154 lines
3.8 KiB
Python
154 lines
3.8 KiB
Python
"""
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Collection of flow strategies
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"""
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from __future__ import print_function
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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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from models.utils import Base
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UNIT_TESTING = False
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class Conv2dReLU(Base):
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def __init__(
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self, n_inputs, n_outputs, kernel_size=3, stride=1, padding=0,
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bias=True):
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super().__init__()
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self.nn = nn.Conv2d(n_inputs, n_outputs, kernel_size, padding=padding)
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def forward(self, x):
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h = self.nn(x)
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y = F.relu(h)
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return y
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class ResidualBlock(Base):
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def __init__(self, n_channels, kernel, Conv2dAct):
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super().__init__()
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self.nn = torch.nn.Sequential(
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Conv2dAct(n_channels, n_channels, kernel, padding=1),
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torch.nn.Conv2d(n_channels, n_channels, kernel, padding=1),
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)
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def forward(self, x):
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h = self.nn(x)
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h = F.relu(h + x)
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return h
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class DenseLayer(Base):
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def __init__(self, args, n_inputs, growth, Conv2dAct):
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super().__init__()
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conv1x1 = Conv2dAct(
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n_inputs, n_inputs, kernel_size=1, stride=1,
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padding=0, bias=True)
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self.nn = torch.nn.Sequential(
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conv1x1,
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Conv2dAct(
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n_inputs, growth, kernel_size=3, stride=1,
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padding=1, bias=True),
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)
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def forward(self, x):
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h = self.nn(x)
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h = torch.cat([x, h], dim=1)
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return h
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class DenseBlock(Base):
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def __init__(
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self, args, n_inputs, n_outputs, kernel, Conv2dAct):
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super().__init__()
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depth = args.densenet_depth
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future_growth = n_outputs - n_inputs
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layers = []
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for d in range(depth):
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growth = future_growth // (depth - d)
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layers.append(DenseLayer(args, n_inputs, growth, Conv2dAct))
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n_inputs += growth
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future_growth -= growth
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self.nn = torch.nn.Sequential(*layers)
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def forward(self, x):
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return self.nn(x)
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class Identity(Base):
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def __init__(self):
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super.__init__()
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def forward(self, x):
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return x
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class NN(Base):
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def __init__(
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self, args, c_in, c_out, height, width, nn_type, kernel=3):
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super().__init__()
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Conv2dAct = Conv2dReLU
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n_channels = args.n_channels
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if nn_type == 'shallow':
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if args.network1x1 == 'standard':
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conv1x1 = Conv2dAct(
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n_channels, n_channels, kernel_size=1, stride=1,
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padding=0, bias=False)
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layers = [
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Conv2dAct(c_in, n_channels, kernel, padding=1),
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conv1x1]
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layers += [torch.nn.Conv2d(n_channels, c_out, kernel, padding=1)]
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elif nn_type == 'resnet':
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layers = [
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Conv2dAct(c_in, n_channels, kernel, padding=1),
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ResidualBlock(n_channels, kernel, Conv2dAct),
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ResidualBlock(n_channels, kernel, Conv2dAct)]
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layers += [
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torch.nn.Conv2d(n_channels, c_out, kernel, padding=1)
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]
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elif nn_type == 'densenet':
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layers = [
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DenseBlock(
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args=args,
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n_inputs=c_in,
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n_outputs=n_channels + c_in,
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kernel=kernel,
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Conv2dAct=Conv2dAct)]
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layers += [
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torch.nn.Conv2d(n_channels + c_in, c_out, kernel, padding=1)
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]
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else:
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raise ValueError
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self.nn = torch.nn.Sequential(*layers)
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# Set parameters of last conv-layer to zero.
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if not UNIT_TESTING:
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self.nn[-1].weight.data.zero_()
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self.nn[-1].bias.data.zero_()
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def forward(self, x):
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return self.nn(x)
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