(residual): Conv2d(1, 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (layer_1): ResidualUnit( (conv): Sequential( (unit0): Convolution( (conv): Conv2d(2, 4, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (adn): ADN( (N): InstanceNorm2d(4, e...
Propagated kernel_size to ConvBlocks within AttentionUnet (#7734) Addressed hard-coded activation layer in ResNet (#7749) bundle Resolved bundle download issue (#7280) Updated bundle_root directory for NNIGen (#7586) Checked for num_fold and failed early if incorrect (#7634) Enhanced logging ...
Network block utilitiesget_[norm, act, dropout, pool]_layer unpack_itemsmode forapply_transformandCompose New eventINNER_ITERATION_STARTEDin the deepgrow interactive workflow set_dataAPI for cache-based datasets to dynamically update the dataset content Fully compatible with PyTorch 1.9 --disttestsand...
conv.children(): if isinstance(layer, conv_type): # type: ignore torch.nn.init.normal_(layer.weight, std=0.01) # type: ignore torch.nn.init.constant_(layer.bias, 0) # type: ignore torch.nn.init.normal_(layer.weight, std=0.01) torch.nn.init.constant_(layer.bias, 0) self.cls_...
However, I still get the error when loading a pre-trained model: Traceback (most recent call last): File "/home/adp20local/anaconda3/envs/monai06_env/lib/python3.6/site-packages/uvicorn/protocols/http/h11_impl.py", line 396, in run_asgi ...
class UnetUp(Unet): ''' Replace the deconv from monai to upsample ''' def _get_up_layer(self, in_channels, out_channels, strides, is_top): conv = Convolution( self.dimensions, in_channels, out_channels, 1, # Changed from strides self.up_kernel_size, self.act, self.norm, self.dr...