Add python current path for inference

02.recognize_digits/README.en.md

@@ -321,14 +321,17 @@ After training is done, user can use the trained model to classify images. The f
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file).convert('L')
     im = im.resize((28, 28), Image.ANTIALIAS)
     im = np.array(im).astype(np.float32).flatten()
     im = im / 255.0
     return im
+
 test_data = []
-test_data.append((load_image('image/infer_3.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/infer_3.png'),))
 
 probs = paddle.infer(
     output_layer=predict, parameters=parameters, input=test_data)

02.recognize_digits/README.md

@@ -317,14 +317,17 @@ trainer.train(
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file).convert('L')
     im = im.resize((28, 28), Image.ANTIALIAS)
     im = np.array(im).astype(np.float32).flatten()
     im = im / 255.0
     return im
+
 test_data = []
-test_data.append((load_image('image/infer_3.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/infer_3.png'),))
 
 probs = paddle.infer(
     output_layer=predict, parameters=parameters, input=test_data)

02.recognize_digits/index.en.html

@@ -363,14 +363,17 @@ After training is done, user can use the trained model to classify images. The f
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file).convert('L')
     im = im.resize((28, 28), Image.ANTIALIAS)
     im = np.array(im).astype(np.float32).flatten()
     im = im / 255.0
     return im
+
 test_data = []
-test_data.append((load_image('image/infer_3.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/infer_3.png'),))
 
 probs = paddle.infer(
     output_layer=predict, parameters=parameters, input=test_data)

02.recognize_digits/index.html

@@ -359,14 +359,17 @@ trainer.train(
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file).convert('L')
     im = im.resize((28, 28), Image.ANTIALIAS)
     im = np.array(im).astype(np.float32).flatten()
     im = im / 255.0
     return im
+
 test_data = []
-test_data.append((load_image('image/infer_3.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/infer_3.png'),))
 
 probs = paddle.infer(
     output_layer=predict, parameters=parameters, input=test_data)

02.recognize_digits/train.py

 import gzip
+import os
 from PIL import Image
 import numpy as np
 import paddle.v2 as paddle
@@ -114,7 +115,8 @@ def main():
         return im
 
     test_data = []
-    test_data.append((load_image('image/infer_3.png'), ))
+    cur_dir = os.path.dirname(os.path.realpath(__file__))
+    test_data.append((load_image(cur_dir + '/image/infer_3.png'), ))
 
     probs = paddle.infer(
         output_layer=predict, parameters=parameters, input=test_data)

03.image_classification/README.en.md

@@ -488,6 +488,7 @@ After training is done, users can use the trained model to classify images. The
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file)
     im = im.resize((32, 32), Image.ANTIALIAS)
@@ -495,7 +496,8 @@ def load_image(file):
     im = im / 255.0
     return im
 test_data = []
-test_data.append((load_image('image/dog.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/dog.png'),)
 
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)

03.image_classification/README.md

@@ -480,6 +480,7 @@ Test with Pass 0, {'classification_error_evaluator': 0.885200023651123}
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file)
     im = im.resize((32, 32), Image.ANTIALIAS)
@@ -487,7 +488,8 @@ def load_image(file):
     im = im / 255.0
     return im
 test_data = []
-test_data.append((load_image('image/dog.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/dog.png'),)
 
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)

03.image_classification/index.en.html

@@ -530,6 +530,7 @@ After training is done, users can use the trained model to classify images. The
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file)
     im = im.resize((32, 32), Image.ANTIALIAS)
@@ -537,7 +538,8 @@ def load_image(file):
     im = im / 255.0
     return im
 test_data = []
-test_data.append((load_image('image/dog.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/dog.png'),)
 
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)

03.image_classification/index.html

@@ -522,6 +522,7 @@ Test with Pass 0, {'classification_error_evaluator': 0.885200023651123}
 ```python
 from PIL import Image
 import numpy as np
+import os
 def load_image(file):
     im = Image.open(file)
     im = im.resize((32, 32), Image.ANTIALIAS)
@@ -529,7 +530,8 @@ def load_image(file):
     im = im / 255.0
     return im
 test_data = []
-test_data.append((load_image('image/dog.png'),))
+cur_dir = os.path.dirname(os.path.realpath(__file__))
+test_data.append((load_image(cur_dir + '/image/dog.png'),)
 
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)

03.image_classification/train.py

@@ -94,6 +94,7 @@ def main():
     # inference
     from PIL import Image
     import numpy as np
+    import os
 
     def load_image(file):
         im = Image.open(file)
@@ -103,7 +104,8 @@ def main():
         return im
 
     test_data = []
-    test_data.append((load_image('image/dog.png'), ))
+    cur_dir = os.path.dirname(os.path.realpath(__file__))
+    test_data.append((load_image(cur_dir + '/image/dog.png'), ))
 
     probs = paddle.infer(
         output_layer=out, parameters=parameters, input=test_data)