Customize Model via subclassing, clean up public-facing Model+Viz api

picasso/examples/keras-vgg16/config.py

@@ -1,12 +1,13 @@
+# Note: By default, Flask doesn't know that this file exists.  If you want
+# Flask to load the settings you specify here, you must set the environment
+# variable `PICASSO_SETTINGS` to point to this file.  E.g.:
+#
+#   export PICASSO_SETTINGS=/path/to/examples/keras-vgg16/config.py
+#
 import os
 
 base_dir = os.path.dirname(os.path.abspath(__file__))
 
-BACKEND_ML = 'keras'
-BACKEND_PREPROCESSOR_NAME = 'preprocess'
-BACKEND_PREPROCESSOR_PATH = os.path.join(base_dir, 'util.py')
-BACKEND_POSTPROCESSOR_NAME = 'postprocess'
-BACKEND_POSTPROCESSOR_PATH = os.path.join(base_dir, 'util.py')
-BACKEND_PROB_DECODER_NAME = 'prob_decode'
-BACKEND_PROB_DECODER_PATH = os.path.join(base_dir, 'util.py')
+MODEL_CLS_PATH = os.path.join(base_dir, 'model.py')
+MODEL_CLS_NAME = 'KerasVGG16Model'
 DATA_DIR = os.path.join(base_dir, 'data-volume')

picasso/examples/keras-vgg16/model.py

@@ -0,0 +1,44 @@
+from keras.applications.imagenet_utils import (decode_predictions,
+                                               preprocess_input)
+import keras.applications.imagenet_utils
+import numpy as np
+from PIL import Image
+
+from picasso.ml_frameworks.keras.model import KerasModel
+
+
+VGG16_DIM = (224, 224, 3)
+
+
+class KerasVGG16Model(KerasModel):
+
+    def preprocess(self, targets):
+        image_arrays = []
+        for target in targets:
+            im = target.resize(VGG16_DIM[:2], Image.ANTIALIAS)
+            im = im.convert('RGB')
+            arr = np.array(im).astype('float32')
+            image_arrays.append(arr)
+
+        all_targets = np.array(image_arrays)
+        return preprocess_input(all_targets)
+
+    def decode_prob(self, probability_array):
+        r = decode_predictions(probability_array, top=self.top_probs)
+        results = [
+            [{'code': entry[0],
+              'name': entry[1],
+              'prob': '{:.3f}'.format(entry[2])}
+             for entry in row]
+            for row in r
+        ]
+        classes = keras.applications.imagenet_utils.CLASS_INDEX
+        class_keys = list(classes.keys())
+        class_values = list(classes.values())
+
+        for result in results:
+            for entry in result:
+                entry['index'] = int(
+                    class_keys[class_values.index([entry['code'],
+                                                   entry['name']])])
+        return results

picasso/examples/keras/config.py

@@ -1,19 +1,13 @@
-# Note: this settings file duplicates the default settings in the top-level
-# file `settings.py`.  If you want to modify settings here, you must export the
-# path to this file:
+# Note: By default, Flask doesn't know that this file exists.  If you want
+# Flask to load the settings you specify here, you must set the environment
+# variable `PICASSO_SETTINGS` to point to this file.  E.g.:
 #
-# export PICASSO_SETTINGS=/path/to/picasso/picasso/examples/keras/config.py
+#   export PICASSO_SETTINGS=/path/to/examples/keras/config.py
 #
-# otherwise, these settings will not be loaded.
 import os
 
 base_dir = os.path.dirname(os.path.abspath(__file__))
 
-BACKEND_ML = 'keras'
-BACKEND_PREPROCESSOR_NAME = 'preprocess'
-BACKEND_PREPROCESSOR_PATH = os.path.join(base_dir, 'util.py')
-BACKEND_POSTPROCESSOR_NAME = 'postprocess'
-BACKEND_POSTPROCESSOR_PATH = os.path.join(base_dir, 'util.py')
-BACKEND_PROB_DECODER_NAME = 'prob_decode'
-BACKEND_PROB_DECODER_PATH = os.path.join(base_dir, 'util.py')
+MODEL_CLS_PATH = os.path.join(base_dir, 'model.py')
+MODEL_CLS_NAME = 'KerasMNISTModel'
 DATA_DIR = os.path.join(base_dir, 'data-volume')

picasso/examples/keras/model.py

@@ -0,0 +1,30 @@
+import numpy as np
+from PIL import Image
+
+from picasso.ml_frameworks.keras.model import KerasModel
+
+
+MNIST_DIM = (28, 28)
+
+
+class KerasMNISTModel(KerasModel):
+
+    def preprocess(self, raw_inputs):
+        """Convert images into the format required by our model.
+
+        Our model requires that inputs be grayscale (mode 'L'), be resized to
+        `MNIST_DIM`, and be represented as float32 numpy arrays in range
+        [0, 1].
+
+        """
+        image_arrays = []
+        for raw_im in raw_inputs:
+            im = raw_im.convert('L')
+            im = im.resize(MNIST_DIM, Image.ANTIALIAS)
+            arr = np.array(im)
+            image_arrays.append(arr)
+
+        inputs = np.array(image_arrays)
+        return inputs.reshape(len(inputs),
+                              MNIST_DIM[0],
+                              MNIST_DIM[1], 1).astype('float32') / 255

picasso/examples/tensorflow/config.py

@@ -1,14 +1,13 @@
+# Note: By default, Flask doesn't know that this file exists.  If you want
+# Flask to load the settings you specify here, you must set the environment
+# variable `PICASSO_SETTINGS` to point to this file.  E.g.:
+#
+#   export PICASSO_SETTINGS=/path/to/examples/tensorflow/config.py
+#
 import os
 
 base_dir = os.path.dirname(os.path.abspath(__file__))
 
-BACKEND_ML = 'tensorflow'
-BACKEND_PREPROCESSOR_NAME = 'preprocess'
-BACKEND_PREPROCESSOR_PATH = os.path.join(base_dir, 'util.py')
-BACKEND_POSTPROCESSOR_NAME = 'postprocess'
-BACKEND_POSTPROCESSOR_PATH = os.path.join(base_dir, 'util.py')
-BACKEND_PROB_DECODER_NAME = 'prob_decode'
-BACKEND_PROB_DECODER_PATH = os.path.join(base_dir, 'util.py')
-BACKEND_TF_PREDICT_VAR = 'Softmax:0'
-BACKEND_TF_INPUT_VAR = 'convolution2d_input_1:0'
+MODEL_CLS_PATH = os.path.join(base_dir, 'model.py')
+MODEL_CLS_NAME = 'TensorflowMNISTModel'
 DATA_DIR = os.path.join(base_dir, 'data-volume')

picasso/examples/tensorflow/model.py

@@ -0,0 +1,34 @@
+import numpy as np
+from PIL import Image
+
+from picasso.ml_frameworks.tensorflow.model import TFModel
+
+
+MNIST_DIM = (28, 28)
+
+
+class TensorflowMNISTModel(TFModel):
+
+    TF_INPUT_VAR = 'convolution2d_input_1:0'
+
+    TF_PREDICT_VAR = 'Softmax:0'
+
+    def preprocess(self, raw_inputs):
+        """Convert images into the format required by our model.
+
+        Our model requires that inputs be grayscale (mode 'L'), be resized to
+        `MNIST_DIM`, and be represented as float32 numpy arrays in range
+        [0, 1].
+
+        """
+        image_arrays = []
+        for raw_im in raw_inputs:
+            im = raw_im.convert('L')
+            im = im.resize(MNIST_DIM, Image.ANTIALIAS)
+            arr = np.array(im)
+            image_arrays.append(arr)
+
+        inputs = np.array(image_arrays)
+        return inputs.reshape(len(inputs),
+                              MNIST_DIM[0],
+                              MNIST_DIM[1], 1).astype('float32') / 255