Reorder loops in im2col_2d_cl given resource strategy issue. Reenable relevant test. Use 5000 MNIST samples rather than full dataset for faster testing

Author: thesps

hls4ml/templates/vivado/nnet_utils/nnet_conv2d_resource.h

@@ -154,20 +154,18 @@ void im2col_2d_cl(
     const int col)
 {
     int index = 0;
-    for (int channel = CONFIG_T::n_chan; channel--; data++) {
+    for (int kernel_row = 0; kernel_row < CONFIG_T::filt_height; kernel_row++) {
         #pragma HLS UNROLL
-        for (int kernel_row = 0; kernel_row < CONFIG_T::filt_height; kernel_row++) {
-            int input_row = -CONFIG_T::pad_top + kernel_row * CONFIG_T::dilation_height + row * CONFIG_T::stride_height;
-            for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+        int input_row = -CONFIG_T::pad_top + kernel_row * CONFIG_T::dilation_height + row * CONFIG_T::stride_height;
+        for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+            for (int channel = 0; channel < CONFIG_T::n_chan; channel++) {
                 if (input_row < 0 || input_row >= CONFIG_T::in_height) {
                     data_col[index++] = 0;
                 } else {
                     int input_col = -CONFIG_T::pad_left + kernel_col * CONFIG_T::dilation_width + col * CONFIG_T::stride_width;
                     if (input_col >= 0 && input_col < CONFIG_T::in_width) {
-                        //*(data_col++) = data[input_row * CONFIG_T::in_width * CONFIG_T::n_chan + input_col * CONFIG_T::n_chan];
-                        data_col[index++] = data[input_row * CONFIG_T::in_width * CONFIG_T::n_chan + input_col * CONFIG_T::n_chan];
+                        data_col[index++] = data[input_row * CONFIG_T::in_width * CONFIG_T::n_chan + input_col * CONFIG_T::n_chan + channel];
                     } else {
-                        //*(data_col++) = 0;
                         data_col[index++] = 0;
                     }
                 }
@@ -209,7 +207,6 @@ void conv_2d_resource_cl(
             FiltLoop:
             for (int k = 0; k < CONFIG_T::n_filt; k++) {
                 res[i * CONFIG_T::out_width * CONFIG_T::n_filt + j * CONFIG_T::n_filt + k] = res_col[k];
-                //res[k * CONFIG_T::out_height * CONFIG_T::out_width + i * CONFIG_T::out_width + j] = res_col[k]; // Transposed order
             }
         }
     }

test/pytest/test_cnn_mnist.py

@@ -28,10 +28,9 @@ def mnist_model():
   model.load_weights('../../example-models/keras/qkeras_mnist_cnn_weights.h5')
   return model
 
-# TODO: add ('io_parallel', 'resource') when it can pass
-# https://github.com/fastmachinelearning/hls4ml/issues/375
 @pytest.fixture      
 @pytest.mark.parametrize('settings', [('io_parallel', 'latency'),
+                                      ('io_parallel', 'resource'),
                                       ('io_stream', 'latency'),
                                       ('io_stream', 'resource')])
 def hls_model(settings):
@@ -49,10 +48,12 @@ def hls_model(settings):
   return hls_model
 
 @pytest.mark.parametrize('settings', [('io_parallel', 'latency'),
+                                      ('io_parallel', 'resource'),
                                       ('io_stream', 'latency'),
                                       ('io_stream', 'resource')])
 def test_accuracy(mnist_data, mnist_model, hls_model):
   x_train, y_train, x_test, y_test = mnist_data
+  x_test, y_test = x_test[:5000], y_test[:5000]
   model = mnist_model
   # model under test predictions and accuracy
   y_keras = model.predict(x_test)