517 lines
20 KiB
Python
517 lines
20 KiB
Python
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from libs.region_loss import RegionLoss
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from libs.yolo_layer import YoloLayer
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from libs.config import *
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from libs.torch_utils import *
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class Mish(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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x = x * (torch.tanh(F.softplus(x)))
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return x
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class MaxPoolDark(nn.Module):
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def __init__(self, size=2, stride=1):
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super(MaxPoolDark, self).__init__()
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self.size = size
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self.stride = stride
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def forward(self, x):
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'''
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darknet output_size = (input_size + p - k) / s +1
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p : padding = k - 1
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k : size
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s : stride
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torch output_size = (input_size + 2*p -k) / s +1
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p : padding = k//2
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'''
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p = self.size // 2
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if ((x.shape[2] - 1) // self.stride) != ((x.shape[2] + 2 * p - self.size) // self.stride):
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padding1 = (self.size - 1) // 2
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padding2 = padding1 + 1
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else:
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padding1 = (self.size - 1) // 2
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padding2 = padding1
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if ((x.shape[3] - 1) // self.stride) != ((x.shape[3] + 2 * p - self.size) // self.stride):
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padding3 = (self.size - 1) // 2
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padding4 = padding3 + 1
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else:
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padding3 = (self.size - 1) // 2
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padding4 = padding3
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x = F.max_pool2d(F.pad(x, (padding3, padding4, padding1, padding2), mode='replicate'),
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self.size, stride=self.stride)
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return x
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class Upsample_expand(nn.Module):
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def __init__(self, stride=2):
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super(Upsample_expand, self).__init__()
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self.stride = stride
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def forward(self, x):
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assert (x.data.dim() == 4)
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x = x.view(x.size(0), x.size(1), x.size(2), 1, x.size(3), 1).\
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expand(x.size(0), x.size(1), x.size(2), self.stride, x.size(3), self.stride).contiguous().\
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view(x.size(0), x.size(1), x.size(2) * self.stride, x.size(3) * self.stride)
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return x
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class Upsample_interpolate(nn.Module):
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def __init__(self, stride):
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super(Upsample_interpolate, self).__init__()
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self.stride = stride
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def forward(self, x):
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assert (x.data.dim() == 4)
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out = F.interpolate(x, size=(x.size(2) * self.stride, x.size(3) * self.stride), mode='nearest')
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return out
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class Reorg(nn.Module):
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def __init__(self, stride=2):
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super(Reorg, self).__init__()
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self.stride = stride
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def forward(self, x):
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stride = self.stride
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assert (x.data.dim() == 4)
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B = x.data.size(0)
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C = x.data.size(1)
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H = x.data.size(2)
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W = x.data.size(3)
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assert (H % stride == 0)
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assert (W % stride == 0)
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ws = stride
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hs = stride
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x = x.view(B, C, H / hs, hs, W / ws, ws).transpose(3, 4).contiguous()
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x = x.view(B, C, H / hs * W / ws, hs * ws).transpose(2, 3).contiguous()
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x = x.view(B, C, hs * ws, H / hs, W / ws).transpose(1, 2).contiguous()
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x = x.view(B, hs * ws * C, H / hs, W / ws)
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return x
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class GlobalAvgPool2d(nn.Module):
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def __init__(self):
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super(GlobalAvgPool2d, self).__init__()
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def forward(self, x):
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N = x.data.size(0)
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C = x.data.size(1)
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H = x.data.size(2)
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W = x.data.size(3)
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x = F.avg_pool2d(x, (H, W))
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x = x.view(N, C)
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return x
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# for route and shortcut
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class EmptyModule(nn.Module):
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def __init__(self):
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super(EmptyModule, self).__init__()
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def forward(self, x):
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return x
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# support route shortcut and reorg
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class Darknet(nn.Module):
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def __init__(self, cfgfile, inference=False):
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super(Darknet, self).__init__()
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self.inference = inference
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self.training = not self.inference
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self.blocks = parse_cfg(cfgfile)
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self.width = int(self.blocks[0]['width'])
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self.height = int(self.blocks[0]['height'])
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self.models = self.create_network(self.blocks) # merge conv, bn,leaky
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self.loss = self.models[len(self.models) - 1]
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if self.blocks[(len(self.blocks) - 1)]['type'] == 'region':
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self.anchors = self.loss.anchors
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self.num_anchors = self.loss.num_anchors
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self.anchor_step = self.loss.anchor_step
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self.num_classes = self.loss.num_classes
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self.header = torch.IntTensor([0, 0, 0, 0])
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self.seen = 0
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def forward(self, x):
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ind = -2
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self.loss = None
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outputs = dict()
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out_boxes = []
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for block in self.blocks:
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ind = ind + 1
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# if ind > 0:
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# return x
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if block['type'] == 'net':
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continue
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elif block['type'] in ['convolutional', 'maxpool', 'reorg', 'upsample', 'avgpool', 'softmax', 'connected']:
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x = self.models[ind](x)
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outputs[ind] = x
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elif block['type'] == 'route':
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layers = block['layers'].split(',')
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layers = [int(i) if int(i) > 0 else int(i) + ind for i in layers]
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if len(layers) == 1:
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if 'groups' not in block.keys() or int(block['groups']) == 1:
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x = outputs[layers[0]]
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outputs[ind] = x
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else:
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groups = int(block['groups'])
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group_id = int(block['group_id'])
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_, b, _, _ = outputs[layers[0]].shape
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x = outputs[layers[0]][:, b // groups * group_id:b // groups * (group_id + 1)]
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outputs[ind] = x
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elif len(layers) == 2:
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x1 = outputs[layers[0]]
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x2 = outputs[layers[1]]
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x = torch.cat((x1, x2), 1)
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outputs[ind] = x
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elif len(layers) == 4:
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x1 = outputs[layers[0]]
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x2 = outputs[layers[1]]
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x3 = outputs[layers[2]]
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x4 = outputs[layers[3]]
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x = torch.cat((x1, x2, x3, x4), 1)
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outputs[ind] = x
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else:
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print("rounte number > 2 ,is {}".format(len(layers)))
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elif block['type'] == 'shortcut':
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from_layer = int(block['from'])
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activation = block['activation']
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from_layer = from_layer if from_layer > 0 else from_layer + ind
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x1 = outputs[from_layer]
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x2 = outputs[ind - 1]
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x = x1 + x2
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if activation == 'leaky':
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x = F.leaky_relu(x, 0.1, inplace=True)
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elif activation == 'relu':
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x = F.relu(x, inplace=True)
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outputs[ind] = x
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elif block['type'] == 'region':
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continue
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if self.loss:
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self.loss = self.loss + self.models[ind](x)
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else:
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self.loss = self.models[ind](x)
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outputs[ind] = None
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elif block['type'] == 'yolo':
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# if self.training:
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# pass
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# else:
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# boxes = self.models[ind](x)
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# out_boxes.append(boxes)
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boxes = self.models[ind](x)
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out_boxes.append(boxes)
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elif block['type'] == 'cost':
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continue
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else:
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print('unknown type %s' % (block['type']))
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if self.training:
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return out_boxes
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else:
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return get_region_boxes(out_boxes)
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def print_network(self):
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print_cfg(self.blocks)
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def create_network(self, blocks):
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models = nn.ModuleList()
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prev_filters = 3
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out_filters = []
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prev_stride = 1
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out_strides = []
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conv_id = 0
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for block in blocks:
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if block['type'] == 'net':
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prev_filters = int(block['channels'])
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continue
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elif block['type'] == 'convolutional':
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conv_id = conv_id + 1
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batch_normalize = int(block['batch_normalize'])
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filters = int(block['filters'])
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kernel_size = int(block['size'])
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stride = int(block['stride'])
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is_pad = int(block['pad'])
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pad = (kernel_size - 1) // 2 if is_pad else 0
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activation = block['activation']
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model = nn.Sequential()
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if batch_normalize:
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model.add_module('conv{0}'.format(conv_id),
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nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False))
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model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters))
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# model.add_module('bn{0}'.format(conv_id), BN2d(filters))
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else:
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model.add_module('conv{0}'.format(conv_id),
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nn.Conv2d(prev_filters, filters, kernel_size, stride, pad))
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if activation == 'leaky':
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model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True))
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elif activation == 'relu':
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model.add_module('relu{0}'.format(conv_id), nn.ReLU(inplace=True))
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elif activation == 'mish':
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model.add_module('mish{0}'.format(conv_id), Mish())
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else:
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print("convalution havn't activate {}".format(activation))
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prev_filters = filters
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out_filters.append(prev_filters)
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prev_stride = stride * prev_stride
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out_strides.append(prev_stride)
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models.append(model)
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elif block['type'] == 'maxpool':
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pool_size = int(block['size'])
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stride = int(block['stride'])
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if stride == 1 and pool_size % 2:
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# You can use Maxpooldark instead, here is convenient to convert onnx.
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# Example: [maxpool] size=3 stride=1
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model = nn.MaxPool2d(kernel_size=pool_size, stride=stride, padding=pool_size // 2)
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elif stride == pool_size:
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# You can use Maxpooldark instead, here is convenient to convert onnx.
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# Example: [maxpool] size=2 stride=2
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model = nn.MaxPool2d(kernel_size=pool_size, stride=stride, padding=0)
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else:
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model = MaxPoolDark(pool_size, stride)
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out_filters.append(prev_filters)
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prev_stride = stride * prev_stride
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out_strides.append(prev_stride)
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models.append(model)
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elif block['type'] == 'avgpool':
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model = GlobalAvgPool2d()
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out_filters.append(prev_filters)
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models.append(model)
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elif block['type'] == 'softmax':
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model = nn.Softmax()
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out_strides.append(prev_stride)
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out_filters.append(prev_filters)
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models.append(model)
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elif block['type'] == 'cost':
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if block['_type'] == 'sse':
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model = nn.MSELoss(reduction='mean')
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elif block['_type'] == 'L1':
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model = nn.L1Loss(reduction='mean')
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elif block['_type'] == 'smooth':
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model = nn.SmoothL1Loss(reduction='mean')
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out_filters.append(1)
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out_strides.append(prev_stride)
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models.append(model)
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elif block['type'] == 'reorg':
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stride = int(block['stride'])
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prev_filters = stride * stride * prev_filters
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out_filters.append(prev_filters)
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prev_stride = prev_stride * stride
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out_strides.append(prev_stride)
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models.append(Reorg(stride))
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elif block['type'] == 'upsample':
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stride = int(block['stride'])
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out_filters.append(prev_filters)
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prev_stride = prev_stride // stride
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out_strides.append(prev_stride)
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models.append(Upsample_expand(stride))
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# models.append(Upsample_interpolate(stride))
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elif block['type'] == 'route':
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layers = block['layers'].split(',')
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ind = len(models)
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layers = [int(i) if int(i) > 0 else int(i) + ind for i in layers]
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if len(layers) == 1:
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if 'groups' not in block.keys() or int(block['groups']) == 1:
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prev_filters = out_filters[layers[0]]
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prev_stride = out_strides[layers[0]]
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else:
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prev_filters = out_filters[layers[0]] // int(block['groups'])
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prev_stride = out_strides[layers[0]] // int(block['groups'])
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elif len(layers) == 2:
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assert (layers[0] == ind - 1 or layers[1] == ind - 1)
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prev_filters = out_filters[layers[0]] + out_filters[layers[1]]
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prev_stride = out_strides[layers[0]]
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elif len(layers) == 4:
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assert (layers[0] == ind - 1)
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prev_filters = out_filters[layers[0]] + out_filters[layers[1]] + out_filters[layers[2]] + \
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out_filters[layers[3]]
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prev_stride = out_strides[layers[0]]
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else:
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print("route error!!!")
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out_filters.append(prev_filters)
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out_strides.append(prev_stride)
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|
|
models.append(EmptyModule())
|
||
|
|
elif block['type'] == 'shortcut':
|
||
|
|
ind = len(models)
|
||
|
|
prev_filters = out_filters[ind - 1]
|
||
|
|
out_filters.append(prev_filters)
|
||
|
|
prev_stride = out_strides[ind - 1]
|
||
|
|
out_strides.append(prev_stride)
|
||
|
|
models.append(EmptyModule())
|
||
|
|
elif block['type'] == 'connected':
|
||
|
|
filters = int(block['output'])
|
||
|
|
if block['activation'] == 'linear':
|
||
|
|
model = nn.Linear(prev_filters, filters)
|
||
|
|
elif block['activation'] == 'leaky':
|
||
|
|
model = nn.Sequential(
|
||
|
|
nn.Linear(prev_filters, filters),
|
||
|
|
nn.LeakyReLU(0.1, inplace=True))
|
||
|
|
elif block['activation'] == 'relu':
|
||
|
|
model = nn.Sequential(
|
||
|
|
nn.Linear(prev_filters, filters),
|
||
|
|
nn.ReLU(inplace=True))
|
||
|
|
prev_filters = filters
|
||
|
|
out_filters.append(prev_filters)
|
||
|
|
out_strides.append(prev_stride)
|
||
|
|
models.append(model)
|
||
|
|
elif block['type'] == 'region':
|
||
|
|
loss = RegionLoss()
|
||
|
|
anchors = block['anchors'].split(',')
|
||
|
|
loss.anchors = [float(i) for i in anchors]
|
||
|
|
loss.num_classes = int(block['classes'])
|
||
|
|
loss.num_anchors = int(block['num'])
|
||
|
|
loss.anchor_step = len(loss.anchors) // loss.num_anchors
|
||
|
|
loss.object_scale = float(block['object_scale'])
|
||
|
|
loss.noobject_scale = float(block['noobject_scale'])
|
||
|
|
loss.class_scale = float(block['class_scale'])
|
||
|
|
loss.coord_scale = float(block['coord_scale'])
|
||
|
|
out_filters.append(prev_filters)
|
||
|
|
out_strides.append(prev_stride)
|
||
|
|
models.append(loss)
|
||
|
|
elif block['type'] == 'yolo':
|
||
|
|
yolo_layer = YoloLayer()
|
||
|
|
anchors = block['anchors'].split(',')
|
||
|
|
anchor_mask = block['mask'].split(',')
|
||
|
|
yolo_layer.anchor_mask = [int(i) for i in anchor_mask]
|
||
|
|
yolo_layer.anchors = [float(i) for i in anchors]
|
||
|
|
yolo_layer.num_classes = int(block['classes'])
|
||
|
|
self.num_classes = yolo_layer.num_classes
|
||
|
|
yolo_layer.num_anchors = int(block['num'])
|
||
|
|
yolo_layer.anchor_step = len(yolo_layer.anchors) // yolo_layer.num_anchors
|
||
|
|
yolo_layer.stride = prev_stride
|
||
|
|
yolo_layer.scale_x_y = float(block['scale_x_y'])
|
||
|
|
# yolo_layer.object_scale = float(block['object_scale'])
|
||
|
|
# yolo_layer.noobject_scale = float(block['noobject_scale'])
|
||
|
|
# yolo_layer.class_scale = float(block['class_scale'])
|
||
|
|
# yolo_layer.coord_scale = float(block['coord_scale'])
|
||
|
|
out_filters.append(prev_filters)
|
||
|
|
out_strides.append(prev_stride)
|
||
|
|
models.append(yolo_layer)
|
||
|
|
else:
|
||
|
|
print('unknown type %s' % (block['type']))
|
||
|
|
|
||
|
|
return models
|
||
|
|
|
||
|
|
def load_weights(self, weightfile):
|
||
|
|
fp = open(weightfile, 'rb')
|
||
|
|
header = np.fromfile(fp, count=5, dtype=np.int32)
|
||
|
|
self.header = torch.from_numpy(header)
|
||
|
|
self.seen = self.header[3]
|
||
|
|
buf = np.fromfile(fp, dtype=np.float32)
|
||
|
|
fp.close()
|
||
|
|
|
||
|
|
start = 0
|
||
|
|
ind = -2
|
||
|
|
for block in self.blocks:
|
||
|
|
if start >= buf.size:
|
||
|
|
break
|
||
|
|
ind = ind + 1
|
||
|
|
if block['type'] == 'net':
|
||
|
|
continue
|
||
|
|
elif block['type'] == 'convolutional':
|
||
|
|
model = self.models[ind]
|
||
|
|
batch_normalize = int(block['batch_normalize'])
|
||
|
|
if batch_normalize:
|
||
|
|
start = load_conv_bn(buf, start, model[0], model[1])
|
||
|
|
else:
|
||
|
|
start = load_conv(buf, start, model[0])
|
||
|
|
elif block['type'] == 'connected':
|
||
|
|
model = self.models[ind]
|
||
|
|
if block['activation'] != 'linear':
|
||
|
|
start = load_fc(buf, start, model[0])
|
||
|
|
else:
|
||
|
|
start = load_fc(buf, start, model)
|
||
|
|
elif block['type'] == 'maxpool':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'reorg':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'upsample':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'route':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'shortcut':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'region':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'yolo':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'avgpool':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'softmax':
|
||
|
|
pass
|
||
|
|
elif block['type'] == 'cost':
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
print('unknown type %s' % (block['type']))
|
||
|
|
|
||
|
|
# def save_weights(self, outfile, cutoff=0):
|
||
|
|
# if cutoff <= 0:
|
||
|
|
# cutoff = len(self.blocks) - 1
|
||
|
|
#
|
||
|
|
# fp = open(outfile, 'wb')
|
||
|
|
# self.header[3] = self.seen
|
||
|
|
# header = self.header
|
||
|
|
# header.numpy().tofile(fp)
|
||
|
|
#
|
||
|
|
# ind = -1
|
||
|
|
# for blockId in range(1, cutoff + 1):
|
||
|
|
# ind = ind + 1
|
||
|
|
# block = self.blocks[blockId]
|
||
|
|
# if block['type'] == 'convolutional':
|
||
|
|
# model = self.models[ind]
|
||
|
|
# batch_normalize = int(block['batch_normalize'])
|
||
|
|
# if batch_normalize:
|
||
|
|
# save_conv_bn(fp, model[0], model[1])
|
||
|
|
# else:
|
||
|
|
# save_conv(fp, model[0])
|
||
|
|
# elif block['type'] == 'connected':
|
||
|
|
# model = self.models[ind]
|
||
|
|
# if block['activation'] != 'linear':
|
||
|
|
# save_fc(fc, model)
|
||
|
|
# else:
|
||
|
|
# save_fc(fc, model[0])
|
||
|
|
# elif block['type'] == 'maxpool':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'reorg':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'upsample':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'route':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'shortcut':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'region':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'yolo':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'avgpool':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'softmax':
|
||
|
|
# pass
|
||
|
|
# elif block['type'] == 'cost':
|
||
|
|
# pass
|
||
|
|
# else:
|
||
|
|
# print('unknown type %s' % (block['type']))
|
||
|
|
# fp.close()
|
||
|
|
|