allow converter to dump torchscript; support torchscript GPU inference

Reviewed By: rbgirshick

Differential Revision: D21364163

fbshipit-source-id: 6d83968b483f91df976939d8682031a5c60dd271

detectron2/export/api.py

@@ -15,7 +15,13 @@
 from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format
 from .shared import get_pb_arg_vali, get_pb_arg_vals, save_graph
 
-__all__ = ["add_export_config", "export_caffe2_model", "Caffe2Model", "export_onnx_model"]
+__all__ = [
+    "add_export_config",
+    "export_caffe2_model",
+    "Caffe2Model",
+    "export_onnx_model",
+    "Caffe2Tracer",
+]
 
 
 def add_export_config(cfg):
@@ -47,7 +53,8 @@ class Caffe2Tracer:
     3. complicated pre/post processing
 
     This class provides a traceable version of a detectron2 model by:
-    1. Rewrite parts of the model using ops in caffe2
+    1. Rewrite parts of the model using ops in caffe2. Note that some ops do
+       not have GPU implementation.
     2. Define the inputs "after pre-processing" as inputs to the model
     3. Remove post-processing and produce raw layer outputs
 
@@ -59,8 +66,6 @@ class Caffe2Tracer:
     model to different deployment formats.
 
     The class currently only supports models using builtin meta architectures.
-
-    Experimental. Don't use.
     """
 
     def __init__(self, cfg, model, inputs):
@@ -127,7 +132,7 @@ def export_torchscript(self):
         logger = logging.getLogger(__name__)
         logger.info("Tracing the model with torch.jit.trace ...")
         with torch.no_grad():
-            return torch.jit.trace(model, (inputs,))
+            return torch.jit.trace(model, (inputs,), optimize=True)
 
 
 def export_caffe2_model(cfg, model, inputs):

detectron2/export/c10.py

@@ -164,9 +164,6 @@ def forward(self, images, features, gt_instances=None):
         features = [features[f] for f in self.in_features]
         objectness_logits_pred, anchor_deltas_pred = self.rpn_head(features)
 
-        # TODO is the needed?
-        # objectness_logits_pred = [t.sigmoid() for t in objectness_logits_pred]
-
         assert isinstance(images, ImageList)
         if self.tensor_mode:
             im_info = images.image_sizes

tools/deploy/CMakeLists.txt

@@ -12,3 +12,10 @@ target_link_libraries(
   caffe2_mask_rcnn
   "${TORCH_LIBRARIES}" gflags glog ${OpenCV_LIBS})
 set_property(TARGET caffe2_mask_rcnn PROPERTY CXX_STANDARD 14)
+
+
+add_executable(torchscript_traced_mask_rcnn torchscript_traced_mask_rcnn.cpp)
+target_link_libraries(
+  torchscript_traced_mask_rcnn
+  "${TORCH_LIBRARIES}" ${OpenCV_LIBS})
+set_property(TARGET torchscript_traced_mask_rcnn PROPERTY CXX_STANDARD 14)

tools/deploy/caffe2_converter.py

@@ -2,13 +2,14 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 import argparse
 import os
+import onnx
 import torch
 
 from detectron2.checkpoint import DetectionCheckpointer
 from detectron2.config import get_cfg
 from detectron2.data import build_detection_test_loader
 from detectron2.evaluation import COCOEvaluator, inference_on_dataset, print_csv_format
-from detectron2.export import add_export_config, export_caffe2_model
+from detectron2.export import Caffe2Tracer, add_export_config
 from detectron2.modeling import build_model
 from detectron2.utils.logger import setup_logger
 
@@ -28,10 +29,16 @@ def setup_cfg(args):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Convert a model to Caffe2")
+    parser = argparse.ArgumentParser(description="Convert a model using caffe2 tracing.")
+    parser.add_argument(
+        "--format",
+        choices=["caffe2", "onnx", "torchscript"],
+        help="output format",
+        default="caffe2",
+    )
     parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
     parser.add_argument("--run-eval", action="store_true")
-    parser.add_argument("--output", help="output directory for the converted caffe2 model")
+    parser.add_argument("--output", help="output directory for the converted model")
     parser.add_argument(
         "opts",
         help="Modify config options using the command-line",
@@ -41,6 +48,7 @@ def setup_cfg(args):
     args = parser.parse_args()
     logger = setup_logger()
     logger.info("Command line arguments: " + str(args))
+    os.makedirs(args.output, exist_ok=True)
 
     cfg = setup_cfg(args)
 
@@ -53,13 +61,35 @@ def setup_cfg(args):
     first_batch = next(iter(data_loader))
 
     # convert and save caffe2 model
-    caffe2_model = export_caffe2_model(cfg, torch_model, first_batch)
-    caffe2_model.save_protobuf(args.output)
-    # draw the caffe2 graph
-    caffe2_model.save_graph(os.path.join(args.output, "model.svg"), inputs=first_batch)
+    tracer = Caffe2Tracer(cfg, torch_model, first_batch)
+    if args.format == "caffe2":
+        caffe2_model = tracer.export_caffe2()
+        caffe2_model.save_protobuf(args.output)
+        # draw the caffe2 graph
+        caffe2_model.save_graph(os.path.join(args.output, "model.svg"), inputs=first_batch)
+    elif args.format == "onnx":
+        onnx_model = tracer.export_onnx()
+        onnx.save(onnx_model, os.path.join(args.output, "model.onnx"))
+    elif args.format == "torchscript":
+        script_model = tracer.export_torchscript()
+        script_model.save(os.path.join(args.output, "model.ts"))
+
+        # Recursively print IR of all modules
+        with open(os.path.join(args.output, "model_ts_IR.txt"), "w") as f:
+            try:
+                f.write(script_model._actual_script_module._c.dump_to_str(True, False, False))
+            except AttributeError:
+                pass
+        # Print IR of the entire graph (all submodules inlined)
+        with open(os.path.join(args.output, "model_ts_IR_inlined.txt"), "w") as f:
+            f.write(str(script_model.inlined_graph))
+        # Print the model structure in pytorch style
+        with open(os.path.join(args.output, "model.txt"), "w") as f:
+            f.write(str(script_model))
 
     # run evaluation with the converted model
     if args.run_eval:
+        assert args.format == "caffe2", "Python inference in other format is not yet supported."
         dataset = cfg.DATASETS.TEST[0]
         data_loader = build_detection_test_loader(cfg, dataset)
         # NOTE: hard-coded evaluator. change to the evaluator for your dataset

tools/deploy/torchscript_traced_mask_rcnn.cpp

@@ -9,7 +9,7 @@
 
 using namespace std;
 
-// Experimental. Don't use.
+// experimental. don't use
 int main(int argc, const char* argv[]) {
   if (argc != 3) {
     return 1;
@@ -19,26 +19,25 @@ int main(int argc, const char* argv[]) {
   torch::autograd::AutoGradMode guard(false);
   auto module = torch::jit::load(argv[1]);
 
+  assert(module.buffers().size() > 0);
+  // Assume that the entire model is on the same device.
+  // We just put input to this device.
+  auto device = (*begin(module.buffers())).device();
+
   cv::Mat input_img = cv::imread(image_file, cv::IMREAD_COLOR);
   const int height = input_img.rows;
   const int width = input_img.cols;
   // FPN models require divisibility of 32
   assert(height % 32 == 0 && width % 32 == 0);
-  const int batch = 1;
   const int channels = 3;
 
-  auto input = torch::empty({1, channels, height, width});
-  float* ptr = input.data_ptr<float>();
-  // HWC to CHW
-  for (int c = 0; c < 3; ++c) {
-    for (int i = 0; i < height * width; ++i) {
-      ptr[c * height * width + i] =
-          static_cast<float>(input_img.data[3 * i + c]);
-    }
-  }
+  auto input = torch::from_blob(
+      input_img.data, {1, height, width, channels}, torch::kUInt8);
+  // NHWC to NCHW
+  input = input.to(device, torch::kFloat).permute({0, 3, 1, 2}).contiguous();
 
-  float im_info_data[] = {height * 1.0f, width * 1.0f, 1.0f};
-  auto im_info = torch::from_blob(im_info_data, {1, 3});
+  std::array<float, 3> im_info_data{height * 1.0f, width * 1.0f, 1.0f};
+  auto im_info = torch::from_blob(im_info_data.data(), {1, 3}).to(device);
 
   // run the network
   auto output = module.forward({std::make_tuple(input, im_info)});