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style: minor style fixes.
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@okunator
okunator committed Dec 29, 2022
1 parent 3d931e2 commit 7a9b4a0
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1 change: 0 additions & 1 deletion README.md
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Expand Up @@ -8,7 +8,6 @@
[![PyTorch - Version](https://img.shields.io/badge/PYTORCH-1.8.1+-red?style=for-the-badge&logo=pytorch)](https://pytorch.org/)
[![Python - Version](https://img.shields.io/badge/PYTHON-3.8+-red?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/)
<br>

[![Github Test](https://img.shields.io/github/actions/workflow/status/okunator/cellseg_models.pytorch/tests.yml?label=Tests&logo=github&&style=for-the-badge)](https://github.com/okunator/cellseg_models.pytorch/actions/workflows/tests.yml)
[![Pypi](https://img.shields.io/pypi/v/cellseg-models-pytorch?color=blue&logo=pypi&style=for-the-badge)](https://pypi.org/project/cellseg-models-pytorch/)
[![Codecov](https://img.shields.io/codecov/c/github/okunator/cellseg_models.pytorch?logo=codecov&style=for-the-badge&token=oGSj7FZ1lm)](https://codecov.io/gh/okunator/cellseg_models.pytorch)
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206 changes: 0 additions & 206 deletions cellseg_models_pytorch/modules/conv_layer.py
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Expand Up @@ -198,209 +198,3 @@ def forward(self, x: torch.Tensor, style: torch.Tensor = None) -> torch.Tensor:
x = x + identity

return x


##############
##############
##############
# from typing import List, Tuple

# import torch
# import torch.nn as nn

# from .conv_block import ConvBlock
# from .misc_modules import ChannelPool

# __all__ = ["ConvLayer"]


# class ConvLayer(nn.Module):
# def __init__(
# self,
# in_channels: int,
# out_channels: int,
# n_blocks: int = 2,
# layer_residual: bool = False,
# short_skip: str = "residual",
# style_channels: int = None,
# expand_ratios: Tuple[float, ...] = (1.0, 1.0),
# block_types: Tuple[str, ...] = ("basic", "basic"),
# normalizations: Tuple[str, ...] = ("bn", "bn"),
# activations: Tuple[str, ...] = ("relu", "relu"),
# convolutions: Tuple[str, ...] = ("conv", "conv"),
# kernel_sizes: Tuple[int, ...] = (3, 3),
# groups: Tuple[int, ...] = (1, 1),
# biases: Tuple[bool, ...] = (True, True),
# preactivates: Tuple[bool, ...] = (False, False),
# attentions: Tuple[str, ...] = (None, None),
# preattends: Tuple[bool, ...] = (False, False),
# use_styles: Tuple[bool, ...] = (False, False),
# **kwargs,
# ) -> None:
# """Chain conv-blocks in a ModuleDict to compose a full layer.

# Optional:
# - add a style vector to the output at the end of each conv block(Cellpose)

# Parameters
# ----------
# in_channels : int
# Number of input channels.
# out_channels : int
# Number of output channels.
# n_blocks : int, default=2
# Number of ConvBlocks used in this layer.
# layer_residual : bool, default=False
# Apply a layer level residual skip. I.e x + layer(x). NOTE: residual
# skips can be also applied inside the ConvBlocks, so this is justextra.
# style_channels : int, default=None
# Number of style vector channels. If None, style vectors are ignored.
# short_skip : str, default="residual"
# The name of the short skip method. One of: "residual", "dense","basic"
# expand_ratios : Tuple[float, ...], default=(1.0, 1.0):
# Expansion/Squeeze ratios for the out channels of each conv block.
# block_types : Tuple[str, ...], default=("basic", "basic")
# The name of the conv-blocks. Length of the tuple has toequal`n_blocks`
# One of: "basic". "mbconv", "fmbconv" "dws", "bottleneck".
# normalizations : Tuple[str, ...], default=("bn", "bn"):
# Normalization methods. One of: "bn", "bcn", "gn", "in", "ln", "lrn"
# activations : Tuple[str, ...], default=("relu", "relu")
# Activation methods. One of: "mish", "swish", "relu", "relu6", "rrelu",
# "selu", "celu", "gelu", "glu", "tanh", "sigmoid", "silu", "prelu",
# "leaky-relu", "elu", "hardshrink", "tanhshrink", "hardsigmoid"
# convolutions : Tuple[str, ...], default=("conv", "conv")
# The convolution method. One of: "conv", "wsconv", "scaled_wsconv"
# preactivates : Tuple[bool, ...], default=(False, False)
# Pre-activations flags for the conv-blocks.
# kernel_sizes : Tuple[int, ...], default=(3, 3)
# The size of the convolution kernels in each conv block.
# groups : int, default=(1, 1)
# Number of groups for the kernels in each convolution blocks.
# biases : Tuple[bool, ...], default=(True, True)
# Include bias terms in the convolution blocks.
# attentions : Tuple[str, ...], default=(None, None)
# Attention methods. One of: "se", "scse", "gc", "eca", None
# preattends : Tuple[bool, ...], default=(False, False)
# If True, Attention is applied at the beginning of forward pass.
# use_styles : Tuple[bool, ...], default=(False, False)
# If True and `style_channels` is not None, adds a style vec to the
# ConvBlock outputs.

# Raises
# ------
# ValueError:
# If lengths of the tuple arguments are not equal to `n_blocks`.
# """
# super().__init__()
# self.layer_residual = layer_residual
# self.short_skip = short_skip
# self.in_channels = in_channels

# illegal_args = [
# (k, a)
# for k, a in locals().items()
# if isinstance(a, tuple) and len(a) != n_blocks
# ]

# if illegal_args:
# raise ValueError(
# f"All the tuple-arg lengths need to be equalto`n_blocks`={n_blocks}. "
# f"Illegal args: {illegal_args}"
# )

# self.conv_blocks = nn.ModuleDict()
# blocks = list(range(n_blocks))
# for i in blocks:
# out = int(out_channels * expand_ratios[i])

# conv_block = ConvBlock(
# name=block_types[i],
# in_channels=in_channels,
# out_channels=out,
# style_channels=style_channels,
# short_skip=short_skip,
# kernel_size=kernel_sizes[i],
# groups=groups[i],
# bias=biases[i],
# normalization=normalizations[i],
# convolution=convolutions[i],
# activation=activations[i],
# attention=attentions[i],
# preactivate=preactivates[i],
# preattend=preattends[i],
# use_style=use_styles[i],
# **kwargs,
# )
# self.conv_blocks[f"{short_skip}_{block_types[i]}_{i + 1}"] = conv_block

# if short_skip == "dense":
# in_channels += conv_block.out_channels
# else:
# in_channels = conv_block.out_channels

# self.out_channels = conv_block.out_channels

# if short_skip == "dense":
# self.transition = ConvBlock(
# name="basic",
# in_channels=in_channels,
# short_skip="basic",
# out_channels=out_channels,
# same_padding=False,
# bias=False,
# kernel_size=1,
# convolution=conv_block.block.conv_choice,
# normalization=normalizations[-1],
# activation=activations[-1],
# preactivate=preactivates[-1],
# )
# self.out_channels = self.transition.out_channels

# self.downsample = None
# if layer_residual and self.in_channels != self.out_channels:
# self.downsample = ChannelPool(
# in_channels=self.in_channels,
# out_channels=self.out_channels,
# convolution=convolutions[-1],
# normalization=normalizations[-1],
# )

# def forward_features_dense(
# self, init_features: List[torch.Tensor], style: torch.Tensor = None
# ) -> torch.Tensor:
# """Dense forward pass."""
# features = [init_features]
# for conv_block in self.conv_blocks.values():
# new_features = conv_block(features, style)
# features.append(new_features)

# x = torch.cat(features, 1)
# x = self.transition(x)

# return x

# def forward_features(
# self, x: torch.Tensor, style: torch.Tensor = None
# ) -> torch.Tensor:
# """Regular forward pass."""
# for conv_block in self.conv_blocks.values():
# x = conv_block(x, style)

# return x

# def forward(self, x: torch.Tensor, style: torch.Tensor = None) -> torch.Tensor:
# """Forward pass of the conv-layer."""
# if self.layer_residual:
# identity = x
# if self.downsample is not None:
# identity = self.downsample(x)

# if self.short_skip == "dense":
# x = self.forward_features_dense(x, style)
# else:
# x = self.forward_features(x, style)

# if self.layer_residual:
# x = x + identity

# return x
6 changes: 3 additions & 3 deletions cellseg_models_pytorch/modules/transformers.py
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Expand Up @@ -203,12 +203,12 @@ def __init__(
n_blocks : int, default=2
Number of SelfAttentionBlocks used in this layer.
block_types : Tuple[str, ...], default=("exact", "exact")
The name of the SelfAttentionBlocks in the TransformerLayer.
The name/type of the SelfAttentionBlocks in the TransformerLayer.
Length of the tuple has to equal `n_blocks`.
Allowed names: ("exact", "linformer").
computation_types : Tuple[str, ...], default=("basic", "basic")
The name of the SelfAttentionBlocks in the TransformerLayer.
Length of the tuple has to equal `n_blocks`
The way of computing the attention matrices in the SelfAttentionBlocks
in the TransformerLayer. Length of the tuple has to equal `n_blocks`
Allowed styles: "basic". "slice", "flash", "memeff", "slice_memeff".
dropouts : Tuple[float, ...], default=(False, False)
Dropout probabilities for the SelfAttention blocks.
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