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+ model=model010
+ gpu=1
+ fold=1
+ conf=./conf/model010.py
+ python -m src.cnn.main train ./conf/model010.py --fold 1 --gpu 1
----- 2019-11-04 01:19:30 -----
/home/xum/kaggle/kaggle-rsna-intracranial-hemorrhage/src/cnn/main.py train ./conf/model010.py --fold 1 --gpu 1
logpath: ./model/model010/train_fold1.log
mode: train
workdir: ./model/model010
fold: 1
batch size: 16
acc: 1
model: efficientnet-b7
pretrained: none
input channel: 3
Loaded pretrained weights for efficientnet-b7
Using loss weight: [2, 1, 1, 1, 1, 1]
loss: BCEWithLogitsLoss
optim: Adam
dataset_policy: all
window_policy: 4
read dataset (533384 records)
applied dataset_policy all (533384 records)
use default(random) sampler
dataset_policy: all
window_policy: 4
read dataset (132030 records)
applied dataset_policy all (132030 records)
use default(random) sampler
train data: loaded 533384 records
valid data: loaded 132030 records
last_epoch: -1
apex True
Selected optimization level O1: Insert automatic casts around Pytorch functions and Tensor methods.
Defaults for this optimization level are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic
Warning: multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback. Original ImportError was: ModuleNotFoundError("No module named 'amp_C'")
----- epoch 0 -----
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Skipping step, loss scaler 0 reducing loss scale to 32768.0
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