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diff --git a/pytorch-image-models/wandb/debug.log b/pytorch-image-models/wandb/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..6d1c2e8833f0fbe361b626f042289fcc3fb2fce5
--- /dev/null
+++ b/pytorch-image-models/wandb/debug.log
@@ -0,0 +1,22 @@
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_setup.py:_flush():67] Current SDK version is 0.19.7
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_setup.py:_flush():67] Configure stats pid to 158
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_setup.py:_flush():67] Loading settings from /home/user/.config/wandb/settings
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_setup.py:_flush():67] Loading settings from /app/pytorch-image-models/wandb/settings
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_setup.py:_flush():67] Loading settings from environment variables
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_init.py:setup_run_log_directory():647] Logging user logs to /app/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/logs/debug.log
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_init.py:setup_run_log_directory():648] Logging internal logs to /app/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/logs/debug-internal.log
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_init.py:init():761] calling init triggers
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_init.py:init():766] wandb.init called with sweep_config: {}
+config: {'data': None, 'data_dir': None, 'dataset': 'hfds/datacomp/imagenet-1k-random-40.0-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': 'ImageNetTraining40.0-frac-1over4', 'eval_metric': 'top1', 'tta': 0, 'use_multi_epochs_loader': False, 'log_wandb': True, 'wandb_project': 'ImageNetTraining40.0-frac-1over4', 'wandb_tags': [], 'wandb_resume_id': '', 'prefetcher': True, 'distributed': True, 'world_size': 4, 'rank': 0, '_wandb': {}}
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_init.py:init():784] starting backend
+2025-02-24 01:57:23,338 INFO    MainThread:158 [wandb_init.py:init():788] sending inform_init request
+2025-02-24 01:57:23,357 INFO    MainThread:158 [backend.py:_multiprocessing_setup():97] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2025-02-24 01:57:23,358 INFO    MainThread:158 [wandb_init.py:init():803] backend started and connected
+2025-02-24 01:57:23,361 INFO    MainThread:158 [wandb_init.py:init():896] updated telemetry
+2025-02-24 01:57:23,383 INFO    MainThread:158 [wandb_init.py:init():920] communicating run to backend with 90.0 second timeout
+2025-02-24 01:57:23,608 INFO    MainThread:158 [wandb_init.py:init():995] starting run threads in backend
+2025-02-24 01:57:23,711 INFO    MainThread:158 [wandb_run.py:_console_start():2377] atexit reg
+2025-02-24 01:57:23,712 INFO    MainThread:158 [wandb_run.py:_redirect():2227] redirect: wrap_raw
+2025-02-24 01:57:23,712 INFO    MainThread:158 [wandb_run.py:_redirect():2292] Wrapping output streams.
+2025-02-24 01:57:23,712 INFO    MainThread:158 [wandb_run.py:_redirect():2317] Redirects installed.
+2025-02-24 01:57:23,714 INFO    MainThread:158 [wandb_init.py:init():1037] run started, returning control to user process
diff --git a/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/files/output.log b/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..58c8efff79f604515d6d0cd90cabba7a48bb1643
--- /dev/null
+++ b/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/files/output.log
@@ -0,0 +1,2338 @@
+Scheduled epochs: 150 (epochs + cooldown_epochs). Warmup within epochs when warmup_prefix=False. LR stepped per epoch.
+Train: 0 [   0/312 (  0%)]  Loss: 6.93 (6.93)  Time: 3.868s,  264.76/s  (3.868s,  264.76/s)  LR: 1.000e-05  Data: 1.239 (1.239)
+Train: 0 [  50/312 ( 16%)]  Loss: 6.95 (6.94)  Time: 0.398s, 2571.53/s  (0.466s, 2198.40/s)  LR: 1.000e-05  Data: 0.027 (0.051)
+Train: 0 [ 100/312 ( 32%)]  Loss: 6.94 (6.94)  Time: 0.402s, 2546.78/s  (0.434s, 2359.68/s)  LR: 1.000e-05  Data: 0.026 (0.039)
+Train: 0 [ 150/312 ( 48%)]  Loss: 6.96 (6.94)  Time: 0.403s, 2542.54/s  (0.424s, 2414.35/s)  LR: 1.000e-05  Data: 0.028 (0.035)
+Train: 0 [ 200/312 ( 64%)]  Loss: 6.94 (6.94)  Time: 0.402s, 2549.84/s  (0.419s, 2442.71/s)  LR: 1.000e-05  Data: 0.025 (0.033)
+Train: 0 [ 250/312 ( 80%)]  Loss: 6.94 (6.94)  Time: 0.406s, 2525.12/s  (0.416s, 2460.34/s)  LR: 1.000e-05  Data: 0.027 (0.032)
+Train: 0 [ 300/312 ( 96%)]  Loss: 6.95 (6.94)  Time: 0.406s, 2523.83/s  (0.414s, 2472.72/s)  LR: 1.000e-05  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.548 (1.548)  Loss:   6.946 ( 6.946)  Acc@1:   0.098 (  0.098)  Acc@5:   0.391 (  0.391)
+Test: [  48/48]  Time: 0.704 (0.340)  Loss:   6.940 ( 6.939)  Acc@1:   0.118 (  0.082)  Acc@5:   0.354 (  0.506)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 1 [   0/312 (  0%)]  Loss: 6.94 (6.94)  Time: 1.813s,  564.81/s  (1.813s,  564.81/s)  LR: 8.001e-02  Data: 1.446 (1.446)
+Train: 1 [  50/312 ( 16%)]  Loss: 6.90 (6.92)  Time: 0.411s, 2490.73/s  (0.436s, 2347.02/s)  LR: 8.001e-02  Data: 0.027 (0.054)
+Train: 1 [ 100/312 ( 32%)]  Loss: 6.88 (6.90)  Time: 0.407s, 2516.64/s  (0.424s, 2413.03/s)  LR: 8.001e-02  Data: 0.027 (0.041)
+Train: 1 [ 150/312 ( 48%)]  Loss: 6.84 (6.89)  Time: 0.405s, 2527.72/s  (0.418s, 2447.28/s)  LR: 8.001e-02  Data: 0.027 (0.036)
+Train: 1 [ 200/312 ( 64%)]  Loss: 6.85 (6.88)  Time: 0.405s, 2527.03/s  (0.415s, 2466.66/s)  LR: 8.001e-02  Data: 0.024 (0.034)
+Train: 1 [ 250/312 ( 80%)]  Loss: 6.84 (6.87)  Time: 0.414s, 2473.66/s  (0.413s, 2477.29/s)  LR: 8.001e-02  Data: 0.028 (0.033)
+Train: 1 [ 300/312 ( 96%)]  Loss: 6.81 (6.86)  Time: 0.412s, 2482.99/s  (0.413s, 2481.12/s)  LR: 8.001e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.431 (1.431)  Loss:   6.600 ( 6.600)  Acc@1:   0.879 (  0.879)  Acc@5:   4.297 (  4.297)
+Test: [  48/48]  Time: 0.089 (0.325)  Loss:   6.546 ( 6.588)  Acc@1:   0.590 (  1.172)  Acc@5:   4.599 (  4.328)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 2 [   0/312 (  0%)]  Loss: 6.79 (6.79)  Time: 1.623s,  631.09/s  (1.623s,  631.09/s)  LR: 1.600e-01  Data: 1.133 (1.133)
+Train: 2 [  50/312 ( 16%)]  Loss: 6.78 (6.80)  Time: 0.411s, 2490.12/s  (0.434s, 2361.87/s)  LR: 1.600e-01  Data: 0.029 (0.048)
+Train: 2 [ 100/312 ( 32%)]  Loss: 6.78 (6.80)  Time: 0.408s, 2511.70/s  (0.422s, 2429.18/s)  LR: 1.600e-01  Data: 0.027 (0.038)
+Train: 2 [ 150/312 ( 48%)]  Loss: 6.78 (6.79)  Time: 0.413s, 2479.35/s  (0.418s, 2447.98/s)  LR: 1.600e-01  Data: 0.026 (0.034)
+Train: 2 [ 200/312 ( 64%)]  Loss: 6.75 (6.79)  Time: 0.414s, 2472.15/s  (0.417s, 2455.18/s)  LR: 1.600e-01  Data: 0.026 (0.032)
+Train: 2 [ 250/312 ( 80%)]  Loss: 6.75 (6.78)  Time: 0.413s, 2479.35/s  (0.416s, 2459.39/s)  LR: 1.600e-01  Data: 0.026 (0.031)
+Train: 2 [ 300/312 ( 96%)]  Loss: 6.72 (6.77)  Time: 0.414s, 2470.91/s  (0.416s, 2463.00/s)  LR: 1.600e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.411 (1.411)  Loss:   6.380 ( 6.380)  Acc@1:   1.562 (  1.562)  Acc@5:   6.152 (  6.152)
+Test: [  48/48]  Time: 0.089 (0.323)  Loss:   6.318 ( 6.369)  Acc@1:   1.651 (  2.062)  Acc@5:   8.137 (  6.894)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 3 [   0/312 (  0%)]  Loss: 6.72 (6.72)  Time: 1.833s,  558.77/s  (1.833s,  558.77/s)  LR: 2.400e-01  Data: 1.162 (1.162)
+Train: 3 [  50/312 ( 16%)]  Loss: 6.74 (6.72)  Time: 0.410s, 2497.60/s  (0.438s, 2338.11/s)  LR: 2.400e-01  Data: 0.027 (0.049)
+Train: 3 [ 100/312 ( 32%)]  Loss: 6.70 (6.72)  Time: 0.415s, 2466.45/s  (0.425s, 2411.86/s)  LR: 2.400e-01  Data: 0.028 (0.038)
+Train: 3 [ 150/312 ( 48%)]  Loss: 6.68 (6.71)  Time: 0.408s, 2511.53/s  (0.421s, 2433.97/s)  LR: 2.400e-01  Data: 0.026 (0.035)
+Train: 3 [ 200/312 ( 64%)]  Loss: 6.68 (6.71)  Time: 0.405s, 2527.38/s  (0.417s, 2453.12/s)  LR: 2.400e-01  Data: 0.026 (0.033)
+Train: 3 [ 250/312 ( 80%)]  Loss: 6.69 (6.70)  Time: 0.405s, 2526.21/s  (0.415s, 2466.50/s)  LR: 2.400e-01  Data: 0.028 (0.032)
+Train: 3 [ 300/312 ( 96%)]  Loss: 6.64 (6.70)  Time: 0.407s, 2514.84/s  (0.414s, 2476.22/s)  LR: 2.400e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.418 (1.418)  Loss:   6.199 ( 6.199)  Acc@1:   2.734 (  2.734)  Acc@5:   8.887 (  8.887)
+Test: [  48/48]  Time: 0.088 (0.324)  Loss:   6.148 ( 6.173)  Acc@1:   1.887 (  2.724)  Acc@5:   9.316 (  8.756)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 4 [   0/312 (  0%)]  Loss: 6.68 (6.68)  Time: 1.654s,  618.96/s  (1.654s,  618.96/s)  LR: 3.200e-01  Data: 1.281 (1.281)
+Train: 4 [  50/312 ( 16%)]  Loss: 6.65 (6.64)  Time: 0.411s, 2489.62/s  (0.433s, 2365.94/s)  LR: 3.200e-01  Data: 0.028 (0.052)
+Train: 4 [ 100/312 ( 32%)]  Loss: 6.61 (6.64)  Time: 0.417s, 2457.93/s  (0.423s, 2419.66/s)  LR: 3.200e-01  Data: 0.030 (0.040)
+Train: 4 [ 150/312 ( 48%)]  Loss: 6.61 (6.63)  Time: 0.416s, 2461.58/s  (0.420s, 2437.14/s)  LR: 3.200e-01  Data: 0.027 (0.036)
+Train: 4 [ 200/312 ( 64%)]  Loss: 6.60 (6.63)  Time: 0.412s, 2487.69/s  (0.419s, 2446.02/s)  LR: 3.200e-01  Data: 0.027 (0.033)
+Train: 4 [ 250/312 ( 80%)]  Loss: 6.62 (6.62)  Time: 0.414s, 2474.98/s  (0.418s, 2450.79/s)  LR: 3.200e-01  Data: 0.028 (0.032)
+Train: 4 [ 300/312 ( 96%)]  Loss: 6.57 (6.61)  Time: 0.417s, 2453.89/s  (0.417s, 2454.18/s)  LR: 3.200e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.411 (1.411)  Loss:   5.937 ( 5.937)  Acc@1:   3.223 (  3.223)  Acc@5:  11.816 ( 11.816)
+Test: [  48/48]  Time: 0.089 (0.324)  Loss:   5.869 ( 5.910)  Acc@1:   4.481 (  4.394)  Acc@5:  14.505 ( 13.086)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 5 [   0/312 (  0%)]  Loss: 6.54 (6.54)  Time: 1.653s,  619.58/s  (1.653s,  619.58/s)  LR: 3.989e-01  Data: 1.277 (1.277)
+Train: 5 [  50/312 ( 16%)]  Loss: 6.55 (6.55)  Time: 0.413s, 2477.17/s  (0.437s, 2344.13/s)  LR: 3.989e-01  Data: 0.027 (0.052)
+Train: 5 [ 100/312 ( 32%)]  Loss: 6.50 (6.54)  Time: 0.414s, 2470.52/s  (0.426s, 2405.13/s)  LR: 3.989e-01  Data: 0.026 (0.040)
+Train: 5 [ 150/312 ( 48%)]  Loss: 6.52 (6.54)  Time: 0.414s, 2473.19/s  (0.422s, 2428.90/s)  LR: 3.989e-01  Data: 0.026 (0.036)
+Train: 5 [ 200/312 ( 64%)]  Loss: 6.53 (6.53)  Time: 0.418s, 2451.95/s  (0.420s, 2439.50/s)  LR: 3.989e-01  Data: 0.028 (0.034)
+Train: 5 [ 250/312 ( 80%)]  Loss: 6.52 (6.52)  Time: 0.411s, 2491.03/s  (0.418s, 2447.58/s)  LR: 3.989e-01  Data: 0.026 (0.032)
+Train: 5 [ 300/312 ( 96%)]  Loss: 6.43 (6.52)  Time: 0.414s, 2474.98/s  (0.418s, 2452.14/s)  LR: 3.989e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.431 (1.431)  Loss:   5.733 ( 5.733)  Acc@1:   4.395 (  4.395)  Acc@5:  14.453 ( 14.453)
+Test: [  48/48]  Time: 0.089 (0.327)  Loss:   5.687 ( 5.707)  Acc@1:   6.486 (  5.044)  Acc@5:  16.627 ( 15.574)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 6 [   0/312 (  0%)]  Loss: 6.42 (6.42)  Time: 2.166s,  472.72/s  (2.166s,  472.72/s)  LR: 3.984e-01  Data: 1.791 (1.791)
+Train: 6 [  50/312 ( 16%)]  Loss: 6.48 (6.44)  Time: 0.410s, 2498.03/s  (0.444s, 2306.40/s)  LR: 3.984e-01  Data: 0.027 (0.062)
+Train: 6 [ 100/312 ( 32%)]  Loss: 6.43 (6.43)  Time: 0.413s, 2480.31/s  (0.429s, 2386.51/s)  LR: 3.984e-01  Data: 0.027 (0.045)
+Train: 6 [ 150/312 ( 48%)]  Loss: 6.38 (6.43)  Time: 0.412s, 2488.12/s  (0.424s, 2417.05/s)  LR: 3.984e-01  Data: 0.028 (0.039)
+Train: 6 [ 200/312 ( 64%)]  Loss: 6.41 (6.42)  Time: 0.411s, 2493.20/s  (0.421s, 2430.83/s)  LR: 3.984e-01  Data: 0.026 (0.036)
+Train: 6 [ 250/312 ( 80%)]  Loss: 6.39 (6.42)  Time: 0.409s, 2501.34/s  (0.419s, 2442.37/s)  LR: 3.984e-01  Data: 0.025 (0.034)
+Train: 6 [ 300/312 ( 96%)]  Loss: 6.29 (6.41)  Time: 0.414s, 2474.90/s  (0.418s, 2449.01/s)  LR: 3.984e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.413 (1.413)  Loss:   5.489 ( 5.489)  Acc@1:   7.715 (  7.715)  Acc@5:  18.457 ( 18.457)
+Test: [  48/48]  Time: 0.089 (0.323)  Loss:   5.395 ( 5.457)  Acc@1:   9.080 (  7.944)  Acc@5:  23.585 ( 20.938)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 7 [   0/312 (  0%)]  Loss: 6.37 (6.37)  Time: 1.503s,  681.25/s  (1.503s,  681.25/s)  LR: 3.979e-01  Data: 1.122 (1.122)
+Train: 7 [  50/312 ( 16%)]  Loss: 6.35 (6.33)  Time: 0.410s, 2494.98/s  (0.431s, 2373.17/s)  LR: 3.979e-01  Data: 0.028 (0.049)
+Train: 7 [ 100/312 ( 32%)]  Loss: 6.32 (6.33)  Time: 0.418s, 2449.24/s  (0.422s, 2424.19/s)  LR: 3.979e-01  Data: 0.027 (0.038)
+Train: 7 [ 150/312 ( 48%)]  Loss: 6.26 (6.33)  Time: 0.406s, 2522.87/s  (0.418s, 2447.17/s)  LR: 3.979e-01  Data: 0.028 (0.035)
+Train: 7 [ 200/312 ( 64%)]  Loss: 6.29 (6.32)  Time: 0.409s, 2501.63/s  (0.415s, 2464.93/s)  LR: 3.979e-01  Data: 0.029 (0.033)
+Train: 7 [ 250/312 ( 80%)]  Loss: 6.26 (6.32)  Time: 0.405s, 2525.92/s  (0.413s, 2476.48/s)  LR: 3.979e-01  Data: 0.028 (0.032)
+Train: 7 [ 300/312 ( 96%)]  Loss: 6.35 (6.31)  Time: 0.409s, 2501.88/s  (0.413s, 2481.59/s)  LR: 3.979e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.422 (1.422)  Loss:   5.083 ( 5.083)  Acc@1:  10.156 ( 10.156)  Acc@5:  27.344 ( 27.344)
+Test: [  48/48]  Time: 0.089 (0.324)  Loss:   5.029 ( 5.094)  Acc@1:  11.557 ( 11.026)  Acc@5:  29.481 ( 27.408)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 8 [   0/312 (  0%)]  Loss: 6.17 (6.17)  Time: 1.719s,  595.87/s  (1.719s,  595.87/s)  LR: 3.972e-01  Data: 1.341 (1.341)
+Train: 8 [  50/312 ( 16%)]  Loss: 6.29 (6.24)  Time: 0.411s, 2491.24/s  (0.439s, 2331.21/s)  LR: 3.972e-01  Data: 0.027 (0.053)
+Train: 8 [ 100/312 ( 32%)]  Loss: 6.23 (6.23)  Time: 0.415s, 2469.64/s  (0.427s, 2397.12/s)  LR: 3.972e-01  Data: 0.027 (0.040)
+Train: 8 [ 150/312 ( 48%)]  Loss: 6.20 (6.23)  Time: 0.410s, 2496.46/s  (0.423s, 2422.31/s)  LR: 3.972e-01  Data: 0.027 (0.036)
+Train: 8 [ 200/312 ( 64%)]  Loss: 6.23 (6.23)  Time: 0.418s, 2450.45/s  (0.420s, 2435.88/s)  LR: 3.972e-01  Data: 0.025 (0.034)
+Train: 8 [ 250/312 ( 80%)]  Loss: 6.29 (6.23)  Time: 0.411s, 2490.35/s  (0.419s, 2443.91/s)  LR: 3.972e-01  Data: 0.029 (0.032)
+Train: 8 [ 300/312 ( 96%)]  Loss: 6.15 (6.22)  Time: 0.417s, 2455.37/s  (0.418s, 2448.94/s)  LR: 3.972e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.421 (1.421)  Loss:   5.200 ( 5.200)  Acc@1:   9.863 (  9.863)  Acc@5:  24.023 ( 24.023)
+Test: [  48/48]  Time: 0.089 (0.326)  Loss:   5.144 ( 5.180)  Acc@1:  10.259 ( 10.976)  Acc@5:  28.774 ( 26.670)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 9 [   0/312 (  0%)]  Loss: 6.22 (6.22)  Time: 1.520s,  673.61/s  (1.520s,  673.61/s)  LR: 3.965e-01  Data: 1.043 (1.043)
+Train: 9 [  50/312 ( 16%)]  Loss: 6.17 (6.14)  Time: 0.407s, 2513.64/s  (0.434s, 2357.25/s)  LR: 3.965e-01  Data: 0.027 (0.047)
+Train: 9 [ 100/312 ( 32%)]  Loss: 6.04 (6.14)  Time: 0.410s, 2500.41/s  (0.421s, 2431.55/s)  LR: 3.965e-01  Data: 0.027 (0.037)
+Train: 9 [ 150/312 ( 48%)]  Loss: 6.10 (6.14)  Time: 0.410s, 2497.82/s  (0.418s, 2448.95/s)  LR: 3.965e-01  Data: 0.027 (0.034)
+Train: 9 [ 200/312 ( 64%)]  Loss: 6.19 (6.14)  Time: 0.409s, 2504.54/s  (0.416s, 2459.82/s)  LR: 3.965e-01  Data: 0.025 (0.032)
+Train: 9 [ 250/312 ( 80%)]  Loss: 6.12 (6.14)  Time: 0.413s, 2479.14/s  (0.416s, 2463.72/s)  LR: 3.965e-01  Data: 0.028 (0.031)
+Train: 9 [ 300/312 ( 96%)]  Loss: 6.14 (6.13)  Time: 0.410s, 2496.24/s  (0.415s, 2466.30/s)  LR: 3.965e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.408 (1.408)  Loss:   4.918 ( 4.918)  Acc@1:  12.012 ( 12.012)  Acc@5:  32.227 ( 32.227)
+Test: [  48/48]  Time: 0.088 (0.325)  Loss:   4.843 ( 4.915)  Acc@1:  14.623 ( 13.284)  Acc@5:  33.608 ( 31.442)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-0.pth.tar', 0.0819999999666214)
+
+Train: 10 [   0/312 (  0%)]  Loss: 6.05 (6.05)  Time: 1.760s,  581.81/s  (1.760s,  581.81/s)  LR: 3.956e-01  Data: 1.388 (1.388)
+Train: 10 [  50/312 ( 16%)]  Loss: 6.06 (6.06)  Time: 0.409s, 2503.22/s  (0.432s, 2372.76/s)  LR: 3.956e-01  Data: 0.030 (0.054)
+Train: 10 [ 100/312 ( 32%)]  Loss: 6.04 (6.06)  Time: 0.408s, 2507.25/s  (0.420s, 2439.34/s)  LR: 3.956e-01  Data: 0.027 (0.041)
+Train: 10 [ 150/312 ( 48%)]  Loss: 6.03 (6.06)  Time: 0.412s, 2482.80/s  (0.417s, 2455.85/s)  LR: 3.956e-01  Data: 0.027 (0.036)
+Train: 10 [ 200/312 ( 64%)]  Loss: 6.06 (6.05)  Time: 0.408s, 2508.70/s  (0.415s, 2465.75/s)  LR: 3.956e-01  Data: 0.026 (0.034)
+Train: 10 [ 250/312 ( 80%)]  Loss: 6.07 (6.05)  Time: 0.415s, 2466.42/s  (0.415s, 2470.25/s)  LR: 3.956e-01  Data: 0.027 (0.033)
+Train: 10 [ 300/312 ( 96%)]  Loss: 6.05 (6.05)  Time: 0.412s, 2487.57/s  (0.414s, 2470.79/s)  LR: 3.956e-01  Data: 0.025 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   4.959 ( 4.959)  Acc@1:  13.184 ( 13.184)  Acc@5:  30.078 ( 30.078)
+Test: [  48/48]  Time: 0.089 (0.325)  Loss:   4.820 ( 4.917)  Acc@1:  13.797 ( 13.280)  Acc@5:  31.840 ( 30.706)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-1.pth.tar', 1.1719999995803834)
+
+Train: 11 [   0/312 (  0%)]  Loss: 5.99 (5.99)  Time: 1.986s,  515.64/s  (1.986s,  515.64/s)  LR: 3.947e-01  Data: 1.608 (1.608)
+Train: 11 [  50/312 ( 16%)]  Loss: 5.99 (5.97)  Time: 0.414s, 2475.60/s  (0.445s, 2302.74/s)  LR: 3.947e-01  Data: 0.026 (0.058)
+Train: 11 [ 100/312 ( 32%)]  Loss: 6.04 (5.97)  Time: 0.405s, 2529.68/s  (0.428s, 2392.48/s)  LR: 3.947e-01  Data: 0.026 (0.042)
+Train: 11 [ 150/312 ( 48%)]  Loss: 5.97 (5.97)  Time: 0.406s, 2521.68/s  (0.421s, 2431.67/s)  LR: 3.947e-01  Data: 0.028 (0.037)
+Train: 11 [ 200/312 ( 64%)]  Loss: 6.00 (5.98)  Time: 0.407s, 2515.91/s  (0.418s, 2451.15/s)  LR: 3.947e-01  Data: 0.025 (0.035)
+Train: 11 [ 250/312 ( 80%)]  Loss: 6.01 (5.97)  Time: 0.413s, 2476.65/s  (0.416s, 2459.57/s)  LR: 3.947e-01  Data: 0.027 (0.033)
+Train: 11 [ 300/312 ( 96%)]  Loss: 5.94 (5.97)  Time: 0.413s, 2478.39/s  (0.416s, 2462.28/s)  LR: 3.947e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.442 (1.442)  Loss:   4.841 ( 4.841)  Acc@1:  13.574 ( 13.574)  Acc@5:  34.180 ( 34.180)
+Test: [  48/48]  Time: 0.089 (0.325)  Loss:   4.724 ( 4.848)  Acc@1:  17.099 ( 15.196)  Acc@5:  38.915 ( 34.582)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-2.pth.tar', 2.0620000020599365)
+
+Train: 12 [   0/312 (  0%)]  Loss: 5.86 (5.86)  Time: 1.496s,  684.52/s  (1.496s,  684.52/s)  LR: 3.937e-01  Data: 1.125 (1.125)
+Train: 12 [  50/312 ( 16%)]  Loss: 5.85 (5.90)  Time: 0.406s, 2520.10/s  (0.432s, 2370.25/s)  LR: 3.937e-01  Data: 0.028 (0.049)
+Train: 12 [ 100/312 ( 32%)]  Loss: 5.90 (5.90)  Time: 0.411s, 2488.46/s  (0.421s, 2433.11/s)  LR: 3.937e-01  Data: 0.027 (0.038)
+Train: 12 [ 150/312 ( 48%)]  Loss: 5.95 (5.91)  Time: 0.408s, 2508.36/s  (0.418s, 2449.56/s)  LR: 3.937e-01  Data: 0.027 (0.035)
+Train: 12 [ 200/312 ( 64%)]  Loss: 5.85 (5.91)  Time: 0.409s, 2504.19/s  (0.416s, 2463.77/s)  LR: 3.937e-01  Data: 0.025 (0.033)
+Train: 12 [ 250/312 ( 80%)]  Loss: 5.86 (5.91)  Time: 0.409s, 2505.00/s  (0.414s, 2472.26/s)  LR: 3.937e-01  Data: 0.027 (0.032)
+Train: 12 [ 300/312 ( 96%)]  Loss: 5.89 (5.90)  Time: 0.414s, 2473.79/s  (0.414s, 2474.16/s)  LR: 3.937e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.416 (1.416)  Loss:   4.359 ( 4.359)  Acc@1:  20.898 ( 20.898)  Acc@5:  44.238 ( 44.238)
+Test: [  48/48]  Time: 0.089 (0.326)  Loss:   4.327 ( 4.410)  Acc@1:  21.344 ( 19.960)  Acc@5:  45.047 ( 42.262)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-3.pth.tar', 2.724000001487732)
+
+Train: 13 [   0/312 (  0%)]  Loss: 5.80 (5.80)  Time: 1.578s,  648.86/s  (1.578s,  648.86/s)  LR: 3.926e-01  Data: 1.201 (1.201)
+Train: 13 [  50/312 ( 16%)]  Loss: 5.76 (5.84)  Time: 0.411s, 2492.68/s  (0.435s, 2352.53/s)  LR: 3.926e-01  Data: 0.028 (0.051)
+Train: 13 [ 100/312 ( 32%)]  Loss: 5.82 (5.84)  Time: 0.411s, 2493.58/s  (0.423s, 2419.74/s)  LR: 3.926e-01  Data: 0.028 (0.039)
+Train: 13 [ 150/312 ( 48%)]  Loss: 5.82 (5.84)  Time: 0.419s, 2445.79/s  (0.420s, 2437.11/s)  LR: 3.926e-01  Data: 0.025 (0.035)
+Train: 13 [ 200/312 ( 64%)]  Loss: 5.86 (5.85)  Time: 0.409s, 2501.31/s  (0.418s, 2448.43/s)  LR: 3.926e-01  Data: 0.027 (0.033)
+Train: 13 [ 250/312 ( 80%)]  Loss: 5.86 (5.84)  Time: 0.411s, 2489.88/s  (0.417s, 2458.23/s)  LR: 3.926e-01  Data: 0.029 (0.032)
+Train: 13 [ 300/312 ( 96%)]  Loss: 5.78 (5.84)  Time: 0.414s, 2472.10/s  (0.416s, 2463.60/s)  LR: 3.926e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.435 (1.435)  Loss:   4.378 ( 4.378)  Acc@1:  19.922 ( 19.922)  Acc@5:  42.578 ( 42.578)
+Test: [  48/48]  Time: 0.089 (0.325)  Loss:   4.280 ( 4.406)  Acc@1:  20.991 ( 19.788)  Acc@5:  44.458 ( 41.392)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-4.pth.tar', 4.39399999923706)
+
+Train: 14 [   0/312 (  0%)]  Loss: 5.79 (5.79)  Time: 1.615s,  633.97/s  (1.615s,  633.97/s)  LR: 3.915e-01  Data: 1.242 (1.242)
+Train: 14 [  50/312 ( 16%)]  Loss: 5.80 (5.76)  Time: 0.409s, 2506.71/s  (0.432s, 2369.12/s)  LR: 3.915e-01  Data: 0.027 (0.051)
+Train: 14 [ 100/312 ( 32%)]  Loss: 5.82 (5.77)  Time: 0.420s, 2439.50/s  (0.422s, 2423.84/s)  LR: 3.915e-01  Data: 0.034 (0.040)
+Train: 14 [ 150/312 ( 48%)]  Loss: 5.82 (5.78)  Time: 0.414s, 2475.41/s  (0.420s, 2438.88/s)  LR: 3.915e-01  Data: 0.027 (0.036)
+Train: 14 [ 200/312 ( 64%)]  Loss: 6.00 (5.78)  Time: 0.411s, 2492.96/s  (0.418s, 2450.38/s)  LR: 3.915e-01  Data: 0.025 (0.034)
+Train: 14 [ 250/312 ( 80%)]  Loss: 5.79 (5.78)  Time: 0.408s, 2511.98/s  (0.416s, 2461.45/s)  LR: 3.915e-01  Data: 0.027 (0.032)
+Train: 14 [ 300/312 ( 96%)]  Loss: 5.70 (5.78)  Time: 0.410s, 2495.36/s  (0.415s, 2467.86/s)  LR: 3.915e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.469 (1.469)  Loss:   4.315 ( 4.315)  Acc@1:  23.633 ( 23.633)  Acc@5:  45.215 ( 45.215)
+Test: [  48/48]  Time: 0.090 (0.323)  Loss:   4.163 ( 4.309)  Acc@1:  23.703 ( 21.922)  Acc@5:  48.467 ( 44.714)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-5.pth.tar', 5.044000005493164)
+
+Train: 15 [   0/312 (  0%)]  Loss: 5.70 (5.70)  Time: 1.709s,  599.19/s  (1.709s,  599.19/s)  LR: 3.902e-01  Data: 1.329 (1.329)
+Train: 15 [  50/312 ( 16%)]  Loss: 5.76 (5.71)  Time: 0.411s, 2494.35/s  (0.439s, 2334.03/s)  LR: 3.902e-01  Data: 0.028 (0.053)
+Train: 15 [ 100/312 ( 32%)]  Loss: 5.78 (5.72)  Time: 0.404s, 2536.81/s  (0.423s, 2421.26/s)  LR: 3.902e-01  Data: 0.027 (0.040)
+Train: 15 [ 150/312 ( 48%)]  Loss: 5.74 (5.72)  Time: 0.408s, 2512.56/s  (0.417s, 2455.36/s)  LR: 3.902e-01  Data: 0.028 (0.036)
+Train: 15 [ 200/312 ( 64%)]  Loss: 5.62 (5.73)  Time: 0.408s, 2512.86/s  (0.414s, 2471.58/s)  LR: 3.902e-01  Data: 0.027 (0.034)
+Train: 15 [ 250/312 ( 80%)]  Loss: 5.59 (5.73)  Time: 0.409s, 2501.03/s  (0.413s, 2478.08/s)  LR: 3.902e-01  Data: 0.027 (0.033)
+Train: 15 [ 300/312 ( 96%)]  Loss: 5.70 (5.73)  Time: 0.415s, 2466.03/s  (0.413s, 2477.61/s)  LR: 3.902e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.413 (1.413)  Loss:   4.164 ( 4.164)  Acc@1:  24.902 ( 24.902)  Acc@5:  46.973 ( 46.973)
+Test: [  48/48]  Time: 0.088 (0.323)  Loss:   4.049 ( 4.164)  Acc@1:  22.877 ( 23.766)  Acc@5:  50.825 ( 47.202)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-6.pth.tar', 7.9440000012207035)
+
+Train: 16 [   0/312 (  0%)]  Loss: 5.50 (5.50)  Time: 1.652s,  619.96/s  (1.652s,  619.96/s)  LR: 3.889e-01  Data: 1.281 (1.281)
+Train: 16 [  50/312 ( 16%)]  Loss: 5.64 (5.65)  Time: 0.406s, 2521.75/s  (0.429s, 2384.90/s)  LR: 3.889e-01  Data: 0.028 (0.052)
+Train: 16 [ 100/312 ( 32%)]  Loss: 5.64 (5.66)  Time: 0.406s, 2523.98/s  (0.418s, 2451.89/s)  LR: 3.889e-01  Data: 0.025 (0.040)
+Train: 16 [ 150/312 ( 48%)]  Loss: 5.69 (5.67)  Time: 0.410s, 2497.69/s  (0.414s, 2472.86/s)  LR: 3.889e-01  Data: 0.033 (0.036)
+Train: 16 [ 200/312 ( 64%)]  Loss: 5.64 (5.67)  Time: 0.408s, 2508.64/s  (0.413s, 2479.00/s)  LR: 3.889e-01  Data: 0.027 (0.034)
+Train: 16 [ 250/312 ( 80%)]  Loss: 5.71 (5.68)  Time: 0.411s, 2490.69/s  (0.413s, 2479.06/s)  LR: 3.889e-01  Data: 0.026 (0.032)
+Train: 16 [ 300/312 ( 96%)]  Loss: 5.73 (5.68)  Time: 0.407s, 2512.95/s  (0.412s, 2483.26/s)  LR: 3.889e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.707 (1.707)  Loss:   4.127 ( 4.127)  Acc@1:  25.879 ( 25.879)  Acc@5:  49.121 ( 49.121)
+Test: [  48/48]  Time: 0.088 (0.324)  Loss:   3.952 ( 4.113)  Acc@1:  26.297 ( 25.064)  Acc@5:  53.420 ( 49.452)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-8.pth.tar', 10.976000012512207)
+
+Train: 17 [   0/312 (  0%)]  Loss: 5.52 (5.52)  Time: 1.565s,  654.41/s  (1.565s,  654.41/s)  LR: 3.875e-01  Data: 1.191 (1.191)
+Train: 17 [  50/312 ( 16%)]  Loss: 5.66 (5.60)  Time: 0.409s, 2502.36/s  (0.431s, 2373.67/s)  LR: 3.875e-01  Data: 0.029 (0.050)
+Train: 17 [ 100/312 ( 32%)]  Loss: 5.76 (5.62)  Time: 0.407s, 2514.03/s  (0.421s, 2430.17/s)  LR: 3.875e-01  Data: 0.027 (0.039)
+Train: 17 [ 150/312 ( 48%)]  Loss: 5.58 (5.63)  Time: 0.404s, 2531.89/s  (0.416s, 2458.82/s)  LR: 3.875e-01  Data: 0.027 (0.035)
+Train: 17 [ 200/312 ( 64%)]  Loss: 5.61 (5.63)  Time: 0.404s, 2532.53/s  (0.413s, 2477.65/s)  LR: 3.875e-01  Data: 0.027 (0.033)
+Train: 17 [ 250/312 ( 80%)]  Loss: 5.61 (5.63)  Time: 0.405s, 2526.85/s  (0.411s, 2488.56/s)  LR: 3.875e-01  Data: 0.028 (0.032)
+Train: 17 [ 300/312 ( 96%)]  Loss: 5.72 (5.63)  Time: 0.411s, 2493.90/s  (0.411s, 2493.18/s)  LR: 3.875e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   4.120 ( 4.120)  Acc@1:  24.316 ( 24.316)  Acc@5:  46.973 ( 46.973)
+Test: [  48/48]  Time: 0.089 (0.323)  Loss:   3.897 ( 4.106)  Acc@1:  27.476 ( 24.186)  Acc@5:  52.712 ( 47.824)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-7.pth.tar', 11.026000010375977)
+
+Train: 18 [   0/312 (  0%)]  Loss: 5.56 (5.56)  Time: 1.708s,  599.64/s  (1.708s,  599.64/s)  LR: 3.860e-01  Data: 1.333 (1.333)
+Train: 18 [  50/312 ( 16%)]  Loss: 5.61 (5.56)  Time: 0.413s, 2482.26/s  (0.438s, 2338.15/s)  LR: 3.860e-01  Data: 0.026 (0.053)
+Train: 18 [ 100/312 ( 32%)]  Loss: 5.52 (5.57)  Time: 0.413s, 2478.13/s  (0.424s, 2413.85/s)  LR: 3.860e-01  Data: 0.027 (0.040)
+Train: 18 [ 150/312 ( 48%)]  Loss: 5.75 (5.57)  Time: 0.417s, 2454.91/s  (0.421s, 2434.96/s)  LR: 3.860e-01  Data: 0.026 (0.036)
+Train: 18 [ 200/312 ( 64%)]  Loss: 5.50 (5.58)  Time: 0.412s, 2484.20/s  (0.419s, 2446.20/s)  LR: 3.860e-01  Data: 0.025 (0.034)
+Train: 18 [ 250/312 ( 80%)]  Loss: 5.64 (5.59)  Time: 0.405s, 2529.77/s  (0.416s, 2459.41/s)  LR: 3.860e-01  Data: 0.027 (0.032)
+Train: 18 [ 300/312 ( 96%)]  Loss: 5.66 (5.58)  Time: 0.408s, 2509.96/s  (0.415s, 2468.70/s)  LR: 3.860e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.404 (1.404)  Loss:   4.003 ( 4.003)  Acc@1:  25.488 ( 25.488)  Acc@5:  51.172 ( 51.172)
+Test: [  48/48]  Time: 0.088 (0.324)  Loss:   3.817 ( 3.949)  Acc@1:  27.830 ( 27.040)  Acc@5:  54.481 ( 51.976)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-10.pth.tar', 13.27999999786377)
+
+Train: 19 [   0/312 (  0%)]  Loss: 5.51 (5.51)  Time: 1.648s,  621.47/s  (1.648s,  621.47/s)  LR: 3.844e-01  Data: 1.273 (1.273)
+Train: 19 [  50/312 ( 16%)]  Loss: 5.62 (5.52)  Time: 0.412s, 2485.48/s  (0.435s, 2356.68/s)  LR: 3.844e-01  Data: 0.027 (0.051)
+Train: 19 [ 100/312 ( 32%)]  Loss: 5.43 (5.53)  Time: 0.410s, 2498.36/s  (0.423s, 2420.16/s)  LR: 3.844e-01  Data: 0.027 (0.039)
+Train: 19 [ 150/312 ( 48%)]  Loss: 5.59 (5.54)  Time: 0.412s, 2486.22/s  (0.419s, 2444.53/s)  LR: 3.844e-01  Data: 0.027 (0.035)
+Train: 19 [ 200/312 ( 64%)]  Loss: 5.56 (5.54)  Time: 0.410s, 2495.79/s  (0.417s, 2453.21/s)  LR: 3.844e-01  Data: 0.026 (0.033)
+Train: 19 [ 250/312 ( 80%)]  Loss: 5.50 (5.54)  Time: 0.414s, 2472.84/s  (0.416s, 2460.89/s)  LR: 3.844e-01  Data: 0.028 (0.032)
+Train: 19 [ 300/312 ( 96%)]  Loss: 5.44 (5.54)  Time: 0.409s, 2505.23/s  (0.415s, 2465.77/s)  LR: 3.844e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   3.887 ( 3.887)  Acc@1:  29.102 ( 29.102)  Acc@5:  53.418 ( 53.418)
+Test: [  48/48]  Time: 0.089 (0.326)  Loss:   3.722 ( 3.880)  Acc@1:  32.547 ( 28.800)  Acc@5:  58.019 ( 53.936)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-9.pth.tar', 13.284000017089843)
+
+Train: 20 [   0/312 (  0%)]  Loss: 5.58 (5.58)  Time: 1.554s,  658.94/s  (1.554s,  658.94/s)  LR: 3.827e-01  Data: 1.143 (1.143)
+Train: 20 [  50/312 ( 16%)]  Loss: 5.41 (5.49)  Time: 0.411s, 2491.66/s  (0.432s, 2370.15/s)  LR: 3.827e-01  Data: 0.027 (0.049)
+Train: 20 [ 100/312 ( 32%)]  Loss: 5.59 (5.50)  Time: 0.407s, 2513.33/s  (0.422s, 2424.88/s)  LR: 3.827e-01  Data: 0.026 (0.038)
+Train: 20 [ 150/312 ( 48%)]  Loss: 5.39 (5.49)  Time: 0.408s, 2509.07/s  (0.418s, 2452.19/s)  LR: 3.827e-01  Data: 0.026 (0.035)
+Train: 20 [ 200/312 ( 64%)]  Loss: 5.50 (5.50)  Time: 0.408s, 2511.81/s  (0.415s, 2465.88/s)  LR: 3.827e-01  Data: 0.027 (0.033)
+Train: 20 [ 250/312 ( 80%)]  Loss: 5.62 (5.50)  Time: 0.412s, 2485.44/s  (0.415s, 2469.59/s)  LR: 3.827e-01  Data: 0.028 (0.032)
+Train: 20 [ 300/312 ( 96%)]  Loss: 5.49 (5.51)  Time: 0.411s, 2492.48/s  (0.414s, 2475.10/s)  LR: 3.827e-01  Data: 0.030 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.426 (1.426)  Loss:   3.746 ( 3.746)  Acc@1:  29.883 ( 29.883)  Acc@5:  54.590 ( 54.590)
+Test: [  48/48]  Time: 0.088 (0.324)  Loss:   3.662 ( 3.762)  Acc@1:  30.425 ( 29.548)  Acc@5:  57.429 ( 54.970)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-11.pth.tar', 15.195999993896484)
+
+Train: 21 [   0/312 (  0%)]  Loss: 5.45 (5.45)  Time: 1.565s,  654.32/s  (1.565s,  654.32/s)  LR: 3.810e-01  Data: 1.192 (1.192)
+Train: 21 [  50/312 ( 16%)]  Loss: 5.48 (5.43)  Time: 0.407s, 2514.37/s  (0.430s, 2382.92/s)  LR: 3.810e-01  Data: 0.027 (0.052)
+Train: 21 [ 100/312 ( 32%)]  Loss: 5.52 (5.45)  Time: 0.411s, 2488.60/s  (0.420s, 2440.61/s)  LR: 3.810e-01  Data: 0.027 (0.040)
+Train: 21 [ 150/312 ( 48%)]  Loss: 5.47 (5.46)  Time: 0.415s, 2467.67/s  (0.417s, 2453.58/s)  LR: 3.810e-01  Data: 0.029 (0.036)
+Train: 21 [ 200/312 ( 64%)]  Loss: 5.58 (5.47)  Time: 0.408s, 2512.70/s  (0.415s, 2465.18/s)  LR: 3.810e-01  Data: 0.027 (0.033)
+Train: 21 [ 250/312 ( 80%)]  Loss: 5.32 (5.47)  Time: 0.407s, 2516.19/s  (0.414s, 2472.74/s)  LR: 3.810e-01  Data: 0.026 (0.032)
+Train: 21 [ 300/312 ( 96%)]  Loss: 5.53 (5.47)  Time: 0.413s, 2479.30/s  (0.414s, 2475.33/s)  LR: 3.810e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.444 (1.444)  Loss:   3.788 ( 3.788)  Acc@1:  30.371 ( 30.371)  Acc@5:  55.273 ( 55.273)
+Test: [  48/48]  Time: 0.088 (0.324)  Loss:   3.642 ( 3.805)  Acc@1:  31.250 ( 29.696)  Acc@5:  58.137 ( 54.774)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-13.pth.tar', 19.78800000366211)
+
+Train: 22 [   0/312 (  0%)]  Loss: 5.47 (5.47)  Time: 1.791s,  571.81/s  (1.791s,  571.81/s)  LR: 3.791e-01  Data: 1.420 (1.420)
+Train: 22 [  50/312 ( 16%)]  Loss: 5.37 (5.41)  Time: 0.400s, 2561.31/s  (0.430s, 2381.85/s)  LR: 3.791e-01  Data: 0.025 (0.055)
+Train: 22 [ 100/312 ( 32%)]  Loss: 5.41 (5.41)  Time: 0.403s, 2543.57/s  (0.416s, 2461.16/s)  LR: 3.791e-01  Data: 0.028 (0.041)
+Train: 22 [ 150/312 ( 48%)]  Loss: 5.50 (5.42)  Time: 0.405s, 2528.48/s  (0.412s, 2486.11/s)  LR: 3.791e-01  Data: 0.027 (0.036)
+Train: 22 [ 200/312 ( 64%)]  Loss: 5.31 (5.42)  Time: 0.413s, 2481.55/s  (0.411s, 2494.43/s)  LR: 3.791e-01  Data: 0.029 (0.034)
+Train: 22 [ 250/312 ( 80%)]  Loss: 5.38 (5.43)  Time: 0.412s, 2483.01/s  (0.411s, 2492.40/s)  LR: 3.791e-01  Data: 0.028 (0.033)
+Train: 22 [ 300/312 ( 96%)]  Loss: 5.46 (5.43)  Time: 0.406s, 2522.13/s  (0.411s, 2493.37/s)  LR: 3.791e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.406 (1.406)  Loss:   3.758 ( 3.758)  Acc@1:  30.273 ( 30.273)  Acc@5:  54.102 ( 54.102)
+Test: [  48/48]  Time: 0.088 (0.322)  Loss:   3.634 ( 3.825)  Acc@1:  31.604 ( 28.346)  Acc@5:  57.665 ( 53.222)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-22.pth.tar', 28.34599999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-12.pth.tar', 19.95999999572754)
+
+Train: 23 [   0/312 (  0%)]  Loss: 5.28 (5.28)  Time: 1.552s,  659.75/s  (1.552s,  659.75/s)  LR: 3.772e-01  Data: 1.180 (1.180)
+Train: 23 [  50/312 ( 16%)]  Loss: 5.33 (5.35)  Time: 0.412s, 2487.89/s  (0.429s, 2389.54/s)  LR: 3.772e-01  Data: 0.026 (0.050)
+Train: 23 [ 100/312 ( 32%)]  Loss: 5.32 (5.37)  Time: 0.412s, 2485.80/s  (0.420s, 2440.07/s)  LR: 3.772e-01  Data: 0.027 (0.038)
+Train: 23 [ 150/312 ( 48%)]  Loss: 5.29 (5.38)  Time: 0.415s, 2469.34/s  (0.417s, 2453.09/s)  LR: 3.772e-01  Data: 0.029 (0.035)
+Train: 23 [ 200/312 ( 64%)]  Loss: 5.40 (5.39)  Time: 0.412s, 2487.13/s  (0.416s, 2460.30/s)  LR: 3.772e-01  Data: 0.028 (0.033)
+Train: 23 [ 250/312 ( 80%)]  Loss: 5.30 (5.39)  Time: 0.412s, 2484.55/s  (0.416s, 2464.26/s)  LR: 3.772e-01  Data: 0.027 (0.032)
+Train: 23 [ 300/312 ( 96%)]  Loss: 5.51 (5.40)  Time: 0.416s, 2463.94/s  (0.415s, 2467.05/s)  LR: 3.772e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.405 (1.405)  Loss:   3.652 ( 3.652)  Acc@1:  30.371 ( 30.371)  Acc@5:  56.445 ( 56.445)
+Test: [  48/48]  Time: 0.088 (0.326)  Loss:   3.498 ( 3.608)  Acc@1:  31.604 ( 31.694)  Acc@5:  60.613 ( 57.268)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-22.pth.tar', 28.34599999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-14.pth.tar', 21.92200001831055)
+
+Train: 24 [   0/312 (  0%)]  Loss: 5.23 (5.23)  Time: 1.692s,  605.30/s  (1.692s,  605.30/s)  LR: 3.753e-01  Data: 1.318 (1.318)
+Train: 24 [  50/312 ( 16%)]  Loss: 5.37 (5.32)  Time: 0.405s, 2529.25/s  (0.431s, 2373.31/s)  LR: 3.753e-01  Data: 0.025 (0.052)
+Train: 24 [ 100/312 ( 32%)]  Loss: 5.44 (5.34)  Time: 0.410s, 2497.48/s  (0.420s, 2440.46/s)  LR: 3.753e-01  Data: 0.028 (0.040)
+Train: 24 [ 150/312 ( 48%)]  Loss: 5.38 (5.35)  Time: 0.414s, 2474.00/s  (0.417s, 2455.95/s)  LR: 3.753e-01  Data: 0.028 (0.036)
+Train: 24 [ 200/312 ( 64%)]  Loss: 5.29 (5.36)  Time: 0.407s, 2514.95/s  (0.415s, 2465.36/s)  LR: 3.753e-01  Data: 0.027 (0.034)
+Train: 24 [ 250/312 ( 80%)]  Loss: 5.33 (5.36)  Time: 0.405s, 2525.98/s  (0.414s, 2474.42/s)  LR: 3.753e-01  Data: 0.028 (0.032)
+Train: 24 [ 300/312 ( 96%)]  Loss: 5.47 (5.37)  Time: 0.413s, 2481.35/s  (0.413s, 2479.73/s)  LR: 3.753e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.412 (1.412)  Loss:   3.689 ( 3.689)  Acc@1:  31.641 ( 31.641)  Acc@5:  56.250 ( 56.250)
+Test: [  48/48]  Time: 0.089 (0.322)  Loss:   3.503 ( 3.665)  Acc@1:  34.788 ( 31.280)  Acc@5:  59.670 ( 56.856)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-22.pth.tar', 28.34599999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-15.pth.tar', 23.765999982910156)
+
+Train: 25 [   0/312 (  0%)]  Loss: 5.36 (5.36)  Time: 2.020s,  506.87/s  (2.020s,  506.87/s)  LR: 3.732e-01  Data: 1.644 (1.644)
+Train: 25 [  50/312 ( 16%)]  Loss: 5.29 (5.29)  Time: 0.409s, 2504.62/s  (0.444s, 2306.79/s)  LR: 3.732e-01  Data: 0.026 (0.059)
+Train: 25 [ 100/312 ( 32%)]  Loss: 5.22 (5.30)  Time: 0.407s, 2514.41/s  (0.426s, 2402.68/s)  LR: 3.732e-01  Data: 0.027 (0.043)
+Train: 25 [ 150/312 ( 48%)]  Loss: 5.28 (5.31)  Time: 0.406s, 2519.30/s  (0.420s, 2438.81/s)  LR: 3.732e-01  Data: 0.028 (0.038)
+Train: 25 [ 200/312 ( 64%)]  Loss: 5.47 (5.32)  Time: 0.410s, 2495.20/s  (0.417s, 2454.22/s)  LR: 3.732e-01  Data: 0.027 (0.035)
+Train: 25 [ 250/312 ( 80%)]  Loss: 5.41 (5.33)  Time: 0.408s, 2507.07/s  (0.416s, 2460.21/s)  LR: 3.732e-01  Data: 0.027 (0.033)
+Train: 25 [ 300/312 ( 96%)]  Loss: 5.35 (5.33)  Time: 0.408s, 2507.85/s  (0.415s, 2468.67/s)  LR: 3.732e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.512 ( 3.512)  Acc@1:  34.375 ( 34.375)  Acc@5:  60.938 ( 60.938)
+Test: [  48/48]  Time: 0.088 (0.323)  Loss:   3.396 ( 3.539)  Acc@1:  34.788 ( 34.428)  Acc@5:  63.915 ( 60.436)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-22.pth.tar', 28.34599999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-17.pth.tar', 24.186000009155272)
+
+Train: 26 [   0/312 (  0%)]  Loss: 5.16 (5.16)  Time: 1.668s,  614.05/s  (1.668s,  614.05/s)  LR: 3.711e-01  Data: 1.296 (1.296)
+Train: 26 [  50/312 ( 16%)]  Loss: 5.26 (5.27)  Time: 0.407s, 2513.65/s  (0.433s, 2365.55/s)  LR: 3.711e-01  Data: 0.027 (0.052)
+Train: 26 [ 100/312 ( 32%)]  Loss: 5.23 (5.28)  Time: 0.413s, 2477.10/s  (0.423s, 2422.05/s)  LR: 3.711e-01  Data: 0.027 (0.040)
+Train: 26 [ 150/312 ( 48%)]  Loss: 5.32 (5.29)  Time: 0.408s, 2509.62/s  (0.419s, 2441.19/s)  LR: 3.711e-01  Data: 0.027 (0.036)
+Train: 26 [ 200/312 ( 64%)]  Loss: 5.38 (5.29)  Time: 0.407s, 2517.70/s  (0.417s, 2458.01/s)  LR: 3.711e-01  Data: 0.027 (0.034)
+Train: 26 [ 250/312 ( 80%)]  Loss: 5.25 (5.30)  Time: 0.404s, 2532.16/s  (0.414s, 2470.56/s)  LR: 3.711e-01  Data: 0.027 (0.033)
+Train: 26 [ 300/312 ( 96%)]  Loss: 5.31 (5.30)  Time: 0.408s, 2507.68/s  (0.413s, 2478.60/s)  LR: 3.711e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   3.600 ( 3.600)  Acc@1:  33.398 ( 33.398)  Acc@5:  58.203 ( 58.203)
+Test: [  48/48]  Time: 0.089 (0.322)  Loss:   3.389 ( 3.581)  Acc@1:  37.146 ( 34.140)  Acc@5:  65.330 ( 59.576)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-22.pth.tar', 28.34599999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-16.pth.tar', 25.064000014038086)
+
+Train: 27 [   0/312 (  0%)]  Loss: 5.17 (5.17)  Time: 1.670s,  613.28/s  (1.670s,  613.28/s)  LR: 3.689e-01  Data: 1.282 (1.282)
+Train: 27 [  50/312 ( 16%)]  Loss: 5.24 (5.22)  Time: 0.415s, 2468.98/s  (0.437s, 2342.41/s)  LR: 3.689e-01  Data: 0.032 (0.052)
+Train: 27 [ 100/312 ( 32%)]  Loss: 5.25 (5.24)  Time: 0.410s, 2499.03/s  (0.424s, 2414.07/s)  LR: 3.689e-01  Data: 0.027 (0.039)
+Train: 27 [ 150/312 ( 48%)]  Loss: 5.35 (5.25)  Time: 0.405s, 2529.49/s  (0.418s, 2447.29/s)  LR: 3.689e-01  Data: 0.027 (0.035)
+Train: 27 [ 200/312 ( 64%)]  Loss: 5.21 (5.26)  Time: 0.406s, 2519.35/s  (0.415s, 2465.94/s)  LR: 3.689e-01  Data: 0.027 (0.033)
+Train: 27 [ 250/312 ( 80%)]  Loss: 5.28 (5.27)  Time: 0.412s, 2488.11/s  (0.414s, 2475.19/s)  LR: 3.689e-01  Data: 0.029 (0.032)
+Train: 27 [ 300/312 ( 96%)]  Loss: 5.32 (5.27)  Time: 0.414s, 2473.14/s  (0.413s, 2476.67/s)  LR: 3.689e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.437 (1.437)  Loss:   3.535 ( 3.535)  Acc@1:  35.059 ( 35.059)  Acc@5:  58.105 ( 58.105)
+Test: [  48/48]  Time: 0.088 (0.325)  Loss:   3.338 ( 3.527)  Acc@1:  36.792 ( 34.324)  Acc@5:  62.972 ( 59.970)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-22.pth.tar', 28.34599999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-18.pth.tar', 27.040000001220704)
+
+Train: 28 [   0/312 (  0%)]  Loss: 5.18 (5.18)  Time: 1.623s,  630.99/s  (1.623s,  630.99/s)  LR: 3.666e-01  Data: 1.198 (1.198)
+Train: 28 [  50/312 ( 16%)]  Loss: 5.20 (5.21)  Time: 0.410s, 2499.23/s  (0.430s, 2382.76/s)  LR: 3.666e-01  Data: 0.035 (0.051)
+Train: 28 [ 100/312 ( 32%)]  Loss: 5.31 (5.22)  Time: 0.411s, 2492.12/s  (0.419s, 2445.56/s)  LR: 3.666e-01  Data: 0.026 (0.039)
+Train: 28 [ 150/312 ( 48%)]  Loss: 5.27 (5.23)  Time: 0.410s, 2499.52/s  (0.416s, 2460.30/s)  LR: 3.666e-01  Data: 0.028 (0.035)
+Train: 28 [ 200/312 ( 64%)]  Loss: 5.36 (5.24)  Time: 0.409s, 2506.42/s  (0.415s, 2470.18/s)  LR: 3.666e-01  Data: 0.024 (0.033)
+Train: 28 [ 250/312 ( 80%)]  Loss: 5.22 (5.25)  Time: 0.413s, 2481.40/s  (0.414s, 2474.52/s)  LR: 3.666e-01  Data: 0.027 (0.032)
+Train: 28 [ 300/312 ( 96%)]  Loss: 5.29 (5.25)  Time: 0.410s, 2495.55/s  (0.414s, 2476.22/s)  LR: 3.666e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.440 (1.440)  Loss:   3.501 ( 3.501)  Acc@1:  36.328 ( 36.328)  Acc@5:  61.426 ( 61.426)
+Test: [  48/48]  Time: 0.089 (0.327)  Loss:   3.393 ( 3.543)  Acc@1:  38.090 ( 34.682)  Acc@5:  63.679 ( 60.430)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-22.pth.tar', 28.34599999206543)
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+Train: 29 [   0/312 (  0%)]  Loss: 5.11 (5.11)  Time: 1.904s,  537.77/s  (1.904s,  537.77/s)  LR: 3.642e-01  Data: 1.530 (1.530)
+Train: 29 [  50/312 ( 16%)]  Loss: 5.26 (5.19)  Time: 0.406s, 2524.53/s  (0.436s, 2346.35/s)  LR: 3.642e-01  Data: 0.028 (0.057)
+Train: 29 [ 100/312 ( 32%)]  Loss: 5.21 (5.20)  Time: 0.406s, 2520.12/s  (0.422s, 2426.05/s)  LR: 3.642e-01  Data: 0.027 (0.042)
+Train: 29 [ 150/312 ( 48%)]  Loss: 5.09 (5.20)  Time: 0.413s, 2482.07/s  (0.418s, 2448.65/s)  LR: 3.642e-01  Data: 0.028 (0.037)
+Train: 29 [ 200/312 ( 64%)]  Loss: 5.24 (5.20)  Time: 0.410s, 2495.48/s  (0.417s, 2456.43/s)  LR: 3.642e-01  Data: 0.026 (0.035)
+Train: 29 [ 250/312 ( 80%)]  Loss: 5.34 (5.21)  Time: 0.406s, 2522.63/s  (0.416s, 2464.02/s)  LR: 3.642e-01  Data: 0.027 (0.033)
+Train: 29 [ 300/312 ( 96%)]  Loss: 5.19 (5.22)  Time: 0.406s, 2522.00/s  (0.414s, 2472.76/s)  LR: 3.642e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.446 (1.446)  Loss:   3.580 ( 3.580)  Acc@1:  31.348 ( 31.348)  Acc@5:  59.473 ( 59.473)
+Test: [  48/48]  Time: 0.088 (0.327)  Loss:   3.335 ( 3.531)  Acc@1:  36.675 ( 34.048)  Acc@5:  62.736 ( 59.390)
+Current checkpoints:
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-29.pth.tar', 34.04800002929687)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-19.pth.tar', 28.800000046386717)
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+Train: 30 [  50/312 ( 16%)]  Loss: 5.18 (5.14)  Time: 0.407s, 2513.45/s  (0.434s, 2358.07/s)  LR: 3.618e-01  Data: 0.027 (0.053)
+Train: 30 [ 100/312 ( 32%)]  Loss: 5.20 (5.14)  Time: 0.414s, 2473.46/s  (0.422s, 2426.02/s)  LR: 3.618e-01  Data: 0.028 (0.040)
+Train: 30 [ 150/312 ( 48%)]  Loss: 5.18 (5.16)  Time: 0.416s, 2464.22/s  (0.419s, 2444.84/s)  LR: 3.618e-01  Data: 0.024 (0.036)
+Train: 30 [ 200/312 ( 64%)]  Loss: 5.21 (5.17)  Time: 0.405s, 2526.55/s  (0.416s, 2460.05/s)  LR: 3.618e-01  Data: 0.028 (0.034)
+Train: 30 [ 250/312 ( 80%)]  Loss: 5.20 (5.18)  Time: 0.411s, 2489.98/s  (0.414s, 2471.92/s)  LR: 3.618e-01  Data: 0.028 (0.033)
+Train: 30 [ 300/312 ( 96%)]  Loss: 5.08 (5.19)  Time: 0.411s, 2488.88/s  (0.413s, 2478.45/s)  LR: 3.618e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.417 (1.417)  Loss:   3.572 ( 3.572)  Acc@1:  33.594 ( 33.594)  Acc@5:  60.156 ( 60.156)
+Test: [  48/48]  Time: 0.089 (0.325)  Loss:   3.430 ( 3.595)  Acc@1:  33.726 ( 33.470)  Acc@5:  63.090 ( 58.684)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-29.pth.tar', 34.04800002929687)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-30.pth.tar', 33.46999997680664)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-20.pth.tar', 29.547999996948242)
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+Train: 31 [   0/312 (  0%)]  Loss: 5.15 (5.15)  Time: 1.611s,  635.52/s  (1.611s,  635.52/s)  LR: 3.593e-01  Data: 1.236 (1.236)
+Train: 31 [  50/312 ( 16%)]  Loss: 5.13 (5.12)  Time: 0.403s, 2539.48/s  (0.430s, 2380.28/s)  LR: 3.593e-01  Data: 0.027 (0.051)
+Train: 31 [ 100/312 ( 32%)]  Loss: 5.20 (5.13)  Time: 0.407s, 2513.07/s  (0.418s, 2449.16/s)  LR: 3.593e-01  Data: 0.027 (0.039)
+Train: 31 [ 150/312 ( 48%)]  Loss: 5.16 (5.14)  Time: 0.412s, 2487.24/s  (0.414s, 2472.53/s)  LR: 3.593e-01  Data: 0.026 (0.035)
+Train: 31 [ 200/312 ( 64%)]  Loss: 5.18 (5.15)  Time: 0.413s, 2478.87/s  (0.413s, 2478.19/s)  LR: 3.593e-01  Data: 0.027 (0.033)
+Train: 31 [ 250/312 ( 80%)]  Loss: 5.16 (5.16)  Time: 0.407s, 2517.97/s  (0.413s, 2480.67/s)  LR: 3.593e-01  Data: 0.027 (0.032)
+Train: 31 [ 300/312 ( 96%)]  Loss: 5.17 (5.17)  Time: 0.403s, 2539.04/s  (0.411s, 2488.95/s)  LR: 3.593e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.475 (1.475)  Loss:   3.367 ( 3.367)  Acc@1:  36.719 ( 36.719)  Acc@5:  63.379 ( 63.379)
+Test: [  48/48]  Time: 0.087 (0.328)  Loss:   3.209 ( 3.393)  Acc@1:  38.443 ( 36.234)  Acc@5:  65.566 ( 62.002)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-29.pth.tar', 34.04800002929687)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-30.pth.tar', 33.46999997680664)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-21.pth.tar', 29.696)
+
+Train: 32 [   0/312 (  0%)]  Loss: 5.03 (5.03)  Time: 1.979s,  517.49/s  (1.979s,  517.49/s)  LR: 3.567e-01  Data: 1.272 (1.272)
+Train: 32 [  50/312 ( 16%)]  Loss: 5.03 (5.07)  Time: 0.402s, 2546.56/s  (0.432s, 2368.09/s)  LR: 3.567e-01  Data: 0.026 (0.052)
+Train: 32 [ 100/312 ( 32%)]  Loss: 5.14 (5.10)  Time: 0.403s, 2541.05/s  (0.418s, 2448.72/s)  LR: 3.567e-01  Data: 0.025 (0.040)
+Train: 32 [ 150/312 ( 48%)]  Loss: 5.19 (5.11)  Time: 0.415s, 2465.75/s  (0.415s, 2468.23/s)  LR: 3.567e-01  Data: 0.030 (0.036)
+Train: 32 [ 200/312 ( 64%)]  Loss: 5.11 (5.12)  Time: 0.414s, 2475.07/s  (0.414s, 2471.55/s)  LR: 3.567e-01  Data: 0.028 (0.033)
+Train: 32 [ 250/312 ( 80%)]  Loss: 5.14 (5.13)  Time: 0.411s, 2490.92/s  (0.414s, 2474.55/s)  LR: 3.567e-01  Data: 0.026 (0.032)
+Train: 32 [ 300/312 ( 96%)]  Loss: 5.18 (5.14)  Time: 0.413s, 2479.93/s  (0.413s, 2477.12/s)  LR: 3.567e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.409 (1.409)  Loss:   3.459 ( 3.459)  Acc@1:  36.719 ( 36.719)  Acc@5:  61.133 ( 61.133)
+Test: [  48/48]  Time: 0.088 (0.323)  Loss:   3.259 ( 3.466)  Acc@1:  38.443 ( 35.620)  Acc@5:  67.099 ( 61.584)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-29.pth.tar', 34.04800002929687)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-30.pth.tar', 33.46999997680664)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-24.pth.tar', 31.279999985351562)
+
+Train: 33 [   0/312 (  0%)]  Loss: 5.05 (5.05)  Time: 1.792s,  571.35/s  (1.792s,  571.35/s)  LR: 3.541e-01  Data: 1.261 (1.261)
+Train: 33 [  50/312 ( 16%)]  Loss: 5.11 (5.06)  Time: 0.410s, 2496.15/s  (0.438s, 2336.88/s)  LR: 3.541e-01  Data: 0.028 (0.052)
+Train: 33 [ 100/312 ( 32%)]  Loss: 5.15 (5.07)  Time: 0.406s, 2525.12/s  (0.422s, 2424.17/s)  LR: 3.541e-01  Data: 0.028 (0.040)
+Train: 33 [ 150/312 ( 48%)]  Loss: 5.13 (5.08)  Time: 0.406s, 2524.07/s  (0.417s, 2457.98/s)  LR: 3.541e-01  Data: 0.027 (0.036)
+Train: 33 [ 200/312 ( 64%)]  Loss: 5.08 (5.10)  Time: 0.413s, 2482.20/s  (0.414s, 2473.09/s)  LR: 3.541e-01  Data: 0.026 (0.034)
+Train: 33 [ 250/312 ( 80%)]  Loss: 5.14 (5.11)  Time: 0.413s, 2478.40/s  (0.414s, 2476.03/s)  LR: 3.541e-01  Data: 0.032 (0.032)
+Train: 33 [ 300/312 ( 96%)]  Loss: 5.10 (5.11)  Time: 0.415s, 2468.53/s  (0.413s, 2477.08/s)  LR: 3.541e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.414 (1.414)  Loss:   3.337 ( 3.337)  Acc@1:  36.816 ( 36.816)  Acc@5:  61.133 ( 61.133)
+Test: [  48/48]  Time: 0.089 (0.324)  Loss:   3.113 ( 3.328)  Acc@1:  39.858 ( 37.398)  Acc@5:  66.156 ( 63.092)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-29.pth.tar', 34.04800002929687)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-30.pth.tar', 33.46999997680664)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-23.pth.tar', 31.69399999206543)
+
+Train: 34 [   0/312 (  0%)]  Loss: 4.96 (4.96)  Time: 1.444s,  708.95/s  (1.444s,  708.95/s)  LR: 3.514e-01  Data: 1.071 (1.071)
+Train: 34 [  50/312 ( 16%)]  Loss: 5.12 (5.03)  Time: 0.411s, 2492.54/s  (0.430s, 2379.43/s)  LR: 3.514e-01  Data: 0.028 (0.048)
+Train: 34 [ 100/312 ( 32%)]  Loss: 5.03 (5.05)  Time: 0.413s, 2477.07/s  (0.421s, 2435.02/s)  LR: 3.514e-01  Data: 0.027 (0.038)
+Train: 34 [ 150/312 ( 48%)]  Loss: 5.09 (5.06)  Time: 0.410s, 2499.00/s  (0.417s, 2455.50/s)  LR: 3.514e-01  Data: 0.027 (0.034)
+Train: 34 [ 200/312 ( 64%)]  Loss: 5.00 (5.07)  Time: 0.406s, 2520.29/s  (0.415s, 2465.86/s)  LR: 3.514e-01  Data: 0.027 (0.032)
+Train: 34 [ 250/312 ( 80%)]  Loss: 5.19 (5.08)  Time: 0.409s, 2504.71/s  (0.414s, 2474.97/s)  LR: 3.514e-01  Data: 0.028 (0.031)
+Train: 34 [ 300/312 ( 96%)]  Loss: 5.13 (5.09)  Time: 0.410s, 2496.30/s  (0.413s, 2480.68/s)  LR: 3.514e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.444 (1.444)  Loss:   3.364 ( 3.364)  Acc@1:  37.891 ( 37.891)  Acc@5:  63.184 ( 63.184)
+Test: [  48/48]  Time: 0.089 (0.322)  Loss:   3.187 ( 3.372)  Acc@1:  39.976 ( 37.860)  Acc@5:  67.217 ( 63.288)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-29.pth.tar', 34.04800002929687)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-30.pth.tar', 33.46999997680664)
+
+Train: 35 [   0/312 (  0%)]  Loss: 4.92 (4.92)  Time: 1.865s,  549.03/s  (1.865s,  549.03/s)  LR: 3.486e-01  Data: 1.489 (1.489)
+Train: 35 [  50/312 ( 16%)]  Loss: 5.07 (5.00)  Time: 0.407s, 2514.58/s  (0.440s, 2327.66/s)  LR: 3.486e-01  Data: 0.027 (0.056)
+Train: 35 [ 100/312 ( 32%)]  Loss: 5.03 (5.01)  Time: 0.405s, 2528.66/s  (0.424s, 2415.56/s)  LR: 3.486e-01  Data: 0.027 (0.041)
+Train: 35 [ 150/312 ( 48%)]  Loss: 5.09 (5.02)  Time: 0.407s, 2519.06/s  (0.418s, 2447.76/s)  LR: 3.486e-01  Data: 0.028 (0.037)
+Train: 35 [ 200/312 ( 64%)]  Loss: 5.00 (5.04)  Time: 0.410s, 2498.58/s  (0.416s, 2460.63/s)  LR: 3.486e-01  Data: 0.026 (0.034)
+Train: 35 [ 250/312 ( 80%)]  Loss: 5.16 (5.05)  Time: 0.408s, 2510.29/s  (0.415s, 2467.23/s)  LR: 3.486e-01  Data: 0.027 (0.033)
+Train: 35 [ 300/312 ( 96%)]  Loss: 5.01 (5.06)  Time: 0.408s, 2511.71/s  (0.414s, 2474.49/s)  LR: 3.486e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.417 (1.417)  Loss:   3.330 ( 3.330)  Acc@1:  37.695 ( 37.695)  Acc@5:  63.867 ( 63.867)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.195 ( 3.340)  Acc@1:  40.448 ( 37.502)  Acc@5:  66.981 ( 63.524)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-29.pth.tar', 34.04800002929687)
+
+Train: 36 [   0/312 (  0%)]  Loss: 4.93 (4.93)  Time: 1.657s,  618.00/s  (1.657s,  618.00/s)  LR: 3.458e-01  Data: 1.137 (1.137)
+Train: 36 [  50/312 ( 16%)]  Loss: 5.04 (4.98)  Time: 0.409s, 2501.04/s  (0.433s, 2366.77/s)  LR: 3.458e-01  Data: 0.028 (0.049)
+Train: 36 [ 100/312 ( 32%)]  Loss: 4.97 (5.00)  Time: 0.410s, 2499.13/s  (0.422s, 2425.64/s)  LR: 3.458e-01  Data: 0.026 (0.038)
+Train: 36 [ 150/312 ( 48%)]  Loss: 4.95 (5.01)  Time: 0.406s, 2521.95/s  (0.418s, 2451.75/s)  LR: 3.458e-01  Data: 0.028 (0.035)
+Train: 36 [ 200/312 ( 64%)]  Loss: 4.93 (5.02)  Time: 0.408s, 2511.93/s  (0.415s, 2467.67/s)  LR: 3.458e-01  Data: 0.027 (0.033)
+Train: 36 [ 250/312 ( 80%)]  Loss: 5.16 (5.03)  Time: 0.409s, 2505.20/s  (0.414s, 2475.48/s)  LR: 3.458e-01  Data: 0.027 (0.032)
+Train: 36 [ 300/312 ( 96%)]  Loss: 5.08 (5.04)  Time: 0.408s, 2511.89/s  (0.413s, 2478.40/s)  LR: 3.458e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   3.394 ( 3.394)  Acc@1:  37.305 ( 37.305)  Acc@5:  63.477 ( 63.477)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.117 ( 3.354)  Acc@1:  44.929 ( 38.714)  Acc@5:  67.217 ( 63.982)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-26.pth.tar', 34.13999997558594)
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+Train: 37 [   0/312 (  0%)]  Loss: 4.97 (4.97)  Time: 1.952s,  524.64/s  (1.952s,  524.64/s)  LR: 3.429e-01  Data: 1.579 (1.579)
+Train: 37 [  50/312 ( 16%)]  Loss: 4.88 (4.94)  Time: 0.409s, 2501.75/s  (0.440s, 2329.76/s)  LR: 3.429e-01  Data: 0.026 (0.057)
+Train: 37 [ 100/312 ( 32%)]  Loss: 4.88 (4.96)  Time: 0.409s, 2503.29/s  (0.425s, 2408.05/s)  LR: 3.429e-01  Data: 0.028 (0.042)
+Train: 37 [ 150/312 ( 48%)]  Loss: 4.98 (4.98)  Time: 0.408s, 2510.02/s  (0.420s, 2436.80/s)  LR: 3.429e-01  Data: 0.028 (0.038)
+Train: 37 [ 200/312 ( 64%)]  Loss: 5.03 (4.99)  Time: 0.411s, 2491.86/s  (0.418s, 2448.87/s)  LR: 3.429e-01  Data: 0.028 (0.035)
+Train: 37 [ 250/312 ( 80%)]  Loss: 5.09 (5.01)  Time: 0.406s, 2525.23/s  (0.416s, 2460.19/s)  LR: 3.429e-01  Data: 0.027 (0.033)
+Train: 37 [ 300/312 ( 96%)]  Loss: 5.05 (5.01)  Time: 0.403s, 2543.02/s  (0.415s, 2470.02/s)  LR: 3.429e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.421 (1.421)  Loss:   3.260 ( 3.260)  Acc@1:  37.207 ( 37.207)  Acc@5:  65.137 ( 65.137)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.055 ( 3.253)  Acc@1:  42.689 ( 38.846)  Acc@5:  68.514 ( 64.960)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-27.pth.tar', 34.32400001586914)
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+Train: 38 [   0/312 (  0%)]  Loss: 4.93 (4.93)  Time: 1.516s,  675.42/s  (1.516s,  675.42/s)  LR: 3.399e-01  Data: 1.143 (1.143)
+Train: 38 [  50/312 ( 16%)]  Loss: 4.96 (4.92)  Time: 0.414s, 2472.99/s  (0.430s, 2381.63/s)  LR: 3.399e-01  Data: 0.027 (0.049)
+Train: 38 [ 100/312 ( 32%)]  Loss: 5.06 (4.94)  Time: 0.413s, 2481.19/s  (0.421s, 2433.30/s)  LR: 3.399e-01  Data: 0.028 (0.038)
+Train: 38 [ 150/312 ( 48%)]  Loss: 4.90 (4.96)  Time: 0.410s, 2497.17/s  (0.418s, 2451.18/s)  LR: 3.399e-01  Data: 0.028 (0.035)
+Train: 38 [ 200/312 ( 64%)]  Loss: 4.97 (4.97)  Time: 0.414s, 2476.22/s  (0.416s, 2459.99/s)  LR: 3.399e-01  Data: 0.028 (0.033)
+Train: 38 [ 250/312 ( 80%)]  Loss: 5.08 (4.98)  Time: 0.411s, 2490.12/s  (0.415s, 2465.98/s)  LR: 3.399e-01  Data: 0.027 (0.032)
+Train: 38 [ 300/312 ( 96%)]  Loss: 5.05 (4.99)  Time: 0.412s, 2484.40/s  (0.415s, 2470.06/s)  LR: 3.399e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.414 (1.414)  Loss:   3.262 ( 3.262)  Acc@1:  40.527 ( 40.527)  Acc@5:  63.086 ( 63.086)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.103 ( 3.275)  Acc@1:  39.151 ( 37.914)  Acc@5:  66.863 ( 63.564)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-25.pth.tar', 34.42799998535156)
+
+Train: 39 [   0/312 (  0%)]  Loss: 4.91 (4.91)  Time: 1.748s,  585.71/s  (1.748s,  585.71/s)  LR: 3.369e-01  Data: 1.374 (1.374)
+Train: 39 [  50/312 ( 16%)]  Loss: 4.94 (4.90)  Time: 0.409s, 2503.72/s  (0.435s, 2352.05/s)  LR: 3.369e-01  Data: 0.027 (0.054)
+Train: 39 [ 100/312 ( 32%)]  Loss: 5.00 (4.92)  Time: 0.411s, 2489.83/s  (0.422s, 2424.12/s)  LR: 3.369e-01  Data: 0.027 (0.041)
+Train: 39 [ 150/312 ( 48%)]  Loss: 4.80 (4.93)  Time: 0.408s, 2508.19/s  (0.419s, 2446.58/s)  LR: 3.369e-01  Data: 0.026 (0.036)
+Train: 39 [ 200/312 ( 64%)]  Loss: 4.91 (4.94)  Time: 0.405s, 2526.97/s  (0.416s, 2463.89/s)  LR: 3.369e-01  Data: 0.028 (0.034)
+Train: 39 [ 250/312 ( 80%)]  Loss: 4.90 (4.96)  Time: 0.402s, 2550.36/s  (0.413s, 2477.65/s)  LR: 3.369e-01  Data: 0.027 (0.033)
+Train: 39 [ 300/312 ( 96%)]  Loss: 4.95 (4.97)  Time: 0.402s, 2544.49/s  (0.412s, 2486.42/s)  LR: 3.369e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.450 (1.450)  Loss:   3.315 ( 3.315)  Acc@1:  38.379 ( 38.379)  Acc@5:  63.281 ( 63.281)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.208 ( 3.355)  Acc@1:  40.566 ( 37.826)  Acc@5:  65.684 ( 63.152)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-39.pth.tar', 37.8260000390625)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-28.pth.tar', 34.68199999755859)
+
+Train: 40 [   0/312 (  0%)]  Loss: 4.82 (4.82)  Time: 1.688s,  606.65/s  (1.688s,  606.65/s)  LR: 3.338e-01  Data: 1.143 (1.143)
+Train: 40 [  50/312 ( 16%)]  Loss: 5.06 (4.88)  Time: 0.409s, 2501.36/s  (0.433s, 2362.73/s)  LR: 3.338e-01  Data: 0.026 (0.049)
+Train: 40 [ 100/312 ( 32%)]  Loss: 5.05 (4.89)  Time: 0.409s, 2501.70/s  (0.422s, 2426.75/s)  LR: 3.338e-01  Data: 0.026 (0.038)
+Train: 40 [ 150/312 ( 48%)]  Loss: 4.99 (4.91)  Time: 0.409s, 2504.99/s  (0.418s, 2448.70/s)  LR: 3.338e-01  Data: 0.027 (0.035)
+Train: 40 [ 200/312 ( 64%)]  Loss: 4.99 (4.92)  Time: 0.408s, 2509.28/s  (0.416s, 2462.92/s)  LR: 3.338e-01  Data: 0.027 (0.033)
+Train: 40 [ 250/312 ( 80%)]  Loss: 4.90 (4.93)  Time: 0.406s, 2519.41/s  (0.414s, 2471.55/s)  LR: 3.338e-01  Data: 0.026 (0.032)
+Train: 40 [ 300/312 ( 96%)]  Loss: 5.02 (4.94)  Time: 0.411s, 2491.22/s  (0.414s, 2475.74/s)  LR: 3.338e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.435 (1.435)  Loss:   3.343 ( 3.343)  Acc@1:  37.598 ( 37.598)  Acc@5:  64.062 ( 64.062)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   3.169 ( 3.305)  Acc@1:  40.094 ( 39.132)  Acc@5:  66.863 ( 64.338)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-39.pth.tar', 37.8260000390625)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-32.pth.tar', 35.62000002197266)
+
+Train: 41 [   0/312 (  0%)]  Loss: 5.05 (5.05)  Time: 1.628s,  628.99/s  (1.628s,  628.99/s)  LR: 3.307e-01  Data: 1.228 (1.228)
+Train: 41 [  50/312 ( 16%)]  Loss: 4.95 (4.88)  Time: 0.411s, 2491.38/s  (0.435s, 2356.53/s)  LR: 3.307e-01  Data: 0.029 (0.051)
+Train: 41 [ 100/312 ( 32%)]  Loss: 4.85 (4.88)  Time: 0.409s, 2502.35/s  (0.423s, 2422.95/s)  LR: 3.307e-01  Data: 0.026 (0.039)
+Train: 41 [ 150/312 ( 48%)]  Loss: 4.80 (4.89)  Time: 0.409s, 2500.82/s  (0.418s, 2448.55/s)  LR: 3.307e-01  Data: 0.027 (0.035)
+Train: 41 [ 200/312 ( 64%)]  Loss: 5.02 (4.91)  Time: 0.411s, 2489.34/s  (0.416s, 2459.55/s)  LR: 3.307e-01  Data: 0.027 (0.033)
+Train: 41 [ 250/312 ( 80%)]  Loss: 4.91 (4.92)  Time: 0.412s, 2486.68/s  (0.415s, 2464.60/s)  LR: 3.307e-01  Data: 0.027 (0.032)
+Train: 41 [ 300/312 ( 96%)]  Loss: 4.89 (4.93)  Time: 0.414s, 2475.35/s  (0.415s, 2468.13/s)  LR: 3.307e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.484 (1.484)  Loss:   3.497 ( 3.497)  Acc@1:  37.402 ( 37.402)  Acc@5:  60.645 ( 60.645)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.306 ( 3.486)  Acc@1:  39.033 ( 35.920)  Acc@5:  63.679 ( 60.722)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-39.pth.tar', 37.8260000390625)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-41.pth.tar', 35.92000001953125)
+
+Train: 42 [   0/312 (  0%)]  Loss: 4.68 (4.68)  Time: 1.655s,  618.77/s  (1.655s,  618.77/s)  LR: 3.275e-01  Data: 1.283 (1.283)
+Train: 42 [  50/312 ( 16%)]  Loss: 4.89 (4.82)  Time: 0.405s, 2528.72/s  (0.427s, 2400.68/s)  LR: 3.275e-01  Data: 0.032 (0.052)
+Train: 42 [ 100/312 ( 32%)]  Loss: 4.83 (4.84)  Time: 0.404s, 2532.37/s  (0.414s, 2471.72/s)  LR: 3.275e-01  Data: 0.031 (0.040)
+Train: 42 [ 150/312 ( 48%)]  Loss: 4.88 (4.86)  Time: 0.406s, 2519.44/s  (0.411s, 2493.22/s)  LR: 3.275e-01  Data: 0.029 (0.036)
+Train: 42 [ 200/312 ( 64%)]  Loss: 4.91 (4.88)  Time: 0.413s, 2479.99/s  (0.409s, 2500.82/s)  LR: 3.275e-01  Data: 0.027 (0.034)
+Train: 42 [ 250/312 ( 80%)]  Loss: 5.02 (4.89)  Time: 0.413s, 2478.81/s  (0.410s, 2499.39/s)  LR: 3.275e-01  Data: 0.027 (0.032)
+Train: 42 [ 300/312 ( 96%)]  Loss: 4.93 (4.90)  Time: 0.418s, 2451.55/s  (0.410s, 2496.35/s)  LR: 3.275e-01  Data: 0.033 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.445 (1.445)  Loss:   3.233 ( 3.233)  Acc@1:  40.625 ( 40.625)  Acc@5:  64.160 ( 64.160)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.046 ( 3.257)  Acc@1:  43.278 ( 38.976)  Acc@5:  68.278 ( 64.068)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-39.pth.tar', 37.8260000390625)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-31.pth.tar', 36.234000021972655)
+
+Train: 43 [   0/312 (  0%)]  Loss: 4.86 (4.86)  Time: 1.826s,  560.83/s  (1.826s,  560.83/s)  LR: 3.242e-01  Data: 1.111 (1.111)
+Train: 43 [  50/312 ( 16%)]  Loss: 4.89 (4.82)  Time: 0.405s, 2526.71/s  (0.431s, 2375.48/s)  LR: 3.242e-01  Data: 0.030 (0.049)
+Train: 43 [ 100/312 ( 32%)]  Loss: 4.81 (4.82)  Time: 0.410s, 2500.21/s  (0.418s, 2449.99/s)  LR: 3.242e-01  Data: 0.029 (0.038)
+Train: 43 [ 150/312 ( 48%)]  Loss: 4.94 (4.84)  Time: 0.411s, 2491.39/s  (0.414s, 2470.83/s)  LR: 3.242e-01  Data: 0.028 (0.035)
+Train: 43 [ 200/312 ( 64%)]  Loss: 4.96 (4.86)  Time: 0.409s, 2502.39/s  (0.414s, 2475.51/s)  LR: 3.242e-01  Data: 0.026 (0.033)
+Train: 43 [ 250/312 ( 80%)]  Loss: 4.87 (4.87)  Time: 0.411s, 2492.67/s  (0.413s, 2480.82/s)  LR: 3.242e-01  Data: 0.027 (0.032)
+Train: 43 [ 300/312 ( 96%)]  Loss: 5.01 (4.88)  Time: 0.412s, 2485.74/s  (0.413s, 2481.56/s)  LR: 3.242e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.445 (1.445)  Loss:   3.192 ( 3.192)  Acc@1:  40.039 ( 40.039)  Acc@5:  64.551 ( 64.551)
+Test: [  48/48]  Time: 0.088 (0.332)  Loss:   3.049 ( 3.204)  Acc@1:  42.099 ( 39.846)  Acc@5:  68.042 ( 65.230)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-39.pth.tar', 37.8260000390625)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-33.pth.tar', 37.397999990234375)
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+Train: 44 [  50/312 ( 16%)]  Loss: 4.77 (4.78)  Time: 0.409s, 2502.44/s  (0.430s, 2380.11/s)  LR: 3.209e-01  Data: 0.028 (0.051)
+Train: 44 [ 100/312 ( 32%)]  Loss: 4.77 (4.80)  Time: 0.406s, 2522.48/s  (0.419s, 2445.51/s)  LR: 3.209e-01  Data: 0.027 (0.040)
+Train: 44 [ 150/312 ( 48%)]  Loss: 4.85 (4.83)  Time: 0.413s, 2481.31/s  (0.416s, 2460.01/s)  LR: 3.209e-01  Data: 0.028 (0.036)
+Train: 44 [ 200/312 ( 64%)]  Loss: 4.82 (4.84)  Time: 0.410s, 2494.59/s  (0.415s, 2469.26/s)  LR: 3.209e-01  Data: 0.027 (0.034)
+Train: 44 [ 250/312 ( 80%)]  Loss: 4.91 (4.85)  Time: 0.407s, 2517.81/s  (0.413s, 2478.21/s)  LR: 3.209e-01  Data: 0.027 (0.032)
+Train: 44 [ 300/312 ( 96%)]  Loss: 4.90 (4.86)  Time: 0.409s, 2506.41/s  (0.412s, 2482.68/s)  LR: 3.209e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
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+Test: [  48/48]  Time: 0.089 (0.332)  Loss:   3.106 ( 3.270)  Acc@1:  43.042 ( 40.144)  Acc@5:  67.335 ( 64.984)
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-39.pth.tar', 37.8260000390625)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-35.pth.tar', 37.50199998779297)
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+Train: 45 [  50/312 ( 16%)]  Loss: 4.86 (4.77)  Time: 0.413s, 2480.62/s  (0.440s, 2327.00/s)  LR: 3.176e-01  Data: 0.029 (0.049)
+Train: 45 [ 100/312 ( 32%)]  Loss: 4.87 (4.79)  Time: 0.405s, 2530.83/s  (0.425s, 2412.15/s)  LR: 3.176e-01  Data: 0.025 (0.039)
+Train: 45 [ 150/312 ( 48%)]  Loss: 4.97 (4.80)  Time: 0.405s, 2527.85/s  (0.418s, 2448.21/s)  LR: 3.176e-01  Data: 0.028 (0.035)
+Train: 45 [ 200/312 ( 64%)]  Loss: 4.88 (4.81)  Time: 0.409s, 2504.48/s  (0.416s, 2464.28/s)  LR: 3.176e-01  Data: 0.028 (0.033)
+Train: 45 [ 250/312 ( 80%)]  Loss: 4.90 (4.82)  Time: 0.419s, 2442.08/s  (0.415s, 2469.55/s)  LR: 3.176e-01  Data: 0.030 (0.032)
+Train: 45 [ 300/312 ( 96%)]  Loss: 4.98 (4.83)  Time: 0.412s, 2486.42/s  (0.414s, 2474.00/s)  LR: 3.176e-01  Data: 0.032 (0.031)
+Distributing BatchNorm running means and vars
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+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.067 ( 3.253)  Acc@1:  40.920 ( 39.188)  Acc@5:  68.160 ( 64.354)
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-39.pth.tar', 37.8260000390625)
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+Train: 46 [   0/312 (  0%)]  Loss: 4.77 (4.77)  Time: 1.669s,  613.36/s  (1.669s,  613.36/s)  LR: 3.141e-01  Data: 1.298 (1.298)
+Train: 46 [  50/312 ( 16%)]  Loss: 4.90 (4.72)  Time: 0.406s, 2521.89/s  (0.435s, 2355.07/s)  LR: 3.141e-01  Data: 0.028 (0.057)
+Train: 46 [ 100/312 ( 32%)]  Loss: 4.72 (4.76)  Time: 0.411s, 2493.07/s  (0.422s, 2427.50/s)  LR: 3.141e-01  Data: 0.028 (0.043)
+Train: 46 [ 150/312 ( 48%)]  Loss: 4.78 (4.78)  Time: 0.407s, 2518.01/s  (0.418s, 2450.71/s)  LR: 3.141e-01  Data: 0.027 (0.038)
+Train: 46 [ 200/312 ( 64%)]  Loss: 4.76 (4.80)  Time: 0.412s, 2485.13/s  (0.416s, 2462.79/s)  LR: 3.141e-01  Data: 0.032 (0.035)
+Train: 46 [ 250/312 ( 80%)]  Loss: 4.80 (4.81)  Time: 0.411s, 2491.33/s  (0.415s, 2468.51/s)  LR: 3.141e-01  Data: 0.026 (0.034)
+Train: 46 [ 300/312 ( 96%)]  Loss: 4.87 (4.82)  Time: 0.407s, 2514.85/s  (0.414s, 2474.43/s)  LR: 3.141e-01  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
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+Test: [  48/48]  Time: 0.087 (0.327)  Loss:   3.111 ( 3.259)  Acc@1:  41.509 ( 39.758)  Acc@5:  66.981 ( 64.898)
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-34.pth.tar', 37.86000004150391)
+
+Train: 47 [   0/312 (  0%)]  Loss: 4.68 (4.68)  Time: 1.681s,  609.20/s  (1.681s,  609.20/s)  LR: 3.107e-01  Data: 1.284 (1.284)
+Train: 47 [  50/312 ( 16%)]  Loss: 4.66 (4.71)  Time: 0.405s, 2530.18/s  (0.430s, 2382.62/s)  LR: 3.107e-01  Data: 0.026 (0.052)
+Train: 47 [ 100/312 ( 32%)]  Loss: 4.73 (4.73)  Time: 0.412s, 2487.34/s  (0.419s, 2446.59/s)  LR: 3.107e-01  Data: 0.027 (0.040)
+Train: 47 [ 150/312 ( 48%)]  Loss: 4.81 (4.74)  Time: 0.408s, 2510.07/s  (0.416s, 2463.44/s)  LR: 3.107e-01  Data: 0.028 (0.036)
+Train: 47 [ 200/312 ( 64%)]  Loss: 4.83 (4.76)  Time: 0.407s, 2515.82/s  (0.413s, 2476.46/s)  LR: 3.107e-01  Data: 0.026 (0.033)
+Train: 47 [ 250/312 ( 80%)]  Loss: 4.96 (4.78)  Time: 0.407s, 2514.58/s  (0.412s, 2484.85/s)  LR: 3.107e-01  Data: 0.030 (0.032)
+Train: 47 [ 300/312 ( 96%)]  Loss: 4.90 (4.79)  Time: 0.412s, 2487.87/s  (0.412s, 2487.23/s)  LR: 3.107e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.464 (1.464)  Loss:   3.268 ( 3.268)  Acc@1:  41.602 ( 41.602)  Acc@5:  65.332 ( 65.332)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.077 ( 3.265)  Acc@1:  43.868 ( 39.924)  Acc@5:  68.514 ( 64.788)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-38.pth.tar', 37.914000006103514)
+
+Train: 48 [   0/312 (  0%)]  Loss: 4.63 (4.63)  Time: 2.294s,  446.41/s  (2.294s,  446.41/s)  LR: 3.072e-01  Data: 1.924 (1.924)
+Train: 48 [  50/312 ( 16%)]  Loss: 4.80 (4.69)  Time: 0.402s, 2548.89/s  (0.439s, 2331.57/s)  LR: 3.072e-01  Data: 0.029 (0.064)
+Train: 48 [ 100/312 ( 32%)]  Loss: 4.75 (4.72)  Time: 0.401s, 2554.24/s  (0.421s, 2431.88/s)  LR: 3.072e-01  Data: 0.026 (0.046)
+Train: 48 [ 150/312 ( 48%)]  Loss: 4.77 (4.73)  Time: 0.403s, 2542.96/s  (0.415s, 2464.57/s)  LR: 3.072e-01  Data: 0.027 (0.040)
+Train: 48 [ 200/312 ( 64%)]  Loss: 4.75 (4.75)  Time: 0.409s, 2506.30/s  (0.414s, 2475.73/s)  LR: 3.072e-01  Data: 0.027 (0.037)
+Train: 48 [ 250/312 ( 80%)]  Loss: 4.91 (4.76)  Time: 0.412s, 2483.22/s  (0.413s, 2478.12/s)  LR: 3.072e-01  Data: 0.029 (0.035)
+Train: 48 [ 300/312 ( 96%)]  Loss: 4.72 (4.77)  Time: 0.409s, 2501.76/s  (0.413s, 2479.46/s)  LR: 3.072e-01  Data: 0.027 (0.034)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.435 (1.435)  Loss:   3.281 ( 3.281)  Acc@1:  41.406 ( 41.406)  Acc@5:  65.625 ( 65.625)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.082 ( 3.275)  Acc@1:  42.453 ( 40.160)  Acc@5:  68.868 ( 64.942)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-36.pth.tar', 38.713999995117184)
+
+Train: 49 [   0/312 (  0%)]  Loss: 4.69 (4.69)  Time: 1.826s,  560.77/s  (1.826s,  560.77/s)  LR: 3.036e-01  Data: 1.451 (1.451)
+Train: 49 [  50/312 ( 16%)]  Loss: 4.69 (4.66)  Time: 0.413s, 2478.12/s  (0.439s, 2333.51/s)  LR: 3.036e-01  Data: 0.026 (0.055)
+Train: 49 [ 100/312 ( 32%)]  Loss: 4.69 (4.69)  Time: 0.410s, 2497.49/s  (0.425s, 2409.66/s)  LR: 3.036e-01  Data: 0.026 (0.041)
+Train: 49 [ 150/312 ( 48%)]  Loss: 4.69 (4.71)  Time: 0.409s, 2505.89/s  (0.420s, 2438.58/s)  LR: 3.036e-01  Data: 0.029 (0.037)
+Train: 49 [ 200/312 ( 64%)]  Loss: 4.76 (4.72)  Time: 0.410s, 2499.68/s  (0.417s, 2452.90/s)  LR: 3.036e-01  Data: 0.027 (0.034)
+Train: 49 [ 250/312 ( 80%)]  Loss: 4.79 (4.74)  Time: 0.410s, 2496.49/s  (0.416s, 2462.13/s)  LR: 3.036e-01  Data: 0.028 (0.033)
+Train: 49 [ 300/312 ( 96%)]  Loss: 4.86 (4.75)  Time: 0.409s, 2503.72/s  (0.415s, 2468.52/s)  LR: 3.036e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.443 (1.443)  Loss:   3.220 ( 3.220)  Acc@1:  42.773 ( 42.773)  Acc@5:  67.285 ( 67.285)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.126 ( 3.301)  Acc@1:  42.689 ( 40.172)  Acc@5:  68.042 ( 64.994)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-37.pth.tar', 38.84599999145508)
+
+Train: 50 [   0/312 (  0%)]  Loss: 4.63 (4.63)  Time: 1.605s,  637.84/s  (1.605s,  637.84/s)  LR: 3.000e-01  Data: 1.231 (1.231)
+Train: 50 [  50/312 ( 16%)]  Loss: 4.66 (4.64)  Time: 0.413s, 2478.32/s  (0.434s, 2359.89/s)  LR: 3.000e-01  Data: 0.030 (0.051)
+Train: 50 [ 100/312 ( 32%)]  Loss: 4.73 (4.67)  Time: 0.407s, 2515.46/s  (0.422s, 2428.28/s)  LR: 3.000e-01  Data: 0.028 (0.040)
+Train: 50 [ 150/312 ( 48%)]  Loss: 4.62 (4.69)  Time: 0.411s, 2489.11/s  (0.417s, 2453.41/s)  LR: 3.000e-01  Data: 0.027 (0.036)
+Train: 50 [ 200/312 ( 64%)]  Loss: 4.66 (4.71)  Time: 0.413s, 2477.08/s  (0.416s, 2463.86/s)  LR: 3.000e-01  Data: 0.028 (0.034)
+Train: 50 [ 250/312 ( 80%)]  Loss: 4.80 (4.72)  Time: 0.413s, 2479.75/s  (0.415s, 2469.35/s)  LR: 3.000e-01  Data: 0.028 (0.033)
+Train: 50 [ 300/312 ( 96%)]  Loss: 4.91 (4.73)  Time: 0.408s, 2507.23/s  (0.414s, 2473.18/s)  LR: 3.000e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.426 (1.426)  Loss:   3.189 ( 3.189)  Acc@1:  41.895 ( 41.895)  Acc@5:  66.406 ( 66.406)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.029 ( 3.214)  Acc@1:  43.868 ( 40.796)  Acc@5:  67.925 ( 65.560)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-42.pth.tar', 38.97600005371094)
+
+Train: 51 [   0/312 (  0%)]  Loss: 4.46 (4.46)  Time: 1.870s,  547.51/s  (1.870s,  547.51/s)  LR: 2.964e-01  Data: 1.500 (1.500)
+Train: 51 [  50/312 ( 16%)]  Loss: 4.74 (4.62)  Time: 0.403s, 2541.36/s  (0.433s, 2365.91/s)  LR: 2.964e-01  Data: 0.026 (0.056)
+Train: 51 [ 100/312 ( 32%)]  Loss: 4.64 (4.64)  Time: 0.407s, 2517.20/s  (0.419s, 2441.97/s)  LR: 2.964e-01  Data: 0.025 (0.042)
+Train: 51 [ 150/312 ( 48%)]  Loss: 4.72 (4.67)  Time: 0.412s, 2488.26/s  (0.416s, 2460.12/s)  LR: 2.964e-01  Data: 0.029 (0.037)
+Train: 51 [ 200/312 ( 64%)]  Loss: 4.63 (4.68)  Time: 0.407s, 2514.01/s  (0.415s, 2469.14/s)  LR: 2.964e-01  Data: 0.027 (0.035)
+Train: 51 [ 250/312 ( 80%)]  Loss: 4.78 (4.70)  Time: 0.403s, 2540.39/s  (0.413s, 2481.06/s)  LR: 2.964e-01  Data: 0.027 (0.033)
+Train: 51 [ 300/312 ( 96%)]  Loss: 4.95 (4.71)  Time: 0.405s, 2528.80/s  (0.411s, 2489.80/s)  LR: 2.964e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   3.278 ( 3.278)  Acc@1:  41.602 ( 41.602)  Acc@5:  64.746 ( 64.746)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.057 ( 3.289)  Acc@1:  44.811 ( 40.160)  Acc@5:  69.222 ( 64.856)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-51.pth.tar', 40.16000000854492)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-40.pth.tar', 39.13200002807617)
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+Train: 52 [  50/312 ( 16%)]  Loss: 4.60 (4.60)  Time: 0.408s, 2506.98/s  (0.432s, 2371.90/s)  LR: 2.927e-01  Data: 0.027 (0.053)
+Train: 52 [ 100/312 ( 32%)]  Loss: 4.66 (4.62)  Time: 0.413s, 2481.92/s  (0.421s, 2431.71/s)  LR: 2.927e-01  Data: 0.027 (0.040)
+Train: 52 [ 150/312 ( 48%)]  Loss: 4.66 (4.64)  Time: 0.414s, 2476.30/s  (0.418s, 2448.02/s)  LR: 2.927e-01  Data: 0.026 (0.036)
+Train: 52 [ 200/312 ( 64%)]  Loss: 4.69 (4.66)  Time: 0.414s, 2476.21/s  (0.417s, 2457.32/s)  LR: 2.927e-01  Data: 0.028 (0.034)
+Train: 52 [ 250/312 ( 80%)]  Loss: 4.67 (4.67)  Time: 0.407s, 2513.42/s  (0.415s, 2465.76/s)  LR: 2.927e-01  Data: 0.027 (0.032)
+Train: 52 [ 300/312 ( 96%)]  Loss: 4.74 (4.69)  Time: 0.409s, 2506.61/s  (0.414s, 2472.52/s)  LR: 2.927e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.433 (1.433)  Loss:   3.114 ( 3.114)  Acc@1:  42.480 ( 42.480)  Acc@5:  67.090 ( 67.090)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   2.895 ( 3.149)  Acc@1:  44.340 ( 41.434)  Acc@5:  71.108 ( 65.810)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-51.pth.tar', 40.16000000854492)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-45.pth.tar', 39.1879999987793)
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+Train: 53 [   0/312 (  0%)]  Loss: 4.56 (4.56)  Time: 1.824s,  561.36/s  (1.824s,  561.36/s)  LR: 2.889e-01  Data: 1.448 (1.448)
+Train: 53 [  50/312 ( 16%)]  Loss: 4.61 (4.58)  Time: 0.407s, 2512.98/s  (0.439s, 2333.02/s)  LR: 2.889e-01  Data: 0.027 (0.055)
+Train: 53 [ 100/312 ( 32%)]  Loss: 4.61 (4.61)  Time: 0.403s, 2538.65/s  (0.423s, 2423.36/s)  LR: 2.889e-01  Data: 0.026 (0.042)
+Train: 53 [ 150/312 ( 48%)]  Loss: 4.61 (4.63)  Time: 0.403s, 2541.90/s  (0.417s, 2458.26/s)  LR: 2.889e-01  Data: 0.027 (0.037)
+Train: 53 [ 200/312 ( 64%)]  Loss: 4.71 (4.65)  Time: 0.407s, 2516.70/s  (0.414s, 2475.24/s)  LR: 2.889e-01  Data: 0.028 (0.035)
+Train: 53 [ 250/312 ( 80%)]  Loss: 4.80 (4.66)  Time: 0.409s, 2505.25/s  (0.412s, 2482.87/s)  LR: 2.889e-01  Data: 0.027 (0.033)
+Train: 53 [ 300/312 ( 96%)]  Loss: 4.76 (4.67)  Time: 0.410s, 2500.07/s  (0.412s, 2483.40/s)  LR: 2.889e-01  Data: 0.023 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.434 (1.434)  Loss:   3.242 ( 3.242)  Acc@1:  39.160 ( 39.160)  Acc@5:  66.406 ( 66.406)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   2.999 ( 3.249)  Acc@1:  44.340 ( 40.256)  Acc@5:  68.514 ( 64.626)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-51.pth.tar', 40.16000000854492)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-46.pth.tar', 39.758000061035155)
+
+Train: 54 [   0/312 (  0%)]  Loss: 4.71 (4.71)  Time: 1.852s,  553.02/s  (1.852s,  553.02/s)  LR: 2.852e-01  Data: 1.476 (1.476)
+Train: 54 [  50/312 ( 16%)]  Loss: 4.63 (4.58)  Time: 0.409s, 2504.40/s  (0.437s, 2344.94/s)  LR: 2.852e-01  Data: 0.028 (0.056)
+Train: 54 [ 100/312 ( 32%)]  Loss: 4.53 (4.59)  Time: 0.410s, 2498.02/s  (0.422s, 2424.01/s)  LR: 2.852e-01  Data: 0.028 (0.042)
+Train: 54 [ 150/312 ( 48%)]  Loss: 4.53 (4.61)  Time: 0.415s, 2464.81/s  (0.419s, 2446.17/s)  LR: 2.852e-01  Data: 0.029 (0.037)
+Train: 54 [ 200/312 ( 64%)]  Loss: 4.71 (4.63)  Time: 0.412s, 2484.47/s  (0.416s, 2458.86/s)  LR: 2.852e-01  Data: 0.030 (0.035)
+Train: 54 [ 250/312 ( 80%)]  Loss: 4.79 (4.64)  Time: 0.410s, 2499.44/s  (0.415s, 2470.03/s)  LR: 2.852e-01  Data: 0.032 (0.033)
+Train: 54 [ 300/312 ( 96%)]  Loss: 4.79 (4.65)  Time: 0.408s, 2508.05/s  (0.414s, 2475.66/s)  LR: 2.852e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.414 (1.414)  Loss:   3.199 ( 3.199)  Acc@1:  40.430 ( 40.430)  Acc@5:  64.355 ( 64.355)
+Test: [  48/48]  Time: 0.089 (0.327)  Loss:   3.035 ( 3.233)  Acc@1:  43.750 ( 40.596)  Acc@5:  69.104 ( 64.642)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-51.pth.tar', 40.16000000854492)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-43.pth.tar', 39.845999993896484)
+
+Train: 55 [   0/312 (  0%)]  Loss: 4.57 (4.57)  Time: 1.675s,  611.18/s  (1.675s,  611.18/s)  LR: 2.813e-01  Data: 1.301 (1.301)
+Train: 55 [  50/312 ( 16%)]  Loss: 4.57 (4.55)  Time: 0.407s, 2517.42/s  (0.433s, 2366.24/s)  LR: 2.813e-01  Data: 0.027 (0.053)
+Train: 55 [ 100/312 ( 32%)]  Loss: 4.66 (4.57)  Time: 0.408s, 2509.72/s  (0.420s, 2438.26/s)  LR: 2.813e-01  Data: 0.027 (0.041)
+Train: 55 [ 150/312 ( 48%)]  Loss: 4.68 (4.59)  Time: 0.411s, 2488.51/s  (0.416s, 2459.02/s)  LR: 2.813e-01  Data: 0.032 (0.036)
+Train: 55 [ 200/312 ( 64%)]  Loss: 4.61 (4.61)  Time: 0.410s, 2496.30/s  (0.415s, 2464.97/s)  LR: 2.813e-01  Data: 0.027 (0.034)
+Train: 55 [ 250/312 ( 80%)]  Loss: 4.62 (4.62)  Time: 0.409s, 2501.31/s  (0.414s, 2471.39/s)  LR: 2.813e-01  Data: 0.029 (0.033)
+Train: 55 [ 300/312 ( 96%)]  Loss: 4.67 (4.63)  Time: 0.405s, 2525.54/s  (0.413s, 2479.38/s)  LR: 2.813e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.439 (1.439)  Loss:   3.160 ( 3.160)  Acc@1:  41.309 ( 41.309)  Acc@5:  65.918 ( 65.918)
+Test: [  48/48]  Time: 0.088 (0.327)  Loss:   2.978 ( 3.225)  Acc@1:  44.340 ( 40.432)  Acc@5:  69.340 ( 64.776)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-51.pth.tar', 40.16000000854492)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-47.pth.tar', 39.92399998657226)
+
+Train: 56 [   0/312 (  0%)]  Loss: 4.52 (4.52)  Time: 1.557s,  657.84/s  (1.557s,  657.84/s)  LR: 2.775e-01  Data: 1.137 (1.137)
+Train: 56 [  50/312 ( 16%)]  Loss: 4.64 (4.51)  Time: 0.412s, 2487.86/s  (0.429s, 2385.11/s)  LR: 2.775e-01  Data: 0.027 (0.050)
+Train: 56 [ 100/312 ( 32%)]  Loss: 4.78 (4.54)  Time: 0.415s, 2468.23/s  (0.420s, 2440.13/s)  LR: 2.775e-01  Data: 0.028 (0.039)
+Train: 56 [ 150/312 ( 48%)]  Loss: 4.63 (4.56)  Time: 0.409s, 2505.42/s  (0.417s, 2457.93/s)  LR: 2.775e-01  Data: 0.026 (0.035)
+Train: 56 [ 200/312 ( 64%)]  Loss: 4.70 (4.58)  Time: 0.412s, 2485.74/s  (0.415s, 2466.36/s)  LR: 2.775e-01  Data: 0.028 (0.033)
+Train: 56 [ 250/312 ( 80%)]  Loss: 4.69 (4.59)  Time: 0.409s, 2501.38/s  (0.414s, 2472.45/s)  LR: 2.775e-01  Data: 0.027 (0.032)
+Train: 56 [ 300/312 ( 96%)]  Loss: 4.79 (4.60)  Time: 0.410s, 2496.01/s  (0.413s, 2477.02/s)  LR: 2.775e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   3.176 ( 3.176)  Acc@1:  41.211 ( 41.211)  Acc@5:  66.113 ( 66.113)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   2.994 ( 3.206)  Acc@1:  44.104 ( 40.982)  Acc@5:  70.047 ( 64.998)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-51.pth.tar', 40.16000000854492)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-44.pth.tar', 40.14400001586914)
+
+Train: 57 [   0/312 (  0%)]  Loss: 4.52 (4.52)  Time: 1.895s,  540.48/s  (1.895s,  540.48/s)  LR: 2.736e-01  Data: 1.520 (1.520)
+Train: 57 [  50/312 ( 16%)]  Loss: 4.56 (4.50)  Time: 0.409s, 2503.25/s  (0.438s, 2339.86/s)  LR: 2.736e-01  Data: 0.026 (0.056)
+Train: 57 [ 100/312 ( 32%)]  Loss: 4.56 (4.52)  Time: 0.409s, 2506.39/s  (0.424s, 2415.22/s)  LR: 2.736e-01  Data: 0.027 (0.042)
+Train: 57 [ 150/312 ( 48%)]  Loss: 4.53 (4.54)  Time: 0.413s, 2478.44/s  (0.420s, 2440.53/s)  LR: 2.736e-01  Data: 0.027 (0.037)
+Train: 57 [ 200/312 ( 64%)]  Loss: 4.58 (4.55)  Time: 0.407s, 2515.42/s  (0.417s, 2453.95/s)  LR: 2.736e-01  Data: 0.027 (0.035)
+Train: 57 [ 250/312 ( 80%)]  Loss: 4.68 (4.57)  Time: 0.409s, 2503.46/s  (0.416s, 2463.55/s)  LR: 2.736e-01  Data: 0.028 (0.033)
+Train: 57 [ 300/312 ( 96%)]  Loss: 4.55 (4.58)  Time: 0.411s, 2489.79/s  (0.415s, 2468.72/s)  LR: 2.736e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.427 (1.427)  Loss:   3.217 ( 3.217)  Acc@1:  42.480 ( 42.480)  Acc@5:  64.648 ( 64.648)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.022 ( 3.222)  Acc@1:  43.396 ( 40.772)  Acc@5:  70.047 ( 64.494)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-57.pth.tar', 40.771999975585935)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-51.pth.tar', 40.16000000854492)
+
+Train: 58 [   0/312 (  0%)]  Loss: 4.47 (4.47)  Time: 1.867s,  548.53/s  (1.867s,  548.53/s)  LR: 2.697e-01  Data: 1.152 (1.152)
+Train: 58 [  50/312 ( 16%)]  Loss: 4.51 (4.47)  Time: 0.401s, 2552.79/s  (0.432s, 2369.50/s)  LR: 2.697e-01  Data: 0.023 (0.050)
+Train: 58 [ 100/312 ( 32%)]  Loss: 4.55 (4.50)  Time: 0.405s, 2525.34/s  (0.418s, 2450.73/s)  LR: 2.697e-01  Data: 0.028 (0.038)
+Train: 58 [ 150/312 ( 48%)]  Loss: 4.67 (4.52)  Time: 0.407s, 2514.85/s  (0.414s, 2472.70/s)  LR: 2.697e-01  Data: 0.027 (0.035)
+Train: 58 [ 200/312 ( 64%)]  Loss: 4.60 (4.54)  Time: 0.413s, 2479.80/s  (0.413s, 2479.37/s)  LR: 2.697e-01  Data: 0.026 (0.033)
+Train: 58 [ 250/312 ( 80%)]  Loss: 4.67 (4.55)  Time: 0.408s, 2508.35/s  (0.412s, 2482.84/s)  LR: 2.697e-01  Data: 0.027 (0.031)
+Train: 58 [ 300/312 ( 96%)]  Loss: 4.68 (4.56)  Time: 0.409s, 2501.31/s  (0.412s, 2487.32/s)  LR: 2.697e-01  Data: 0.023 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.439 (1.439)  Loss:   3.166 ( 3.166)  Acc@1:  43.066 ( 43.066)  Acc@5:  64.941 ( 64.941)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.047 ( 3.238)  Acc@1:  42.571 ( 40.746)  Acc@5:  70.165 ( 64.900)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-57.pth.tar', 40.771999975585935)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-58.pth.tar', 40.74600000488281)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-48.pth.tar', 40.16000001831055)
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+Train: 59 [  50/312 ( 16%)]  Loss: 4.51 (4.46)  Time: 0.412s, 2487.52/s  (0.435s, 2356.34/s)  LR: 2.658e-01  Data: 0.027 (0.051)
+Train: 59 [ 100/312 ( 32%)]  Loss: 4.31 (4.48)  Time: 0.413s, 2480.17/s  (0.424s, 2415.70/s)  LR: 2.658e-01  Data: 0.028 (0.039)
+Train: 59 [ 150/312 ( 48%)]  Loss: 4.48 (4.50)  Time: 0.405s, 2530.60/s  (0.419s, 2445.00/s)  LR: 2.658e-01  Data: 0.027 (0.036)
+Train: 59 [ 200/312 ( 64%)]  Loss: 4.64 (4.52)  Time: 0.406s, 2524.21/s  (0.416s, 2463.85/s)  LR: 2.658e-01  Data: 0.028 (0.034)
+Train: 59 [ 250/312 ( 80%)]  Loss: 4.65 (4.53)  Time: 0.408s, 2507.64/s  (0.414s, 2474.70/s)  LR: 2.658e-01  Data: 0.027 (0.032)
+Train: 59 [ 300/312 ( 96%)]  Loss: 4.66 (4.55)  Time: 0.412s, 2482.61/s  (0.413s, 2477.93/s)  LR: 2.658e-01  Data: 0.028 (0.032)
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+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.043 ( 3.261)  Acc@1:  43.160 ( 39.904)  Acc@5:  68.986 ( 64.388)
+Train: 60 [   0/312 (  0%)]  Loss: 4.37 (4.37)  Time: 1.609s,  636.58/s  (1.609s,  636.58/s)  LR: 2.618e-01  Data: 1.237 (1.237)
+Train: 60 [  50/312 ( 16%)]  Loss: 4.52 (4.43)  Time: 0.403s, 2543.99/s  (0.428s, 2395.18/s)  LR: 2.618e-01  Data: 0.025 (0.051)
+Train: 60 [ 100/312 ( 32%)]  Loss: 4.57 (4.45)  Time: 0.409s, 2501.79/s  (0.417s, 2456.10/s)  LR: 2.618e-01  Data: 0.026 (0.039)
+Train: 60 [ 150/312 ( 48%)]  Loss: 4.43 (4.47)  Time: 0.410s, 2498.00/s  (0.415s, 2466.81/s)  LR: 2.618e-01  Data: 0.027 (0.035)
+Train: 60 [ 200/312 ( 64%)]  Loss: 4.42 (4.49)  Time: 0.408s, 2507.92/s  (0.414s, 2471.77/s)  LR: 2.618e-01  Data: 0.028 (0.033)
+Train: 60 [ 250/312 ( 80%)]  Loss: 4.58 (4.51)  Time: 0.405s, 2526.24/s  (0.413s, 2480.73/s)  LR: 2.618e-01  Data: 0.027 (0.032)
+Train: 60 [ 300/312 ( 96%)]  Loss: 4.74 (4.52)  Time: 0.409s, 2501.25/s  (0.412s, 2487.57/s)  LR: 2.618e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   3.264 ( 3.264)  Acc@1:  40.332 ( 40.332)  Acc@5:  63.574 ( 63.574)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.043 ( 3.295)  Acc@1:  44.458 ( 40.166)  Acc@5:  67.099 ( 63.708)
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-58.pth.tar', 40.74600000488281)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-60.pth.tar', 40.165999984130856)
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+Train: 61 [  50/312 ( 16%)]  Loss: 4.39 (4.43)  Time: 0.410s, 2497.17/s  (0.439s, 2333.63/s)  LR: 2.578e-01  Data: 0.027 (0.050)
+Train: 61 [ 100/312 ( 32%)]  Loss: 4.42 (4.44)  Time: 0.407s, 2516.17/s  (0.424s, 2413.47/s)  LR: 2.578e-01  Data: 0.027 (0.039)
+Train: 61 [ 150/312 ( 48%)]  Loss: 4.54 (4.46)  Time: 0.404s, 2532.11/s  (0.418s, 2447.85/s)  LR: 2.578e-01  Data: 0.028 (0.035)
+Train: 61 [ 200/312 ( 64%)]  Loss: 4.55 (4.48)  Time: 0.406s, 2519.98/s  (0.415s, 2466.84/s)  LR: 2.578e-01  Data: 0.027 (0.033)
+Train: 61 [ 250/312 ( 80%)]  Loss: 4.59 (4.49)  Time: 0.407s, 2516.83/s  (0.414s, 2475.85/s)  LR: 2.578e-01  Data: 0.027 (0.032)
+Train: 61 [ 300/312 ( 96%)]  Loss: 4.61 (4.50)  Time: 0.410s, 2496.99/s  (0.413s, 2477.70/s)  LR: 2.578e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
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+Test: [  48/48]  Time: 0.089 (0.331)  Loss:   3.015 ( 3.233)  Acc@1:  42.807 ( 41.030)  Acc@5:  68.986 ( 64.602)
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-61.pth.tar', 41.02999997802734)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-57.pth.tar', 40.771999975585935)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-58.pth.tar', 40.74600000488281)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-49.pth.tar', 40.17200005615234)
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+Train: 62 [  50/312 ( 16%)]  Loss: 4.50 (4.39)  Time: 0.412s, 2485.91/s  (0.434s, 2358.63/s)  LR: 2.538e-01  Data: 0.027 (0.048)
+Train: 62 [ 100/312 ( 32%)]  Loss: 4.46 (4.41)  Time: 0.405s, 2531.26/s  (0.421s, 2432.06/s)  LR: 2.538e-01  Data: 0.028 (0.038)
+Train: 62 [ 150/312 ( 48%)]  Loss: 4.45 (4.43)  Time: 0.403s, 2543.08/s  (0.416s, 2464.26/s)  LR: 2.538e-01  Data: 0.027 (0.035)
+Train: 62 [ 200/312 ( 64%)]  Loss: 4.50 (4.45)  Time: 0.406s, 2521.05/s  (0.413s, 2480.84/s)  LR: 2.538e-01  Data: 0.028 (0.033)
+Train: 62 [ 250/312 ( 80%)]  Loss: 4.58 (4.47)  Time: 0.409s, 2503.63/s  (0.412s, 2487.58/s)  LR: 2.538e-01  Data: 0.028 (0.032)
+Train: 62 [ 300/312 ( 96%)]  Loss: 4.66 (4.48)  Time: 0.410s, 2498.44/s  (0.412s, 2488.13/s)  LR: 2.538e-01  Data: 0.030 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.418 (1.418)  Loss:   3.488 ( 3.488)  Acc@1:  36.719 ( 36.719)  Acc@5:  60.938 ( 60.938)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.286 ( 3.435)  Acc@1:  39.269 ( 37.694)  Acc@5:  63.797 ( 61.188)
+Train: 63 [   0/312 (  0%)]  Loss: 4.36 (4.36)  Time: 1.825s,  560.94/s  (1.825s,  560.94/s)  LR: 2.497e-01  Data: 1.454 (1.454)
+Train: 63 [  50/312 ( 16%)]  Loss: 4.35 (4.37)  Time: 0.405s, 2526.50/s  (0.433s, 2365.91/s)  LR: 2.497e-01  Data: 0.027 (0.055)
+Train: 63 [ 100/312 ( 32%)]  Loss: 4.37 (4.39)  Time: 0.407s, 2514.64/s  (0.420s, 2438.23/s)  LR: 2.497e-01  Data: 0.025 (0.041)
+Train: 63 [ 150/312 ( 48%)]  Loss: 4.39 (4.41)  Time: 0.413s, 2482.34/s  (0.417s, 2454.00/s)  LR: 2.497e-01  Data: 0.028 (0.037)
+Train: 63 [ 200/312 ( 64%)]  Loss: 4.59 (4.43)  Time: 0.408s, 2512.83/s  (0.415s, 2466.99/s)  LR: 2.497e-01  Data: 0.028 (0.034)
+Train: 63 [ 250/312 ( 80%)]  Loss: 4.52 (4.45)  Time: 0.407s, 2513.81/s  (0.413s, 2478.01/s)  LR: 2.497e-01  Data: 0.027 (0.033)
+Train: 63 [ 300/312 ( 96%)]  Loss: 4.70 (4.46)  Time: 0.410s, 2496.04/s  (0.412s, 2484.41/s)  LR: 2.497e-01  Data: 0.032 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.433 (1.433)  Loss:   3.253 ( 3.253)  Acc@1:  41.016 ( 41.016)  Acc@5:  65.332 ( 65.332)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   3.046 ( 3.251)  Acc@1:  43.514 ( 40.760)  Acc@5:  67.807 ( 64.484)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-61.pth.tar', 41.02999997802734)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-57.pth.tar', 40.771999975585935)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-63.pth.tar', 40.76000002685547)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-58.pth.tar', 40.74600000488281)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-53.pth.tar', 40.2559999975586)
+
+Train: 64 [   0/312 (  0%)]  Loss: 4.38 (4.38)  Time: 1.955s,  523.83/s  (1.955s,  523.83/s)  LR: 2.457e-01  Data: 1.579 (1.579)
+Train: 64 [  50/312 ( 16%)]  Loss: 4.50 (4.35)  Time: 0.407s, 2513.34/s  (0.441s, 2320.53/s)  LR: 2.457e-01  Data: 0.028 (0.058)
+Train: 64 [ 100/312 ( 32%)]  Loss: 4.51 (4.38)  Time: 0.407s, 2514.13/s  (0.424s, 2412.26/s)  LR: 2.457e-01  Data: 0.027 (0.043)
+Train: 64 [ 150/312 ( 48%)]  Loss: 4.48 (4.39)  Time: 0.407s, 2513.57/s  (0.419s, 2446.10/s)  LR: 2.457e-01  Data: 0.027 (0.038)
+Train: 64 [ 200/312 ( 64%)]  Loss: 4.51 (4.41)  Time: 0.408s, 2506.84/s  (0.416s, 2458.91/s)  LR: 2.457e-01  Data: 0.026 (0.035)
+Train: 64 [ 250/312 ( 80%)]  Loss: 4.54 (4.43)  Time: 0.412s, 2488.40/s  (0.416s, 2463.24/s)  LR: 2.457e-01  Data: 0.026 (0.034)
+Train: 64 [ 300/312 ( 96%)]  Loss: 4.49 (4.44)  Time: 0.408s, 2511.12/s  (0.414s, 2470.70/s)  LR: 2.457e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.495 (1.495)  Loss:   3.323 ( 3.323)  Acc@1:  40.137 ( 40.137)  Acc@5:  62.891 ( 62.891)
+Test: [  48/48]  Time: 0.088 (0.332)  Loss:   3.101 ( 3.318)  Acc@1:  40.802 ( 39.330)  Acc@5:  66.038 ( 62.732)
+Train: 65 [   0/312 (  0%)]  Loss: 4.32 (4.32)  Time: 1.466s,  698.65/s  (1.466s,  698.65/s)  LR: 2.416e-01  Data: 1.093 (1.093)
+Train: 65 [  50/312 ( 16%)]  Loss: 4.41 (4.33)  Time: 0.405s, 2526.99/s  (0.428s, 2391.90/s)  LR: 2.416e-01  Data: 0.027 (0.049)
+Train: 65 [ 100/312 ( 32%)]  Loss: 4.41 (4.34)  Time: 0.410s, 2499.69/s  (0.419s, 2445.18/s)  LR: 2.416e-01  Data: 0.026 (0.039)
+Train: 65 [ 150/312 ( 48%)]  Loss: 4.47 (4.37)  Time: 0.408s, 2509.24/s  (0.416s, 2461.88/s)  LR: 2.416e-01  Data: 0.027 (0.035)
+Train: 65 [ 200/312 ( 64%)]  Loss: 4.39 (4.39)  Time: 0.407s, 2515.66/s  (0.414s, 2475.69/s)  LR: 2.416e-01  Data: 0.028 (0.033)
+Train: 65 [ 250/312 ( 80%)]  Loss: 4.47 (4.41)  Time: 0.405s, 2530.44/s  (0.412s, 2485.97/s)  LR: 2.416e-01  Data: 0.025 (0.032)
+Train: 65 [ 300/312 ( 96%)]  Loss: 4.46 (4.42)  Time: 0.406s, 2522.76/s  (0.411s, 2491.84/s)  LR: 2.416e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   3.184 ( 3.184)  Acc@1:  42.676 ( 42.676)  Acc@5:  65.137 ( 65.137)
+Test: [  48/48]  Time: 0.088 (0.332)  Loss:   3.043 ( 3.275)  Acc@1:  41.274 ( 40.108)  Acc@5:  66.156 ( 63.474)
+Train: 66 [   0/312 (  0%)]  Loss: 4.36 (4.36)  Time: 1.960s,  522.47/s  (1.960s,  522.47/s)  LR: 2.375e-01  Data: 1.072 (1.072)
+Train: 66 [  50/312 ( 16%)]  Loss: 4.29 (4.29)  Time: 0.407s, 2513.89/s  (0.440s, 2327.87/s)  LR: 2.375e-01  Data: 0.027 (0.048)
+Train: 66 [ 100/312 ( 32%)]  Loss: 4.42 (4.33)  Time: 0.410s, 2500.15/s  (0.424s, 2412.43/s)  LR: 2.375e-01  Data: 0.028 (0.038)
+Train: 66 [ 150/312 ( 48%)]  Loss: 4.43 (4.35)  Time: 0.409s, 2503.93/s  (0.420s, 2438.29/s)  LR: 2.375e-01  Data: 0.027 (0.034)
+Train: 66 [ 200/312 ( 64%)]  Loss: 4.43 (4.37)  Time: 0.407s, 2517.78/s  (0.418s, 2452.29/s)  LR: 2.375e-01  Data: 0.026 (0.033)
+Train: 66 [ 250/312 ( 80%)]  Loss: 4.59 (4.39)  Time: 0.411s, 2492.66/s  (0.415s, 2464.66/s)  LR: 2.375e-01  Data: 0.035 (0.032)
+Train: 66 [ 300/312 ( 96%)]  Loss: 4.52 (4.40)  Time: 0.404s, 2533.20/s  (0.414s, 2474.40/s)  LR: 2.375e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.440 (1.440)  Loss:   3.256 ( 3.256)  Acc@1:  39.844 ( 39.844)  Acc@5:  63.965 ( 63.965)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.073 ( 3.300)  Acc@1:  43.160 ( 39.854)  Acc@5:  66.745 ( 63.218)
+Train: 67 [   0/312 (  0%)]  Loss: 4.08 (4.08)  Time: 1.548s,  661.30/s  (1.548s,  661.30/s)  LR: 2.334e-01  Data: 1.176 (1.176)
+Train: 67 [  50/312 ( 16%)]  Loss: 4.26 (4.28)  Time: 0.412s, 2484.39/s  (0.432s, 2372.79/s)  LR: 2.334e-01  Data: 0.029 (0.049)
+Train: 67 [ 100/312 ( 32%)]  Loss: 4.34 (4.31)  Time: 0.405s, 2527.65/s  (0.421s, 2434.94/s)  LR: 2.334e-01  Data: 0.028 (0.038)
+Train: 67 [ 150/312 ( 48%)]  Loss: 4.35 (4.33)  Time: 0.403s, 2540.64/s  (0.415s, 2467.23/s)  LR: 2.334e-01  Data: 0.028 (0.035)
+Train: 67 [ 200/312 ( 64%)]  Loss: 4.37 (4.35)  Time: 0.404s, 2536.09/s  (0.412s, 2485.03/s)  LR: 2.334e-01  Data: 0.026 (0.033)
+Train: 67 [ 250/312 ( 80%)]  Loss: 4.37 (4.37)  Time: 0.407s, 2518.42/s  (0.411s, 2493.84/s)  LR: 2.334e-01  Data: 0.028 (0.032)
+Train: 67 [ 300/312 ( 96%)]  Loss: 4.41 (4.38)  Time: 0.410s, 2499.09/s  (0.410s, 2496.24/s)  LR: 2.334e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.427 (1.427)  Loss:   3.347 ( 3.347)  Acc@1:  39.160 ( 39.160)  Acc@5:  61.523 ( 61.523)
+Test: [  48/48]  Time: 0.089 (0.329)  Loss:   3.121 ( 3.331)  Acc@1:  42.453 ( 39.534)  Acc@5:  66.981 ( 62.798)
+Train: 68 [   0/312 (  0%)]  Loss: 4.23 (4.23)  Time: 1.590s,  644.12/s  (1.590s,  644.12/s)  LR: 2.292e-01  Data: 1.068 (1.068)
+Train: 68 [  50/312 ( 16%)]  Loss: 4.23 (4.24)  Time: 0.407s, 2513.72/s  (0.436s, 2346.97/s)  LR: 2.292e-01  Data: 0.027 (0.048)
+Train: 68 [ 100/312 ( 32%)]  Loss: 4.43 (4.28)  Time: 0.409s, 2505.48/s  (0.423s, 2422.37/s)  LR: 2.292e-01  Data: 0.027 (0.038)
+Train: 68 [ 150/312 ( 48%)]  Loss: 4.25 (4.30)  Time: 0.412s, 2485.43/s  (0.419s, 2444.51/s)  LR: 2.292e-01  Data: 0.027 (0.034)
+Train: 68 [ 200/312 ( 64%)]  Loss: 4.45 (4.32)  Time: 0.409s, 2504.30/s  (0.417s, 2456.95/s)  LR: 2.292e-01  Data: 0.025 (0.033)
+Train: 68 [ 250/312 ( 80%)]  Loss: 4.47 (4.34)  Time: 0.406s, 2522.73/s  (0.415s, 2469.36/s)  LR: 2.292e-01  Data: 0.028 (0.031)
+Train: 68 [ 300/312 ( 96%)]  Loss: 4.51 (4.36)  Time: 0.407s, 2517.74/s  (0.413s, 2476.79/s)  LR: 2.292e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   3.221 ( 3.221)  Acc@1:  41.309 ( 41.309)  Acc@5:  63.477 ( 63.477)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.067 ( 3.257)  Acc@1:  41.745 ( 40.278)  Acc@5:  68.278 ( 63.566)
+Current checkpoints:
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-52.pth.tar', 41.433999997558594)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-61.pth.tar', 41.02999997802734)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-57.pth.tar', 40.771999975585935)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-63.pth.tar', 40.76000002685547)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-58.pth.tar', 40.74600000488281)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-68.pth.tar', 40.27800003417969)
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+Train: 69 [   0/312 (  0%)]  Loss: 4.19 (4.19)  Time: 1.532s,  668.25/s  (1.532s,  668.25/s)  LR: 2.251e-01  Data: 1.128 (1.128)
+Train: 69 [  50/312 ( 16%)]  Loss: 4.30 (4.24)  Time: 0.408s, 2512.33/s  (0.434s, 2360.82/s)  LR: 2.251e-01  Data: 0.025 (0.049)
+Train: 69 [ 100/312 ( 32%)]  Loss: 4.39 (4.27)  Time: 0.406s, 2522.21/s  (0.421s, 2430.83/s)  LR: 2.251e-01  Data: 0.027 (0.038)
+Train: 69 [ 150/312 ( 48%)]  Loss: 4.42 (4.29)  Time: 0.408s, 2511.00/s  (0.417s, 2457.29/s)  LR: 2.251e-01  Data: 0.028 (0.034)
+Train: 69 [ 200/312 ( 64%)]  Loss: 4.42 (4.31)  Time: 0.411s, 2491.28/s  (0.415s, 2468.44/s)  LR: 2.251e-01  Data: 0.029 (0.033)
+Train: 69 [ 250/312 ( 80%)]  Loss: 4.27 (4.32)  Time: 0.410s, 2496.54/s  (0.414s, 2470.49/s)  LR: 2.251e-01  Data: 0.029 (0.032)
+Train: 69 [ 300/312 ( 96%)]  Loss: 4.45 (4.34)  Time: 0.408s, 2512.59/s  (0.414s, 2475.49/s)  LR: 2.251e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.419 (1.419)  Loss:   3.298 ( 3.298)  Acc@1:  39.648 ( 39.648)  Acc@5:  63.281 ( 63.281)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.136 ( 3.379)  Acc@1:  40.920 ( 38.722)  Acc@5:  64.623 ( 61.396)
+Train: 70 [   0/312 (  0%)]  Loss: 4.26 (4.26)  Time: 1.908s,  536.70/s  (1.908s,  536.70/s)  LR: 2.209e-01  Data: 1.078 (1.078)
+Train: 70 [  50/312 ( 16%)]  Loss: 4.37 (4.23)  Time: 0.409s, 2503.77/s  (0.437s, 2342.47/s)  LR: 2.209e-01  Data: 0.028 (0.048)
+Train: 70 [ 100/312 ( 32%)]  Loss: 4.30 (4.24)  Time: 0.410s, 2497.97/s  (0.423s, 2417.96/s)  LR: 2.209e-01  Data: 0.027 (0.038)
+Train: 70 [ 150/312 ( 48%)]  Loss: 4.33 (4.27)  Time: 0.402s, 2545.81/s  (0.418s, 2450.92/s)  LR: 2.209e-01  Data: 0.026 (0.034)
+Train: 70 [ 200/312 ( 64%)]  Loss: 4.36 (4.29)  Time: 0.403s, 2541.28/s  (0.414s, 2470.86/s)  LR: 2.209e-01  Data: 0.027 (0.032)
+Train: 70 [ 250/312 ( 80%)]  Loss: 4.29 (4.30)  Time: 0.410s, 2499.60/s  (0.413s, 2480.12/s)  LR: 2.209e-01  Data: 0.027 (0.031)
+Train: 70 [ 300/312 ( 96%)]  Loss: 4.48 (4.32)  Time: 0.412s, 2486.22/s  (0.412s, 2482.56/s)  LR: 2.209e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.426 (1.426)  Loss:   3.314 ( 3.314)  Acc@1:  40.332 ( 40.332)  Acc@5:  62.891 ( 62.891)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.078 ( 3.292)  Acc@1:  44.340 ( 40.444)  Acc@5:  65.566 ( 63.378)
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+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-61.pth.tar', 41.02999997802734)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-56.pth.tar', 40.981999959716795)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-50.pth.tar', 40.79600005126953)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-57.pth.tar', 40.771999975585935)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-63.pth.tar', 40.76000002685547)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-58.pth.tar', 40.74600000488281)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-54.pth.tar', 40.596)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-70.pth.tar', 40.44399999755859)
+ ('./output/train/ImageNetTraining40.0-frac-1over4/checkpoint-55.pth.tar', 40.43199999755859)
+
+Train: 71 [   0/312 (  0%)]  Loss: 4.19 (4.19)  Time: 1.612s,  635.19/s  (1.612s,  635.19/s)  LR: 2.167e-01  Data: 1.239 (1.239)
+Train: 71 [  50/312 ( 16%)]  Loss: 4.18 (4.18)  Time: 0.403s, 2540.50/s  (0.428s, 2390.75/s)  LR: 2.167e-01  Data: 0.027 (0.051)
+Train: 71 [ 100/312 ( 32%)]  Loss: 4.22 (4.22)  Time: 0.407s, 2513.79/s  (0.418s, 2450.52/s)  LR: 2.167e-01  Data: 0.027 (0.040)
+Train: 71 [ 150/312 ( 48%)]  Loss: 4.21 (4.24)  Time: 0.412s, 2488.39/s  (0.415s, 2464.56/s)  LR: 2.167e-01  Data: 0.027 (0.036)
+Train: 71 [ 200/312 ( 64%)]  Loss: 4.31 (4.26)  Time: 0.407s, 2518.39/s  (0.414s, 2473.74/s)  LR: 2.167e-01  Data: 0.027 (0.034)
+Train: 71 [ 250/312 ( 80%)]  Loss: 4.36 (4.28)  Time: 0.408s, 2510.19/s  (0.413s, 2481.70/s)  LR: 2.167e-01  Data: 0.027 (0.032)
+Train: 71 [ 300/312 ( 96%)]  Loss: 4.40 (4.29)  Time: 0.408s, 2508.79/s  (0.412s, 2485.02/s)  LR: 2.167e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.431 ( 3.431)  Acc@1:  38.574 ( 38.574)  Acc@5:  61.426 ( 61.426)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   3.156 ( 3.386)  Acc@1:  41.863 ( 39.072)  Acc@5:  66.745 ( 61.836)
+Train: 72 [   0/312 (  0%)]  Loss: 4.16 (4.16)  Time: 1.637s,  625.51/s  (1.637s,  625.51/s)  LR: 2.126e-01  Data: 1.262 (1.262)
+Train: 72 [  50/312 ( 16%)]  Loss: 4.32 (4.17)  Time: 0.408s, 2507.61/s  (0.434s, 2358.40/s)  LR: 2.126e-01  Data: 0.027 (0.051)
+Train: 72 [ 100/312 ( 32%)]  Loss: 4.25 (4.20)  Time: 0.412s, 2483.64/s  (0.423s, 2423.52/s)  LR: 2.126e-01  Data: 0.026 (0.040)
+Train: 72 [ 150/312 ( 48%)]  Loss: 4.31 (4.22)  Time: 0.407s, 2515.69/s  (0.418s, 2447.00/s)  LR: 2.126e-01  Data: 0.027 (0.035)
+Train: 72 [ 200/312 ( 64%)]  Loss: 4.34 (4.24)  Time: 0.412s, 2484.92/s  (0.416s, 2461.10/s)  LR: 2.126e-01  Data: 0.029 (0.034)
+Train: 72 [ 250/312 ( 80%)]  Loss: 4.36 (4.25)  Time: 0.407s, 2517.35/s  (0.415s, 2467.77/s)  LR: 2.126e-01  Data: 0.027 (0.032)
+Train: 72 [ 300/312 ( 96%)]  Loss: 4.35 (4.27)  Time: 0.408s, 2510.91/s  (0.414s, 2473.56/s)  LR: 2.126e-01  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   3.375 ( 3.375)  Acc@1:  40.820 ( 40.820)  Acc@5:  61.328 ( 61.328)
+Test: [  48/48]  Time: 0.089 (0.329)  Loss:   3.217 ( 3.426)  Acc@1:  41.863 ( 38.456)  Acc@5:  65.212 ( 60.690)
+Train: 73 [   0/312 (  0%)]  Loss: 4.14 (4.14)  Time: 1.890s,  541.92/s  (1.890s,  541.92/s)  LR: 2.084e-01  Data: 1.514 (1.514)
+Train: 73 [  50/312 ( 16%)]  Loss: 4.26 (4.16)  Time: 0.409s, 2504.79/s  (0.439s, 2331.72/s)  LR: 2.084e-01  Data: 0.027 (0.056)
+Train: 73 [ 100/312 ( 32%)]  Loss: 4.06 (4.19)  Time: 0.407s, 2513.81/s  (0.424s, 2412.86/s)  LR: 2.084e-01  Data: 0.027 (0.042)
+Train: 73 [ 150/312 ( 48%)]  Loss: 4.34 (4.21)  Time: 0.409s, 2505.76/s  (0.419s, 2441.90/s)  LR: 2.084e-01  Data: 0.027 (0.037)
+Train: 73 [ 200/312 ( 64%)]  Loss: 4.28 (4.22)  Time: 0.411s, 2489.08/s  (0.417s, 2456.30/s)  LR: 2.084e-01  Data: 0.027 (0.035)
+Train: 73 [ 250/312 ( 80%)]  Loss: 4.34 (4.24)  Time: 0.405s, 2527.60/s  (0.415s, 2467.26/s)  LR: 2.084e-01  Data: 0.027 (0.033)
+Train: 73 [ 300/312 ( 96%)]  Loss: 4.31 (4.26)  Time: 0.410s, 2498.29/s  (0.414s, 2473.74/s)  LR: 2.084e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.502 (1.502)  Loss:   3.398 ( 3.398)  Acc@1:  39.062 ( 39.062)  Acc@5:  61.328 ( 61.328)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   3.141 ( 3.385)  Acc@1:  42.689 ( 39.418)  Acc@5:  66.863 ( 61.622)
+Train: 74 [   0/312 (  0%)]  Loss: 4.02 (4.02)  Time: 1.897s,  539.73/s  (1.897s,  539.73/s)  LR: 2.042e-01  Data: 1.522 (1.522)
+Train: 74 [  50/312 ( 16%)]  Loss: 4.18 (4.13)  Time: 0.413s, 2481.87/s  (0.440s, 2329.18/s)  LR: 2.042e-01  Data: 0.027 (0.056)
+Train: 74 [ 100/312 ( 32%)]  Loss: 4.12 (4.16)  Time: 0.405s, 2531.24/s  (0.424s, 2416.35/s)  LR: 2.042e-01  Data: 0.026 (0.042)
+Train: 74 [ 150/312 ( 48%)]  Loss: 4.33 (4.18)  Time: 0.405s, 2527.54/s  (0.418s, 2450.91/s)  LR: 2.042e-01  Data: 0.026 (0.037)
+Train: 74 [ 200/312 ( 64%)]  Loss: 4.26 (4.20)  Time: 0.410s, 2496.33/s  (0.415s, 2467.70/s)  LR: 2.042e-01  Data: 0.028 (0.035)
+Train: 74 [ 250/312 ( 80%)]  Loss: 4.35 (4.22)  Time: 0.410s, 2500.46/s  (0.414s, 2475.35/s)  LR: 2.042e-01  Data: 0.027 (0.033)
+Train: 74 [ 300/312 ( 96%)]  Loss: 4.35 (4.23)  Time: 0.408s, 2506.97/s  (0.413s, 2478.24/s)  LR: 2.042e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.471 (1.471)  Loss:   3.344 ( 3.344)  Acc@1:  41.699 ( 41.699)  Acc@5:  63.086 ( 63.086)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.210 ( 3.402)  Acc@1:  39.387 ( 38.902)  Acc@5:  63.561 ( 61.286)
+Train: 75 [   0/312 (  0%)]  Loss: 4.13 (4.13)  Time: 1.636s,  625.82/s  (1.636s,  625.82/s)  LR: 2.000e-01  Data: 1.102 (1.102)
+Train: 75 [  50/312 ( 16%)]  Loss: 4.09 (4.12)  Time: 0.405s, 2525.50/s  (0.429s, 2386.53/s)  LR: 2.000e-01  Data: 0.027 (0.048)
+Train: 75 [ 100/312 ( 32%)]  Loss: 4.16 (4.14)  Time: 0.409s, 2502.13/s  (0.419s, 2445.48/s)  LR: 2.000e-01  Data: 0.027 (0.038)
+Train: 75 [ 150/312 ( 48%)]  Loss: 4.31 (4.17)  Time: 0.410s, 2495.89/s  (0.416s, 2461.48/s)  LR: 2.000e-01  Data: 0.028 (0.035)
+Train: 75 [ 200/312 ( 64%)]  Loss: 4.09 (4.18)  Time: 0.406s, 2519.68/s  (0.414s, 2472.44/s)  LR: 2.000e-01  Data: 0.028 (0.033)
+Train: 75 [ 250/312 ( 80%)]  Loss: 4.27 (4.20)  Time: 0.404s, 2536.22/s  (0.412s, 2482.66/s)  LR: 2.000e-01  Data: 0.027 (0.032)
+Train: 75 [ 300/312 ( 96%)]  Loss: 4.38 (4.21)  Time: 0.407s, 2517.38/s  (0.411s, 2488.55/s)  LR: 2.000e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   3.235 ( 3.235)  Acc@1:  41.699 ( 41.699)  Acc@5:  64.551 ( 64.551)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.111 ( 3.339)  Acc@1:  40.920 ( 39.356)  Acc@5:  66.274 ( 62.078)
+Train: 76 [   0/312 (  0%)]  Loss: 4.11 (4.11)  Time: 1.913s,  535.39/s  (1.913s,  535.39/s)  LR: 1.958e-01  Data: 1.537 (1.537)
+Train: 76 [  50/312 ( 16%)]  Loss: 4.14 (4.09)  Time: 0.406s, 2521.54/s  (0.438s, 2335.44/s)  LR: 1.958e-01  Data: 0.027 (0.057)
+Train: 76 [ 100/312 ( 32%)]  Loss: 4.19 (4.11)  Time: 0.404s, 2535.95/s  (0.422s, 2424.66/s)  LR: 1.958e-01  Data: 0.029 (0.043)
+Train: 76 [ 150/312 ( 48%)]  Loss: 4.17 (4.14)  Time: 0.408s, 2512.80/s  (0.417s, 2457.28/s)  LR: 1.958e-01  Data: 0.029 (0.038)
+Train: 76 [ 200/312 ( 64%)]  Loss: 4.29 (4.15)  Time: 0.414s, 2472.98/s  (0.415s, 2469.09/s)  LR: 1.958e-01  Data: 0.027 (0.035)
+Train: 76 [ 250/312 ( 80%)]  Loss: 4.30 (4.17)  Time: 0.404s, 2531.59/s  (0.414s, 2476.27/s)  LR: 1.958e-01  Data: 0.028 (0.034)
+Train: 76 [ 300/312 ( 96%)]  Loss: 4.29 (4.18)  Time: 0.399s, 2568.67/s  (0.412s, 2487.41/s)  LR: 1.958e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.415 (1.415)  Loss:   3.357 ( 3.357)  Acc@1:  39.941 ( 39.941)  Acc@5:  61.328 ( 61.328)
+Test: [  48/48]  Time: 0.087 (0.328)  Loss:   3.153 ( 3.442)  Acc@1:  40.212 ( 38.378)  Acc@5:  66.038 ( 60.404)
+Train: 77 [   0/312 (  0%)]  Loss: 4.08 (4.08)  Time: 1.705s,  600.66/s  (1.705s,  600.66/s)  LR: 1.916e-01  Data: 1.159 (1.159)
+Train: 77 [  50/312 ( 16%)]  Loss: 4.12 (4.09)  Time: 0.400s, 2561.26/s  (0.425s, 2408.31/s)  LR: 1.916e-01  Data: 0.027 (0.050)
+Train: 77 [ 100/312 ( 32%)]  Loss: 4.01 (4.10)  Time: 0.402s, 2547.21/s  (0.413s, 2477.71/s)  LR: 1.916e-01  Data: 0.028 (0.039)
+Train: 77 [ 150/312 ( 48%)]  Loss: 4.18 (4.13)  Time: 0.403s, 2543.20/s  (0.410s, 2498.11/s)  LR: 1.916e-01  Data: 0.027 (0.035)
+Train: 77 [ 200/312 ( 64%)]  Loss: 4.20 (4.14)  Time: 0.409s, 2505.76/s  (0.409s, 2502.90/s)  LR: 1.916e-01  Data: 0.028 (0.033)
+Train: 77 [ 250/312 ( 80%)]  Loss: 4.30 (4.15)  Time: 0.413s, 2481.48/s  (0.409s, 2500.65/s)  LR: 1.916e-01  Data: 0.027 (0.032)
+Train: 77 [ 300/312 ( 96%)]  Loss: 4.28 (4.17)  Time: 0.404s, 2534.53/s  (0.409s, 2504.79/s)  LR: 1.916e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.442 (1.442)  Loss:   3.446 ( 3.446)  Acc@1:  39.258 ( 39.258)  Acc@5:  60.059 ( 60.059)
+Test: [  48/48]  Time: 0.087 (0.329)  Loss:   3.227 ( 3.476)  Acc@1:  42.925 ( 38.278)  Acc@5:  64.151 ( 60.046)
+Train: 78 [   0/312 (  0%)]  Loss: 4.01 (4.01)  Time: 2.055s,  498.40/s  (2.055s,  498.40/s)  LR: 1.874e-01  Data: 1.688 (1.688)
+Train: 78 [  50/312 ( 16%)]  Loss: 4.03 (4.04)  Time: 0.400s, 2560.19/s  (0.432s, 2371.48/s)  LR: 1.874e-01  Data: 0.028 (0.060)
+Train: 78 [ 100/312 ( 32%)]  Loss: 4.17 (4.08)  Time: 0.399s, 2568.85/s  (0.416s, 2460.59/s)  LR: 1.874e-01  Data: 0.026 (0.044)
+Train: 78 [ 150/312 ( 48%)]  Loss: 4.11 (4.10)  Time: 0.408s, 2507.82/s  (0.412s, 2487.92/s)  LR: 1.874e-01  Data: 0.032 (0.038)
+Train: 78 [ 200/312 ( 64%)]  Loss: 4.30 (4.11)  Time: 0.407s, 2517.04/s  (0.410s, 2498.58/s)  LR: 1.874e-01  Data: 0.028 (0.036)
+Train: 78 [ 250/312 ( 80%)]  Loss: 4.20 (4.13)  Time: 0.409s, 2500.97/s  (0.410s, 2499.44/s)  LR: 1.874e-01  Data: 0.027 (0.034)
+Train: 78 [ 300/312 ( 96%)]  Loss: 4.17 (4.14)  Time: 0.411s, 2491.44/s  (0.410s, 2497.17/s)  LR: 1.874e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   3.526 ( 3.526)  Acc@1:  36.621 ( 36.621)  Acc@5:  57.812 ( 57.812)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.288 ( 3.539)  Acc@1:  39.858 ( 36.852)  Acc@5:  61.792 ( 58.972)
+Train: 79 [   0/312 (  0%)]  Loss: 4.07 (4.07)  Time: 1.572s,  651.59/s  (1.572s,  651.59/s)  LR: 1.833e-01  Data: 1.161 (1.161)
+Train: 79 [  50/312 ( 16%)]  Loss: 4.01 (4.03)  Time: 0.405s, 2526.47/s  (0.427s, 2396.10/s)  LR: 1.833e-01  Data: 0.026 (0.050)
+Train: 79 [ 100/312 ( 32%)]  Loss: 4.08 (4.05)  Time: 0.409s, 2501.15/s  (0.417s, 2453.12/s)  LR: 1.833e-01  Data: 0.027 (0.038)
+Train: 79 [ 150/312 ( 48%)]  Loss: 4.12 (4.07)  Time: 0.410s, 2496.03/s  (0.415s, 2466.44/s)  LR: 1.833e-01  Data: 0.028 (0.034)
+Train: 79 [ 200/312 ( 64%)]  Loss: 4.09 (4.09)  Time: 0.412s, 2484.72/s  (0.414s, 2475.87/s)  LR: 1.833e-01  Data: 0.028 (0.033)
+Train: 79 [ 250/312 ( 80%)]  Loss: 4.16 (4.11)  Time: 0.411s, 2493.24/s  (0.413s, 2481.60/s)  LR: 1.833e-01  Data: 0.028 (0.032)
+Train: 79 [ 300/312 ( 96%)]  Loss: 4.28 (4.12)  Time: 0.415s, 2469.30/s  (0.412s, 2483.43/s)  LR: 1.833e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   3.334 ( 3.334)  Acc@1:  40.137 ( 40.137)  Acc@5:  62.598 ( 62.598)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.163 ( 3.367)  Acc@1:  41.274 ( 39.428)  Acc@5:  64.269 ( 61.242)
+Train: 80 [   0/312 (  0%)]  Loss: 3.98 (3.98)  Time: 1.659s,  617.06/s  (1.659s,  617.06/s)  LR: 1.791e-01  Data: 1.089 (1.089)
+Train: 80 [  50/312 ( 16%)]  Loss: 4.07 (4.00)  Time: 0.406s, 2523.24/s  (0.429s, 2384.80/s)  LR: 1.791e-01  Data: 0.027 (0.049)
+Train: 80 [ 100/312 ( 32%)]  Loss: 4.22 (4.03)  Time: 0.407s, 2518.20/s  (0.418s, 2451.56/s)  LR: 1.791e-01  Data: 0.026 (0.038)
+Train: 80 [ 150/312 ( 48%)]  Loss: 4.07 (4.04)  Time: 0.409s, 2503.21/s  (0.415s, 2467.07/s)  LR: 1.791e-01  Data: 0.028 (0.034)
+Train: 80 [ 200/312 ( 64%)]  Loss: 4.07 (4.07)  Time: 0.407s, 2517.21/s  (0.414s, 2472.56/s)  LR: 1.791e-01  Data: 0.027 (0.033)
+Train: 80 [ 250/312 ( 80%)]  Loss: 4.19 (4.08)  Time: 0.404s, 2537.12/s  (0.412s, 2483.19/s)  LR: 1.791e-01  Data: 0.028 (0.032)
+Train: 80 [ 300/312 ( 96%)]  Loss: 4.25 (4.10)  Time: 0.403s, 2541.07/s  (0.411s, 2492.90/s)  LR: 1.791e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.416 (1.416)  Loss:   3.511 ( 3.511)  Acc@1:  37.988 ( 37.988)  Acc@5:  60.059 ( 60.059)
+Test: [  48/48]  Time: 0.087 (0.330)  Loss:   3.312 ( 3.565)  Acc@1:  37.972 ( 36.762)  Acc@5:  61.792 ( 58.264)
+Train: 81 [   0/312 (  0%)]  Loss: 3.97 (3.97)  Time: 1.495s,  684.81/s  (1.495s,  684.81/s)  LR: 1.749e-01  Data: 1.127 (1.127)
+Train: 81 [  50/312 ( 16%)]  Loss: 3.95 (3.98)  Time: 0.403s, 2539.46/s  (0.426s, 2402.31/s)  LR: 1.749e-01  Data: 0.027 (0.051)
+Train: 81 [ 100/312 ( 32%)]  Loss: 4.07 (4.01)  Time: 0.407s, 2518.26/s  (0.416s, 2458.89/s)  LR: 1.749e-01  Data: 0.027 (0.039)
+Train: 81 [ 150/312 ( 48%)]  Loss: 4.15 (4.03)  Time: 0.410s, 2499.11/s  (0.414s, 2471.33/s)  LR: 1.749e-01  Data: 0.027 (0.035)
+Train: 81 [ 200/312 ( 64%)]  Loss: 4.06 (4.04)  Time: 0.406s, 2523.21/s  (0.413s, 2480.37/s)  LR: 1.749e-01  Data: 0.026 (0.034)
+Train: 81 [ 250/312 ( 80%)]  Loss: 4.15 (4.06)  Time: 0.406s, 2521.75/s  (0.412s, 2488.11/s)  LR: 1.749e-01  Data: 0.028 (0.032)
+Train: 81 [ 300/312 ( 96%)]  Loss: 4.11 (4.08)  Time: 0.410s, 2498.20/s  (0.411s, 2492.02/s)  LR: 1.749e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.423 (1.423)  Loss:   3.365 ( 3.365)  Acc@1:  40.234 ( 40.234)  Acc@5:  62.500 ( 62.500)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.214 ( 3.440)  Acc@1:  41.038 ( 38.550)  Acc@5:  63.443 ( 60.332)
+Train: 82 [   0/312 (  0%)]  Loss: 3.92 (3.92)  Time: 1.524s,  672.02/s  (1.524s,  672.02/s)  LR: 1.708e-01  Data: 1.126 (1.126)
+Train: 82 [  50/312 ( 16%)]  Loss: 3.94 (3.96)  Time: 0.406s, 2520.24/s  (0.430s, 2379.42/s)  LR: 1.708e-01  Data: 0.027 (0.049)
+Train: 82 [ 100/312 ( 32%)]  Loss: 4.10 (3.99)  Time: 0.411s, 2493.47/s  (0.420s, 2438.04/s)  LR: 1.708e-01  Data: 0.028 (0.039)
+Train: 82 [ 150/312 ( 48%)]  Loss: 4.07 (4.01)  Time: 0.408s, 2509.10/s  (0.417s, 2457.12/s)  LR: 1.708e-01  Data: 0.026 (0.035)
+Train: 82 [ 200/312 ( 64%)]  Loss: 4.10 (4.02)  Time: 0.405s, 2527.34/s  (0.414s, 2471.85/s)  LR: 1.708e-01  Data: 0.028 (0.033)
+Train: 82 [ 250/312 ( 80%)]  Loss: 4.15 (4.04)  Time: 0.409s, 2505.23/s  (0.413s, 2480.27/s)  LR: 1.708e-01  Data: 0.028 (0.032)
+Train: 82 [ 300/312 ( 96%)]  Loss: 3.99 (4.06)  Time: 0.412s, 2483.35/s  (0.412s, 2483.01/s)  LR: 1.708e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.426 (1.426)  Loss:   3.453 ( 3.453)  Acc@1:  36.914 ( 36.914)  Acc@5:  60.645 ( 60.645)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.160 ( 3.465)  Acc@1:  43.396 ( 38.472)  Acc@5:  63.679 ( 59.696)
+Train: 83 [   0/312 (  0%)]  Loss: 3.87 (3.87)  Time: 1.927s,  531.41/s  (1.927s,  531.41/s)  LR: 1.666e-01  Data: 1.556 (1.556)
+Train: 83 [  50/312 ( 16%)]  Loss: 3.94 (3.94)  Time: 0.403s, 2540.82/s  (0.433s, 2366.49/s)  LR: 1.666e-01  Data: 0.027 (0.058)
+Train: 83 [ 100/312 ( 32%)]  Loss: 4.05 (3.97)  Time: 0.403s, 2542.79/s  (0.418s, 2452.25/s)  LR: 1.666e-01  Data: 0.026 (0.043)
+Train: 83 [ 150/312 ( 48%)]  Loss: 4.01 (4.00)  Time: 0.404s, 2536.71/s  (0.413s, 2479.17/s)  LR: 1.666e-01  Data: 0.026 (0.038)
+Train: 83 [ 200/312 ( 64%)]  Loss: 4.03 (4.01)  Time: 0.411s, 2489.75/s  (0.412s, 2487.18/s)  LR: 1.666e-01  Data: 0.028 (0.035)
+Train: 83 [ 250/312 ( 80%)]  Loss: 4.13 (4.03)  Time: 0.406s, 2522.60/s  (0.411s, 2489.02/s)  LR: 1.666e-01  Data: 0.027 (0.034)
+Train: 83 [ 300/312 ( 96%)]  Loss: 4.15 (4.04)  Time: 0.406s, 2522.13/s  (0.411s, 2493.79/s)  LR: 1.666e-01  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.430 (1.430)  Loss:   3.449 ( 3.449)  Acc@1:  38.184 ( 38.184)  Acc@5:  61.426 ( 61.426)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.302 ( 3.512)  Acc@1:  42.099 ( 38.060)  Acc@5:  62.146 ( 59.254)
+Train: 84 [   0/312 (  0%)]  Loss: 3.96 (3.96)  Time: 1.731s,  591.41/s  (1.731s,  591.41/s)  LR: 1.625e-01  Data: 1.359 (1.359)
+Train: 84 [  50/312 ( 16%)]  Loss: 4.06 (3.92)  Time: 0.408s, 2511.60/s  (0.434s, 2359.83/s)  LR: 1.625e-01  Data: 0.028 (0.053)
+Train: 84 [ 100/312 ( 32%)]  Loss: 3.99 (3.95)  Time: 0.410s, 2498.97/s  (0.423s, 2423.37/s)  LR: 1.625e-01  Data: 0.024 (0.041)
+Train: 84 [ 150/312 ( 48%)]  Loss: 4.08 (3.97)  Time: 0.407s, 2517.12/s  (0.417s, 2456.55/s)  LR: 1.625e-01  Data: 0.032 (0.036)
+Train: 84 [ 200/312 ( 64%)]  Loss: 4.12 (3.99)  Time: 0.403s, 2543.56/s  (0.413s, 2477.21/s)  LR: 1.625e-01  Data: 0.027 (0.034)
+Train: 84 [ 250/312 ( 80%)]  Loss: 4.06 (4.00)  Time: 0.405s, 2527.42/s  (0.411s, 2489.16/s)  LR: 1.625e-01  Data: 0.025 (0.033)
+Train: 84 [ 300/312 ( 96%)]  Loss: 4.14 (4.02)  Time: 0.411s, 2494.14/s  (0.411s, 2494.21/s)  LR: 1.625e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.471 ( 3.471)  Acc@1:  38.770 ( 38.770)  Acc@5:  59.277 ( 59.277)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.228 ( 3.474)  Acc@1:  40.920 ( 37.936)  Acc@5:  61.439 ( 59.408)
+Train: 85 [   0/312 (  0%)]  Loss: 3.90 (3.90)  Time: 1.775s,  577.00/s  (1.775s,  577.00/s)  LR: 1.584e-01  Data: 1.144 (1.144)
+Train: 85 [  50/312 ( 16%)]  Loss: 4.03 (3.90)  Time: 0.415s, 2465.09/s  (0.438s, 2340.28/s)  LR: 1.584e-01  Data: 0.029 (0.049)
+Train: 85 [ 100/312 ( 32%)]  Loss: 3.97 (3.92)  Time: 0.406s, 2521.91/s  (0.424s, 2414.83/s)  LR: 1.584e-01  Data: 0.028 (0.039)
+Train: 85 [ 150/312 ( 48%)]  Loss: 3.86 (3.94)  Time: 0.403s, 2541.55/s  (0.418s, 2447.92/s)  LR: 1.584e-01  Data: 0.026 (0.035)
+Train: 85 [ 200/312 ( 64%)]  Loss: 4.05 (3.96)  Time: 0.409s, 2503.12/s  (0.415s, 2465.76/s)  LR: 1.584e-01  Data: 0.027 (0.033)
+Train: 85 [ 250/312 ( 80%)]  Loss: 4.09 (3.98)  Time: 0.411s, 2491.85/s  (0.414s, 2472.82/s)  LR: 1.584e-01  Data: 0.027 (0.032)
+Train: 85 [ 300/312 ( 96%)]  Loss: 4.12 (4.00)  Time: 0.414s, 2471.91/s  (0.414s, 2474.93/s)  LR: 1.584e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.444 (1.444)  Loss:   3.485 ( 3.485)  Acc@1:  37.891 ( 37.891)  Acc@5:  59.570 ( 59.570)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.248 ( 3.491)  Acc@1:  39.741 ( 38.134)  Acc@5:  63.090 ( 59.760)
+Train: 86 [   0/312 (  0%)]  Loss: 3.87 (3.87)  Time: 1.597s,  641.38/s  (1.597s,  641.38/s)  LR: 1.543e-01  Data: 1.190 (1.190)
+Train: 86 [  50/312 ( 16%)]  Loss: 3.88 (3.89)  Time: 0.410s, 2499.30/s  (0.433s, 2363.97/s)  LR: 1.543e-01  Data: 0.028 (0.050)
+Train: 86 [ 100/312 ( 32%)]  Loss: 3.95 (3.90)  Time: 0.411s, 2489.82/s  (0.422s, 2428.08/s)  LR: 1.543e-01  Data: 0.025 (0.038)
+Train: 86 [ 150/312 ( 48%)]  Loss: 3.93 (3.92)  Time: 0.409s, 2500.78/s  (0.418s, 2450.39/s)  LR: 1.543e-01  Data: 0.027 (0.035)
+Train: 86 [ 200/312 ( 64%)]  Loss: 4.00 (3.94)  Time: 0.409s, 2500.83/s  (0.416s, 2462.63/s)  LR: 1.543e-01  Data: 0.028 (0.033)
+Train: 86 [ 250/312 ( 80%)]  Loss: 3.98 (3.96)  Time: 0.409s, 2500.81/s  (0.415s, 2469.05/s)  LR: 1.543e-01  Data: 0.028 (0.032)
+Train: 86 [ 300/312 ( 96%)]  Loss: 3.92 (3.97)  Time: 0.412s, 2484.51/s  (0.414s, 2474.22/s)  LR: 1.543e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.441 (1.441)  Loss:   3.528 ( 3.528)  Acc@1:  35.742 ( 35.742)  Acc@5:  57.910 ( 57.910)
+Test: [  48/48]  Time: 0.089 (0.332)  Loss:   3.324 ( 3.618)  Acc@1:  39.741 ( 35.878)  Acc@5:  62.028 ( 57.392)
+Train: 87 [   0/312 (  0%)]  Loss: 3.81 (3.81)  Time: 1.606s,  637.70/s  (1.606s,  637.70/s)  LR: 1.503e-01  Data: 1.232 (1.232)
+Train: 87 [  50/312 ( 16%)]  Loss: 3.88 (3.86)  Time: 0.410s, 2497.11/s  (0.433s, 2364.10/s)  LR: 1.503e-01  Data: 0.027 (0.051)
+Train: 87 [ 100/312 ( 32%)]  Loss: 3.96 (3.88)  Time: 0.408s, 2509.46/s  (0.422s, 2427.46/s)  LR: 1.503e-01  Data: 0.028 (0.040)
+Train: 87 [ 150/312 ( 48%)]  Loss: 3.98 (3.89)  Time: 0.402s, 2546.79/s  (0.416s, 2464.13/s)  LR: 1.503e-01  Data: 0.028 (0.035)
+Train: 87 [ 200/312 ( 64%)]  Loss: 3.99 (3.91)  Time: 0.402s, 2546.76/s  (0.412s, 2485.42/s)  LR: 1.503e-01  Data: 0.028 (0.033)
+Train: 87 [ 250/312 ( 80%)]  Loss: 3.90 (3.93)  Time: 0.403s, 2541.67/s  (0.410s, 2498.29/s)  LR: 1.503e-01  Data: 0.027 (0.032)
+Train: 87 [ 300/312 ( 96%)]  Loss: 3.97 (3.95)  Time: 0.402s, 2545.90/s  (0.409s, 2505.45/s)  LR: 1.503e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.440 (1.440)  Loss:   3.501 ( 3.501)  Acc@1:  37.891 ( 37.891)  Acc@5:  57.422 ( 57.422)
+Test: [  48/48]  Time: 0.088 (0.332)  Loss:   3.326 ( 3.577)  Acc@1:  41.274 ( 36.874)  Acc@5:  60.495 ( 57.810)
+Train: 88 [   0/312 (  0%)]  Loss: 3.83 (3.83)  Time: 1.814s,  564.42/s  (1.814s,  564.42/s)  LR: 1.462e-01  Data: 1.441 (1.441)
+Train: 88 [  50/312 ( 16%)]  Loss: 3.96 (3.83)  Time: 0.408s, 2509.30/s  (0.435s, 2355.13/s)  LR: 1.462e-01  Data: 0.028 (0.055)
+Train: 88 [ 100/312 ( 32%)]  Loss: 4.00 (3.86)  Time: 0.411s, 2488.90/s  (0.423s, 2420.97/s)  LR: 1.462e-01  Data: 0.028 (0.041)
+Train: 88 [ 150/312 ( 48%)]  Loss: 3.96 (3.88)  Time: 0.408s, 2511.38/s  (0.419s, 2443.55/s)  LR: 1.462e-01  Data: 0.027 (0.037)
+Train: 88 [ 200/312 ( 64%)]  Loss: 3.92 (3.90)  Time: 0.411s, 2489.63/s  (0.417s, 2456.09/s)  LR: 1.462e-01  Data: 0.028 (0.034)
+Train: 88 [ 250/312 ( 80%)]  Loss: 4.08 (3.91)  Time: 0.411s, 2492.57/s  (0.416s, 2464.33/s)  LR: 1.462e-01  Data: 0.028 (0.033)
+Train: 88 [ 300/312 ( 96%)]  Loss: 4.03 (3.93)  Time: 0.411s, 2494.21/s  (0.415s, 2469.31/s)  LR: 1.462e-01  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.453 (1.453)  Loss:   3.478 ( 3.478)  Acc@1:  37.500 ( 37.500)  Acc@5:  59.473 ( 59.473)
+Test: [  48/48]  Time: 0.089 (0.329)  Loss:   3.248 ( 3.513)  Acc@1:  40.566 ( 37.620)  Acc@5:  62.618 ( 58.934)
+Train: 89 [   0/312 (  0%)]  Loss: 3.82 (3.82)  Time: 1.862s,  550.00/s  (1.862s,  550.00/s)  LR: 1.422e-01  Data: 1.485 (1.485)
+Train: 89 [  50/312 ( 16%)]  Loss: 3.85 (3.82)  Time: 0.411s, 2493.90/s  (0.441s, 2323.56/s)  LR: 1.422e-01  Data: 0.026 (0.056)
+Train: 89 [ 100/312 ( 32%)]  Loss: 3.91 (3.84)  Time: 0.404s, 2534.59/s  (0.424s, 2412.39/s)  LR: 1.422e-01  Data: 0.029 (0.042)
+Train: 89 [ 150/312 ( 48%)]  Loss: 4.00 (3.86)  Time: 0.402s, 2546.25/s  (0.418s, 2448.95/s)  LR: 1.422e-01  Data: 0.027 (0.037)
+Train: 89 [ 200/312 ( 64%)]  Loss: 3.96 (3.88)  Time: 0.405s, 2528.24/s  (0.415s, 2468.52/s)  LR: 1.422e-01  Data: 0.026 (0.035)
+Train: 89 [ 250/312 ( 80%)]  Loss: 3.78 (3.89)  Time: 0.407s, 2517.33/s  (0.413s, 2478.15/s)  LR: 1.422e-01  Data: 0.027 (0.033)
+Train: 89 [ 300/312 ( 96%)]  Loss: 4.11 (3.90)  Time: 0.416s, 2463.74/s  (0.413s, 2479.80/s)  LR: 1.422e-01  Data: 0.032 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.441 (1.441)  Loss:   3.479 ( 3.479)  Acc@1:  38.770 ( 38.770)  Acc@5:  61.230 ( 61.230)
+Test: [  48/48]  Time: 0.089 (0.332)  Loss:   3.269 ( 3.516)  Acc@1:  41.392 ( 37.874)  Acc@5:  63.443 ( 59.096)
+Train: 90 [   0/312 (  0%)]  Loss: 3.74 (3.74)  Time: 1.568s,  653.16/s  (1.568s,  653.16/s)  LR: 1.382e-01  Data: 1.193 (1.193)
+Train: 90 [  50/312 ( 16%)]  Loss: 3.87 (3.79)  Time: 0.411s, 2489.65/s  (0.431s, 2378.43/s)  LR: 1.382e-01  Data: 0.026 (0.051)
+Train: 90 [ 100/312 ( 32%)]  Loss: 3.92 (3.82)  Time: 0.407s, 2515.25/s  (0.420s, 2440.27/s)  LR: 1.382e-01  Data: 0.027 (0.039)
+Train: 90 [ 150/312 ( 48%)]  Loss: 3.91 (3.84)  Time: 0.411s, 2492.57/s  (0.417s, 2456.84/s)  LR: 1.382e-01  Data: 0.028 (0.035)
+Train: 90 [ 200/312 ( 64%)]  Loss: 3.97 (3.85)  Time: 0.407s, 2516.06/s  (0.415s, 2467.01/s)  LR: 1.382e-01  Data: 0.027 (0.033)
+Train: 90 [ 250/312 ( 80%)]  Loss: 3.92 (3.87)  Time: 0.407s, 2518.27/s  (0.413s, 2477.64/s)  LR: 1.382e-01  Data: 0.026 (0.032)
+Train: 90 [ 300/312 ( 96%)]  Loss: 3.90 (3.88)  Time: 0.407s, 2518.54/s  (0.412s, 2485.12/s)  LR: 1.382e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.464 ( 3.464)  Acc@1:  37.793 ( 37.793)  Acc@5:  59.473 ( 59.473)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.267 ( 3.519)  Acc@1:  42.217 ( 37.716)  Acc@5:  61.321 ( 58.834)
+Train: 91 [   0/312 (  0%)]  Loss: 3.72 (3.72)  Time: 1.754s,  583.94/s  (1.754s,  583.94/s)  LR: 1.342e-01  Data: 1.381 (1.381)
+Train: 91 [  50/312 ( 16%)]  Loss: 3.56 (3.76)  Time: 0.411s, 2491.28/s  (0.434s, 2358.61/s)  LR: 1.342e-01  Data: 0.027 (0.053)
+Train: 91 [ 100/312 ( 32%)]  Loss: 3.74 (3.79)  Time: 0.409s, 2504.70/s  (0.422s, 2425.81/s)  LR: 1.342e-01  Data: 0.026 (0.040)
+Train: 91 [ 150/312 ( 48%)]  Loss: 3.93 (3.81)  Time: 0.411s, 2493.78/s  (0.418s, 2448.50/s)  LR: 1.342e-01  Data: 0.028 (0.036)
+Train: 91 [ 200/312 ( 64%)]  Loss: 3.97 (3.83)  Time: 0.411s, 2492.00/s  (0.416s, 2461.02/s)  LR: 1.342e-01  Data: 0.027 (0.034)
+Train: 91 [ 250/312 ( 80%)]  Loss: 3.91 (3.84)  Time: 0.408s, 2507.93/s  (0.415s, 2465.57/s)  LR: 1.342e-01  Data: 0.027 (0.032)
+Train: 91 [ 300/312 ( 96%)]  Loss: 4.00 (3.86)  Time: 0.406s, 2519.38/s  (0.414s, 2472.79/s)  LR: 1.342e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.421 (1.421)  Loss:   3.550 ( 3.550)  Acc@1:  38.477 ( 38.477)  Acc@5:  58.691 ( 58.691)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.350 ( 3.591)  Acc@1:  39.033 ( 36.826)  Acc@5:  59.788 ( 57.516)
+Train: 92 [   0/312 (  0%)]  Loss: 3.78 (3.78)  Time: 1.800s,  568.76/s  (1.800s,  568.76/s)  LR: 1.303e-01  Data: 1.053 (1.053)
+Train: 92 [  50/312 ( 16%)]  Loss: 3.84 (3.75)  Time: 0.406s, 2522.16/s  (0.432s, 2369.75/s)  LR: 1.303e-01  Data: 0.028 (0.047)
+Train: 92 [ 100/312 ( 32%)]  Loss: 3.63 (3.76)  Time: 0.413s, 2476.63/s  (0.421s, 2434.17/s)  LR: 1.303e-01  Data: 0.028 (0.038)
+Train: 92 [ 150/312 ( 48%)]  Loss: 3.83 (3.78)  Time: 0.403s, 2540.87/s  (0.417s, 2456.62/s)  LR: 1.303e-01  Data: 0.028 (0.034)
+Train: 92 [ 200/312 ( 64%)]  Loss: 3.96 (3.80)  Time: 0.402s, 2545.51/s  (0.413s, 2477.60/s)  LR: 1.303e-01  Data: 0.027 (0.033)
+Train: 92 [ 250/312 ( 80%)]  Loss: 3.87 (3.81)  Time: 0.403s, 2538.67/s  (0.411s, 2492.51/s)  LR: 1.303e-01  Data: 0.029 (0.032)
+Train: 92 [ 300/312 ( 96%)]  Loss: 3.86 (3.83)  Time: 0.399s, 2568.86/s  (0.409s, 2503.21/s)  LR: 1.303e-01  Data: 0.025 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.409 (1.409)  Loss:   3.559 ( 3.559)  Acc@1:  38.965 ( 38.965)  Acc@5:  57.812 ( 57.812)
+Test: [  48/48]  Time: 0.087 (0.327)  Loss:   3.283 ( 3.544)  Acc@1:  41.038 ( 37.390)  Acc@5:  61.792 ( 58.418)
+Train: 93 [   0/312 (  0%)]  Loss: 3.79 (3.79)  Time: 1.477s,  693.52/s  (1.477s,  693.52/s)  LR: 1.264e-01  Data: 1.109 (1.109)
+Train: 93 [  50/312 ( 16%)]  Loss: 3.73 (3.73)  Time: 0.405s, 2525.33/s  (0.425s, 2407.82/s)  LR: 1.264e-01  Data: 0.028 (0.049)
+Train: 93 [ 100/312 ( 32%)]  Loss: 3.68 (3.74)  Time: 0.408s, 2510.15/s  (0.416s, 2460.89/s)  LR: 1.264e-01  Data: 0.027 (0.038)
+Train: 93 [ 150/312 ( 48%)]  Loss: 3.91 (3.77)  Time: 0.412s, 2485.60/s  (0.414s, 2470.69/s)  LR: 1.264e-01  Data: 0.027 (0.035)
+Train: 93 [ 200/312 ( 64%)]  Loss: 4.00 (3.79)  Time: 0.409s, 2505.92/s  (0.414s, 2475.19/s)  LR: 1.264e-01  Data: 0.028 (0.033)
+Train: 93 [ 250/312 ( 80%)]  Loss: 3.85 (3.80)  Time: 0.405s, 2527.91/s  (0.412s, 2483.24/s)  LR: 1.264e-01  Data: 0.027 (0.032)
+Train: 93 [ 300/312 ( 96%)]  Loss: 3.93 (3.81)  Time: 0.405s, 2531.23/s  (0.411s, 2488.80/s)  LR: 1.264e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   3.541 ( 3.541)  Acc@1:  37.500 ( 37.500)  Acc@5:  57.715 ( 57.715)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.300 ( 3.564)  Acc@1:  40.802 ( 37.228)  Acc@5:  61.675 ( 58.018)
+Train: 94 [   0/312 (  0%)]  Loss: 3.55 (3.55)  Time: 1.624s,  630.68/s  (1.624s,  630.68/s)  LR: 1.225e-01  Data: 1.249 (1.249)
+Train: 94 [  50/312 ( 16%)]  Loss: 3.60 (3.70)  Time: 0.411s, 2491.76/s  (0.434s, 2361.60/s)  LR: 1.225e-01  Data: 0.029 (0.051)
+Train: 94 [ 100/312 ( 32%)]  Loss: 3.82 (3.73)  Time: 0.406s, 2523.75/s  (0.421s, 2435.17/s)  LR: 1.225e-01  Data: 0.027 (0.039)
+Train: 94 [ 150/312 ( 48%)]  Loss: 3.65 (3.75)  Time: 0.408s, 2508.03/s  (0.416s, 2463.07/s)  LR: 1.225e-01  Data: 0.028 (0.036)
+Train: 94 [ 200/312 ( 64%)]  Loss: 3.90 (3.76)  Time: 0.410s, 2497.90/s  (0.414s, 2474.30/s)  LR: 1.225e-01  Data: 0.026 (0.033)
+Train: 94 [ 250/312 ( 80%)]  Loss: 3.90 (3.78)  Time: 0.408s, 2508.52/s  (0.413s, 2480.14/s)  LR: 1.225e-01  Data: 0.029 (0.032)
+Train: 94 [ 300/312 ( 96%)]  Loss: 3.90 (3.79)  Time: 0.403s, 2543.17/s  (0.411s, 2488.54/s)  LR: 1.225e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.409 (1.409)  Loss:   3.650 ( 3.650)  Acc@1:  37.012 ( 37.012)  Acc@5:  56.152 ( 56.152)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.425 ( 3.671)  Acc@1:  39.269 ( 35.926)  Acc@5:  60.849 ( 56.446)
+Train: 95 [   0/312 (  0%)]  Loss: 3.59 (3.59)  Time: 1.815s,  564.09/s  (1.815s,  564.09/s)  LR: 1.187e-01  Data: 1.076 (1.076)
+Train: 95 [  50/312 ( 16%)]  Loss: 3.61 (3.69)  Time: 0.406s, 2523.96/s  (0.431s, 2374.21/s)  LR: 1.187e-01  Data: 0.027 (0.048)
+Train: 95 [ 100/312 ( 32%)]  Loss: 3.69 (3.71)  Time: 0.411s, 2493.95/s  (0.420s, 2439.73/s)  LR: 1.187e-01  Data: 0.027 (0.038)
+Train: 95 [ 150/312 ( 48%)]  Loss: 3.78 (3.72)  Time: 0.412s, 2485.60/s  (0.417s, 2455.93/s)  LR: 1.187e-01  Data: 0.027 (0.035)
+Train: 95 [ 200/312 ( 64%)]  Loss: 3.77 (3.74)  Time: 0.410s, 2495.62/s  (0.415s, 2465.59/s)  LR: 1.187e-01  Data: 0.027 (0.033)
+Train: 95 [ 250/312 ( 80%)]  Loss: 3.76 (3.76)  Time: 0.410s, 2496.00/s  (0.414s, 2474.27/s)  LR: 1.187e-01  Data: 0.028 (0.032)
+Train: 95 [ 300/312 ( 96%)]  Loss: 3.77 (3.77)  Time: 0.410s, 2495.78/s  (0.413s, 2478.88/s)  LR: 1.187e-01  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.436 (1.436)  Loss:   3.658 ( 3.658)  Acc@1:  37.207 ( 37.207)  Acc@5:  56.445 ( 56.445)
+Test: [  48/48]  Time: 0.089 (0.329)  Loss:   3.375 ( 3.662)  Acc@1:  39.387 ( 36.204)  Acc@5:  60.377 ( 56.500)
+Train: 96 [   0/312 (  0%)]  Loss: 3.64 (3.64)  Time: 1.867s,  548.60/s  (1.867s,  548.60/s)  LR: 1.148e-01  Data: 1.491 (1.491)
+Train: 96 [  50/312 ( 16%)]  Loss: 3.66 (3.66)  Time: 0.411s, 2493.38/s  (0.439s, 2333.75/s)  LR: 1.148e-01  Data: 0.031 (0.058)
+Train: 96 [ 100/312 ( 32%)]  Loss: 3.68 (3.69)  Time: 0.411s, 2490.67/s  (0.425s, 2411.22/s)  LR: 1.148e-01  Data: 0.027 (0.043)
+Train: 96 [ 150/312 ( 48%)]  Loss: 3.60 (3.70)  Time: 0.414s, 2475.41/s  (0.420s, 2436.26/s)  LR: 1.148e-01  Data: 0.027 (0.038)
+Train: 96 [ 200/312 ( 64%)]  Loss: 3.80 (3.71)  Time: 0.406s, 2522.51/s  (0.417s, 2453.15/s)  LR: 1.148e-01  Data: 0.027 (0.035)
+Train: 96 [ 250/312 ( 80%)]  Loss: 3.74 (3.73)  Time: 0.405s, 2525.93/s  (0.415s, 2467.78/s)  LR: 1.148e-01  Data: 0.028 (0.034)
+Train: 96 [ 300/312 ( 96%)]  Loss: 3.87 (3.74)  Time: 0.404s, 2531.99/s  (0.413s, 2477.75/s)  LR: 1.148e-01  Data: 0.027 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   3.572 ( 3.572)  Acc@1:  37.598 ( 37.598)  Acc@5:  57.715 ( 57.715)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.374 ( 3.633)  Acc@1:  38.679 ( 36.486)  Acc@5:  60.377 ( 56.946)
+Train: 97 [   0/312 (  0%)]  Loss: 3.50 (3.50)  Time: 1.627s,  629.35/s  (1.627s,  629.35/s)  LR: 1.111e-01  Data: 1.255 (1.255)
+Train: 97 [  50/312 ( 16%)]  Loss: 3.71 (3.63)  Time: 0.408s, 2509.60/s  (0.431s, 2375.10/s)  LR: 1.111e-01  Data: 0.027 (0.051)
+Train: 97 [ 100/312 ( 32%)]  Loss: 3.80 (3.66)  Time: 0.412s, 2483.41/s  (0.421s, 2433.76/s)  LR: 1.111e-01  Data: 0.027 (0.039)
+Train: 97 [ 150/312 ( 48%)]  Loss: 3.83 (3.68)  Time: 0.409s, 2501.02/s  (0.417s, 2452.75/s)  LR: 1.111e-01  Data: 0.027 (0.035)
+Train: 97 [ 200/312 ( 64%)]  Loss: 3.76 (3.69)  Time: 0.414s, 2475.68/s  (0.416s, 2461.46/s)  LR: 1.111e-01  Data: 0.028 (0.034)
+Train: 97 [ 250/312 ( 80%)]  Loss: 3.89 (3.71)  Time: 0.415s, 2468.10/s  (0.415s, 2466.85/s)  LR: 1.111e-01  Data: 0.031 (0.032)
+Train: 97 [ 300/312 ( 96%)]  Loss: 3.78 (3.72)  Time: 0.410s, 2495.62/s  (0.415s, 2469.98/s)  LR: 1.111e-01  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.447 (1.447)  Loss:   3.568 ( 3.568)  Acc@1:  36.328 ( 36.328)  Acc@5:  57.227 ( 57.227)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.382 ( 3.592)  Acc@1:  39.976 ( 36.910)  Acc@5:  61.439 ( 57.656)
+Train: 98 [   0/312 (  0%)]  Loss: 3.57 (3.57)  Time: 1.796s,  570.03/s  (1.796s,  570.03/s)  LR: 1.073e-01  Data: 1.238 (1.238)
+Train: 98 [  50/312 ( 16%)]  Loss: 3.73 (3.62)  Time: 0.409s, 2504.32/s  (0.435s, 2355.22/s)  LR: 1.073e-01  Data: 0.028 (0.050)
+Train: 98 [ 100/312 ( 32%)]  Loss: 3.73 (3.63)  Time: 0.408s, 2507.80/s  (0.423s, 2420.77/s)  LR: 1.073e-01  Data: 0.027 (0.039)
+Train: 98 [ 150/312 ( 48%)]  Loss: 3.68 (3.65)  Time: 0.404s, 2534.85/s  (0.417s, 2454.75/s)  LR: 1.073e-01  Data: 0.026 (0.035)
+Train: 98 [ 200/312 ( 64%)]  Loss: 3.72 (3.67)  Time: 0.404s, 2536.95/s  (0.414s, 2475.48/s)  LR: 1.073e-01  Data: 0.026 (0.033)
+Train: 98 [ 250/312 ( 80%)]  Loss: 3.66 (3.69)  Time: 0.403s, 2541.54/s  (0.411s, 2488.46/s)  LR: 1.073e-01  Data: 0.028 (0.032)
+Train: 98 [ 300/312 ( 96%)]  Loss: 3.92 (3.70)  Time: 0.406s, 2519.57/s  (0.410s, 2494.96/s)  LR: 1.073e-01  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.674 ( 3.674)  Acc@1:  35.254 ( 35.254)  Acc@5:  56.445 ( 56.445)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.425 ( 3.685)  Acc@1:  37.854 ( 35.594)  Acc@5:  60.495 ( 56.220)
+Train: 99 [   0/312 (  0%)]  Loss: 3.57 (3.57)  Time: 1.601s,  639.79/s  (1.601s,  639.79/s)  LR: 1.036e-01  Data: 1.105 (1.105)
+Train: 99 [  50/312 ( 16%)]  Loss: 3.62 (3.59)  Time: 0.412s, 2483.83/s  (0.435s, 2352.32/s)  LR: 1.036e-01  Data: 0.027 (0.050)
+Train: 99 [ 100/312 ( 32%)]  Loss: 3.57 (3.62)  Time: 0.411s, 2491.08/s  (0.424s, 2415.24/s)  LR: 1.036e-01  Data: 0.027 (0.039)
+Train: 99 [ 150/312 ( 48%)]  Loss: 3.68 (3.63)  Time: 0.407s, 2518.71/s  (0.420s, 2440.19/s)  LR: 1.036e-01  Data: 0.027 (0.035)
+Train: 99 [ 200/312 ( 64%)]  Loss: 3.67 (3.65)  Time: 0.405s, 2527.81/s  (0.416s, 2459.85/s)  LR: 1.036e-01  Data: 0.027 (0.033)
+Train: 99 [ 250/312 ( 80%)]  Loss: 3.78 (3.67)  Time: 0.405s, 2526.49/s  (0.414s, 2473.17/s)  LR: 1.036e-01  Data: 0.027 (0.032)
+Train: 99 [ 300/312 ( 96%)]  Loss: 3.83 (3.68)  Time: 0.412s, 2485.95/s  (0.413s, 2480.65/s)  LR: 1.036e-01  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.459 (1.459)  Loss:   3.754 ( 3.754)  Acc@1:  35.254 ( 35.254)  Acc@5:  54.688 ( 54.688)
+Test: [  48/48]  Time: 0.089 (0.329)  Loss:   3.481 ( 3.755)  Acc@1:  38.090 ( 35.156)  Acc@5:  60.024 ( 55.428)
+Train: 100 [   0/312 (  0%)]  Loss: 3.70 (3.70)  Time: 1.630s,  628.04/s  (1.630s,  628.04/s)  LR: 1.000e-01  Data: 1.255 (1.255)
+Train: 100 [  50/312 ( 16%)]  Loss: 3.60 (3.56)  Time: 0.402s, 2544.34/s  (0.430s, 2382.25/s)  LR: 1.000e-01  Data: 0.027 (0.051)
+Train: 100 [ 100/312 ( 32%)]  Loss: 3.60 (3.59)  Time: 0.404s, 2536.25/s  (0.418s, 2452.56/s)  LR: 1.000e-01  Data: 0.027 (0.039)
+Train: 100 [ 150/312 ( 48%)]  Loss: 3.73 (3.61)  Time: 0.405s, 2527.46/s  (0.414s, 2476.22/s)  LR: 1.000e-01  Data: 0.025 (0.035)
+Train: 100 [ 200/312 ( 64%)]  Loss: 3.81 (3.63)  Time: 0.414s, 2470.88/s  (0.412s, 2484.44/s)  LR: 1.000e-01  Data: 0.033 (0.033)
+Train: 100 [ 250/312 ( 80%)]  Loss: 3.65 (3.64)  Time: 0.410s, 2499.72/s  (0.412s, 2485.76/s)  LR: 1.000e-01  Data: 0.027 (0.032)
+Train: 100 [ 300/312 ( 96%)]  Loss: 3.85 (3.65)  Time: 0.409s, 2503.39/s  (0.412s, 2487.06/s)  LR: 1.000e-01  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   3.659 ( 3.659)  Acc@1:  36.523 ( 36.523)  Acc@5:  58.008 ( 58.008)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.392 ( 3.699)  Acc@1:  39.151 ( 36.010)  Acc@5:  60.495 ( 55.918)
+Train: 101 [   0/312 (  0%)]  Loss: 3.50 (3.50)  Time: 1.801s,  568.61/s  (1.801s,  568.61/s)  LR: 9.639e-02  Data: 1.426 (1.426)
+Train: 101 [  50/312 ( 16%)]  Loss: 3.55 (3.55)  Time: 0.412s, 2486.49/s  (0.438s, 2336.31/s)  LR: 9.639e-02  Data: 0.027 (0.055)
+Train: 101 [ 100/312 ( 32%)]  Loss: 3.57 (3.57)  Time: 0.408s, 2512.05/s  (0.425s, 2411.71/s)  LR: 9.639e-02  Data: 0.027 (0.041)
+Train: 101 [ 150/312 ( 48%)]  Loss: 3.72 (3.59)  Time: 0.404s, 2532.14/s  (0.418s, 2450.82/s)  LR: 9.639e-02  Data: 0.028 (0.037)
+Train: 101 [ 200/312 ( 64%)]  Loss: 3.79 (3.61)  Time: 0.404s, 2536.88/s  (0.414s, 2473.12/s)  LR: 9.639e-02  Data: 0.027 (0.035)
+Train: 101 [ 250/312 ( 80%)]  Loss: 3.67 (3.62)  Time: 0.402s, 2548.57/s  (0.412s, 2486.52/s)  LR: 9.639e-02  Data: 0.027 (0.033)
+Train: 101 [ 300/312 ( 96%)]  Loss: 3.71 (3.63)  Time: 0.406s, 2524.24/s  (0.411s, 2493.90/s)  LR: 9.639e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.472 (1.472)  Loss:   3.630 ( 3.630)  Acc@1:  36.035 ( 36.035)  Acc@5:  56.445 ( 56.445)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.417 ( 3.672)  Acc@1:  40.802 ( 35.996)  Acc@5:  60.495 ( 56.428)
+Train: 102 [   0/312 (  0%)]  Loss: 3.59 (3.59)  Time: 1.665s,  615.15/s  (1.665s,  615.15/s)  LR: 9.283e-02  Data: 1.291 (1.291)
+Train: 102 [  50/312 ( 16%)]  Loss: 3.65 (3.53)  Time: 0.412s, 2488.38/s  (0.434s, 2359.83/s)  LR: 9.283e-02  Data: 0.027 (0.052)
+Train: 102 [ 100/312 ( 32%)]  Loss: 3.69 (3.54)  Time: 0.408s, 2510.20/s  (0.423s, 2423.33/s)  LR: 9.283e-02  Data: 0.027 (0.040)
+Train: 102 [ 150/312 ( 48%)]  Loss: 3.60 (3.57)  Time: 0.409s, 2502.15/s  (0.418s, 2452.23/s)  LR: 9.283e-02  Data: 0.028 (0.036)
+Train: 102 [ 200/312 ( 64%)]  Loss: 3.65 (3.58)  Time: 0.408s, 2511.58/s  (0.415s, 2467.75/s)  LR: 9.283e-02  Data: 0.027 (0.034)
+Train: 102 [ 250/312 ( 80%)]  Loss: 3.65 (3.60)  Time: 0.418s, 2447.85/s  (0.414s, 2473.67/s)  LR: 9.283e-02  Data: 0.027 (0.032)
+Train: 102 [ 300/312 ( 96%)]  Loss: 3.69 (3.61)  Time: 0.413s, 2476.51/s  (0.414s, 2476.20/s)  LR: 9.283e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.457 (1.457)  Loss:   3.793 ( 3.793)  Acc@1:  35.742 ( 35.742)  Acc@5:  52.832 ( 52.832)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.586 ( 3.810)  Acc@1:  35.613 ( 34.154)  Acc@5:  56.722 ( 54.334)
+Train: 103 [   0/312 (  0%)]  Loss: 3.48 (3.48)  Time: 1.877s,  545.53/s  (1.877s,  545.53/s)  LR: 8.932e-02  Data: 1.504 (1.504)
+Train: 103 [  50/312 ( 16%)]  Loss: 3.49 (3.50)  Time: 0.408s, 2510.68/s  (0.434s, 2358.02/s)  LR: 8.932e-02  Data: 0.028 (0.056)
+Train: 103 [ 100/312 ( 32%)]  Loss: 3.50 (3.53)  Time: 0.414s, 2475.14/s  (0.422s, 2423.70/s)  LR: 8.932e-02  Data: 0.027 (0.042)
+Train: 103 [ 150/312 ( 48%)]  Loss: 3.64 (3.55)  Time: 0.406s, 2520.24/s  (0.419s, 2445.52/s)  LR: 8.932e-02  Data: 0.027 (0.037)
+Train: 103 [ 200/312 ( 64%)]  Loss: 3.57 (3.56)  Time: 0.406s, 2520.87/s  (0.416s, 2462.19/s)  LR: 8.932e-02  Data: 0.028 (0.035)
+Train: 103 [ 250/312 ( 80%)]  Loss: 3.64 (3.57)  Time: 0.408s, 2507.07/s  (0.414s, 2471.46/s)  LR: 8.932e-02  Data: 0.027 (0.033)
+Train: 103 [ 300/312 ( 96%)]  Loss: 3.69 (3.59)  Time: 0.412s, 2486.37/s  (0.414s, 2473.90/s)  LR: 8.932e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.463 (1.463)  Loss:   3.693 ( 3.693)  Acc@1:  36.230 ( 36.230)  Acc@5:  56.445 ( 56.445)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   3.421 ( 3.668)  Acc@1:  38.443 ( 36.200)  Acc@5:  58.844 ( 56.554)
+Train: 104 [   0/312 (  0%)]  Loss: 3.58 (3.58)  Time: 1.601s,  639.54/s  (1.601s,  639.54/s)  LR: 8.586e-02  Data: 1.138 (1.138)
+Train: 104 [  50/312 ( 16%)]  Loss: 3.49 (3.48)  Time: 0.404s, 2537.68/s  (0.432s, 2372.72/s)  LR: 8.586e-02  Data: 0.028 (0.050)
+Train: 104 [ 100/312 ( 32%)]  Loss: 3.63 (3.51)  Time: 0.405s, 2527.83/s  (0.419s, 2446.20/s)  LR: 8.586e-02  Data: 0.027 (0.039)
+Train: 104 [ 150/312 ( 48%)]  Loss: 3.59 (3.52)  Time: 0.411s, 2490.80/s  (0.415s, 2466.86/s)  LR: 8.586e-02  Data: 0.028 (0.035)
+Train: 104 [ 200/312 ( 64%)]  Loss: 3.60 (3.54)  Time: 0.410s, 2496.49/s  (0.414s, 2474.31/s)  LR: 8.586e-02  Data: 0.028 (0.033)
+Train: 104 [ 250/312 ( 80%)]  Loss: 3.72 (3.55)  Time: 0.408s, 2512.47/s  (0.413s, 2478.13/s)  LR: 8.586e-02  Data: 0.027 (0.032)
+Train: 104 [ 300/312 ( 96%)]  Loss: 3.63 (3.56)  Time: 0.407s, 2515.46/s  (0.412s, 2486.65/s)  LR: 8.586e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.415 (1.415)  Loss:   3.683 ( 3.683)  Acc@1:  36.719 ( 36.719)  Acc@5:  55.664 ( 55.664)
+Test: [  48/48]  Time: 0.087 (0.328)  Loss:   3.414 ( 3.634)  Acc@1:  36.910 ( 36.482)  Acc@5:  61.085 ( 57.040)
+Train: 105 [   0/312 (  0%)]  Loss: 3.52 (3.52)  Time: 1.643s,  623.15/s  (1.643s,  623.15/s)  LR: 8.244e-02  Data: 1.276 (1.276)
+Train: 105 [  50/312 ( 16%)]  Loss: 3.55 (3.47)  Time: 0.402s, 2549.94/s  (0.426s, 2401.42/s)  LR: 8.244e-02  Data: 0.028 (0.053)
+Train: 105 [ 100/312 ( 32%)]  Loss: 3.56 (3.48)  Time: 0.404s, 2533.62/s  (0.415s, 2467.31/s)  LR: 8.244e-02  Data: 0.027 (0.040)
+Train: 105 [ 150/312 ( 48%)]  Loss: 3.51 (3.50)  Time: 0.409s, 2505.69/s  (0.412s, 2485.05/s)  LR: 8.244e-02  Data: 0.028 (0.036)
+Train: 105 [ 200/312 ( 64%)]  Loss: 3.52 (3.52)  Time: 0.412s, 2486.05/s  (0.412s, 2487.15/s)  LR: 8.244e-02  Data: 0.027 (0.034)
+Train: 105 [ 250/312 ( 80%)]  Loss: 3.67 (3.53)  Time: 0.410s, 2498.11/s  (0.411s, 2488.65/s)  LR: 8.244e-02  Data: 0.029 (0.033)
+Train: 105 [ 300/312 ( 96%)]  Loss: 3.68 (3.55)  Time: 0.412s, 2483.83/s  (0.411s, 2489.26/s)  LR: 8.244e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   3.648 ( 3.648)  Acc@1:  37.402 ( 37.402)  Acc@5:  57.129 ( 57.129)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.417 ( 3.681)  Acc@1:  38.090 ( 36.224)  Acc@5:  59.906 ( 56.430)
+Train: 106 [   0/312 (  0%)]  Loss: 3.38 (3.38)  Time: 1.674s,  611.61/s  (1.674s,  611.61/s)  LR: 7.908e-02  Data: 1.302 (1.302)
+Train: 106 [  50/312 ( 16%)]  Loss: 3.45 (3.45)  Time: 0.407s, 2517.59/s  (0.432s, 2371.42/s)  LR: 7.908e-02  Data: 0.028 (0.053)
+Train: 106 [ 100/312 ( 32%)]  Loss: 3.45 (3.47)  Time: 0.416s, 2460.67/s  (0.421s, 2433.10/s)  LR: 7.908e-02  Data: 0.027 (0.040)
+Train: 106 [ 150/312 ( 48%)]  Loss: 3.52 (3.48)  Time: 0.408s, 2511.52/s  (0.417s, 2456.23/s)  LR: 7.908e-02  Data: 0.027 (0.036)
+Train: 106 [ 200/312 ( 64%)]  Loss: 3.65 (3.49)  Time: 0.411s, 2490.48/s  (0.415s, 2466.65/s)  LR: 7.908e-02  Data: 0.029 (0.034)
+Train: 106 [ 250/312 ( 80%)]  Loss: 3.59 (3.51)  Time: 0.408s, 2510.62/s  (0.414s, 2473.19/s)  LR: 7.908e-02  Data: 0.029 (0.032)
+Train: 106 [ 300/312 ( 96%)]  Loss: 3.57 (3.52)  Time: 0.405s, 2529.94/s  (0.413s, 2480.68/s)  LR: 7.908e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.417 (1.417)  Loss:   3.708 ( 3.708)  Acc@1:  34.961 ( 34.961)  Acc@5:  55.176 ( 55.176)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.424 ( 3.684)  Acc@1:  39.151 ( 35.820)  Acc@5:  61.321 ( 56.422)
+Train: 107 [   0/312 (  0%)]  Loss: 3.39 (3.39)  Time: 1.775s,  576.87/s  (1.775s,  576.87/s)  LR: 7.577e-02  Data: 1.403 (1.403)
+Train: 107 [  50/312 ( 16%)]  Loss: 3.44 (3.44)  Time: 0.409s, 2501.14/s  (0.435s, 2355.21/s)  LR: 7.577e-02  Data: 0.027 (0.054)
+Train: 107 [ 100/312 ( 32%)]  Loss: 3.51 (3.45)  Time: 0.411s, 2494.44/s  (0.423s, 2421.05/s)  LR: 7.577e-02  Data: 0.034 (0.041)
+Train: 107 [ 150/312 ( 48%)]  Loss: 3.47 (3.46)  Time: 0.404s, 2535.20/s  (0.417s, 2454.82/s)  LR: 7.577e-02  Data: 0.027 (0.037)
+Train: 107 [ 200/312 ( 64%)]  Loss: 3.51 (3.47)  Time: 0.402s, 2548.46/s  (0.414s, 2474.23/s)  LR: 7.577e-02  Data: 0.028 (0.034)
+Train: 107 [ 250/312 ( 80%)]  Loss: 3.44 (3.48)  Time: 0.404s, 2534.74/s  (0.412s, 2485.62/s)  LR: 7.577e-02  Data: 0.027 (0.033)
+Train: 107 [ 300/312 ( 96%)]  Loss: 3.52 (3.50)  Time: 0.406s, 2519.43/s  (0.411s, 2490.73/s)  LR: 7.577e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.434 (1.434)  Loss:   3.677 ( 3.677)  Acc@1:  37.500 ( 37.500)  Acc@5:  56.445 ( 56.445)
+Test: [  48/48]  Time: 0.089 (0.329)  Loss:   3.543 ( 3.727)  Acc@1:  36.675 ( 35.876)  Acc@5:  58.373 ( 55.742)
+Train: 108 [   0/312 (  0%)]  Loss: 3.41 (3.41)  Time: 1.587s,  645.23/s  (1.587s,  645.23/s)  LR: 7.252e-02  Data: 1.211 (1.211)
+Train: 108 [  50/312 ( 16%)]  Loss: 3.49 (3.40)  Time: 0.409s, 2504.57/s  (0.434s, 2359.85/s)  LR: 7.252e-02  Data: 0.027 (0.050)
+Train: 108 [ 100/312 ( 32%)]  Loss: 3.54 (3.41)  Time: 0.407s, 2518.04/s  (0.421s, 2433.18/s)  LR: 7.252e-02  Data: 0.029 (0.039)
+Train: 108 [ 150/312 ( 48%)]  Loss: 3.46 (3.43)  Time: 0.406s, 2520.82/s  (0.416s, 2459.72/s)  LR: 7.252e-02  Data: 0.028 (0.035)
+Train: 108 [ 200/312 ( 64%)]  Loss: 3.47 (3.45)  Time: 0.408s, 2510.62/s  (0.414s, 2470.68/s)  LR: 7.252e-02  Data: 0.027 (0.033)
+Train: 108 [ 250/312 ( 80%)]  Loss: 3.52 (3.46)  Time: 0.408s, 2512.22/s  (0.413s, 2477.58/s)  LR: 7.252e-02  Data: 0.027 (0.032)
+Train: 108 [ 300/312 ( 96%)]  Loss: 3.59 (3.47)  Time: 0.411s, 2489.62/s  (0.413s, 2481.52/s)  LR: 7.252e-02  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.447 (1.447)  Loss:   3.766 ( 3.766)  Acc@1:  35.645 ( 35.645)  Acc@5:  55.566 ( 55.566)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   3.532 ( 3.720)  Acc@1:  37.618 ( 35.570)  Acc@5:  59.788 ( 55.736)
+Train: 109 [   0/312 (  0%)]  Loss: 3.38 (3.38)  Time: 1.615s,  633.87/s  (1.615s,  633.87/s)  LR: 6.932e-02  Data: 1.241 (1.241)
+Train: 109 [  50/312 ( 16%)]  Loss: 3.51 (3.39)  Time: 0.410s, 2500.23/s  (0.433s, 2362.34/s)  LR: 6.932e-02  Data: 0.028 (0.051)
+Train: 109 [ 100/312 ( 32%)]  Loss: 3.41 (3.40)  Time: 0.410s, 2495.63/s  (0.422s, 2426.89/s)  LR: 6.932e-02  Data: 0.028 (0.039)
+Train: 109 [ 150/312 ( 48%)]  Loss: 3.63 (3.41)  Time: 0.410s, 2499.44/s  (0.418s, 2451.44/s)  LR: 6.932e-02  Data: 0.029 (0.035)
+Train: 109 [ 200/312 ( 64%)]  Loss: 3.47 (3.43)  Time: 0.415s, 2468.21/s  (0.415s, 2464.87/s)  LR: 6.932e-02  Data: 0.027 (0.033)
+Train: 109 [ 250/312 ( 80%)]  Loss: 3.50 (3.44)  Time: 0.407s, 2513.85/s  (0.414s, 2472.76/s)  LR: 6.932e-02  Data: 0.028 (0.032)
+Train: 109 [ 300/312 ( 96%)]  Loss: 3.56 (3.45)  Time: 0.404s, 2531.64/s  (0.413s, 2482.26/s)  LR: 6.932e-02  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.416 (1.416)  Loss:   3.699 ( 3.699)  Acc@1:  35.742 ( 35.742)  Acc@5:  55.176 ( 55.176)
+Test: [  48/48]  Time: 0.087 (0.328)  Loss:   3.496 ( 3.722)  Acc@1:  36.910 ( 35.720)  Acc@5:  60.142 ( 55.780)
+Train: 110 [   0/312 (  0%)]  Loss: 3.35 (3.35)  Time: 1.660s,  616.81/s  (1.660s,  616.81/s)  LR: 6.617e-02  Data: 1.292 (1.292)
+Train: 110 [  50/312 ( 16%)]  Loss: 3.36 (3.37)  Time: 0.404s, 2535.29/s  (0.429s, 2389.45/s)  LR: 6.617e-02  Data: 0.028 (0.052)
+Train: 110 [ 100/312 ( 32%)]  Loss: 3.45 (3.38)  Time: 0.407s, 2516.61/s  (0.418s, 2450.11/s)  LR: 6.617e-02  Data: 0.027 (0.040)
+Train: 110 [ 150/312 ( 48%)]  Loss: 3.43 (3.40)  Time: 0.412s, 2485.18/s  (0.415s, 2466.09/s)  LR: 6.617e-02  Data: 0.029 (0.036)
+Train: 110 [ 200/312 ( 64%)]  Loss: 3.38 (3.41)  Time: 0.413s, 2479.64/s  (0.414s, 2472.18/s)  LR: 6.617e-02  Data: 0.028 (0.034)
+Train: 110 [ 250/312 ( 80%)]  Loss: 3.38 (3.42)  Time: 0.408s, 2510.75/s  (0.413s, 2476.71/s)  LR: 6.617e-02  Data: 0.028 (0.033)
+Train: 110 [ 300/312 ( 96%)]  Loss: 3.39 (3.43)  Time: 0.415s, 2467.65/s  (0.413s, 2481.38/s)  LR: 6.617e-02  Data: 0.032 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.438 (1.438)  Loss:   3.723 ( 3.723)  Acc@1:  35.449 ( 35.449)  Acc@5:  56.250 ( 56.250)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.494 ( 3.747)  Acc@1:  39.387 ( 35.450)  Acc@5:  59.316 ( 55.564)
+Train: 111 [   0/312 (  0%)]  Loss: 3.30 (3.30)  Time: 1.582s,  647.27/s  (1.582s,  647.27/s)  LR: 6.309e-02  Data: 1.062 (1.062)
+Train: 111 [  50/312 ( 16%)]  Loss: 3.44 (3.36)  Time: 0.408s, 2511.37/s  (0.432s, 2368.46/s)  LR: 6.309e-02  Data: 0.024 (0.047)
+Train: 111 [ 100/312 ( 32%)]  Loss: 3.39 (3.37)  Time: 0.410s, 2498.94/s  (0.421s, 2434.33/s)  LR: 6.309e-02  Data: 0.027 (0.037)
+Train: 111 [ 150/312 ( 48%)]  Loss: 3.51 (3.37)  Time: 0.410s, 2500.61/s  (0.417s, 2456.02/s)  LR: 6.309e-02  Data: 0.027 (0.034)
+Train: 111 [ 200/312 ( 64%)]  Loss: 3.41 (3.39)  Time: 0.408s, 2512.14/s  (0.415s, 2469.14/s)  LR: 6.309e-02  Data: 0.027 (0.032)
+Train: 111 [ 250/312 ( 80%)]  Loss: 3.55 (3.40)  Time: 0.414s, 2472.64/s  (0.414s, 2475.66/s)  LR: 6.309e-02  Data: 0.028 (0.031)
+Train: 111 [ 300/312 ( 96%)]  Loss: 3.35 (3.41)  Time: 0.408s, 2512.26/s  (0.413s, 2480.65/s)  LR: 6.309e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.456 (1.456)  Loss:   3.734 ( 3.734)  Acc@1:  33.984 ( 33.984)  Acc@5:  56.250 ( 56.250)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.539 ( 3.760)  Acc@1:  36.439 ( 35.396)  Acc@5:  57.901 ( 55.146)
+Train: 112 [   0/312 (  0%)]  Loss: 3.35 (3.35)  Time: 1.584s,  646.59/s  (1.584s,  646.59/s)  LR: 6.007e-02  Data: 1.151 (1.151)
+Train: 112 [  50/312 ( 16%)]  Loss: 3.42 (3.32)  Time: 0.407s, 2513.74/s  (0.430s, 2380.53/s)  LR: 6.007e-02  Data: 0.027 (0.049)
+Train: 112 [ 100/312 ( 32%)]  Loss: 3.42 (3.33)  Time: 0.410s, 2495.54/s  (0.420s, 2436.63/s)  LR: 6.007e-02  Data: 0.028 (0.038)
+Train: 112 [ 150/312 ( 48%)]  Loss: 3.44 (3.35)  Time: 0.407s, 2514.99/s  (0.416s, 2460.50/s)  LR: 6.007e-02  Data: 0.027 (0.035)
+Train: 112 [ 200/312 ( 64%)]  Loss: 3.36 (3.36)  Time: 0.407s, 2516.01/s  (0.414s, 2476.25/s)  LR: 6.007e-02  Data: 0.027 (0.033)
+Train: 112 [ 250/312 ( 80%)]  Loss: 3.36 (3.37)  Time: 0.409s, 2503.02/s  (0.412s, 2484.84/s)  LR: 6.007e-02  Data: 0.027 (0.032)
+Train: 112 [ 300/312 ( 96%)]  Loss: 3.45 (3.38)  Time: 0.412s, 2485.80/s  (0.412s, 2486.77/s)  LR: 6.007e-02  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.434 (1.434)  Loss:   3.719 ( 3.719)  Acc@1:  35.254 ( 35.254)  Acc@5:  52.930 ( 52.930)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.457 ( 3.731)  Acc@1:  39.033 ( 35.416)  Acc@5:  58.491 ( 55.512)
+Train: 113 [   0/312 (  0%)]  Loss: 3.25 (3.25)  Time: 1.643s,  623.12/s  (1.643s,  623.12/s)  LR: 5.711e-02  Data: 1.203 (1.203)
+Train: 113 [  50/312 ( 16%)]  Loss: 3.38 (3.30)  Time: 0.407s, 2515.79/s  (0.430s, 2383.11/s)  LR: 5.711e-02  Data: 0.024 (0.051)
+Train: 113 [ 100/312 ( 32%)]  Loss: 3.38 (3.32)  Time: 0.407s, 2515.03/s  (0.418s, 2450.68/s)  LR: 5.711e-02  Data: 0.026 (0.039)
+Train: 113 [ 150/312 ( 48%)]  Loss: 3.32 (3.33)  Time: 0.408s, 2510.48/s  (0.415s, 2466.11/s)  LR: 5.711e-02  Data: 0.027 (0.035)
+Train: 113 [ 200/312 ( 64%)]  Loss: 3.43 (3.35)  Time: 0.409s, 2504.04/s  (0.414s, 2472.99/s)  LR: 5.711e-02  Data: 0.027 (0.033)
+Train: 113 [ 250/312 ( 80%)]  Loss: 3.40 (3.35)  Time: 0.408s, 2512.44/s  (0.413s, 2479.72/s)  LR: 5.711e-02  Data: 0.027 (0.032)
+Train: 113 [ 300/312 ( 96%)]  Loss: 3.27 (3.36)  Time: 0.410s, 2499.83/s  (0.412s, 2483.51/s)  LR: 5.711e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.416 (1.416)  Loss:   3.771 ( 3.771)  Acc@1:  34.961 ( 34.961)  Acc@5:  55.859 ( 55.859)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.523 ( 3.788)  Acc@1:  38.090 ( 35.078)  Acc@5:  58.255 ( 54.914)
+Train: 114 [   0/312 (  0%)]  Loss: 3.30 (3.30)  Time: 1.760s,  581.88/s  (1.760s,  581.88/s)  LR: 5.421e-02  Data: 1.384 (1.384)
+Train: 114 [  50/312 ( 16%)]  Loss: 3.31 (3.28)  Time: 0.413s, 2476.52/s  (0.438s, 2338.20/s)  LR: 5.421e-02  Data: 0.028 (0.054)
+Train: 114 [ 100/312 ( 32%)]  Loss: 3.31 (3.30)  Time: 0.406s, 2521.91/s  (0.424s, 2416.37/s)  LR: 5.421e-02  Data: 0.028 (0.041)
+Train: 114 [ 150/312 ( 48%)]  Loss: 3.44 (3.31)  Time: 0.404s, 2531.90/s  (0.418s, 2447.75/s)  LR: 5.421e-02  Data: 0.029 (0.037)
+Train: 114 [ 200/312 ( 64%)]  Loss: 3.36 (3.33)  Time: 0.408s, 2512.32/s  (0.415s, 2465.43/s)  LR: 5.421e-02  Data: 0.029 (0.034)
+Train: 114 [ 250/312 ( 80%)]  Loss: 3.34 (3.33)  Time: 0.413s, 2479.20/s  (0.414s, 2472.11/s)  LR: 5.421e-02  Data: 0.027 (0.033)
+Train: 114 [ 300/312 ( 96%)]  Loss: 3.36 (3.34)  Time: 0.412s, 2485.96/s  (0.414s, 2474.44/s)  LR: 5.421e-02  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   3.829 ( 3.829)  Acc@1:  31.934 ( 31.934)  Acc@5:  55.176 ( 55.176)
+Test: [  48/48]  Time: 0.088 (0.327)  Loss:   3.570 ( 3.814)  Acc@1:  35.377 ( 34.604)  Acc@5:  56.132 ( 54.438)
+Train: 115 [   0/312 (  0%)]  Loss: 3.37 (3.37)  Time: 1.664s,  615.46/s  (1.664s,  615.46/s)  LR: 5.137e-02  Data: 1.222 (1.222)
+Train: 115 [  50/312 ( 16%)]  Loss: 3.41 (3.27)  Time: 0.408s, 2509.51/s  (0.432s, 2368.90/s)  LR: 5.137e-02  Data: 0.028 (0.051)
+Train: 115 [ 100/312 ( 32%)]  Loss: 3.31 (3.29)  Time: 0.416s, 2459.26/s  (0.422s, 2428.47/s)  LR: 5.137e-02  Data: 0.032 (0.039)
+Train: 115 [ 150/312 ( 48%)]  Loss: 3.35 (3.30)  Time: 0.408s, 2511.14/s  (0.418s, 2451.96/s)  LR: 5.137e-02  Data: 0.027 (0.035)
+Train: 115 [ 200/312 ( 64%)]  Loss: 3.35 (3.30)  Time: 0.414s, 2473.06/s  (0.416s, 2461.03/s)  LR: 5.137e-02  Data: 0.029 (0.034)
+Train: 115 [ 250/312 ( 80%)]  Loss: 3.34 (3.31)  Time: 0.406s, 2519.96/s  (0.415s, 2469.95/s)  LR: 5.137e-02  Data: 0.027 (0.032)
+Train: 115 [ 300/312 ( 96%)]  Loss: 3.28 (3.32)  Time: 0.406s, 2522.45/s  (0.413s, 2477.53/s)  LR: 5.137e-02  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.426 (1.426)  Loss:   3.831 ( 3.831)  Acc@1:  34.082 ( 34.082)  Acc@5:  54.980 ( 54.980)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.610 ( 3.845)  Acc@1:  36.675 ( 34.430)  Acc@5:  55.189 ( 54.088)
+Train: 116 [   0/312 (  0%)]  Loss: 3.16 (3.16)  Time: 1.540s,  665.06/s  (1.540s,  665.06/s)  LR: 4.860e-02  Data: 1.117 (1.117)
+Train: 116 [  50/312 ( 16%)]  Loss: 3.16 (3.24)  Time: 0.416s, 2463.96/s  (0.437s, 2343.73/s)  LR: 4.860e-02  Data: 0.028 (0.049)
+Train: 116 [ 100/312 ( 32%)]  Loss: 3.30 (3.25)  Time: 0.414s, 2471.23/s  (0.424s, 2417.55/s)  LR: 4.860e-02  Data: 0.034 (0.039)
+Train: 116 [ 150/312 ( 48%)]  Loss: 3.33 (3.27)  Time: 0.406s, 2521.78/s  (0.418s, 2450.65/s)  LR: 4.860e-02  Data: 0.027 (0.035)
+Train: 116 [ 200/312 ( 64%)]  Loss: 3.42 (3.28)  Time: 0.411s, 2494.33/s  (0.415s, 2466.89/s)  LR: 4.860e-02  Data: 0.028 (0.033)
+Train: 116 [ 250/312 ( 80%)]  Loss: 3.22 (3.29)  Time: 0.413s, 2479.04/s  (0.414s, 2472.08/s)  LR: 4.860e-02  Data: 0.027 (0.032)
+Train: 116 [ 300/312 ( 96%)]  Loss: 3.38 (3.30)  Time: 0.407s, 2515.97/s  (0.414s, 2476.31/s)  LR: 4.860e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.465 (1.465)  Loss:   3.758 ( 3.758)  Acc@1:  34.766 ( 34.766)  Acc@5:  53.613 ( 53.613)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.562 ( 3.797)  Acc@1:  37.146 ( 34.794)  Acc@5:  58.019 ( 54.754)
+Train: 117 [   0/312 (  0%)]  Loss: 3.20 (3.20)  Time: 1.619s,  632.39/s  (1.619s,  632.39/s)  LR: 4.590e-02  Data: 1.248 (1.248)
+Train: 117 [  50/312 ( 16%)]  Loss: 3.29 (3.23)  Time: 0.410s, 2496.27/s  (0.432s, 2368.92/s)  LR: 4.590e-02  Data: 0.026 (0.051)
+Train: 117 [ 100/312 ( 32%)]  Loss: 3.28 (3.25)  Time: 0.412s, 2482.77/s  (0.421s, 2432.32/s)  LR: 4.590e-02  Data: 0.027 (0.040)
+Train: 117 [ 150/312 ( 48%)]  Loss: 3.19 (3.26)  Time: 0.407s, 2515.95/s  (0.417s, 2453.41/s)  LR: 4.590e-02  Data: 0.026 (0.036)
+Train: 117 [ 200/312 ( 64%)]  Loss: 3.30 (3.27)  Time: 0.409s, 2502.60/s  (0.415s, 2465.21/s)  LR: 4.590e-02  Data: 0.026 (0.034)
+Train: 117 [ 250/312 ( 80%)]  Loss: 3.28 (3.28)  Time: 0.412s, 2483.87/s  (0.415s, 2470.00/s)  LR: 4.590e-02  Data: 0.028 (0.032)
+Train: 117 [ 300/312 ( 96%)]  Loss: 3.26 (3.28)  Time: 0.411s, 2490.42/s  (0.414s, 2472.90/s)  LR: 4.590e-02  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.449 (1.449)  Loss:   3.790 ( 3.790)  Acc@1:  34.570 ( 34.570)  Acc@5:  55.176 ( 55.176)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.541 ( 3.766)  Acc@1:  38.090 ( 35.426)  Acc@5:  56.250 ( 55.044)
+Train: 118 [   0/312 (  0%)]  Loss: 3.21 (3.21)  Time: 1.706s,  600.17/s  (1.706s,  600.17/s)  LR: 4.326e-02  Data: 1.282 (1.282)
+Train: 118 [  50/312 ( 16%)]  Loss: 3.23 (3.21)  Time: 0.407s, 2516.00/s  (0.430s, 2383.15/s)  LR: 4.326e-02  Data: 0.032 (0.052)
+Train: 118 [ 100/312 ( 32%)]  Loss: 3.25 (3.22)  Time: 0.406s, 2519.57/s  (0.418s, 2451.13/s)  LR: 4.326e-02  Data: 0.028 (0.040)
+Train: 118 [ 150/312 ( 48%)]  Loss: 3.22 (3.23)  Time: 0.414s, 2471.49/s  (0.415s, 2466.82/s)  LR: 4.326e-02  Data: 0.028 (0.036)
+Train: 118 [ 200/312 ( 64%)]  Loss: 3.25 (3.24)  Time: 0.404s, 2532.76/s  (0.414s, 2474.46/s)  LR: 4.326e-02  Data: 0.029 (0.034)
+Train: 118 [ 250/312 ( 80%)]  Loss: 3.29 (3.25)  Time: 0.404s, 2532.59/s  (0.412s, 2485.96/s)  LR: 4.326e-02  Data: 0.028 (0.032)
+Train: 118 [ 300/312 ( 96%)]  Loss: 3.30 (3.26)  Time: 0.404s, 2536.47/s  (0.410s, 2494.56/s)  LR: 4.326e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   3.785 ( 3.785)  Acc@1:  35.938 ( 35.938)  Acc@5:  53.711 ( 53.711)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.553 ( 3.796)  Acc@1:  36.910 ( 34.922)  Acc@5:  58.726 ( 54.938)
+Train: 119 [   0/312 (  0%)]  Loss: 3.14 (3.14)  Time: 1.563s,  655.00/s  (1.563s,  655.00/s)  LR: 4.069e-02  Data: 1.192 (1.192)
+Train: 119 [  50/312 ( 16%)]  Loss: 3.30 (3.20)  Time: 0.411s, 2494.36/s  (0.429s, 2387.69/s)  LR: 4.069e-02  Data: 0.028 (0.050)
+Train: 119 [ 100/312 ( 32%)]  Loss: 3.25 (3.21)  Time: 0.410s, 2496.22/s  (0.420s, 2439.29/s)  LR: 4.069e-02  Data: 0.026 (0.039)
+Train: 119 [ 150/312 ( 48%)]  Loss: 3.26 (3.22)  Time: 0.408s, 2510.89/s  (0.416s, 2463.08/s)  LR: 4.069e-02  Data: 0.028 (0.035)
+Train: 119 [ 200/312 ( 64%)]  Loss: 3.27 (3.23)  Time: 0.405s, 2528.13/s  (0.414s, 2476.31/s)  LR: 4.069e-02  Data: 0.026 (0.033)
+Train: 119 [ 250/312 ( 80%)]  Loss: 3.33 (3.24)  Time: 0.413s, 2480.16/s  (0.413s, 2481.35/s)  LR: 4.069e-02  Data: 0.029 (0.032)
+Train: 119 [ 300/312 ( 96%)]  Loss: 3.23 (3.24)  Time: 0.407s, 2513.40/s  (0.413s, 2482.31/s)  LR: 4.069e-02  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.428 (1.428)  Loss:   3.832 ( 3.832)  Acc@1:  34.570 ( 34.570)  Acc@5:  55.078 ( 55.078)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.542 ( 3.787)  Acc@1:  35.377 ( 35.100)  Acc@5:  57.311 ( 54.972)
+Train: 120 [   0/312 (  0%)]  Loss: 3.22 (3.22)  Time: 1.625s,  630.30/s  (1.625s,  630.30/s)  LR: 3.820e-02  Data: 1.256 (1.256)
+Train: 120 [  50/312 ( 16%)]  Loss: 3.30 (3.19)  Time: 0.403s, 2539.00/s  (0.428s, 2393.59/s)  LR: 3.820e-02  Data: 0.023 (0.051)
+Train: 120 [ 100/312 ( 32%)]  Loss: 3.18 (3.20)  Time: 0.410s, 2494.75/s  (0.418s, 2452.46/s)  LR: 3.820e-02  Data: 0.028 (0.040)
+Train: 120 [ 150/312 ( 48%)]  Loss: 3.30 (3.21)  Time: 0.417s, 2457.27/s  (0.415s, 2464.58/s)  LR: 3.820e-02  Data: 0.028 (0.036)
+Train: 120 [ 200/312 ( 64%)]  Loss: 3.25 (3.22)  Time: 0.414s, 2472.30/s  (0.415s, 2469.78/s)  LR: 3.820e-02  Data: 0.032 (0.034)
+Train: 120 [ 250/312 ( 80%)]  Loss: 3.31 (3.23)  Time: 0.410s, 2498.41/s  (0.414s, 2473.99/s)  LR: 3.820e-02  Data: 0.028 (0.033)
+Train: 120 [ 300/312 ( 96%)]  Loss: 3.22 (3.23)  Time: 0.410s, 2499.62/s  (0.413s, 2476.69/s)  LR: 3.820e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.410 (1.410)  Loss:   3.808 ( 3.808)  Acc@1:  33.789 ( 33.789)  Acc@5:  54.492 ( 54.492)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.631 ( 3.843)  Acc@1:  35.495 ( 34.616)  Acc@5:  56.604 ( 54.060)
+Train: 121 [   0/312 (  0%)]  Loss: 3.29 (3.29)  Time: 1.794s,  570.94/s  (1.794s,  570.94/s)  LR: 3.577e-02  Data: 1.420 (1.420)
+Train: 121 [  50/312 ( 16%)]  Loss: 3.20 (3.16)  Time: 0.411s, 2490.78/s  (0.437s, 2342.17/s)  LR: 3.577e-02  Data: 0.026 (0.055)
+Train: 121 [ 100/312 ( 32%)]  Loss: 3.32 (3.17)  Time: 0.410s, 2496.59/s  (0.423s, 2418.06/s)  LR: 3.577e-02  Data: 0.029 (0.041)
+Train: 121 [ 150/312 ( 48%)]  Loss: 3.25 (3.18)  Time: 0.412s, 2485.08/s  (0.419s, 2443.82/s)  LR: 3.577e-02  Data: 0.027 (0.037)
+Train: 121 [ 200/312 ( 64%)]  Loss: 3.31 (3.19)  Time: 0.408s, 2511.85/s  (0.417s, 2458.17/s)  LR: 3.577e-02  Data: 0.027 (0.034)
+Train: 121 [ 250/312 ( 80%)]  Loss: 3.25 (3.20)  Time: 0.408s, 2512.25/s  (0.415s, 2466.59/s)  LR: 3.577e-02  Data: 0.028 (0.033)
+Train: 121 [ 300/312 ( 96%)]  Loss: 3.33 (3.21)  Time: 0.408s, 2512.52/s  (0.414s, 2473.43/s)  LR: 3.577e-02  Data: 0.026 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.418 (1.418)  Loss:   3.811 ( 3.811)  Acc@1:  35.254 ( 35.254)  Acc@5:  54.883 ( 54.883)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.604 ( 3.844)  Acc@1:  34.906 ( 34.194)  Acc@5:  55.307 ( 54.210)
+Train: 122 [   0/312 (  0%)]  Loss: 3.10 (3.10)  Time: 1.636s,  625.89/s  (1.636s,  625.89/s)  LR: 3.342e-02  Data: 1.262 (1.262)
+Train: 122 [  50/312 ( 16%)]  Loss: 3.15 (3.15)  Time: 0.409s, 2502.99/s  (0.435s, 2353.77/s)  LR: 3.342e-02  Data: 0.028 (0.052)
+Train: 122 [ 100/312 ( 32%)]  Loss: 3.18 (3.16)  Time: 0.405s, 2530.81/s  (0.420s, 2437.33/s)  LR: 3.342e-02  Data: 0.028 (0.040)
+Train: 122 [ 150/312 ( 48%)]  Loss: 3.20 (3.17)  Time: 0.402s, 2546.17/s  (0.414s, 2472.96/s)  LR: 3.342e-02  Data: 0.028 (0.035)
+Train: 122 [ 200/312 ( 64%)]  Loss: 3.25 (3.17)  Time: 0.402s, 2550.34/s  (0.411s, 2490.79/s)  LR: 3.342e-02  Data: 0.028 (0.034)
+Train: 122 [ 250/312 ( 80%)]  Loss: 3.10 (3.18)  Time: 0.404s, 2537.11/s  (0.409s, 2500.87/s)  LR: 3.342e-02  Data: 0.028 (0.032)
+Train: 122 [ 300/312 ( 96%)]  Loss: 3.29 (3.19)  Time: 0.409s, 2504.00/s  (0.409s, 2505.58/s)  LR: 3.342e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.425 (1.425)  Loss:   3.881 ( 3.881)  Acc@1:  34.375 ( 34.375)  Acc@5:  52.734 ( 52.734)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.579 ( 3.858)  Acc@1:  37.028 ( 34.342)  Acc@5:  57.075 ( 53.970)
+Train: 123 [   0/312 (  0%)]  Loss: 3.15 (3.15)  Time: 1.766s,  579.76/s  (1.766s,  579.76/s)  LR: 3.113e-02  Data: 1.209 (1.209)
+Train: 123 [  50/312 ( 16%)]  Loss: 3.06 (3.14)  Time: 0.408s, 2507.61/s  (0.441s, 2321.49/s)  LR: 3.113e-02  Data: 0.026 (0.050)
+Train: 123 [ 100/312 ( 32%)]  Loss: 3.05 (3.14)  Time: 0.413s, 2477.17/s  (0.426s, 2402.24/s)  LR: 3.113e-02  Data: 0.028 (0.039)
+Train: 123 [ 150/312 ( 48%)]  Loss: 3.04 (3.14)  Time: 0.406s, 2522.43/s  (0.421s, 2430.49/s)  LR: 3.113e-02  Data: 0.028 (0.035)
+Train: 123 [ 200/312 ( 64%)]  Loss: 3.19 (3.15)  Time: 0.415s, 2467.70/s  (0.418s, 2447.59/s)  LR: 3.113e-02  Data: 0.028 (0.033)
+Train: 123 [ 250/312 ( 80%)]  Loss: 3.09 (3.16)  Time: 0.411s, 2492.05/s  (0.417s, 2455.94/s)  LR: 3.113e-02  Data: 0.023 (0.032)
+Train: 123 [ 300/312 ( 96%)]  Loss: 3.21 (3.17)  Time: 0.412s, 2487.49/s  (0.416s, 2460.87/s)  LR: 3.113e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.442 (1.442)  Loss:   3.847 ( 3.847)  Acc@1:  34.570 ( 34.570)  Acc@5:  52.539 ( 52.539)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.631 ( 3.845)  Acc@1:  37.028 ( 34.716)  Acc@5:  55.542 ( 54.140)
+Train: 124 [   0/312 (  0%)]  Loss: 3.06 (3.06)  Time: 2.061s,  496.89/s  (2.061s,  496.89/s)  LR: 2.893e-02  Data: 1.685 (1.685)
+Train: 124 [  50/312 ( 16%)]  Loss: 3.26 (3.11)  Time: 0.407s, 2515.30/s  (0.442s, 2316.80/s)  LR: 2.893e-02  Data: 0.027 (0.060)
+Train: 124 [ 100/312 ( 32%)]  Loss: 3.15 (3.13)  Time: 0.405s, 2528.44/s  (0.425s, 2410.62/s)  LR: 2.893e-02  Data: 0.027 (0.044)
+Train: 124 [ 150/312 ( 48%)]  Loss: 3.17 (3.14)  Time: 0.410s, 2497.38/s  (0.419s, 2442.79/s)  LR: 2.893e-02  Data: 0.026 (0.038)
+Train: 124 [ 200/312 ( 64%)]  Loss: 3.09 (3.14)  Time: 0.408s, 2510.10/s  (0.417s, 2454.77/s)  LR: 2.893e-02  Data: 0.027 (0.036)
+Train: 124 [ 250/312 ( 80%)]  Loss: 3.13 (3.15)  Time: 0.408s, 2510.20/s  (0.415s, 2465.16/s)  LR: 2.893e-02  Data: 0.029 (0.034)
+Train: 124 [ 300/312 ( 96%)]  Loss: 3.18 (3.15)  Time: 0.409s, 2506.54/s  (0.414s, 2473.56/s)  LR: 2.893e-02  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.822 ( 3.822)  Acc@1:  35.059 ( 35.059)  Acc@5:  54.004 ( 54.004)
+Test: [  48/48]  Time: 0.088 (0.332)  Loss:   3.608 ( 3.838)  Acc@1:  36.321 ( 34.664)  Acc@5:  56.368 ( 54.290)
+Train: 125 [   0/312 (  0%)]  Loss: 3.12 (3.12)  Time: 1.641s,  624.17/s  (1.641s,  624.17/s)  LR: 2.679e-02  Data: 1.268 (1.268)
+Train: 125 [  50/312 ( 16%)]  Loss: 3.03 (3.11)  Time: 0.411s, 2489.54/s  (0.435s, 2351.82/s)  LR: 2.679e-02  Data: 0.027 (0.052)
+Train: 125 [ 100/312 ( 32%)]  Loss: 3.14 (3.11)  Time: 0.407s, 2515.54/s  (0.422s, 2423.78/s)  LR: 2.679e-02  Data: 0.025 (0.040)
+Train: 125 [ 150/312 ( 48%)]  Loss: 3.06 (3.12)  Time: 0.410s, 2498.45/s  (0.418s, 2451.45/s)  LR: 2.679e-02  Data: 0.027 (0.036)
+Train: 125 [ 200/312 ( 64%)]  Loss: 3.14 (3.12)  Time: 0.410s, 2497.25/s  (0.416s, 2463.41/s)  LR: 2.679e-02  Data: 0.027 (0.034)
+Train: 125 [ 250/312 ( 80%)]  Loss: 3.12 (3.13)  Time: 0.411s, 2490.11/s  (0.415s, 2467.88/s)  LR: 2.679e-02  Data: 0.028 (0.032)
+Train: 125 [ 300/312 ( 96%)]  Loss: 3.12 (3.13)  Time: 0.412s, 2485.54/s  (0.414s, 2472.24/s)  LR: 2.679e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.443 (1.443)  Loss:   3.813 ( 3.813)  Acc@1:  34.766 ( 34.766)  Acc@5:  54.297 ( 54.297)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.601 ( 3.835)  Acc@1:  35.142 ( 34.566)  Acc@5:  56.958 ( 54.220)
+Train: 126 [   0/312 (  0%)]  Loss: 3.13 (3.13)  Time: 1.703s,  601.27/s  (1.703s,  601.27/s)  LR: 2.474e-02  Data: 1.330 (1.330)
+Train: 126 [  50/312 ( 16%)]  Loss: 3.00 (3.08)  Time: 0.403s, 2543.20/s  (0.431s, 2375.93/s)  LR: 2.474e-02  Data: 0.026 (0.052)
+Train: 126 [ 100/312 ( 32%)]  Loss: 3.01 (3.09)  Time: 0.402s, 2545.29/s  (0.417s, 2453.85/s)  LR: 2.474e-02  Data: 0.028 (0.040)
+Train: 126 [ 150/312 ( 48%)]  Loss: 3.20 (3.10)  Time: 0.402s, 2546.31/s  (0.412s, 2482.53/s)  LR: 2.474e-02  Data: 0.028 (0.036)
+Train: 126 [ 200/312 ( 64%)]  Loss: 3.14 (3.11)  Time: 0.404s, 2534.84/s  (0.410s, 2494.60/s)  LR: 2.474e-02  Data: 0.027 (0.034)
+Train: 126 [ 250/312 ( 80%)]  Loss: 3.11 (3.11)  Time: 0.410s, 2496.66/s  (0.410s, 2497.37/s)  LR: 2.474e-02  Data: 0.028 (0.033)
+Train: 126 [ 300/312 ( 96%)]  Loss: 3.09 (3.12)  Time: 0.411s, 2493.02/s  (0.410s, 2496.09/s)  LR: 2.474e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.415 (1.415)  Loss:   3.812 ( 3.812)  Acc@1:  35.156 ( 35.156)  Acc@5:  54.980 ( 54.980)
+Test: [  48/48]  Time: 0.089 (0.326)  Loss:   3.587 ( 3.810)  Acc@1:  36.557 ( 35.162)  Acc@5:  55.896 ( 54.578)
+Train: 127 [   0/312 (  0%)]  Loss: 3.09 (3.09)  Time: 1.784s,  573.98/s  (1.784s,  573.98/s)  LR: 2.276e-02  Data: 1.408 (1.408)
+Train: 127 [  50/312 ( 16%)]  Loss: 3.02 (3.08)  Time: 0.413s, 2482.19/s  (0.437s, 2341.77/s)  LR: 2.276e-02  Data: 0.027 (0.054)
+Train: 127 [ 100/312 ( 32%)]  Loss: 3.12 (3.08)  Time: 0.408s, 2509.13/s  (0.423s, 2423.67/s)  LR: 2.276e-02  Data: 0.028 (0.041)
+Train: 127 [ 150/312 ( 48%)]  Loss: 3.12 (3.09)  Time: 0.403s, 2537.97/s  (0.417s, 2456.39/s)  LR: 2.276e-02  Data: 0.026 (0.037)
+Train: 127 [ 200/312 ( 64%)]  Loss: 3.12 (3.09)  Time: 0.405s, 2526.61/s  (0.414s, 2471.62/s)  LR: 2.276e-02  Data: 0.028 (0.034)
+Train: 127 [ 250/312 ( 80%)]  Loss: 3.02 (3.10)  Time: 0.410s, 2497.53/s  (0.413s, 2477.23/s)  LR: 2.276e-02  Data: 0.028 (0.033)
+Train: 127 [ 300/312 ( 96%)]  Loss: 3.17 (3.10)  Time: 0.413s, 2479.98/s  (0.413s, 2478.32/s)  LR: 2.276e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   3.831 ( 3.831)  Acc@1:  35.059 ( 35.059)  Acc@5:  52.734 ( 52.734)
+Test: [  48/48]  Time: 0.087 (0.331)  Loss:   3.595 ( 3.843)  Acc@1:  36.675 ( 34.792)  Acc@5:  57.075 ( 54.230)
+Train: 128 [   0/312 (  0%)]  Loss: 3.05 (3.05)  Time: 1.482s,  691.06/s  (1.482s,  691.06/s)  LR: 2.086e-02  Data: 1.110 (1.110)
+Train: 128 [  50/312 ( 16%)]  Loss: 3.01 (3.07)  Time: 0.403s, 2541.68/s  (0.425s, 2408.35/s)  LR: 2.086e-02  Data: 0.027 (0.048)
+Train: 128 [ 100/312 ( 32%)]  Loss: 3.05 (3.07)  Time: 0.407s, 2514.51/s  (0.415s, 2468.38/s)  LR: 2.086e-02  Data: 0.026 (0.038)
+Train: 128 [ 150/312 ( 48%)]  Loss: 3.03 (3.07)  Time: 0.412s, 2484.68/s  (0.413s, 2480.75/s)  LR: 2.086e-02  Data: 0.028 (0.034)
+Train: 128 [ 200/312 ( 64%)]  Loss: 3.02 (3.08)  Time: 0.411s, 2490.48/s  (0.413s, 2482.23/s)  LR: 2.086e-02  Data: 0.028 (0.032)
+Train: 128 [ 250/312 ( 80%)]  Loss: 3.13 (3.08)  Time: 0.405s, 2528.70/s  (0.412s, 2488.41/s)  LR: 2.086e-02  Data: 0.027 (0.031)
+Train: 128 [ 300/312 ( 96%)]  Loss: 3.08 (3.09)  Time: 0.406s, 2524.42/s  (0.411s, 2492.88/s)  LR: 2.086e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.441 (1.441)  Loss:   3.833 ( 3.833)  Acc@1:  34.668 ( 34.668)  Acc@5:  53.418 ( 53.418)
+Test: [  48/48]  Time: 0.089 (0.331)  Loss:   3.609 ( 3.833)  Acc@1:  37.028 ( 34.760)  Acc@5:  56.250 ( 54.252)
+Train: 129 [   0/312 (  0%)]  Loss: 2.99 (2.99)  Time: 1.899s,  539.17/s  (1.899s,  539.17/s)  LR: 1.903e-02  Data: 1.261 (1.261)
+Train: 129 [  50/312 ( 16%)]  Loss: 2.99 (3.05)  Time: 0.407s, 2516.10/s  (0.440s, 2327.58/s)  LR: 1.903e-02  Data: 0.027 (0.052)
+Train: 129 [ 100/312 ( 32%)]  Loss: 3.10 (3.05)  Time: 0.404s, 2533.73/s  (0.424s, 2416.19/s)  LR: 1.903e-02  Data: 0.027 (0.040)
+Train: 129 [ 150/312 ( 48%)]  Loss: 3.05 (3.06)  Time: 0.412s, 2483.57/s  (0.418s, 2447.70/s)  LR: 1.903e-02  Data: 0.034 (0.036)
+Train: 129 [ 200/312 ( 64%)]  Loss: 3.14 (3.07)  Time: 0.416s, 2463.20/s  (0.416s, 2458.90/s)  LR: 1.903e-02  Data: 0.028 (0.034)
+Train: 129 [ 250/312 ( 80%)]  Loss: 3.02 (3.07)  Time: 0.410s, 2495.45/s  (0.415s, 2464.80/s)  LR: 1.903e-02  Data: 0.028 (0.032)
+Train: 129 [ 300/312 ( 96%)]  Loss: 3.04 (3.07)  Time: 0.409s, 2502.40/s  (0.414s, 2472.32/s)  LR: 1.903e-02  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.408 (1.408)  Loss:   3.809 ( 3.809)  Acc@1:  34.668 ( 34.668)  Acc@5:  52.246 ( 52.246)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.599 ( 3.818)  Acc@1:  36.557 ( 34.956)  Acc@5:  56.722 ( 54.496)
+Train: 130 [   0/312 (  0%)]  Loss: 2.96 (2.96)  Time: 1.833s,  558.78/s  (1.833s,  558.78/s)  LR: 1.729e-02  Data: 1.460 (1.460)
+Train: 130 [  50/312 ( 16%)]  Loss: 3.00 (3.03)  Time: 0.409s, 2503.53/s  (0.435s, 2351.98/s)  LR: 1.729e-02  Data: 0.027 (0.056)
+Train: 130 [ 100/312 ( 32%)]  Loss: 3.09 (3.04)  Time: 0.411s, 2490.43/s  (0.423s, 2421.67/s)  LR: 1.729e-02  Data: 0.028 (0.042)
+Train: 130 [ 150/312 ( 48%)]  Loss: 3.00 (3.05)  Time: 0.407s, 2517.39/s  (0.419s, 2445.70/s)  LR: 1.729e-02  Data: 0.027 (0.037)
+Train: 130 [ 200/312 ( 64%)]  Loss: 3.08 (3.05)  Time: 0.405s, 2528.41/s  (0.415s, 2464.93/s)  LR: 1.729e-02  Data: 0.027 (0.035)
+Train: 130 [ 250/312 ( 80%)]  Loss: 3.10 (3.06)  Time: 0.404s, 2533.61/s  (0.413s, 2476.96/s)  LR: 1.729e-02  Data: 0.026 (0.033)
+Train: 130 [ 300/312 ( 96%)]  Loss: 3.06 (3.06)  Time: 0.408s, 2510.26/s  (0.412s, 2483.39/s)  LR: 1.729e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.446 (1.446)  Loss:   3.791 ( 3.791)  Acc@1:  34.766 ( 34.766)  Acc@5:  54.004 ( 54.004)
+Test: [  48/48]  Time: 0.089 (0.327)  Loss:   3.556 ( 3.785)  Acc@1:  37.736 ( 35.440)  Acc@5:  56.132 ( 54.834)
+Train: 131 [   0/312 (  0%)]  Loss: 2.96 (2.96)  Time: 1.520s,  673.86/s  (1.520s,  673.86/s)  LR: 1.563e-02  Data: 1.144 (1.144)
+Train: 131 [  50/312 ( 16%)]  Loss: 3.04 (3.02)  Time: 0.413s, 2478.85/s  (0.434s, 2359.63/s)  LR: 1.563e-02  Data: 0.027 (0.049)
+Train: 131 [ 100/312 ( 32%)]  Loss: 3.01 (3.03)  Time: 0.409s, 2503.46/s  (0.423s, 2420.20/s)  LR: 1.563e-02  Data: 0.025 (0.038)
+Train: 131 [ 150/312 ( 48%)]  Loss: 3.02 (3.03)  Time: 0.411s, 2490.50/s  (0.419s, 2441.22/s)  LR: 1.563e-02  Data: 0.029 (0.035)
+Train: 131 [ 200/312 ( 64%)]  Loss: 2.96 (3.04)  Time: 0.409s, 2501.29/s  (0.418s, 2452.22/s)  LR: 1.563e-02  Data: 0.025 (0.033)
+Train: 131 [ 250/312 ( 80%)]  Loss: 2.99 (3.04)  Time: 0.414s, 2472.93/s  (0.416s, 2458.71/s)  LR: 1.563e-02  Data: 0.028 (0.032)
+Train: 131 [ 300/312 ( 96%)]  Loss: 3.17 (3.05)  Time: 0.413s, 2480.70/s  (0.416s, 2463.28/s)  LR: 1.563e-02  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   3.781 ( 3.781)  Acc@1:  35.547 ( 35.547)  Acc@5:  54.980 ( 54.980)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.580 ( 3.791)  Acc@1:  35.259 ( 35.124)  Acc@5:  56.604 ( 54.838)
+Train: 132 [   0/312 (  0%)]  Loss: 3.00 (3.00)  Time: 1.589s,  644.47/s  (1.589s,  644.47/s)  LR: 1.404e-02  Data: 1.212 (1.212)
+Train: 132 [  50/312 ( 16%)]  Loss: 2.92 (3.02)  Time: 0.408s, 2510.50/s  (0.438s, 2335.91/s)  LR: 1.404e-02  Data: 0.027 (0.050)
+Train: 132 [ 100/312 ( 32%)]  Loss: 3.03 (3.03)  Time: 0.413s, 2482.24/s  (0.424s, 2417.93/s)  LR: 1.404e-02  Data: 0.028 (0.039)
+Train: 132 [ 150/312 ( 48%)]  Loss: 2.97 (3.03)  Time: 0.408s, 2508.74/s  (0.419s, 2443.98/s)  LR: 1.404e-02  Data: 0.027 (0.035)
+Train: 132 [ 200/312 ( 64%)]  Loss: 3.01 (3.03)  Time: 0.409s, 2505.79/s  (0.417s, 2457.29/s)  LR: 1.404e-02  Data: 0.028 (0.033)
+Train: 132 [ 250/312 ( 80%)]  Loss: 2.97 (3.03)  Time: 0.408s, 2510.57/s  (0.415s, 2467.86/s)  LR: 1.404e-02  Data: 0.027 (0.032)
+Train: 132 [ 300/312 ( 96%)]  Loss: 2.98 (3.03)  Time: 0.409s, 2503.72/s  (0.414s, 2473.86/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:   3.861 ( 3.861)  Acc@1:  35.059 ( 35.059)  Acc@5:  53.320 ( 53.320)
+Test: [  48/48]  Time: 0.089 (0.331)  Loss:   3.592 ( 3.828)  Acc@1:  36.910 ( 34.882)  Acc@5:  56.486 ( 54.292)
+Train: 133 [   0/312 (  0%)]  Loss: 2.91 (2.91)  Time: 1.649s,  621.02/s  (1.649s,  621.02/s)  LR: 1.254e-02  Data: 1.274 (1.274)
+Train: 133 [  50/312 ( 16%)]  Loss: 3.05 (3.00)  Time: 0.408s, 2507.50/s  (0.434s, 2359.41/s)  LR: 1.254e-02  Data: 0.027 (0.052)
+Train: 133 [ 100/312 ( 32%)]  Loss: 3.02 (3.01)  Time: 0.407s, 2515.11/s  (0.421s, 2431.15/s)  LR: 1.254e-02  Data: 0.028 (0.040)
+Train: 133 [ 150/312 ( 48%)]  Loss: 3.01 (3.02)  Time: 0.409s, 2502.35/s  (0.417s, 2454.79/s)  LR: 1.254e-02  Data: 0.027 (0.036)
+Train: 133 [ 200/312 ( 64%)]  Loss: 3.08 (3.02)  Time: 0.405s, 2531.43/s  (0.415s, 2466.48/s)  LR: 1.254e-02  Data: 0.028 (0.034)
+Train: 133 [ 250/312 ( 80%)]  Loss: 3.02 (3.02)  Time: 0.399s, 2564.58/s  (0.413s, 2481.40/s)  LR: 1.254e-02  Data: 0.026 (0.032)
+Train: 133 [ 300/312 ( 96%)]  Loss: 2.96 (3.02)  Time: 0.400s, 2559.15/s  (0.411s, 2493.61/s)  LR: 1.254e-02  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.422 (1.422)  Loss:   3.857 ( 3.857)  Acc@1:  33.789 ( 33.789)  Acc@5:  52.832 ( 52.832)
+Test: [  48/48]  Time: 0.087 (0.329)  Loss:   3.621 ( 3.851)  Acc@1:  35.613 ( 34.612)  Acc@5:  55.542 ( 54.000)
+Train: 134 [   0/312 (  0%)]  Loss: 2.97 (2.97)  Time: 2.036s,  502.95/s  (2.036s,  502.95/s)  LR: 1.112e-02  Data: 1.670 (1.670)
+Train: 134 [  50/312 ( 16%)]  Loss: 3.00 (3.00)  Time: 0.400s, 2558.06/s  (0.432s, 2367.94/s)  LR: 1.112e-02  Data: 0.026 (0.060)
+Train: 134 [ 100/312 ( 32%)]  Loss: 3.06 (3.01)  Time: 0.402s, 2549.68/s  (0.418s, 2451.63/s)  LR: 1.112e-02  Data: 0.027 (0.044)
+Train: 134 [ 150/312 ( 48%)]  Loss: 3.03 (3.01)  Time: 0.405s, 2528.91/s  (0.414s, 2475.84/s)  LR: 1.112e-02  Data: 0.026 (0.038)
+Train: 134 [ 200/312 ( 64%)]  Loss: 3.02 (3.01)  Time: 0.413s, 2480.96/s  (0.412s, 2482.79/s)  LR: 1.112e-02  Data: 0.028 (0.036)
+Train: 134 [ 250/312 ( 80%)]  Loss: 2.93 (3.01)  Time: 0.410s, 2495.35/s  (0.412s, 2484.86/s)  LR: 1.112e-02  Data: 0.029 (0.034)
+Train: 134 [ 300/312 ( 96%)]  Loss: 3.09 (3.02)  Time: 0.410s, 2496.48/s  (0.412s, 2487.99/s)  LR: 1.112e-02  Data: 0.029 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.420 (1.420)  Loss:   3.836 ( 3.836)  Acc@1:  35.059 ( 35.059)  Acc@5:  53.418 ( 53.418)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.612 ( 3.833)  Acc@1:  35.142 ( 34.906)  Acc@5:  56.250 ( 54.358)
+Train: 135 [   0/312 (  0%)]  Loss: 2.98 (2.98)  Time: 1.466s,  698.65/s  (1.466s,  698.65/s)  LR: 9.789e-03  Data: 1.093 (1.093)
+Train: 135 [  50/312 ( 16%)]  Loss: 3.01 (3.00)  Time: 0.405s, 2527.52/s  (0.427s, 2397.48/s)  LR: 9.789e-03  Data: 0.025 (0.049)
+Train: 135 [ 100/312 ( 32%)]  Loss: 3.03 (3.00)  Time: 0.405s, 2526.47/s  (0.416s, 2461.10/s)  LR: 9.789e-03  Data: 0.028 (0.038)
+Train: 135 [ 150/312 ( 48%)]  Loss: 2.93 (3.00)  Time: 0.407s, 2513.19/s  (0.413s, 2478.65/s)  LR: 9.789e-03  Data: 0.027 (0.034)
+Train: 135 [ 200/312 ( 64%)]  Loss: 2.92 (3.00)  Time: 0.414s, 2473.52/s  (0.413s, 2481.80/s)  LR: 9.789e-03  Data: 0.027 (0.033)
+Train: 135 [ 250/312 ( 80%)]  Loss: 2.97 (3.00)  Time: 0.406s, 2521.13/s  (0.412s, 2484.62/s)  LR: 9.789e-03  Data: 0.028 (0.032)
+Train: 135 [ 300/312 ( 96%)]  Loss: 3.04 (3.01)  Time: 0.406s, 2520.26/s  (0.411s, 2489.69/s)  LR: 9.789e-03  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.417 (1.417)  Loss:   3.829 ( 3.829)  Acc@1:  34.863 ( 34.863)  Acc@5:  53.125 ( 53.125)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.568 ( 3.811)  Acc@1:  36.321 ( 35.144)  Acc@5:  55.307 ( 54.488)
+Train: 136 [   0/312 (  0%)]  Loss: 3.03 (3.03)  Time: 1.739s,  588.92/s  (1.739s,  588.92/s)  LR: 8.536e-03  Data: 1.365 (1.365)
+Train: 136 [  50/312 ( 16%)]  Loss: 3.11 (2.98)  Time: 0.409s, 2503.16/s  (0.443s, 2311.19/s)  LR: 8.536e-03  Data: 0.026 (0.054)
+Train: 136 [ 100/312 ( 32%)]  Loss: 3.05 (2.98)  Time: 0.408s, 2509.34/s  (0.426s, 2401.84/s)  LR: 8.536e-03  Data: 0.027 (0.041)
+Train: 136 [ 150/312 ( 48%)]  Loss: 2.87 (2.98)  Time: 0.413s, 2477.01/s  (0.421s, 2431.79/s)  LR: 8.536e-03  Data: 0.027 (0.036)
+Train: 136 [ 200/312 ( 64%)]  Loss: 2.96 (2.99)  Time: 0.415s, 2468.22/s  (0.419s, 2443.91/s)  LR: 8.536e-03  Data: 0.027 (0.034)
+Train: 136 [ 250/312 ( 80%)]  Loss: 3.05 (2.99)  Time: 0.404s, 2537.34/s  (0.417s, 2454.06/s)  LR: 8.536e-03  Data: 0.025 (0.033)
+Train: 136 [ 300/312 ( 96%)]  Loss: 3.05 (2.99)  Time: 0.405s, 2531.34/s  (0.415s, 2465.80/s)  LR: 8.536e-03  Data: 0.027 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.432 (1.432)  Loss:   3.819 ( 3.819)  Acc@1:  34.961 ( 34.961)  Acc@5:  53.711 ( 53.711)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.614 ( 3.823)  Acc@1:  36.085 ( 34.930)  Acc@5:  56.722 ( 54.300)
+Train: 137 [   0/312 (  0%)]  Loss: 2.88 (2.88)  Time: 1.605s,  638.11/s  (1.605s,  638.11/s)  LR: 7.367e-03  Data: 1.077 (1.077)
+Train: 137 [  50/312 ( 16%)]  Loss: 3.08 (2.98)  Time: 0.405s, 2525.94/s  (0.428s, 2394.20/s)  LR: 7.367e-03  Data: 0.027 (0.048)
+Train: 137 [ 100/312 ( 32%)]  Loss: 2.99 (2.98)  Time: 0.408s, 2510.12/s  (0.418s, 2452.34/s)  LR: 7.367e-03  Data: 0.028 (0.038)
+Train: 137 [ 150/312 ( 48%)]  Loss: 2.94 (2.98)  Time: 0.408s, 2509.99/s  (0.415s, 2467.78/s)  LR: 7.367e-03  Data: 0.028 (0.034)
+Train: 137 [ 200/312 ( 64%)]  Loss: 2.90 (2.98)  Time: 0.411s, 2489.27/s  (0.413s, 2477.02/s)  LR: 7.367e-03  Data: 0.033 (0.033)
+Train: 137 [ 250/312 ( 80%)]  Loss: 2.93 (2.99)  Time: 0.417s, 2455.10/s  (0.413s, 2481.76/s)  LR: 7.367e-03  Data: 0.035 (0.032)
+Train: 137 [ 300/312 ( 96%)]  Loss: 3.02 (2.99)  Time: 0.406s, 2523.22/s  (0.412s, 2484.43/s)  LR: 7.367e-03  Data: 0.024 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.431 (1.431)  Loss:   3.806 ( 3.806)  Acc@1:  35.352 ( 35.352)  Acc@5:  53.613 ( 53.613)
+Test: [  48/48]  Time: 0.087 (0.333)  Loss:   3.608 ( 3.820)  Acc@1:  36.085 ( 35.026)  Acc@5:  56.486 ( 54.444)
+Train: 138 [   0/312 (  0%)]  Loss: 2.95 (2.95)  Time: 1.611s,  635.55/s  (1.611s,  635.55/s)  LR: 6.283e-03  Data: 1.239 (1.239)
+Train: 138 [  50/312 ( 16%)]  Loss: 2.93 (2.97)  Time: 0.405s, 2529.45/s  (0.428s, 2394.87/s)  LR: 6.283e-03  Data: 0.025 (0.051)
+Train: 138 [ 100/312 ( 32%)]  Loss: 2.93 (2.97)  Time: 0.409s, 2502.41/s  (0.417s, 2454.00/s)  LR: 6.283e-03  Data: 0.027 (0.040)
+Train: 138 [ 150/312 ( 48%)]  Loss: 2.89 (2.96)  Time: 0.409s, 2505.45/s  (0.415s, 2468.71/s)  LR: 6.283e-03  Data: 0.026 (0.036)
+Train: 138 [ 200/312 ( 64%)]  Loss: 2.92 (2.97)  Time: 0.407s, 2514.47/s  (0.413s, 2478.69/s)  LR: 6.283e-03  Data: 0.028 (0.034)
+Train: 138 [ 250/312 ( 80%)]  Loss: 3.06 (2.97)  Time: 0.406s, 2519.10/s  (0.412s, 2487.07/s)  LR: 6.283e-03  Data: 0.028 (0.032)
+Train: 138 [ 300/312 ( 96%)]  Loss: 2.91 (2.98)  Time: 0.409s, 2501.03/s  (0.411s, 2491.33/s)  LR: 6.283e-03  Data: 0.029 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.435 (1.435)  Loss:   3.836 ( 3.836)  Acc@1:  35.645 ( 35.645)  Acc@5:  53.027 ( 53.027)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.606 ( 3.838)  Acc@1:  35.377 ( 34.848)  Acc@5:  56.014 ( 54.144)
+Train: 139 [   0/312 (  0%)]  Loss: 2.93 (2.93)  Time: 1.640s,  624.38/s  (1.640s,  624.38/s)  LR: 5.284e-03  Data: 1.267 (1.267)
+Train: 139 [  50/312 ( 16%)]  Loss: 2.97 (2.94)  Time: 0.405s, 2525.80/s  (0.429s, 2387.24/s)  LR: 5.284e-03  Data: 0.027 (0.051)
+Train: 139 [ 100/312 ( 32%)]  Loss: 2.98 (2.96)  Time: 0.403s, 2539.28/s  (0.416s, 2460.36/s)  LR: 5.284e-03  Data: 0.028 (0.039)
+Train: 139 [ 150/312 ( 48%)]  Loss: 2.92 (2.96)  Time: 0.403s, 2542.38/s  (0.412s, 2485.79/s)  LR: 5.284e-03  Data: 0.027 (0.035)
+Train: 139 [ 200/312 ( 64%)]  Loss: 2.95 (2.96)  Time: 0.408s, 2507.90/s  (0.410s, 2495.72/s)  LR: 5.284e-03  Data: 0.026 (0.033)
+Train: 139 [ 250/312 ( 80%)]  Loss: 3.09 (2.97)  Time: 0.409s, 2503.13/s  (0.410s, 2495.46/s)  LR: 5.284e-03  Data: 0.026 (0.032)
+Train: 139 [ 300/312 ( 96%)]  Loss: 3.00 (2.97)  Time: 0.414s, 2475.73/s  (0.411s, 2493.38/s)  LR: 5.284e-03  Data: 0.030 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.450 (1.450)  Loss:   3.849 ( 3.849)  Acc@1:  34.961 ( 34.961)  Acc@5:  52.832 ( 52.832)
+Test: [  48/48]  Time: 0.088 (0.331)  Loss:   3.617 ( 3.843)  Acc@1:  35.259 ( 34.790)  Acc@5:  55.778 ( 54.112)
+Train: 140 [   0/312 (  0%)]  Loss: 2.89 (2.89)  Time: 1.702s,  601.79/s  (1.702s,  601.79/s)  LR: 4.370e-03  Data: 1.330 (1.330)
+Train: 140 [  50/312 ( 16%)]  Loss: 3.02 (2.95)  Time: 0.402s, 2546.01/s  (0.428s, 2390.59/s)  LR: 4.370e-03  Data: 0.026 (0.052)
+Train: 140 [ 100/312 ( 32%)]  Loss: 2.92 (2.95)  Time: 0.405s, 2531.29/s  (0.416s, 2460.73/s)  LR: 4.370e-03  Data: 0.028 (0.040)
+Train: 140 [ 150/312 ( 48%)]  Loss: 2.96 (2.95)  Time: 0.406s, 2521.48/s  (0.413s, 2479.15/s)  LR: 4.370e-03  Data: 0.026 (0.036)
+Train: 140 [ 200/312 ( 64%)]  Loss: 2.93 (2.96)  Time: 0.412s, 2483.17/s  (0.412s, 2482.44/s)  LR: 4.370e-03  Data: 0.028 (0.034)
+Train: 140 [ 250/312 ( 80%)]  Loss: 2.97 (2.96)  Time: 0.410s, 2496.49/s  (0.412s, 2485.05/s)  LR: 4.370e-03  Data: 0.029 (0.033)
+Train: 140 [ 300/312 ( 96%)]  Loss: 2.96 (2.96)  Time: 0.405s, 2526.73/s  (0.411s, 2491.06/s)  LR: 4.370e-03  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.424 (1.424)  Loss:   3.828 ( 3.828)  Acc@1:  34.473 ( 34.473)  Acc@5:  53.027 ( 53.027)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.594 ( 3.820)  Acc@1:  35.731 ( 35.016)  Acc@5:  56.486 ( 54.416)
+Train: 141 [   0/312 (  0%)]  Loss: 2.83 (2.83)  Time: 1.475s,  694.30/s  (1.475s,  694.30/s)  LR: 3.543e-03  Data: 1.103 (1.103)
+Train: 141 [  50/312 ( 16%)]  Loss: 2.97 (2.95)  Time: 0.406s, 2521.83/s  (0.427s, 2399.89/s)  LR: 3.543e-03  Data: 0.028 (0.049)
+Train: 141 [ 100/312 ( 32%)]  Loss: 3.15 (2.96)  Time: 0.410s, 2496.28/s  (0.418s, 2449.26/s)  LR: 3.543e-03  Data: 0.027 (0.038)
+Train: 141 [ 150/312 ( 48%)]  Loss: 2.88 (2.96)  Time: 0.413s, 2479.96/s  (0.416s, 2460.21/s)  LR: 3.543e-03  Data: 0.025 (0.035)
+Train: 141 [ 200/312 ( 64%)]  Loss: 2.95 (2.96)  Time: 0.407s, 2518.79/s  (0.414s, 2470.46/s)  LR: 3.543e-03  Data: 0.027 (0.033)
+Train: 141 [ 250/312 ( 80%)]  Loss: 2.99 (2.96)  Time: 0.404s, 2532.61/s  (0.413s, 2480.10/s)  LR: 3.543e-03  Data: 0.028 (0.032)
+Train: 141 [ 300/312 ( 96%)]  Loss: 2.94 (2.96)  Time: 0.407s, 2512.90/s  (0.412s, 2486.16/s)  LR: 3.543e-03  Data: 0.027 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.454 (1.454)  Loss:   3.834 ( 3.834)  Acc@1:  35.059 ( 35.059)  Acc@5:  52.930 ( 52.930)
+Test: [  48/48]  Time: 0.088 (0.333)  Loss:   3.592 ( 3.824)  Acc@1:  36.910 ( 34.952)  Acc@5:  55.071 ( 54.322)
+Train: 142 [   0/312 (  0%)]  Loss: 2.96 (2.96)  Time: 1.681s,  609.24/s  (1.681s,  609.24/s)  LR: 2.801e-03  Data: 1.164 (1.164)
+Train: 142 [  50/312 ( 16%)]  Loss: 2.97 (2.96)  Time: 0.412s, 2484.54/s  (0.435s, 2352.96/s)  LR: 2.801e-03  Data: 0.032 (0.049)
+Train: 142 [ 100/312 ( 32%)]  Loss: 2.98 (2.95)  Time: 0.404s, 2537.28/s  (0.422s, 2428.44/s)  LR: 2.801e-03  Data: 0.023 (0.038)
+Train: 142 [ 150/312 ( 48%)]  Loss: 3.03 (2.95)  Time: 0.407s, 2515.88/s  (0.417s, 2458.30/s)  LR: 2.801e-03  Data: 0.028 (0.035)
+Train: 142 [ 200/312 ( 64%)]  Loss: 2.86 (2.95)  Time: 0.409s, 2505.67/s  (0.415s, 2469.28/s)  LR: 2.801e-03  Data: 0.027 (0.033)
+Train: 142 [ 250/312 ( 80%)]  Loss: 2.92 (2.95)  Time: 0.410s, 2494.54/s  (0.414s, 2473.73/s)  LR: 2.801e-03  Data: 0.027 (0.032)
+Train: 142 [ 300/312 ( 96%)]  Loss: 2.85 (2.95)  Time: 0.410s, 2500.47/s  (0.413s, 2480.52/s)  LR: 2.801e-03  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.499 (1.499)  Loss:   3.832 ( 3.832)  Acc@1:  35.059 ( 35.059)  Acc@5:  53.516 ( 53.516)
+Test: [  48/48]  Time: 0.088 (0.330)  Loss:   3.595 ( 3.819)  Acc@1:  36.557 ( 35.068)  Acc@5:  56.604 ( 54.410)
+Train: 143 [   0/312 (  0%)]  Loss: 2.97 (2.97)  Time: 1.545s,  662.74/s  (1.545s,  662.74/s)  LR: 2.146e-03  Data: 1.172 (1.172)
+Train: 143 [  50/312 ( 16%)]  Loss: 2.96 (2.95)  Time: 0.409s, 2504.58/s  (0.430s, 2378.96/s)  LR: 2.146e-03  Data: 0.026 (0.050)
+Train: 143 [ 100/312 ( 32%)]  Loss: 3.00 (2.95)  Time: 0.407s, 2514.75/s  (0.420s, 2436.75/s)  LR: 2.146e-03  Data: 0.028 (0.039)
+Train: 143 [ 150/312 ( 48%)]  Loss: 3.03 (2.95)  Time: 0.405s, 2530.81/s  (0.416s, 2461.85/s)  LR: 2.146e-03  Data: 0.027 (0.035)
+Train: 143 [ 200/312 ( 64%)]  Loss: 2.96 (2.95)  Time: 0.408s, 2512.09/s  (0.414s, 2474.38/s)  LR: 2.146e-03  Data: 0.026 (0.033)
+Train: 143 [ 250/312 ( 80%)]  Loss: 2.98 (2.95)  Time: 0.413s, 2476.94/s  (0.413s, 2478.72/s)  LR: 2.146e-03  Data: 0.027 (0.032)
+Train: 143 [ 300/312 ( 96%)]  Loss: 2.91 (2.95)  Time: 0.406s, 2524.92/s  (0.412s, 2485.53/s)  LR: 2.146e-03  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.410 (1.410)  Loss:   3.826 ( 3.826)  Acc@1:  35.156 ( 35.156)  Acc@5:  53.125 ( 53.125)
+Test: [  48/48]  Time: 0.087 (0.330)  Loss:   3.601 ( 3.818)  Acc@1:  36.085 ( 35.034)  Acc@5:  55.425 ( 54.342)
+Train: 144 [   0/312 (  0%)]  Loss: 2.93 (2.93)  Time: 1.748s,  585.80/s  (1.748s,  585.80/s)  LR: 1.577e-03  Data: 1.063 (1.063)
+Train: 144 [  50/312 ( 16%)]  Loss: 2.96 (2.94)  Time: 0.397s, 2580.20/s  (0.424s, 2412.50/s)  LR: 1.577e-03  Data: 0.027 (0.048)
+Train: 144 [ 100/312 ( 32%)]  Loss: 2.85 (2.94)  Time: 0.398s, 2570.92/s  (0.412s, 2487.88/s)  LR: 1.577e-03  Data: 0.028 (0.038)
+Train: 144 [ 150/312 ( 48%)]  Loss: 2.87 (2.94)  Time: 0.405s, 2528.92/s  (0.407s, 2513.25/s)  LR: 1.577e-03  Data: 0.026 (0.034)
+Train: 144 [ 200/312 ( 64%)]  Loss: 2.94 (2.94)  Time: 0.403s, 2540.22/s  (0.406s, 2523.20/s)  LR: 1.577e-03  Data: 0.028 (0.033)
+Train: 144 [ 250/312 ( 80%)]  Loss: 3.00 (2.95)  Time: 0.407s, 2515.75/s  (0.406s, 2524.95/s)  LR: 1.577e-03  Data: 0.027 (0.032)
+Train: 144 [ 300/312 ( 96%)]  Loss: 2.94 (2.95)  Time: 0.407s, 2515.59/s  (0.406s, 2523.07/s)  LR: 1.577e-03  Data: 0.028 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.416 (1.416)  Loss:   3.814 ( 3.814)  Acc@1:  35.254 ( 35.254)  Acc@5:  54.492 ( 54.492)
+Test: [  48/48]  Time: 0.089 (0.328)  Loss:   3.580 ( 3.805)  Acc@1:  36.439 ( 35.154)  Acc@5:  57.193 ( 54.614)
+Train: 145 [   0/312 (  0%)]  Loss: 2.90 (2.90)  Time: 1.860s,  550.41/s  (1.860s,  550.41/s)  LR: 1.096e-03  Data: 1.483 (1.483)
+Train: 145 [  50/312 ( 16%)]  Loss: 3.10 (2.94)  Time: 0.411s, 2494.44/s  (0.442s, 2316.83/s)  LR: 1.096e-03  Data: 0.026 (0.056)
+Train: 145 [ 100/312 ( 32%)]  Loss: 2.96 (2.94)  Time: 0.415s, 2465.30/s  (0.428s, 2391.33/s)  LR: 1.096e-03  Data: 0.027 (0.042)
+Train: 145 [ 150/312 ( 48%)]  Loss: 2.89 (2.94)  Time: 0.405s, 2528.93/s  (0.423s, 2423.35/s)  LR: 1.096e-03  Data: 0.027 (0.037)
+Train: 145 [ 200/312 ( 64%)]  Loss: 2.91 (2.93)  Time: 0.404s, 2533.37/s  (0.418s, 2447.03/s)  LR: 1.096e-03  Data: 0.028 (0.035)
+Train: 145 [ 250/312 ( 80%)]  Loss: 2.88 (2.93)  Time: 0.406s, 2523.65/s  (0.416s, 2461.79/s)  LR: 1.096e-03  Data: 0.027 (0.033)
+Train: 145 [ 300/312 ( 96%)]  Loss: 2.95 (2.94)  Time: 0.406s, 2519.14/s  (0.415s, 2470.07/s)  LR: 1.096e-03  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.434 (1.434)  Loss:   3.827 ( 3.827)  Acc@1:  35.156 ( 35.156)  Acc@5:  54.102 ( 54.102)
+Test: [  48/48]  Time: 0.088 (0.329)  Loss:   3.595 ( 3.825)  Acc@1:  35.731 ( 34.918)  Acc@5:  56.722 ( 54.348)
+Train: 146 [   0/312 (  0%)]  Loss: 2.96 (2.96)  Time: 1.897s,  539.76/s  (1.897s,  539.76/s)  LR: 7.014e-04  Data: 1.522 (1.522)
+Train: 146 [  50/312 ( 16%)]  Loss: 2.85 (2.94)  Time: 0.409s, 2505.91/s  (0.441s, 2322.00/s)  LR: 7.014e-04  Data: 0.028 (0.057)
+Train: 146 [ 100/312 ( 32%)]  Loss: 2.77 (2.94)  Time: 0.406s, 2524.72/s  (0.424s, 2416.23/s)  LR: 7.014e-04  Data: 0.028 (0.042)
+Train: 146 [ 150/312 ( 48%)]  Loss: 2.94 (2.94)  Time: 0.406s, 2522.77/s  (0.417s, 2455.26/s)  LR: 7.014e-04  Data: 0.029 (0.037)
+Train: 146 [ 200/312 ( 64%)]  Loss: 2.95 (2.94)  Time: 0.406s, 2524.23/s  (0.414s, 2473.95/s)  LR: 7.014e-04  Data: 0.027 (0.035)
+Train: 146 [ 250/312 ( 80%)]  Loss: 2.87 (2.94)  Time: 0.411s, 2493.87/s  (0.413s, 2482.08/s)  LR: 7.014e-04  Data: 0.028 (0.033)
+Train: 146 [ 300/312 ( 96%)]  Loss: 2.99 (2.94)  Time: 0.412s, 2482.82/s  (0.412s, 2483.55/s)  LR: 7.014e-04  Data: 0.028 (0.033)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.419 (1.419)  Loss:   3.816 ( 3.816)  Acc@1:  35.254 ( 35.254)  Acc@5:  53.809 ( 53.809)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.592 ( 3.812)  Acc@1:  35.495 ( 35.136)  Acc@5:  56.486 ( 54.472)
+Train: 147 [   0/312 (  0%)]  Loss: 3.04 (3.04)  Time: 1.554s,  659.04/s  (1.554s,  659.04/s)  LR: 3.947e-04  Data: 1.158 (1.158)
+Train: 147 [  50/312 ( 16%)]  Loss: 2.87 (2.94)  Time: 0.408s, 2511.85/s  (0.428s, 2389.93/s)  LR: 3.947e-04  Data: 0.029 (0.050)
+Train: 147 [ 100/312 ( 32%)]  Loss: 2.91 (2.93)  Time: 0.408s, 2507.59/s  (0.418s, 2451.29/s)  LR: 3.947e-04  Data: 0.025 (0.039)
+Train: 147 [ 150/312 ( 48%)]  Loss: 2.87 (2.93)  Time: 0.415s, 2466.27/s  (0.416s, 2464.38/s)  LR: 3.947e-04  Data: 0.026 (0.035)
+Train: 147 [ 200/312 ( 64%)]  Loss: 2.96 (2.93)  Time: 0.414s, 2472.93/s  (0.415s, 2469.30/s)  LR: 3.947e-04  Data: 0.028 (0.033)
+Train: 147 [ 250/312 ( 80%)]  Loss: 2.95 (2.94)  Time: 0.412s, 2486.78/s  (0.414s, 2473.61/s)  LR: 3.947e-04  Data: 0.027 (0.032)
+Train: 147 [ 300/312 ( 96%)]  Loss: 2.93 (2.94)  Time: 0.409s, 2505.43/s  (0.414s, 2475.56/s)  LR: 3.947e-04  Data: 0.026 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.454 (1.454)  Loss:   3.813 ( 3.813)  Acc@1:  35.449 ( 35.449)  Acc@5:  53.809 ( 53.809)
+Test: [  48/48]  Time: 0.089 (0.330)  Loss:   3.578 ( 3.806)  Acc@1:  35.377 ( 35.134)  Acc@5:  57.075 ( 54.550)
+Train: 148 [   0/312 (  0%)]  Loss: 2.90 (2.90)  Time: 1.804s,  567.48/s  (1.804s,  567.48/s)  LR: 1.754e-04  Data: 1.177 (1.177)
+Train: 148 [  50/312 ( 16%)]  Loss: 3.06 (2.94)  Time: 0.410s, 2495.47/s  (0.437s, 2344.98/s)  LR: 1.754e-04  Data: 0.028 (0.050)
+Train: 148 [ 100/312 ( 32%)]  Loss: 2.92 (2.93)  Time: 0.408s, 2512.81/s  (0.424s, 2412.98/s)  LR: 1.754e-04  Data: 0.028 (0.039)
+Train: 148 [ 150/312 ( 48%)]  Loss: 2.91 (2.93)  Time: 0.407s, 2515.36/s  (0.419s, 2445.24/s)  LR: 1.754e-04  Data: 0.027 (0.035)
+Train: 148 [ 200/312 ( 64%)]  Loss: 2.94 (2.94)  Time: 0.405s, 2525.77/s  (0.416s, 2463.34/s)  LR: 1.754e-04  Data: 0.029 (0.033)
+Train: 148 [ 250/312 ( 80%)]  Loss: 2.93 (2.94)  Time: 0.417s, 2457.62/s  (0.414s, 2473.47/s)  LR: 1.754e-04  Data: 0.036 (0.032)
+Train: 148 [ 300/312 ( 96%)]  Loss: 2.89 (2.94)  Time: 0.413s, 2479.42/s  (0.413s, 2476.57/s)  LR: 1.754e-04  Data: 0.029 (0.031)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.429 (1.429)  Loss:   3.821 ( 3.821)  Acc@1:  34.961 ( 34.961)  Acc@5:  53.613 ( 53.613)
+Test: [  48/48]  Time: 0.087 (0.327)  Loss:   3.601 ( 3.817)  Acc@1:  35.613 ( 34.988)  Acc@5:  56.132 ( 54.476)
+Train: 149 [   0/312 (  0%)]  Loss: 2.97 (2.97)  Time: 1.765s,  580.31/s  (1.765s,  580.31/s)  LR: 4.386e-05  Data: 1.394 (1.394)
+Train: 149 [  50/312 ( 16%)]  Loss: 2.94 (2.95)  Time: 0.400s, 2560.46/s  (0.428s, 2394.33/s)  LR: 4.386e-05  Data: 0.027 (0.054)
+Train: 149 [ 100/312 ( 32%)]  Loss: 2.91 (2.94)  Time: 0.399s, 2564.34/s  (0.413s, 2477.09/s)  LR: 4.386e-05  Data: 0.028 (0.041)
+Train: 149 [ 150/312 ( 48%)]  Loss: 2.90 (2.94)  Time: 0.402s, 2548.04/s  (0.408s, 2507.38/s)  LR: 4.386e-05  Data: 0.033 (0.037)
+Train: 149 [ 200/312 ( 64%)]  Loss: 2.94 (2.94)  Time: 0.400s, 2560.82/s  (0.406s, 2521.94/s)  LR: 4.386e-05  Data: 0.028 (0.034)
+Train: 149 [ 250/312 ( 80%)]  Loss: 3.00 (2.94)  Time: 0.399s, 2567.49/s  (0.405s, 2530.39/s)  LR: 4.386e-05  Data: 0.027 (0.033)
+Train: 149 [ 300/312 ( 96%)]  Loss: 2.82 (2.93)  Time: 0.401s, 2551.61/s  (0.404s, 2533.63/s)  LR: 4.386e-05  Data: 0.028 (0.032)
+Distributing BatchNorm running means and vars
+Test: [   0/48]  Time: 1.439 (1.439)  Loss:   3.834 ( 3.834)  Acc@1:  34.863 ( 34.863)  Acc@5:  53.809 ( 53.809)
+Test: [  48/48]  Time: 0.088 (0.328)  Loss:   3.602 ( 3.823)  Acc@1:  35.495 ( 34.894)  Acc@5:  56.368 ( 54.404)
+*** Best metric: 41.433999997558594 (epoch 52)
+--result
+[
+    {
+        "epoch": 55,
+        "train": {
+            "loss": 4.632839679718018
+        },
+        "validation": {
+            "loss": 3.225374926071167,
+            "top1": 40.43199999755859,
+            "top5": 64.7759999975586
+        }
+    },
+    {
+        "epoch": 70,
+        "train": {
+            "loss": 4.318325996398926
+        },
+        "validation": {
+            "loss": 3.291542735748291,
+            "top1": 40.44399999755859,
+            "top5": 63.37799990966797
+        }
+    },
+    {
+        "epoch": 54,
+        "train": {
+            "loss": 4.649861812591553
+        },
+        "validation": {
+            "loss": 3.2325782968139647,
+            "top1": 40.596,
+            "top5": 64.64200002441406
+        }
+    },
+    {
+        "epoch": 58,
+        "train": {
+            "loss": 4.566425323486328
+        },
+        "validation": {
+            "loss": 3.2375895790863036,
+            "top1": 40.74600000488281,
+            "top5": 64.89999996826172
+        }
+    },
+    {
+        "epoch": 63,
+        "train": {
+            "loss": 4.463349342346191
+        },
+        "validation": {
+            "loss": 3.2511073503112793,
+            "top1": 40.76000002685547,
+            "top5": 64.48399997802734
+        }
+    },
+    {
+        "epoch": 57,
+        "train": {
+            "loss": 4.585275650024414
+        },
+        "validation": {
+            "loss": 3.2218094375610353,
+            "top1": 40.771999975585935,
+            "top5": 64.49400004638672
+        }
+    },
+    {
+        "epoch": 50,
+        "train": {
+            "loss": 4.7323479652404785
+        },
+        "validation": {
+            "loss": 3.214202807006836,
+            "top1": 40.79600005126953,
+            "top5": 65.56000002929687
+        }
+    },
+    {
+        "epoch": 56,
+        "train": {
+            "loss": 4.607300281524658
+        },
+        "validation": {
+            "loss": 3.2064396517944336,
+            "top1": 40.981999959716795,
+            "top5": 64.99800004638672
+        }
+    },
+    {
+        "epoch": 61,
+        "train": {
+            "loss": 4.507087707519531
+        },
+        "validation": {
+            "loss": 3.2332502903747558,
+            "top1": 41.02999997802734,
+            "top5": 64.60199997314453
+        }
+    },
+    {
+        "epoch": 52,
+        "train": {
+            "loss": 4.688541412353516
+        },
+        "validation": {
+            "loss": 3.14884043838501,
+            "top1": 41.433999997558594,
+            "top5": 65.80999999023437
+        }
+    }
+]
diff --git a/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/files/requirements.txt b/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d388cd950a4a88b1d9f37efc5c83fee7de76a1be
--- /dev/null
+++ b/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/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_015723-igbnn5u2/files/wandb-metadata.json b/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..6ebe1db1e55449002647bccebd516e9957915239
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@@ -0,0 +1,88 @@
+{
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+  "python":  "CPython 3.9.21",
+  "startedAt":  "2025-02-24T01:57:23.358427Z",
+  "args":  [
+    "--dataset",
+    "hfds/datacomp/imagenet-1k-random-40.0-frac-1over4",
+    "--log-wandb",
+    "--wandb-project",
+    "ImageNetTraining40.0-frac-1over4",
+    "--experiment",
+    "ImageNetTraining40.0-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"
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+    "countLogical":  48
+  },
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+    {
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+      "memoryTotal":  "24152899584",
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+      "architecture":  "Ada"
+    },
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+      "name":  "NVIDIA L4",
+      "memoryTotal":  "24152899584",
+      "cudaCores":  7424,
+      "architecture":  "Ada"
+    },
+    {
+      "name":  "NVIDIA L4",
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+      "architecture":  "Ada"
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+      "cudaCores":  7424,
+      "architecture":  "Ada"
+    }
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+}
\ No newline at end of file
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new file mode 100644
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diff --git a/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/logs/debug.log b/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/logs/debug.log
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diff --git a/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/run-igbnn5u2.wandb b/pytorch-image-models/wandb/run-20250224_015723-igbnn5u2/run-igbnn5u2.wandb
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diff --git a/requirements.txt b/requirements.txt
index 32191d5a0fb9f649cdf72c1ec245baac3c1c6212..3f839a7d886a627e58a4da5438bf7180761e5a1a 100644
--- a/requirements.txt
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 gradio
 datasets
-wandb
\ No newline at end of file
+wandb
+importlib_metadata
\ No newline at end of file