Train: 73 [ 250/312 ( 80%)]  Loss: 3.43 (3.32)  Time: 0.409s, 2501.45/s  (0.415s, 2465.13/s)  LR: 2.084e-01  Data: 0.028 (0.033)
+Train: 73 [ 300/312 ( 96%)]  Loss: 3.31 (3.33)  Time: 0.410s, 2498.47/s  (0.415s, 2470.15/s)  LR: 2.084e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.412 (1.412)  Loss:   2.641 ( 2.641)  Acc@1:  49.902 ( 49.902)  Acc@5:  70.508 ( 70.508)
+Test: [  48/48]  Time: 0.091 (0.318)  Loss:   2.488 ( 2.699)  Acc@1:  50.943 ( 47.866)  Acc@5:  73.821 ( 71.060)
+Train: 74 [   0/312 (  0%)]  Loss: 3.18 (3.18)  Time: 2.022s,  506.55/s  (2.022s,  506.55/s)  LR: 2.042e-01  Data: 1.646 (1.646)
+Train: 74 [  50/312 ( 16%)]  Loss: 3.16 (3.23)  Time: 0.412s, 2485.06/s  (0.447s, 2291.53/s)  LR: 2.042e-01  Data: 0.026 (0.063)
+Train: 74 [ 100/312 ( 32%)]  Loss: 3.29 (3.25)  Time: 0.410s, 2497.80/s  (0.429s, 2387.37/s)  LR: 2.042e-01  Data: 0.026 (0.046)
+Train: 74 [ 150/312 ( 48%)]  Loss: 3.29 (3.28)  Time: 0.415s, 2469.42/s  (0.423s, 2420.04/s)  LR: 2.042e-01  Data: 0.027 (0.040)
+Train: 74 [ 200/312 ( 64%)]  Loss: 3.33 (3.29)  Time: 0.412s, 2486.80/s  (0.421s, 2434.29/s)  LR: 2.042e-01  Data: 0.028 (0.037)
+Train: 74 [ 250/312 ( 80%)]  Loss: 3.41 (3.31)  Time: 0.409s, 2501.16/s  (0.419s, 2444.34/s)  LR: 2.042e-01  Data: 0.025 (0.035)
+Train: 74 [ 300/312 ( 96%)]  Loss: 3.29 (3.32)  Time: 0.411s, 2494.42/s  (0.418s, 2452.31/s)  LR: 2.042e-01  Data: 0.028 (0.034)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.481 (1.481)  Loss:   2.617 ( 2.617)  Acc@1:  50.879 ( 50.879)  Acc@5:  71.973 ( 71.973)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.421 ( 2.688)  Acc@1:  52.241 ( 48.072)  Acc@5:  75.943 ( 71.170)
+Train: 75 [   0/312 (  0%)]  Loss: 3.18 (3.18)  Time: 1.996s,  513.09/s  (1.996s,  513.09/s)  LR: 2.000e-01  Data: 1.359 (1.359)
+Train: 75 [  50/312 ( 16%)]  Loss: 3.32 (3.22)  Time: 0.413s, 2478.63/s  (0.442s, 2314.49/s)  LR: 2.000e-01  Data: 0.030 (0.055)
+Train: 75 [ 100/312 ( 32%)]  Loss: 3.17 (3.24)  Time: 0.408s, 2509.75/s  (0.427s, 2399.88/s)  LR: 2.000e-01  Data: 0.027 (0.042)
+Train: 75 [ 150/312 ( 48%)]  Loss: 3.33 (3.26)  Time: 0.408s, 2509.64/s  (0.421s, 2433.35/s)  LR: 2.000e-01  Data: 0.027 (0.037)
+Train: 75 [ 200/312 ( 64%)]  Loss: 3.29 (3.28)  Time: 0.408s, 2508.38/s  (0.418s, 2447.26/s)  LR: 2.000e-01  Data: 0.027 (0.035)
+Train: 75 [ 250/312 ( 80%)]  Loss: 3.37 (3.29)  Time: 0.416s, 2459.48/s  (0.417s, 2456.13/s)  LR: 2.000e-01  Data: 0.027 (0.033)
+Train: 75 [ 300/312 ( 96%)]  Loss: 3.36 (3.30)  Time: 0.410s, 2495.48/s  (0.416s, 2460.93/s)  LR: 2.000e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.422 (1.422)  Loss:   2.533 ( 2.533)  Acc@1:  50.098 ( 50.098)  Acc@5:  73.926 ( 73.926)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.409 ( 2.673)  Acc@1:  54.481 ( 48.090)  Acc@5:  76.179 ( 71.414)
+Train: 76 [   0/312 (  0%)]  Loss: 3.18 (3.18)  Time: 1.686s,  607.32/s  (1.686s,  607.32/s)  LR: 1.958e-01  Data: 1.311 (1.311)
+Train: 76 [  50/312 ( 16%)]  Loss: 3.19 (3.21)  Time: 0.410s, 2496.96/s  (0.437s, 2345.24/s)  LR: 1.958e-01  Data: 0.027 (0.053)
+Train: 76 [ 100/312 ( 32%)]  Loss: 3.36 (3.24)  Time: 0.414s, 2475.67/s  (0.425s, 2410.85/s)  LR: 1.958e-01  Data: 0.027 (0.040)
+Train: 76 [ 150/312 ( 48%)]  Loss: 3.23 (3.25)  Time: 0.410s, 2497.08/s  (0.420s, 2435.25/s)  LR: 1.958e-01  Data: 0.027 (0.036)
+Train: 76 [ 200/312 ( 64%)]  Loss: 3.28 (3.26)  Time: 0.407s, 2514.99/s  (0.418s, 2450.51/s)  LR: 1.958e-01  Data: 0.026 (0.034)
+Train: 76 [ 250/312 ( 80%)]  Loss: 3.28 (3.27)  Time: 0.413s, 2480.22/s  (0.416s, 2458.65/s)  LR: 1.958e-01  Data: 0.028 (0.033)
+Train: 76 [ 300/312 ( 96%)]  Loss: 3.31 (3.28)  Time: 0.411s, 2490.64/s  (0.416s, 2463.09/s)  LR: 1.958e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   2.709 ( 2.709)  Acc@1:  48.340 ( 48.340)  Acc@5:  70.312 ( 70.312)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.542 ( 2.717)  Acc@1:  50.825 ( 47.690)  Acc@5:  72.524 ( 70.394)
+Train: 77 [   0/312 (  0%)]  Loss: 3.13 (3.13)  Time: 1.730s,  591.81/s  (1.730s,  591.81/s)  LR: 1.916e-01  Data: 1.180 (1.180)
+Train: 77 [  50/312 ( 16%)]  Loss: 3.16 (3.21)  Time: 0.412s, 2484.44/s  (0.436s, 2349.53/s)  LR: 1.916e-01  Data: 0.029 (0.050)
+Train: 77 [ 100/312 ( 32%)]  Loss: 3.34 (3.21)  Time: 0.409s, 2502.54/s  (0.423s, 2418.88/s)  LR: 1.916e-01  Data: 0.028 (0.039)
+Train: 77 [ 150/312 ( 48%)]  Loss: 3.27 (3.23)  Time: 0.408s, 2507.57/s  (0.419s, 2442.93/s)  LR: 1.916e-01  Data: 0.027 (0.036)
+Train: 77 [ 200/312 ( 64%)]  Loss: 3.34 (3.24)  Time: 0.412s, 2483.17/s  (0.417s, 2454.84/s)  LR: 1.916e-01  Data: 0.029 (0.034)
+Train: 77 [ 250/312 ( 80%)]  Loss: 3.26 (3.25)  Time: 0.417s, 2455.45/s  (0.416s, 2462.01/s)  LR: 1.916e-01  Data: 0.029 (0.032)
+Train: 77 [ 300/312 ( 96%)]  Loss: 3.44 (3.27)  Time: 0.408s, 2512.16/s  (0.415s, 2467.61/s)  LR: 1.916e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   2.690 ( 2.690)  Acc@1:  46.973 ( 46.973)  Acc@5:  72.070 ( 72.070)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.472 ( 2.722)  Acc@1:  51.415 ( 47.640)  Acc@5:  74.292 ( 70.704)
+Train: 78 [   0/312 (  0%)]  Loss: 3.23 (3.23)  Time: 1.587s,  645.43/s  (1.587s,  645.43/s)  LR: 1.874e-01  Data: 1.213 (1.213)
+Train: 78 [  50/312 ( 16%)]  Loss: 3.18 (3.15)  Time: 0.410s, 2496.56/s  (0.432s, 2367.94/s)  LR: 1.874e-01  Data: 0.028 (0.051)
+Train: 78 [ 100/312 ( 32%)]  Loss: 3.30 (3.18)  Time: 0.409s, 2502.62/s  (0.422s, 2427.28/s)  LR: 1.874e-01  Data: 0.028 (0.039)
+Train: 78 [ 150/312 ( 48%)]  Loss: 3.25 (3.21)  Time: 0.409s, 2501.76/s  (0.418s, 2451.21/s)  LR: 1.874e-01  Data: 0.028 (0.036)
+Train: 78 [ 200/312 ( 64%)]  Loss: 3.32 (3.22)  Time: 0.411s, 2492.45/s  (0.416s, 2462.54/s)  LR: 1.874e-01  Data: 0.027 (0.034)
+Train: 78 [ 250/312 ( 80%)]  Loss: 3.35 (3.24)  Time: 0.410s, 2500.18/s  (0.415s, 2468.20/s)  LR: 1.874e-01  Data: 0.027 (0.033)
+Train: 78 [ 300/312 ( 96%)]  Loss: 3.35 (3.25)  Time: 0.408s, 2512.47/s  (0.414s, 2473.65/s)  LR: 1.874e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   2.605 ( 2.605)  Acc@1:  50.977 ( 50.977)  Acc@5:  71.680 ( 71.680)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.372 ( 2.666)  Acc@1:  53.066 ( 48.628)  Acc@5:  76.769 ( 71.536)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-69.pth.tar', 48.180000052490236)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-67.pth.tar', 48.150000032958985)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-58.pth.tar', 48.12800004638672)
+
+Train: 79 [   0/312 (  0%)]  Loss: 3.15 (3.15)  Time: 1.774s,  577.13/s  (1.774s,  577.13/s)  LR: 1.833e-01  Data: 1.400 (1.400)
+Train: 79 [  50/312 ( 16%)]  Loss: 3.16 (3.17)  Time: 0.410s, 2495.78/s  (0.438s, 2340.11/s)  LR: 1.833e-01  Data: 0.025 (0.055)
+Train: 79 [ 100/312 ( 32%)]  Loss: 3.24 (3.18)  Time: 0.408s, 2506.92/s  (0.424s, 2414.39/s)  LR: 1.833e-01  Data: 0.028 (0.042)
+Train: 79 [ 150/312 ( 48%)]  Loss: 3.23 (3.19)  Time: 0.408s, 2508.18/s  (0.419s, 2442.80/s)  LR: 1.833e-01  Data: 0.027 (0.037)
+Train: 79 [ 200/312 ( 64%)]  Loss: 3.23 (3.21)  Time: 0.414s, 2471.02/s  (0.417s, 2455.44/s)  LR: 1.833e-01  Data: 0.032 (0.035)
+Train: 79 [ 250/312 ( 80%)]  Loss: 3.34 (3.22)  Time: 0.414s, 2475.17/s  (0.416s, 2463.00/s)  LR: 1.833e-01  Data: 0.032 (0.033)
+Train: 79 [ 300/312 ( 96%)]  Loss: 3.37 (3.23)  Time: 0.410s, 2500.32/s  (0.415s, 2468.07/s)  LR: 1.833e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.443 (1.443)  Loss:   2.607 ( 2.607)  Acc@1:  49.219 ( 49.219)  Acc@5:  72.363 ( 72.363)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.426 ( 2.703)  Acc@1:  53.184 ( 47.904)  Acc@5:  75.000 ( 70.948)
+Train: 80 [   0/312 (  0%)]  Loss: 3.21 (3.21)  Time: 1.689s,  606.38/s  (1.689s,  606.38/s)  LR: 1.791e-01  Data: 1.313 (1.313)
+Train: 80 [  50/312 ( 16%)]  Loss: 3.08 (3.16)  Time: 0.411s, 2490.71/s  (0.438s, 2338.21/s)  LR: 1.791e-01  Data: 0.029 (0.055)
+Train: 80 [ 100/312 ( 32%)]  Loss: 3.10 (3.16)  Time: 0.407s, 2514.65/s  (0.424s, 2415.73/s)  LR: 1.791e-01  Data: 0.028 (0.042)
+Train: 80 [ 150/312 ( 48%)]  Loss: 3.27 (3.18)  Time: 0.408s, 2512.04/s  (0.419s, 2445.42/s)  LR: 1.791e-01  Data: 0.027 (0.037)
+Train: 80 [ 200/312 ( 64%)]  Loss: 3.30 (3.19)  Time: 0.410s, 2499.58/s  (0.416s, 2459.22/s)  LR: 1.791e-01  Data: 0.027 (0.035)
+Train: 80 [ 250/312 ( 80%)]  Loss: 3.12 (3.20)  Time: 0.412s, 2487.32/s  (0.415s, 2465.38/s)  LR: 1.791e-01  Data: 0.027 (0.033)
+Train: 80 [ 300/312 ( 96%)]  Loss: 3.37 (3.21)  Time: 0.403s, 2540.05/s  (0.414s, 2475.67/s)  LR: 1.791e-01  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.453 (1.453)  Loss:   2.654 ( 2.654)  Acc@1:  48.438 ( 48.438)  Acc@5:  69.629 ( 69.629)
+Test: [  48/48]  Time: 0.088 (0.321)  Loss:   2.421 ( 2.732)  Acc@1:  55.189 ( 47.722)  Acc@5:  76.061 ( 70.458)
+Train: 81 [   0/312 (  0%)]  Loss: 3.04 (3.04)  Time: 1.818s,  563.12/s  (1.818s,  563.12/s)  LR: 1.749e-01  Data: 1.124 (1.124)
+Train: 81 [  50/312 ( 16%)]  Loss: 3.19 (3.14)  Time: 0.403s, 2544.07/s  (0.430s, 2383.69/s)  LR: 1.749e-01  Data: 0.028 (0.050)
+Train: 81 [ 100/312 ( 32%)]  Loss: 3.24 (3.15)  Time: 0.403s, 2543.24/s  (0.416s, 2460.71/s)  LR: 1.749e-01  Data: 0.029 (0.039)
+Train: 81 [ 150/312 ( 48%)]  Loss: 3.21 (3.17)  Time: 0.406s, 2525.16/s  (0.412s, 2485.06/s)  LR: 1.749e-01  Data: 0.028 (0.035)
+Train: 81 [ 200/312 ( 64%)]  Loss: 3.24 (3.18)  Time: 0.409s, 2503.66/s  (0.411s, 2493.41/s)  LR: 1.749e-01  Data: 0.028 (0.033)
+Train: 81 [ 250/312 ( 80%)]  Loss: 3.18 (3.19)  Time: 0.410s, 2498.14/s  (0.411s, 2494.02/s)  LR: 1.749e-01  Data: 0.026 (0.032)
+Train: 81 [ 300/312 ( 96%)]  Loss: 3.33 (3.20)  Time: 0.411s, 2493.94/s  (0.411s, 2492.61/s)  LR: 1.749e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.494 (1.494)  Loss:   2.656 ( 2.656)  Acc@1:  49.219 ( 49.219)  Acc@5:  70.898 ( 70.898)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.479 ( 2.743)  Acc@1:  51.887 ( 47.332)  Acc@5:  74.882 ( 70.024)
+Train: 82 [   0/312 (  0%)]  Loss: 3.16 (3.16)  Time: 2.194s,  466.67/s  (2.194s,  466.67/s)  LR: 1.708e-01  Data: 1.138 (1.138)
+Train: 82 [  50/312 ( 16%)]  Loss: 3.21 (3.12)  Time: 0.413s, 2477.53/s  (0.447s, 2290.14/s)  LR: 1.708e-01  Data: 0.028 (0.049)
+Train: 82 [ 100/312 ( 32%)]  Loss: 3.07 (3.13)  Time: 0.411s, 2490.13/s  (0.430s, 2380.76/s)  LR: 1.708e-01  Data: 0.029 (0.038)
+Train: 82 [ 150/312 ( 48%)]  Loss: 3.28 (3.15)  Time: 0.413s, 2481.70/s  (0.425s, 2411.72/s)  LR: 1.708e-01  Data: 0.028 (0.035)
+Train: 82 [ 200/312 ( 64%)]  Loss: 3.22 (3.16)  Time: 0.412s, 2484.97/s  (0.422s, 2429.05/s)  LR: 1.708e-01  Data: 0.029 (0.033)
+Train: 82 [ 250/312 ( 80%)]  Loss: 3.22 (3.17)  Time: 0.411s, 2489.30/s  (0.420s, 2438.97/s)  LR: 1.708e-01  Data: 0.028 (0.032)
+Train: 82 [ 300/312 ( 96%)]  Loss: 3.26 (3.18)  Time: 0.410s, 2500.14/s  (0.419s, 2446.80/s)  LR: 1.708e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.409 (1.409)  Loss:   2.750 ( 2.750)  Acc@1:  48.047 ( 48.047)  Acc@5:  70.020 ( 70.020)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.510 ( 2.729)  Acc@1:  51.533 ( 47.668)  Acc@5:  76.061 ( 70.664)
+Train: 83 [   0/312 (  0%)]  Loss: 3.09 (3.09)  Time: 1.629s,  628.45/s  (1.629s,  628.45/s)  LR: 1.666e-01  Data: 1.255 (1.255)
+Train: 83 [  50/312 ( 16%)]  Loss: 3.01 (3.09)  Time: 0.410s, 2499.85/s  (0.434s, 2359.83/s)  LR: 1.666e-01  Data: 0.028 (0.052)
+Train: 83 [ 100/312 ( 32%)]  Loss: 3.15 (3.11)  Time: 0.410s, 2496.56/s  (0.423s, 2421.95/s)  LR: 1.666e-01  Data: 0.029 (0.040)
+Train: 83 [ 150/312 ( 48%)]  Loss: 3.11 (3.12)  Time: 0.408s, 2512.72/s  (0.419s, 2444.23/s)  LR: 1.666e-01  Data: 0.026 (0.036)
+Train: 83 [ 200/312 ( 64%)]  Loss: 3.21 (3.14)  Time: 0.407s, 2518.22/s  (0.417s, 2457.95/s)  LR: 1.666e-01  Data: 0.027 (0.034)
+Train: 83 [ 250/312 ( 80%)]  Loss: 3.20 (3.15)  Time: 0.410s, 2498.09/s  (0.415s, 2466.26/s)  LR: 1.666e-01  Data: 0.028 (0.033)
+Train: 83 [ 300/312 ( 96%)]  Loss: 3.26 (3.16)  Time: 0.409s, 2501.26/s  (0.415s, 2470.17/s)  LR: 1.666e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.408 (1.408)  Loss:   2.719 ( 2.719)  Acc@1:  46.875 ( 46.875)  Acc@5:  69.434 ( 69.434)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.474 ( 2.735)  Acc@1:  54.009 ( 47.882)  Acc@5:  75.472 ( 70.510)
+Train: 84 [   0/312 (  0%)]  Loss: 3.01 (3.01)  Time: 1.928s,  531.14/s  (1.928s,  531.14/s)  LR: 1.625e-01  Data: 1.556 (1.556)
+Train: 84 [  50/312 ( 16%)]  Loss: 3.12 (3.08)  Time: 0.409s, 2503.07/s  (0.440s, 2328.64/s)  LR: 1.625e-01  Data: 0.028 (0.059)
+Train: 84 [ 100/312 ( 32%)]  Loss: 3.19 (3.10)  Time: 0.411s, 2494.45/s  (0.426s, 2406.31/s)  LR: 1.625e-01  Data: 0.028 (0.044)
+Train: 84 [ 150/312 ( 48%)]  Loss: 3.14 (3.11)  Time: 0.413s, 2478.01/s  (0.421s, 2430.24/s)  LR: 1.625e-01  Data: 0.027 (0.038)
+Train: 84 [ 200/312 ( 64%)]  Loss: 3.17 (3.12)  Time: 0.417s, 2455.93/s  (0.419s, 2442.59/s)  LR: 1.625e-01  Data: 0.029 (0.036)
+Train: 84 [ 250/312 ( 80%)]  Loss: 3.21 (3.14)  Time: 0.410s, 2497.42/s  (0.418s, 2450.40/s)  LR: 1.625e-01  Data: 0.027 (0.034)
+Train: 84 [ 300/312 ( 96%)]  Loss: 3.28 (3.15)  Time: 0.413s, 2476.48/s  (0.417s, 2455.57/s)  LR: 1.625e-01  Data: 0.029 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.443 (1.443)  Loss:   2.630 ( 2.630)  Acc@1:  50.293 ( 50.293)  Acc@5:  71.387 ( 71.387)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.440 ( 2.696)  Acc@1:  52.241 ( 48.088)  Acc@5:  75.000 ( 71.012)
+Train: 85 [   0/312 (  0%)]  Loss: 3.10 (3.10)  Time: 1.763s,  580.75/s  (1.763s,  580.75/s)  LR: 1.584e-01  Data: 1.247 (1.247)
+Train: 85 [  50/312 ( 16%)]  Loss: 3.13 (3.06)  Time: 0.410s, 2496.66/s  (0.436s, 2349.35/s)  LR: 1.584e-01  Data: 0.029 (0.052)
+Train: 85 [ 100/312 ( 32%)]  Loss: 3.16 (3.08)  Time: 0.407s, 2514.64/s  (0.424s, 2417.13/s)  LR: 1.584e-01  Data: 0.027 (0.040)
+Train: 85 [ 150/312 ( 48%)]  Loss: 3.11 (3.09)  Time: 0.409s, 2506.34/s  (0.418s, 2449.30/s)  LR: 1.584e-01  Data: 0.032 (0.036)
+Train: 85 [ 200/312 ( 64%)]  Loss: 3.13 (3.11)  Time: 0.405s, 2527.79/s  (0.415s, 2466.99/s)  LR: 1.584e-01  Data: 0.027 (0.034)
+Train: 85 [ 250/312 ( 80%)]  Loss: 3.23 (3.12)  Time: 0.406s, 2522.19/s  (0.413s, 2477.17/s)  LR: 1.584e-01  Data: 0.026 (0.033)
+Train: 85 [ 300/312 ( 96%)]  Loss: 3.29 (3.13)  Time: 0.410s, 2499.10/s  (0.413s, 2481.29/s)  LR: 1.584e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.448 (1.448)  Loss:   2.754 ( 2.754)  Acc@1:  47.461 ( 47.461)  Acc@5:  69.238 ( 69.238)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.517 ( 2.770)  Acc@1:  52.123 ( 47.500)  Acc@5:  74.175 ( 70.038)
+Train: 86 [   0/312 (  0%)]  Loss: 3.07 (3.07)  Time: 1.875s,  546.10/s  (1.875s,  546.10/s)  LR: 1.543e-01  Data: 1.217 (1.217)
+Train: 86 [  50/312 ( 16%)]  Loss: 3.05 (3.06)  Time: 0.406s, 2523.17/s  (0.436s, 2350.33/s)  LR: 1.543e-01  Data: 0.027 (0.051)
+Train: 86 [ 100/312 ( 32%)]  Loss: 3.12 (3.06)  Time: 0.408s, 2510.33/s  (0.421s, 2430.42/s)  LR: 1.543e-01  Data: 0.027 (0.039)
+Train: 86 [ 150/312 ( 48%)]  Loss: 3.14 (3.08)  Time: 0.410s, 2498.20/s  (0.417s, 2453.22/s)  LR: 1.543e-01  Data: 0.024 (0.035)
+Train: 86 [ 200/312 ( 64%)]  Loss: 3.15 (3.09)  Time: 0.411s, 2489.93/s  (0.416s, 2462.37/s)  LR: 1.543e-01  Data: 0.032 (0.034)
+Train: 86 [ 250/312 ( 80%)]  Loss: 3.23 (3.10)  Time: 0.408s, 2511.70/s  (0.414s, 2471.73/s)  LR: 1.543e-01  Data: 0.027 (0.032)
+Train: 86 [ 300/312 ( 96%)]  Loss: 3.13 (3.11)  Time: 0.409s, 2502.92/s  (0.413s, 2478.42/s)  LR: 1.543e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.454 (1.454)  Loss:   2.722 ( 2.722)  Acc@1:  47.949 ( 47.949)  Acc@5:  69.238 ( 69.238)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   2.533 ( 2.755)  Acc@1:  50.236 ( 47.228)  Acc@5:  74.646 ( 70.062)
+Train: 87 [   0/312 (  0%)]  Loss: 3.09 (3.09)  Time: 1.709s,  599.19/s  (1.709s,  599.19/s)  LR: 1.503e-01  Data: 1.242 (1.242)
+Train: 87 [  50/312 ( 16%)]  Loss: 3.08 (3.05)  Time: 0.412s, 2487.40/s  (0.439s, 2334.38/s)  LR: 1.503e-01  Data: 0.028 (0.052)
+Train: 87 [ 100/312 ( 32%)]  Loss: 3.06 (3.05)  Time: 0.411s, 2493.03/s  (0.426s, 2403.73/s)  LR: 1.503e-01  Data: 0.027 (0.040)
+Train: 87 [ 150/312 ( 48%)]  Loss: 3.05 (3.06)  Time: 0.407s, 2514.37/s  (0.420s, 2436.26/s)  LR: 1.503e-01  Data: 0.028 (0.036)
+Train: 87 [ 200/312 ( 64%)]  Loss: 3.01 (3.07)  Time: 0.407s, 2516.98/s  (0.417s, 2454.96/s)  LR: 1.503e-01  Data: 0.026 (0.034)
+Train: 87 [ 250/312 ( 80%)]  Loss: 3.19 (3.08)  Time: 0.409s, 2505.77/s  (0.415s, 2465.31/s)  LR: 1.503e-01  Data: 0.027 (0.033)
+Train: 87 [ 300/312 ( 96%)]  Loss: 3.10 (3.09)  Time: 0.414s, 2475.38/s  (0.415s, 2469.70/s)  LR: 1.503e-01  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.407 (1.407)  Loss:   2.757 ( 2.757)  Acc@1:  47.754 ( 47.754)  Acc@5:  68.848 ( 68.848)
+Test: [  48/48]  Time: 0.091 (0.319)  Loss:   2.503 ( 2.787)  Acc@1:  49.882 ( 47.186)  Acc@5:  74.882 ( 69.534)
+Train: 88 [   0/312 (  0%)]  Loss: 2.88 (2.88)  Time: 1.676s,  610.81/s  (1.676s,  610.81/s)  LR: 1.462e-01  Data: 1.300 (1.300)
+Train: 88 [  50/312 ( 16%)]  Loss: 3.06 (3.01)  Time: 0.409s, 2505.76/s  (0.443s, 2312.93/s)  LR: 1.462e-01  Data: 0.029 (0.060)
+Train: 88 [ 100/312 ( 32%)]  Loss: 3.11 (3.03)  Time: 0.409s, 2501.42/s  (0.425s, 2408.14/s)  LR: 1.462e-01  Data: 0.032 (0.045)
+Train: 88 [ 150/312 ( 48%)]  Loss: 3.03 (3.05)  Time: 0.407s, 2514.51/s  (0.419s, 2442.89/s)  LR: 1.462e-01  Data: 0.029 (0.039)
+Train: 88 [ 200/312 ( 64%)]  Loss: 3.19 (3.06)  Time: 0.410s, 2499.00/s  (0.416s, 2459.07/s)  LR: 1.462e-01  Data: 0.028 (0.036)
+Train: 88 [ 250/312 ( 80%)]  Loss: 3.10 (3.07)  Time: 0.410s, 2497.24/s  (0.415s, 2464.75/s)  LR: 1.462e-01  Data: 0.028 (0.035)
+Train: 88 [ 300/312 ( 96%)]  Loss: 3.16 (3.08)  Time: 0.408s, 2507.72/s  (0.415s, 2470.38/s)  LR: 1.462e-01  Data: 0.028 (0.034)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   2.711 ( 2.711)  Acc@1:  47.656 ( 47.656)  Acc@5:  69.727 ( 69.727)
+Test: [  48/48]  Time: 0.091 (0.319)  Loss:   2.462 ( 2.739)  Acc@1:  51.297 ( 47.822)  Acc@5:  74.057 ( 70.386)
+Train: 89 [   0/312 (  0%)]  Loss: 3.06 (3.06)  Time: 1.695s,  604.02/s  (1.695s,  604.02/s)  LR: 1.422e-01  Data: 1.319 (1.319)
+Train: 89 [  50/312 ( 16%)]  Loss: 3.04 (3.00)  Time: 0.413s, 2481.55/s  (0.438s, 2340.05/s)  LR: 1.422e-01  Data: 0.029 (0.054)
+Train: 89 [ 100/312 ( 32%)]  Loss: 3.13 (3.01)  Time: 0.411s, 2491.99/s  (0.425s, 2408.27/s)  LR: 1.422e-01  Data: 0.028 (0.041)
+Train: 89 [ 150/312 ( 48%)]  Loss: 3.16 (3.03)  Time: 0.416s, 2464.35/s  (0.421s, 2431.93/s)  LR: 1.422e-01  Data: 0.033 (0.037)
+Train: 89 [ 200/312 ( 64%)]  Loss: 2.98 (3.04)  Time: 0.412s, 2484.98/s  (0.419s, 2445.02/s)  LR: 1.422e-01  Data: 0.028 (0.035)
+Train: 89 [ 250/312 ( 80%)]  Loss: 3.16 (3.05)  Time: 0.409s, 2503.76/s  (0.418s, 2452.00/s)  LR: 1.422e-01  Data: 0.027 (0.033)
+Train: 89 [ 300/312 ( 96%)]  Loss: 3.08 (3.06)  Time: 0.412s, 2487.76/s  (0.417s, 2457.78/s)  LR: 1.422e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.463 (1.463)  Loss:   2.667 ( 2.667)  Acc@1:  48.633 ( 48.633)  Acc@5:  70.508 ( 70.508)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.455 ( 2.728)  Acc@1:  53.538 ( 48.214)  Acc@5:  75.236 ( 70.552)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-89.pth.tar', 48.21400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-69.pth.tar', 48.180000052490236)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-67.pth.tar', 48.150000032958985)
+
+Train: 90 [   0/312 (  0%)]  Loss: 3.04 (3.04)  Time: 1.643s,  623.06/s  (1.643s,  623.06/s)  LR: 1.382e-01  Data: 1.119 (1.119)
+Train: 90 [  50/312 ( 16%)]  Loss: 2.95 (2.98)  Time: 0.408s, 2509.27/s  (0.438s, 2335.24/s)  LR: 1.382e-01  Data: 0.028 (0.050)
+Train: 90 [ 100/312 ( 32%)]  Loss: 3.02 (3.00)  Time: 0.408s, 2510.85/s  (0.424s, 2412.96/s)  LR: 1.382e-01  Data: 0.027 (0.039)
+Train: 90 [ 150/312 ( 48%)]  Loss: 2.96 (3.01)  Time: 0.411s, 2491.30/s  (0.420s, 2438.65/s)  LR: 1.382e-01  Data: 0.028 (0.035)
+Train: 90 [ 200/312 ( 64%)]  Loss: 3.02 (3.02)  Time: 0.412s, 2487.81/s  (0.418s, 2451.44/s)  LR: 1.382e-01  Data: 0.029 (0.033)
+Train: 90 [ 250/312 ( 80%)]  Loss: 3.13 (3.03)  Time: 0.410s, 2500.56/s  (0.416s, 2459.81/s)  LR: 1.382e-01  Data: 0.027 (0.032)
+Train: 90 [ 300/312 ( 96%)]  Loss: 3.11 (3.04)  Time: 0.408s, 2508.21/s  (0.415s, 2464.76/s)  LR: 1.382e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.427 (1.427)  Loss:   2.626 ( 2.626)  Acc@1:  49.609 ( 49.609)  Acc@5:  70.996 ( 70.996)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.392 ( 2.690)  Acc@1:  52.830 ( 48.640)  Acc@5:  75.000 ( 70.844)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
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+Train: 91 [   0/312 (  0%)]  Loss: 3.05 (3.05)  Time: 1.810s,  565.60/s  (1.810s,  565.60/s)  LR: 1.342e-01  Data: 1.217 (1.217)
+Train: 91 [  50/312 ( 16%)]  Loss: 3.00 (2.97)  Time: 0.404s, 2532.34/s  (0.434s, 2360.73/s)  LR: 1.342e-01  Data: 0.026 (0.051)
+Train: 91 [ 100/312 ( 32%)]  Loss: 2.98 (2.99)  Time: 0.407s, 2517.80/s  (0.421s, 2434.77/s)  LR: 1.342e-01  Data: 0.023 (0.039)
+Train: 91 [ 150/312 ( 48%)]  Loss: 3.04 (3.00)  Time: 0.411s, 2489.50/s  (0.417s, 2455.33/s)  LR: 1.342e-01  Data: 0.028 (0.036)
+Train: 91 [ 200/312 ( 64%)]  Loss: 3.07 (3.01)  Time: 0.408s, 2507.28/s  (0.415s, 2464.75/s)  LR: 1.342e-01  Data: 0.028 (0.034)
+Train: 91 [ 250/312 ( 80%)]  Loss: 3.09 (3.02)  Time: 0.412s, 2486.10/s  (0.414s, 2473.09/s)  LR: 1.342e-01  Data: 0.026 (0.033)
+Train: 91 [ 300/312 ( 96%)]  Loss: 3.20 (3.03)  Time: 0.414s, 2471.49/s  (0.413s, 2477.67/s)  LR: 1.342e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.496 (1.496)  Loss:   2.786 ( 2.786)  Acc@1:  45.605 ( 45.605)  Acc@5:  69.336 ( 69.336)
+Test: [  48/48]  Time: 0.090 (0.324)  Loss:   2.526 ( 2.840)  Acc@1:  51.651 ( 46.524)  Acc@5:  73.349 ( 68.786)
+Train: 92 [   0/312 (  0%)]  Loss: 2.82 (2.82)  Time: 1.667s,  614.27/s  (1.667s,  614.27/s)  LR: 1.303e-01  Data: 1.293 (1.293)
+Train: 92 [  50/312 ( 16%)]  Loss: 2.97 (2.94)  Time: 0.410s, 2498.97/s  (0.435s, 2353.61/s)  LR: 1.303e-01  Data: 0.028 (0.052)
+Train: 92 [ 100/312 ( 32%)]  Loss: 3.10 (2.96)  Time: 0.416s, 2462.55/s  (0.424s, 2416.04/s)  LR: 1.303e-01  Data: 0.029 (0.040)
+Train: 92 [ 150/312 ( 48%)]  Loss: 2.97 (2.97)  Time: 0.408s, 2512.21/s  (0.420s, 2439.34/s)  LR: 1.303e-01  Data: 0.026 (0.036)
+Train: 92 [ 200/312 ( 64%)]  Loss: 3.08 (2.99)  Time: 0.409s, 2501.54/s  (0.417s, 2454.92/s)  LR: 1.303e-01  Data: 0.028 (0.034)
+Train: 92 [ 250/312 ( 80%)]  Loss: 3.15 (3.00)  Time: 0.413s, 2479.90/s  (0.416s, 2463.02/s)  LR: 1.303e-01  Data: 0.028 (0.033)
+Train: 92 [ 300/312 ( 96%)]  Loss: 3.17 (3.01)  Time: 0.416s, 2459.34/s  (0.415s, 2465.65/s)  LR: 1.303e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.433 (1.433)  Loss:   2.748 ( 2.748)  Acc@1:  46.973 ( 46.973)  Acc@5:  69.824 ( 69.824)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.539 ( 2.812)  Acc@1:  51.297 ( 46.898)  Acc@5:  74.292 ( 69.092)
+Train: 93 [   0/312 (  0%)]  Loss: 2.99 (2.99)  Time: 1.979s,  517.32/s  (1.979s,  517.32/s)  LR: 1.264e-01  Data: 1.606 (1.606)
+Train: 93 [  50/312 ( 16%)]  Loss: 2.95 (2.95)  Time: 0.411s, 2491.10/s  (0.438s, 2337.63/s)  LR: 1.264e-01  Data: 0.031 (0.058)
+Train: 93 [ 100/312 ( 32%)]  Loss: 2.97 (2.95)  Time: 0.411s, 2488.58/s  (0.424s, 2416.68/s)  LR: 1.264e-01  Data: 0.028 (0.043)
+Train: 93 [ 150/312 ( 48%)]  Loss: 3.04 (2.97)  Time: 0.416s, 2463.94/s  (0.420s, 2439.34/s)  LR: 1.264e-01  Data: 0.029 (0.038)
+Train: 93 [ 200/312 ( 64%)]  Loss: 2.96 (2.98)  Time: 0.407s, 2515.97/s  (0.417s, 2454.47/s)  LR: 1.264e-01  Data: 0.029 (0.036)
+Train: 93 [ 250/312 ( 80%)]  Loss: 2.94 (2.99)  Time: 0.408s, 2507.81/s  (0.415s, 2465.86/s)  LR: 1.264e-01  Data: 0.026 (0.034)
+Train: 93 [ 300/312 ( 96%)]  Loss: 3.09 (3.00)  Time: 0.410s, 2498.34/s  (0.414s, 2471.54/s)  LR: 1.264e-01  Data: 0.026 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.431 (1.431)  Loss:   2.718 ( 2.718)  Acc@1:  47.559 ( 47.559)  Acc@5:  70.410 ( 70.410)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.574 ( 2.761)  Acc@1:  50.236 ( 47.514)  Acc@5:  73.467 ( 69.856)
+Train: 94 [   0/312 (  0%)]  Loss: 2.88 (2.88)  Time: 1.588s,  644.64/s  (1.588s,  644.64/s)  LR: 1.225e-01  Data: 1.199 (1.199)
+Train: 94 [  50/312 ( 16%)]  Loss: 2.95 (2.91)  Time: 0.407s, 2516.81/s  (0.432s, 2370.31/s)  LR: 1.225e-01  Data: 0.027 (0.051)
+Train: 94 [ 100/312 ( 32%)]  Loss: 2.91 (2.93)  Time: 0.411s, 2490.66/s  (0.422s, 2428.27/s)  LR: 1.225e-01  Data: 0.028 (0.039)
+Train: 94 [ 150/312 ( 48%)]  Loss: 3.06 (2.95)  Time: 0.406s, 2522.47/s  (0.417s, 2453.49/s)  LR: 1.225e-01  Data: 0.029 (0.036)
+Train: 94 [ 200/312 ( 64%)]  Loss: 3.15 (2.96)  Time: 0.408s, 2510.67/s  (0.415s, 2470.21/s)  LR: 1.225e-01  Data: 0.030 (0.034)
+Train: 94 [ 250/312 ( 80%)]  Loss: 3.01 (2.96)  Time: 0.406s, 2523.52/s  (0.413s, 2480.17/s)  LR: 1.225e-01  Data: 0.027 (0.033)
+Train: 94 [ 300/312 ( 96%)]  Loss: 3.10 (2.97)  Time: 0.409s, 2506.62/s  (0.412s, 2484.88/s)  LR: 1.225e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.444 (1.444)  Loss:   2.696 ( 2.696)  Acc@1:  49.023 ( 49.023)  Acc@5:  70.996 ( 70.996)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.501 ( 2.749)  Acc@1:  51.415 ( 48.100)  Acc@5:  73.821 ( 69.994)
+Train: 95 [   0/312 (  0%)]  Loss: 2.90 (2.90)  Time: 1.730s,  592.04/s  (1.730s,  592.04/s)  LR: 1.187e-01  Data: 1.354 (1.354)
+Train: 95 [  50/312 ( 16%)]  Loss: 2.91 (2.89)  Time: 0.408s, 2510.87/s  (0.436s, 2346.03/s)  LR: 1.187e-01  Data: 0.029 (0.054)
+Train: 95 [ 100/312 ( 32%)]  Loss: 2.90 (2.91)  Time: 0.407s, 2513.56/s  (0.422s, 2424.09/s)  LR: 1.187e-01  Data: 0.027 (0.041)
+Train: 95 [ 150/312 ( 48%)]  Loss: 2.96 (2.93)  Time: 0.413s, 2476.97/s  (0.418s, 2449.71/s)  LR: 1.187e-01  Data: 0.026 (0.037)
+Train: 95 [ 200/312 ( 64%)]  Loss: 2.89 (2.94)  Time: 0.414s, 2470.81/s  (0.416s, 2459.34/s)  LR: 1.187e-01  Data: 0.028 (0.034)
+Train: 95 [ 250/312 ( 80%)]  Loss: 3.03 (2.95)  Time: 0.406s, 2520.64/s  (0.415s, 2466.87/s)  LR: 1.187e-01  Data: 0.026 (0.033)
+Train: 95 [ 300/312 ( 96%)]  Loss: 3.08 (2.96)  Time: 0.413s, 2479.79/s  (0.414s, 2472.98/s)  LR: 1.187e-01  Data: 0.030 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   2.732 ( 2.732)  Acc@1:  49.121 ( 49.121)  Acc@5:  69.629 ( 69.629)
+Test: [  48/48]  Time: 0.091 (0.323)  Loss:   2.565 ( 2.795)  Acc@1:  51.297 ( 47.260)  Acc@5:  73.113 ( 69.292)
+Train: 96 [   0/312 (  0%)]  Loss: 2.90 (2.90)  Time: 1.568s,  652.94/s  (1.568s,  652.94/s)  LR: 1.148e-01  Data: 1.195 (1.195)
+Train: 96 [  50/312 ( 16%)]  Loss: 3.01 (2.88)  Time: 0.411s, 2490.10/s  (0.435s, 2352.87/s)  LR: 1.148e-01  Data: 0.027 (0.051)
+Train: 96 [ 100/312 ( 32%)]  Loss: 2.92 (2.89)  Time: 0.409s, 2501.43/s  (0.424s, 2415.62/s)  LR: 1.148e-01  Data: 0.029 (0.040)
+Train: 96 [ 150/312 ( 48%)]  Loss: 2.87 (2.90)  Time: 0.407s, 2515.16/s  (0.419s, 2444.66/s)  LR: 1.148e-01  Data: 0.028 (0.036)
+Train: 96 [ 200/312 ( 64%)]  Loss: 2.87 (2.92)  Time: 0.409s, 2502.84/s  (0.416s, 2462.06/s)  LR: 1.148e-01  Data: 0.033 (0.034)
+Train: 96 [ 250/312 ( 80%)]  Loss: 3.02 (2.93)  Time: 0.409s, 2505.26/s  (0.414s, 2472.11/s)  LR: 1.148e-01  Data: 0.027 (0.033)
+Train: 96 [ 300/312 ( 96%)]  Loss: 3.06 (2.94)  Time: 0.410s, 2500.48/s  (0.413s, 2476.66/s)  LR: 1.148e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.431 (1.431)  Loss:   2.723 ( 2.723)  Acc@1:  48.047 ( 48.047)  Acc@5:  69.434 ( 69.434)
+Test: [  48/48]  Time: 0.091 (0.322)  Loss:   2.567 ( 2.807)  Acc@1:  51.651 ( 47.394)  Acc@5:  73.703 ( 69.126)
+Train: 97 [   0/312 (  0%)]  Loss: 2.84 (2.84)  Time: 1.552s,  659.86/s  (1.552s,  659.86/s)  LR: 1.111e-01  Data: 1.169 (1.169)
+Train: 97 [  50/312 ( 16%)]  Loss: 2.82 (2.87)  Time: 0.413s, 2480.06/s  (0.435s, 2351.93/s)  LR: 1.111e-01  Data: 0.027 (0.050)
+Train: 97 [ 100/312 ( 32%)]  Loss: 2.89 (2.88)  Time: 0.409s, 2501.86/s  (0.424s, 2415.44/s)  LR: 1.111e-01  Data: 0.027 (0.039)
+Train: 97 [ 150/312 ( 48%)]  Loss: 2.80 (2.89)  Time: 0.415s, 2467.37/s  (0.420s, 2438.56/s)  LR: 1.111e-01  Data: 0.027 (0.035)
+Train: 97 [ 200/312 ( 64%)]  Loss: 2.94 (2.90)  Time: 0.412s, 2483.26/s  (0.418s, 2448.47/s)  LR: 1.111e-01  Data: 0.027 (0.033)
+Train: 97 [ 250/312 ( 80%)]  Loss: 2.98 (2.91)  Time: 0.411s, 2491.62/s  (0.417s, 2454.61/s)  LR: 1.111e-01  Data: 0.027 (0.032)
+Train: 97 [ 300/312 ( 96%)]  Loss: 2.95 (2.92)  Time: 0.412s, 2485.07/s  (0.416s, 2459.46/s)  LR: 1.111e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.427 (1.427)  Loss:   2.819 ( 2.819)  Acc@1:  45.801 ( 45.801)  Acc@5:  68.750 ( 68.750)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.625 ( 2.848)  Acc@1:  52.005 ( 46.598)  Acc@5:  71.934 ( 68.610)
+Train: 98 [   0/312 (  0%)]  Loss: 2.77 (2.77)  Time: 1.473s,  695.27/s  (1.473s,  695.27/s)  LR: 1.073e-01  Data: 1.093 (1.093)
+Train: 98 [  50/312 ( 16%)]  Loss: 2.85 (2.84)  Time: 0.411s, 2493.89/s  (0.434s, 2360.22/s)  LR: 1.073e-01  Data: 0.028 (0.049)
+Train: 98 [ 100/312 ( 32%)]  Loss: 2.87 (2.87)  Time: 0.412s, 2485.69/s  (0.423s, 2419.92/s)  LR: 1.073e-01  Data: 0.026 (0.038)
+Train: 98 [ 150/312 ( 48%)]  Loss: 2.97 (2.87)  Time: 0.410s, 2496.58/s  (0.419s, 2443.17/s)  LR: 1.073e-01  Data: 0.028 (0.035)
+Train: 98 [ 200/312 ( 64%)]  Loss: 3.01 (2.89)  Time: 0.412s, 2486.95/s  (0.417s, 2454.40/s)  LR: 1.073e-01  Data: 0.028 (0.033)
+Train: 98 [ 250/312 ( 80%)]  Loss: 2.93 (2.89)  Time: 0.412s, 2484.70/s  (0.416s, 2460.43/s)  LR: 1.073e-01  Data: 0.027 (0.032)
+Train: 98 [ 300/312 ( 96%)]  Loss: 2.93 (2.90)  Time: 0.410s, 2495.34/s  (0.416s, 2464.47/s)  LR: 1.073e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.436 (1.436)  Loss:   2.797 ( 2.797)  Acc@1:  46.777 ( 46.777)  Acc@5:  68.555 ( 68.555)
+Test: [  48/48]  Time: 0.091 (0.326)  Loss:   2.527 ( 2.815)  Acc@1:  51.297 ( 47.406)  Acc@5:  73.467 ( 69.138)
+Train: 99 [   0/312 (  0%)]  Loss: 2.79 (2.79)  Time: 1.495s,  685.15/s  (1.495s,  685.15/s)  LR: 1.036e-01  Data: 1.121 (1.121)
+Train: 99 [  50/312 ( 16%)]  Loss: 2.85 (2.83)  Time: 0.413s, 2478.04/s  (0.439s, 2335.19/s)  LR: 1.036e-01  Data: 0.028 (0.050)
+Train: 99 [ 100/312 ( 32%)]  Loss: 2.94 (2.85)  Time: 0.409s, 2501.96/s  (0.426s, 2406.07/s)  LR: 1.036e-01  Data: 0.028 (0.039)
+Train: 99 [ 150/312 ( 48%)]  Loss: 2.85 (2.86)  Time: 0.412s, 2487.63/s  (0.421s, 2431.51/s)  LR: 1.036e-01  Data: 0.028 (0.036)
+Train: 99 [ 200/312 ( 64%)]  Loss: 2.92 (2.87)  Time: 0.413s, 2481.21/s  (0.419s, 2444.35/s)  LR: 1.036e-01  Data: 0.028 (0.034)
+Train: 99 [ 250/312 ( 80%)]  Loss: 2.88 (2.88)  Time: 0.411s, 2493.87/s  (0.417s, 2454.64/s)  LR: 1.036e-01  Data: 0.028 (0.032)
+Train: 99 [ 300/312 ( 96%)]  Loss: 2.99 (2.89)  Time: 0.413s, 2479.82/s  (0.416s, 2461.20/s)  LR: 1.036e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   2.787 ( 2.787)  Acc@1:  47.559 ( 47.559)  Acc@5:  68.457 ( 68.457)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.625 ( 2.835)  Acc@1:  50.590 ( 46.966)  Acc@5:  72.759 ( 68.642)
+Train: 100 [   0/312 (  0%)]  Loss: 2.75 (2.75)  Time: 1.771s,  578.12/s  (1.771s,  578.12/s)  LR: 1.000e-01  Data: 1.067 (1.067)
+Train: 100 [  50/312 ( 16%)]  Loss: 2.74 (2.82)  Time: 0.408s, 2512.86/s  (0.437s, 2344.57/s)  LR: 1.000e-01  Data: 0.027 (0.048)
+Train: 100 [ 100/312 ( 32%)]  Loss: 2.91 (2.83)  Time: 0.410s, 2499.22/s  (0.424s, 2416.27/s)  LR: 1.000e-01  Data: 0.028 (0.038)
+Train: 100 [ 150/312 ( 48%)]  Loss: 2.85 (2.84)  Time: 0.410s, 2497.82/s  (0.419s, 2441.43/s)  LR: 1.000e-01  Data: 0.028 (0.035)
+Train: 100 [ 200/312 ( 64%)]  Loss: 2.96 (2.85)  Time: 0.409s, 2503.05/s  (0.417s, 2454.77/s)  LR: 1.000e-01  Data: 0.028 (0.033)
+Train: 100 [ 250/312 ( 80%)]  Loss: 2.89 (2.87)  Time: 0.410s, 2499.27/s  (0.416s, 2462.14/s)  LR: 1.000e-01  Data: 0.028 (0.032)
+Train: 100 [ 300/312 ( 96%)]  Loss: 3.00 (2.88)  Time: 0.408s, 2507.96/s  (0.415s, 2467.36/s)  LR: 1.000e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.409 (1.409)  Loss:   2.742 ( 2.742)  Acc@1:  46.680 ( 46.680)  Acc@5:  70.508 ( 70.508)
+Test: [  48/48]  Time: 0.091 (0.322)  Loss:   2.611 ( 2.815)  Acc@1:  52.594 ( 47.228)  Acc@5:  73.231 ( 69.180)
+Train: 101 [   0/312 (  0%)]  Loss: 2.82 (2.82)  Time: 1.704s,  601.08/s  (1.704s,  601.08/s)  LR: 9.639e-02  Data: 1.328 (1.328)
+Train: 101 [  50/312 ( 16%)]  Loss: 2.81 (2.79)  Time: 0.412s, 2485.75/s  (0.437s, 2345.89/s)  LR: 9.639e-02  Data: 0.028 (0.053)
+Train: 101 [ 100/312 ( 32%)]  Loss: 2.86 (2.81)  Time: 0.412s, 2483.26/s  (0.424s, 2414.48/s)  LR: 9.639e-02  Data: 0.028 (0.041)
+Train: 101 [ 150/312 ( 48%)]  Loss: 2.80 (2.83)  Time: 0.410s, 2499.27/s  (0.420s, 2438.27/s)  LR: 9.639e-02  Data: 0.026 (0.036)
+Train: 101 [ 200/312 ( 64%)]  Loss: 2.90 (2.84)  Time: 0.411s, 2494.17/s  (0.418s, 2449.93/s)  LR: 9.639e-02  Data: 0.028 (0.034)
+Train: 101 [ 250/312 ( 80%)]  Loss: 2.96 (2.85)  Time: 0.410s, 2496.37/s  (0.417s, 2457.16/s)  LR: 9.639e-02  Data: 0.027 (0.033)
+Train: 101 [ 300/312 ( 96%)]  Loss: 2.96 (2.86)  Time: 0.410s, 2498.58/s  (0.416s, 2461.83/s)  LR: 9.639e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.421 (1.421)  Loss:   2.736 ( 2.736)  Acc@1:  49.121 ( 49.121)  Acc@5:  68.359 ( 68.359)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.537 ( 2.798)  Acc@1:  51.061 ( 47.444)  Acc@5:  74.292 ( 69.164)
+Train: 102 [   0/312 (  0%)]  Loss: 2.92 (2.92)  Time: 1.535s,  667.20/s  (1.535s,  667.20/s)  LR: 9.283e-02  Data: 1.094 (1.094)
+Train: 102 [  50/312 ( 16%)]  Loss: 2.78 (2.80)  Time: 0.411s, 2494.01/s  (0.433s, 2362.62/s)  LR: 9.283e-02  Data: 0.028 (0.049)
+Train: 102 [ 100/312 ( 32%)]  Loss: 2.77 (2.80)  Time: 0.410s, 2498.49/s  (0.422s, 2424.74/s)  LR: 9.283e-02  Data: 0.022 (0.038)
+Train: 102 [ 150/312 ( 48%)]  Loss: 2.87 (2.81)  Time: 0.411s, 2489.88/s  (0.419s, 2445.36/s)  LR: 9.283e-02  Data: 0.028 (0.035)
+Train: 102 [ 200/312 ( 64%)]  Loss: 2.84 (2.82)  Time: 0.409s, 2504.24/s  (0.417s, 2455.46/s)  LR: 9.283e-02  Data: 0.028 (0.033)
+Train: 102 [ 250/312 ( 80%)]  Loss: 2.84 (2.83)  Time: 0.412s, 2486.67/s  (0.416s, 2461.95/s)  LR: 9.283e-02  Data: 0.027 (0.032)
+Train: 102 [ 300/312 ( 96%)]  Loss: 2.92 (2.84)  Time: 0.411s, 2490.94/s  (0.415s, 2465.83/s)  LR: 9.283e-02  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   2.748 ( 2.748)  Acc@1:  48.145 ( 48.145)  Acc@5:  69.336 ( 69.336)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.531 ( 2.794)  Acc@1:  51.179 ( 47.370)  Acc@5:  74.646 ( 69.324)
+Train: 103 [   0/312 (  0%)]  Loss: 2.77 (2.77)  Time: 1.960s,  522.38/s  (1.960s,  522.38/s)  LR: 8.932e-02  Data: 1.586 (1.586)
+Train: 103 [  50/312 ( 16%)]  Loss: 2.71 (2.78)  Time: 0.409s, 2505.67/s  (0.440s, 2326.63/s)  LR: 8.932e-02  Data: 0.027 (0.058)
+Train: 103 [ 100/312 ( 32%)]  Loss: 2.80 (2.79)  Time: 0.411s, 2494.11/s  (0.425s, 2406.82/s)  LR: 8.932e-02  Data: 0.028 (0.043)
+Train: 103 [ 150/312 ( 48%)]  Loss: 2.76 (2.81)  Time: 0.409s, 2504.41/s  (0.420s, 2436.22/s)  LR: 8.932e-02  Data: 0.026 (0.038)
+Train: 103 [ 200/312 ( 64%)]  Loss: 2.85 (2.81)  Time: 0.409s, 2501.56/s  (0.418s, 2451.12/s)  LR: 8.932e-02  Data: 0.027 (0.036)
+Train: 103 [ 250/312 ( 80%)]  Loss: 2.88 (2.82)  Time: 0.409s, 2502.69/s  (0.416s, 2459.24/s)  LR: 8.932e-02  Data: 0.028 (0.034)
+Train: 103 [ 300/312 ( 96%)]  Loss: 2.91 (2.82)  Time: 0.408s, 2508.99/s  (0.415s, 2465.34/s)  LR: 8.932e-02  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.416 (1.416)  Loss:   2.709 ( 2.709)  Acc@1:  48.926 ( 48.926)  Acc@5:  69.434 ( 69.434)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.529 ( 2.778)  Acc@1:  52.594 ( 47.880)  Acc@5:  73.939 ( 69.436)
+Train: 104 [   0/312 (  0%)]  Loss: 2.70 (2.70)  Time: 2.251s,  454.94/s  (2.251s,  454.94/s)  LR: 8.586e-02  Data: 1.293 (1.293)
+Train: 104 [  50/312 ( 16%)]  Loss: 2.81 (2.75)  Time: 0.412s, 2483.55/s  (0.447s, 2291.16/s)  LR: 8.586e-02  Data: 0.027 (0.052)
+Train: 104 [ 100/312 ( 32%)]  Loss: 2.80 (2.77)  Time: 0.410s, 2495.48/s  (0.430s, 2382.47/s)  LR: 8.586e-02  Data: 0.029 (0.040)
+Train: 104 [ 150/312 ( 48%)]  Loss: 2.81 (2.78)  Time: 0.409s, 2504.04/s  (0.423s, 2419.31/s)  LR: 8.586e-02  Data: 0.028 (0.036)
+Train: 104 [ 200/312 ( 64%)]  Loss: 2.76 (2.79)  Time: 0.410s, 2496.61/s  (0.420s, 2436.73/s)  LR: 8.586e-02  Data: 0.029 (0.034)
+Train: 104 [ 250/312 ( 80%)]  Loss: 2.87 (2.80)  Time: 0.414s, 2474.41/s  (0.418s, 2446.95/s)  LR: 8.586e-02  Data: 0.028 (0.033)
+Train: 104 [ 300/312 ( 96%)]  Loss: 2.84 (2.81)  Time: 0.416s, 2458.82/s  (0.417s, 2453.88/s)  LR: 8.586e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.438 (1.438)  Loss:   2.772 ( 2.772)  Acc@1:  47.949 ( 47.949)  Acc@5:  69.824 ( 69.824)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.578 ( 2.826)  Acc@1:  50.708 ( 47.132)  Acc@5:  74.175 ( 68.804)
+Train: 105 [   0/312 (  0%)]  Loss: 2.69 (2.69)  Time: 1.642s,  623.59/s  (1.642s,  623.59/s)  LR: 8.244e-02  Data: 1.267 (1.267)
+Train: 105 [  50/312 ( 16%)]  Loss: 2.78 (2.74)  Time: 0.410s, 2494.98/s  (0.435s, 2356.32/s)  LR: 8.244e-02  Data: 0.026 (0.052)
+Train: 105 [ 100/312 ( 32%)]  Loss: 2.71 (2.75)  Time: 0.406s, 2521.17/s  (0.422s, 2426.97/s)  LR: 8.244e-02  Data: 0.025 (0.040)
+Train: 105 [ 150/312 ( 48%)]  Loss: 2.82 (2.77)  Time: 0.404s, 2537.32/s  (0.417s, 2456.70/s)  LR: 8.244e-02  Data: 0.027 (0.036)
+Train: 105 [ 200/312 ( 64%)]  Loss: 2.79 (2.77)  Time: 0.408s, 2511.77/s  (0.414s, 2472.36/s)  LR: 8.244e-02  Data: 0.031 (0.034)
+Train: 105 [ 250/312 ( 80%)]  Loss: 2.88 (2.78)  Time: 0.408s, 2509.16/s  (0.413s, 2479.87/s)  LR: 8.244e-02  Data: 0.029 (0.033)
+Train: 105 [ 300/312 ( 96%)]  Loss: 2.84 (2.79)  Time: 0.412s, 2488.24/s  (0.413s, 2482.25/s)  LR: 8.244e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.418 (1.418)  Loss:   2.799 ( 2.799)  Acc@1:  46.875 ( 46.875)  Acc@5:  69.043 ( 69.043)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.598 ( 2.870)  Acc@1:  50.472 ( 46.918)  Acc@5:  73.467 ( 68.238)
+Train: 106 [   0/312 (  0%)]  Loss: 2.68 (2.68)  Time: 1.999s,  512.36/s  (1.999s,  512.36/s)  LR: 7.908e-02  Data: 1.432 (1.432)
+Train: 106 [  50/312 ( 16%)]  Loss: 2.71 (2.73)  Time: 0.408s, 2512.03/s  (0.441s, 2320.22/s)  LR: 7.908e-02  Data: 0.027 (0.055)
+Train: 106 [ 100/312 ( 32%)]  Loss: 2.72 (2.73)  Time: 0.412s, 2485.90/s  (0.427s, 2399.92/s)  LR: 7.908e-02  Data: 0.027 (0.042)
+Train: 106 [ 150/312 ( 48%)]  Loss: 2.78 (2.74)  Time: 0.413s, 2477.33/s  (0.422s, 2429.27/s)  LR: 7.908e-02  Data: 0.027 (0.037)
+Train: 106 [ 200/312 ( 64%)]  Loss: 2.85 (2.76)  Time: 0.413s, 2481.19/s  (0.419s, 2442.79/s)  LR: 7.908e-02  Data: 0.028 (0.035)
+Train: 106 [ 250/312 ( 80%)]  Loss: 2.65 (2.77)  Time: 0.413s, 2477.55/s  (0.418s, 2450.08/s)  LR: 7.908e-02  Data: 0.028 (0.033)
+Train: 106 [ 300/312 ( 96%)]  Loss: 2.80 (2.77)  Time: 0.413s, 2482.11/s  (0.417s, 2454.81/s)  LR: 7.908e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.447 (1.447)  Loss:   2.793 ( 2.793)  Acc@1:  48.047 ( 48.047)  Acc@5:  68.262 ( 68.262)
+Test: [  48/48]  Time: 0.090 (0.325)  Loss:   2.556 ( 2.821)  Acc@1:  51.297 ( 47.478)  Acc@5:  74.292 ( 68.698)
+Train: 107 [   0/312 (  0%)]  Loss: 2.58 (2.58)  Time: 1.605s,  637.89/s  (1.605s,  637.89/s)  LR: 7.577e-02  Data: 1.230 (1.230)
+Train: 107 [  50/312 ( 16%)]  Loss: 2.68 (2.71)  Time: 0.410s, 2497.14/s  (0.434s, 2358.26/s)  LR: 7.577e-02  Data: 0.027 (0.051)
+Train: 107 [ 100/312 ( 32%)]  Loss: 2.77 (2.72)  Time: 0.410s, 2498.72/s  (0.423s, 2423.36/s)  LR: 7.577e-02  Data: 0.028 (0.040)
+Train: 107 [ 150/312 ( 48%)]  Loss: 2.74 (2.72)  Time: 0.412s, 2485.27/s  (0.419s, 2446.32/s)  LR: 7.577e-02  Data: 0.028 (0.036)
+Train: 107 [ 200/312 ( 64%)]  Loss: 2.82 (2.74)  Time: 0.412s, 2488.18/s  (0.417s, 2457.54/s)  LR: 7.577e-02  Data: 0.028 (0.034)
+Train: 107 [ 250/312 ( 80%)]  Loss: 2.67 (2.75)  Time: 0.410s, 2498.06/s  (0.415s, 2464.83/s)  LR: 7.577e-02  Data: 0.028 (0.033)
+Train: 107 [ 300/312 ( 96%)]  Loss: 2.75 (2.75)  Time: 0.408s, 2510.17/s  (0.415s, 2469.33/s)  LR: 7.577e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.443 (1.443)  Loss:   2.780 ( 2.780)  Acc@1:  47.363 ( 47.363)  Acc@5:  69.922 ( 69.922)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.602 ( 2.823)  Acc@1:  51.061 ( 47.190)  Acc@5:  73.939 ( 68.832)
+Train: 108 [   0/312 (  0%)]  Loss: 2.76 (2.76)  Time: 1.686s,  607.38/s  (1.686s,  607.38/s)  LR: 7.252e-02  Data: 1.312 (1.312)
+Train: 108 [  50/312 ( 16%)]  Loss: 2.68 (2.71)  Time: 0.409s, 2503.68/s  (0.434s, 2358.68/s)  LR: 7.252e-02  Data: 0.027 (0.053)
+Train: 108 [ 100/312 ( 32%)]  Loss: 2.80 (2.71)  Time: 0.408s, 2510.03/s  (0.423s, 2422.73/s)  LR: 7.252e-02  Data: 0.029 (0.041)
+Train: 108 [ 150/312 ( 48%)]  Loss: 2.71 (2.72)  Time: 0.411s, 2492.74/s  (0.419s, 2445.76/s)  LR: 7.252e-02  Data: 0.028 (0.036)
+Train: 108 [ 200/312 ( 64%)]  Loss: 2.71 (2.72)  Time: 0.415s, 2468.95/s  (0.417s, 2455.91/s)  LR: 7.252e-02  Data: 0.028 (0.034)
+Train: 108 [ 250/312 ( 80%)]  Loss: 2.78 (2.73)  Time: 0.409s, 2501.55/s  (0.416s, 2464.35/s)  LR: 7.252e-02  Data: 0.028 (0.033)
+Train: 108 [ 300/312 ( 96%)]  Loss: 2.67 (2.74)  Time: 0.411s, 2492.29/s  (0.415s, 2469.49/s)  LR: 7.252e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.485 (1.485)  Loss:   2.775 ( 2.775)  Acc@1:  46.973 ( 46.973)  Acc@5:  69.238 ( 69.238)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.567 ( 2.848)  Acc@1:  52.241 ( 47.196)  Acc@5:  73.113 ( 68.604)
+Train: 109 [   0/312 (  0%)]  Loss: 2.69 (2.69)  Time: 1.963s,  521.58/s  (1.963s,  521.58/s)  LR: 6.932e-02  Data: 1.588 (1.588)
+Train: 109 [  50/312 ( 16%)]  Loss: 2.65 (2.67)  Time: 0.408s, 2508.23/s  (0.442s, 2314.78/s)  LR: 6.932e-02  Data: 0.028 (0.059)
+Train: 109 [ 100/312 ( 32%)]  Loss: 2.78 (2.69)  Time: 0.410s, 2495.44/s  (0.427s, 2399.78/s)  LR: 6.932e-02  Data: 0.028 (0.043)
+Train: 109 [ 150/312 ( 48%)]  Loss: 2.81 (2.70)  Time: 0.410s, 2499.09/s  (0.422s, 2427.84/s)  LR: 6.932e-02  Data: 0.028 (0.038)
+Train: 109 [ 200/312 ( 64%)]  Loss: 2.71 (2.70)  Time: 0.413s, 2477.91/s  (0.419s, 2441.92/s)  LR: 6.932e-02  Data: 0.027 (0.036)
+Train: 109 [ 250/312 ( 80%)]  Loss: 2.71 (2.71)  Time: 0.412s, 2488.38/s  (0.418s, 2450.30/s)  LR: 6.932e-02  Data: 0.028 (0.034)
+Train: 109 [ 300/312 ( 96%)]  Loss: 2.65 (2.72)  Time: 0.413s, 2479.66/s  (0.417s, 2456.03/s)  LR: 6.932e-02  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.490 (1.490)  Loss:   2.775 ( 2.775)  Acc@1:  46.484 ( 46.484)  Acc@5:  69.141 ( 69.141)
+Test: [  48/48]  Time: 0.091 (0.324)  Loss:   2.536 ( 2.851)  Acc@1:  51.887 ( 47.334)  Acc@5:  75.236 ( 68.512)
+Train: 110 [   0/312 (  0%)]  Loss: 2.75 (2.75)  Time: 1.620s,  632.04/s  (1.620s,  632.04/s)  LR: 6.617e-02  Data: 1.245 (1.245)
+Train: 110 [  50/312 ( 16%)]  Loss: 2.65 (2.66)  Time: 0.410s, 2497.80/s  (0.435s, 2353.35/s)  LR: 6.617e-02  Data: 0.028 (0.052)
+Train: 110 [ 100/312 ( 32%)]  Loss: 2.78 (2.68)  Time: 0.409s, 2502.23/s  (0.424s, 2416.53/s)  LR: 6.617e-02  Data: 0.028 (0.040)
+Train: 110 [ 150/312 ( 48%)]  Loss: 2.72 (2.68)  Time: 0.415s, 2464.55/s  (0.420s, 2437.48/s)  LR: 6.617e-02  Data: 0.029 (0.036)
+Train: 110 [ 200/312 ( 64%)]  Loss: 2.65 (2.69)  Time: 0.412s, 2486.02/s  (0.418s, 2448.27/s)  LR: 6.617e-02  Data: 0.029 (0.034)
+Train: 110 [ 250/312 ( 80%)]  Loss: 2.77 (2.70)  Time: 0.410s, 2497.75/s  (0.417s, 2455.70/s)  LR: 6.617e-02  Data: 0.027 (0.033)
+Train: 110 [ 300/312 ( 96%)]  Loss: 2.78 (2.70)  Time: 0.410s, 2498.06/s  (0.416s, 2462.03/s)  LR: 6.617e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.443 (1.443)  Loss:   2.779 ( 2.779)  Acc@1:  46.973 ( 46.973)  Acc@5:  68.945 ( 68.945)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.521 ( 2.802)  Acc@1:  52.476 ( 48.208)  Acc@5:  74.646 ( 69.154)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-89.pth.tar', 48.21400005004883)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-110.pth.tar', 48.20800004150391)
+
+Train: 111 [   0/312 (  0%)]  Loss: 2.61 (2.61)  Time: 1.723s,  594.35/s  (1.723s,  594.35/s)  LR: 6.309e-02  Data: 1.348 (1.348)
+Train: 111 [  50/312 ( 16%)]  Loss: 2.70 (2.65)  Time: 0.415s, 2466.00/s  (0.437s, 2341.03/s)  LR: 6.309e-02  Data: 0.034 (0.054)
+Train: 111 [ 100/312 ( 32%)]  Loss: 2.61 (2.65)  Time: 0.413s, 2477.92/s  (0.425s, 2412.11/s)  LR: 6.309e-02  Data: 0.028 (0.042)
+Train: 111 [ 150/312 ( 48%)]  Loss: 2.71 (2.67)  Time: 0.410s, 2499.13/s  (0.420s, 2438.51/s)  LR: 6.309e-02  Data: 0.028 (0.037)
+Train: 111 [ 200/312 ( 64%)]  Loss: 2.66 (2.68)  Time: 0.410s, 2494.99/s  (0.418s, 2451.37/s)  LR: 6.309e-02  Data: 0.027 (0.035)
+Train: 111 [ 250/312 ( 80%)]  Loss: 2.68 (2.68)  Time: 0.411s, 2493.70/s  (0.417s, 2458.20/s)  LR: 6.309e-02  Data: 0.029 (0.034)
+Train: 111 [ 300/312 ( 96%)]  Loss: 2.72 (2.69)  Time: 0.415s, 2467.78/s  (0.416s, 2462.68/s)  LR: 6.309e-02  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   2.785 ( 2.785)  Acc@1:  46.875 ( 46.875)  Acc@5:  68.555 ( 68.555)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.539 ( 2.839)  Acc@1:  52.123 ( 47.338)  Acc@5:  74.764 ( 68.618)
+Train: 112 [   0/312 (  0%)]  Loss: 2.68 (2.68)  Time: 1.709s,  599.12/s  (1.709s,  599.12/s)  LR: 6.007e-02  Data: 1.335 (1.335)
+Train: 112 [  50/312 ( 16%)]  Loss: 2.71 (2.65)  Time: 0.410s, 2498.58/s  (0.436s, 2347.22/s)  LR: 6.007e-02  Data: 0.026 (0.053)
+Train: 112 [ 100/312 ( 32%)]  Loss: 2.71 (2.65)  Time: 0.410s, 2497.60/s  (0.423s, 2418.70/s)  LR: 6.007e-02  Data: 0.028 (0.041)
+Train: 112 [ 150/312 ( 48%)]  Loss: 2.71 (2.66)  Time: 0.409s, 2502.70/s  (0.419s, 2442.13/s)  LR: 6.007e-02  Data: 0.028 (0.037)
+Train: 112 [ 200/312 ( 64%)]  Loss: 2.54 (2.66)  Time: 0.410s, 2500.21/s  (0.417s, 2454.61/s)  LR: 6.007e-02  Data: 0.027 (0.034)
+Train: 112 [ 250/312 ( 80%)]  Loss: 2.70 (2.67)  Time: 0.410s, 2496.09/s  (0.416s, 2461.93/s)  LR: 6.007e-02  Data: 0.026 (0.033)
+Train: 112 [ 300/312 ( 96%)]  Loss: 2.81 (2.67)  Time: 0.415s, 2467.41/s  (0.415s, 2466.74/s)  LR: 6.007e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   2.777 ( 2.777)  Acc@1:  46.875 ( 46.875)  Acc@5:  69.434 ( 69.434)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.606 ( 2.831)  Acc@1:  50.590 ( 47.382)  Acc@5:  72.524 ( 68.762)
+Train: 113 [   0/312 (  0%)]  Loss: 2.66 (2.66)  Time: 1.617s,  633.11/s  (1.617s,  633.11/s)  LR: 5.711e-02  Data: 1.243 (1.243)
+Train: 113 [  50/312 ( 16%)]  Loss: 2.71 (2.62)  Time: 0.412s, 2488.38/s  (0.434s, 2358.47/s)  LR: 5.711e-02  Data: 0.028 (0.052)
+Train: 113 [ 100/312 ( 32%)]  Loss: 2.68 (2.63)  Time: 0.411s, 2489.10/s  (0.423s, 2423.21/s)  LR: 5.711e-02  Data: 0.029 (0.040)
+Train: 113 [ 150/312 ( 48%)]  Loss: 2.63 (2.64)  Time: 0.411s, 2493.75/s  (0.419s, 2445.95/s)  LR: 5.711e-02  Data: 0.028 (0.036)
+Train: 113 [ 200/312 ( 64%)]  Loss: 2.68 (2.65)  Time: 0.412s, 2487.91/s  (0.417s, 2457.46/s)  LR: 5.711e-02  Data: 0.028 (0.034)
+Train: 113 [ 250/312 ( 80%)]  Loss: 2.68 (2.65)  Time: 0.413s, 2481.53/s  (0.415s, 2464.60/s)  LR: 5.711e-02  Data: 0.029 (0.033)
+Train: 113 [ 300/312 ( 96%)]  Loss: 2.79 (2.66)  Time: 0.411s, 2492.01/s  (0.415s, 2469.38/s)  LR: 5.711e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.419 (1.419)  Loss:   2.754 ( 2.754)  Acc@1:  47.852 ( 47.852)  Acc@5:  69.043 ( 69.043)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.552 ( 2.832)  Acc@1:  51.179 ( 47.492)  Acc@5:  74.528 ( 68.612)
+Train: 114 [   0/312 (  0%)]  Loss: 2.62 (2.62)  Time: 1.669s,  613.51/s  (1.669s,  613.51/s)  LR: 5.421e-02  Data: 1.237 (1.237)
+Train: 114 [  50/312 ( 16%)]  Loss: 2.62 (2.60)  Time: 0.409s, 2502.67/s  (0.435s, 2356.36/s)  LR: 5.421e-02  Data: 0.027 (0.052)
+Train: 114 [ 100/312 ( 32%)]  Loss: 2.74 (2.62)  Time: 0.410s, 2499.91/s  (0.422s, 2423.77/s)  LR: 5.421e-02  Data: 0.027 (0.040)
+Train: 114 [ 150/312 ( 48%)]  Loss: 2.63 (2.63)  Time: 0.408s, 2510.32/s  (0.419s, 2446.65/s)  LR: 5.421e-02  Data: 0.028 (0.036)
+Train: 114 [ 200/312 ( 64%)]  Loss: 2.61 (2.63)  Time: 0.411s, 2492.29/s  (0.417s, 2458.33/s)  LR: 5.421e-02  Data: 0.028 (0.034)
+Train: 114 [ 250/312 ( 80%)]  Loss: 2.73 (2.64)  Time: 0.411s, 2492.28/s  (0.415s, 2465.42/s)  LR: 5.421e-02  Data: 0.027 (0.033)
+Train: 114 [ 300/312 ( 96%)]  Loss: 2.68 (2.65)  Time: 0.409s, 2506.38/s  (0.415s, 2469.69/s)  LR: 5.421e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   2.757 ( 2.757)  Acc@1:  46.680 ( 46.680)  Acc@5:  68.848 ( 68.848)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.547 ( 2.849)  Acc@1:  51.887 ( 47.160)  Acc@5:  74.292 ( 68.508)
+Train: 115 [   0/312 (  0%)]  Loss: 2.57 (2.57)  Time: 1.807s,  566.66/s  (1.807s,  566.66/s)  LR: 5.137e-02  Data: 1.332 (1.332)
+Train: 115 [  50/312 ( 16%)]  Loss: 2.65 (2.60)  Time: 0.415s, 2469.75/s  (0.439s, 2331.86/s)  LR: 5.137e-02  Data: 0.028 (0.053)
+Train: 115 [ 100/312 ( 32%)]  Loss: 2.55 (2.61)  Time: 0.412s, 2482.70/s  (0.426s, 2405.44/s)  LR: 5.137e-02  Data: 0.029 (0.041)
+Train: 115 [ 150/312 ( 48%)]  Loss: 2.64 (2.62)  Time: 0.412s, 2487.97/s  (0.421s, 2431.34/s)  LR: 5.137e-02  Data: 0.027 (0.036)
+Train: 115 [ 200/312 ( 64%)]  Loss: 2.61 (2.62)  Time: 0.412s, 2482.76/s  (0.419s, 2443.95/s)  LR: 5.137e-02  Data: 0.028 (0.034)
+Train: 115 [ 250/312 ( 80%)]  Loss: 2.72 (2.62)  Time: 0.409s, 2502.41/s  (0.418s, 2451.55/s)  LR: 5.137e-02  Data: 0.027 (0.033)
+Train: 115 [ 300/312 ( 96%)]  Loss: 2.70 (2.63)  Time: 0.411s, 2494.20/s  (0.417s, 2456.70/s)  LR: 5.137e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.433 (1.433)  Loss:   2.796 ( 2.796)  Acc@1:  46.777 ( 46.777)  Acc@5:  68.945 ( 68.945)
+Test: [  48/48]  Time: 0.090 (0.318)  Loss:   2.562 ( 2.854)  Acc@1:  51.061 ( 47.358)  Acc@5:  72.642 ( 68.522)
+Train: 116 [   0/312 (  0%)]  Loss: 2.56 (2.56)  Time: 1.602s,  639.36/s  (1.602s,  639.36/s)  LR: 4.860e-02  Data: 1.228 (1.228)
+Train: 116 [  50/312 ( 16%)]  Loss: 2.56 (2.58)  Time: 0.410s, 2495.70/s  (0.435s, 2353.62/s)  LR: 4.860e-02  Data: 0.027 (0.051)
+Train: 116 [ 100/312 ( 32%)]  Loss: 2.59 (2.59)  Time: 0.414s, 2474.14/s  (0.424s, 2416.72/s)  LR: 4.860e-02  Data: 0.028 (0.040)
+Train: 116 [ 150/312 ( 48%)]  Loss: 2.47 (2.60)  Time: 0.410s, 2496.66/s  (0.420s, 2438.29/s)  LR: 4.860e-02  Data: 0.026 (0.036)
+Train: 116 [ 200/312 ( 64%)]  Loss: 2.69 (2.61)  Time: 0.412s, 2484.55/s  (0.418s, 2449.63/s)  LR: 4.860e-02  Data: 0.026 (0.034)
+Train: 116 [ 250/312 ( 80%)]  Loss: 2.73 (2.61)  Time: 0.411s, 2493.81/s  (0.417s, 2458.30/s)  LR: 4.860e-02  Data: 0.026 (0.033)
+Train: 116 [ 300/312 ( 96%)]  Loss: 2.72 (2.62)  Time: 0.409s, 2502.86/s  (0.415s, 2465.13/s)  LR: 4.860e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.411 (1.411)  Loss:   2.772 ( 2.772)  Acc@1:  47.949 ( 47.949)  Acc@5:  69.238 ( 69.238)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.534 ( 2.810)  Acc@1:  51.297 ( 47.778)  Acc@5:  73.113 ( 69.110)
+Train: 117 [   0/312 (  0%)]  Loss: 2.58 (2.58)  Time: 1.840s,  556.54/s  (1.840s,  556.54/s)  LR: 4.590e-02  Data: 1.465 (1.465)
+Train: 117 [  50/312 ( 16%)]  Loss: 2.58 (2.58)  Time: 0.409s, 2504.47/s  (0.440s, 2329.42/s)  LR: 4.590e-02  Data: 0.027 (0.056)
+Train: 117 [ 100/312 ( 32%)]  Loss: 2.72 (2.58)  Time: 0.410s, 2500.28/s  (0.425s, 2410.79/s)  LR: 4.590e-02  Data: 0.027 (0.042)
+Train: 117 [ 150/312 ( 48%)]  Loss: 2.55 (2.59)  Time: 0.410s, 2500.43/s  (0.420s, 2440.05/s)  LR: 4.590e-02  Data: 0.027 (0.037)
+Train: 117 [ 200/312 ( 64%)]  Loss: 2.60 (2.59)  Time: 0.410s, 2496.17/s  (0.417s, 2454.49/s)  LR: 4.590e-02  Data: 0.028 (0.035)
+Train: 117 [ 250/312 ( 80%)]  Loss: 2.60 (2.60)  Time: 0.411s, 2493.66/s  (0.416s, 2462.48/s)  LR: 4.590e-02  Data: 0.027 (0.034)
+Train: 117 [ 300/312 ( 96%)]  Loss: 2.67 (2.60)  Time: 0.413s, 2476.43/s  (0.415s, 2467.51/s)  LR: 4.590e-02  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.441 (1.441)  Loss:   2.829 ( 2.829)  Acc@1:  45.996 ( 45.996)  Acc@5:  67.969 ( 67.969)
+Test: [  48/48]  Time: 0.091 (0.319)  Loss:   2.582 ( 2.871)  Acc@1:  50.943 ( 46.960)  Acc@5:  72.759 ( 68.088)
+Train: 118 [   0/312 (  0%)]  Loss: 2.58 (2.58)  Time: 1.846s,  554.86/s  (1.846s,  554.86/s)  LR: 4.326e-02  Data: 1.471 (1.471)
+Train: 118 [  50/312 ( 16%)]  Loss: 2.54 (2.56)  Time: 0.410s, 2499.83/s  (0.439s, 2330.62/s)  LR: 4.326e-02  Data: 0.028 (0.056)
+Train: 118 [ 100/312 ( 32%)]  Loss: 2.63 (2.57)  Time: 0.412s, 2483.57/s  (0.426s, 2405.89/s)  LR: 4.326e-02  Data: 0.029 (0.042)
+Train: 118 [ 150/312 ( 48%)]  Loss: 2.52 (2.57)  Time: 0.410s, 2495.40/s  (0.421s, 2432.55/s)  LR: 4.326e-02  Data: 0.028 (0.037)
+Train: 118 [ 200/312 ( 64%)]  Loss: 2.62 (2.58)  Time: 0.411s, 2493.25/s  (0.419s, 2446.00/s)  LR: 4.326e-02  Data: 0.027 (0.035)
+Train: 118 [ 250/312 ( 80%)]  Loss: 2.59 (2.58)  Time: 0.412s, 2484.34/s  (0.417s, 2453.77/s)  LR: 4.326e-02  Data: 0.029 (0.034)
+Train: 118 [ 300/312 ( 96%)]  Loss: 2.65 (2.59)  Time: 0.414s, 2476.35/s  (0.416s, 2459.21/s)  LR: 4.326e-02  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   2.801 ( 2.801)  Acc@1:  47.168 ( 47.168)  Acc@5:  67.773 ( 67.773)
+Test: [  48/48]  Time: 0.089 (0.321)  Loss:   2.655 ( 2.872)  Acc@1:  49.764 ( 47.024)  Acc@5:  72.170 ( 68.078)
+Train: 119 [   0/312 (  0%)]  Loss: 2.59 (2.59)  Time: 1.492s,  686.49/s  (1.492s,  686.49/s)  LR: 4.069e-02  Data: 1.118 (1.118)
+Train: 119 [  50/312 ( 16%)]  Loss: 2.55 (2.55)  Time: 0.411s, 2494.06/s  (0.436s, 2350.72/s)  LR: 4.069e-02  Data: 0.028 (0.051)
+Train: 119 [ 100/312 ( 32%)]  Loss: 2.54 (2.55)  Time: 0.412s, 2483.41/s  (0.424s, 2413.27/s)  LR: 4.069e-02  Data: 0.033 (0.040)
+Train: 119 [ 150/312 ( 48%)]  Loss: 2.60 (2.55)  Time: 0.412s, 2482.55/s  (0.420s, 2439.65/s)  LR: 4.069e-02  Data: 0.030 (0.036)
+Train: 119 [ 200/312 ( 64%)]  Loss: 2.63 (2.56)  Time: 0.414s, 2472.34/s  (0.417s, 2452.84/s)  LR: 4.069e-02  Data: 0.028 (0.034)
+Train: 119 [ 250/312 ( 80%)]  Loss: 2.56 (2.57)  Time: 0.414s, 2473.83/s  (0.416s, 2460.60/s)  LR: 4.069e-02  Data: 0.028 (0.033)
+Train: 119 [ 300/312 ( 96%)]  Loss: 2.62 (2.57)  Time: 0.411s, 2488.89/s  (0.415s, 2465.13/s)  LR: 4.069e-02  Data: 0.031 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   2.745 ( 2.745)  Acc@1:  46.973 ( 46.973)  Acc@5:  69.043 ( 69.043)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.573 ( 2.794)  Acc@1:  52.358 ( 48.118)  Acc@5:  72.759 ( 69.142)
+Train: 120 [   0/312 (  0%)]  Loss: 2.52 (2.52)  Time: 1.678s,  610.12/s  (1.678s,  610.12/s)  LR: 3.820e-02  Data: 1.305 (1.305)
+Train: 120 [  50/312 ( 16%)]  Loss: 2.57 (2.53)  Time: 0.409s, 2502.14/s  (0.435s, 2352.79/s)  LR: 3.820e-02  Data: 0.025 (0.053)
+Train: 120 [ 100/312 ( 32%)]  Loss: 2.64 (2.54)  Time: 0.409s, 2506.30/s  (0.423s, 2423.53/s)  LR: 3.820e-02  Data: 0.030 (0.040)
+Train: 120 [ 150/312 ( 48%)]  Loss: 2.59 (2.55)  Time: 0.407s, 2513.80/s  (0.418s, 2450.88/s)  LR: 3.820e-02  Data: 0.027 (0.036)
+Train: 120 [ 200/312 ( 64%)]  Loss: 2.63 (2.55)  Time: 0.409s, 2502.95/s  (0.416s, 2464.38/s)  LR: 3.820e-02  Data: 0.025 (0.034)
+Train: 120 [ 250/312 ( 80%)]  Loss: 2.54 (2.55)  Time: 0.412s, 2482.65/s  (0.415s, 2470.12/s)  LR: 3.820e-02  Data: 0.028 (0.033)
+Train: 120 [ 300/312 ( 96%)]  Loss: 2.67 (2.56)  Time: 0.410s, 2498.32/s  (0.414s, 2475.50/s)  LR: 3.820e-02  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.402 (1.402)  Loss:   2.744 ( 2.744)  Acc@1:  47.070 ( 47.070)  Acc@5:  69.434 ( 69.434)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   2.528 ( 2.801)  Acc@1:  50.354 ( 48.120)  Acc@5:  73.821 ( 69.132)
+Train: 121 [   0/312 (  0%)]  Loss: 2.47 (2.47)  Time: 1.802s,  568.39/s  (1.802s,  568.39/s)  LR: 3.577e-02  Data: 1.428 (1.428)
+Train: 121 [  50/312 ( 16%)]  Loss: 2.50 (2.52)  Time: 0.413s, 2478.53/s  (0.439s, 2332.30/s)  LR: 3.577e-02  Data: 0.028 (0.055)
+Train: 121 [ 100/312 ( 32%)]  Loss: 2.45 (2.53)  Time: 0.408s, 2512.68/s  (0.425s, 2412.17/s)  LR: 3.577e-02  Data: 0.028 (0.042)
+Train: 121 [ 150/312 ( 48%)]  Loss: 2.53 (2.53)  Time: 0.408s, 2510.75/s  (0.419s, 2445.52/s)  LR: 3.577e-02  Data: 0.026 (0.037)
+Train: 121 [ 200/312 ( 64%)]  Loss: 2.55 (2.54)  Time: 0.408s, 2511.81/s  (0.416s, 2462.09/s)  LR: 3.577e-02  Data: 0.028 (0.035)
+Train: 121 [ 250/312 ( 80%)]  Loss: 2.56 (2.54)  Time: 0.412s, 2484.43/s  (0.415s, 2468.96/s)  LR: 3.577e-02  Data: 0.028 (0.034)
+Train: 121 [ 300/312 ( 96%)]  Loss: 2.55 (2.55)  Time: 0.412s, 2485.31/s  (0.414s, 2471.08/s)  LR: 3.577e-02  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.414 (1.414)  Loss:   2.725 ( 2.725)  Acc@1:  47.559 ( 47.559)  Acc@5:  69.531 ( 69.531)
+Test: [  48/48]  Time: 0.091 (0.318)  Loss:   2.540 ( 2.795)  Acc@1:  50.118 ( 48.184)  Acc@5:  73.939 ( 69.252)
+Train: 122 [   0/312 (  0%)]  Loss: 2.57 (2.57)  Time: 1.775s,  576.76/s  (1.775s,  576.76/s)  LR: 3.342e-02  Data: 1.400 (1.400)
+Train: 122 [  50/312 ( 16%)]  Loss: 2.44 (2.52)  Time: 0.411s, 2488.70/s  (0.438s, 2340.09/s)  LR: 3.342e-02  Data: 0.030 (0.055)
+Train: 122 [ 100/312 ( 32%)]  Loss: 2.51 (2.53)  Time: 0.409s, 2502.56/s  (0.425s, 2409.57/s)  LR: 3.342e-02  Data: 0.028 (0.042)
+Train: 122 [ 150/312 ( 48%)]  Loss: 2.44 (2.53)  Time: 0.406s, 2522.84/s  (0.419s, 2442.27/s)  LR: 3.342e-02  Data: 0.028 (0.037)
+Train: 122 [ 200/312 ( 64%)]  Loss: 2.43 (2.53)  Time: 0.407s, 2514.48/s  (0.416s, 2460.92/s)  LR: 3.342e-02  Data: 0.028 (0.035)
+Train: 122 [ 250/312 ( 80%)]  Loss: 2.56 (2.53)  Time: 0.414s, 2473.36/s  (0.414s, 2470.90/s)  LR: 3.342e-02  Data: 0.027 (0.033)
+Train: 122 [ 300/312 ( 96%)]  Loss: 2.58 (2.54)  Time: 0.413s, 2478.23/s  (0.414s, 2474.56/s)  LR: 3.342e-02  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   2.746 ( 2.746)  Acc@1:  47.949 ( 47.949)  Acc@5:  68.359 ( 68.359)
+Test: [  48/48]  Time: 0.089 (0.321)  Loss:   2.547 ( 2.784)  Acc@1:  51.415 ( 48.344)  Acc@5:  71.934 ( 69.412)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-122.pth.tar', 48.34399996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-89.pth.tar', 48.21400005004883)
+
+Train: 123 [   0/312 (  0%)]  Loss: 2.48 (2.48)  Time: 1.976s,  518.20/s  (1.976s,  518.20/s)  LR: 3.113e-02  Data: 1.483 (1.483)
+Train: 123 [  50/312 ( 16%)]  Loss: 2.56 (2.50)  Time: 0.408s, 2509.89/s  (0.439s, 2333.28/s)  LR: 3.113e-02  Data: 0.029 (0.057)
+Train: 123 [ 100/312 ( 32%)]  Loss: 2.60 (2.51)  Time: 0.413s, 2478.52/s  (0.424s, 2415.03/s)  LR: 3.113e-02  Data: 0.033 (0.043)
+Train: 123 [ 150/312 ( 48%)]  Loss: 2.57 (2.51)  Time: 0.413s, 2479.33/s  (0.420s, 2437.02/s)  LR: 3.113e-02  Data: 0.028 (0.038)
+Train: 123 [ 200/312 ( 64%)]  Loss: 2.52 (2.52)  Time: 0.408s, 2506.99/s  (0.418s, 2448.64/s)  LR: 3.113e-02  Data: 0.025 (0.035)
+Train: 123 [ 250/312 ( 80%)]  Loss: 2.63 (2.52)  Time: 0.408s, 2509.88/s  (0.416s, 2458.88/s)  LR: 3.113e-02  Data: 0.027 (0.034)
+Train: 123 [ 300/312 ( 96%)]  Loss: 2.53 (2.52)  Time: 0.414s, 2472.59/s  (0.415s, 2465.45/s)  LR: 3.113e-02  Data: 0.033 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   2.766 ( 2.766)  Acc@1:  47.461 ( 47.461)  Acc@5:  68.848 ( 68.848)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   2.569 ( 2.803)  Acc@1:  52.005 ( 48.140)  Acc@5:  73.231 ( 69.184)
+Train: 124 [   0/312 (  0%)]  Loss: 2.55 (2.55)  Time: 1.686s,  607.49/s  (1.686s,  607.49/s)  LR: 2.893e-02  Data: 1.138 (1.138)
+Train: 124 [  50/312 ( 16%)]  Loss: 2.55 (2.49)  Time: 0.411s, 2489.82/s  (0.437s, 2340.86/s)  LR: 2.893e-02  Data: 0.028 (0.050)
+Train: 124 [ 100/312 ( 32%)]  Loss: 2.55 (2.50)  Time: 0.406s, 2521.67/s  (0.423s, 2419.25/s)  LR: 2.893e-02  Data: 0.027 (0.038)
+Train: 124 [ 150/312 ( 48%)]  Loss: 2.48 (2.50)  Time: 0.407s, 2517.15/s  (0.418s, 2449.53/s)  LR: 2.893e-02  Data: 0.025 (0.035)
+Train: 124 [ 200/312 ( 64%)]  Loss: 2.48 (2.50)  Time: 0.409s, 2505.82/s  (0.416s, 2463.90/s)  LR: 2.893e-02  Data: 0.027 (0.033)
+Train: 124 [ 250/312 ( 80%)]  Loss: 2.54 (2.51)  Time: 0.416s, 2462.95/s  (0.415s, 2469.22/s)  LR: 2.893e-02  Data: 0.027 (0.032)
+Train: 124 [ 300/312 ( 96%)]  Loss: 2.53 (2.51)  Time: 0.410s, 2498.79/s  (0.414s, 2472.95/s)  LR: 2.893e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   2.763 ( 2.763)  Acc@1:  46.582 ( 46.582)  Acc@5:  68.750 ( 68.750)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.552 ( 2.803)  Acc@1:  51.533 ( 48.054)  Acc@5:  72.524 ( 69.000)
+Train: 125 [   0/312 (  0%)]  Loss: 2.53 (2.53)  Time: 1.566s,  654.09/s  (1.566s,  654.09/s)  LR: 2.679e-02  Data: 1.077 (1.077)
+Train: 125 [  50/312 ( 16%)]  Loss: 2.46 (2.49)  Time: 0.409s, 2506.37/s  (0.431s, 2374.14/s)  LR: 2.679e-02  Data: 0.028 (0.048)
+Train: 125 [ 100/312 ( 32%)]  Loss: 2.51 (2.49)  Time: 0.416s, 2462.93/s  (0.421s, 2431.03/s)  LR: 2.679e-02  Data: 0.030 (0.038)
+Train: 125 [ 150/312 ( 48%)]  Loss: 2.51 (2.49)  Time: 0.408s, 2512.71/s  (0.418s, 2449.29/s)  LR: 2.679e-02  Data: 0.028 (0.035)
+Train: 125 [ 200/312 ( 64%)]  Loss: 2.53 (2.50)  Time: 0.408s, 2508.55/s  (0.416s, 2463.37/s)  LR: 2.679e-02  Data: 0.028 (0.033)
+Train: 125 [ 250/312 ( 80%)]  Loss: 2.51 (2.50)  Time: 0.410s, 2499.81/s  (0.414s, 2472.28/s)  LR: 2.679e-02  Data: 0.029 (0.032)
+Train: 125 [ 300/312 ( 96%)]  Loss: 2.48 (2.50)  Time: 0.410s, 2496.99/s  (0.414s, 2476.27/s)  LR: 2.679e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   2.773 ( 2.773)  Acc@1:  47.168 ( 47.168)  Acc@5:  69.336 ( 69.336)
+Test: [  48/48]  Time: 0.091 (0.318)  Loss:   2.594 ( 2.814)  Acc@1:  53.184 ( 47.998)  Acc@5:  73.349 ( 68.886)
+Train: 126 [   0/312 (  0%)]  Loss: 2.55 (2.55)  Time: 1.700s,  602.49/s  (1.700s,  602.49/s)  LR: 2.474e-02  Data: 1.324 (1.324)
+Train: 126 [  50/312 ( 16%)]  Loss: 2.34 (2.48)  Time: 0.410s, 2499.08/s  (0.437s, 2340.70/s)  LR: 2.474e-02  Data: 0.028 (0.054)
+Train: 126 [ 100/312 ( 32%)]  Loss: 2.51 (2.48)  Time: 0.404s, 2535.05/s  (0.423s, 2422.21/s)  LR: 2.474e-02  Data: 0.029 (0.041)
+Train: 126 [ 150/312 ( 48%)]  Loss: 2.54 (2.48)  Time: 0.409s, 2506.63/s  (0.417s, 2455.06/s)  LR: 2.474e-02  Data: 0.033 (0.037)
+Train: 126 [ 200/312 ( 64%)]  Loss: 2.51 (2.48)  Time: 0.406s, 2521.43/s  (0.414s, 2472.66/s)  LR: 2.474e-02  Data: 0.028 (0.035)
+Train: 126 [ 250/312 ( 80%)]  Loss: 2.58 (2.49)  Time: 0.408s, 2511.85/s  (0.413s, 2482.18/s)  LR: 2.474e-02  Data: 0.028 (0.033)
+Train: 126 [ 300/312 ( 96%)]  Loss: 2.47 (2.49)  Time: 0.410s, 2496.85/s  (0.412s, 2484.94/s)  LR: 2.474e-02  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   2.835 ( 2.835)  Acc@1:  47.754 ( 47.754)  Acc@5:  68.164 ( 68.164)
+Test: [  48/48]  Time: 0.091 (0.321)  Loss:   2.590 ( 2.846)  Acc@1:  50.472 ( 47.742)  Acc@5:  72.995 ( 68.518)
+Train: 127 [   0/312 (  0%)]  Loss: 2.47 (2.47)  Time: 1.770s,  578.47/s  (1.770s,  578.47/s)  LR: 2.276e-02  Data: 1.394 (1.394)
+Train: 127 [  50/312 ( 16%)]  Loss: 2.45 (2.45)  Time: 0.408s, 2507.78/s  (0.439s, 2332.27/s)  LR: 2.276e-02  Data: 0.028 (0.058)
+Train: 127 [ 100/312 ( 32%)]  Loss: 2.47 (2.46)  Time: 0.409s, 2505.16/s  (0.425s, 2411.45/s)  LR: 2.276e-02  Data: 0.027 (0.043)
+Train: 127 [ 150/312 ( 48%)]  Loss: 2.45 (2.47)  Time: 0.410s, 2497.96/s  (0.420s, 2436.06/s)  LR: 2.276e-02  Data: 0.028 (0.038)
+Train: 127 [ 200/312 ( 64%)]  Loss: 2.43 (2.47)  Time: 0.409s, 2506.72/s  (0.417s, 2453.43/s)  LR: 2.276e-02  Data: 0.028 (0.035)
+Train: 127 [ 250/312 ( 80%)]  Loss: 2.50 (2.47)  Time: 0.411s, 2489.98/s  (0.416s, 2464.49/s)  LR: 2.276e-02  Data: 0.028 (0.034)
+Train: 127 [ 300/312 ( 96%)]  Loss: 2.50 (2.48)  Time: 0.409s, 2504.18/s  (0.415s, 2470.35/s)  LR: 2.276e-02  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   2.816 ( 2.816)  Acc@1:  46.289 ( 46.289)  Acc@5:  68.066 ( 68.066)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.566 ( 2.817)  Acc@1:  51.769 ( 48.078)  Acc@5:  74.175 ( 68.846)
+Train: 128 [   0/312 (  0%)]  Loss: 2.39 (2.39)  Time: 1.936s,  528.82/s  (1.936s,  528.82/s)  LR: 2.086e-02  Data: 1.562 (1.562)
+Train: 128 [  50/312 ( 16%)]  Loss: 2.40 (2.44)  Time: 0.410s, 2500.18/s  (0.440s, 2326.92/s)  LR: 2.086e-02  Data: 0.027 (0.058)
+Train: 128 [ 100/312 ( 32%)]  Loss: 2.38 (2.45)  Time: 0.410s, 2494.70/s  (0.426s, 2403.96/s)  LR: 2.086e-02  Data: 0.028 (0.043)
+Train: 128 [ 150/312 ( 48%)]  Loss: 2.42 (2.46)  Time: 0.408s, 2511.39/s  (0.421s, 2430.69/s)  LR: 2.086e-02  Data: 0.027 (0.038)
+Train: 128 [ 200/312 ( 64%)]  Loss: 2.45 (2.46)  Time: 0.408s, 2511.10/s  (0.418s, 2447.96/s)  LR: 2.086e-02  Data: 0.029 (0.036)
+Train: 128 [ 250/312 ( 80%)]  Loss: 2.46 (2.46)  Time: 0.414s, 2472.10/s  (0.417s, 2458.58/s)  LR: 2.086e-02  Data: 0.026 (0.034)
+Train: 128 [ 300/312 ( 96%)]  Loss: 2.47 (2.46)  Time: 0.416s, 2463.84/s  (0.416s, 2463.10/s)  LR: 2.086e-02  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.430 (1.430)  Loss:   2.818 ( 2.818)  Acc@1:  46.680 ( 46.680)  Acc@5:  67.383 ( 67.383)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.576 ( 2.838)  Acc@1:  50.472 ( 47.776)  Acc@5:  72.995 ( 68.514)
+Train: 129 [   0/312 (  0%)]  Loss: 2.44 (2.44)  Time: 1.777s,  576.19/s  (1.777s,  576.19/s)  LR: 1.903e-02  Data: 1.402 (1.402)
+Train: 129 [  50/312 ( 16%)]  Loss: 2.36 (2.44)  Time: 0.414s, 2476.00/s  (0.443s, 2313.61/s)  LR: 1.903e-02  Data: 0.029 (0.055)
+Train: 129 [ 100/312 ( 32%)]  Loss: 2.36 (2.44)  Time: 0.410s, 2495.62/s  (0.428s, 2393.15/s)  LR: 1.903e-02  Data: 0.028 (0.042)
+Train: 129 [ 150/312 ( 48%)]  Loss: 2.44 (2.45)  Time: 0.414s, 2471.14/s  (0.423s, 2421.64/s)  LR: 1.903e-02  Data: 0.030 (0.037)
+Train: 129 [ 200/312 ( 64%)]  Loss: 2.40 (2.45)  Time: 0.414s, 2472.62/s  (0.420s, 2436.55/s)  LR: 1.903e-02  Data: 0.028 (0.035)
+Train: 129 [ 250/312 ( 80%)]  Loss: 2.48 (2.45)  Time: 0.410s, 2495.08/s  (0.419s, 2446.70/s)  LR: 1.903e-02  Data: 0.028 (0.034)
+Train: 129 [ 300/312 ( 96%)]  Loss: 2.43 (2.46)  Time: 0.411s, 2493.03/s  (0.417s, 2453.43/s)  LR: 1.903e-02  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.435 (1.435)  Loss:   2.789 ( 2.789)  Acc@1:  47.461 ( 47.461)  Acc@5:  67.578 ( 67.578)
+Test: [  48/48]  Time: 0.092 (0.322)  Loss:   2.563 ( 2.826)  Acc@1:  51.651 ( 48.066)  Acc@5:  72.995 ( 68.778)
+Train: 130 [   0/312 (  0%)]  Loss: 2.34 (2.34)  Time: 1.592s,  643.23/s  (1.592s,  643.23/s)  LR: 1.729e-02  Data: 1.170 (1.170)
+Train: 130 [  50/312 ( 16%)]  Loss: 2.33 (2.43)  Time: 0.412s, 2483.73/s  (0.435s, 2351.59/s)  LR: 1.729e-02  Data: 0.029 (0.050)
+Train: 130 [ 100/312 ( 32%)]  Loss: 2.39 (2.44)  Time: 0.412s, 2488.14/s  (0.424s, 2417.50/s)  LR: 1.729e-02  Data: 0.029 (0.039)
+Train: 130 [ 150/312 ( 48%)]  Loss: 2.43 (2.44)  Time: 0.409s, 2501.99/s  (0.420s, 2441.00/s)  LR: 1.729e-02  Data: 0.028 (0.036)
+Train: 130 [ 200/312 ( 64%)]  Loss: 2.39 (2.44)  Time: 0.414s, 2475.05/s  (0.418s, 2452.14/s)  LR: 1.729e-02  Data: 0.028 (0.034)
+Train: 130 [ 250/312 ( 80%)]  Loss: 2.47 (2.44)  Time: 0.415s, 2470.08/s  (0.417s, 2457.75/s)  LR: 1.729e-02  Data: 0.028 (0.033)
+Train: 130 [ 300/312 ( 96%)]  Loss: 2.50 (2.45)  Time: 0.410s, 2494.92/s  (0.416s, 2463.16/s)  LR: 1.729e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.414 (1.414)  Loss:   2.785 ( 2.785)  Acc@1:  47.949 ( 47.949)  Acc@5:  68.652 ( 68.652)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.540 ( 2.822)  Acc@1:  52.005 ( 48.124)  Acc@5:  73.585 ( 68.766)
+Train: 131 [   0/312 (  0%)]  Loss: 2.52 (2.52)  Time: 1.847s,  554.47/s  (1.847s,  554.47/s)  LR: 1.563e-02  Data: 1.472 (1.472)
+Train: 131 [  50/312 ( 16%)]  Loss: 2.40 (2.42)  Time: 0.410s, 2499.86/s  (0.439s, 2331.62/s)  LR: 1.563e-02  Data: 0.027 (0.056)
+Train: 131 [ 100/312 ( 32%)]  Loss: 2.46 (2.42)  Time: 0.410s, 2496.50/s  (0.425s, 2407.81/s)  LR: 1.563e-02  Data: 0.028 (0.042)
+Train: 131 [ 150/312 ( 48%)]  Loss: 2.39 (2.43)  Time: 0.410s, 2499.46/s  (0.420s, 2435.21/s)  LR: 1.563e-02  Data: 0.027 (0.037)
+Train: 131 [ 200/312 ( 64%)]  Loss: 2.45 (2.43)  Time: 0.414s, 2475.71/s  (0.418s, 2448.94/s)  LR: 1.563e-02  Data: 0.027 (0.035)
+Train: 131 [ 250/312 ( 80%)]  Loss: 2.41 (2.43)  Time: 0.410s, 2498.69/s  (0.417s, 2456.35/s)  LR: 1.563e-02  Data: 0.027 (0.034)
+Train: 131 [ 300/312 ( 96%)]  Loss: 2.51 (2.44)  Time: 0.409s, 2501.68/s  (0.416s, 2463.53/s)  LR: 1.563e-02  Data: 0.029 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.433 (1.433)  Loss:   2.749 ( 2.749)  Acc@1:  48.535 ( 48.535)  Acc@5:  68.262 ( 68.262)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.536 ( 2.805)  Acc@1:  52.005 ( 48.208)  Acc@5:  73.349 ( 69.092)
+Train: 132 [   0/312 (  0%)]  Loss: 2.31 (2.31)  Time: 1.719s,  595.74/s  (1.719s,  595.74/s)  LR: 1.404e-02  Data: 1.130 (1.130)
+Train: 132 [  50/312 ( 16%)]  Loss: 2.45 (2.42)  Time: 0.411s, 2491.27/s  (0.438s, 2337.59/s)  LR: 1.404e-02  Data: 0.028 (0.049)
+Train: 132 [ 100/312 ( 32%)]  Loss: 2.48 (2.42)  Time: 0.409s, 2504.63/s  (0.425s, 2410.32/s)  LR: 1.404e-02  Data: 0.027 (0.039)
+Train: 132 [ 150/312 ( 48%)]  Loss: 2.45 (2.43)  Time: 0.411s, 2491.02/s  (0.420s, 2436.81/s)  LR: 1.404e-02  Data: 0.027 (0.035)
+Train: 132 [ 200/312 ( 64%)]  Loss: 2.39 (2.43)  Time: 0.412s, 2486.18/s  (0.418s, 2449.11/s)  LR: 1.404e-02  Data: 0.029 (0.033)
+Train: 132 [ 250/312 ( 80%)]  Loss: 2.45 (2.43)  Time: 0.408s, 2507.91/s  (0.417s, 2456.16/s)  LR: 1.404e-02  Data: 0.028 (0.032)
+Train: 132 [ 300/312 ( 96%)]  Loss: 2.41 (2.43)  Time: 0.410s, 2496.45/s  (0.416s, 2462.77/s)  LR: 1.404e-02  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.427 (1.427)  Loss:   2.803 ( 2.803)  Acc@1:  48.145 ( 48.145)  Acc@5:  68.262 ( 68.262)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.553 ( 2.827)  Acc@1:  50.943 ( 48.002)  Acc@5:  73.939 ( 68.856)
+Train: 133 [   0/312 (  0%)]  Loss: 2.38 (2.38)  Time: 1.518s,  674.74/s  (1.518s,  674.74/s)  LR: 1.254e-02  Data: 1.122 (1.122)
+Train: 133 [  50/312 ( 16%)]  Loss: 2.46 (2.41)  Time: 0.413s, 2480.71/s  (0.434s, 2359.70/s)  LR: 1.254e-02  Data: 0.029 (0.049)
+Train: 133 [ 100/312 ( 32%)]  Loss: 2.40 (2.41)  Time: 0.410s, 2499.84/s  (0.422s, 2424.38/s)  LR: 1.254e-02  Data: 0.027 (0.038)
+Train: 133 [ 150/312 ( 48%)]  Loss: 2.35 (2.41)  Time: 0.410s, 2498.83/s  (0.418s, 2448.23/s)  LR: 1.254e-02  Data: 0.027 (0.035)
+Train: 133 [ 200/312 ( 64%)]  Loss: 2.45 (2.42)  Time: 0.410s, 2496.65/s  (0.417s, 2458.43/s)  LR: 1.254e-02  Data: 0.027 (0.033)
+Train: 133 [ 250/312 ( 80%)]  Loss: 2.45 (2.42)  Time: 0.411s, 2489.31/s  (0.416s, 2464.44/s)  LR: 1.254e-02  Data: 0.028 (0.032)
+Train: 133 [ 300/312 ( 96%)]  Loss: 2.34 (2.42)  Time: 0.412s, 2483.96/s  (0.415s, 2468.22/s)  LR: 1.254e-02  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.419 (1.419)  Loss:   2.815 ( 2.815)  Acc@1:  47.852 ( 47.852)  Acc@5:  67.773 ( 67.773)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.577 ( 2.838)  Acc@1:  51.415 ( 47.814)  Acc@5:  73.231 ( 68.568)
+Train: 134 [   0/312 (  0%)]  Loss: 2.47 (2.47)  Time: 1.791s,  571.74/s  (1.791s,  571.74/s)  LR: 1.112e-02  Data: 1.416 (1.416)
+Train: 134 [  50/312 ( 16%)]  Loss: 2.34 (2.41)  Time: 0.409s, 2502.65/s  (0.437s, 2342.55/s)  LR: 1.112e-02  Data: 0.027 (0.055)
+Train: 134 [ 100/312 ( 32%)]  Loss: 2.36 (2.41)  Time: 0.409s, 2504.50/s  (0.424s, 2414.73/s)  LR: 1.112e-02  Data: 0.027 (0.042)
+Train: 134 [ 150/312 ( 48%)]  Loss: 2.34 (2.41)  Time: 0.409s, 2504.51/s  (0.420s, 2440.60/s)  LR: 1.112e-02  Data: 0.029 (0.037)
+Train: 134 [ 200/312 ( 64%)]  Loss: 2.44 (2.42)  Time: 0.409s, 2503.63/s  (0.417s, 2454.16/s)  LR: 1.112e-02  Data: 0.028 (0.035)
+Train: 134 [ 250/312 ( 80%)]  Loss: 2.36 (2.42)  Time: 0.409s, 2502.78/s  (0.416s, 2461.87/s)  LR: 1.112e-02  Data: 0.028 (0.033)
+Train: 134 [ 300/312 ( 96%)]  Loss: 2.38 (2.42)  Time: 0.412s, 2487.43/s  (0.415s, 2466.73/s)  LR: 1.112e-02  Data: 0.029 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.446 (1.446)  Loss:   2.776 ( 2.776)  Acc@1:  47.754 ( 47.754)  Acc@5:  67.871 ( 67.871)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.544 ( 2.806)  Acc@1:  51.415 ( 48.262)  Acc@5:  73.939 ( 69.038)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-122.pth.tar', 48.34399996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-134.pth.tar', 48.26199996826172)
+
+Train: 135 [   0/312 (  0%)]  Loss: 2.42 (2.42)  Time: 1.587s,  645.31/s  (1.587s,  645.31/s)  LR: 9.789e-03  Data: 1.212 (1.212)
+Train: 135 [  50/312 ( 16%)]  Loss: 2.33 (2.40)  Time: 0.409s, 2502.42/s  (0.433s, 2364.54/s)  LR: 9.789e-03  Data: 0.023 (0.051)
+Train: 135 [ 100/312 ( 32%)]  Loss: 2.37 (2.41)  Time: 0.410s, 2496.42/s  (0.422s, 2426.03/s)  LR: 9.789e-03  Data: 0.028 (0.039)
+Train: 135 [ 150/312 ( 48%)]  Loss: 2.31 (2.41)  Time: 0.411s, 2494.13/s  (0.418s, 2447.93/s)  LR: 9.789e-03  Data: 0.030 (0.036)
+Train: 135 [ 200/312 ( 64%)]  Loss: 2.48 (2.41)  Time: 0.410s, 2497.94/s  (0.416s, 2459.46/s)  LR: 9.789e-03  Data: 0.027 (0.034)
+Train: 135 [ 250/312 ( 80%)]  Loss: 2.44 (2.41)  Time: 0.410s, 2499.71/s  (0.415s, 2465.63/s)  LR: 9.789e-03  Data: 0.028 (0.033)
+Train: 135 [ 300/312 ( 96%)]  Loss: 2.44 (2.41)  Time: 0.410s, 2497.92/s  (0.415s, 2469.86/s)  LR: 9.789e-03  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.509 (1.509)  Loss:   2.809 ( 2.809)  Acc@1:  47.949 ( 47.949)  Acc@5:  68.555 ( 68.555)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.600 ( 2.841)  Acc@1:  50.354 ( 47.830)  Acc@5:  73.231 ( 68.498)
+Train: 136 [   0/312 (  0%)]  Loss: 2.36 (2.36)  Time: 1.677s,  610.70/s  (1.677s,  610.70/s)  LR: 8.536e-03  Data: 1.302 (1.302)
+Train: 136 [  50/312 ( 16%)]  Loss: 2.51 (2.40)  Time: 0.412s, 2482.83/s  (0.436s, 2346.36/s)  LR: 8.536e-03  Data: 0.028 (0.054)
+Train: 136 [ 100/312 ( 32%)]  Loss: 2.42 (2.39)  Time: 0.411s, 2488.89/s  (0.424s, 2417.54/s)  LR: 8.536e-03  Data: 0.027 (0.041)
+Train: 136 [ 150/312 ( 48%)]  Loss: 2.40 (2.40)  Time: 0.410s, 2495.59/s  (0.419s, 2442.14/s)  LR: 8.536e-03  Data: 0.028 (0.037)
+Train: 136 [ 200/312 ( 64%)]  Loss: 2.32 (2.40)  Time: 0.410s, 2498.02/s  (0.417s, 2454.90/s)  LR: 8.536e-03  Data: 0.028 (0.035)
+Train: 136 [ 250/312 ( 80%)]  Loss: 2.24 (2.40)  Time: 0.411s, 2492.32/s  (0.416s, 2462.46/s)  LR: 8.536e-03  Data: 0.029 (0.033)
+Train: 136 [ 300/312 ( 96%)]  Loss: 2.42 (2.40)  Time: 0.410s, 2499.48/s  (0.415s, 2467.34/s)  LR: 8.536e-03  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.417 (1.417)  Loss:   2.787 ( 2.787)  Acc@1:  48.438 ( 48.438)  Acc@5:  68.750 ( 68.750)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.566 ( 2.814)  Acc@1:  50.943 ( 48.142)  Acc@5:  73.585 ( 68.982)
+Train: 137 [   0/312 (  0%)]  Loss: 2.31 (2.31)  Time: 1.616s,  633.81/s  (1.616s,  633.81/s)  LR: 7.367e-03  Data: 1.066 (1.066)
+Train: 137 [  50/312 ( 16%)]  Loss: 2.44 (2.39)  Time: 0.406s, 2525.13/s  (0.429s, 2388.16/s)  LR: 7.367e-03  Data: 0.027 (0.048)
+Train: 137 [ 100/312 ( 32%)]  Loss: 2.48 (2.40)  Time: 0.406s, 2519.83/s  (0.417s, 2453.56/s)  LR: 7.367e-03  Data: 0.029 (0.038)
+Train: 137 [ 150/312 ( 48%)]  Loss: 2.41 (2.40)  Time: 0.408s, 2509.80/s  (0.414s, 2471.93/s)  LR: 7.367e-03  Data: 0.026 (0.035)
+Train: 137 [ 200/312 ( 64%)]  Loss: 2.45 (2.40)  Time: 0.410s, 2496.56/s  (0.413s, 2477.42/s)  LR: 7.367e-03  Data: 0.028 (0.033)
+Train: 137 [ 250/312 ( 80%)]  Loss: 2.32 (2.40)  Time: 0.410s, 2498.50/s  (0.413s, 2481.31/s)  LR: 7.367e-03  Data: 0.029 (0.032)
+Train: 137 [ 300/312 ( 96%)]  Loss: 2.41 (2.40)  Time: 0.410s, 2497.82/s  (0.412s, 2483.76/s)  LR: 7.367e-03  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.438 (1.438)  Loss:   2.783 ( 2.783)  Acc@1:  48.242 ( 48.242)  Acc@5:  67.871 ( 67.871)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.567 ( 2.812)  Acc@1:  51.415 ( 48.232)  Acc@5:  73.585 ( 68.852)
+Train: 138 [   0/312 (  0%)]  Loss: 2.35 (2.35)  Time: 1.611s,  635.70/s  (1.611s,  635.70/s)  LR: 6.283e-03  Data: 1.235 (1.235)
+Train: 138 [  50/312 ( 16%)]  Loss: 2.43 (2.39)  Time: 0.410s, 2496.92/s  (0.436s, 2346.29/s)  LR: 6.283e-03  Data: 0.023 (0.051)
+Train: 138 [ 100/312 ( 32%)]  Loss: 2.42 (2.39)  Time: 0.408s, 2508.07/s  (0.424s, 2416.61/s)  LR: 6.283e-03  Data: 0.027 (0.040)
+Train: 138 [ 150/312 ( 48%)]  Loss: 2.40 (2.39)  Time: 0.406s, 2520.47/s  (0.419s, 2445.45/s)  LR: 6.283e-03  Data: 0.028 (0.036)
+Train: 138 [ 200/312 ( 64%)]  Loss: 2.40 (2.39)  Time: 0.408s, 2511.58/s  (0.416s, 2461.57/s)  LR: 6.283e-03  Data: 0.027 (0.034)
+Train: 138 [ 250/312 ( 80%)]  Loss: 2.41 (2.40)  Time: 0.410s, 2499.47/s  (0.415s, 2468.75/s)  LR: 6.283e-03  Data: 0.029 (0.033)
+Train: 138 [ 300/312 ( 96%)]  Loss: 2.40 (2.40)  Time: 0.410s, 2497.56/s  (0.414s, 2471.16/s)  LR: 6.283e-03  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.497 (1.497)  Loss:   2.790 ( 2.790)  Acc@1:  47.656 ( 47.656)  Acc@5:  67.871 ( 67.871)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.564 ( 2.817)  Acc@1:  51.061 ( 48.122)  Acc@5:  74.057 ( 68.866)
+Train: 139 [   0/312 (  0%)]  Loss: 2.43 (2.43)  Time: 1.574s,  650.38/s  (1.574s,  650.38/s)  LR: 5.284e-03  Data: 1.203 (1.203)
+Train: 139 [  50/312 ( 16%)]  Loss: 2.38 (2.38)  Time: 0.408s, 2508.61/s  (0.430s, 2380.10/s)  LR: 5.284e-03  Data: 0.028 (0.050)
+Train: 139 [ 100/312 ( 32%)]  Loss: 2.41 (2.38)  Time: 0.409s, 2502.25/s  (0.420s, 2438.68/s)  LR: 5.284e-03  Data: 0.028 (0.039)
+Train: 139 [ 150/312 ( 48%)]  Loss: 2.36 (2.38)  Time: 0.411s, 2489.07/s  (0.418s, 2452.60/s)  LR: 5.284e-03  Data: 0.028 (0.035)
+Train: 139 [ 200/312 ( 64%)]  Loss: 2.41 (2.39)  Time: 0.411s, 2492.15/s  (0.416s, 2458.61/s)  LR: 5.284e-03  Data: 0.028 (0.034)
+Train: 139 [ 250/312 ( 80%)]  Loss: 2.37 (2.39)  Time: 0.412s, 2486.90/s  (0.416s, 2462.14/s)  LR: 5.284e-03  Data: 0.026 (0.032)
+Train: 139 [ 300/312 ( 96%)]  Loss: 2.40 (2.39)  Time: 0.412s, 2484.86/s  (0.415s, 2465.80/s)  LR: 5.284e-03  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.435 (1.435)  Loss:   2.801 ( 2.801)  Acc@1:  48.145 ( 48.145)  Acc@5:  68.066 ( 68.066)
+Test: [  48/48]  Time: 0.091 (0.320)  Loss:   2.562 ( 2.824)  Acc@1:  51.297 ( 48.080)  Acc@5:  73.703 ( 68.844)
+Train: 140 [   0/312 (  0%)]  Loss: 2.41 (2.41)  Time: 1.734s,  590.44/s  (1.734s,  590.44/s)  LR: 4.370e-03  Data: 1.360 (1.360)
+Train: 140 [  50/312 ( 16%)]  Loss: 2.47 (2.38)  Time: 0.410s, 2499.70/s  (0.438s, 2337.41/s)  LR: 4.370e-03  Data: 0.025 (0.054)
+Train: 140 [ 100/312 ( 32%)]  Loss: 2.42 (2.38)  Time: 0.410s, 2494.66/s  (0.425s, 2410.49/s)  LR: 4.370e-03  Data: 0.027 (0.041)
+Train: 140 [ 150/312 ( 48%)]  Loss: 2.37 (2.38)  Time: 0.413s, 2478.15/s  (0.421s, 2434.64/s)  LR: 4.370e-03  Data: 0.030 (0.036)
+Train: 140 [ 200/312 ( 64%)]  Loss: 2.39 (2.38)  Time: 0.410s, 2500.07/s  (0.418s, 2447.67/s)  LR: 4.370e-03  Data: 0.028 (0.034)
+Train: 140 [ 250/312 ( 80%)]  Loss: 2.39 (2.38)  Time: 0.410s, 2494.93/s  (0.417s, 2456.16/s)  LR: 4.370e-03  Data: 0.027 (0.033)
+Train: 140 [ 300/312 ( 96%)]  Loss: 2.41 (2.38)  Time: 0.414s, 2471.38/s  (0.416s, 2459.72/s)  LR: 4.370e-03  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.421 (1.421)  Loss:   2.800 ( 2.800)  Acc@1:  47.754 ( 47.754)  Acc@5:  67.773 ( 67.773)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.567 ( 2.829)  Acc@1:  51.887 ( 48.072)  Acc@5:  73.821 ( 68.640)
+Train: 141 [   0/312 (  0%)]  Loss: 2.32 (2.32)  Time: 1.836s,  557.72/s  (1.836s,  557.72/s)  LR: 3.543e-03  Data: 1.462 (1.462)
+Train: 141 [  50/312 ( 16%)]  Loss: 2.39 (2.39)  Time: 0.412s, 2486.42/s  (0.437s, 2341.67/s)  LR: 3.543e-03  Data: 0.029 (0.056)
+Train: 141 [ 100/312 ( 32%)]  Loss: 2.37 (2.38)  Time: 0.409s, 2504.66/s  (0.424s, 2413.07/s)  LR: 3.543e-03  Data: 0.028 (0.042)
+Train: 141 [ 150/312 ( 48%)]  Loss: 2.44 (2.38)  Time: 0.414s, 2473.86/s  (0.420s, 2437.87/s)  LR: 3.543e-03  Data: 0.028 (0.037)
+Train: 141 [ 200/312 ( 64%)]  Loss: 2.38 (2.38)  Time: 0.410s, 2498.26/s  (0.418s, 2450.87/s)  LR: 3.543e-03  Data: 0.028 (0.035)
+Train: 141 [ 250/312 ( 80%)]  Loss: 2.42 (2.38)  Time: 0.408s, 2512.22/s  (0.416s, 2460.57/s)  LR: 3.543e-03  Data: 0.027 (0.034)
+Train: 141 [ 300/312 ( 96%)]  Loss: 2.48 (2.38)  Time: 0.411s, 2493.88/s  (0.415s, 2466.22/s)  LR: 3.543e-03  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.449 (1.449)  Loss:   2.770 ( 2.770)  Acc@1:  48.438 ( 48.438)  Acc@5:  68.262 ( 68.262)
+Test: [  48/48]  Time: 0.091 (0.318)  Loss:   2.551 ( 2.803)  Acc@1:  51.651 ( 48.498)  Acc@5:  74.057 ( 69.110)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-141.pth.tar', 48.49800007080078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-122.pth.tar', 48.34399996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-49.pth.tar', 48.28799996582031)
+
+Train: 142 [   0/312 (  0%)]  Loss: 2.35 (2.35)  Time: 1.416s,  723.16/s  (1.416s,  723.16/s)  LR: 2.801e-03  Data: 1.028 (1.028)
+Train: 142 [  50/312 ( 16%)]  Loss: 2.32 (2.36)  Time: 0.414s, 2471.86/s  (0.434s, 2357.27/s)  LR: 2.801e-03  Data: 0.032 (0.048)
+Train: 142 [ 100/312 ( 32%)]  Loss: 2.41 (2.37)  Time: 0.413s, 2481.73/s  (0.424s, 2416.84/s)  LR: 2.801e-03  Data: 0.027 (0.038)
+Train: 142 [ 150/312 ( 48%)]  Loss: 2.32 (2.37)  Time: 0.409s, 2500.67/s  (0.420s, 2440.11/s)  LR: 2.801e-03  Data: 0.028 (0.035)
+Train: 142 [ 200/312 ( 64%)]  Loss: 2.27 (2.37)  Time: 0.414s, 2470.54/s  (0.417s, 2452.94/s)  LR: 2.801e-03  Data: 0.033 (0.033)
+Train: 142 [ 250/312 ( 80%)]  Loss: 2.40 (2.37)  Time: 0.412s, 2486.56/s  (0.416s, 2459.60/s)  LR: 2.801e-03  Data: 0.028 (0.032)
+Train: 142 [ 300/312 ( 96%)]  Loss: 2.35 (2.37)  Time: 0.410s, 2495.39/s  (0.416s, 2464.09/s)  LR: 2.801e-03  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.450 (1.450)  Loss:   2.775 ( 2.775)  Acc@1:  48.535 ( 48.535)  Acc@5:  68.164 ( 68.164)
+Test: [  48/48]  Time: 0.090 (0.320)  Loss:   2.559 ( 2.810)  Acc@1:  50.825 ( 48.440)  Acc@5:  73.703 ( 69.016)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-141.pth.tar', 48.49800007080078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-142.pth.tar', 48.43999997070313)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-122.pth.tar', 48.34399996826172)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-55.pth.tar', 48.33800000366211)
+
+Train: 143 [   0/312 (  0%)]  Loss: 2.37 (2.37)  Time: 1.639s,  624.75/s  (1.639s,  624.75/s)  LR: 2.146e-03  Data: 1.264 (1.264)
+Train: 143 [  50/312 ( 16%)]  Loss: 2.41 (2.37)  Time: 0.410s, 2498.81/s  (0.437s, 2343.00/s)  LR: 2.146e-03  Data: 0.028 (0.052)
+Train: 143 [ 100/312 ( 32%)]  Loss: 2.34 (2.37)  Time: 0.411s, 2492.84/s  (0.424s, 2415.23/s)  LR: 2.146e-03  Data: 0.027 (0.040)
+Train: 143 [ 150/312 ( 48%)]  Loss: 2.45 (2.37)  Time: 0.412s, 2482.86/s  (0.420s, 2439.33/s)  LR: 2.146e-03  Data: 0.026 (0.036)
+Train: 143 [ 200/312 ( 64%)]  Loss: 2.36 (2.37)  Time: 0.409s, 2501.68/s  (0.417s, 2453.09/s)  LR: 2.146e-03  Data: 0.028 (0.034)
+Train: 143 [ 250/312 ( 80%)]  Loss: 2.35 (2.37)  Time: 0.410s, 2499.21/s  (0.416s, 2463.37/s)  LR: 2.146e-03  Data: 0.027 (0.033)
+Train: 143 [ 300/312 ( 96%)]  Loss: 2.46 (2.37)  Time: 0.411s, 2491.64/s  (0.415s, 2468.52/s)  LR: 2.146e-03  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.419 (1.419)  Loss:   2.766 ( 2.766)  Acc@1:  48.633 ( 48.633)  Acc@5:  68.066 ( 68.066)
+Test: [  48/48]  Time: 0.090 (0.319)  Loss:   2.549 ( 2.802)  Acc@1:  51.533 ( 48.556)  Acc@5:  73.349 ( 68.978)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-143.pth.tar', 48.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-141.pth.tar', 48.49800007080078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-142.pth.tar', 48.43999997070313)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-122.pth.tar', 48.34399996826172)
+
+Train: 144 [   0/312 (  0%)]  Loss: 2.44 (2.44)  Time: 1.589s,  644.44/s  (1.589s,  644.44/s)  LR: 1.577e-03  Data: 1.215 (1.215)
+Train: 144 [  50/312 ( 16%)]  Loss: 2.37 (2.38)  Time: 0.409s, 2504.94/s  (0.433s, 2366.80/s)  LR: 1.577e-03  Data: 0.028 (0.052)
+Train: 144 [ 100/312 ( 32%)]  Loss: 2.30 (2.37)  Time: 0.411s, 2492.10/s  (0.422s, 2427.70/s)  LR: 1.577e-03  Data: 0.028 (0.040)
+Train: 144 [ 150/312 ( 48%)]  Loss: 2.42 (2.37)  Time: 0.412s, 2484.14/s  (0.419s, 2446.45/s)  LR: 1.577e-03  Data: 0.027 (0.036)
+Train: 144 [ 200/312 ( 64%)]  Loss: 2.40 (2.37)  Time: 0.409s, 2504.15/s  (0.417s, 2456.57/s)  LR: 1.577e-03  Data: 0.028 (0.034)
+Train: 144 [ 250/312 ( 80%)]  Loss: 2.40 (2.37)  Time: 0.413s, 2479.98/s  (0.416s, 2461.11/s)  LR: 1.577e-03  Data: 0.027 (0.033)
+Train: 144 [ 300/312 ( 96%)]  Loss: 2.35 (2.37)  Time: 0.407s, 2515.41/s  (0.415s, 2467.83/s)  LR: 1.577e-03  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.436 (1.436)  Loss:   2.786 ( 2.786)  Acc@1:  48.340 ( 48.340)  Acc@5:  67.969 ( 67.969)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.572 ( 2.818)  Acc@1:  51.887 ( 48.354)  Acc@5:  73.113 ( 68.776)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-143.pth.tar', 48.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-141.pth.tar', 48.49800007080078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-142.pth.tar', 48.43999997070313)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-144.pth.tar', 48.353999979248044)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-68.pth.tar', 48.34400006835938)
+
+Train: 145 [   0/312 (  0%)]  Loss: 2.31 (2.31)  Time: 1.815s,  564.21/s  (1.815s,  564.21/s)  LR: 1.096e-03  Data: 1.204 (1.204)
+Train: 145 [  50/312 ( 16%)]  Loss: 2.38 (2.37)  Time: 0.409s, 2506.57/s  (0.436s, 2348.33/s)  LR: 1.096e-03  Data: 0.028 (0.051)
+Train: 145 [ 100/312 ( 32%)]  Loss: 2.37 (2.37)  Time: 0.411s, 2490.21/s  (0.424s, 2415.77/s)  LR: 1.096e-03  Data: 0.027 (0.040)
+Train: 145 [ 150/312 ( 48%)]  Loss: 2.40 (2.37)  Time: 0.410s, 2499.93/s  (0.420s, 2438.12/s)  LR: 1.096e-03  Data: 0.028 (0.036)
+Train: 145 [ 200/312 ( 64%)]  Loss: 2.35 (2.37)  Time: 0.410s, 2500.53/s  (0.417s, 2454.60/s)  LR: 1.096e-03  Data: 0.027 (0.034)
+Train: 145 [ 250/312 ( 80%)]  Loss: 2.41 (2.37)  Time: 0.411s, 2491.42/s  (0.416s, 2463.37/s)  LR: 1.096e-03  Data: 0.028 (0.033)
+Train: 145 [ 300/312 ( 96%)]  Loss: 2.41 (2.37)  Time: 0.413s, 2478.77/s  (0.415s, 2467.03/s)  LR: 1.096e-03  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.440 (1.440)  Loss:   2.781 ( 2.781)  Acc@1:  48.535 ( 48.535)  Acc@5:  67.773 ( 67.773)
+Test: [  48/48]  Time: 0.090 (0.324)  Loss:   2.567 ( 2.818)  Acc@1:  51.769 ( 48.324)  Acc@5:  72.642 ( 68.816)
+Train: 146 [   0/312 (  0%)]  Loss: 2.32 (2.32)  Time: 1.792s,  571.27/s  (1.792s,  571.27/s)  LR: 7.014e-04  Data: 1.419 (1.419)
+Train: 146 [  50/312 ( 16%)]  Loss: 2.38 (2.36)  Time: 0.409s, 2506.32/s  (0.436s, 2347.81/s)  LR: 7.014e-04  Data: 0.026 (0.055)
+Train: 146 [ 100/312 ( 32%)]  Loss: 2.38 (2.37)  Time: 0.410s, 2497.27/s  (0.423s, 2418.83/s)  LR: 7.014e-04  Data: 0.027 (0.041)
+Train: 146 [ 150/312 ( 48%)]  Loss: 2.38 (2.37)  Time: 0.412s, 2485.51/s  (0.420s, 2440.51/s)  LR: 7.014e-04  Data: 0.028 (0.037)
+Train: 146 [ 200/312 ( 64%)]  Loss: 2.38 (2.37)  Time: 0.409s, 2506.41/s  (0.417s, 2454.86/s)  LR: 7.014e-04  Data: 0.027 (0.035)
+Train: 146 [ 250/312 ( 80%)]  Loss: 2.26 (2.37)  Time: 0.405s, 2527.67/s  (0.415s, 2467.65/s)  LR: 7.014e-04  Data: 0.027 (0.033)
+Train: 146 [ 300/312 ( 96%)]  Loss: 2.36 (2.37)  Time: 0.406s, 2525.19/s  (0.414s, 2476.25/s)  LR: 7.014e-04  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.451 (1.451)  Loss:   2.786 ( 2.786)  Acc@1:  48.145 ( 48.145)  Acc@5:  67.773 ( 67.773)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   2.571 ( 2.818)  Acc@1:  52.123 ( 48.362)  Acc@5:  72.995 ( 68.766)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-143.pth.tar', 48.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-141.pth.tar', 48.49800007080078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-142.pth.tar', 48.43999997070313)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-146.pth.tar', 48.36200001708984)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-144.pth.tar', 48.353999979248044)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-63.pth.tar', 48.35200002197266)
+
+Train: 147 [   0/312 (  0%)]  Loss: 2.38 (2.38)  Time: 1.673s,  612.14/s  (1.673s,  612.14/s)  LR: 3.947e-04  Data: 1.299 (1.299)
+Train: 147 [  50/312 ( 16%)]  Loss: 2.32 (2.37)  Time: 0.413s, 2481.79/s  (0.438s, 2339.82/s)  LR: 3.947e-04  Data: 0.030 (0.056)
+Train: 147 [ 100/312 ( 32%)]  Loss: 2.31 (2.37)  Time: 0.407s, 2515.60/s  (0.425s, 2411.40/s)  LR: 3.947e-04  Data: 0.027 (0.042)
+Train: 147 [ 150/312 ( 48%)]  Loss: 2.48 (2.37)  Time: 0.409s, 2506.47/s  (0.419s, 2442.17/s)  LR: 3.947e-04  Data: 0.028 (0.038)
+Train: 147 [ 200/312 ( 64%)]  Loss: 2.37 (2.37)  Time: 0.409s, 2503.07/s  (0.417s, 2456.69/s)  LR: 3.947e-04  Data: 0.029 (0.035)
+Train: 147 [ 250/312 ( 80%)]  Loss: 2.37 (2.37)  Time: 0.414s, 2471.28/s  (0.416s, 2462.12/s)  LR: 3.947e-04  Data: 0.026 (0.034)
+Train: 147 [ 300/312 ( 96%)]  Loss: 2.43 (2.37)  Time: 0.408s, 2511.03/s  (0.415s, 2467.44/s)  LR: 3.947e-04  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   2.770 ( 2.770)  Acc@1:  48.145 ( 48.145)  Acc@5:  67.285 ( 67.285)
+Test: [  48/48]  Time: 0.090 (0.321)  Loss:   2.560 ( 2.806)  Acc@1:  52.005 ( 48.460)  Acc@5:  72.995 ( 69.006)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-143.pth.tar', 48.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-141.pth.tar', 48.49800007080078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-147.pth.tar', 48.46000003051758)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-142.pth.tar', 48.43999997070313)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-146.pth.tar', 48.36200001708984)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-144.pth.tar', 48.353999979248044)
+
+Train: 148 [   0/312 (  0%)]  Loss: 2.36 (2.36)  Time: 1.718s,  595.87/s  (1.718s,  595.87/s)  LR: 1.754e-04  Data: 1.346 (1.346)
+Train: 148 [  50/312 ( 16%)]  Loss: 2.36 (2.36)  Time: 0.408s, 2507.66/s  (0.432s, 2368.51/s)  LR: 1.754e-04  Data: 0.027 (0.054)
+Train: 148 [ 100/312 ( 32%)]  Loss: 2.44 (2.37)  Time: 0.410s, 2500.28/s  (0.421s, 2433.44/s)  LR: 1.754e-04  Data: 0.027 (0.041)
+Train: 148 [ 150/312 ( 48%)]  Loss: 2.38 (2.37)  Time: 0.410s, 2495.60/s  (0.418s, 2449.79/s)  LR: 1.754e-04  Data: 0.027 (0.037)
+Train: 148 [ 200/312 ( 64%)]  Loss: 2.36 (2.37)  Time: 0.407s, 2514.34/s  (0.416s, 2461.87/s)  LR: 1.754e-04  Data: 0.028 (0.035)
+Train: 148 [ 250/312 ( 80%)]  Loss: 2.40 (2.37)  Time: 0.410s, 2498.95/s  (0.415s, 2469.66/s)  LR: 1.754e-04  Data: 0.026 (0.033)
+Train: 148 [ 300/312 ( 96%)]  Loss: 2.32 (2.37)  Time: 0.413s, 2481.77/s  (0.414s, 2473.69/s)  LR: 1.754e-04  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.421 (1.421)  Loss:   2.780 ( 2.780)  Acc@1:  47.754 ( 47.754)  Acc@5:  68.164 ( 68.164)
+Test: [  48/48]  Time: 0.090 (0.322)  Loss:   2.565 ( 2.813)  Acc@1:  51.887 ( 48.380)  Acc@5:  73.231 ( 68.958)
+Current checkpoints:
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-60.pth.tar', 48.73400004516601)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-90.pth.tar', 48.64000006591797)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-78.pth.tar', 48.6280000390625)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-143.pth.tar', 48.55600001953125)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-141.pth.tar', 48.49800007080078)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-147.pth.tar', 48.46000003051758)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-142.pth.tar', 48.43999997070313)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-62.pth.tar', 48.390000036621096)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-148.pth.tar', 48.37999997924805)
+ ('./output/train/ImageNetTraining20.0-frac-1over4/checkpoint-146.pth.tar', 48.36200001708984)
+
+Train: 149 [   0/312 (  0%)]  Loss: 2.41 (2.41)  Time: 2.077s,  492.91/s  (2.077s,  492.91/s)  LR: 4.386e-05  Data: 1.702 (1.702)
+Train: 149 [  50/312 ( 16%)]  Loss: 2.46 (2.37)  Time: 0.411s, 2489.77/s  (0.446s, 2298.51/s)  LR: 4.386e-05  Data: 0.031 (0.061)
+Train: 149 [ 100/312 ( 32%)]  Loss: 2.43 (2.36)  Time: 0.405s, 2527.98/s  (0.427s, 2399.42/s)  LR: 4.386e-05  Data: 0.029 (0.045)
+Train: 149 [ 150/312 ( 48%)]  Loss: 2.34 (2.36)  Time: 0.405s, 2525.78/s  (0.420s, 2440.24/s)  LR: 4.386e-05  Data: 0.027 (0.039)
+Train: 149 [ 200/312 ( 64%)]  Loss: 2.35 (2.37)  Time: 0.404s, 2532.51/s  (0.416s, 2460.98/s)  LR: 4.386e-05  Data: 0.027 (0.036)
+Train: 149 [ 250/312 ( 80%)]  Loss: 2.47 (2.37)  Time: 0.408s, 2508.01/s  (0.414s, 2472.73/s)  LR: 4.386e-05  Data: 0.028 (0.035)
+Train: 149 [ 300/312 ( 96%)]  Loss: 2.33 (2.37)  Time: 0.412s, 2483.56/s  (0.413s, 2478.20/s)  LR: 4.386e-05  Data: 0.032 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   2.787 ( 2.787)  Acc@1:  47.656 ( 47.656)  Acc@5:  67.578 ( 67.578)
+Test: [  48/48]  Time: 0.091 (0.323)  Loss:   2.573 ( 2.818)  Acc@1:  51.415 ( 48.274)  Acc@5:  72.877 ( 68.830)
+*** Best metric: 48.73400004516601 (epoch 60)
+--result
+[
+    {
+        "epoch": 146,
+        "train": {
+            "loss": 2.3669257164001465
+        },
+        "validation": {
+            "loss": 2.8177401666259767,
+            "top1": 48.36200001708984,
+            "top5": 68.76600003417968
+        }
+    },
+    {
+        "epoch": 148,
+        "train": {
+            "loss": 2.3682570457458496
+        },
+        "validation": {
+            "loss": 2.8133713523864747,
+            "top1": 48.37999997924805,
+            "top5": 68.95800000732422
+        }
+    },
+    {
+        "epoch": 62,
+        "train": {
+            "loss": 3.524199962615967
+        },
+        "validation": {
+            "loss": 2.630918745880127,
+            "top1": 48.390000036621096,
+            "top5": 71.93200004638672
+        }
+    },
+    {
+        "epoch": 142,
+        "train": {
+            "loss": 2.3723840713500977
+        },
+        "validation": {
+            "loss": 2.8102378479003907,
+            "top1": 48.43999997070313,
+            "top5": 69.01600008300781
+        }
+    },
+    {
+        "epoch": 147,
+        "train": {
+            "loss": 2.3710074424743652
+        },
+        "validation": {
+            "loss": 2.805688974609375,
+            "top1": 48.46000003051758,
+            "top5": 69.00600003417969
+        }
+    },
+    {
+        "epoch": 141,
+        "train": {
+            "loss": 2.3747737407684326
+        },
+        "validation": {
+            "loss": 2.8027838427734375,
+            "top1": 48.49800007080078,
+            "top5": 69.11000010742187
+        }
+    },
+    {
+        "epoch": 143,
+        "train": {
+            "loss": 2.371351718902588
+        },
+        "validation": {
+            "loss": 2.802447621307373,
+            "top1": 48.55600001953125,
+            "top5": 68.97800005859375
+        }
+    },
+    {
+        "epoch": 78,
+        "train": {
+            "loss": 3.2513880729675293
+        },
+        "validation": {
+            "loss": 2.6659132025909424,
+            "top1": 48.6280000390625,
+            "top5": 71.5360001220703
+        }
+    },
+    {
+        "epoch": 90,
+        "train": {
+            "loss": 3.0457143783569336
+        },
+        "validation": {
+            "loss": 2.690470294418335,
+            "top1": 48.64000006591797,
+            "top5": 70.844
+        }
+    },
+    {
+        "epoch": 60,
+        "train": {
+            "loss": 3.5596461296081543
+        },
+        "validation": {
+            "loss": 2.631229259338379,
+            "top1": 48.73400004516601,
+            "top5": 72.43400006835938
+        }
+    }
+]
diff --git a/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/requirements.txt b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d388cd950a4a88b1d9f37efc5c83fee7de76a1be
--- /dev/null
+++ b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/requirements.txt
@@ -0,0 +1,108 @@
+GitPython==3.1.44
+MarkupSafe==2.1.5
+PyYAML==6.0.2
+aiofiles==23.2.1
+aiohappyeyeballs==2.4.6
+aiohttp==3.11.12
+aiosignal==1.3.2
+annotated-types==0.7.0
+anyio==4.8.0
+async-timeout==5.0.1
+attrs==25.1.0
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+contourpy==1.3.0
+cycler==0.12.1
+datasets==3.3.2
+dill==0.3.8
+docker-pycreds==0.4.0
+eval_type_backport==0.2.2
+exceptiongroup==1.2.2
+fastapi==0.115.8
+ffmpy==0.5.0
+filelock==3.17.0
+fonttools==4.56.0
+frozenlist==1.5.0
+fsspec==2024.12.0
+gitdb==4.0.12
+gradio==4.44.1
+gradio_client==1.3.0
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.29.1
+idna==3.10
+importlib_metadata==8.6.1
+importlib_resources==6.5.2
+Jinja2==3.1.5
+kiwisolver==1.4.7
+markdown-it-py==3.0.0
+matplotlib==3.9.4
+mdurl==0.1.2
+multidict==6.1.0
+multiprocess==0.70.16
+numpy==2.0.2
+orjson==3.10.15
+packaging==24.2
+pandas==2.2.3
+pillow==10.4.0
+platformdirs==4.3.6
+propcache==0.3.0
+protobuf==5.29.3
+psutil==7.0.0
+pyarrow==19.0.1
+pydantic==2.10.6
+pydantic_core==2.27.2
+pydub==0.25.1
+Pygments==2.19.1
+pyparsing==3.2.1
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.1
+requests==2.32.3
+rich==13.9.4
+ruff==0.9.7
+semantic-version==2.10.0
+sentry-sdk==2.22.0
+setproctitle==1.3.4
+shellingham==1.5.4
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+starlette==0.45.3
+tomlkit==0.12.0
+tqdm==4.67.1
+typer==0.15.1
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0
+uvicorn==0.34.0
+wandb==0.19.7
+websockets==12.0
+xxhash==3.5.0
+yarl==1.18.3
+zipp==3.21.0
+mpmath==1.3.0
+networkx==3.2.1
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-runtime-cu12==12.4.127
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.2.1.3
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-cusparselt-cu12==0.6.2
+nvidia-nccl-cu12==2.21.5
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.4.127
+safetensors==0.5.2
+sympy==1.13.1
+torch==2.6.0
+torchvision==0.21.0
+triton==3.2.0
+pip==23.0.1
+setuptools==58.1.0
+wheel==0.45.1
diff --git a/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/wandb-metadata.json b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..4ccd2a5cd2be84c6e3dfd5d08d66b3b2298e189b
--- /dev/null
+++ b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/files/wandb-metadata.json
@@ -0,0 +1,88 @@
+{
+  "os":  "Linux-5.10.233-224.894.amzn2.x86_64-x86_64-with-glibc2.36",
+  "python":  "CPython 3.9.21",
+  "startedAt":  "2025-02-24T01:13:34.719874Z",
+  "args":  [
+    "--dataset",
+    "hfds/datacomp/imagenet-1k-random-20.0-frac-1over4",
+    "--log-wandb",
+    "--wandb-project",
+    "ImageNetTraining20.0-frac-1over4",
+    "--experiment",
+    "ImageNetTraining20.0-frac-1over4",
+    "--model",
+    "seresnet34",
+    "--sched",
+    "cosine",
+    "--epochs",
+    "150",
+    "--warmup-epochs",
+    "5",
+    "--lr",
+    "0.4",
+    "--reprob",
+    "0.5",
+    "--remode",
+    "pixel",
+    "--batch-size",
+    "256",
+    "--amp",
+    "-j",
+    "4"
+  ],
+  "program":  "/app/pytorch-image-models/train.py",
+  "codePath":  "train.py",
+  "git":  {
+    "remote":  "https://github.com/huggingface/pytorch-image-models.git",
+    "commit":  "e76ea5474db7c0f3cc00424efc6a4e52fb4e945e"
+  },
+  "email":  "meg@huggingface.co",
+  "root":  "/app/pytorch-image-models",
+  "host":  "r-datacomp-imagenettraining20-0-frac-1over4-ixy510w3-af1dd-avuh",
+  "executable":  "/usr/local/bin/python3.9",
+  "codePathLocal":  "train.py",
+  "cpu_count":  24,
+  "cpu_count_logical":  48,
+  "gpu":  "NVIDIA L4",
+  "gpu_count":  4,
+  "disk":  {
+    "/":  {
+      "total":  "3757625933824",
+      "used":  "136043196416"
+    }
+  },
+  "memory":  {
+    "total":  "195171028992"
+  },
+  "cpu":  {
+    "count":  24,
+    "countLogical":  48
+  },
+  "gpu_nvidia":  [
+    {
+      "name":  "NVIDIA L4",
+      "memoryTotal":  "24152899584",
+      "cudaCores":  7424,
+      "architecture":  "Ada"
+    },
+    {
+      "name":  "NVIDIA L4",
+      "memoryTotal":  "24152899584",
+      "cudaCores":  7424,
+      "architecture":  "Ada"
+    },
+    {
+      "name":  "NVIDIA L4",
+      "memoryTotal":  "24152899584",
+      "cudaCores":  7424,
+      "architecture":  "Ada"
+    },
+    {
+      "name":  "NVIDIA L4",
+      "memoryTotal":  "24152899584",
+      "cudaCores":  7424,
+      "architecture":  "Ada"
+    }
+  ],
+  "cudaVersion":  "12.4"
+}
\ No newline at end of file
diff --git a/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-core.log b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..59974320cd17e23338401658b26533a3faf4a725
--- /dev/null
+++ b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-core.log
@@ -0,0 +1,7 @@
+{"time":"2025-02-24T00:57:01.216995823Z","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpalcqokby/port-1.txt","pid":1,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false}
+{"time":"2025-02-24T00:57:01.218056989Z","level":"INFO","msg":"Will exit if parent process dies.","ppid":1}
+{"time":"2025-02-24T00:57:01.218051989Z","level":"INFO","msg":"server is running","addr":{"IP":"127.0.0.1","Port":39583,"Zone":""}}
+{"time":"2025-02-24T00:57:01.408084946Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"127.0.0.1:34126"}
+{"time":"2025-02-24T01:13:34.443708914Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"127.0.0.1:47696"}
+{"time":"2025-02-24T01:13:34.720851379Z","level":"INFO","msg":"handleInformInit: received","streamId":"w1u5xl1c","id":"127.0.0.1:47696"}
+{"time":"2025-02-24T01:13:34.826586583Z","level":"INFO","msg":"handleInformInit: stream started","streamId":"w1u5xl1c","id":"127.0.0.1:47696"}
diff --git a/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-internal.log b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..2a0011d94f7bcc8ed55f0805b551e10de01e3006
--- /dev/null
+++ b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-internal.log
@@ -0,0 +1,9 @@
+{"time":"2025-02-24T01:13:34.721009141Z","level":"INFO","msg":"stream: starting","core version":"0.19.7","symlink path":"/app/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug-core.log"}
+{"time":"2025-02-24T01:13:34.826536368Z","level":"INFO","msg":"created new stream","id":"w1u5xl1c"}
+{"time":"2025-02-24T01:13:34.826579152Z","level":"INFO","msg":"stream: started","id":"w1u5xl1c"}
+{"time":"2025-02-24T01:13:34.826642277Z","level":"INFO","msg":"writer: Do: started","stream_id":"w1u5xl1c"}
+{"time":"2025-02-24T01:13:34.82668172Z","level":"INFO","msg":"sender: started","stream_id":"w1u5xl1c"}
+{"time":"2025-02-24T01:13:34.826767286Z","level":"INFO","msg":"handler: started","stream_id":"w1u5xl1c"}
+{"time":"2025-02-24T01:13:35.017665625Z","level":"INFO","msg":"Starting system monitor"}
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+{"time":"2025-02-24T07:17:05.018275432Z","level":"ERROR","msg":"monitor: memory: error sampling metrics: open /proc/144/statm: no such file or directory"}
diff --git a/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug.log b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/logs/debug.log
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index 0000000000000000000000000000000000000000..d0c661847d7d864226c44b93fe43fa628cb292b7
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+2025-02-24 01:13:34,681 INFO    MainThread:144 [wandb_init.py:init():788] sending inform_init request
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+2025-02-24 01:13:35,117 INFO    MainThread:144 [wandb_init.py:init():1037] run started, returning control to user process
diff --git a/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/run-w1u5xl1c.wandb b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/run-w1u5xl1c.wandb
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+++ b/pytorch-image-models/wandb/run-20250224_011334-w1u5xl1c/run-w1u5xl1c.wandb
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diff --git a/requirements.txt b/requirements.txt
index 32191d5a0fb9f649cdf72c1ec245baac3c1c6212..3f839a7d886a627e58a4da5438bf7180761e5a1a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
 gradio
 datasets
-wandb
\ No newline at end of file
+wandb
+importlib_metadata
\ No newline at end of file