Chris MyauandClaude Sonnet 4.6
df9d9eb7c0
Wraps the Adobe Research PIH model (CVPR 2023) as ComfyUI nodes. Includes auto-download of the pretrained checkpoint on first use. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
# Copyright 2023 Adobe. All rights reserved.
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# This file is licensed to you under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License. You may obtain a copy
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# of the License at http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software distributed under
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# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
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# OF ANY KIND, either express or implied. See the License for the specific language
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# governing permissions and limitations under the License.
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import torch
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import torch.nn as nn
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class IBN(nn.Module):
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r"""Instance-Batch Normalization layer from
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`"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net"
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<https://arxiv.org/pdf/1807.09441.pdf>`
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Args:
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planes (int): Number of channels for the input tensor
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ratio (float): Ratio of instance normalization in the IBN layer
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"""
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def __init__(self, planes, ratio=0.5):
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super(IBN, self).__init__()
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self.half = int(planes * ratio)
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self.IN = nn.InstanceNorm2d(self.half, affine=True)
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self.BN = nn.BatchNorm2d(planes - self.half)
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def forward(self, x):
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split = torch.split(x, self.half, 1)
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out1 = self.IN(split[0].contiguous())
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out2 = self.BN(split[1].contiguous())
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out = torch.cat((out1, out2), 1)
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return out
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=16):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, int(channel / reduction), bias=False),
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nn.ReLU(inplace=True),
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nn.Linear(int(channel / reduction), channel, bias=False),
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nn.Sigmoid(),
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y.expand_as(x)
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