fix(): first commit

Signed-off-by: Abhiram Iyer <[email protected]>
Signed-off-by: Abhiram Iyer <[email protected]>

core/conversion/converters/BUILD

@@ -29,6 +29,7 @@ cc_library(
         "impl/shuffle.cpp",
         "impl/softmax.cpp",
         "impl/unary.cpp",
+        "impl/interpolate.cpp"
     ],
     deps = [
         "@tensorrt//:nvinfer",

core/conversion/converters/impl/interpolate.cpp

@@ -0,0 +1,34 @@
+#include "torch/torch.h"
+#include "core/util/prelude.h"
+#include "core/conversion/converters/converters.h"
+
+namespace trtorch {
+namespace core {
+namespace conversion {
+namespace converters {
+namespace impl {
+namespace {
+
+auto interpolate_registrations = RegisterNodeConversionPatterns()
+    .pattern({
+        "aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> (Tensor)",
+        [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+            auto in = args[0].ITensor();
+
+            auto shape = util::toVec(in->getDimensions());
+
+            LOG_DEBUG("Shape of input is" << in);
+
+            std::cout << "TEST!" << std::endl;
+
+            return true;
+        }
+    });
+
+
+} // namespace
+} // namespace impl
+} // namespace converters
+} // namespace conversion
+} // namespace core
+} // namespace trtorch

test.py

@@ -0,0 +1,70 @@
+# import torch.nn as nn
+# import torch
+
+# class FeatureExtractor(nn.Module):
+#     def __init__(self):
+#         super(FeatureExtractor, self).__init__()
+#         self.conv1 = nn.Conv2d(1, 6, 3)
+#         self.conv2 = nn.Conv2d(6, 16, 3)
+
+#     def forward(self, x):
+#         x = torch.max_pool2d(torch.relu(self.conv1(x)), (2, 2))
+#         x = torch.max_pool2d(torch.relu(self.conv2(x)), 2)
+
+#         return x
+
+# class Classifier(nn.Module):
+#     def __init__(self):
+#         super(Classifier, self).__init__()
+
+#         self.fc1 = nn.Linear(16*6*6, 120)
+#         self.fc2 = nn.Linear(120, 84)
+#         self.fc3 = nn.Linear(84, 10)
+
+#     def forward(self, x):
+#         x = torch.flatten(x, 1)
+#         x = torch.relu(self.fc1(x))
+#         x = torch.relu(self.fc2(x))
+#         x = self.fc3(x)
+
+#         return x
+
+# class LeNet(nn.Module):
+#     def __init__(self):
+#         super(LeNet, self).__init__()
+#         self.feat = FeatureExtractor()
+#         self.classifier = Classifier()
+
+#     def forward(self, x):
+#         x = self.feat(x)
+#         x = self.classifier(x)
+
+#         return x
+
+# model = LeNet()
+# model.eval()
+# traced_model = torch.jit.trace(model, torch.empty([1, 1, 32, 32]))
+# torch.jit.save(traced_model, 'traced_model.ts')
+# torch.jit.save(torch.jit.script(model), 'script_model.ts')
+
+
+import torch.nn as nn
+import torch
+import torch.nn.functional as F
+#import trtorch
+
+class Interp(nn.Module):
+    def __init__(self):
+        super(Interp, self).__init__()
+
+    def forward(self, x):
+        return F.interpolate(x, scale_factor=(5,5), mode='nearest')
+
+model = Interp()
+model.eval()
+trace = torch.jit.trace(model, torch.empty([1, 1, 2, 2]))
+torch.jit.save(trace, 'trace.ts')
+
+#trtorch.check_method_op_support(trace, "forward")
+
+