py2/darknet.py [8:229]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class MaxPoolStride1(nn.Module): def __init__(self): super(MaxPoolStride1, self).__init__() def forward(self, x): x = F.max_pool2d(F.pad(x, (0,1,0,1), mode='replicate'), 2, stride=1) return x class Reorg(nn.Module): def __init__(self, stride=2): super(Reorg, self).__init__() self.stride = stride def forward(self, x): stride = self.stride assert(x.data.dim() == 4) B = x.data.size(0) C = x.data.size(1) H = x.data.size(2) W = x.data.size(3) assert(H % stride == 0) assert(W % stride == 0) ws = stride hs = stride x = x.view(B, C, H/hs, hs, W/ws, ws).transpose(3,4).contiguous() x = x.view(B, C, H/hs*W/ws, hs*ws).transpose(2,3).contiguous() x = x.view(B, C, hs*ws, H/hs, W/ws).transpose(1,2).contiguous() x = x.view(B, hs*ws*C, H/hs, W/ws) return x class GlobalAvgPool2d(nn.Module): def __init__(self): super(GlobalAvgPool2d, self).__init__() def forward(self, x): N = x.data.size(0) C = x.data.size(1) H = x.data.size(2) W = x.data.size(3) x = F.avg_pool2d(x, (H, W)) x = x.view(N, C) return x # for route and shortcut class EmptyModule(nn.Module): def __init__(self): super(EmptyModule, self).__init__() def forward(self, x): return x # support route shortcut and reorg class Darknet(nn.Module): def __init__(self, cfgfile): super(Darknet, self).__init__() self.blocks = parse_cfg(cfgfile) self.models = self.create_network(self.blocks) # merge conv, bn,leaky self.loss = self.models[len(self.models)-1] self.width = int(self.blocks[0]['width']) self.height = int(self.blocks[0]['height']) if self.blocks[(len(self.blocks)-1)]['type'] == 'region': self.anchors = self.loss.anchors self.num_anchors = self.loss.num_anchors self.anchor_step = self.loss.anchor_step self.num_classes = self.loss.num_classes self.header = torch.IntTensor([0,0,0,0]) self.seen = 0 self.iter = 0 def forward(self, x): ind = -2 self.loss = None outputs = dict() for block in self.blocks: ind = ind + 1 #if ind > 0: # return x if block['type'] == 'net': continue elif block['type'] == 'convolutional' or block['type'] == 'maxpool' or block['type'] == 'reorg' or block['type'] == 'avgpool' or block['type'] == 'softmax' or block['type'] == 'connected': x = self.models[ind](x) outputs[ind] = x elif block['type'] == 'route': layers = block['layers'].split(',') layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] if len(layers) == 1: x = outputs[layers[0]] outputs[ind] = x elif len(layers) == 2: x1 = outputs[layers[0]] x2 = outputs[layers[1]] x = torch.cat((x1,x2),1) outputs[ind] = x elif block['type'] == 'shortcut': from_layer = int(block['from']) activation = block['activation'] from_layer = from_layer if from_layer > 0 else from_layer + ind x1 = outputs[from_layer] x2 = outputs[ind-1] x = x1 + x2 if activation == 'leaky': x = F.leaky_relu(x, 0.1, inplace=True) elif activation == 'relu': x = F.relu(x, inplace=True) outputs[ind] = x elif block['type'] == 'region': continue if self.loss: self.loss = self.loss + self.models[ind](x) else: self.loss = self.models[ind](x) outputs[ind] = None elif block['type'] == 'cost': continue else: print('unknown type %s' % (block['type'])) return x def print_network(self): print_cfg(self.blocks) def create_network(self, blocks): models = nn.ModuleList() prev_filters = 3 out_filters =[] conv_id = 0 for block in blocks: if block['type'] == 'net': prev_filters = int(block['channels']) continue elif block['type'] == 'convolutional': conv_id = conv_id + 1 batch_normalize = int(block['batch_normalize']) filters = int(block['filters']) kernel_size = int(block['size']) stride = int(block['stride']) is_pad = int(block['pad']) pad = (kernel_size-1)/2 if is_pad else 0 activation = block['activation'] model = nn.Sequential() if batch_normalize: model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False)) model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters, eps=1e-4)) #model.add_module('bn{0}'.format(conv_id), BN2d(filters)) else: model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad)) if activation == 'leaky': model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True)) elif activation == 'relu': model.add_module('relu{0}'.format(conv_id), nn.ReLU(inplace=True)) prev_filters = filters out_filters.append(prev_filters) models.append(model) elif block['type'] == 'maxpool': pool_size = int(block['size']) stride = int(block['stride']) if stride > 1: model = nn.MaxPool2d(pool_size, stride) else: model = MaxPoolStride1() out_filters.append(prev_filters) models.append(model) elif block['type'] == 'avgpool': model = GlobalAvgPool2d() out_filters.append(prev_filters) models.append(model) elif block['type'] == 'softmax': model = nn.Softmax() out_filters.append(prev_filters) models.append(model) elif block['type'] == 'cost': if block['_type'] == 'sse': model = nn.MSELoss(size_average=True) elif block['_type'] == 'L1': model = nn.L1Loss(size_average=True) elif block['_type'] == 'smooth': model = nn.SmoothL1Loss(size_average=True) out_filters.append(1) models.append(model) elif block['type'] == 'reorg': stride = int(block['stride']) prev_filters = stride * stride * prev_filters out_filters.append(prev_filters) models.append(Reorg(stride)) elif block['type'] == 'route': layers = block['layers'].split(',') ind = len(models) layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] if len(layers) == 1: prev_filters = out_filters[layers[0]] elif len(layers) == 2: assert(layers[0] == ind - 1) prev_filters = out_filters[layers[0]] + out_filters[layers[1]] out_filters.append(prev_filters) models.append(EmptyModule()) elif block['type'] == 'shortcut': ind = len(models) prev_filters = out_filters[ind-1] out_filters.append(prev_filters) 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) models.append(model) elif block['type'] == 'region': loss = RegionLoss() anchors = block['anchors'].split(',') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - py2/multi_obj_pose_estimation/darknet_multi.py [8:229]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class MaxPoolStride1(nn.Module): def __init__(self): super(MaxPoolStride1, self).__init__() def forward(self, x): x = F.max_pool2d(F.pad(x, (0,1,0,1), mode='replicate'), 2, stride=1) return x class Reorg(nn.Module): def __init__(self, stride=2): super(Reorg, self).__init__() self.stride = stride def forward(self, x): stride = self.stride assert(x.data.dim() == 4) B = x.data.size(0) C = x.data.size(1) H = x.data.size(2) W = x.data.size(3) assert(H % stride == 0) assert(W % stride == 0) ws = stride hs = stride x = x.view(B, C, H/hs, hs, W/ws, ws).transpose(3,4).contiguous() x = x.view(B, C, H/hs*W/ws, hs*ws).transpose(2,3).contiguous() x = x.view(B, C, hs*ws, H/hs, W/ws).transpose(1,2).contiguous() x = x.view(B, hs*ws*C, H/hs, W/ws) return x class GlobalAvgPool2d(nn.Module): def __init__(self): super(GlobalAvgPool2d, self).__init__() def forward(self, x): N = x.data.size(0) C = x.data.size(1) H = x.data.size(2) W = x.data.size(3) x = F.avg_pool2d(x, (H, W)) x = x.view(N, C) return x # for route and shortcut class EmptyModule(nn.Module): def __init__(self): super(EmptyModule, self).__init__() def forward(self, x): return x # support route shortcut and reorg class Darknet(nn.Module): def __init__(self, cfgfile): super(Darknet, self).__init__() self.blocks = parse_cfg(cfgfile) self.models = self.create_network(self.blocks) # merge conv, bn,leaky self.loss = self.models[len(self.models)-1] self.width = int(self.blocks[0]['width']) self.height = int(self.blocks[0]['height']) if self.blocks[(len(self.blocks)-1)]['type'] == 'region': self.anchors = self.loss.anchors self.num_anchors = self.loss.num_anchors self.anchor_step = self.loss.anchor_step self.num_classes = self.loss.num_classes self.header = torch.IntTensor([0,0,0,0]) self.seen = 0 self.iter = 0 def forward(self, x): ind = -2 self.loss = None outputs = dict() for block in self.blocks: ind = ind + 1 #if ind > 0: # return x if block['type'] == 'net': continue elif block['type'] == 'convolutional' or block['type'] == 'maxpool' or block['type'] == 'reorg' or block['type'] == 'avgpool' or block['type'] == 'softmax' or block['type'] == 'connected': x = self.models[ind](x) outputs[ind] = x elif block['type'] == 'route': layers = block['layers'].split(',') layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] if len(layers) == 1: x = outputs[layers[0]] outputs[ind] = x elif len(layers) == 2: x1 = outputs[layers[0]] x2 = outputs[layers[1]] x = torch.cat((x1,x2),1) outputs[ind] = x elif block['type'] == 'shortcut': from_layer = int(block['from']) activation = block['activation'] from_layer = from_layer if from_layer > 0 else from_layer + ind x1 = outputs[from_layer] x2 = outputs[ind-1] x = x1 + x2 if activation == 'leaky': x = F.leaky_relu(x, 0.1, inplace=True) elif activation == 'relu': x = F.relu(x, inplace=True) outputs[ind] = x elif block['type'] == 'region': continue if self.loss: self.loss = self.loss + self.models[ind](x) else: self.loss = self.models[ind](x) outputs[ind] = None elif block['type'] == 'cost': continue else: print('unknown type %s' % (block['type'])) return x def print_network(self): print_cfg(self.blocks) def create_network(self, blocks): models = nn.ModuleList() prev_filters = 3 out_filters =[] conv_id = 0 for block in blocks: if block['type'] == 'net': prev_filters = int(block['channels']) continue elif block['type'] == 'convolutional': conv_id = conv_id + 1 batch_normalize = int(block['batch_normalize']) filters = int(block['filters']) kernel_size = int(block['size']) stride = int(block['stride']) is_pad = int(block['pad']) pad = (kernel_size-1)/2 if is_pad else 0 activation = block['activation'] model = nn.Sequential() if batch_normalize: model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False)) model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters, eps=1e-4)) #model.add_module('bn{0}'.format(conv_id), BN2d(filters)) else: model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad)) if activation == 'leaky': model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True)) elif activation == 'relu': model.add_module('relu{0}'.format(conv_id), nn.ReLU(inplace=True)) prev_filters = filters out_filters.append(prev_filters) models.append(model) elif block['type'] == 'maxpool': pool_size = int(block['size']) stride = int(block['stride']) if stride > 1: model = nn.MaxPool2d(pool_size, stride) else: model = MaxPoolStride1() out_filters.append(prev_filters) models.append(model) elif block['type'] == 'avgpool': model = GlobalAvgPool2d() out_filters.append(prev_filters) models.append(model) elif block['type'] == 'softmax': model = nn.Softmax() out_filters.append(prev_filters) models.append(model) elif block['type'] == 'cost': if block['_type'] == 'sse': model = nn.MSELoss(size_average=True) elif block['_type'] == 'L1': model = nn.L1Loss(size_average=True) elif block['_type'] == 'smooth': model = nn.SmoothL1Loss(size_average=True) out_filters.append(1) models.append(model) elif block['type'] == 'reorg': stride = int(block['stride']) prev_filters = stride * stride * prev_filters out_filters.append(prev_filters) models.append(Reorg(stride)) elif block['type'] == 'route': layers = block['layers'].split(',') ind = len(models) layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] if len(layers) == 1: prev_filters = out_filters[layers[0]] elif len(layers) == 2: assert(layers[0] == ind - 1) prev_filters = out_filters[layers[0]] + out_filters[layers[1]] out_filters.append(prev_filters) models.append(EmptyModule()) elif block['type'] == 'shortcut': ind = len(models) prev_filters = out_filters[ind-1] out_filters.append(prev_filters) 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) models.append(model) elif block['type'] == 'region': loss = RegionLoss() anchors = block['anchors'].split(',') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -