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dronet_torch.py
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dronet_torch.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import onnx
import onnx_tensorrt.backend as backend
# dronet implementation in pytorch.
class DronetTorch(nn.Module):
def __init__(self, img_dims, img_channels, output_dim):
"""
Define model architecture.
## Arguments
`img_dim`: image dimensions.
`img_channels`: Target image channels.
`output_dim`: Dimension of model output.
"""
super(DronetTorch, self).__init__()
# get the device
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
self.img_dims = img_dims
self.channels = img_channels
self.output_dim = output_dim
self.conv_modules = nn.ModuleList()
self.beta = torch.Tensor([0]).float().to(self.device)
# Initialize number of samples for hard-mining
self.conv_modules.append(nn.Conv2d(self.channels, 32, (5,5), stride=(2,2), padding=(2,2)))
filter_amt = np.array([32,64,128])
for f in filter_amt:
x1 = int(f/2) if f!=32 else f
x2 = f
self.conv_modules.append(nn.Conv2d(x1, x2, (3,3), stride=(2,2), padding=(1,1)))
self.conv_modules.append(nn.Conv2d(x2, x2, (3,3), padding=(1,1)))
self.conv_modules.append(nn.Conv2d(x1, x2, (1,1), stride=(2,2)))
# create convolutional modules
self.maxpool1 = nn.MaxPool2d((3,3), (2,2))
bn_amt = np.array([32,32,32,64,64,128])
self.bn_modules = nn.ModuleList()
for i in range(6):
self.bn_modules.append(nn.BatchNorm2d(bn_amt[i]))
self.relu_modules = nn.ModuleList()
for i in range(7):
self.relu_modules.append(nn.ReLU())
self.dropout1 = nn.Dropout()
self.linear1 = nn.Linear(6272, output_dim)
self.linear2 = nn.Linear(6272, output_dim)
self.sigmoid1 = nn.Sigmoid()
self.init_weights()
self.decay = 0.1
def init_weights(self):
'''
intializes weights according to He initialization.
## parameters
None
'''
torch.nn.init.kaiming_normal_(self.conv_modules[1].weight)
torch.nn.init.kaiming_normal_(self.conv_modules[2].weight)
torch.nn.init.kaiming_normal_(self.conv_modules[4].weight)
torch.nn.init.kaiming_normal_(self.conv_modules[5].weight)
torch.nn.init.kaiming_normal_(self.conv_modules[7].weight)
torch.nn.init.kaiming_normal_(self.conv_modules[8].weight)
def forward(self, x):
'''
forward pass of dronet
## parameters
`x`: `Tensor`: The provided input tensor`
'''
bn_idx = 0
conv_idx = 1
relu_idx = 0
x = self.conv_modules[0](x)
x1 = self.maxpool1(x)
for i in range(3):
x2 = self.bn_modules[bn_idx](x1)
x2 = self.relu_modules[relu_idx](x2)
x2 = self.conv_modules[conv_idx](x2)
x2 = self.bn_modules[bn_idx+1](x2)
x2 = self.relu_modules[relu_idx+1](x2)
x2 = self.conv_modules[conv_idx+1](x2)
x1 = self.conv_modules[conv_idx+2](x1)
x3 = torch.add(x1,x2)
x1 = x3
bn_idx+=2
relu_idx+=2
conv_idx+=3
x4 = torch.flatten(x3).reshape(-1, 6272)
x4 = self.relu_modules[-1](x4)
x5 = self.dropout1(x4)
steer = self.linear1(x5)
collision = self.linear2(x5)
collision = self.sigmoid1(collision)
return steer, collision
def loss(self, k, steer_true, steer_pred, coll_true, coll_pred):
'''
loss function for dronet. Is a weighted sum of hard mined mean square
error and hard mined binary cross entropy.
## parameters
`k`: `int`: the value for hard mining; the `k` highest losses will be learned first,
and the others ignored.
`steer_true`: `Tensor`: the torch tensor for the true steering angles. Is of shape
`(N,1)`, where `N` is the amount of samples in the batch.
`steer_pred`: `Tensor`: the torch tensor for the predicted steering angles. Also is of shape
`(N,1)`.
`coll_true`: `Tensor`: the torch tensor for the true probabilities of collision. Is of
shape `(N,1)`
`coll_pred`: `Tensor`: the torch tensor for the predicted probabilities of collision.
Is of shape `(N,1)`
'''
# for steering angle
mse_loss = self.hard_mining_mse(k, steer_true, steer_pred)
# for collision probability
bce_loss = self.beta * (self.hard_mining_entropy(k, coll_true, coll_pred))
return mse_loss + bce_loss
def hard_mining_mse(self, k, y_true, y_pred):
'''
Compute Mean Square Error for steering
evaluation and hard-mining for the current batch.
### parameters
`k`: `int`: number of samples for hard-mining
`y_true`: `Tensor`: torch Tensor of the expected steering angles.
`y_pred`: `Tensor`: torch Tensor of the predicted steering angles.
'''
loss_steer = (y_true - y_pred)**2
# hard mining
# get value of k that is minimum of batch size or the selected value of k
k_min = min(k, y_true.shape[0])
_, indices = torch.topk(loss_steer, k=k_min, dim=0)
max_loss_steer = torch.gather(loss_steer, dim=0, index=indices)
# mean square error
hard_loss_steer = torch.div(torch.sum(max_loss_steer), k_min)
return hard_loss_steer
def hard_mining_entropy(self, k, y_true, y_pred):
'''
computes binary cross entropy for probability collisions and hard-mining.
## parameters
`k`: `int`: number of samples for hard-mining
`y_true`: `Tensor`: torch Tensor of the expected probabilities of collision.
`y_pred`: `Tensor`: torch Tensor of the predicted probabilities of collision.
'''
loss_coll = F.binary_cross_entropy(y_pred, y_true, reduction='none')
k_min = min(k, y_true.shape[0])
_, indices = torch.topk(loss_coll, k=k_min, dim=0)
max_loss_coll = torch.gather(loss_coll, dim=0, index=indices)
hard_loss_coll = torch.div(torch.sum(max_loss_coll), k_min)
return hard_loss_coll
class DronetOnnx():
def __init__(self, img_dims, img_channels, output_dim, verbose=False):
super(DronetOnnx).__init__()
self.dronet = DronetTorch(img_dims, img_channels, output_dim)
self.dronet.to(self.dronet.device)
inference_shape = (1, img_channels, img_dims[0], img_dims[1])
inputs = torch.randn(inference_shape).to(self.dronet.device)
torch.onnx.export(self.dronet, inputs, 'dronet.onnx', verbose=verbose,
output_names=['steer', 'coll'])
def load_model(self, path):
self.model = onnx.load(path)
if not torch.cuda.is_available():
raise NotImplementedError('TensorRT backend does not work for non-CUDA devices.')
# get first gpu
self.engine = backend.prepare(self.model, device='CUDA:0')
def infer(self, data, verbose=False):
input_data = data.numpy().astype(np.float32)
output_data = self.engine.run(input_data)
steer = output_data['steer']
coll = output_data['coll']
if verbose:
print(f'Steering Angle: {steer[0]} radians')
print(f'Collision Prob: {coll[0]}')
return steer, coll
# one dim for steering angle, another for prob. of collision
# dronet = DronetTorch(img_dims=(224,224), img_channels=3, output_dim=1)
# dronet.cuda()
# steer_true = torch.Tensor([[0.1], [0.1], [0.1], [0.1]]).cuda()
# coll_true = torch.Tensor([[0.1], [0.1], [0.1], [0.1]]).cuda()
# m = torch.ones((4,3, 224, 224)).cuda()
# steer_pred, coll_pred = dronet(m)