ds: done

ds/25-1/5/config.txt

@@ -0,0 +1,50 @@
+model_config {
+  arch: "vgg"
+  n_layers: 19
+  use_batch_norm: True
+  freeze_blocks: 0
+  input_image_size: "3,224,224"
+}
+
+train_config {
+  train_dataset_path: "/workspace/tao-experiments/data/train"
+  val_dataset_path: "/workspace/tao-experiments/data/val"
+  pretrained_model_path: "/workspace/tao-experiments/classification/pretrained_vgg19/pretrained_classification_vvgg19/vgg_19.hdf5"
+  optimizer {
+    sgd {
+    lr: 0.01
+    decay: 0.0
+    momentum: 0.9
+    nesterov: False
+    }
+  }
+  n_epochs: 5
+  batch_size_per_gpu: 32
+  n_workers: 8
+  enable_random_crop: False
+  enable_center_crop: False
+  enable_color_augmentation: False
+  preprocess_mode: "caffe"
+  reg_config {
+    type: "L2"
+    scope: "Conv2D, Dense"
+    weight_decay: 0.00005
+  }
+  lr_config {
+    step {
+      learning_rate: 0.006
+      step_size: 10
+      gamma: 0.1
+    }
+  }
+}
+
+eval_config {
+  eval_dataset_path: "/workspace/tao-experiments/data/val"
+  model_path: "/workspace/tao-experiments/classification/vgg19/weights/vgg_005.hdf5"
+  top_k: 1
+  batch_size: 32
+  n_workers: 8
+  enable_center_crop: False
+}
+

ds/25-1/5/log_file.txt

@@ -0,0 +1,226 @@
+Using TensorFlow backend.
+WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
+WARNING:tensorflow:TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable  TF_ALLOW_IOLIBS=1.
+WARNING: TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable  TF_ALLOW_IOLIBS=1.
+WARNING:tensorflow:TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable  TF_ALLOW_IOLIBS=1.
+WARNING: TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable  TF_ALLOW_IOLIBS=1.
+WARNING:tensorflow:TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable  TF_ALLOW_IOLIBS=1.
+WARNING: TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable  TF_ALLOW_IOLIBS=1.
+/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:150: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
+  def random_hue(img, max_delta=10.0):
+/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:173: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
+  def random_saturation(img, max_shift):
+/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:183: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
+  def random_contrast(img, center, max_contrast_scale):
+/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/utils/helper.py:192: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
+  def random_shift(x_img, shift_stddev):
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:44: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:44: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:46: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:46: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/makenet/scripts/evaluate.py:157: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.
+
+INFO: Loading experiment spec at /workspace/tao-experiments/spec_files/vgg19/config.txt.
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:245: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:245: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/third_party/keras/tensorflow_backend.py:199: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/third_party/keras/tensorflow_backend.py:199: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
+
+WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.
+
+WARNING: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.
+
+INFO: Processing dataset (evaluation): /workspace/tao-experiments/data/val
+INFO: Calculating per-class P/R and confusion matrix. It may take a while...
+/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+_________________________________________________________________
+Layer (type)                 Output Shape              Param #   
+=================================================================
+input_1 (InputLayer)         (None, 3, 224, 224)       0         
+_________________________________________________________________
+block_1a_conv_1 (Conv2D)     (None, 64, 224, 224)      1728      
+_________________________________________________________________
+block_1a_bn_1 (BatchNormaliz (None, 64, 224, 224)      256       
+_________________________________________________________________
+block_1a_relu (Activation)   (None, 64, 224, 224)      0         
+_________________________________________________________________
+block_1b_conv_1 (Conv2D)     (None, 64, 224, 224)      36864     
+_________________________________________________________________
+block_1b_bn_1 (BatchNormaliz (None, 64, 224, 224)      256       
+_________________________________________________________________
+block_1b_relu (Activation)   (None, 64, 224, 224)      0         
+_________________________________________________________________
+block_2a_conv_1 (Conv2D)     (None, 128, 112, 112)     73728     
+_________________________________________________________________
+block_2a_bn_1 (BatchNormaliz (None, 128, 112, 112)     512       
+_________________________________________________________________
+block_2a_relu (Activation)   (None, 128, 112, 112)     0         
+_________________________________________________________________
+block_2b_conv_1 (Conv2D)     (None, 128, 112, 112)     147456    
+_________________________________________________________________
+block_2b_bn_1 (BatchNormaliz (None, 128, 112, 112)     512       
+_________________________________________________________________
+block_2b_relu (Activation)   (None, 128, 112, 112)     0         
+_________________________________________________________________
+block_3a_conv_1 (Conv2D)     (None, 256, 56, 56)       294912    
+_________________________________________________________________
+block_3a_bn_1 (BatchNormaliz (None, 256, 56, 56)       1024      
+_________________________________________________________________
+block_3a_relu (Activation)   (None, 256, 56, 56)       0         
+_________________________________________________________________
+block_3b_conv_1 (Conv2D)     (None, 256, 56, 56)       589824    
+_________________________________________________________________
+block_3b_bn_1 (BatchNormaliz (None, 256, 56, 56)       1024      
+_________________________________________________________________
+block_3b_relu (Activation)   (None, 256, 56, 56)       0         
+_________________________________________________________________
+block_3c_conv_1 (Conv2D)     (None, 256, 56, 56)       589824    
+_________________________________________________________________
+block_3c_bn_1 (BatchNormaliz (None, 256, 56, 56)       1024      
+_________________________________________________________________
+block_3c_relu (Activation)   (None, 256, 56, 56)       0         
+_________________________________________________________________
+block_3d_conv_1 (Conv2D)     (None, 256, 56, 56)       589824    
+_________________________________________________________________
+block_3d_bn_1 (BatchNormaliz (None, 256, 56, 56)       1024      
+_________________________________________________________________
+block_3d_relu (Activation)   (None, 256, 56, 56)       0         
+_________________________________________________________________
+block_4a_conv_1 (Conv2D)     (None, 512, 28, 28)       1179648   
+_________________________________________________________________
+block_4a_bn_1 (BatchNormaliz (None, 512, 28, 28)       2048      
+_________________________________________________________________
+block_4a_relu (Activation)   (None, 512, 28, 28)       0         
+_________________________________________________________________
+block_4b_conv_1 (Conv2D)     (None, 512, 28, 28)       2359296   
+_________________________________________________________________
+block_4b_bn_1 (BatchNormaliz (None, 512, 28, 28)       2048      
+_________________________________________________________________
+block_4b_relu (Activation)   (None, 512, 28, 28)       0         
+_________________________________________________________________
+block_4c_conv_1 (Conv2D)     (None, 512, 28, 28)       2359296   
+_________________________________________________________________
+block_4c_bn_1 (BatchNormaliz (None, 512, 28, 28)       2048      
+_________________________________________________________________
+block_4c_relu (Activation)   (None, 512, 28, 28)       0         
+_________________________________________________________________
+block_4d_conv_1 (Conv2D)     (None, 512, 28, 28)       2359296   
+_________________________________________________________________
+block_4d_bn_1 (BatchNormaliz (None, 512, 28, 28)       2048      
+_________________________________________________________________
+block_4d_relu (Activation)   (None, 512, 28, 28)       0         
+_________________________________________________________________
+block_5a_conv_1 (Conv2D)     (None, 512, 14, 14)       2359296   
+_________________________________________________________________
+block_5a_bn_1 (BatchNormaliz (None, 512, 14, 14)       2048      
+_________________________________________________________________
+block_5a_relu (Activation)   (None, 512, 14, 14)       0         
+_________________________________________________________________
+block_5b_conv_1 (Conv2D)     (None, 512, 14, 14)       2359296   
+_________________________________________________________________
+block_5b_bn_1 (BatchNormaliz (None, 512, 14, 14)       2048      
+_________________________________________________________________
+block_5b_relu (Activation)   (None, 512, 14, 14)       0         
+_________________________________________________________________
+block_5c_conv_1 (Conv2D)     (None, 512, 14, 14)       2359296   
+_________________________________________________________________
+block_5c_bn_1 (BatchNormaliz (None, 512, 14, 14)       2048      
+_________________________________________________________________
+block_5c_relu (Activation)   (None, 512, 14, 14)       0         
+_________________________________________________________________
+block_5d_conv_1 (Conv2D)     (None, 512, 14, 14)       2359296   
+_________________________________________________________________
+block_5d_bn_1 (BatchNormaliz (None, 512, 14, 14)       2048      
+_________________________________________________________________
+block_5d_relu (Activation)   (None, 512, 14, 14)       0         
+_________________________________________________________________
+avg_pool (AveragePooling2D)  (None, 512, 1, 1)         0         
+_________________________________________________________________
+flatten (Flatten)            (None, 512)               0         
+_________________________________________________________________
+predictions (Dense)          (None, 4)                 2052      
+=================================================================
+Total params: 20,042,948
+Trainable params: 20,031,940
+Non-trainable params: 11,008
+_________________________________________________________________
+Found 36 images belonging to 4 classes.
+Evaluation Loss: 1.073743396335178
+Evaluation Top K accuracy: 0.8333333333333334
+Found 36 images belonging to 4 classes.
+Confusion Matrix
+[[30  0  0]
+ [ 4  0  0]
+ [ 2  0  0]]
+Classification Report
+              precision    recall  f1-score   support
+
+           C       0.83      1.00      0.91        30
+           Q       0.00      0.00      0.00         0
+           R       0.00      0.00      0.00         4
+           U       0.00      0.00      0.00         2
+
+   micro avg       0.83      0.83      0.83        36
+   macro avg       0.21      0.25      0.23        36
+weighted avg       0.69      0.83      0.76        36
+