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
import cv2 
import matplotlib.pyplot as plt
from util import count_parameters as count
from util import convert2cpu as cpu
from util import predict_transform

#from roi_align.roi_align import RoIAlign

""" def to_varabile(arr, requires_grad=False, is_cuda=True):
    tensor = torch.from_numpy(arr)
    if is_cuda:
        tensor = tensor.cuda()
    var = Variable(tensor, requires_grad=requires_grad)
    return var """

def to_varabile(tensor, requires_grad=False, is_cuda=True):
    if is_cuda:
        tensor = tensor.cuda()
    var = Variable(tensor, requires_grad=requires_grad)
    return var

        
def get_test_input():
    img = cv2.imread("dog-cycle-car.png")
    img = cv2.resize(img, (416,416)) 
    img_ =  img[:,:,::-1].transpose((2,0,1))
    img_ = img_[np.newaxis,:,:,:]/255.0
    img_ = torch.from_numpy(img_).float()
    img_ = Variable(img_)
    return img_


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] != '#']  
    lines = [x.rstrip().lstrip() for x in lines]

    
    block = {}
    blocks = []
    
    for line in lines:
        if line[0] == "[":               #This marks the start of a new block
            if len(block) != 0:
                blocks.append(block)
                block = {}
            block["type"] = line[1:-1].rstrip()
        else:
            key,value = line.split("=")
            block[key.rstrip()] = value.lstrip()
    blocks.append(block)

#    print('\n\n'.join([repr(x) for x in blocks]))
    return blocks
#    print('\n\n'.join([repr(x) for x in blocks]))

import pickle as pkl

class MaxPoolStride1(nn.Module):
    def __init__(self, kernel_size):
        super(MaxPoolStride1, self).__init__()
        self.kernel_size = kernel_size
        self.pad = kernel_size - 1
    
    def forward(self, x):
        padded_x = F.pad(x, (0,self.pad,0,self.pad), mode="replicate")
        pooled_x = nn.MaxPool2d(self.kernel_size, self.pad)(padded_x)
        return pooled_x
    

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 forward(self, x, inp_dim, num_classes, confidence):
        x = x.data
        global CUDA
        prediction = x
        prediction = predict_transform(prediction, inp_dim, self.anchors, num_classes, confidence, device)
        return prediction
        

        


class Upsample(nn.Module):
    def __init__(self, stride=2):
        super(Upsample, 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)
        ws = stride
        hs = stride
        x = x.view(B, C, H, 1, W, 1).expand(B, C, H, stride, W, stride).contiguous().view(B, C, H*stride, W*stride)
        return x
#       
        
class ReOrgLayer(nn.Module):
    def __init__(self, stride = 2):
        super(ReOrgLayer, self).__init__()
        self.stride= stride
        
    def forward(self,x):
        assert(x.data.dim() == 4)
        B,C,H,W = x.data.shape
        hs = self.stride
        ws = self.stride
        assert(H % hs == 0),  "The stride " + str(self.stride) + " is not a proper divisor of height " + str(H)
        assert(W % ws == 0),  "The stride " + str(self.stride) + " is not a proper divisor of height " + str(W)
        x = x.view(B,C, H // hs, hs, W // ws, ws).transpose(-2,-3).contiguous()
        x = x.view(B,C, H // hs * W // ws, hs, ws)
        x = x.view(B,C, H // hs * W // ws, hs*ws).transpose(-1,-2).contiguous()
        x = x.view(B, C, ws*hs, H // ws, W // ws).transpose(1,2).contiguous()
        x = x.view(B, C*ws*hs, H // ws, W // ws)
        return x


def create_modules(blocks):
    net_info = blocks[0]     #Captures the information about the input and pre-processing    
    
    module_list = nn.ModuleList()
    
    index = 0    #indexing blocks helps with implementing route  layers (skip connections)

    
    prev_filters = 3
    
    output_filters = []
    
    for x in blocks:
        module = nn.Sequential()
        
        if (x["type"] == "net"):
            continue
        
        #If it's a convolutional layer
        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 = Upsample(stride)
            upsample = nn.Upsample(scale_factor = 2, mode = "nearest")
        #    upsample = nn.functional.interpolate(None, scale_factor = 2, mode = "nearest")
            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:
                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":
            from_ = int(x["from"])
            shortcut = EmptyLayer()
            module.add_module("shortcut_{}".format(index), shortcut)
            
            
        elif x["type"] == "maxpool":
            stride = int(x["stride"])
            size = int(x["size"])
            if stride != 1:
                maxpool = nn.MaxPool2d(size, stride)
            else:
                maxpool = MaxPoolStride1(size)
            
            module.add_module("maxpool_{}".format(index), maxpool)
        
        #Yolo is the detection layer
        elif x["type"] == "yolo":
            mask = x["mask"].split(",")
            mask = [int(xx) for xx 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)
        
            
            
        else:
            print("Something I dunno")
            assert False


        module_list.append(module)
        prev_filters = filters
        output_filters.append(filters)
        index += 1
        
    
    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)
        self.header = torch.IntTensor([0,0,0,0])
        self.seen = 0

        self.feature_map = None

        
        
    def get_blocks(self):
        return self.blocks
    
    def get_module_list(self):
        return self.module_list

    def get_feature(self):
        return self.feature_map

                                
    def forward(self, x, device, flag_eval):
        detections = []
        modules = self.blocks[1:]
        outputs = {}   #We cache the outputs for the route layer
        
        layer_index = 1
        write = 0
        for i in range(len(modules)):        
            
            module_type = (modules[i]["type"])
            if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
                
                x = self.module_list[i](x)
                outputs[i] = x
            #    print(x.size(), layer_index, module_type)
                layer_index += 1

                
            elif module_type == "route":
                layers = modules[i]["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)
                outputs[i] = x
            #    print(x.size(), layer_index, module_type)
                layer_index += 1
            
            elif  module_type == "shortcut":
                from_ = int(modules[i]["from"])
                x = outputs[i-1] + outputs[i+from_]
                outputs[i] = x
            #    print(x.size(), layer_index, module_type)

                if i == 61:
                    self.feature_map = x
                    
                layer_index += 1
                
            
            elif module_type == 'yolo' and flag_eval:        
                
                anchors = self.module_list[i][0].anchors
                #Get the input dimensions
                inp_dim = int (self.net_info["height"])
                
                #Get the number of classes
                num_classes = int (modules[i]["classes"])
                
                #Output the result
                x = x.data
                x = predict_transform(x, inp_dim, anchors, num_classes, device)
                
                if type(x) == int:
                    continue

                
                if not write:
                    detections = x
                    write = 1
                
                else:
                    detections = torch.cat((detections, x), 1)
                
                outputs[i] = outputs[i-1]
                
        
        if flag_eval:
            try:
                return detections
            except:
                return 0
        else:
            return outputs[61]

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

        #The first 4 values are header information 
        # 1. Major version number
        # 2. Minor Version Number
        # 3. Subversion number 
        # 4. IMages seen 
        header = np.fromfile(fp, dtype = np.int32, count = 5)
        self.header = torch.from_numpy(header)
        self.seen = self.header[3]
        
        #The rest of the values are the weights
        # Let's load them up
        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 == "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)
                
    def save_weights(self, savedfile, cutoff = 0):
            
        if cutoff <= 0:
            cutoff = len(self.blocks) - 1
        
        fp = open(savedfile, 'wb')
        
        # Attach the header at the top of the file
        self.header[3] = self.seen
        header = self.header

        header = header.numpy()
        header.tofile(fp)
        
        # Now, let us save the weights 
        for i in range(len(self.module_list)):
            module_type = self.blocks[i+1]["type"]
            
            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]
                
                    #If the parameters are on GPU, convert them back to CPU
                    #We don't convert the parameter to GPU
                    #Instead. we copy the parameter and then convert it to CPU
                    #This is done as weight are need to be saved during training
                    cpu(bn.bias.data).numpy().tofile(fp)
                    cpu(bn.weight.data).numpy().tofile(fp)
                    cpu(bn.running_mean).numpy().tofile(fp)
                    cpu(bn.running_var).numpy().tofile(fp)
                
            
                else:
                    cpu(conv.bias.data).numpy().tofile(fp)
                
                
                #Let us save the weights for the Convolutional layers
                cpu(conv.weight.data).numpy().tofile(fp)
               




#
#dn = Darknet('cfg/yolov3.cfg')
#dn.load_weights("yolov3.weights")
#inp = get_test_input()
#a, interms = dn(inp)
#dn.eval()
#a_i, interms_i = dn(inp)