855 lines
29 KiB
Python
855 lines
29 KiB
Python
import functools
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import numpy as np
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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 torch.nn import init
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from torch.optim import lr_scheduler
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###############################################################################
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# Helper Functions
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###############################################################################
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def get_filter(filt_size=3):
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if filt_size == 1:
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a = np.array(
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[
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1.0,
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]
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)
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elif filt_size == 2:
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a = np.array([1.0, 1.0])
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elif filt_size == 3:
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a = np.array([1.0, 2.0, 1.0])
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elif filt_size == 4:
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a = np.array([1.0, 3.0, 3.0, 1.0])
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elif filt_size == 5:
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a = np.array([1.0, 4.0, 6.0, 4.0, 1.0])
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elif filt_size == 6:
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a = np.array([1.0, 5.0, 10.0, 10.0, 5.0, 1.0])
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elif filt_size == 7:
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a = np.array([1.0, 6.0, 15.0, 20.0, 15.0, 6.0, 1.0])
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filt = torch.Tensor(a[:, None] * a[None, :])
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filt = filt / torch.sum(filt)
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return filt
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class Downsample(nn.Module):
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def __init__(self, channels, pad_type="reflect", filt_size=3, stride=2, pad_off=0):
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super(Downsample, self).__init__()
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self.filt_size = filt_size
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self.pad_off = pad_off
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self.pad_sizes = [
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int(1.0 * (filt_size - 1) / 2),
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int(np.ceil(1.0 * (filt_size - 1) / 2)),
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int(1.0 * (filt_size - 1) / 2),
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int(np.ceil(1.0 * (filt_size - 1) / 2)),
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]
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self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes]
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self.stride = stride
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self.off = int((self.stride - 1) / 2.0)
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self.channels = channels
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filt = get_filter(filt_size=self.filt_size)
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self.register_buffer(
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"filt", filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
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)
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self.pad = get_pad_layer(pad_type)(self.pad_sizes)
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def forward(self, inp):
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if self.filt_size == 1:
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if self.pad_off == 0:
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return inp[:, :, :: self.stride, :: self.stride]
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else:
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return self.pad(inp)[:, :, :: self.stride, :: self.stride]
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else:
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return F.conv2d(
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self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1]
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)
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class Upsample2(nn.Module):
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def __init__(self, scale_factor, mode="nearest"):
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super().__init__()
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self.factor = scale_factor
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self.mode = mode
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def forward(self, x):
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return torch.nn.functional.interpolate(
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x, scale_factor=self.factor, mode=self.mode
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)
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class Upsample(nn.Module):
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def __init__(self, channels, pad_type="repl", filt_size=4, stride=2):
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super(Upsample, self).__init__()
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self.filt_size = filt_size
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self.filt_odd = np.mod(filt_size, 2) == 1
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self.pad_size = int((filt_size - 1) / 2)
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self.stride = stride
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self.off = int((self.stride - 1) / 2.0)
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self.channels = channels
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filt = get_filter(filt_size=self.filt_size) * (stride**2)
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self.register_buffer(
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"filt", filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
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)
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self.pad = get_pad_layer(pad_type)([1, 1, 1, 1])
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def forward(self, inp):
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ret_val = F.conv_transpose2d(
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self.pad(inp),
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self.filt,
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stride=self.stride,
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padding=1 + self.pad_size,
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groups=inp.shape[1],
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)[:, :, 1:, 1:]
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if self.filt_odd:
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return ret_val
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else:
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return ret_val[:, :, :-1, :-1]
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def get_pad_layer(pad_type):
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if pad_type in ["refl", "reflect"]:
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PadLayer = nn.ReflectionPad2d
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elif pad_type in ["repl", "replicate"]:
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PadLayer = nn.ReplicationPad2d
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elif pad_type == "zero":
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PadLayer = nn.ZeroPad2d
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else:
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print("Pad type [%s] not recognized" % pad_type)
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return PadLayer
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class Identity(nn.Module):
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def forward(self, x):
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return x
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def get_norm_layer(norm_type="instance"):
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"""Return a normalization layer
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Parameters:
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norm_type (str) -- the name of the normalization layer: batch | instance | none
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For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
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For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
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"""
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if norm_type == "batch":
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norm_layer = functools.partial(
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nn.BatchNorm2d, affine=True, track_running_stats=True
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)
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elif norm_type == "instance":
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norm_layer = functools.partial(
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nn.InstanceNorm2d, affine=False, track_running_stats=False
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)
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elif norm_type == "none":
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def norm_layer(x):
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return Identity()
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else:
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raise NotImplementedError("normalization layer [%s] is not found" % norm_type)
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return norm_layer
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def get_scheduler(optimizer, opt):
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"""Return a learning rate scheduler
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Parameters:
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optimizer -- the optimizer of the network
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opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．
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opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
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For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
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and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
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For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
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See https://pytorch.org/docs/stable/optim.html for more details.
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"""
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if opt.lr_policy == "linear":
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def lambda_rule(epoch):
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lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(
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opt.n_epochs_decay + 1
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)
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return lr_l
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
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elif opt.lr_policy == "step":
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scheduler = lr_scheduler.StepLR(
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optimizer, step_size=opt.lr_decay_iters, gamma=0.1
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)
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elif opt.lr_policy == "plateau":
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scheduler = lr_scheduler.ReduceLROnPlateau(
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optimizer, mode="min", factor=0.2, threshold=0.01, patience=5
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)
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elif opt.lr_policy == "cosine":
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scheduler = lr_scheduler.CosineAnnealingLR(
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optimizer, T_max=opt.n_epochs, eta_min=0
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)
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else:
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return NotImplementedError(
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"learning rate policy [%s] is not implemented", opt.lr_policy
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)
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return scheduler
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def init_weights(net, init_type="kaiming", init_gain=0.02, debug=False):
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"""Initialize network weights.
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Parameters:
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net (network) -- network to be initialized
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init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
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init_gain (float) -- scaling factor for normal, xavier and orthogonal.
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We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
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work better for some applications. Feel free to try yourself.
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"""
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def init_func(m): # define the initialization function
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classname = m.__class__.__name__
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if hasattr(m, "weight") and (
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classname.find("Conv") != -1 or classname.find("Linear") != -1
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):
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if debug:
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print(classname)
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if init_type == "normal":
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init.normal_(m.weight.data, 0.0, init_gain)
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elif init_type == "xavier":
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init.xavier_normal_(m.weight.data, gain=init_gain)
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elif init_type == "kaiming":
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init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
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elif init_type == "orthogonal":
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init.orthogonal_(m.weight.data, gain=init_gain)
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else:
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raise NotImplementedError(
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"initialization method [%s] is not implemented" % init_type
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)
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if hasattr(m, "bias") and m.bias is not None:
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init.constant_(m.bias.data, 0.0)
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elif (
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classname.find("BatchNorm2d") != -1
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): # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
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init.normal_(m.weight.data, 1.0, init_gain)
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init.constant_(m.bias.data, 0.0)
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net.apply(init_func) # apply the initialization function <init_func>
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def init_net(
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net,
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init_type="normal",
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init_gain=0.02,
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gpu_ids=[],
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debug=False,
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initialize_weights=True,
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):
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"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
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Parameters:
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net (network) -- the network to be initialized
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init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
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gain (float) -- scaling factor for normal, xavier and orthogonal.
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gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
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Return an initialized network.
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"""
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if len(gpu_ids) > 0:
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assert torch.cuda.is_available()
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net.to(gpu_ids[0])
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# if not amp:
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# net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs for non-AMP training
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if initialize_weights:
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init_weights(net, init_type, init_gain=init_gain, debug=debug)
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return net
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def define_D(
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input_nc,
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ndf,
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netD,
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n_layers_D=3,
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image_size=256,
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feature_dim=256,
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max_conv_dim=512,
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norm="batch",
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init_type="normal",
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init_gain=0.02,
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no_antialias=False,
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gpu_ids=[],
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opt=None,
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):
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"""Create a discriminator
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Parameters:
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input_nc (int) -- the number of channels in input images
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ndf (int) -- the number of filters in the first conv layer
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netD (str) -- the architecture's name: basic | n_layers | pixel
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n_layers_D (int) -- the number of conv layers in the discriminator; effective when netD=='n_layers'
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norm (str) -- the type of normalization layers used in the network.
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init_type (str) -- the name of the initialization method.
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init_gain (float) -- scaling factor for normal, xavier and orthogonal.
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gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
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Returns a discriminator
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Our current implementation provides three types of discriminators:
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[basic]: 'PatchGAN' classifier described in the original pix2pix paper.
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It can classify whether 70x70 overlapping patches are real or fake.
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Such a patch-level discriminator architecture has fewer parameters
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than a full-image discriminator and can work on arbitrarily-sized images
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in a fully convolutional fashion.
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[n_layers]: With this mode, you cna specify the number of conv layers in the discriminator
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with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
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[pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
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It encourages greater color diversity but has no effect on spatial statistics.
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The discriminator has been initialized by <init_net>. It uses Leaky RELU for non-linearity.
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"""
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net = None
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norm_layer = get_norm_layer(norm_type=norm)
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if netD == "basic": # default PatchGAN classifier
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net = NLayerDiscriminator(
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input_nc,
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ndf,
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n_layers=3,
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image_size=image_size,
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norm_layer=norm_layer,
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no_antialias=no_antialias,
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)
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elif netD == "n_layers": # more options
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net = NLayerDiscriminator(
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input_nc,
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ndf,
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n_layers_D,
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norm_layer=norm_layer,
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no_antialias=no_antialias,
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)
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elif netD == "pixel": # classify if each pixel is real or fake
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net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
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# elif 'stylegan2' in netD:
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# net = StyleGAN2Discriminator(input_nc, ndf, n_layers_D, no_antialias=no_antialias, opt=opt)
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# elif netD == 'NCE': # default PatchGAN classifier
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# net = NCEDiscriminator(input_nc, ndf, n_layers=3, image_size = image_size, feature_dim = feature_dim, max_conv_dim = max_conv_dim, norm_layer=norm_layer, no_antialias=no_antialias,)
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else:
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raise NotImplementedError(
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"Discriminator model name [%s] is not recognized" % netD
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)
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return init_net(
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net, init_type, init_gain, gpu_ids, initialize_weights=("stylegan2" not in netD)
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)
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##############################################################################
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# Classes
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##############################################################################
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class GANLoss(nn.Module):
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"""Define different GAN objectives.
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The GANLoss class abstracts away the need to create the target label tensor
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that has the same size as the input.
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"""
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def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
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"""Initialize the GANLoss class.
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Parameters:
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gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
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target_real_label (bool) - - label for a real image
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target_fake_label (bool) - - label of a fake image
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Note: Do not use sigmoid as the last layer of Discriminator.
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LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
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"""
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super(GANLoss, self).__init__()
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self.register_buffer("real_label", torch.tensor(target_real_label))
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self.register_buffer("fake_label", torch.tensor(target_fake_label))
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self.gan_mode = gan_mode
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if gan_mode == "lsgan":
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self.loss = nn.MSELoss()
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elif gan_mode == "vanilla":
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self.loss = nn.BCEWithLogitsLoss()
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elif gan_mode in ["wgangp", "nonsaturating"]:
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self.loss = None
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elif gan_mode == "hinge":
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self.loss = None
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else:
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raise NotImplementedError("gan mode %s not implemented" % gan_mode)
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def get_target_tensor(self, prediction, target_is_real):
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"""Create label tensors with the same size as the input.
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Parameters:
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prediction (tensor) - - tpyically the prediction from a discriminator
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target_is_real (bool) - - if the ground truth label is for real images or fake images
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Returns:
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A label tensor filled with ground truth label, and with the size of the input
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"""
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if target_is_real:
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target_tensor = self.real_label
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else:
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target_tensor = self.fake_label
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return target_tensor.expand_as(prediction)
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def __call__(self, prediction, target_is_real):
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"""Calculate loss given Discriminator's output and grount truth labels.
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Parameters:
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prediction (tensor) - - tpyically the prediction output from a discriminator
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target_is_real (bool) - - if the ground truth label is for real images or fake images
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Returns:
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the calculated loss.
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"""
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bs = prediction.size(0)
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if self.gan_mode in ["lsgan", "vanilla"]:
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target_tensor = self.get_target_tensor(prediction, target_is_real)
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loss = self.loss(prediction, target_tensor)
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elif self.gan_mode == "wgangp":
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if target_is_real:
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loss = -prediction.mean()
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else:
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loss = prediction.mean()
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elif self.gan_mode == "nonsaturating":
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if target_is_real:
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loss = F.softplus(-prediction).view(bs, -1).mean(dim=1)
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else:
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loss = F.softplus(prediction).view(bs, -1).mean(dim=1)
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elif self.gan_mode == "hinge":
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if target_is_real:
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minvalue = torch.min(
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prediction - 1, torch.zeros(prediction.shape).to(prediction.device)
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)
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loss = -torch.mean(minvalue)
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else:
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minvalue = torch.min(
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-prediction - 1, torch.zeros(prediction.shape).to(prediction.device)
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)
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loss = -torch.mean(minvalue)
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return loss
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def cal_gradient_penalty(
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netD, real_data, fake_data, device, type="mixed", constant=1.0, lambda_gp=10.0
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):
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"""Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
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Arguments:
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netD (network) -- discriminator network
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real_data (tensor array) -- real images
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fake_data (tensor array) -- generated images from the generator
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device (str) -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
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type (str) -- if we mix real and fake data or not [real | fake | mixed].
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constant (float) -- the constant used in formula ( | |gradient||_2 - constant)^2
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lambda_gp (float) -- weight for this loss
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Returns the gradient penalty loss
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"""
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if lambda_gp > 0.0:
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if (
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type == "real"
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): # either use real images, fake images, or a linear interpolation of two.
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interpolatesv = real_data
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elif type == "fake":
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interpolatesv = fake_data
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elif type == "mixed":
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alpha = torch.rand(real_data.shape[0], 1, device=device)
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alpha = (
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alpha.expand(
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real_data.shape[0], real_data.nelement() // real_data.shape[0]
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)
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.contiguous()
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.view(*real_data.shape)
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)
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interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
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else:
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raise NotImplementedError("{} not implemented".format(type))
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||
interpolatesv.requires_grad_(True)
|
||
disc_interpolates = netD(interpolatesv)
|
||
gradients = torch.autograd.grad(
|
||
outputs=disc_interpolates,
|
||
inputs=interpolatesv,
|
||
grad_outputs=torch.ones(disc_interpolates.size()).to(device),
|
||
create_graph=True,
|
||
retain_graph=True,
|
||
only_inputs=True,
|
||
)
|
||
gradients = gradients[0].view(real_data.size(0), -1) # flat the data
|
||
gradient_penalty = (
|
||
((gradients + 1e-16).norm(2, dim=1) - constant) ** 2
|
||
).mean() * lambda_gp # added eps
|
||
return gradient_penalty, gradients
|
||
else:
|
||
return 0.0, None
|
||
|
||
|
||
class Normalize(nn.Module):
|
||
def __init__(self, power=2):
|
||
super(Normalize, self).__init__()
|
||
self.power = power
|
||
|
||
def forward(self, x):
|
||
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
|
||
out = x.div(norm + 1e-7)
|
||
return out
|
||
|
||
|
||
##################################################################################
|
||
# Sequential Models
|
||
##################################################################################
|
||
|
||
|
||
# class ResBlocks(nn.Module):
|
||
# def __init__(
|
||
# self, num_blocks, dim, norm="inst", activation="relu", pad_type="zero", nz=0
|
||
# ):
|
||
# super(ResBlocks, self).__init__()
|
||
# self.model = []
|
||
# for i in range(num_blocks):
|
||
# self.model += [
|
||
# ResBlock(
|
||
# dim, norm=norm, activation=activation, pad_type=pad_type, nz=nz
|
||
# )
|
||
# ]
|
||
# self.model = nn.Sequential(*self.model)
|
||
|
||
# def forward(self, x):
|
||
# return self.model(x)
|
||
|
||
|
||
##################################################################################
|
||
# Basic Blocks
|
||
##################################################################################
|
||
def cat_feature(x, y):
|
||
y_expand = y.view(y.size(0), y.size(1), 1, 1).expand(
|
||
y.size(0), y.size(1), x.size(2), x.size(3)
|
||
)
|
||
x_cat = torch.cat([x, y_expand], 1)
|
||
return x_cat
|
||
|
||
|
||
class Conv2dBlock(nn.Module):
|
||
def __init__(
|
||
self,
|
||
input_dim,
|
||
output_dim,
|
||
kernel_size,
|
||
stride,
|
||
padding=0,
|
||
norm="none",
|
||
activation="relu",
|
||
pad_type="zero",
|
||
):
|
||
super(Conv2dBlock, self).__init__()
|
||
self.use_bias = True
|
||
# initialize padding
|
||
if pad_type == "reflect":
|
||
self.pad = nn.ReflectionPad2d(padding)
|
||
elif pad_type == "zero":
|
||
self.pad = nn.ZeroPad2d(padding)
|
||
else:
|
||
assert 0, "Unsupported padding type: {}".format(pad_type)
|
||
|
||
# initialize normalization
|
||
norm_dim = output_dim
|
||
if norm == "batch":
|
||
self.norm = nn.BatchNorm2d(norm_dim)
|
||
elif norm == "inst":
|
||
self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=False)
|
||
elif norm == "ln":
|
||
self.norm = LayerNorm(norm_dim)
|
||
elif norm == "none":
|
||
self.norm = None
|
||
else:
|
||
assert 0, "Unsupported normalization: {}".format(norm)
|
||
|
||
# initialize activation
|
||
if activation == "relu":
|
||
self.activation = nn.ReLU(inplace=True)
|
||
elif activation == "lrelu":
|
||
self.activation = nn.LeakyReLU(0.2, inplace=True)
|
||
elif activation == "prelu":
|
||
self.activation = nn.PReLU()
|
||
elif activation == "selu":
|
||
self.activation = nn.SELU(inplace=True)
|
||
elif activation == "tanh":
|
||
self.activation = nn.Tanh()
|
||
elif activation == "none":
|
||
self.activation = None
|
||
else:
|
||
assert 0, "Unsupported activation: {}".format(activation)
|
||
|
||
# initialize convolution
|
||
self.conv = nn.Conv2d(
|
||
input_dim, output_dim, kernel_size, stride, bias=self.use_bias
|
||
)
|
||
|
||
def forward(self, x):
|
||
x = self.conv(self.pad(x))
|
||
if self.norm:
|
||
x = self.norm(x)
|
||
if self.activation:
|
||
x = self.activation(x)
|
||
return x
|
||
|
||
|
||
class LinearBlock(nn.Module):
|
||
def __init__(self, input_dim, output_dim, norm="none", activation="relu"):
|
||
super(LinearBlock, self).__init__()
|
||
use_bias = True
|
||
# initialize fully connected layer
|
||
self.fc = nn.Linear(input_dim, output_dim, bias=use_bias)
|
||
|
||
# initialize normalization
|
||
norm_dim = output_dim
|
||
if norm == "batch":
|
||
self.norm = nn.BatchNorm1d(norm_dim)
|
||
elif norm == "inst":
|
||
self.norm = nn.InstanceNorm1d(norm_dim)
|
||
elif norm == "ln":
|
||
self.norm = LayerNorm(norm_dim)
|
||
elif norm == "none":
|
||
self.norm = None
|
||
else:
|
||
assert 0, "Unsupported normalization: {}".format(norm)
|
||
|
||
# initialize activation
|
||
if activation == "relu":
|
||
self.activation = nn.ReLU(inplace=True)
|
||
elif activation == "lrelu":
|
||
self.activation = nn.LeakyReLU(0.2, inplace=True)
|
||
elif activation == "prelu":
|
||
self.activation = nn.PReLU()
|
||
elif activation == "selu":
|
||
self.activation = nn.SELU(inplace=True)
|
||
elif activation == "tanh":
|
||
self.activation = nn.Tanh()
|
||
elif activation == "none":
|
||
self.activation = None
|
||
else:
|
||
assert 0, "Unsupported activation: {}".format(activation)
|
||
|
||
def forward(self, x):
|
||
out = self.fc(x)
|
||
if self.norm:
|
||
out = self.norm(out)
|
||
if self.activation:
|
||
out = self.activation(out)
|
||
return out
|
||
|
||
|
||
##################################################################################
|
||
# Normalization layers
|
||
##################################################################################
|
||
|
||
|
||
class LayerNorm(nn.Module):
|
||
def __init__(self, num_features, eps=1e-5, affine=True):
|
||
super(LayerNorm, self).__init__()
|
||
self.num_features = num_features
|
||
self.affine = affine
|
||
self.eps = eps
|
||
|
||
if self.affine:
|
||
self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
|
||
self.beta = nn.Parameter(torch.zeros(num_features))
|
||
|
||
def forward(self, x):
|
||
shape = [-1] + [1] * (x.dim() - 1)
|
||
mean = x.view(x.size(0), -1).mean(1).view(*shape)
|
||
std = x.view(x.size(0), -1).std(1).view(*shape)
|
||
x = (x - mean) / (std + self.eps)
|
||
|
||
if self.affine:
|
||
shape = [1, -1] + [1] * (x.dim() - 2)
|
||
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
|
||
return x
|
||
|
||
|
||
class NLayerDiscriminator(nn.Module):
|
||
"""Defines a PatchGAN discriminator"""
|
||
|
||
def __init__(
|
||
self,
|
||
input_nc,
|
||
ndf=64,
|
||
n_layers=3,
|
||
image_size=256,
|
||
norm_layer=nn.BatchNorm2d,
|
||
no_antialias=False,
|
||
):
|
||
"""Construct a PatchGAN discriminator
|
||
|
||
Parameters:
|
||
input_nc (int) -- the number of channels in input images
|
||
ndf (int) -- the number of filters in the last conv layer
|
||
n_layers (int) -- the number of conv layers in the discriminator
|
||
norm_layer -- normalization layer
|
||
"""
|
||
super(NLayerDiscriminator, self).__init__()
|
||
if (
|
||
type(norm_layer) == functools.partial
|
||
): # no need to use bias as BatchNorm2d has affine parameters
|
||
use_bias = norm_layer.func == nn.InstanceNorm2d
|
||
else:
|
||
use_bias = norm_layer == nn.InstanceNorm2d
|
||
|
||
kw = 4
|
||
padw = 1
|
||
if no_antialias:
|
||
sequence = [
|
||
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
|
||
nn.LeakyReLU(0.2, True),
|
||
]
|
||
else:
|
||
sequence = [
|
||
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw),
|
||
nn.LeakyReLU(0.2, True),
|
||
Downsample(ndf),
|
||
]
|
||
nf_mult = 1
|
||
nf_mult_prev = 1
|
||
for n in range(1, n_layers): # gradually increase the number of filters
|
||
nf_mult_prev = nf_mult
|
||
nf_mult = min(2**n, 8)
|
||
if no_antialias:
|
||
sequence += [
|
||
nn.Conv2d(
|
||
ndf * nf_mult_prev,
|
||
ndf * nf_mult,
|
||
kernel_size=kw,
|
||
stride=2,
|
||
padding=padw,
|
||
bias=use_bias,
|
||
),
|
||
norm_layer(ndf * nf_mult),
|
||
nn.LeakyReLU(0.2, True),
|
||
]
|
||
else:
|
||
sequence += [
|
||
nn.Conv2d(
|
||
ndf * nf_mult_prev,
|
||
ndf * nf_mult,
|
||
kernel_size=kw,
|
||
stride=1,
|
||
padding=padw,
|
||
bias=use_bias,
|
||
),
|
||
norm_layer(ndf * nf_mult),
|
||
nn.LeakyReLU(0.2, True),
|
||
Downsample(ndf * nf_mult),
|
||
]
|
||
|
||
nf_mult_prev = nf_mult
|
||
nf_mult = min(2**n_layers, 8)
|
||
sequence += [
|
||
nn.Conv2d(
|
||
ndf * nf_mult_prev,
|
||
ndf * nf_mult,
|
||
kernel_size=kw,
|
||
stride=1,
|
||
padding=padw,
|
||
bias=use_bias,
|
||
),
|
||
norm_layer(ndf * nf_mult),
|
||
nn.LeakyReLU(0.2, True),
|
||
]
|
||
|
||
sequence += [
|
||
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
|
||
] # output 1 channel prediction map
|
||
self.model = nn.Sequential(*sequence)
|
||
|
||
def forward(self, input):
|
||
"""Standard forward."""
|
||
logit = self.model(input)
|
||
return logit
|
||
|
||
|
||
class PixelDiscriminator(nn.Module):
|
||
"""Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
|
||
|
||
def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
|
||
"""Construct a 1x1 PatchGAN discriminator
|
||
|
||
Parameters:
|
||
input_nc (int) -- the number of channels in input images
|
||
ndf (int) -- the number of filters in the last conv layer
|
||
norm_layer -- normalization layer
|
||
"""
|
||
super(PixelDiscriminator, self).__init__()
|
||
if (
|
||
type(norm_layer) == functools.partial
|
||
): # no need to use bias as BatchNorm2d has affine parameters
|
||
use_bias = norm_layer.func == nn.InstanceNorm2d
|
||
else:
|
||
use_bias = norm_layer == nn.InstanceNorm2d
|
||
|
||
self.net = [
|
||
nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
|
||
nn.LeakyReLU(0.2, True),
|
||
nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
|
||
norm_layer(ndf * 2),
|
||
nn.LeakyReLU(0.2, True),
|
||
nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias),
|
||
]
|
||
|
||
self.net = nn.Sequential(*self.net)
|
||
|
||
def forward(self, input):
|
||
"""Standard forward."""
|
||
return self.net(input)
|
||
|
||
|
||
class PatchDiscriminator(NLayerDiscriminator):
|
||
"""Defines a PatchGAN discriminator"""
|
||
|
||
def __init__(
|
||
self,
|
||
input_nc,
|
||
ndf=64,
|
||
n_layers=3,
|
||
norm_layer=nn.BatchNorm2d,
|
||
no_antialias=False,
|
||
):
|
||
super().__init__(input_nc, ndf, 2, norm_layer, no_antialias)
|
||
|
||
def forward(self, input):
|
||
B, C, H, W = input.size(0), input.size(1), input.size(2), input.size(3)
|
||
size = 16
|
||
Y = H // size
|
||
X = W // size
|
||
input = input.view(B, C, Y, size, X, size)
|
||
input = (
|
||
input.permute(0, 2, 4, 1, 3, 5).contiguous().view(B * Y * X, C, size, size)
|
||
)
|
||
return super().forward(input)
|
||
|
||
|
||
class GroupedChannelNorm(nn.Module):
|
||
def __init__(self, num_groups):
|
||
super().__init__()
|
||
self.num_groups = num_groups
|
||
|
||
def forward(self, x):
|
||
shape = list(x.shape)
|
||
new_shape = [shape[0], self.num_groups, shape[1] // self.num_groups] + shape[2:]
|
||
x = x.view(*new_shape)
|
||
mean = x.mean(dim=2, keepdim=True)
|
||
std = x.std(dim=2, keepdim=True)
|
||
x_norm = (x - mean) / (std + 1e-7)
|
||
return x_norm.view(*shape)
|