jonstreeterandClaude
36860b09dd
This fixes the ModuleNotFoundError issues that occurred during clean installations. Changes: - Embedded colormnet_source dependencies directly into repository as: - colormnet_model/ (model architecture) - colormnet_inference/ (inference pipeline) - colormnet_util/ (utility functions) - colormnet_dataset/ (data transforms and utilities) - Updated all internal imports to use new embedded paths - Removed external colormnet_source path dependency from __init__.py - Added graceful fallback for spatial_correlation_sampler CUDA extension - Fixed shape calculation bugs in PyTorch fallback implementation - Added missing dependencies (hickle, scikit-image) to auto-install The repository is now self-contained and can be installed without requiring the separate colormnet_source directory. Tested and verified: - Node imports work correctly - Colorization pipeline runs successfully - Model loads and processes video frames Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
78 lines
3.0 KiB
Python
78 lines
3.0 KiB
Python
# Modified from https://github.com/Jongchan/attention-module/blob/master/MODELS/cbam.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class BasicConv(nn.Module):
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def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
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super(BasicConv, self).__init__()
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self.out_channels = out_planes
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self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
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def forward(self, x):
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x = self.conv(x)
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return x
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class Flatten(nn.Module):
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def forward(self, x):
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return x.view(x.size(0), -1)
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class ChannelGate(nn.Module):
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def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
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super(ChannelGate, self).__init__()
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self.gate_channels = gate_channels
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self.mlp = nn.Sequential(
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Flatten(),
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nn.Linear(gate_channels, gate_channels // reduction_ratio),
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nn.ReLU(),
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nn.Linear(gate_channels // reduction_ratio, gate_channels)
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)
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self.pool_types = pool_types
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def forward(self, x):
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channel_att_sum = None
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for pool_type in self.pool_types:
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if pool_type=='avg':
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avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
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channel_att_raw = self.mlp( avg_pool )
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elif pool_type=='max':
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max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
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channel_att_raw = self.mlp( max_pool )
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if channel_att_sum is None:
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channel_att_sum = channel_att_raw
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else:
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channel_att_sum = channel_att_sum + channel_att_raw
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scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)
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return x * scale
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class ChannelPool(nn.Module):
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def forward(self, x):
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return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )
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class SpatialGate(nn.Module):
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def __init__(self):
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super(SpatialGate, self).__init__()
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kernel_size = 7
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self.compress = ChannelPool()
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self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)
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def forward(self, x):
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x_compress = self.compress(x)
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x_out = self.spatial(x_compress)
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scale = torch.sigmoid(x_out) # broadcasting
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return x * scale
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class CBAM(nn.Module):
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def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):
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super(CBAM, self).__init__()
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self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)
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self.no_spatial=no_spatial
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if not no_spatial:
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self.SpatialGate = SpatialGate()
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def forward(self, x):
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x_out = self.ChannelGate(x)
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if not self.no_spatial:
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x_out = self.SpatialGate(x_out)
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return x_out
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