bug fix

example_alexnet.py

@@ -124,7 +124,7 @@ def run_test(path):
     param_dict = np.load(path).item()
 
     pred_config = PredictConfig(
-        model=Models(),
+        model=Model(),
         input_data_mapping=[0],
         session_init=ParamRestore(param_dict),
         output_var_names=['output:0']   # output:0 is the probability distribution
@@ -139,7 +139,9 @@ def run_test(path):
     outputs = predict_func([im])[0]
     prob = outputs[0]
     print prob.shape
-    print prob.argsort()[-10:][::-1]
+    ret = prob.argsort()[-10:][::-1]
+    print ret
+    assert ret[0] == 285
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
@@ -148,7 +150,7 @@ if __name__ == '__main__':
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
-    start_train(get_config())
+    #start_train(get_config())
 
     # run alexnet with given model (in npy format)
-    run_test('alexnet-tuned.npy')
+    run_test('alexnet.npy')

tensorpack/predict.py

@@ -35,11 +35,12 @@ class PredictConfig(object):
                 If not given, defaults to range(len(input_vars))
 
                 For example, with image classification task, the testing
-                dataset only provides datapoints of images (no labels). The
-                arguments should look like:
-                    inputs: [image_var, label_var]
+                dataset only provides datapoints of images (no labels). When
+                the input variables of the model is:
+                    input_vars: [image_var, label_var]
+                the mapping should look like:
                     input_data_mapping: [0]
-                If this argument is not set, the inputs and the data points won't be aligned.
+                If this argument is not set in this case, the inputs and the data points won't be aligned.
             model: a ModelDesc instance
             output_var_names: a list of names of the output variable to predict, the
                 variables can be any computable tensor in the graph.
@@ -53,7 +54,7 @@ class PredictConfig(object):
         assert_type(self.session_config, tf.ConfigProto)
         self.session_init = kwargs.pop('session_init')
         self.model = kwargs.pop('model')
-        self.input_data_mapping = kwargs.pop('input_dataset_mapping', None)
+        self.input_data_mapping = kwargs.pop('input_data_mapping', None)
         self.output_var_names = kwargs.pop('output_var_names', None)
         assert len(kwargs) == 0, 'Unknown arguments: {}'.format(str(kwargs.keys()))