Fix in_chans parameter usage

models_mae.py

@@ -27,6 +27,7 @@ def __init__(self, img_size=224, patch_size=16, in_chans=3,
                  decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
                  mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):
         super().__init__()
+        self.in_chans = in_chans
 
         # --------------------------------------------------------------------------
         # MAE encoder specifics
@@ -94,30 +95,30 @@ def _init_weights(self, m):
 
     def patchify(self, imgs):
         """
-        imgs: (N, 3, H, W)
-        x: (N, L, patch_size**2 *3)
+        imgs: (N, in_chans, H, W)
+        x: (N, L, patch_size**2 * in_chans)
         """
         p = self.patch_embed.patch_size[0]
         assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0
 
         h = w = imgs.shape[2] // p
-        x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
+        x = imgs.reshape(shape=(imgs.shape[0], self.in_chans, h, p, w, p))
         x = torch.einsum('nchpwq->nhwpqc', x)
-        x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 3))
+        x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * self.in_chans))
         return x
 
     def unpatchify(self, x):
         """
-        x: (N, L, patch_size**2 *3)
-        imgs: (N, 3, H, W)
+        x: (N, L, patch_size**2 * in_chans)
+        imgs: (N, in_chans, H, W)
         """
         p = self.patch_embed.patch_size[0]
         h = w = int(x.shape[1]**.5)
         assert h * w == x.shape[1]
 
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, self.in_chans))
         x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
+        imgs = x.reshape(shape=(x.shape[0], self.in_chans, h * p, h * p))
         return imgs
 
     def random_masking(self, x, mask_ratio):
@@ -197,8 +198,8 @@ def forward_decoder(self, x, ids_restore):
 
     def forward_loss(self, imgs, pred, mask):
         """
-        imgs: [N, 3, H, W]
-        pred: [N, L, p*p*3]
+        imgs: [N, in_chans, H, W]
+        pred: [N, L, p*p*in_chans]
         mask: [N, L], 0 is keep, 1 is remove, 
         """
         target = self.patchify(imgs)
@@ -215,7 +216,7 @@ def forward_loss(self, imgs, pred, mask):
 
     def forward(self, imgs, mask_ratio=0.75):
         latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
-        pred = self.forward_decoder(latent, ids_restore)  # [N, L, p*p*3]
+        pred = self.forward_decoder(latent, ids_restore)  # [N, L, p*p*in_chans]
         loss = self.forward_loss(imgs, pred, mask)
         return loss, pred, mask