update bilinear upsampling results

README.md

@@ -41,10 +41,10 @@ bash scripts/run_quantization.sh
 | yolov8n | [ultralytics](https://github.com/ultralytics/ultralytics) | 37.1    | 37.1    | 0.0    | 0.0          | 35.1           |
 
 ### camvid (val, mIOU)
-| Model | Source                                          | Float32 | Fixed16 | Fixed8 | BFP8 (Layer) | BFP8 (Channel) |
-|-------|-------------------------------------------------|---------|---------|--------|--------------|----------------|
-| unet  | [nncf](https://github.com/openvinotoolkit/nncf) | 71.95   | 71.95   | 61.02  | 71.60        | 71.85          |
-| unet-approx  | [nncf](https://github.com/openvinotoolkit/nncf) | 71.67   | -   | -  | -        | -          |
+| Model         | Source                                          | Float32 | Fixed16 | Fixed8 | BFP8 (Layer) | BFP8 (Channel) |
+|---------------|-------------------------------------------------|---------|---------|--------|--------------|----------------|
+| unet          | [nncf](https://github.com/openvinotoolkit/nncf) | 71.95   | 71.95   | 61.02  | 71.60        | 71.85          |
+| unet-bilinear | [nncf](https://github.com/openvinotoolkit/nncf) | 71.67   | 71.67   | 60.62  | 71.40        | 71.75          |
 
 ### cityscapes (val, mIOU)
 | Model | Source                                                         | Float32 | Fixed16 | Fixed8 | BFP8 (Layer) | BFP8 (Channel) |

models/base.py

@@ -28,5 +28,8 @@ def onnx_exporter(self):
     def forward(self, x):
         return self.model(x)
 
-    from models.utils import replace_modules
+    def replace_modules(self, replace_dict):
+        from models.utils import replace_modules
+        replace_modules(self.model, replace_dict)
+
     from models.utils import generate_onnx_files

models/segmentation/camvid.py

@@ -8,6 +8,7 @@
 
 from collections import OrderedDict
 from models.base import TorchModelWrapper
+from models.segmentation.utils import apply_conv_transp_approx
 from PIL import Image
 from torch.utils import data
 
@@ -18,7 +19,7 @@ def __init__(self, model_name, input_size=(1, 3, 368, 480), num_classes=12):
         self.num_classes = num_classes
         super().__init__(model_name)
 
-    def load_model(self, eval=True):
+    def load_model(self, eval=True, approx_transpose_conv=True):
         assert self.model_name == 'unet'
 
         self.model = UNet(input_size_hw=self.input_size[2:], in_channels=self.input_size[1], n_classes=self.num_classes)
@@ -27,6 +28,10 @@ def load_model(self, eval=True):
         # remove 'module.' prefix
         state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
         self.model.load_state_dict(state_dict)
+
+        if approx_transpose_conv:
+            apply_conv_transp_approx(self.model)
+
         if torch.cuda.is_available():
             self.model = self.model.cuda()
 

conv_transp_approx/utils.py → models/segmentation/utils.py

@@ -1,9 +1,10 @@
+import math
 import torch
+
+from models.utils import replace_modules
 from torch import nn
-import math
 
-CONV_TRANSP_MODULES = (
-    nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d)
+CONV_TRANSP_MODULES = (nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d)
 
 class ConvTranspApproxLayer(nn.Module):
     def __init__(self, parent_module, upsampling_mode, kernel_approx_strategy):
@@ -62,12 +63,11 @@ def forward(self, x):
 
         return x
 
-
-def apply_conv_transp_approx(model_wrapper, upsampling_mode, kernel_approx_strategy):
+def apply_conv_transp_approx(model, upsampling_mode="bilinear", kernel_approx_strategy="average"):
     replace_dict = {}
-    for name, module in model_wrapper.named_modules():
+    for name, module in model.named_modules():
         if isinstance(module, CONV_TRANSP_MODULES):
             new_module = ConvTranspApproxLayer(
                 parent_module=module, upsampling_mode=upsampling_mode, kernel_approx_strategy=kernel_approx_strategy)
             replace_dict[module] = new_module
-    model_wrapper.replace_modules(replace_dict)
+    replace_modules(model, replace_dict)

models/utils.py

@@ -5,8 +5,8 @@
 
 import torch.nn as nn
 
-def replace_modules(self, replace_dict):
-    for name, module in self.model.named_modules(): 
+def replace_modules(model, replace_dict):
+    for name, module in model.named_modules(): 
         for subname, submodule in module.named_children():
             if submodule in replace_dict.keys():
                 new_submodule = replace_dict[submodule]