updated freeze file and readme

Author: zeryx

README.md

@@ -129,18 +129,19 @@ from PIL import Image
 import json
 from torchvision import models, transforms
 
-def load_labels(label_path, client):
-    local_path = client.file(label_path).getFile().name
-    with open(local_path) as f:
+
+client = Algorithmia.client()
+
+def load_labels(label_path):
+    with open(label_path) as f:
         labels = json.load(f)
     labels = [labels[str(k)][1] for k in range(len(labels))]
     return labels
 
 
-def load_model(model_paths, client):
+def load_model(model_path):
     model = models.squeezenet1_1()
-    local_file = client.file(model_paths["filepath"]).getFile().name
-    weights = torch.load(local_file)
+    weights = torch.load(model_path)
     model.load_state_dict(weights)
     return model.float().eval()
 
@@ -176,26 +177,25 @@ def infer_image(image_url, n, globals):
 
 def load(manifest):
 
-    globals = {}
-    client = Algorithmia.client()
-    globals["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
-    globals["model"] = load_model(manifest["squeezenet"], client)
-    globals["labels"] = load_labels(manifest["label_file"], client)
-    return globals
+    state = {}
+    state["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
+    state["model"] = load_model(manifest.get_model("squeezenet"))
+    state["labels"] = load_labels(manifest.get_model("labels"))
+    return state
 
 
-def apply(input, globals):
+def apply(input, state):
     if isinstance(input, dict):
         if "n" in input:
             n = input["n"]
         else:
             n = 3
         if "data" in input:
             if isinstance(input["data"], str):
-                output = infer_image(input["data"], n, globals)
+                output = infer_image(input["data"], n, state)
             elif isinstance(input["data"], list):
                 for row in input["data"]:
-                    row["predictions"] = infer_image(row["image_url"], n, globals)
+                    row["predictions"] = infer_image(row["image_url"], n, state)
                 output = input["data"]
             else:
                 raise Exception("\"data\" must be a image url or a list of image urls (with labels)")
@@ -206,7 +206,7 @@ def apply(input, globals):
         raise Exception("input must be a json object")
 
 
-algorithm = ADK(apply_func=apply, load_func=load)
+algorithm = ADK(apply_func=apply, load_func=load, client=client)
 algorithm.init({"data": "https://i.imgur.com/bXdORXl.jpeg"})
 
 ```

examples/pytorch_image_classification/model_manifest.json.freeze

@@ -1 +1,34 @@
-{"required_files": [{"name": "squeezenet", "source_uri": "data://AlgorithmiaSE/image_cassification_demo/squeezenet1_1-f364aa15.pth", "fail_on_tamper": true, "metadata": {"dataset_md5_checksum": "46a44d32d2c5c07f7f66324bef4c7266"}, "md5_checksum": "46a44d32d2c5c07f7f66324bef4c7266"}, {"name": "labels", "source_uri": "data://AlgorithmiaSE/image_cassification_demo/imagenet_class_index.json", "fail_on_tamper": true, "metadata": {"dataset_md5_checksum": "46a44d32d2c5c07f7f66324bef4c7266"}, "md5_checksum": "c2c37ea517e94d9795004a39431a14cb"}], "optional_files": [{"name": "mobilenet", "source_uri": "data://AlgorithmiaSE/image_cassification_demo/mobilenet_v2-b0353104.pth", "fail_on_tamper": false, "metadata": {"dataset_md5_checksum": "46a44d32d2c5c07f7f66324bef4c7266"}}], "timestamp": "1633450866.985464", "lock_checksum": "24f5eca888d87661ca6fc08042e40cb7"}
+{
+   "required_files":[
+      {
+         "name":"squeezenet",
+         "source_uri":"data://AlgorithmiaSE/image_cassification_demo/squeezenet1_1-f364aa15.pth",
+         "fail_on_tamper":true,
+         "metadata":{
+            "dataset_md5_checksum":"46a44d32d2c5c07f7f66324bef4c7266"
+         },
+         "md5_checksum":"46a44d32d2c5c07f7f66324bef4c7266"
+      },
+      {
+         "name":"labels",
+         "source_uri":"data://AlgorithmiaSE/image_cassification_demo/imagenet_class_index.json",
+         "fail_on_tamper":true,
+         "metadata":{
+            "dataset_md5_checksum":"46a44d32d2c5c07f7f66324bef4c7266"
+         },
+         "md5_checksum":"c2c37ea517e94d9795004a39431a14cb"
+      }
+   ],
+   "optional_files":[
+      {
+         "name":"mobilenet",
+         "source_uri":"data://AlgorithmiaSE/image_cassification_demo/mobilenet_v2-b0353104.pth",
+         "fail_on_tamper":false,
+         "metadata":{
+            "dataset_md5_checksum":"46a44d32d2c5c07f7f66324bef4c7266"
+         }
+      }
+   ],
+   "timestamp":"1633450866.985464",
+   "lock_checksum":"24f5eca888d87661ca6fc08042e40cb7"
+}