added rudimentary support for dumping nn.Conv2d layers

dnn/torch/weight-exchange/wexchange/c_export/__init__.py

@@ -28,4 +28,4 @@
 */
 """
 
-from .common import print_gru_layer, print_dense_layer, print_conv1d_layer, print_vector
+from .common import print_gru_layer, print_dense_layer, print_conv1d_layer, print_conv2d_layer, print_vector

dnn/torch/weight-exchange/wexchange/c_export/common.py

@@ -291,13 +291,37 @@ def print_conv1d_layer(writer : CWriter,
     lin_weight = np.reshape(weight, (-1, weight.shape[-1]))
     print_linear_layer(writer, name, lin_weight, bias, scale=scale, sparse=False, diagonal=False, quantize=quantize)
 
+
     writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {weight.shape[2]}\n")
     writer.header.write(f"\n#define {name.upper()}_IN_SIZE {weight.shape[1]}\n")
     writer.header.write(f"\n#define {name.upper()}_STATE_SIZE ({weight.shape[1]} * ({weight.shape[0] - 1}))\n")
     writer.header.write(f"\n#define {name.upper()}_DELAY {(weight.shape[0] - 1) // 2}\n") # CAVE: delay is not a property of the conv layer
 
     return weight.shape[0] * weight.shape[1]
 
+def print_conv2d_layer(writer : CWriter,
+                       name : str,
+                       weight : np.ndarray,
+                       bias : np.ndarray,
+                       scale : float=1/128,
+                       quantize : bool=False):
+
+    if quantize:
+        print("[print_conv2d_layer] warning: quantize argument ignored")
+
+    bias_name = name + "_bias"
+    float_weight_name = name + "_weight_float"
+
+    print_vector(writer, weight, float_weight_name)
+    print_vector(writer, bias, bias_name)
+
+    # init function
+    out_channels, in_channels, ksize1, ksize2 = weight.shape
+    init_call = f'conv2d_init(&model->{name}, arrays, "{bias_name}", "{float_weight_name}", {in_channels}, {out_channels}, {ksize1}, {ksize2})'
+
+    writer.layer_dict[name] = ('Conv2dLayer', init_call)
+
+
 
 def print_gru_layer(writer : CWriter,
                     name : str,

dnn/torch/weight-exchange/wexchange/torch/__init__.py

@@ -28,6 +28,7 @@
 """
 
 from .torch import dump_torch_conv1d_weights, load_torch_conv1d_weights
+from .torch import dump_torch_conv2d_weights, load_torch_conv2d_weights
 from .torch import dump_torch_dense_weights, load_torch_dense_weights
 from .torch import dump_torch_gru_weights, load_torch_gru_weights
 from .torch import dump_torch_embedding_weights, load_torch_embedding_weights

dnn/torch/weight-exchange/wexchange/torch/torch.py

@@ -32,7 +32,7 @@
 import torch
 import numpy as np
 
-from wexchange.c_export import CWriter, print_gru_layer, print_dense_layer, print_conv1d_layer
+from wexchange.c_export import CWriter, print_gru_layer, print_dense_layer, print_conv1d_layer, print_conv2d_layer
 
 def dump_torch_gru_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128):
 
@@ -138,6 +138,33 @@ def load_torch_conv1d_weights(where, conv):
                 conv.bias.set_(torch.from_numpy(b))
 
 
+def dump_torch_conv2d_weights(where, conv, name='conv', scale=1/128, quantize=False):
+    w = conv.weight.detach().cpu().permute(0, 1, 3, 2).numpy().copy()
+    if conv.bias is None:
+        b = np.zeros(conv.out_channels, dtype=w.dtype)
+    else:
+        b = conv.bias.detach().cpu().numpy().copy()
+
+    if isinstance(where, CWriter):
+        return print_conv2d_layer(where, name, w, b, scale=scale, quantize=quantize)
+
+    else:
+        os.makedirs(where, exist_ok=True)
+
+        np.save(os.path.join(where, 'weight_oiwh.npy'), w)
+
+        np.save(os.path.join(where, 'bias.npy'), b)
+
+def load_torch_conv2d_weights(where, conv):
+    with torch.no_grad():
+        w = np.load(os.path.join(where, 'weight_oiwh.npy'))
+        conv.weight.set_(torch.from_numpy(w).permute(0, 1, 3, 2))
+        if type(conv.bias) != type(None):
+            b = np.load(os.path.join(where, 'bias.npy'))
+            if conv.bias is not None:
+                conv.bias.set_(torch.from_numpy(b))
+
+
 def dump_torch_embedding_weights(where, emb):
     os.makedirs(where, exist_ok=True)
 
@@ -162,6 +189,8 @@ def dump_torch_weights(where, module, name=None, verbose=False, **kwargs):
         return dump_torch_gru_weights(where, module, name, **kwargs)
     elif isinstance(module, torch.nn.Conv1d):
         return dump_torch_conv1d_weights(where, module, name, **kwargs)
+    elif isinstance(module, torch.nn.Conv2d):
+        return dump_torch_conv2d_weights(where, module, name, **kwargs)
     elif isinstance(module, torch.nn.Embedding):
         return dump_torch_embedding_weights(where, module)
     else:
@@ -175,6 +204,8 @@ def load_torch_weights(where, module):
         load_torch_gru_weights(where, module)
     elif isinstance(module, torch.nn.Conv1d):
         load_torch_conv1d_weights(where, module)
+    elif isinstance(module, torch.nn.Conv2d):
+        load_torch_conv2d_weights(where, module)
     elif isinstance(module, torch.nn.Embedding):
         load_torch_embedding_weights(where, module)
     else: