monai Add norm param to ResNet (Project-MONAI#7752) May 23, 2024 tests Add norm param to ResNet (Project-MONAI#7752) May 23, 2024 .clang-format adds a basic clang formatter (Project-MONAI#1006) Sep 12, 2020 .deepsource.toml CI: pre-commit (Project-MONAI#2843) Aug 27, 2021 ...
INSTANCE_NVFUSER for enhanced performance in 3D instance norm Support of string representation of dtype in convert_data_type Added new options iteration_log, iteration_log to the logging handlers Base Docker image upgraded to nvcr.io/nvidia/pytorch:22.04-py3 from nvcr.io/nvidia/pytorch:21.10-py3...
AI Toolkit for Healthcare Imaging. Contribute to Project-MONAI/MONAI development by creating an account on GitHub.
(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, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (A): PReLU(num_parameters...
@@ -678,7 +678,7 @@ def load_from(self, weights, n_block, layer): self.norm1.weight.copy_(weights["state_dict"][root + block_names[0]]) self.norm1.bias.copy_(weights["state_dict"][root + block_names[1]]) self.attn.relative_position_bias_table.copy_(weights["state_dict"]...
Generic module loaders and Gaussian mixture models using the PyTorch JIT compilation Inverse of image patch sampling transforms Network block utilitiesget_[norm, act, dropout, pool]_layer unpack_itemsmode forapply_transformandCompose New eventINNER_ITERATION_STARTEDin the deepgrow interactive workflow ...
0.norm2.bias", "downsamples.0.norm3.weight", "downsamples.0.norm3.bias", "downsamples.1.norm1.weight", "downsamples.1.norm1.bias", "downsamples.1.norm2.weight", "downsamples.1.norm2.bias", "downsamples.1.norm3.weight", "downsamples.1.norm3.bias", "downsamples.2.norm1....
Acti-norm-dropout block Upsampling layer Autoencoder, Variational autoencoder FCNet Support of initialisation from pre-trained weights for densenet, SENet, multichannel AHNet Layer-wise learning rate API New model metrics and event handlers based on occlusion sensitivity, confusion matrix, surface distance...
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...