fromatting changes and bug fixes

frontend/server/src/main/java/org/pytorch/serve/util/codec/ModelRequestEncoder.java

@@ -34,12 +34,12 @@ protected void encode(ChannelHandlerContext ctx, BaseModelRequest msg, ByteBuf o
                 batchSize = 1;
             }
             out.writeInt(batchSize);
-            
+
             String handler = request.getHandler();
-            if (handler != null){
-            	buf = handler.getBytes(StandardCharsets.UTF_8);
+            if (handler != null) {
+                buf = handler.getBytes(StandardCharsets.UTF_8);
             }
-            
+
             out.writeInt(buf.length);
             out.writeBytes(buf);
 

frontend/server/src/main/java/org/pytorch/serve/wlm/WorkerLifeCycle.java

@@ -43,21 +43,20 @@ private String[] getEnvString(String cwd, String modelPath, String handler) {
         ArrayList<String> envList = new ArrayList<>();
         Pattern blackList = configManager.getBlacklistPattern();
         StringBuilder pythonPath = new StringBuilder();
-        
+
         if (handler != null && handler.contains(":")) {
-        	String handlerFile = handler;
+            String handlerFile = handler;
             handlerFile = handler.split(":")[0];
             if (handlerFile.contains("/")) {
                 handlerFile = handlerFile.substring(0, handlerFile.lastIndexOf('/'));
             }
-            
+
             pythonPath.append(handlerFile).append(File.pathSeparatorChar);
         }
 
-
         HashMap<String, String> environment = new HashMap<>(System.getenv());
         environment.putAll(configManager.getBackendConfiguration());
-        
+
         if (System.getenv("PYTHONPATH") != null) {
             pythonPath.append(System.getenv("PYTHONPATH")).append(File.pathSeparatorChar);
         }

setup.py

@@ -132,7 +132,7 @@ def run(self):
 if __name__ == '__main__':
     version = detect_model_server_version()
 
-    requirements = ['Pillow', 'psutil', 'future', 'model-archiver', 'torch']
+    requirements = ['Pillow', 'psutil', 'future', 'torch']
 
     setup(
         name='torchserve',

ts/torch_hanlder/base_handler.py

@@ -1,10 +1,13 @@
+
+
 """
 Base default handler to load torchscript or eager mode [state_dict] models
 Also, provides handle method per torch serve custom model specification
 """
 import abc
 import logging
 import os
+import json
 
 import torch
 
@@ -14,9 +17,10 @@
 class BaseHandler(abc.ABC):
     def __init__(self):
         self.model = None
+        self.mapping = None
         self.device = "cpu"
+        self.initialized = False
 
-    @abc.abstractmethod
     def initialize(self, ctx):
         """First try to load torchscript else load eager mode state_dict based model"""
 
@@ -26,7 +30,7 @@ def initialize(self, ctx):
         # Read model serialize/pt file
         model_pt_path = os.path.join(model_dir, "model.pt")
         if not os.path.isfile(model_pt_path):
-            raise RuntimeError("Missing model.pt file")
+            raise RuntimeError("Missing the model.pt file")
 
         try:
             logger.debug('Loading torchscript model')
@@ -35,7 +39,7 @@ def initialize(self, ctx):
             # Read model definition file
             model_def_path = os.path.join(model_dir, "model.py")
             if not os.path.isfile(model_def_path):
-                raise RuntimeError("Missing model.py file")
+                raise RuntimeError("Missing the model.py file")
 
             import importlib
             from ..utils import list_classes_from_module
@@ -53,6 +57,17 @@ def initialize(self, ctx):
 
         logger.debug('Model file {0} loaded successfully'.format(model_pt_path))
 
+        # Read the mapping file, index to object name
+        mapping_file_path = os.path.join(model_dir, "index_to_name.json")
+
+        if os.path.isfile(mapping_file_path):
+            with open(mapping_file_path) as f:
+                self.mapping = json.load(f)
+        else:
+            logger.warning('Missing the index_to_name.json file. Inference output will not include class name.')
+
+        self.initialized = True
+
     @abc.abstractmethod
     def preprocess(self, data):
         pass
@@ -64,15 +79,3 @@ def inference(self, data):
     @abc.abstractmethod
     def postprocess(self, data):
         pass
-
-    def handle(self, data, context):
-        if not self.initialized:
-            self.initialize(context)
-
-        if data is None:
-            return None
-
-        data = self.preprocess(data)
-        data = self.inference(data)
-        data = self.postprocess(data)
-        return data

ts/torch_hanlder/encoder.py

@@ -0,0 +1,107 @@
+# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the 'License'). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the 'license' file accompanying this file. This file is
+# distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+from __future__ import absolute_import
+
+import json
+from typing import Iterable  # noqa ignore=F401 imported but unused
+
+import numpy as np
+from six import BytesIO, StringIO
+
+JSON = 'application/json'
+CSV = "text/csv"
+OCTET_STREAM = "application/octet-stream"
+ANY = '*/*'
+NPY = 'application/x-npy'
+UTF8_TYPES = [JSON, CSV]
+
+
+def _array_to_json(array_like):  # type: (np.array or Iterable or int or float) -> str
+    """Convert an array-like object to JSON.
+
+    To understand better what an array-like object is see:
+    https://docs.scipy.org/doc/numpy/user/basics.creation.html#converting-python-array-like-objects-to-numpy-arrays
+
+    Args:
+        array_like (np.array or Iterable or int or float): array-like object to be converted to JSON.
+
+    Returns:
+        (str): object serialized to JSON
+    """
+
+    def default(_array_like):
+        if hasattr(_array_like, 'tolist'):
+            return _array_like.tolist()
+        return json.JSONEncoder().default(_array_like)
+
+    return json.dumps(array_like, default=default)
+
+
+def _array_to_npy(array_like):  # type: (np.array or Iterable or int or float) -> object
+    """Convert an array-like object to the NPY format.
+
+    To understand better what an array-like object is see:
+    https://docs.scipy.org/doc/numpy/user/basics.creation.html#converting-python-array-like-objects-to-numpy-arrays
+
+    Args:
+        array_like (np.array or Iterable or int or float): array-like object to be converted to NPY.
+
+    Returns:
+        (obj): NPY array.
+    """
+    buffer = BytesIO()
+    np.save(buffer, array_like)
+    return buffer.getvalue()
+
+
+def _array_to_csv(array_like):  # type: (np.array or Iterable or int or float) -> str
+    """Convert an array-like object to CSV.
+
+    To understand better what an array-like object is see:
+    https://docs.scipy.org/doc/numpy/user/basics.creation.html#converting-python-array-like-objects-to-numpy-arrays
+
+    Args:
+        array_like (np.array or Iterable or int or float): array-like object to be converted to CSV.
+
+    Returns:
+        (str): object serialized to CSV
+    """
+    stream = StringIO()
+    np.savetxt(stream, array_like, delimiter=',', fmt='%s')
+    return stream.getvalue()
+
+
+_encoder_map = {NPY: _array_to_npy,
+                CSV: _array_to_csv,
+                JSON: _array_to_json}
+
+
+def encode(array_like, content_type):
+    # type: (np.array or Iterable or int or float, str) -> np.array
+    """Encode an array-like object in a specific content_type to a numpy array.
+
+    To understand better what an array-like object is see:
+    https://docs.scipy.org/doc/numpy/user/basics.creation.html#converting-python-array-like-objects-to-numpy-arrays
+
+    Args:
+        array_like (np.array or Iterable or int or float): to be converted to numpy.
+        content_type (str): content type to be used.
+
+    Returns:
+        (np.array): object converted as numpy array.
+    """
+    try:
+        encoder = _encoder_map[content_type]
+        return encoder(array_like)
+    except KeyError:
+        raise Exception(content_type)

ts/torch_hanlder/image_classifier.py

@@ -19,30 +19,7 @@ class ImageClassifier(BaseHandler):
     """
 
     def __init__(self):
-        self.checkpoint_file_path = None
-        self.mapping = None
-        self.initialized = False
-
-    def initialize(self, context):
-        """
-            Load the model and mapping file to perform infernece.
-        """
-
-        # Initialize model
-        super(ImageClassifier, self).initialize(context)
-
-        properties = context.system_properties
-        model_dir = properties.get("model_dir")
-
-        # Read the mapping file, index to object name
-        mapping_file_path = os.path.join(model_dir, "index_to_name.json")
-        if not os.path.isfile(mapping_file_path):
-            raise RuntimeError("Missing the mapping file")
-        with open(mapping_file_path) as f:
-            self.mapping = json.load(f)
-
-        self.initialized = True
-
+        super(ImageClassifier, self).__init__()
 
     def preprocess(self, data):
         """
@@ -69,25 +46,50 @@ def inference(self, img, topk=5):
         '''
         # Convert 2D image to 1D vector
         img = np.expand_dims(img, 0)
-
         img = torch.from_numpy(img)
 
         self.model.eval()
         inputs = Variable(img).to(self.device)
-        logits = self.model.forward(inputs)
+        outputs = self.model.forward(inputs)
 
-        ps = F.softmax(logits, dim=1)
+        ps = F.softmax(outputs, dim=1)
         topk = ps.cpu().topk(topk)
 
         probs, classes = (e.data.numpy().squeeze().tolist() for e in topk)
 
         results = []
         for i in range(len(probs)):
-            tmp = dict()
-            tmp[self.mapping[str(classes[i])][1]] = probs[i]
-            # tmp[self.mapping[str(classes[i])]] = probs[i]
-            results.append(tmp)
+            if self.mapping:
+                tmp = dict()
+                if isinstance(self.mapping, dict) and isinstance(list(self.mapping.values())[0], list):
+                    tmp[self.mapping[str(classes[i])][1]] = probs[i]
+                elif isinstance(self.mapping, dict) and isinstance(list(self.mapping.values())[0], str):
+                    tmp[self.mapping[str(classes[i])]] = probs[i]
+                else:
+                    raise Exception('index_to_name mapping should be in "class":"label" json format')
+
+                results.append(tmp)
+            else:
+                results.append({str(classes[i]), probs[i]})
+
         return [results]
 
     def postprocess(self, inference_output):
         return inference_output
+
+
+_service = ImageClassifier()
+
+
+def handle(data, context):
+    if not _service.initialized:
+        _service.initialize(context)
+
+    if data is None:
+        return None
+
+    data = _service.preprocess(data)
+    data = _service.inference(data)
+    data = _service.postprocess(data)
+
+    return data