385 lines
16 KiB
Python
Executable File
385 lines
16 KiB
Python
Executable File
from collections import defaultdict
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import os
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import torch.nn as nn
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from utils.parse_config import *
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from utils.utils import *
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ONNX_EXPORT = False
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def create_modules(module_defs):
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"""
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Constructs module list of layer blocks from module configuration in module_defs
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"""
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hyperparams = module_defs.pop(0)
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output_filters = [int(hyperparams['channels'])]
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module_list = nn.ModuleList()
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for i, module_def in enumerate(module_defs):
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modules = nn.Sequential()
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if module_def['type'] == 'convolutional':
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bn = int(module_def['batch_normalize'])
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filters = int(module_def['filters'])
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kernel_size = int(module_def['size'])
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pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
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modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
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out_channels=filters,
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kernel_size=kernel_size,
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stride=int(module_def['stride']),
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padding=pad,
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bias=not bn))
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if bn:
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modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
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if module_def['activation'] == 'leaky':
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modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
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elif module_def['type'] == 'maxpool':
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kernel_size = int(module_def['size'])
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stride = int(module_def['stride'])
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if kernel_size == 2 and stride == 1:
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modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
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maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
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modules.add_module('maxpool_%d' % i, maxpool)
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elif module_def['type'] == 'upsample':
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# upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest') # WARNING: deprecated
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upsample = Upsample(scale_factor=int(module_def['stride']))
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modules.add_module('upsample_%d' % i, upsample)
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elif module_def['type'] == 'route':
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layers = [int(x) for x in module_def['layers'].split(',')]
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filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
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modules.add_module('route_%d' % i, EmptyLayer())
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elif module_def['type'] == 'shortcut':
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filters = output_filters[int(module_def['from'])]
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modules.add_module('shortcut_%d' % i, EmptyLayer())
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elif module_def['type'] == 'yolo':
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anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
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# Extract anchors
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anchors = [float(x) for x in module_def['anchors'].split(',')]
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anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
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anchors = [anchors[i] for i in anchor_idxs]
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num_classes = int(module_def['classes'])
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img_height = int(hyperparams['height'])
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# Define detection layer
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yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs, cfg=hyperparams['cfg'])
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modules.add_module('yolo_%d' % i, yolo_layer)
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# Register module list and number of output filters
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module_list.append(modules)
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output_filters.append(filters)
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return hyperparams, module_list
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class EmptyLayer(nn.Module):
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"""Placeholder for 'route' and 'shortcut' layers"""
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def __init__(self):
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super(EmptyLayer, self).__init__()
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class Upsample(nn.Module):
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# Custom Upsample layer (nn.Upsample gives deprecated warning message)
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def __init__(self, scale_factor=1, mode='nearest'):
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super(Upsample, self).__init__()
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self.scale_factor = scale_factor
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self.mode = mode
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def forward(self, x):
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return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
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class YOLOLayer(nn.Module):
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def __init__(self, anchors, nC, img_dim, anchor_idxs, cfg):
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super(YOLOLayer, self).__init__()
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anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
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nA = len(anchors)
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self.anchors = anchors
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self.nA = nA # number of anchors (3)
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self.nC = nC # number of classes (80)
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self.bbox_attrs = 5 + nC
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self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
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if anchor_idxs[0] == (nA * 2): # 6
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stride = 32
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elif anchor_idxs[0] == nA: # 3
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stride = 16
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else:
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stride = 8
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if cfg.endswith('yolov3-tiny.cfg'):
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stride *= 2
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# Build anchor grids
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nG = int(self.img_dim / stride) # number grid points
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self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
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self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float()
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self.anchor_wh = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors]) # scale anchors
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self.anchor_w = self.anchor_wh[:, 0].view((1, nA, 1, 1))
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self.anchor_h = self.anchor_wh[:, 1].view((1, nA, 1, 1))
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self.weights = class_weights()
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self.loss_means = torch.ones(6)
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self.yolo_layer = anchor_idxs[0] / nA # 2, 1, 0
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self.stride = stride
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self.nG = nG
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if ONNX_EXPORT: # use fully populated and reshaped tensors
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self.anchor_w = self.anchor_w.repeat((1, 1, nG, nG)).view(1, -1, 1)
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self.anchor_h = self.anchor_h.repeat((1, 1, nG, nG)).view(1, -1, 1)
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self.grid_x = self.grid_x.repeat(1, nA, 1, 1).view(1, -1, 1)
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self.grid_y = self.grid_y.repeat(1, nA, 1, 1).view(1, -1, 1)
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self.grid_xy = torch.cat((self.grid_x, self.grid_y), 2)
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self.anchor_wh = torch.cat((self.anchor_w, self.anchor_h), 2) / nG
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def forward(self, p, targets=None, var=None):
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FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
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bs = 1 if ONNX_EXPORT else p.shape[0] # batch size
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nG = self.nG # number of grid points
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if p.is_cuda and not self.weights.is_cuda:
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self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
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self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
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self.weights, self.loss_means = self.weights.cuda(), self.loss_means.cuda()
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# p.view(12, 255, 13, 13) -- > (12, 3, 13, 13, 80) # (bs, anchors, grid, grid, classes + xywh)
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p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
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# Training
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if targets is not None:
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MSELoss = nn.MSELoss()
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BCEWithLogitsLoss = nn.BCEWithLogitsLoss()
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CrossEntropyLoss = nn.CrossEntropyLoss()
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# Get outputs
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x = torch.sigmoid(p[..., 0]) # Center x
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y = torch.sigmoid(p[..., 1]) # Center y
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p_conf = p[..., 4] # Conf
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p_cls = p[..., 5:] # Class
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# Width and height (yolo method)
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w = p[..., 2] # Width
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h = p[..., 3] # Height
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width = torch.exp(w.data) * self.anchor_w
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height = torch.exp(h.data) * self.anchor_h
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# Width and height (power method)
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# w = torch.sigmoid(p[..., 2]) # Width
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# h = torch.sigmoid(p[..., 3]) # Height
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# width = ((w.data * 2) ** 2) * self.anchor_w
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# height = ((h.data * 2) ** 2) * self.anchor_h
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tx, ty, tw, th, mask, tcls = build_targets(targets, self.anchor_wh, self.nA, self.nC, nG)
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tcls = tcls[mask]
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if x.is_cuda:
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tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda()
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# Compute losses
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nT = sum([len(x) for x in targets]) # number of targets
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nM = mask.sum().float() # number of anchors (assigned to targets)
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nB = len(targets) # batch size
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k = nM / nB
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if nM > 0:
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lx = k * MSELoss(x[mask], tx[mask])
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ly = k * MSELoss(y[mask], ty[mask])
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lw = k * MSELoss(w[mask], tw[mask])
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lh = k * MSELoss(h[mask], th[mask])
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lcls = (k / 4) * CrossEntropyLoss(p_cls[mask], torch.argmax(tcls, 1))
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# lcls = (k * 10) * BCEWithLogitsLoss(p_cls[mask], tcls.float())
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else:
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lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
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lconf = (k * 64) * BCEWithLogitsLoss(p_conf, mask.float())
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# Sum loss components
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loss = lx + ly + lw + lh + lconf + lcls
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return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), nT
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else:
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if ONNX_EXPORT:
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p = p.view(1, -1, 85)
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xy = torch.sigmoid(p[..., 0:2]) + self.grid_xy # x, y
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width_height = torch.exp(p[..., 2:4]) * self.anchor_wh # width, height
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p_conf = torch.sigmoid(p[..., 4:5]) # Conf
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p_cls = p[..., 5:85]
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# Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py
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# p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf
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p_cls = torch.exp(p_cls).permute(2, 1, 0)
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p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute(2, 1, 0) # F.softmax() equivalent
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p_cls = p_cls.permute(2, 1, 0)
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return torch.cat((xy / nG, width_height, p_conf, p_cls), 2).squeeze().t()
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p[..., 0] = torch.sigmoid(p[..., 0]) + self.grid_x # x
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p[..., 1] = torch.sigmoid(p[..., 1]) + self.grid_y # y
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p[..., 2] = torch.exp(p[..., 2]) * self.anchor_w # width
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p[..., 3] = torch.exp(p[..., 3]) * self.anchor_h # height
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p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf
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p[..., :4] *= self.stride
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# reshape from [1, 3, 13, 13, 85] to [1, 507, 85]
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return p.view(bs, -1, 5 + self.nC)
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class Darknet(nn.Module):
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"""YOLOv3 object detection model"""
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def __init__(self, cfg_path, img_size=416):
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super(Darknet, self).__init__()
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self.module_defs = parse_model_config(cfg_path)
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self.module_defs[0]['cfg'] = cfg_path
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self.module_defs[0]['height'] = img_size
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self.hyperparams, self.module_list = create_modules(self.module_defs)
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self.img_size = img_size
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self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT']
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def forward(self, x, targets=None, var=0):
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self.losses = defaultdict(float)
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is_training = targets is not None
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layer_outputs = []
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output = []
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for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
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if module_def['type'] in ['convolutional', 'upsample', 'maxpool']:
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x = module(x)
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elif module_def['type'] == 'route':
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layer_i = [int(x) for x in module_def['layers'].split(',')]
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if len(layer_i) == 1:
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x = layer_outputs[layer_i[0]]
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else:
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x = torch.cat([layer_outputs[i] for i in layer_i], 1)
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elif module_def['type'] == 'shortcut':
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layer_i = int(module_def['from'])
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x = layer_outputs[-1] + layer_outputs[layer_i]
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elif module_def['type'] == 'yolo':
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# Train phase: get loss
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if is_training:
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x, *losses = module[0](x, targets, var)
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for name, loss in zip(self.loss_names, losses):
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self.losses[name] += loss
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# Test phase: Get detections
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else:
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x = module(x)
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output.append(x)
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layer_outputs.append(x)
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if is_training:
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self.losses['nT'] /= 3
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if ONNX_EXPORT:
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output = torch.cat(output, 1) # merge the 3 layers 85 x (507, 2028, 8112) to 85 x 10647
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return output[5:85].t(), output[:4].t() # ONNX scores, boxes
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return sum(output) if is_training else torch.cat(output, 1)
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def load_darknet_weights(self, weights, cutoff=-1):
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# Parses and loads the weights stored in 'weights'
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# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
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weights_file = weights.split(os.sep)[-1]
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# Try to download weights if not available locally
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if not os.path.isfile(weights):
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try:
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os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -P ' + weights)
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except:
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assert os.path.isfile(weights)
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# Establish cutoffs
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if weights_file == 'darknet53.conv.74':
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cutoff = 75
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elif weights_file == 'yolov3-tiny.conv.15':
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cutoff = 16
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# Open the weights file
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fp = open(weights, 'rb')
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header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
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# Needed to write header when saving weights
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self.header_info = header
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self.seen = header[3]
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weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
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fp.close()
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ptr = 0
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for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
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if module_def['type'] == 'convolutional':
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conv_layer = module[0]
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if module_def['batch_normalize']:
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# Load BN bias, weights, running mean and running variance
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bn_layer = module[1]
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num_b = bn_layer.bias.numel() # Number of biases
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# Bias
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bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
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bn_layer.bias.data.copy_(bn_b)
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ptr += num_b
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# Weight
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bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
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bn_layer.weight.data.copy_(bn_w)
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ptr += num_b
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# Running Mean
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bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
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bn_layer.running_mean.data.copy_(bn_rm)
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ptr += num_b
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# Running Var
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bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
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bn_layer.running_var.data.copy_(bn_rv)
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ptr += num_b
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else:
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# Load conv. bias
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num_b = conv_layer.bias.numel()
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conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
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conv_layer.bias.data.copy_(conv_b)
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ptr += num_b
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# Load conv. weights
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num_w = conv_layer.weight.numel()
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conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
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conv_layer.weight.data.copy_(conv_w)
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ptr += num_w
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"""
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@:param path - path of the new weights file
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@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
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"""
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def save_weights(self, path, cutoff=-1):
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fp = open(path, 'wb')
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self.header_info[3] = self.seen
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self.header_info.tofile(fp)
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# Iterate through layers
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for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
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if module_def['type'] == 'convolutional':
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conv_layer = module[0]
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# If batch norm, load bn first
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if module_def['batch_normalize']:
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bn_layer = module[1]
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bn_layer.bias.data.cpu().numpy().tofile(fp)
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bn_layer.weight.data.cpu().numpy().tofile(fp)
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bn_layer.running_mean.data.cpu().numpy().tofile(fp)
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bn_layer.running_var.data.cpu().numpy().tofile(fp)
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# Load conv bias
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else:
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conv_layer.bias.data.cpu().numpy().tofile(fp)
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# Load conv weights
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conv_layer.weight.data.cpu().numpy().tofile(fp)
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fp.close()
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