Update examples

(cherry picked from commit 6aea035)

Author: ospillinger

examples/pytorch/image-classifier/predictor.py

@@ -1,9 +1,9 @@
 import requests
+import torch
+import torchvision
+from torchvision import transforms
 from PIL import Image
 from io import BytesIO
-from torchvision import transforms
-import torchvision
-import torch
 
 model = torchvision.models.alexnet(pretrained=True)
 model.eval()

examples/pytorch/image-classifier/sample.json

@@ -1,3 +1,3 @@
 {
-    "url": "https://bowwowinsurance.com.au/wp-content/uploads/2018/10/akita-700x700.jpg"
+    "url": "https://i.imgur.com/PzXprwl.jpg"
 }

examples/pytorch/iris-classifier/src/predictor.py

@@ -2,7 +2,6 @@
 import torch
 from model import IrisNet
 
-labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
 
 model = IrisNet()
 
@@ -12,6 +11,9 @@ def init(model_path, metadata):
     model.eval()
 
 
+labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
+
+
 def predict(sample, metadata):
     input_tensor = torch.FloatTensor(
         [

examples/pytorch/text-generator/cortex.yaml

@@ -6,7 +6,7 @@
   predictor:
     path: predictor.py
     metadata:
-      num_words: 20
+      num_words: 50
       device: cuda  # use "cpu" to run on CPUs
   compute:
     gpu: 1

examples/pytorch/text-generator/predictor.py

@@ -1,18 +1,18 @@
+# This file includes code which was modified from https://github.com/huggingface/transformers/blob/master/examples/run_generation.py
+
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-import numpy as np
-import argparse
-import logging
-from tqdm import trange
-import torch.nn.functional as F
 import torch
+import torch.nn.functional as F
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from tqdm import trange
 
+
+tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
 model = GPT2LMHeadModel.from_pretrained("distilgpt2")
 model.eval()
-tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
 
-# adapted from: https://github.com/huggingface/transformers/blob/master/examples/run_generation.py
+
 def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
     """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
         Args:

examples/tensorflow/image-classifier/sample.json

@@ -1,3 +1,3 @@
 {
-  "url": "https://bowwowinsurance.com.au/wp-content/uploads/2018/10/akita-700x700.jpg"
+  "url": "https://i.imgur.com/PzXprwl.jpg"
 }