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""" Demonstrates the easy of integration of a custom layer """ | |
import math | |
import torch | |
import torch.nn as nn | |
import numpy as np | |
class MyLinearLayer(nn.Module): | |
""" Custom Linear layer but mimics a standard linear layer """ | |
def __init__(self, size_in, size_out): | |
super().__init__() | |
self.size_in, self.size_out = size_in, size_out | |
weights = torch.Tensor(size_out, size_in) | |
self.weights = nn.Parameter(weights) # nn.Parameter is a Tensor that's a module parameter. | |
bias = torch.Tensor(size_out) | |
self.bias = nn.Parameter(bias) | |
# initialize weights and biases | |
nn.init.kaiming_uniform_(self.weights, a=math.sqrt(5)) # weight init | |
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weights) | |
bound = 1 / math.sqrt(fan_in) | |
nn.init.uniform_(self.bias, -bound, bound) # bias init | |
def forward(self, x): | |
w_times_x= torch.mm(x, self.weights.t()) | |
return torch.add(w_times_x, self.bias) # w times x + b | |
class BasicModel(nn.Module): | |
def __init__(self): | |
super().__init__() | |
self.conv = nn.Conv2d(1, 128, 3) | |
# self.linear = nn.Linear(256, 2) | |
self.linear = MyLinearLayer(256, 2) | |
def forward(self, x): | |
x = self. conv(x) | |
x = x.view(-1, 256) | |
return self.linear(x) | |
torch.manual_seed(0) # for repeatable results | |
basic_model = BasicModel() | |
inp = np.array([[[[1,2,3,4], # batch(=1) x channels(=1) x height x width | |
[1,2,3,4], | |
[1,2,3,4]]]]) | |
x = torch.tensor(inp, dtype=torch.float) | |
print('Forward computation thru model:', basic_model(x)) |
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