added samples for fine-grained weights

Readme.md

@@ -1,7 +1,9 @@
 # Image Segmentation Using Text and Image Prompts
 This repository contains the code used in the paper ["Image Segmentation Using Text and Image Prompts"](https://arxiv.org/abs/2112.10003).
 
-**The Paper has been accepted to CVPR 2022!**
+**September 2022:** We released new weights for fine-grained predictions (see below for details).  
+**March 2022:** The Paper has been accepted to CVPR 2022!
+
 
 <img src="overview.png" alt="drawing" height="200em"/>
 
@@ -46,12 +48,25 @@ git clone https://github.com/juhongm999/hsnet.git
 
 The MIT license does not apply to these weights. 
 
-We provide two model weights, for D=64 (4.1MB) and D=16 (1.1MB).
+We provide three model weights, for D=64 (2x, ~4MB each) and D=16 (~1MB).
 ```
 wget https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download -O weights.zip
 unzip -d weights -j weights.zip
 ```
 
+#### New Fine-grained Weights
+We introduced a more complex module for transforming tokens into predictions that allow for more refined predictions (in contrast to the square-like predictions of other weights). Corresponding weights are available in the weight download above called `rd64-uni-refined.pth`.
+They can be loaded by:
+```python
+model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
+model.load_state_dict(torch.load('weights/rd64-uni-refined.pth'), strict=False)
+```
+
+See below for a direct comparison of the new fine-grained weights (top) and the old weights (below).  
+<img src="sample_rd64_refined.png" alt="drawing" height="80em"/>  
+<img src="sample_rd64.png" alt="drawing" height="80em"/>
+
+
 
 ### Training and Evaluation
 

models/clipseg.py

@@ -338,11 +338,11 @@ def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, r
             trans_conv_ks = (trans_conv, trans_conv)
 
         if not complex_trans_conv:
-            self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, self.trans_conv_ks, stride=self.trans_conv_ks)
+            self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
         else:
-            assert self.trans_conv_ks[0] == self.trans_conv_ks[1]
+            assert trans_conv_ks[0] == trans_conv_ks[1]
 
-            tp_kernels = (self.trans_conv_ks[0] // 4, self.trans_conv_ks[0] // 4)
+            tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)
 
             self.trans_conv = nn.Sequential(
                 nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),