Merge branch 'dev' into feature/vitis_hls

src/finn/transformation/fpgadataflow/convert_to_hls_layers.py

@@ -65,8 +65,17 @@ class InferConvInpGen(Transformation):
                     continue
                 i2c_inst = getCustomOp(n)
                 stride = i2c_inst.get_nodeattr("stride")
-                k = i2c_inst.get_nodeattr("kernel_size")
-                pad = i2c_inst.get_nodeattr("pad_amount")
+                k_attr = i2c_inst.get_nodeattr("kernel_size")
+                k_h = k_attr[0]
+                k_w = k_attr[1]
+                pad_attr = i2c_inst.get_nodeattr("pad_amount")
+                pad_h = pad_attr[0] + pad_attr[2]
+                pad_w = pad_attr[1] + pad_attr[3]
+                # temporary checks until non-square conv support is finalized
+                assert pad_h == pad_w, "Non-square images not yet supported."
+                assert k_h == k_w, "Non-square kernels not yet supported."
+                k = k_h
+                pad = pad_attr[0]
                 pad_val = i2c_inst.get_nodeattr("pad_value")
                 depthwise = i2c_inst.get_nodeattr("depthwise")
                 ifm_ch = i2c_in_shape[-1]
@@ -330,8 +339,8 @@ class InferPool_Batch(Transformation):
                     [im2col_out],
                     domain="finn.custom_op.general",
                     stride=stride,
-                    kernel_size=k,
-                    pad_amount=pad,
+                    kernel_size=[k, k],
+                    pad_amount=[pad, pad, pad, pad],
                     pad_value=pad_value,
                     depthwise=1,
                     input_shape="(1,{},{},{})".format(ifm_dim, ifm_dim, ifm_ch),

tests/fpgadataflow/test_convert_to_hls_conv_layer.py

@@ -77,7 +77,10 @@ def test_convert_to_hls_conv_layer(conv_config, depthwise, exec_mode):
         out_chn = 20
         conv_param_shape = [out_chn, in_chn, kernel_size, kernel_size]
 
-    out_feature_dim = compute_conv_output_dim(in_feature_dim, kernel_size, stride, pad)
+    total_pad = 2 * pad
+    out_feature_dim = compute_conv_output_dim(
+        in_feature_dim, kernel_size, stride, total_pad
+    )
 
     input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
     output_shape = [1, out_chn, out_feature_dim, out_feature_dim]

tests/fpgadataflow/test_depthwise_convolution.py

@@ -57,7 +57,8 @@ def set_up_reference_model(act, idt, wdt, k, ifm_dim, ifm_ch, stride, padding):
 
     # set up reference model consisting of Im2Col + MatMul (+ MultiThreshold)
     ofm_ch = ifm_ch
-    ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, pad=padding)
+    total_pad = 2 * padding
+    ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, total_pad=total_pad)
 
     if act is None:
         odt = DataType.INT32
@@ -96,9 +97,9 @@ def set_up_reference_model(act, idt, wdt, k, ifm_dim, ifm_ch, stride, padding):
         domain="finn.custom_op.general",
         inputs=["inp"],
         outputs=["im2col_out"],
-        kernel_size=k,
+        kernel_size=[k, k],
         stride=stride,
-        pad_amount=padding,
+        pad_amount=[padding, padding, padding, padding],
         input_shape="(1, {}, {}, {})".format(ifm_dim, ifm_dim, ifm_ch),
         depthwise=1,
     )

tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py

@@ -63,9 +63,9 @@ def make_single_im2col_modelwrapper(k, ifm_ch, ifm_dim, ofm_dim, simd, stride, i
         domain="finn.custom_op.general",
         backend="fpgadataflow",
         stride=stride,
-        kernel_size=k,
+        kernel_size=[k, k],
         input_shape=str((1, ifm_dim, ifm_dim, ifm_ch)),
-        pad_amount=0,
+        pad_amount=[0, 0, 0, 0],
         pad_value=0,
     )
     graph = helper.make_graph(

tests/transformation/streamline/test_move_mul_past_dw_conv.py

@@ -32,8 +32,8 @@ def test_move_mul_past_dw_conv(ifm_dim, ifm_ch, k, stride, pad_amt, dw):
         ofm_ch = ifm_ch + 2
         groups = 1
         W_shape = [ofm_ch, ifm_ch, k, k]
-
-    ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, pad_amt)
+    total_pad = 2 * pad_amt
+    ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, total_pad)
 
     # set up onnx model
     inp = helper.make_tensor_value_info(