Add YOLOv8 support
This commit is contained in:
Marcos Luciano
315
utils/gen_wts_yoloV8.py
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315
utils/gen_wts_yoloV8.py
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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 ultralytics.yolo.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 = -1
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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.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 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 C2f(self, child):
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self.current = child.i
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self.fc.write('\n# C2f\n')
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self.convolutional(child.cv1)
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self.c2f(child.m)
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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 = []
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for i in range(1, len(child.f)):
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r.append(self.get_route(child.f[i]))
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self.route('-1, %s' % str(r)[1:-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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output_idxs = [0 for _ in range(child.nl)]
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for i in range(child.nl):
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r = self.get_route(child.f[i])
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self.route('%d' % r)
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for j in range(len(child.cv3[i])):
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self.convolutional(child.cv3[i][j])
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self.route('%d' % (-1 - len(child.cv3[i])))
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for j in range(len(child.cv2[i])):
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self.convolutional(child.cv2[i][j])
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self.route('-1, %d' % (-2 - len(child.cv2[i])))
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self.shuffle(reshape=[child.no, -1])
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output_idxs[i] = (-1 + i * (-4 - len(child.cv3[i]) - len(child.cv2[i])))
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self.route('%s' % str(output_idxs[::-1])[1:-1], axis=1)
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self.yolo(child)
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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 convolutional(self, cv, act=None, 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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if act is None:
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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' % self.get_value(size) +
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'stride=%s\n' % self.get_value(stride) +
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'pad=%s\n' % self.get_value(pad) +
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g +
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w +
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'activation=%s\n' % act)
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def c2f(self, m):
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self.blocks[self.current] += 1
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for x in m:
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self.get_state_dict(x.state_dict())
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n = len(m)
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shortcut = 1 if m[0].add else 0
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filters = m[0].cv1.conv.out_channels
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size = m[0].cv1.conv.kernel_size
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stride = m[0].cv1.conv.stride
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pad = m[0].cv1.conv.padding
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groups = m[0].cv1.conv.groups
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bias = m[0].cv1.conv.bias
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bn = True if hasattr(m[0].cv1, 'bn') else False
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act = 'linear'
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if hasattr(m[0].cv1, 'act'):
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act = self.get_activation(m[0].cv1.act._get_name())
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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[c2f]\n' +
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'n=%d\n' % n +
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'shortcut=%d\n' % shortcut +
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b +
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'filters=%d\n' % filters +
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'size=%s\n' % self.get_value(size) +
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'stride=%s\n' % self.get_value(stride) +
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'pad=%s\n' % self.get_value(pad) +
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g +
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w +
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'activation=%s\n' % act)
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def route(self, layers, axis=0):
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self.blocks[self.current] += 1
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a = 'axis=%d\n' % axis if axis != 0 else ''
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self.fc.write('\n[route]\n' +
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'layers=%s\n' % layers +
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a)
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def shortcut(self, r, ew='add', act='linear'):
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self.blocks[self.current] += 1
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m = 'mode=mul\n' if ew == 'mul' else ''
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self.fc.write('\n[shortcut]\n' +
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'from=%d\n' % r +
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m +
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'activation=%s\n' % act)
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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 shuffle(self, reshape=None, transpose1=None, transpose2=None, route=None):
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self.blocks[self.current] += 1
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r = 'reshape=%s\n' % ', '.join(str(x) for x in reshape) if reshape is not None else ''
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t1 = 'transpose1=%s\n' % ', '.join(str(x) for x in transpose1) if transpose1 is not None else ''
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t2 = 'transpose2=%s\n' % ', '.join(str(x) for x in transpose2) if transpose2 is not None else ''
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f = 'from=%d\n' % route if route is not None else ''
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self.fc.write('\n[shuffle]\n' +
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r +
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t1 +
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t2 +
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f)
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def yolo(self, child):
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self.blocks[self.current] += 1
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self.fc.write('\n[detect_v8]\n' +
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'num=%d\n' % (child.reg_max * 4) +
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'classes=%d\n' % child.nc)
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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, anchor_points, stride_tensor):
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vr = anchor_points.numpy()
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self.fw.write('{} {} '.format('anchor_points', 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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vr = stride_tensor.numpy()
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self.fw.write('{} {} '.format('stride_tensor', 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_value(self, key):
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if type(key) == int:
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return key
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return key[0] if key[0] == key[1] else str(key)[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 - 1
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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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return 'linear'
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def parse_args():
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parser = argparse.ArgumentParser(description='PyTorch YOLOv8 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 'yolov8' in model_name else 'yolov8_' + model_name + '.wts'
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cfg_file = model_name + '.cfg' if 'yolov8' in model_name else 'yolov8_' + 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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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() == 'Conv':
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layers.Conv(child)
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elif child._get_name() == 'C2f':
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layers.C2f(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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layers.get_anchors(child.anchors.reshape([-1]), child.strides.reshape([-1]))
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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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