Implement Squeezenet using Squeezenet1.1

examples/onnx/squeezenet.py

@@ -0,0 +1,118 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License 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 th
+
+import os
+import numpy as np
+from PIL import Image
+
+from singa import device
+from singa import tensor
+from singa import autograd
+from singa import sonnx
+import onnx
+from utils import download_model, update_batch_size, check_exist_or_download
+
+import logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(message)s')
+
+
+def preprocess(img):
+    img = img.resize((224, 224))
+    img = img.crop((0, 0, 224, 224))
+    img = np.array(img).astype(np.float32) / 255.
+    img = np.rollaxis(img, 2, 0)
+    for channel, mean, std in zip(range(3), [0.485, 0.456, 0.406],
+                                  [0.229, 0.224, 0.225]):
+        img[channel, :, :] -= mean
+        img[channel, :, :] /= std
+    img = np.expand_dims(img, axis=0)
+    return img
+
+
+def get_image_label():
+    # download label
+    label_url = 'https://s3.amazonaws.com/onnx-model-zoo/synset.txt'
+    with open(check_exist_or_download(label_url), 'r') as f:
+        labels = [l.rstrip() for l in f]
+
+    # download image
+    image_url = 'https://s3.amazonaws.com/model-server/inputs/kitten.jpg'
+    img = Image.open(check_exist_or_download(image_url))
+    return img, labels
+
+
+class Infer:
+
+    def __init__(self, sg_ir):
+        self.sg_ir = sg_ir
+        for idx, tens in sg_ir.tensor_map.items():
+            # allow the tensors to be updated
+            tens.requires_grad = True
+            tens.stores_grad = True
+            sg_ir.tensor_map[idx] = tens
+
+    def forward(self, x):
+        return sg_ir.run([x])[0]
+
+
+if __name__ == "__main__":
+
+    url = 'https://github.com/onnx/models/raw/master/vision/classification/squeezenet/model/squeezenet1.1-7.tar.gz'
+    download_dir = '/tmp/'
+    model_path = os.path.join(download_dir, 'squeezenet1.1',
+                              'squeezenet1.1.onnx')
+
+    logging.info("onnx load model...")
+    download_model(url)
+    onnx_model = onnx.load(model_path)
+
+    # set batch size
+    onnx_model = update_batch_size(onnx_model, 1)
+
+    # prepare the model
+    logging.info("prepare model...")
+    dev = device.create_cuda_gpu()
+    sg_ir = sonnx.prepare(onnx_model, device=dev)
+    autograd.training = False
+    model = Infer(sg_ir)
+
+    # verify the test
+    # from utils import load_dataset
+    # inputs, ref_outputs = load_dataset(
+    #     os.path.join('/tmp', 'squeezenet1.1', 'test_data_set_0'))
+    # x_batch = tensor.Tensor(device=dev, data=inputs[0])
+    # outputs = model.forward(x_batch)
+    # for ref_o, o in zip(ref_outputs, outputs):
+    #     np.testing.assert_almost_equal(ref_o, tensor.to_numpy(o), 4)
+
+    # inference
+    logging.info("preprocessing...")
+    img, labels = get_image_label()
+    img = preprocess(img)
+
+    logging.info("model running...")
+    x_batch = tensor.Tensor(device=dev, data=img)
+    y = model.forward(x_batch)
+
+    logging.info("postprocessing...")
+    y = tensor.softmax(y)
+    scores = tensor.to_numpy(y)
+    scores = np.squeeze(scores)
+    a = np.argsort(scores)[::-1]
+    for i in a[0:5]:
+        logging.info('class=%s ; probability=%f' % (labels[i], scores[i]))