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06-tutorial-classification/6.4-eval-with-model.md

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+# 使用训练的模型运行推理服务
+
+## 下载模型
+
+在之前创建的训练进行过程中，经过一些 epoch 会生成模型，训练代码中的 AVA-SDK 会将这些模型上传至 AVA 平台。用户在 "模型列表 -> 训练产生模型" 的页面中，可以查看到对应的训练任务，以及训练任务产生的模型列表，如下图：
+
+![](/images/ch-06/6.4/snapshot-models.png)
+
+点击右侧的 "下载"，即可将训练产生的模型下载至本地。
+
+```bash
+➜  ll models
+total 6256
+-rw-r--r--@ 1 quxiao  staff   2.7M  7  1 10:35 5b337b3a5710da0001333594-snapshot-model_epoch_30.model.tar.gz
+```
+
+解压之后，就可以看到模型对应的网络结构和权重文件了。
+
+```bash
+➜  tar -zxvf 5b337b3a5710da0001333594-snapshot-model_epoch_30.model.tar.gz
+x snapshot-symbol.json
+x snapshot-0030.params
+➜  ll
+total 12464
+-rw-r--r--@ 1 quxiao  staff   2.7M  7  1 10:35 5b337b3a5710da0001333594-snapshot-model_epoch_30.model.tar.gz
+-rw-r--r--@ 1 quxiao  staff   2.9M  6 27 20:24 snapshot-0030.params
+-rw-r--r--@ 1 quxiao  staff   105K  6 27 20:24 snapshot-symbol.json
+```
+
+## 编写推理代码
+
+接着，我们可以根据 mxnet 提供的教程，对上面的推理代码进行一些修改。demo 代码如下：
+
+```python
+import os
+import base64
+import mxnet as mx
+import numpy as np
+from collections import namedtuple
+from bottle import route, run
+import urllib
+from urlparse import urlparse
+
+Batch = namedtuple('Batch', ['data'])
+
+
+class ImagePredictor (object):
+    def __init__(self):
+        self.Batch = namedtuple('Batch', ['data'])
+        self.model_dir = "model"
+        self.data_dir = "data"
+        self.mod = None
+        self.labels = []
+        self.symbol_filename = 'deploy.symbol.json'
+        self.weight_filename = 'weight.params'
+        self.label_filename = 'labels.csv'
+
+    def load_model(self, custom_model_dir=None, symbol_fn=None, weight_fn=None, label_fn=None):
+        if custom_model_dir != None:
+            self.model_dir = custom_model_dir
+        if symbol_fn != None:
+            self.symbol_filename = symbol_fn
+        if weight_fn != None:
+            self.weight_filename = weight_fn
+        if label_fn != None:
+            self.label_filename = label_fn
+
+        print "set cpu/gpu model"
+        # set the context on CPU, switch to GPU if there is one available
+        ctx = mx.cpu()
+
+        print "loading model"
+        sym, arg_params, aux_params, labels = self._load_model()
+        self.mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
+        self.mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))], 
+                label_shapes=self.mod._label_shapes)
+        self.mod.set_params(arg_params, aux_params, allow_missing=True)
+        self.labels = labels
+
+    def _load_model(self):
+        sym = mx.symbol.load(os.path.join(self.model_dir, self.symbol_filename))
+        save_dict = mx.ndarray.load(os.path.join(self.model_dir, self.weight_filename))
+        arg_params = {}
+        aux_params = {}
+        for k, v in save_dict.items():
+            tp, name = k.split(':', 1)
+            if tp == 'arg':
+                arg_params[name] = v
+            if tp == 'aux':
+                aux_params[name] = v
+
+        labels = []
+        with open(os.path.join(self.model_dir, self.label_filename), 'r') as f:
+            labels = [l.rstrip() for l in f]
+
+        return sym, arg_params, aux_params, labels
+
+    def get_image(self, uri):
+        # download and show the image
+        # fname = mx.test_utils.download(url, dirname=self.data_dir)
+        # img = mx.image.imread(fname)
+        content = urllib.urlopen(uri).read()
+        img = mx.image.imdecode(content)
+        if img is None:
+            return None
+        # convert into format (batch, RGB, width, height)
+        img = mx.image.imresize(img, 224, 224) # resize
+        img = img.transpose((2, 0, 1)) # Channel first
+        img = img.expand_dims(axis=0) # batchify
+        return img
+
+    def predict(self, uri, topN=5):
+        img = self.get_image(uri)
+        # compute the predict probabilities
+        self.mod.forward(Batch([img]))
+        prob = self.mod.get_outputs()[0].asnumpy()
+        # print the top-5
+        prob = np.squeeze(prob)
+        a = np.argsort(prob)[::-1]
+        ret = []
+        for i in a[0:topN]:
+            print('probability=%f, class=%s' %(prob[i], self.labels[i]))
+            ret.append({'prob': float(prob[i]), 'label': self.labels[i]})
+        return ret
+
+```
+
+另外，我们还需要准备一份分类新的文件 `label.csv`，包含了 cifar10 数据集中数据分类 ID 和名称的映射关系，文件可参考：https://github.com/quxiao/cifar10-example/blob/master/image-predict/models/ava-snapshot-model/labels.csv
+
+## 查看推理结果
+
+之后，我们运行几张图片的推理：
+
+```python
+def main():
+    worker = predictor.ImagePredictor()
+    worker.load_model(
+        custom_model_dir='models/ava-snapshot-model',
+        symbol_fn='snapshot-symbol.json',
+        weight_fn='snapshot-0030.params',
+        label_fn='labels.csv')
+    worker.predict('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true')
+    worker.predict('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/dog.jpg?raw=true')
+```
+
+就可以得到推理的结果：
+
+```
+➜  python local.py
+set cpu/gpu model
+loading model
+[12:15:51] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v1.1.0. Attempting to upgrade...
+[12:15:51] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
+uri: https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true
+probability=0.533429, class=n01491361 tiger shark, Galeocerdo cuvieri
+probability=0.309057, class=n01494475 hammerhead, hammerhead shark
+probability=0.089500, class=n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+probability=0.042574, class=n01498041 stingray
+probability=0.024384, class=n01496331 electric ray, crampfish, numbfish, torpedo
+
+uri: https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/dog.jpg?raw=true
+probability=0.656869, class=n01491361 tiger shark, Galeocerdo cuvieri
+probability=0.165087, class=n01496331 electric ray, crampfish, numbfish, torpedo
+probability=0.063514, class=n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+probability=0.056640, class=n01494475 hammerhead, hammerhead shark
+probability=0.037440, class=n01498041 stingray
+```
+
+PS：从结果可以看出，推理的效果与预期存在差距，这是由多方面原因造成的，例如训练数据集编码的参数、训练代码的超参等等。