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update swin_unetr block parameter names #4311

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Merged
wyli merged 7 commits into from
May 22, 2022
Merged

update swin_unetr block parameter names #4311

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3952dc2
update swin_unetr block name
ahatamiz May 21, 2022
c2e57a0
update swin_unetr block name
ahatamiz May 21, 2022
b92cdb2
update swin_unetr block name
ahatamiz May 22, 2022
96c7bbd
update swin_unetr block name
ahatamiz May 22, 2022
cf6debe
update swin_unetr block name
ahatamiz May 22, 2022
02fae63
update swin_unetr block name
ahatamiz May 22, 2022
0997daf
Merge branch 'dev' into swin_unetr_v2
wyli May 22, 2022
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17 changes: 7 additions & 10 deletions monai/networks/nets/swin_unetr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,12 +40,12 @@ def __init__(
out_channels: int,
depths: Sequence[int] = (2, 2, 2, 2),
num_heads: Sequence[int] = (3, 6, 12, 24),
feature_size: int = 48,
feature_size: int = 24,
norm_name: Union[Tuple, str] = "instance",
drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
dropout_path_rate: float = 0.0,
normalize: bool = False,
normalize: bool = True,
use_checkpoint: bool = False,
spatial_dims: int = 3,
) -> None:
Expand Down Expand Up @@ -275,8 +275,6 @@ def load_from(self, weights):
self.swinViT.layers4[0].downsample.norm.bias.copy_(
weights["state_dict"]["module.layers4.0.downsample.norm.bias"]
)
self.swinViT.norm.weight.copy_(weights["state_dict"]["module.norm.weight"])
self.swinViT.norm.bias.copy_(weights["state_dict"]["module.norm.bias"])

def forward(self, x_in):
hidden_states_out = self.swinViT(x_in, self.normalize)
Expand Down Expand Up @@ -626,10 +624,10 @@ def load_from(self, weights, n_block, layer):
"attn.proj.bias",
"norm2.weight",
"norm2.bias",
"mlp.linear1.weight",
"mlp.linear1.bias",
"mlp.linear2.weight",
"mlp.linear2.bias",
"mlp.fc1.weight",
"mlp.fc1.bias",
"mlp.fc2.weight",
"mlp.fc2.bias",
]
with torch.no_grad():
self.norm1.weight.copy_(weights["state_dict"][root + block_names[0]])
Expand Down Expand Up @@ -950,7 +948,6 @@ def __init__(
elif i_layer == 3:
self.layers4.append(layer)
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.norm = norm_layer(self.num_features)

def proj_out(self, x, normalize=False):
if normalize:
Expand All @@ -967,7 +964,7 @@ def proj_out(self, x, normalize=False):
x = rearrange(x, "n h w c -> n c h w")
return x

def forward(self, x, normalize=False):
def forward(self, x, normalize=True):
x0 = self.patch_embed(x)
x0 = self.pos_drop(x0)
x0_out = self.proj_out(x0, normalize)
Expand Down