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darknet.py
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darknet.py
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import torch
import torch.nn as nn
import numpy as np
import os
from util import *
cfgfile = 'cfg/yolov3.cfg'
def parse_cfg(cfgfile):
"""
Takes a configuration file
Returns a list of blocks. Each blocks describes a block in the neural
network to be built. Block is represented as a dictionary in the list
"""
file = open(cfgfile, 'r')
lines = file.read().split('\n')
lines = [x for x in lines if len(x) > 0]
lines = [x for x in lines if x[0] != '#']
lines = [x.rstrip().lstrip() for x in lines]
block = {}
blocks = []
for line in lines:
if line[0] == '[':
if len(block) != 0:
blocks.append(block)
block = {}
# print(line, sep='\n')
block['type'] = line[1: -1].rstrip()
else:
key, value = line.split('=')
block[key.rstrip()] = value.lstrip()
blocks.append(block)
return blocks
def create_modules(blocks):
net_info = blocks[0] # Capture information about the input and pre-processing
module_list = nn.ModuleList()
prev_filters = 3
output_filters = []
for index, x in enumerate(blocks[1:]):
module = nn.Sequential()
# check the type of block
# create a new module for the block
# append to module_list
if x['type'] == 'convolutional':
# get the info about the layer
activation = x['activation']
try: # has BN
batch_normalize = int(x['batch_normalize'])
bias = False
except:
batch_normalize = 0
bias = True
filters = int(x['filters'])
padding = int(x['pad'])
kernel_size = int(x['size'])
stride = int(x['stride'])
if padding:
pad = (kernel_size - 1) // 2
else:
pad = 0
# add the convolutional layer
conv = nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=bias)
module.add_module('conv_{0}'.format(index), conv)
# add BN layer
if batch_normalize:
bn = nn.BatchNorm2d(filters)
module.add_module("batch_norm_{0}".format(index), bn)
# check the activation
# it is either Linear or Leaky ReLU for YOLO
if activation == 'leaky':
activn = nn.LeakyReLU(0.1, inplace=True)
module.add_module('leaky_{0}'.format(index), activn)
# if it's an upsampling layer, we use Bilinear2dUpsampling
elif x['type'] == 'upsample':
stride = int(x['stride'])
upsample = nn.UpsamplingBilinear2d(scale_factor=stride)
module.add_module('upsample_{0}'.format(index), upsample)
# if it is a route layer, one or two paras
# one para: route to one layer
# two para: route to two layers
elif x['type'] == 'route':
x['layers'] = x['layers'].split(',')
# start of a route
start = int(x['layers'][0])
# end, if there exists one
try:
end = int(x['layers'][1])
except:
end = 0
# positive anotation
if start > 0:
start -= index
if end > 0:
end -= index
route = EmptyLayer()
module.add_module('route_{0}'.format(index), route)
if end < 0:
filters = output_filters[index + start] + output_filters[index + end]
else:
filters = output_filters[index + start]
elif x['type'] == 'yolo':
mask = x['mask'].split(',')
mask = [int(x) for x in mask]
anchors = x['anchors'].split(',')
anchors = [int(a) for a in anchors]
anchors = [(anchors[i], anchors[i+1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in mask]
detection = DetectionLayer(anchors)
module.add_module('Detection_{0}'.format(index), detection)
# shortcut corresponds to skip connection
elif x['type'] == 'shortcut':
shortcut = EmptyLayer()
module.add_module('shortcut_{}'.format(index), shortcut)
# do some bookkeeping
module_list.append(module)
prev_filters = filters
output_filters.append(filters)
return (net_info, module_list)
class EmptyLayer(nn.Module):
def __init__(self):
super().__init__()
class DetectionLayer(nn.Module):
def __init__(self, anchors):
super().__init__()
self.anchors = anchors
class Darknet(nn.Module):
def __init__(self, cfgfile):
super().__init__()
self.blocks = parse_cfg(cfgfile)
self.net_info, self.module_list = create_modules(self.blocks)
def forward(self, x, CUDA):
# two purposes
# first: calculate the output
# second: transform the output detection feature maps in a way
# that it can be processed easier (like normalize the dimensions)
modules = self.blocks[1:]
outputs = {}
# print("QWQWQWQ")
# print(len(self.blocks), len(self.module_list))
# print(self.module_list)
# types = [i['type'] for i in self.blocks]
# for i in range(len(types)):
# print(types[i], sep='\n')
# print("QWQWQWQ")
write = 0
for i, module in enumerate(modules):
module_type = module['type']
if module_type == 'convolutional' or module_type == 'upsample':
x = x.float()
x = self.module_list[i](x)
elif module_type == 'route':
layers = module['layers']
layers = [int(a) for a in layers]
if layers[0] > 0:
layers[0] -= - i
if len(layers) == 1:
x = outputs[i + layers[0]]
else:
if layers[1] > 0:
layers[1] -= i
if len(layers) == 1:
x = outputs[i + layers[0]]
else:
if layers[1] > 0:
layers[1] -= i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif module_type == 'shortcut':
from_ = int(module['from'])
x = outputs[i - 1] + outputs[i + from_]
elif module_type == 'yolo':
# print("DEBUG: yolo")
# print(i)
anchors = self.module_list[i][0].anchors
# get the input dimensions
inp_dim = int(self.net_info['height'])
# get the number of classes
num_classes = int(module['classes'])
# transform
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def get_test_input():
img_ = cv2.imread('./imgs/dog-cycle-car.png')
img_ = cv2.resize(img_, (416, 416))
img_ = img_[:,:,::-1].transpose((2, 0, 1)) # BGR -> RGB
img_ = img_[np.newaxis,:,:,:] / 255.0 # convert to float
img_ = torch.tensor(img_, dtype=torch.float)
return img_
print(os.getcwd())
model = Darknet('cfg/yolov3.cfg')
inp = get_test_input()
pred = model(inp, torch.cuda.is_available())
print(pred)