338 lines
10 KiB
Python
338 lines
10 KiB
Python
import argparse
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import os
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import struct
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import torch
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from utils.torch_utils import select_device
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class Layers(object):
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def __init__(self, n, size, fw, fc):
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self.blocks = [0 for _ in range(n)]
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self.current = 0
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self.width = size[0] if len(size) == 1 else size[1]
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self.height = size[0]
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self.num = 0
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self.nc = 0
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self.anchors = ''
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self.masks = []
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self.fw = fw
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self.fc = fc
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self.wc = 0
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self.net()
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def Focus(self, child):
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self.current = child.i
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self.fc.write('\n# Focus\n')
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self.reorg()
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self.convolutional(child.conv)
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def Conv(self, child):
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self.current = child.i
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self.fc.write('\n# Conv\n')
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self.convolutional(child)
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def BottleneckCSP(self, child):
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self.current = child.i
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self.fc.write('\n# BottleneckCSP\n')
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self.convolutional(child.cv2)
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self.route('-2')
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self.convolutional(child.cv1)
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idx = -3
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for m in child.m:
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if m.add:
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self.convolutional(m.cv1)
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self.convolutional(m.cv2)
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self.shortcut(-3)
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idx -= 3
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else:
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self.convolutional(m.cv1)
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self.convolutional(m.cv2)
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idx -= 2
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self.convolutional(child.cv3)
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self.route('-1, %d' % (idx - 1))
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self.batchnorm(child.bn, child.act)
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self.convolutional(child.cv4)
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def C3(self, child):
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self.current = child.i
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self.fc.write('\n# C3\n')
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self.convolutional(child.cv2)
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self.route('-2')
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self.convolutional(child.cv1)
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idx = -3
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for m in child.m:
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if m.add:
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self.convolutional(m.cv1)
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self.convolutional(m.cv2)
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self.shortcut(-3)
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idx -= 3
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else:
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self.convolutional(m.cv1)
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self.convolutional(m.cv2)
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idx -= 2
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self.route('-1, %d' % idx)
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self.convolutional(child.cv3)
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def SPP(self, child):
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self.current = child.i
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self.fc.write('\n# SPP\n')
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self.convolutional(child.cv1)
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self.maxpool(child.m[0])
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self.route('-2')
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self.maxpool(child.m[1])
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self.route('-4')
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self.maxpool(child.m[2])
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self.route('-6, -5, -3, -1')
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self.convolutional(child.cv2)
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def SPPF(self, child):
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self.current = child.i
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self.fc.write('\n# SPPF\n')
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self.convolutional(child.cv1)
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self.maxpool(child.m)
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self.maxpool(child.m)
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self.maxpool(child.m)
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self.route('-4, -3, -2, -1')
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self.convolutional(child.cv2)
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def Upsample(self, child):
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self.current = child.i
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self.fc.write('\n# Upsample\n')
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self.upsample(child)
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def Concat(self, child):
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self.current = child.i
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self.fc.write('\n# Concat\n')
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r = self.get_route(child.f[1])
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self.route('-1, %d' % (r - 1))
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def Detect(self, child):
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self.current = child.i
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self.fc.write('\n# Detect\n')
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self.get_anchors(child.state_dict(), child.m[0].out_channels)
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for i, m in enumerate(child.m):
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r = self.get_route(child.f[i])
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self.route('%d' % (r - 1))
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self.convolutional(m, detect=True)
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self.yolo(i)
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def net(self):
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self.fc.write('[net]\n' +
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'width=%d\n' % self.width +
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'height=%d\n' % self.height +
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'channels=3\n' +
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'letter_box=1\n')
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def reorg(self):
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self.blocks[self.current] += 1
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self.fc.write('\n[reorg]\n')
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def convolutional(self, cv, detect=False):
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self.blocks[self.current] += 1
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self.get_state_dict(cv.state_dict())
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if cv._get_name() == 'Conv2d':
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filters = cv.out_channels
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size = cv.kernel_size
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stride = cv.stride
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pad = cv.padding
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groups = cv.groups
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bias = cv.bias
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bn = False
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act = 'linear' if not detect else 'logistic'
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else:
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filters = cv.conv.out_channels
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size = cv.conv.kernel_size
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stride = cv.conv.stride
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pad = cv.conv.padding
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groups = cv.conv.groups
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bias = cv.conv.bias
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bn = True if hasattr(cv, 'bn') else False
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act = self.get_activation(cv.act._get_name()) if hasattr(cv, 'act') else 'linear'
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b = 'batch_normalize=1\n' if bn is True else ''
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g = 'groups=%d\n' % groups if groups > 1 else ''
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w = 'bias=0\n' if bias is None and bn is False else ''
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self.fc.write('\n[convolutional]\n' +
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b +
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'filters=%d\n' % filters +
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'size=%s\n' % (size[0] if len(size) == 2 and size[0] == size[1] else str(size)[1:-1]) +
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'stride=%s\n' % (stride[0] if len(stride) == 2 and stride[0] == stride[1] else str(stride)[1:-1]) +
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'pad=%s\n' % (pad[0] if len(pad) == 2 and pad[0] == pad[1] else str(pad)[1:-1]) +
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g +
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w +
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'activation=%s\n' % act)
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def batchnorm(self, bn, act):
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self.blocks[self.current] += 1
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self.get_state_dict(bn.state_dict())
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filters = bn.num_features
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act = self.get_activation(act._get_name())
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self.fc.write('\n[batchnorm]\n' +
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'filters=%d\n' % filters +
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'activation=%s\n' % act)
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def route(self, layers):
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self.blocks[self.current] += 1
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self.fc.write('\n[route]\n' +
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'layers=%s\n' % layers)
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def shortcut(self, r, activation='linear'):
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self.blocks[self.current] += 1
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self.fc.write('\n[shortcut]\n' +
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'from=%d\n' % r +
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'activation=%s\n' % activation)
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def maxpool(self, m):
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self.blocks[self.current] += 1
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stride = m.stride
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size = m.kernel_size
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mode = m.ceil_mode
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m = 'maxpool_up' if mode else 'maxpool'
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self.fc.write('\n[%s]\n' % m +
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'stride=%d\n' % stride +
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'size=%d\n' % size)
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def upsample(self, child):
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self.blocks[self.current] += 1
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stride = child.scale_factor
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self.fc.write('\n[upsample]\n' +
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'stride=%d\n' % stride)
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def yolo(self, i):
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self.blocks[self.current] += 1
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self.fc.write('\n[yolo]\n' +
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'mask=%s\n' % self.masks[i] +
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'anchors=%s\n' % self.anchors +
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'classes=%d\n' % self.nc +
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'num=%d\n' % self.num +
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'scale_x_y=2.0\n' +
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'new_coords=1\n')
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def get_state_dict(self, state_dict):
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for k, v in state_dict.items():
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if 'num_batches_tracked' not in k:
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vr = v.reshape(-1).numpy()
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self.fw.write('{} {} '.format(k, len(vr)))
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for vv in vr:
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self.fw.write(' ')
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self.fw.write(struct.pack('>f', float(vv)).hex())
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self.fw.write('\n')
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self.wc += 1
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def get_anchors(self, state_dict, out_channels):
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anchor_grid = state_dict['anchor_grid']
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aa = anchor_grid.reshape(-1).tolist()
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am = anchor_grid.tolist()
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self.num = (len(aa) / 2)
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self.nc = int((out_channels / (self.num / len(am))) - 5)
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self.anchors = str(aa)[1:-1]
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n = 0
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for m in am:
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mask = []
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for _ in range(len(m)):
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mask.append(n)
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n += 1
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self.masks.append(str(mask)[1:-1])
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def get_route(self, n):
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r = 0
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for i, b in enumerate(self.blocks):
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if i <= n:
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r += b
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else:
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break
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return r
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def get_activation(self, act):
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if act == 'Hardswish':
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return 'hardswish'
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elif act == 'LeakyReLU':
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return 'leaky'
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elif act == 'SiLU':
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return 'silu'
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def parse_args():
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parser = argparse.ArgumentParser(description='PyTorch YOLOv5 conversion')
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parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
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parser.add_argument(
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'-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
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args = parser.parse_args()
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if not os.path.isfile(args.weights):
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raise SystemExit('Invalid weights file')
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return args.weights, args.size
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pt_file, inference_size = parse_args()
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model_name = os.path.basename(pt_file).split('.pt')[0]
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wts_file = model_name + '.wts' if 'yolov5' in model_name else 'yolov5_' + model_name + '.wts'
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cfg_file = model_name + '.cfg' if 'yolov5' in model_name else 'yolov5_' + model_name + '.cfg'
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device = select_device('cpu')
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model = torch.load(pt_file, map_location=device)['model'].float()
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anchor_grid = model.model[-1].anchors * model.model[-1].stride[..., None, None]
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delattr(model.model[-1], 'anchor_grid')
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model.model[-1].register_buffer('anchor_grid', anchor_grid)
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model.to(device).eval()
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with open(wts_file, 'w') as fw, open(cfg_file, 'w') as fc:
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layers = Layers(len(model.model), inference_size, fw, fc)
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for child in model.model.children():
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if child._get_name() == 'Focus':
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layers.Focus(child)
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elif child._get_name() == 'Conv':
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layers.Conv(child)
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elif child._get_name() == 'BottleneckCSP':
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layers.BottleneckCSP(child)
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elif child._get_name() == 'C3':
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layers.C3(child)
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elif child._get_name() == 'SPP':
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layers.SPP(child)
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elif child._get_name() == 'SPPF':
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layers.SPPF(child)
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elif child._get_name() == 'Upsample':
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layers.Upsample(child)
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elif child._get_name() == 'Concat':
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layers.Concat(child)
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elif child._get_name() == 'Detect':
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layers.Detect(child)
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else:
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raise SystemExit('Model not supported')
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os.system('echo "%d" | cat - %s > temp && mv temp %s' % (layers.wc, wts_file, wts_file))
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