64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
import torch.nn as nn
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import torch.nn.functional as F
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from inference.models.grasp_model import GraspModel, ResidualBlock
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class GenerativeResnet(GraspModel):
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def __init__(self, input_channels=1, dropout=False, prob=0.0, channel_size=32):
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super(GenerativeResnet, self).__init__()
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self.conv1 = nn.Conv2d(input_channels, 32, kernel_size=9, stride=1, padding=4)
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self.bn1 = nn.BatchNorm2d(32)
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self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1)
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self.bn2 = nn.BatchNorm2d(64)
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self.conv3 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
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self.bn3 = nn.BatchNorm2d(128)
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self.res1 = ResidualBlock(128, 128)
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self.res2 = ResidualBlock(128, 128)
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self.res3 = ResidualBlock(128, 128)
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self.res4 = ResidualBlock(128, 128)
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self.res5 = ResidualBlock(128, 128)
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self.conv4 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, output_padding=1)
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self.bn4 = nn.BatchNorm2d(64)
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self.conv5 = nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=2, output_padding=1)
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self.bn5 = nn.BatchNorm2d(32)
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self.conv6 = nn.ConvTranspose2d(32, 32, kernel_size=9, stride=1, padding=4)
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self.pos_output = nn.Conv2d(32, 1, kernel_size=2)
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self.cos_output = nn.Conv2d(32, 1, kernel_size=2)
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self.sin_output = nn.Conv2d(32, 1, kernel_size=2)
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self.width_output = nn.Conv2d(32, 1, kernel_size=2)
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self.dropout1 = nn.Dropout(p=prob)
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for m in self.modules():
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if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
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nn.init.xavier_uniform_(m.weight, gain=1)
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def forward(self, x_in):
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x = F.relu(self.bn1(self.conv1(x_in)))
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)))
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x = self.res1(x)
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x = self.res2(x)
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x = self.res3(x)
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x = self.res4(x)
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x = self.res5(x)
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x = F.relu(self.bn4(self.conv4(x)))
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x = F.relu(self.bn5(self.conv5(x)))
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x = self.conv6(x)
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pos_output = self.pos_output(self.dropout1(x))
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cos_output = self.cos_output(self.dropout1(x))
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sin_output = self.sin_output(self.dropout1(x))
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width_output = self.width_output(self.dropout1(x))
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return pos_output, cos_output, sin_output, width_output
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