testing dataset ok; trainer.py take effects

trainer/config_files/4digit_config.yaml

@@ -0,0 +1,46 @@
+number: '0123456789'
+experiment_name: '4digit'
+symbol: ""
+lang_char: ''
+train_data: 'all_data'
+valid_data: 'all_data/4digit_valid'
+manualSeed: 1111
+workers: 6
+batch_size: 32 #32
+num_iter: 3000
+valInterval: 5
+# saved_model: '' #'saved_models/en_filtered/iter_300000.pth'
+svaed_model: 'saved_models/4digit/iter_3000.pth'
+FT: False
+optim: False # default is Adadelta
+lr: 1.
+beta1: 0.9
+rho: 0.95
+eps: 0.00000001
+grad_clip: 5
+#Data processing
+select_data: '4digit_train' # this is dataset folder in train_data
+batch_ratio: '1' 
+total_data_usage_ratio: 1.0
+batch_max_length: 34 
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: False
+sensitive: True
+PAD: True
+contrast_adjust: 0.0
+data_filtering_off: False
+# Model Architecture
+Transformation: 'TPS'
+FeatureExtraction: 'ResNet'
+SequenceModeling: 'BiLSTM'
+Prediction: 'CTC'
+num_fiducial: 20
+input_channel: 1
+output_channel: 256
+hidden_size: 256
+decode: 'greedy'
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False

trainer/config_files/en_filtered_config.yaml

@@ -7,8 +7,8 @@ valid_data: 'all_data/valid'
 manualSeed: 1111
 workers: 6
 batch_size: 32 #32
-num_iter: 300000
-valInterval: 20000
+num_iter: 300
+valInterval: 5
 saved_model: '' #'saved_models/en_filtered/iter_300000.pth'
 FT: False
 optim: False # default is Adadelta
@@ -31,8 +31,8 @@ PAD: True
 contrast_adjust: 0.0
 data_filtering_off: False
 # Model Architecture
-Transformation: 'ResNet'
-FeatureExtraction: 'VGG'
+Transformation: 'None'
+FeatureExtraction: 'ResNet'
 SequenceModeling: 'BiLSTM'
 Prediction: 'CTC'
 num_fiducial: 20