Merge pull request #33 from borisfom/fp16

Added data size and ONNX export options, FP16 inference is working

1 files changed, 34 insertions, 2 deletions

diff --git a/test.py b/test.py
index a9c8729..203d887 100755
--- a/test.py
+++ b/test.py
@@ -8,6 +8,8 @@ from models.models import create_model
 import util.util as util
 from util.visualizer import Visualizer
 from util import html
+import torch
+from run_engine import run_trt_engine, run_onnx
 
 opt = TestOptions().parse(save=False)
 opt.nThreads = 1   # test code only supports nThreads = 1
@@ -17,16 +19,46 @@ opt.no_flip = True  # no flip
 
 data_loader = CreateDataLoader(opt)
 dataset = data_loader.load_data()
-model = create_model(opt)
 visualizer = Visualizer(opt)
 # create website
 web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
 webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
 # test
+
+if not opt.engine and not opt.onnx:
+        model = create_model(opt)
+        if opt.data_type == 16:
+            model.half()
+        elif opt.data_type == 8:
+            model.type(torch.uint8)
+            
+        if opt.verbose:
+            print(model)
+
+    
 for i, data in enumerate(dataset):
     if i >= opt.how_many:
         break
-    generated = model.inference(data['label'], data['inst'])
+    if opt.data_type == 16:
+        data['label'] = data['label'].half()
+        data['inst']  = data['inst'].half()
+    elif opt.data_type == 8:
+        data['label'] = data['label'].uint8()
+        data['inst']  = data['inst'].uint8()
+    if opt.export_onnx:
+        print ("Exporting to ONNX: ", opt.export_onnx)
+        assert opt.export_onnx.endswith("onnx"), "Export model file should end with .onnx"
+        torch.onnx.export(model, [data['label'], data['inst']],
+                          opt.export_onnx, verbose=True)
+        exit(0)
+    minibatch = 1 
+    if opt.engine:
+        generated = run_trt_engine(opt.engine, minibatch, [data['label'], data['inst']])
+    elif opt.onnx:
+        generated = run_onnx(opt.onnx, opt.data_type, minibatch, [data['label'], data['inst']])
+    else:
+        generated = model.inference(data['label'], data['inst'])
+        
     visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
                            ('synthesized_image', util.tensor2im(generated.data[0]))])
     img_path = data['path']