Merge pull request fastmachinelearning#487 from apfusco/predict-multiple-outputs

Use correct number of args for multiple outputs

Author: jmduarte

hls4ml/model/graph.py

@@ -591,6 +591,7 @@ def _get_top_function(self, x):
             xlist = [x]
         else: 
             xlist = x
+        n_outputs = len(self.get_output_variables())
 
         for xi in xlist:
             if not isinstance(xi, np.ndarray):
@@ -610,7 +611,7 @@ def _get_top_function(self, x):
 
 
         top_function.restype = None
-        top_function.argtypes = [npc.ndpointer(ctype, flags="C_CONTIGUOUS") for i in range(len(xlist)+1)]
+        top_function.argtypes = [npc.ndpointer(ctype, flags="C_CONTIGUOUS") for i in range(len(xlist) + n_outputs)]
 
         return top_function, ctype
 
@@ -637,6 +638,7 @@ def predict(self, x):
         top_function, ctype = self._get_top_function(x)
         n_samples = self._compute_n_samples(x)
         n_inputs = len(self.get_input_variables())
+        n_outputs = len(self.get_output_variables())
 
         curr_dir = os.getcwd()
         os.chdir(self.config.get_output_dir() + '/firmware')
@@ -647,25 +649,29 @@ def predict(self, x):
 
         try:
             for i in range(n_samples):
-                predictions = np.zeros(self.get_output_variables()[0].size(), dtype=ctype)
+                predictions = [np.zeros(yj.size(), dtype=ctype) for yj in self.get_output_variables()]
                 if n_inputs == 1:
-                    top_function(x[i], predictions)
+                    inp = [x[i]]
                 else:
                     inp = [xj[i] for xj in x]
-                    argtuple = inp
-                    argtuple += [predictions]
-                    argtuple = tuple(argtuple)
-                    top_function(*argtuple)
+                argtuple = inp
+                argtuple += predictions
+                argtuple = tuple(argtuple)
+                top_function(*argtuple)
                 output.append(predictions)
 
 
-            #Convert to numpy array
-            output = np.asarray(output)
+            # Convert to list of numpy arrays (one for each output)
+            output = [np.asarray([output[i_sample][i_output] for i_sample in range(n_samples)]) for i_output in range(n_outputs)]
         finally:
             os.chdir(curr_dir)
-
-        if n_samples == 1:
+            
+        if n_samples == 1 and n_outputs == 1:
+            return output[0][0]
+        elif n_outputs == 1:
             return output[0]
+        elif n_samples == 1:
+            return [output_i[0] for output_i in output]
         else:
             return output
 

test/pytest/test_graph.py

@@ -175,3 +175,31 @@ def test_broadcast_stream(shapes, layer):
   y = model.predict([X1, X2])
   y_hls = hls_model.predict([X1, X2]).reshape(y.shape)
   np.testing.assert_allclose(y, y_hls, rtol=0)
+
+@pytest.mark.parametrize('batch', [1, 32])
+def test_multiple_outputs(batch):
+  ''' Test case for multple outputs '''
+  input1 = tf.keras.layers.Input(shape=(10,))
+  inputs = [input1]
+  output1 = tf.keras.layers.Dense(5, kernel_initializer='ones', use_bias=False)(input1)
+  output2 = tf.keras.layers.Dense(2, kernel_initializer='ones', use_bias=False)(input1)
+  outputs = [output1, output2]
+  model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
+
+  # create the ModelGraph
+  config = hls4ml.utils.config_from_keras_model(model, granularity='model', default_precision='ap_fixed<32,16>')
+  odir = str(test_root_path / 'hls4mlprj_graph_multiple_outputs')
+  hls_model = hls4ml.converters.convert_from_keras_model(model,
+                                                         output_dir=odir,
+                                                         backend='Vivado',
+                                                         io_type='io_serial',
+                                                         hls_config=config)
+  hls_model.compile()
+
+  # Test with integers (for exact agreement)
+  X1 = np.random.randint(0, 100, size=(batch, 10)).astype(float)
+  y = model.predict(X1)
+  y_hls = hls_model.predict(X1)
+  for y_i, y_hls_i in zip(y, y_hls):
+    y_hls_i = y_hls_i.reshape(y_i.shape)
+    np.testing.assert_allclose(y_i, y_hls_i, rtol=0)