import os import sys import torch import torch.nn as nn import torch.utils.model_zoo as model_zoo from torch.nn import functional as F class BlockTypeA(nn.Module): def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True): super(BlockTypeA, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(in_c2, out_c2, kernel_size=1), nn.BatchNorm2d(out_c2), nn.ReLU(inplace=True) ) self.conv2 = nn.Sequential( nn.Conv2d(in_c1, out_c1, kernel_size=1), nn.BatchNorm2d(out_c1), nn.ReLU(inplace=True) ) self.upscale = upscale def forward(self, a, b): b = self.conv1(b) a = self.conv2(a) b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True) return torch.cat((a, b), dim=1) class BlockTypeB(nn.Module): def __init__(self, in_c, out_c): super(BlockTypeB, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(in_c, in_c, kernel_size=3, padding=1), nn.BatchNorm2d(in_c), nn.ReLU() ) self.conv2 = nn.Sequential( nn.Conv2d(in_c, out_c, kernel_size=3, padding=1), nn.BatchNorm2d(out_c), nn.ReLU() ) def forward(self, x): x = self.conv1(x) + x x = self.conv2(x) return x class BlockTypeC(nn.Module): def __init__(self, in_c, out_c): super(BlockTypeC, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(in_c, in_c, kernel_size=3, padding=5, dilation=5), nn.BatchNorm2d(in_c), nn.ReLU() ) self.conv2 = nn.Sequential( nn.Conv2d(in_c, in_c, kernel_size=3, padding=1), nn.BatchNorm2d(in_c), nn.ReLU() ) self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) return x def _make_divisible(v, divisor, min_value=None): """ This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py :param v: :param divisor: :param min_value: :return: """ if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_v < 0.9 * v: new_v += divisor return new_v class ConvBNReLU(nn.Sequential): def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1): self.channel_pad = out_planes - in_planes self.stride = stride #padding = (kernel_size - 1) // 2 # TFLite uses slightly different padding than PyTorch if stride == 2: padding = 0 else: padding = (kernel_size - 1) // 2 super(ConvBNReLU, self).__init__( nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False), nn.BatchNorm2d(out_planes), nn.ReLU6(inplace=True) ) self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride) def forward(self, x): # TFLite uses different padding if self.stride == 2: x = F.pad(x, (0, 1, 0, 1), "constant", 0) #print(x.shape) for module in self: if not isinstance(module, nn.MaxPool2d): x = module(x) return x class InvertedResidual(nn.Module): def __init__(self, inp, oup, stride, expand_ratio): super(InvertedResidual, self).__init__() self.stride = stride assert stride in [1, 2] hidden_dim = int(round(inp * expand_ratio)) self.use_res_connect = self.stride == 1 and inp == oup layers = [] if expand_ratio != 1: # pw layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) layers.extend([ # dw ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), # pw-linear nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), ]) self.conv = nn.Sequential(*layers) def forward(self, x): if self.use_res_connect: return x + self.conv(x) else: return self.conv(x) class MobileNetV2(nn.Module): def __init__(self, pretrained=True): """ MobileNet V2 main class Args: num_classes (int): Number of classes width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount inverted_residual_setting: Network structure round_nearest (int): Round the number of channels in each layer to be a multiple of this number Set to 1 to turn off rounding block: Module specifying inverted residual building block for mobilenet """ super(MobileNetV2, self).__init__() block = InvertedResidual input_channel = 32 last_channel = 1280 width_mult = 1.0 round_nearest = 8 inverted_residual_setting = [ # t, c, n, s [1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2], [6, 64, 4, 2], #[6, 96, 3, 1], #[6, 160, 3, 2], #[6, 320, 1, 1], ] # only check the first element, assuming user knows t,c,n,s are required if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4: raise ValueError("inverted_residual_setting should be non-empty " "or a 4-element list, got {}".format(inverted_residual_setting)) # building first layer input_channel = _make_divisible(input_channel * width_mult, round_nearest) self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) features = [ConvBNReLU(4, input_channel, stride=2)] # building inverted residual blocks for t, c, n, s in inverted_residual_setting: output_channel = _make_divisible(c * width_mult, round_nearest) for i in range(n): stride = s if i == 0 else 1 features.append(block(input_channel, output_channel, stride, expand_ratio=t)) input_channel = output_channel self.features = nn.Sequential(*features) self.fpn_selected = [3, 6, 10] # weight initialization for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.BatchNorm2d): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.zeros_(m.bias) #if pretrained: # self._load_pretrained_model() def _forward_impl(self, x): # This exists since TorchScript doesn't support inheritance, so the superclass method # (this one) needs to have a name other than `forward` that can be accessed in a subclass fpn_features = [] for i, f in enumerate(self.features): if i > self.fpn_selected[-1]: break x = f(x) if i in self.fpn_selected: fpn_features.append(x) c2, c3, c4 = fpn_features return c2, c3, c4 def forward(self, x): return self._forward_impl(x) def _load_pretrained_model(self): pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth') model_dict = {} state_dict = self.state_dict() for k, v in pretrain_dict.items(): if k in state_dict: model_dict[k] = v state_dict.update(model_dict) self.load_state_dict(state_dict) class MobileV2_MLSD_Tiny(nn.Module): def __init__(self): super(MobileV2_MLSD_Tiny, self).__init__() self.backbone = MobileNetV2(pretrained=True) self.block12 = BlockTypeA(in_c1= 32, in_c2= 64, out_c1= 64, out_c2=64) self.block13 = BlockTypeB(128, 64) self.block14 = BlockTypeA(in_c1 = 24, in_c2 = 64, out_c1= 32, out_c2= 32) self.block15 = BlockTypeB(64, 64) self.block16 = BlockTypeC(64, 16) def forward(self, x): c2, c3, c4 = self.backbone(x) x = self.block12(c3, c4) x = self.block13(x) x = self.block14(c2, x) x = self.block15(x) x = self.block16(x) x = x[:, 7:, :, :] #print(x.shape) x = F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=True) return x