darknet.py

from __future__ import division

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np

from util import *


def get_test_input():
    img = cv2.imread("dog-cycle-car.png")
    img = cv2.resize(img, (416, 416))  # Resize to the input dimension
    img_ = img[:, :, ::-1].transpose((2, 0, 1))  # BGR -> RGB | H X W C -> C X H X W
    img_ = img_[np.newaxis, :, :, :] / 255.0  # Add a channel at 0 (for batch) | Normalise
    img_ = torch.from_numpy(img_).float()  # Convert to float
    # img_ = Variable(img_)  # Convert to Variable
    return img_


class EmptyLayer(nn.Module):
    def __init__(self):
        super(EmptyLayer, self).__init__()


class DetectionLayer(nn.Module):
    def __init__(self, anchors):
        super(DetectionLayer, self).__init__()
        self.anchors = anchors


def parse_cfg(cfgfile):
    """
    Takes a configuration file

    Returns a list of blocks. Each blocks describes a block in the neural
    network to be built. Block is represented as a dictionary in the list

    """

    file = open(cfgfile, 'r')
    lines = file.read().split('\n')  # store the lines in a list
    lines = [x for x in lines if len(x) > 0]  # get read of the empty lines
    lines = [x for x in lines if x[0] != '#']  # get rid of comments
    lines = [x.rstrip().lstrip() for x in lines]  # get rid of fringe whitespaces

    block = {}
    blocks = []

    for line in lines:
        if line[0] == "[":  # This marks the start of a new block
            if len(block) != 0:  # If block is not empty, implies it is storing values of previous block.
                blocks.append(block)  # add it the blocks list
                block = {}  # re-init the block
            block["type"] = line[1:-1].rstrip()
        else:
            key, value = line.split("=")
            block[key.rstrip()] = value.lstrip()
    blocks.append(block)

    return blocks


def create_modules(blocks):
    net_info = blocks[0]  # Captures the information about the input and pre-processing
    module_list = nn.ModuleList()
    prev_filters = 3
    output_filters = []

    for index, x in enumerate(blocks[1:]):
        module = nn.Sequential()

        # check the type of block
        # create a new module for the block
        # append to module_list
        if (x["type"] == "convolutional"):
            # Get the info about the layer
            activation = x["activation"]
            try:
                batch_normalize = int(x["batch_normalize"])
                bias = False
            except:
                batch_normalize = 0
                bias = True

            filters = int(x["filters"])
            padding = int(x["pad"])
            kernel_size = int(x["size"])
            stride = int(x["stride"])

            if padding:
                pad = (kernel_size - 1) // 2
            else:
                pad = 0

            # Add the convolutional layer
            conv = nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=bias)
            module.add_module("conv_{0}".format(index), conv)

            # Add the Batch Norm Layer
            if batch_normalize:
                bn = nn.BatchNorm2d(filters)
                module.add_module("batch_norm_{0}".format(index), bn)

            # Check the activation.
            # It is either Linear or a Leaky ReLU for YOLO
            if activation == "leaky":
                activn = nn.LeakyReLU(0.1, inplace=True)
                module.add_module("leaky_{0}".format(index), activn)

        # If it's an upsampling layer
        # We use Bilinear2dUpsampling
        elif (x["type"] == "upsample"):
            stride = int(x["stride"])
            upsample = nn.Upsample(scale_factor=2, mode="bilinear")
            module.add_module("upsample_{}".format(index), upsample)

        # If it is a route layer
        elif (x["type"] == "route"):
            x["layers"] = x["layers"].split(',')
            # Start  of a route
            start = int(x["layers"][0])
            # end, if there exists one.
            try:
                end = int(x["layers"][1])
            except:
                end = 0
            # Positive anotation
            if start > 0:
                start = start - index
            if end > 0:  # make it negative
                end = end - index
            route = EmptyLayer()
            module.add_module("route_{0}".format(index), route)
            if end < 0:
                filters = output_filters[index + start] + output_filters[index + end]
            else:
                filters = output_filters[index + start]

        # shortcut corresponds to skip connection
        elif x["type"] == "shortcut":
            shortcut = EmptyLayer()
            module.add_module("shortcut_{}".format(index), shortcut)

        # Yolo is the detection layer
        elif x["type"] == "yolo":
            mask = x["mask"].split(",")
            mask = [int(x) for x in mask]

            anchors = x["anchors"].split(",")
            anchors = [int(a) for a in anchors]
            anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
            anchors = [anchors[i] for i in mask]

            detection = DetectionLayer(anchors)
            module.add_module("Detection_{}".format(index), detection)

        module_list.append(module)
        prev_filters = filters
        output_filters.append(filters)

    return (net_info, module_list)


class Darknet(nn.Module):
    def __init__(self, cfgfile):
        super(Darknet, self).__init__()
        self.blocks = parse_cfg(cfgfile)
        self.net_info, self.module_list = create_modules(self.blocks)

    def forward(self, x, CUDA):
        modules = self.blocks[1:]
        outputs = {}  # We cache the outputs for the route layer

        write = 0  # This is explained a bit later
        for i, module in enumerate(modules):
            module_type = (module["type"])

            if module_type == "convolutional" or module_type == "upsample":
                x = self.module_list[i](x)

            elif module_type == "route":
                layers = module["layers"]
                layers = [int(a) for a in layers]

                if (layers[0]) > 0:
                    layers[0] = layers[0] - i

                if len(layers) == 1:
                    x = outputs[i + (layers[0])]
                else:
                    if (layers[1]) > 0:
                        layers[1] = layers[1] - i
                    map1 = outputs[i + layers[0]]
                    map2 = outputs[i + layers[1]]
                    x = torch.cat((map1, map2), 1)  # (N,C,H,W)

            elif module_type == "shortcut":
                from_ = int(module["from"])
                x = outputs[i - 1] + outputs[i + from_]

            elif module_type == 'yolo':
                anchors = self.module_list[i][0].anchors  # self.module_list[i]是个Sequential
                # Get the input dimensions
                inp_dim = int(self.net_info["height"])

                # Get the number of classes
                num_classes = int(module["classes"])

                # Transform
                x = x.data
                x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
                if not write:  # if no collector has been intialised.
                    detections = x
                    write = 1
                else:
                    detections = torch.cat((detections, x), 1)

            outputs[i] = x

        return detections

    def load_weights(self, weight_file):
        # Open the weights file
        fp = open(weight_file, "rb")

        # The first 5 values are header information
        # 1. Major version number
        # 2. Minor Version Number
        # 3. Subversion number
        # 4,5. Images seen by the network (during training)
        header = np.fromfile(fp, dtype=np.int32, count=5)
        self.header = torch.from_numpy(header)
        self.seen = self.header[3]

        weights = np.fromfile(fp, dtype=np.float32)

        ptr = 0
        for i in range(len(self.module_list)):
            module_type = self.blocks[i + 1]["type"]

            # If module_type is convolutional load weights
            # Otherwise ignore.
            if module_type == "convolutional":
                model = self.module_list[i]
                try:
                    batch_normalize = int(self.blocks[i + 1]["batch_normalize"])
                except:
                    batch_normalize = 0

                conv = model[0]


                if (batch_normalize):
                    bn = model[1]

                    # Get the number of weights of Batch Norm Layer
                    num_bn_biases = bn.bias.numel()

                    # Load the weights
                    bn_biases = torch.from_numpy(weights[ptr:ptr + num_bn_biases])
                    ptr += num_bn_biases

                    bn_weights = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
                    ptr += num_bn_biases

                    bn_running_mean = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
                    ptr += num_bn_biases

                    bn_running_var = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
                    ptr += num_bn_biases

                    # Cast the loaded weights into dims of model weights.
                    bn_biases = bn_biases.view_as(bn.bias.data)
                    bn_weights = bn_weights.view_as(bn.weight.data)
                    bn_running_mean = bn_running_mean.view_as(bn.running_mean)
                    bn_running_var = bn_running_var.view_as(bn.running_var)

                    # Copy the data to model
                    bn.bias.data.copy_(bn_biases)
                    bn.weight.data.copy_(bn_weights)
                    bn.running_mean.copy_(bn_running_mean)
                    bn.running_var.copy_(bn_running_var)
                else:
                    # Number of biases
                    num_biases = conv.bias.numel()

                    # Load the weights
                    conv_biases = torch.from_numpy(weights[ptr: ptr + num_biases])
                    ptr = ptr + num_biases

                    # reshape the loaded weights according to the dims of the model weights
                    conv_biases = conv_biases.view_as(conv.bias.data)

                    # Finally copy the data
                    conv.bias.data.copy_(conv_biases)

                # Let us load the weights for the Convolutional layers
                num_weights = conv.weight.numel()

                # Do the same as above for weights
                conv_weights = torch.from_numpy(weights[ptr:ptr + num_weights])
                ptr = ptr + num_weights

                conv_weights = conv_weights.view_as(conv.weight.data)
                conv.weight.data.copy_(conv_weights)




if __name__ == '__main__':
    model = Darknet("cfg/yolov3.cfg")
    model.load_weights("yolov3.weights")
    print(model)
    # inp = get_test_input()
    # pred = model(inp, 0)
    # print(pred.shape)