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Merge pull request #18 from chairc/dev
Reconstruct modules.py, and update package path; Rewrite checkpoint storage and loading functions, add checkpoint files
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,36 @@ | ||
| #!/usr/bin/env python | ||
| # -*- coding:utf-8 -*- | ||
| """ | ||
| @Date : 2023/12/5 10:19 | ||
| @Author : chairc | ||
| @Site : https://github.com/chairc | ||
| """ | ||
| import logging | ||
| import coloredlogs | ||
| import torch.nn as nn | ||
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| logger = logging.getLogger(__name__) | ||
| coloredlogs.install(level="INFO") | ||
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| def get_activation_function(name="silu", inplace=False): | ||
| """ | ||
| Get activation function | ||
| :param name: Activation function name | ||
| :param inplace: can optionally do the operation in-place | ||
| :return Activation function | ||
| """ | ||
| if name == "relu": | ||
| act = nn.ReLU(inplace=inplace) | ||
| elif name == "relu6": | ||
| act = nn.ReLU6(inplace=inplace) | ||
| elif name == "silu": | ||
| act = nn.SiLU(inplace=inplace) | ||
| elif name == "lrelu": | ||
| act = nn.LeakyReLU(0.1, inplace=inplace) | ||
| elif name == "gelu": | ||
| act = nn.GELU() | ||
| else: | ||
| logger.warning(msg=f"Unsupported activation function type: {name}") | ||
| act = nn.SiLU(inplace=inplace) | ||
| return act |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| #!/usr/bin/env python | ||
| # -*- coding:utf-8 -*- | ||
| """ | ||
| @Date : 2023/12/5 10:19 | ||
| @Author : chairc | ||
| @Site : https://github.com/chairc | ||
| """ | ||
| import torch.nn as nn | ||
| from model.modules.activation import get_activation_function | ||
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| class SelfAttention(nn.Module): | ||
| """ | ||
| SelfAttention block | ||
| """ | ||
|
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| def __init__(self, channels, size, act="silu"): | ||
| """ | ||
| Initialize the self-attention block | ||
| :param channels: Channels | ||
| :param size: Size | ||
| :param act: Activation function | ||
| """ | ||
| super(SelfAttention, self).__init__() | ||
| self.channels = channels | ||
| self.size = size | ||
| # batch_first is not supported in pytorch 1.8. | ||
| # If you want to support upgrading to 1.9 and above, or use the following code to transpose | ||
| self.mha = nn.MultiheadAttention(embed_dim=channels, num_heads=4, batch_first=True) | ||
| self.ln = nn.LayerNorm(normalized_shape=[channels]) | ||
| self.ff_self = nn.Sequential( | ||
| nn.LayerNorm(normalized_shape=[channels]), | ||
| nn.Linear(in_features=channels, out_features=channels), | ||
| get_activation_function(name=act), | ||
| nn.Linear(in_features=channels, out_features=channels), | ||
| ) | ||
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| def forward(self, x): | ||
| """ | ||
| SelfAttention forward | ||
| :param x: Input | ||
| :return: attention_value | ||
| """ | ||
| # First perform the shape transformation, and then use 'swapaxes' to exchange the first | ||
| # second dimensions of the new tensor | ||
| x = x.view(-1, self.channels, self.size * self.size).swapaxes(1, 2) | ||
| x_ln = self.ln(x) | ||
| # batch_first is not supported in pytorch 1.8. | ||
| # If you want to support upgrading to 1.9 and above, or use the following code to transpose | ||
| attention_value, _ = self.mha(x_ln, x_ln, x_ln) | ||
| attention_value = attention_value + x | ||
| attention_value = self.ff_self(attention_value) + attention_value | ||
| return attention_value.swapaxes(2, 1).view(-1, self.channels, self.size, self.size) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,131 @@ | ||
| #!/usr/bin/env python | ||
| # -*- coding:utf-8 -*- | ||
| """ | ||
| @Date : 2023/12/5 10:21 | ||
| @Author : chairc | ||
| @Site : https://github.com/chairc | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
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| from model.modules.conv import BaseConv, DoubleConv | ||
| from model.modules.module import CSPLayer | ||
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| class DownBlock(nn.Module): | ||
| """ | ||
| Downsample block | ||
| """ | ||
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| def __init__(self, in_channels, out_channels, emb_channels=256, act="silu"): | ||
| """ | ||
| Initialize the downsample block | ||
| :param in_channels: Input channels | ||
| :param out_channels: Output channels | ||
| :param emb_channels: Embed channels | ||
| :param act: Activation function | ||
| """ | ||
| super().__init__() | ||
| self.maxpool_conv = nn.Sequential( | ||
| nn.MaxPool2d(2), | ||
| DoubleConv(in_channels=in_channels, out_channels=in_channels, residual=True, act=act), | ||
| DoubleConv(in_channels=in_channels, out_channels=out_channels, act=act), | ||
| ) | ||
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| self.emb_layer = nn.Sequential( | ||
| nn.SiLU(), | ||
| nn.Linear(in_features=emb_channels, out_features=out_channels), | ||
| ) | ||
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| def forward(self, x, time): | ||
| """ | ||
| DownBlock forward | ||
| :param x: Input | ||
| :param time: Time | ||
| :return: x + emb | ||
| """ | ||
| x = self.maxpool_conv(x) | ||
| emb = self.emb_layer(time)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1]) | ||
| return x + emb | ||
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| class UpBlock(nn.Module): | ||
| """ | ||
| Upsample Block | ||
| """ | ||
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| def __init__(self, in_channels, out_channels, emb_channels=256, act="silu"): | ||
| """ | ||
| Initialize the upsample block | ||
| :param in_channels: Input channels | ||
| :param out_channels: Output channels | ||
| :param emb_channels: Embed channels | ||
| :param act: Activation function | ||
| """ | ||
| super().__init__() | ||
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| self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True) | ||
| self.conv = nn.Sequential( | ||
| DoubleConv(in_channels=in_channels, out_channels=in_channels, residual=True, act=act), | ||
| DoubleConv(in_channels=in_channels, out_channels=out_channels, mid_channels=in_channels // 2, act=act), | ||
| ) | ||
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| self.emb_layer = nn.Sequential( | ||
| nn.SiLU(), | ||
| nn.Linear(in_features=emb_channels, out_features=out_channels), | ||
| ) | ||
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| def forward(self, x, skip_x, time): | ||
| """ | ||
| UpBlock forward | ||
| :param x: Input | ||
| :param skip_x: Merged input | ||
| :param time: Time | ||
| :return: x + emb | ||
| """ | ||
| x = self.up(x) | ||
| x = torch.cat([skip_x, x], dim=1) | ||
| x = self.conv(x) | ||
| emb = self.emb_layer(time)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1]) | ||
| return x + emb | ||
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| class CSPDarkDownBlock(nn.Module): | ||
| def __init__(self, in_channels, out_channels, emb_channels=256, n=1, act="silu"): | ||
| super().__init__() | ||
| self.conv_csp = nn.Sequential( | ||
| BaseConv(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, act=act), | ||
| CSPLayer(in_channels=out_channels, out_channels=out_channels, n=n, act=act) | ||
| ) | ||
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| self.emb_layer = nn.Sequential( | ||
| nn.SiLU(), | ||
| nn.Linear(in_features=emb_channels, out_features=out_channels), | ||
| ) | ||
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| def forward(self, x, time): | ||
| x = self.conv_csp(x) | ||
| emb = self.emb_layer(time)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1]) | ||
| return x + emb | ||
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| class CSPDarkUpBlock(nn.Module): | ||
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| def __init__(self, in_channels, out_channels, emb_channels=256, n=1, act="silu"): | ||
| super().__init__() | ||
| self.up = nn.Upsample(scale_factor=2, mode="nearest") | ||
| self.conv = BaseConv(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, act=act) | ||
| self.csp = CSPLayer(in_channels=in_channels, out_channels=out_channels, n=n, shortcut=False, act=act) | ||
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| self.emb_layer = nn.Sequential( | ||
| nn.SiLU(), | ||
| nn.Linear(in_features=emb_channels, out_features=out_channels), | ||
| ) | ||
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| def forward(self, x, skip_x, time): | ||
| x = self.conv(x) | ||
| x = self.up(x) | ||
| x = torch.cat([skip_x, x], dim=1) | ||
| x = self.conv(x) | ||
| emb = self.emb_layer(time)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1]) | ||
| return x + emb |
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