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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/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_relu (Activation) (None, 64, 224, 224) 0
_________________________________________________________________
block_1b_conv_1 (Conv2D) (None, 64, 224, 224) 36864
_________________________________________________________________
block_1b_relu (Activation) (None, 64, 224, 224) 0
_________________________________________________________________
block_2a_conv_1 (Conv2D) (None, 128, 112, 112) 73728
_________________________________________________________________
block_2a_relu (Activation) (None, 128, 112, 112) 0
_________________________________________________________________
block_2b_conv_1 (Conv2D) (None, 128, 112, 112) 147456
_________________________________________________________________
block_2b_relu (Activation) (None, 128, 112, 112) 0
_________________________________________________________________
block_3a_conv_1 (Conv2D) (None, 256, 56, 56) 294912
_________________________________________________________________
block_3a_relu (Activation) (None, 256, 56, 56) 0
_________________________________________________________________
block_3b_conv_1 (Conv2D) (None, 256, 56, 56) 589824
_________________________________________________________________
block_3b_relu (Activation) (None, 256, 56, 56) 0
_________________________________________________________________
block_3c_conv_1 (Conv2D) (None, 256, 56, 56) 589824
_________________________________________________________________
block_3c_relu (Activation) (None, 256, 56, 56) 0
_________________________________________________________________
block_3d_conv_1 (Conv2D) (None, 256, 56, 56) 589824
_________________________________________________________________
block_3d_relu (Activation) (None, 256, 56, 56) 0
_________________________________________________________________
block_4a_conv_1 (Conv2D) (None, 512, 28, 28) 1179648
_________________________________________________________________
block_4a_relu (Activation) (None, 512, 28, 28) 0
_________________________________________________________________
block_4b_conv_1 (Conv2D) (None, 512, 28, 28) 2359296
_________________________________________________________________
block_4b_relu (Activation) (None, 512, 28, 28) 0
_________________________________________________________________
block_4c_conv_1 (Conv2D) (None, 512, 28, 28) 2359296
_________________________________________________________________
block_4c_relu (Activation) (None, 512, 28, 28) 0
_________________________________________________________________
block_4d_conv_1 (Conv2D) (None, 512, 28, 28) 2359296
_________________________________________________________________
block_4d_relu (Activation) (None, 512, 28, 28) 0
_________________________________________________________________
block_5a_conv_1 (Conv2D) (None, 512, 14, 14) 2359296
_________________________________________________________________
block_5a_relu (Activation) (None, 512, 14, 14) 0
_________________________________________________________________
block_5b_conv_1 (Conv2D) (None, 512, 14, 14) 2359296
_________________________________________________________________
block_5b_relu (Activation) (None, 512, 14, 14) 0
_________________________________________________________________
block_5c_conv_1 (Conv2D) (None, 512, 14, 14) 2359296
_________________________________________________________________
block_5c_relu (Activation) (None, 512, 14, 14) 0
_________________________________________________________________
block_5d_conv_1 (Conv2D) (None, 512, 14, 14) 2359296
_________________________________________________________________
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: 20,020,932
Trainable params: 20,020,932
Non-trainable params: 0
_________________________________________________________________
Found 36 images belonging to 4 classes.
Evaluation Loss: 14.586379369099935
Evaluation Top K accuracy: 0.1111111111111111
Found 36 images belonging to 4 classes.
Confusion Matrix
[[ 0 30 0]
[ 0 4 0]
[ 0 2 0]]
Classification Report
precision recall f1-score support
C 0.00 0.00 0.00 30
Q 0.00 0.00 0.00 0
R 0.11 1.00 0.20 4
U 0.00 0.00 0.00 2
micro avg 0.11 0.11 0.11 36
macro avg 0.03 0.25 0.05 36
weighted avg 0.01 0.11 0.02 36
|
