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from torch import nn | |
from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 | |
class Residual(nn.Module): | |
def __init__(self, inp_dim, out_dim, use_gn=False): | |
super(Residual, self).__init__() | |
self.relu = nn.ReLU() | |
# self.bn1 = nn.BatchNorm2d(inp_dim) | |
self.conv1 = make_conv3x3(inp_dim, int(out_dim / 2), 1, use_relu=False, use_gn=use_gn) | |
# self.bn2 = nn.BatchNorm2d(int(out_dim / 2)) | |
self.conv2 = make_conv3x3(int(out_dim / 2), int(out_dim / 2), 3, use_relu=False, use_gn=use_gn) | |
# self.bn3 = nn.BatchNorm2d(int(out_dim / 2)) | |
self.conv3 = make_conv3x3(int(out_dim / 2), out_dim, 1, use_relu=False, use_gn=use_gn) | |
if inp_dim == out_dim: | |
self.need_skip = False | |
else: | |
self.need_skip = True | |
self.skip_layer = make_conv3x3(inp_dim, out_dim, 1, use_relu=False, use_gn=False) | |
def forward(self, x): | |
if self.need_skip: | |
residual = self.skip_layer(x) | |
else: | |
residual = x | |
out = x | |
# out = self.bn1(out) | |
out = self.relu(out) | |
out = self.conv1(out) | |
# out = self.bn2(out) | |
out = self.relu(out) | |
out = self.conv2(out) | |
# out = self.bn3(out) | |
out = self.relu(out) | |
out = self.conv3(out) | |
out += residual | |
return out | |
class Hourglass(nn.Module): | |
def __init__(self, n, f, gn=False, increase=0): | |
super(Hourglass, self).__init__() | |
nf = f + increase | |
self.up1 = Residual(f, f) | |
# Lower branch | |
self.pool1 = nn.MaxPool2d(2, 2) | |
self.low1 = Residual(f, nf) | |
self.n = n | |
# Recursive hourglass | |
if self.n > 1: | |
self.low2 = Hourglass(n - 1, nf, gn=gn) | |
else: | |
self.low2 = Residual(nf, nf, gn) | |
self.low3 = Residual(nf, f, gn) | |
self.up2 = nn.Upsample(scale_factor=2, mode="nearest") | |
def forward(self, x): | |
up1 = self.up1(x) | |
pool1 = self.pool1(x) | |
low1 = self.low1(pool1) | |
low2 = self.low2(low1) | |
low3 = self.low3(low2) | |
up2 = self.up2(low3) | |
return up1 + up2 | |