348 lines
14 KiB
Python
Executable File
348 lines
14 KiB
Python
Executable File
from collections import defaultdict
|
|
|
|
import torch.nn as nn
|
|
|
|
from utils.parse_config import *
|
|
from utils.utils import *
|
|
|
|
|
|
def create_modules(module_defs):
|
|
"""
|
|
Constructs module list of layer blocks from module configuration in module_defs
|
|
"""
|
|
hyperparams = module_defs.pop(0)
|
|
output_filters = [int(hyperparams['channels'])]
|
|
module_list = nn.ModuleList()
|
|
for i, module_def in enumerate(module_defs):
|
|
modules = nn.Sequential()
|
|
|
|
if module_def['type'] == 'convolutional':
|
|
bn = int(module_def['batch_normalize'])
|
|
filters = int(module_def['filters'])
|
|
kernel_size = int(module_def['size'])
|
|
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
|
|
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
|
|
out_channels=filters,
|
|
kernel_size=kernel_size,
|
|
stride=int(module_def['stride']),
|
|
padding=pad,
|
|
bias=not bn))
|
|
if bn:
|
|
modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
|
|
if module_def['activation'] == 'leaky':
|
|
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
|
|
|
|
elif module_def['type'] == 'upsample':
|
|
upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
|
|
modules.add_module('upsample_%d' % i, upsample)
|
|
|
|
elif module_def['type'] == 'route':
|
|
layers = [int(x) for x in module_def['layers'].split(',')]
|
|
filters = sum([output_filters[layer_i] for layer_i in layers])
|
|
modules.add_module('route_%d' % i, EmptyLayer())
|
|
|
|
elif module_def['type'] == 'shortcut':
|
|
filters = output_filters[int(module_def['from'])]
|
|
modules.add_module('shortcut_%d' % i, EmptyLayer())
|
|
|
|
elif module_def['type'] == 'yolo':
|
|
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
|
|
# Extract anchors
|
|
anchors = [float(x) for x in module_def['anchors'].split(',')]
|
|
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
|
|
anchors = [anchors[i] for i in anchor_idxs]
|
|
num_classes = int(module_def['classes'])
|
|
img_height = int(hyperparams['height'])
|
|
# Define detection layer
|
|
yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs)
|
|
modules.add_module('yolo_%d' % i, yolo_layer)
|
|
|
|
# Register module list and number of output filters
|
|
module_list.append(modules)
|
|
output_filters.append(filters)
|
|
|
|
return hyperparams, module_list
|
|
|
|
|
|
class EmptyLayer(nn.Module):
|
|
"""Placeholder for 'route' and 'shortcut' layers"""
|
|
|
|
def __init__(self):
|
|
super(EmptyLayer, self).__init__()
|
|
|
|
|
|
class YOLOLayer(nn.Module):
|
|
|
|
def __init__(self, anchors, nC, img_dim, anchor_idxs):
|
|
super(YOLOLayer, self).__init__()
|
|
|
|
anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
|
|
nA = len(anchors)
|
|
|
|
self.anchors = anchors
|
|
self.nA = nA # number of anchors (3)
|
|
self.nC = nC # number of classes (80)
|
|
self.bbox_attrs = 5 + nC
|
|
self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
|
|
|
|
if anchor_idxs[0] == (nA * 2): # 6
|
|
stride = 32
|
|
elif anchor_idxs[0] == nA: # 3
|
|
stride = 16
|
|
else:
|
|
stride = 8
|
|
|
|
# Build anchor grids
|
|
nG = int(self.img_dim / stride)
|
|
self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
|
|
self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float()
|
|
self.scaled_anchors = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors])
|
|
self.anchor_w = self.scaled_anchors[:, 0:1].view((1, nA, 1, 1))
|
|
self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
|
|
|
|
def forward(self, p, targets=None, requestPrecision=False):
|
|
FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
|
|
|
|
bs = p.shape[0] # batch size
|
|
nG = p.shape[2] # number of grid points
|
|
stride = self.img_dim / nG
|
|
|
|
if p.is_cuda and not self.grid_x.is_cuda:
|
|
self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
|
|
self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
|
|
|
|
# p.view(12, 255, 13, 13) -- > (12, 3, 13, 13, 80) # (bs, anchors, grid, grid, classes + xywh)
|
|
p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
|
|
|
|
# Get outputs
|
|
x = torch.sigmoid(p[..., 0]) # Center x
|
|
y = torch.sigmoid(p[..., 1]) # Center y
|
|
|
|
# Width and height (yolo method)
|
|
w = p[..., 2] # Width
|
|
h = p[..., 3] # Height
|
|
width = torch.exp(w.data) * self.anchor_w
|
|
height = torch.exp(h.data) * self.anchor_h
|
|
|
|
# Width and height (power method)
|
|
# w = torch.sigmoid(p[..., 2]) # Width
|
|
# h = torch.sigmoid(p[..., 3]) # Height
|
|
# width = ((w.data * 2) ** 2) * self.anchor_w
|
|
# height = ((h.data * 2) ** 2) * self.anchor_h
|
|
|
|
# Add offset and scale with anchors (in grid space, i.e. 0-13)
|
|
pred_boxes = FT(bs, self.nA, nG, nG, 4)
|
|
pred_conf = p[..., 4] # Conf
|
|
pred_cls = p[..., 5:] # Class
|
|
|
|
# Training
|
|
if targets is not None:
|
|
MSELoss = nn.MSELoss(size_average=True)
|
|
BCEWithLogitsLoss = nn.BCEWithLogitsLoss(size_average=True)
|
|
CrossEntropyLoss = nn.CrossEntropyLoss()
|
|
|
|
if requestPrecision:
|
|
gx = self.grid_x[:, :, :nG, :nG]
|
|
gy = self.grid_y[:, :, :nG, :nG]
|
|
pred_boxes[..., 0] = x.data + gx - width / 2
|
|
pred_boxes[..., 1] = y.data + gy - height / 2
|
|
pred_boxes[..., 2] = x.data + gx + width / 2
|
|
pred_boxes[..., 3] = y.data + gy + height / 2
|
|
|
|
tx, ty, tw, th, mask, tcls, TP, FP, FN, TC = \
|
|
build_targets(pred_boxes, pred_conf, pred_cls, targets, self.scaled_anchors, self.nA, self.nC, nG,
|
|
requestPrecision)
|
|
tcls = tcls[mask]
|
|
if x.is_cuda:
|
|
tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda()
|
|
|
|
# Mask outputs to ignore non-existing objects (but keep confidence predictions)
|
|
nT = sum([len(x) for x in targets]) # number of targets
|
|
nM = mask.sum().float() # number of anchors (assigned to targets)
|
|
nB = len(targets) # batch size
|
|
k = nM / nB
|
|
if nM > 0:
|
|
lx = k * MSELoss(x[mask], tx[mask])
|
|
ly = k * MSELoss(y[mask], ty[mask])
|
|
lw = k * MSELoss(w[mask], tw[mask])
|
|
lh = k * MSELoss(h[mask], th[mask])
|
|
|
|
# lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
|
|
lconf = k * BCEWithLogitsLoss(pred_conf, mask.float())
|
|
|
|
# lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
|
|
lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
|
|
else:
|
|
lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
|
|
|
|
# Add confidence loss for background anchors (noobj)
|
|
#lconf += k * BCEWithLogitsLoss(pred_conf[~mask], mask[~mask].float())
|
|
|
|
# Sum loss components
|
|
loss = lx + ly + lw + lh + lconf + lcls
|
|
|
|
# Sum False Positives from unassigned anchors
|
|
i = torch.sigmoid(pred_conf[~mask]) > 0.5
|
|
if i.sum() > 0:
|
|
FP_classes = torch.argmax(pred_cls[~mask][i], 1)
|
|
FPe = torch.bincount(FP_classes, minlength=self.nC).float().cpu() # extra FPs
|
|
else:
|
|
FPe = torch.zeros(self.nC)
|
|
|
|
return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \
|
|
nT, TP, FP, FPe, FN, TC
|
|
|
|
else:
|
|
pred_boxes[..., 0] = x.data + self.grid_x
|
|
pred_boxes[..., 1] = y.data + self.grid_y
|
|
pred_boxes[..., 2] = width
|
|
pred_boxes[..., 3] = height
|
|
|
|
# If not in training phase return predictions
|
|
output = torch.cat((pred_boxes.view(bs, -1, 4) * stride,
|
|
torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1)
|
|
return output.data
|
|
|
|
|
|
class Darknet(nn.Module):
|
|
"""YOLOv3 object detection model"""
|
|
|
|
def __init__(self, config_path, img_size=416):
|
|
super(Darknet, self).__init__()
|
|
self.module_defs = parse_model_config(config_path)
|
|
self.module_defs[0]['height'] = img_size
|
|
self.hyperparams, self.module_list = create_modules(self.module_defs)
|
|
self.img_size = img_size
|
|
self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT', 'TP', 'FP', 'FPe', 'FN', 'TC']
|
|
|
|
def forward(self, x, targets=None, requestPrecision=False):
|
|
is_training = targets is not None
|
|
output = []
|
|
self.losses = defaultdict(float)
|
|
layer_outputs = []
|
|
|
|
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
|
|
if module_def['type'] in ['convolutional', 'upsample']:
|
|
x = module(x)
|
|
elif module_def['type'] == 'route':
|
|
layer_i = [int(x) for x in module_def['layers'].split(',')]
|
|
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
|
|
elif module_def['type'] == 'shortcut':
|
|
layer_i = int(module_def['from'])
|
|
x = layer_outputs[-1] + layer_outputs[layer_i]
|
|
elif module_def['type'] == 'yolo':
|
|
# Train phase: get loss
|
|
if is_training:
|
|
x, *losses = module[0](x, targets, requestPrecision)
|
|
for name, loss in zip(self.loss_names, losses):
|
|
self.losses[name] += loss
|
|
# Test phase: Get detections
|
|
else:
|
|
x = module(x)
|
|
output.append(x)
|
|
layer_outputs.append(x)
|
|
|
|
if is_training:
|
|
self.losses['nT'] /= 3
|
|
self.losses['TC'] /= 3
|
|
metrics = torch.zeros(4, len(self.losses['FPe'])) # TP, FP, FN, target_count
|
|
|
|
ui = np.unique(self.losses['TC'])[1:]
|
|
for i in ui:
|
|
j = self.losses['TC'] == float(i)
|
|
metrics[0, i] = (self.losses['TP'][j] > 0).sum().float() # TP
|
|
metrics[1, i] = (self.losses['FP'][j] > 0).sum().float() # FP
|
|
metrics[2, i] = (self.losses['FN'][j] == 3).sum().float() # FN
|
|
metrics[3] = metrics.sum(0)
|
|
metrics[1] += self.losses['FPe']
|
|
|
|
self.losses['TP'] = metrics[0].sum()
|
|
self.losses['FP'] = metrics[1].sum()
|
|
self.losses['FN'] = metrics[2].sum()
|
|
self.losses['TC'] = 0
|
|
self.losses['metrics'] = metrics
|
|
|
|
return sum(output) if is_training else torch.cat(output, 1)
|
|
|
|
|
|
def load_weights(self, weights_path):
|
|
"""Parses and loads the weights stored in 'weights_path'"""
|
|
|
|
# Open the weights file
|
|
fp = open(weights_path, 'rb')
|
|
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
|
|
|
|
# Needed to write header when saving weights
|
|
self.header_info = header
|
|
|
|
self.seen = header[3]
|
|
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
|
|
fp.close()
|
|
|
|
ptr = 0
|
|
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
|
|
if module_def['type'] == 'convolutional':
|
|
conv_layer = module[0]
|
|
if module_def['batch_normalize']:
|
|
# Load BN bias, weights, running mean and running variance
|
|
bn_layer = module[1]
|
|
num_b = bn_layer.bias.numel() # Number of biases
|
|
# Bias
|
|
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
|
|
bn_layer.bias.data.copy_(bn_b)
|
|
ptr += num_b
|
|
# Weight
|
|
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
|
|
bn_layer.weight.data.copy_(bn_w)
|
|
ptr += num_b
|
|
# Running Mean
|
|
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
|
|
bn_layer.running_mean.data.copy_(bn_rm)
|
|
ptr += num_b
|
|
# Running Var
|
|
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
|
|
bn_layer.running_var.data.copy_(bn_rv)
|
|
ptr += num_b
|
|
else:
|
|
# Load conv. bias
|
|
num_b = conv_layer.bias.numel()
|
|
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
|
|
conv_layer.bias.data.copy_(conv_b)
|
|
ptr += num_b
|
|
# Load conv. weights
|
|
num_w = conv_layer.weight.numel()
|
|
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
|
|
conv_layer.weight.data.copy_(conv_w)
|
|
ptr += num_w
|
|
|
|
|
|
"""
|
|
@:param path - path of the new weights file
|
|
@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
|
|
"""
|
|
|
|
|
|
def save_weights(self, path, cutoff=-1):
|
|
fp = open(path, 'wb')
|
|
self.header_info[3] = self.seen
|
|
self.header_info.tofile(fp)
|
|
|
|
# Iterate through layers
|
|
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
|
if module_def['type'] == 'convolutional':
|
|
conv_layer = module[0]
|
|
# If batch norm, load bn first
|
|
if module_def['batch_normalize']:
|
|
bn_layer = module[1]
|
|
bn_layer.bias.data.cpu().numpy().tofile(fp)
|
|
bn_layer.weight.data.cpu().numpy().tofile(fp)
|
|
bn_layer.running_mean.data.cpu().numpy().tofile(fp)
|
|
bn_layer.running_var.data.cpu().numpy().tofile(fp)
|
|
# Load conv bias
|
|
else:
|
|
conv_layer.bias.data.cpu().numpy().tofile(fp)
|
|
# Load conv weights
|
|
conv_layer.weight.data.cpu().numpy().tofile(fp)
|
|
|
|
fp.close()
|