fix conflict

gcvit/models/gcvit.py

@@ -2,25 +2,12 @@ import numpy as np
 import tensorflow as tf
 
 from ..layers import Stem, GCViTLevel, Identity
-from ..layers import Stem, GCViTLevel, Identity
 
                   
                   
 BASE_URL = 'https://github.com/awsaf49/gcvit-tf/releases/download'
 TAG = 'v1.1.1'
 NAME2CONFIG = {
-    'gcvit_xxtiny': {'window_size': (7, 7, 14, 7),
-                    'dim': 64,
-                    'depths': (2, 2, 6, 2),
-                    'num_heads': (2, 4, 8, 16),
-                    'mlp_ratio': 3.,
-                    'path_drop': 0.2},
-    'gcvit_xtiny': {'window_size': (7, 7, 14, 7),
-                    'dim': 64,
-                    'depths': (3, 4, 6, 5),
-                    'num_heads': (2, 4, 8, 16),
-                    'mlp_ratio': 3.,
-                    'path_drop': 0.2},
     'gcvit_xxtiny': {'window_size': (7, 7, 14, 7),
                     'dim': 64,
                     'depths': (2, 2, 6, 2),
@@ -94,7 +81,6 @@ class GCViT(tf.keras.Model):
         self.levels = []
         for i in range(len(depths)):
             path_drop = path_drops[sum(depths[:i]):sum(depths[:i + 1])].tolist()
-            level = GCViTLevel(depth=depths[i], num_heads=num_heads[i], window_size=window_size[i], keep_dims=keep_dims[i],
             level = GCViTLevel(depth=depths[i], num_heads=num_heads[i], window_size=window_size[i], keep_dims=keep_dims[i],
                     downsample=(i < len(depths) - 1), mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, 
                     drop=drop_rate, attn_drop=attn_drop, path_drop=path_drop, layer_scale=layer_scale, resize_query=resize_query,
@@ -110,17 +96,14 @@ class GCViT(tf.keras.Model):
         else:
             raise ValueError(f'Expecting pooling to be one of None/avg/max. Found: {global_pool}')
         self.head = tf.keras.layers.Dense(num_classes, name='head', activation=head_act)
-        self.head = tf.keras.layers.Dense(num_classes, name='head', activation=head_act)
 
-    def reset_classifier(self, num_classes, head_act, global_pool=None, in_channels=3):
+
     def reset_classifier(self, num_classes, head_act, global_pool=None, in_channels=3):
         self.num_classes = num_classes
         if global_pool is not None:
             self.global_pool = global_pool
         self.head = tf.keras.layers.Dense(num_classes, name='head', activation=head_act) if num_classes else Identity(name='head')
         super().build((1, 224, 224, in_channels)) # for head we only need info from the input channel
-        self.head = tf.keras.layers.Dense(num_classes, name='head', activation=head_act) if num_classes else Identity(name='head')
-        super().build((1, 224, 224, in_channels)) # for head we only need info from the input channel
         
     def forward_features(self, inputs):
         x = self.patch_embed(inputs)
@@ -137,7 +120,6 @@ class GCViT(tf.keras.Model):
             x = self.pool(x)
         if not pre_logits:
             x = self.head(x)
-            x = self.head(x)
         return x 
 
     def call(self, inputs, **kwargs):
@@ -153,8 +135,6 @@ class GCViT(tf.keras.Model):
     def summary(self, input_shape=(224, 224, 3)):
         return self.build_graph(input_shape).summary()
 
-    def summary(self, input_shape=(224, 224, 3)):
-        return self.build_graph(input_shape).summary()
 
 # load standard models
 def GCViTXXTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
@@ -179,7 +159,6 @@ def GCViTXTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **
         model.load_weights(ckpt_path)
     return model
 
-def GCViTTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
 def GCViTXXTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
     name = 'gcvit_xxtiny'
     config = NAME2CONFIG[name]
@@ -215,7 +194,6 @@ def GCViTTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **k
         model.load_weights(ckpt_path)
     return model
 
-def GCViTSmall(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
 def GCViTSmall(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
     name = 'gcvit_small'
     config = NAME2CONFIG[name]
@@ -229,7 +207,6 @@ def GCViTSmall(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **
         model.load_weights(ckpt_path)
     return model
 
-def GCViTBase(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
 def GCViTBase(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
     name = 'gcvit_base'
     config = NAME2CONFIG[name]