Merge branch 'develop' of https://github.com/NRCan/geo-deep-learning …

…into geo-inference

config/training/default_training.yaml

@@ -13,6 +13,7 @@ training:
   max_used_perc:
   state_dict_path:
   state_dict_strict_load: True
+  script_model: False
   compute_sampler_weights: False
 
 #  precision: 16

tests/utils/test_script_model.py

@@ -0,0 +1,14 @@
+import torch
+import pytest
+from utils.script_model import ScriptModel
+
+def test_script_model():
+    model = torch.nn.Linear(3, 1)
+    script_model = ScriptModel(model,
+                               input_shape=(1, 3),)
+
+    input_tensor = torch.rand((1, 3))
+    output = script_model.forward(input_tensor)
+
+    assert output.shape == (1, 1)
+    assert isinstance(output, torch.Tensor)

train_segmentation.py

@@ -20,6 +20,7 @@
 from tiling_segmentation import Tiler
 from utils import augmentation as aug
 from dataset import create_dataset
+from utils.script_model import ScriptModel
 from utils.logger import InformationLogger, tsv_line, get_logger, set_tracker
 from utils.loss import verify_weights, define_loss
 from utils.metrics import create_metrics_dict, calculate_batch_metrics
@@ -553,6 +554,7 @@ def train(cfg: DictConfig) -> None:
     train_state_dict_path = get_key_def('state_dict_path', cfg['training'], default=None, expected_type=str)
     state_dict_strict = get_key_def('state_dict_strict_load', cfg['training'], default=True, expected_type=bool)
     dropout_prob = get_key_def('factor', cfg['scheduler']['params'], default=None, expected_type=float)
+    scriptmodel = get_key_def('script_model', cfg['training'], default=False, expected_type=bool)
     # if error
     if train_state_dict_path and not Path(train_state_dict_path).is_file():
         raise logging.critical(
@@ -792,6 +794,19 @@ def train(cfg: DictConfig) -> None:
 
         cur_elapsed = time.time() - since
         # logging.info(f'\nCurrent elapsed time {cur_elapsed // 60:.0f}m {cur_elapsed % 60:.0f}s')
+
+    # Script model
+    if scriptmodel:
+        model_to_script = ScriptModel(model,
+                                      device=device,
+                                      input_shape=(1, num_bands, patches_size, patches_size),
+                                      mean=mean,
+                                      std=std,
+                                      min=scale[0],
+                                      max=scale[1])
+
+        scripted_model = torch.jit.script(model_to_script)
+        scripted_model.save(output_path.joinpath('scripted_model.pt'))
 
     # load checkpoint model and evaluate it on test dataset.
     if int(cfg['general']['max_epochs']) > 0:   # if num_epochs is set to 0, model is loaded to evaluate on test set

utils/script_model.py

@@ -0,0 +1,32 @@
+import torch
+
+class ScriptModel(torch.nn.Module):
+    def __init__(self, 
+                 model,
+                 device = torch.device("cpu"),
+                 input_shape = (1, 3, 512, 512), 
+                 mean = [0.405,0.432,0.397],
+                 std = [0.164,0.173,0.153],
+                 min = 0,
+                 max = 255,
+                 scaled_min = 0.0,
+                 scaled_max = 1.0):
+        super().__init__()
+        self.device = device
+        self.mean = torch.tensor(mean).resize_(len(mean), 1)
+        self.std = torch.tensor(std).resize_(len(std), 1)
+        self.min = min
+        self.max = max
+        self.min_val = scaled_min
+        self.max_val = scaled_max
+
+        input_tensor = torch.rand(input_shape).to(self.device)
+        self.model_scripted = torch.jit.trace(model.eval(), input_tensor)
+
+    def forward(self, input):
+        shape = input.shape
+        B, C = shape[0], shape[1]
+        input = (self.max_val - self.min_val) * (input - self.min) / (self.max -self.min) + self.min_val
+        input = (input.view(B, C, -1) - self.mean) / self.std
+        input = input.view(shape)
+        return self.model_scripted(input.to(self.device))