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import ast | |
import contextlib | |
import json | |
import math | |
import platform | |
import warnings | |
import zipfile | |
from collections import OrderedDict, namedtuple | |
from copy import copy | |
from pathlib import Path | |
from urllib.parse import urlparse | |
from typing import Optional | |
import cv2 | |
import numpy as np | |
import pandas as pd | |
import requests | |
import torch | |
import torch.nn as nn | |
from IPython.display import display | |
from PIL import Image | |
from torch.cuda import amp | |
from utils import TryExcept | |
from utils.dataloaders import exif_transpose, letterbox | |
from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr, | |
increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, | |
xywh2xyxy, xyxy2xywh, yaml_load) | |
from utils.plots import Annotator, colors, save_one_box | |
from utils.torch_utils import copy_attr, smart_inference_mode | |
def autopad(k, p=None, d=1): # kernel, padding, dilation | |
# Pad to 'same' shape outputs | |
if d > 1: | |
k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size | |
if p is None: | |
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad | |
return p | |
class Conv(nn.Module): | |
# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation) | |
default_act = nn.SiLU() # default activation | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): | |
super().__init__() | |
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) | |
self.bn = nn.BatchNorm2d(c2) | |
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() | |
def forward(self, x): | |
return self.act(self.bn(self.conv(x))) | |
def forward_fuse(self, x): | |
return self.act(self.conv(x)) | |
class Convb(nn.Module): | |
# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation) | |
default_act = nn.SiLU() # default activation | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): | |
super().__init__() | |
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=True) | |
self.bn = nn.BatchNorm2d(c2) | |
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() | |
def forward(self, x): | |
return self.act(self.bn(self.conv(x))) | |
def forward_fuse(self, x): | |
return self.act(self.conv(x)) | |
class AConv(nn.Module): | |
def __init__(self, c1, c2): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
self.cv1 = Conv(c1, c2, 3, 2, 1) | |
def forward(self, x): | |
x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True) | |
return self.cv1(x) | |
class ADown(nn.Module): | |
def __init__(self, c1, c2): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
self.c = c2 // 2 | |
self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1) | |
self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0) | |
def forward(self, x): | |
x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True) | |
x1,x2 = x.chunk(2, 1) | |
x1 = self.cv1(x1) | |
x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1) | |
x2 = self.cv2(x2) | |
return torch.cat((x1, x2), 1) | |
class RepConvN(nn.Module): | |
"""RepConv is a basic rep-style block, including training and deploy status | |
This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py | |
""" | |
default_act = nn.SiLU() # default activation | |
def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False): | |
super().__init__() | |
assert k == 3 and p == 1 | |
self.g = g | |
self.c1 = c1 | |
self.c2 = c2 | |
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() | |
self.bn = None | |
self.conv1 = Conv(c1, c2, k, s, p=p, g=g, act=False) | |
self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False) | |
def forward_fuse(self, x): | |
"""Forward process""" | |
return self.act(self.conv(x)) | |
def forward(self, x): | |
"""Forward process""" | |
id_out = 0 if self.bn is None else self.bn(x) | |
return self.act(self.conv1(x) + self.conv2(x) + id_out) | |
def get_equivalent_kernel_bias(self): | |
kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1) | |
kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2) | |
kernelid, biasid = self._fuse_bn_tensor(self.bn) | |
return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid | |
def _avg_to_3x3_tensor(self, avgp): | |
channels = self.c1 | |
groups = self.g | |
kernel_size = avgp.kernel_size | |
input_dim = channels // groups | |
k = torch.zeros((channels, input_dim, kernel_size, kernel_size)) | |
k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2 | |
return k | |
def _pad_1x1_to_3x3_tensor(self, kernel1x1): | |
if kernel1x1 is None: | |
return 0 | |
else: | |
return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1]) | |
def _fuse_bn_tensor(self, branch): | |
if branch is None: | |
return 0, 0 | |
if isinstance(branch, Conv): | |
kernel = branch.conv.weight | |
running_mean = branch.bn.running_mean | |
running_var = branch.bn.running_var | |
gamma = branch.bn.weight | |
beta = branch.bn.bias | |
eps = branch.bn.eps | |
elif isinstance(branch, nn.BatchNorm2d): | |
if not hasattr(self, 'id_tensor'): | |
input_dim = self.c1 // self.g | |
kernel_value = np.zeros((self.c1, input_dim, 3, 3), dtype=np.float32) | |
for i in range(self.c1): | |
kernel_value[i, i % input_dim, 1, 1] = 1 | |
self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) | |
kernel = self.id_tensor | |
running_mean = branch.running_mean | |
running_var = branch.running_var | |
gamma = branch.weight | |
beta = branch.bias | |
eps = branch.eps | |
std = (running_var + eps).sqrt() | |
t = (gamma / std).reshape(-1, 1, 1, 1) | |
return kernel * t, beta - running_mean * gamma / std | |
def fuse_convs(self): | |
if hasattr(self, 'conv'): | |
return | |
kernel, bias = self.get_equivalent_kernel_bias() | |
self.conv = nn.Conv2d(in_channels=self.conv1.conv.in_channels, | |
out_channels=self.conv1.conv.out_channels, | |
kernel_size=self.conv1.conv.kernel_size, | |
stride=self.conv1.conv.stride, | |
padding=self.conv1.conv.padding, | |
dilation=self.conv1.conv.dilation, | |
groups=self.conv1.conv.groups, | |
bias=True).requires_grad_(False) | |
self.conv.weight.data = kernel | |
self.conv.bias.data = bias | |
for para in self.parameters(): | |
para.detach_() | |
self.__delattr__('conv1') | |
self.__delattr__('conv2') | |
if hasattr(self, 'nm'): | |
self.__delattr__('nm') | |
if hasattr(self, 'bn'): | |
self.__delattr__('bn') | |
if hasattr(self, 'id_tensor'): | |
self.__delattr__('id_tensor') | |
class SP(nn.Module): | |
def __init__(self, k=3, s=1): | |
super(SP, self).__init__() | |
self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2) | |
def forward(self, x): | |
return self.m(x) | |
class MP(nn.Module): | |
# Max pooling | |
def __init__(self, k=2): | |
super(MP, self).__init__() | |
self.m = nn.MaxPool2d(kernel_size=k, stride=k) | |
def forward(self, x): | |
return self.m(x) | |
class ConvTranspose(nn.Module): | |
# Convolution transpose 2d layer | |
default_act = nn.SiLU() # default activation | |
def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True): | |
super().__init__() | |
self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn) | |
self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity() | |
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() | |
def forward(self, x): | |
return self.act(self.bn(self.conv_transpose(x))) | |
class DWConv(Conv): | |
# Depth-wise convolution | |
def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation | |
super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) | |
class DWConvTranspose2d(nn.ConvTranspose2d): | |
# Depth-wise transpose convolution | |
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out | |
super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) | |
class DWConvTranspose(nn.Module): | |
# Convolution transpose 2d layer | |
default_act = nn.SiLU() # default activation | |
def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True): | |
super().__init__() | |
self.dwconv_transpose = DWConvTranspose2d(c1, c2, k, s, p) | |
self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity() | |
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() | |
def forward(self, x): | |
return self.act(self.bn(self.dwconv_transpose(x))) | |
class DFL(nn.Module): | |
# DFL module | |
def __init__(self, c1=17): | |
super().__init__() | |
self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False) | |
self.conv.weight.data[:] = nn.Parameter(torch.arange(c1, dtype=torch.float).view(1, c1, 1, 1)) # / 120.0 | |
self.c1 = c1 | |
# self.bn = nn.BatchNorm2d(4) | |
def forward(self, x): | |
b, c, a = x.shape # batch, channels, anchors | |
return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a) | |
# return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a) | |
class BottleneckBase(nn.Module): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(1, 3), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, k[0], 1) | |
self.cv2 = Conv(c_, c2, k[1], 1, g=g) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
class RBottleneckBase(nn.Module): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 1), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, k[0], 1) | |
self.cv2 = Conv(c_, c2, k[1], 1, g=g) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
class RepNRBottleneckBase(nn.Module): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 1), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = RepConvN(c1, c_, k[0], 1) | |
self.cv2 = Conv(c_, c2, k[1], 1, g=g) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
class Bottleneck(nn.Module): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, k[0], 1) | |
self.cv2 = Conv(c_, c2, k[1], 1, g=g) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
class RepNBottleneck(nn.Module): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = RepConvN(c1, c_, k[0], 1) | |
self.cv2 = Conv(c_, c2, k[1], 1, g=g) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
class Res(nn.Module): | |
# ResNet bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super(Res, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_, c_, 3, 1, g=g) | |
self.cv3 = Conv(c_, c2, 1, 1) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x))) | |
class RepNRes(nn.Module): | |
# ResNet bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super(RepNRes, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = RepConvN(c_, c_, 3, 1, g=g) | |
self.cv3 = Conv(c_, c2, 1, 1) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x))) | |
class BottleneckCSP(nn.Module): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) | |
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) | |
self.cv4 = Conv(2 * c_, c2, 1, 1) | |
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) | |
self.act = nn.SiLU() | |
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
def forward(self, x): | |
y1 = self.cv3(self.m(self.cv1(x))) | |
y2 = self.cv2(x) | |
return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) | |
class CSP(nn.Module): | |
# CSP Bottleneck with 3 convolutions | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) | |
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
def forward(self, x): | |
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) | |
class RepNCSP(nn.Module): | |
# CSP Bottleneck with 3 convolutions | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) | |
self.m = nn.Sequential(*(RepNBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
def forward(self, x): | |
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) | |
class CSPBase(nn.Module): | |
# CSP Bottleneck with 3 convolutions | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) | |
self.m = nn.Sequential(*(BottleneckBase(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
def forward(self, x): | |
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) | |
class SPP(nn.Module): | |
# Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 | |
def __init__(self, c1, c2, k=(5, 9, 13)): | |
super().__init__() | |
c_ = c1 // 2 # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) | |
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) | |
def forward(self, x): | |
x = self.cv1(x) | |
with warnings.catch_warnings(): | |
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning | |
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) | |
class ASPP(torch.nn.Module): | |
def __init__(self, in_channels, out_channels): | |
super().__init__() | |
kernel_sizes = [1, 3, 3, 1] | |
dilations = [1, 3, 6, 1] | |
paddings = [0, 3, 6, 0] | |
self.aspp = torch.nn.ModuleList() | |
for aspp_idx in range(len(kernel_sizes)): | |
conv = torch.nn.Conv2d( | |
in_channels, | |
out_channels, | |
kernel_size=kernel_sizes[aspp_idx], | |
stride=1, | |
dilation=dilations[aspp_idx], | |
padding=paddings[aspp_idx], | |
bias=True) | |
self.aspp.append(conv) | |
self.gap = torch.nn.AdaptiveAvgPool2d(1) | |
self.aspp_num = len(kernel_sizes) | |
for m in self.modules(): | |
if isinstance(m, torch.nn.Conv2d): | |
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
m.weight.data.normal_(0, math.sqrt(2. / n)) | |
m.bias.data.fill_(0) | |
def forward(self, x): | |
avg_x = self.gap(x) | |
out = [] | |
for aspp_idx in range(self.aspp_num): | |
inp = avg_x if (aspp_idx == self.aspp_num - 1) else x | |
out.append(F.relu_(self.aspp[aspp_idx](inp))) | |
out[-1] = out[-1].expand_as(out[-2]) | |
out = torch.cat(out, dim=1) | |
return out | |
class SPPCSPC(nn.Module): | |
# CSP SPP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): | |
super(SPPCSPC, self).__init__() | |
c_ = int(2 * c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(c_, c_, 3, 1) | |
self.cv4 = Conv(c_, c_, 1, 1) | |
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) | |
self.cv5 = Conv(4 * c_, c_, 1, 1) | |
self.cv6 = Conv(c_, c_, 3, 1) | |
self.cv7 = Conv(2 * c_, c2, 1, 1) | |
def forward(self, x): | |
x1 = self.cv4(self.cv3(self.cv1(x))) | |
y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1))) | |
y2 = self.cv2(x) | |
return self.cv7(torch.cat((y1, y2), dim=1)) | |
class SPPF(nn.Module): | |
# Spatial Pyramid Pooling - Fast (SPPF) layer by Glenn Jocher | |
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) | |
super().__init__() | |
c_ = c1 // 2 # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_ * 4, c2, 1, 1) | |
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) | |
# self.m = SoftPool2d(kernel_size=k, stride=1, padding=k // 2) | |
def forward(self, x): | |
x = self.cv1(x) | |
with warnings.catch_warnings(): | |
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning | |
y1 = self.m(x) | |
y2 = self.m(y1) | |
return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) | |
import torch.nn.functional as F | |
from torch.nn.modules.utils import _pair | |
class ReOrg(nn.Module): | |
# yolo | |
def __init__(self): | |
super(ReOrg, self).__init__() | |
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) | |
return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1) | |
class Contract(nn.Module): | |
# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) | |
def __init__(self, gain=2): | |
super().__init__() | |
self.gain = gain | |
def forward(self, x): | |
b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' | |
s = self.gain | |
x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) | |
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) | |
return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) | |
class Expand(nn.Module): | |
# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) | |
def __init__(self, gain=2): | |
super().__init__() | |
self.gain = gain | |
def forward(self, x): | |
b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' | |
s = self.gain | |
x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) | |
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) | |
return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) | |
class Concat(nn.Module): | |
# Concatenate a list of tensors along dimension | |
def __init__(self, dimension=1): | |
super().__init__() | |
self.d = dimension | |
def forward(self, x): | |
return torch.cat(x, self.d) | |
class Shortcut(nn.Module): | |
def __init__(self, dimension=0): | |
super(Shortcut, self).__init__() | |
self.d = dimension | |
def forward(self, x): | |
return x[0]+x[1] | |
class Silence(nn.Module): | |
def __init__(self): | |
super(Silence, self).__init__() | |
def forward(self, x): | |
return x | |
##### GELAN ##### | |
class SPPELAN(nn.Module): | |
# spp-elan | |
def __init__(self, c1, c2, c3): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
self.c = c3 | |
self.cv1 = Conv(c1, c3, 1, 1) | |
self.cv2 = SP(5) | |
self.cv3 = SP(5) | |
self.cv4 = SP(5) | |
self.cv5 = Conv(4*c3, c2, 1, 1) | |
def forward(self, x): | |
y = [self.cv1(x)] | |
y.extend(m(y[-1]) for m in [self.cv2, self.cv3, self.cv4]) | |
return self.cv5(torch.cat(y, 1)) | |
class RepNCSPELAN4(nn.Module): | |
# csp-elan | |
def __init__(self, c1, c2, c3, c4, c5=1): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
self.c = c3//2 | |
self.cv1 = Conv(c1, c3, 1, 1) | |
self.cv2 = nn.Sequential(RepNCSP(c3//2, c4, c5), Conv(c4, c4, 3, 1)) | |
self.cv3 = nn.Sequential(RepNCSP(c4, c4, c5), Conv(c4, c4, 3, 1)) | |
self.cv4 = Conv(c3+(2*c4), c2, 1, 1) | |
def forward(self, x): | |
y = list(self.cv1(x).chunk(2, 1)) | |
y.extend((m(y[-1])) for m in [self.cv2, self.cv3]) | |
return self.cv4(torch.cat(y, 1)) | |
def forward_split(self, x): | |
y = list(self.cv1(x).split((self.c, self.c), 1)) | |
y.extend(m(y[-1]) for m in [self.cv2, self.cv3]) | |
return self.cv4(torch.cat(y, 1)) | |
################# | |
##### YOLOR ##### | |
class ImplicitA(nn.Module): | |
def __init__(self, channel): | |
super(ImplicitA, self).__init__() | |
self.channel = channel | |
self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1)) | |
nn.init.normal_(self.implicit, std=.02) | |
def forward(self, x): | |
return self.implicit + x | |
class ImplicitM(nn.Module): | |
def __init__(self, channel): | |
super(ImplicitM, self).__init__() | |
self.channel = channel | |
self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1)) | |
nn.init.normal_(self.implicit, mean=1., std=.02) | |
def forward(self, x): | |
return self.implicit * x | |
################# | |
##### CBNet ##### | |
class CBLinear(nn.Module): | |
def __init__(self, c1, c2s, k=1, s=1, p=None, g=1): # ch_in, ch_outs, kernel, stride, padding, groups | |
super(CBLinear, self).__init__() | |
self.c2s = c2s | |
self.conv = nn.Conv2d(c1, sum(c2s), k, s, autopad(k, p), groups=g, bias=True) | |
def forward(self, x): | |
outs = self.conv(x).split(self.c2s, dim=1) | |
return outs | |
class CBFuse(nn.Module): | |
def __init__(self, idx): | |
super(CBFuse, self).__init__() | |
self.idx = idx | |
def forward(self, xs): | |
target_size = xs[-1].shape[2:] | |
res = [F.interpolate(x[self.idx[i]], size=target_size, mode='nearest') for i, x in enumerate(xs[:-1])] | |
out = torch.sum(torch.stack(res + xs[-1:]), dim=0) | |
return out | |
################# | |
class DetectMultiBackend(nn.Module): | |
# YOLO MultiBackend class for python inference on various backends | |
def __init__(self, weights='yolo.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True): | |
# Usage: | |
# PyTorch: weights = *.pt | |
# TorchScript: *.torchscript | |
# ONNX Runtime: *.onnx | |
# ONNX OpenCV DNN: *.onnx --dnn | |
# OpenVINO: *_openvino_model | |
# CoreML: *.mlmodel | |
# TensorRT: *.engine | |
# TensorFlow SavedModel: *_saved_model | |
# TensorFlow GraphDef: *.pb | |
# TensorFlow Lite: *.tflite | |
# TensorFlow Edge TPU: *_edgetpu.tflite | |
# PaddlePaddle: *_paddle_model | |
from models.experimental import attempt_download, attempt_load # scoped to avoid circular import | |
super().__init__() | |
w = str(weights[0] if isinstance(weights, list) else weights) | |
pt, jit, onnx, onnx_end2end, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) | |
fp16 &= pt or jit or onnx or engine # FP16 | |
nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) | |
stride = 32 # default stride | |
cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA | |
if not (pt or triton): | |
w = attempt_download(w) # download if not local | |
if pt: # PyTorch | |
model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) | |
stride = max(int(model.stride.max()), 32) # model stride | |
names = model.module.names if hasattr(model, 'module') else model.names # get class names | |
model.half() if fp16 else model.float() | |
self.model = model # explicitly assign for to(), cpu(), cuda(), half() | |
elif jit: # TorchScript | |
LOGGER.info(f'Loading {w} for TorchScript inference...') | |
extra_files = {'config.txt': ''} # model metadata | |
model = torch.jit.load(w, _extra_files=extra_files, map_location=device) | |
model.half() if fp16 else model.float() | |
if extra_files['config.txt']: # load metadata dict | |
d = json.loads(extra_files['config.txt'], | |
object_hook=lambda d: {int(k) if k.isdigit() else k: v | |
for k, v in d.items()}) | |
stride, names = int(d['stride']), d['names'] | |
elif dnn: # ONNX OpenCV DNN | |
LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') | |
check_requirements('opencv-python>=4.5.4') | |
net = cv2.dnn.readNetFromONNX(w) | |
elif onnx: # ONNX Runtime | |
LOGGER.info(f'Loading {w} for ONNX Runtime inference...') | |
check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) | |
import onnxruntime | |
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] | |
session = onnxruntime.InferenceSession(w, providers=providers) | |
output_names = [x.name for x in session.get_outputs()] | |
meta = session.get_modelmeta().custom_metadata_map # metadata | |
if 'stride' in meta: | |
stride, names = int(meta['stride']), eval(meta['names']) | |
elif xml: # OpenVINO | |
LOGGER.info(f'Loading {w} for OpenVINO inference...') | |
check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/ | |
from openvino.runtime import Core, Layout, get_batch | |
ie = Core() | |
if not Path(w).is_file(): # if not *.xml | |
w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir | |
network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin')) | |
if network.get_parameters()[0].get_layout().empty: | |
network.get_parameters()[0].set_layout(Layout("NCHW")) | |
batch_dim = get_batch(network) | |
if batch_dim.is_static: | |
batch_size = batch_dim.get_length() | |
executable_network = ie.compile_model(network, device_name="CPU") # device_name="MYRIAD" for Intel NCS2 | |
stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata | |
elif engine: # TensorRT | |
LOGGER.info(f'Loading {w} for TensorRT inference...') | |
import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download | |
check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 | |
if device.type == 'cpu': | |
device = torch.device('cuda:0') | |
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) | |
logger = trt.Logger(trt.Logger.INFO) | |
with open(w, 'rb') as f, trt.Runtime(logger) as runtime: | |
model = runtime.deserialize_cuda_engine(f.read()) | |
context = model.create_execution_context() | |
bindings = OrderedDict() | |
output_names = [] | |
fp16 = False # default updated below | |
dynamic = False | |
for i in range(model.num_bindings): | |
name = model.get_binding_name(i) | |
dtype = trt.nptype(model.get_binding_dtype(i)) | |
if model.binding_is_input(i): | |
if -1 in tuple(model.get_binding_shape(i)): # dynamic | |
dynamic = True | |
context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) | |
if dtype == np.float16: | |
fp16 = True | |
else: # output | |
output_names.append(name) | |
shape = tuple(context.get_binding_shape(i)) | |
im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) | |
bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) | |
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) | |
batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size | |
elif coreml: # CoreML | |
LOGGER.info(f'Loading {w} for CoreML inference...') | |
import coremltools as ct | |
model = ct.models.MLModel(w) | |
elif saved_model: # TF SavedModel | |
LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') | |
import tensorflow as tf | |
keras = False # assume TF1 saved_model | |
model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) | |
elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt | |
LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') | |
import tensorflow as tf | |
def wrap_frozen_graph(gd, inputs, outputs): | |
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped | |
ge = x.graph.as_graph_element | |
return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) | |
def gd_outputs(gd): | |
name_list, input_list = [], [] | |
for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef | |
name_list.append(node.name) | |
input_list.extend(node.input) | |
return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp')) | |
gd = tf.Graph().as_graph_def() # TF GraphDef | |
with open(w, 'rb') as f: | |
gd.ParseFromString(f.read()) | |
frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) | |
elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python | |
try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu | |
from tflite_runtime.interpreter import Interpreter, load_delegate | |
except ImportError: | |
import tensorflow as tf | |
Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, | |
if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime | |
LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') | |
delegate = { | |
'Linux': 'libedgetpu.so.1', | |
'Darwin': 'libedgetpu.1.dylib', | |
'Windows': 'edgetpu.dll'}[platform.system()] | |
interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) | |
else: # TFLite | |
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') | |
interpreter = Interpreter(model_path=w) # load TFLite model | |
interpreter.allocate_tensors() # allocate | |
input_details = interpreter.get_input_details() # inputs | |
output_details = interpreter.get_output_details() # outputs | |
# load metadata | |
with contextlib.suppress(zipfile.BadZipFile): | |
with zipfile.ZipFile(w, "r") as model: | |
meta_file = model.namelist()[0] | |
meta = ast.literal_eval(model.read(meta_file).decode("utf-8")) | |
stride, names = int(meta['stride']), meta['names'] | |
elif tfjs: # TF.js | |
raise NotImplementedError('ERROR: YOLO TF.js inference is not supported') | |
elif paddle: # PaddlePaddle | |
LOGGER.info(f'Loading {w} for PaddlePaddle inference...') | |
check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle') | |
import paddle.inference as pdi | |
if not Path(w).is_file(): # if not *.pdmodel | |
w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir | |
weights = Path(w).with_suffix('.pdiparams') | |
config = pdi.Config(str(w), str(weights)) | |
if cuda: | |
config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) | |
predictor = pdi.create_predictor(config) | |
input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) | |
output_names = predictor.get_output_names() | |
elif triton: # NVIDIA Triton Inference Server | |
LOGGER.info(f'Using {w} as Triton Inference Server...') | |
check_requirements('tritonclient[all]') | |
from utils.triton import TritonRemoteModel | |
model = TritonRemoteModel(url=w) | |
nhwc = model.runtime.startswith("tensorflow") | |
else: | |
raise NotImplementedError(f'ERROR: {w} is not a supported format') | |
# class names | |
if 'names' not in locals(): | |
names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)} | |
if names[0] == 'n01440764' and len(names) == 1000: # ImageNet | |
names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names | |
self.__dict__.update(locals()) # assign all variables to self | |
def forward(self, im, augment=False, visualize=False): | |
# YOLO MultiBackend inference | |
b, ch, h, w = im.shape # batch, channel, height, width | |
if self.fp16 and im.dtype != torch.float16: | |
im = im.half() # to FP16 | |
if self.nhwc: | |
im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) | |
if self.pt: # PyTorch | |
y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) | |
elif self.jit: # TorchScript | |
y = self.model(im) | |
elif self.dnn: # ONNX OpenCV DNN | |
im = im.cpu().numpy() # torch to numpy | |
self.net.setInput(im) | |
y = self.net.forward() | |
elif self.onnx: # ONNX Runtime | |
im = im.cpu().numpy() # torch to numpy | |
y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) | |
elif self.xml: # OpenVINO | |
im = im.cpu().numpy() # FP32 | |
y = list(self.executable_network([im]).values()) | |
elif self.engine: # TensorRT | |
if self.dynamic and im.shape != self.bindings['images'].shape: | |
i = self.model.get_binding_index('images') | |
self.context.set_binding_shape(i, im.shape) # reshape if dynamic | |
self.bindings['images'] = self.bindings['images']._replace(shape=im.shape) | |
for name in self.output_names: | |
i = self.model.get_binding_index(name) | |
self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) | |
s = self.bindings['images'].shape | |
assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" | |
self.binding_addrs['images'] = int(im.data_ptr()) | |
self.context.execute_v2(list(self.binding_addrs.values())) | |
y = [self.bindings[x].data for x in sorted(self.output_names)] | |
elif self.coreml: # CoreML | |
im = im.cpu().numpy() | |
im = Image.fromarray((im[0] * 255).astype('uint8')) | |
# im = im.resize((192, 320), Image.ANTIALIAS) | |
y = self.model.predict({'image': im}) # coordinates are xywh normalized | |
if 'confidence' in y: | |
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels | |
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) | |
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) | |
else: | |
y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) | |
elif self.paddle: # PaddlePaddle | |
im = im.cpu().numpy().astype(np.float32) | |
self.input_handle.copy_from_cpu(im) | |
self.predictor.run() | |
y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] | |
elif self.triton: # NVIDIA Triton Inference Server | |
y = self.model(im) | |
else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) | |
im = im.cpu().numpy() | |
if self.saved_model: # SavedModel | |
y = self.model(im, training=False) if self.keras else self.model(im) | |
elif self.pb: # GraphDef | |
y = self.frozen_func(x=self.tf.constant(im)) | |
else: # Lite or Edge TPU | |
input = self.input_details[0] | |
int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model | |
if int8: | |
scale, zero_point = input['quantization'] | |
im = (im / scale + zero_point).astype(np.uint8) # de-scale | |
self.interpreter.set_tensor(input['index'], im) | |
self.interpreter.invoke() | |
y = [] | |
for output in self.output_details: | |
x = self.interpreter.get_tensor(output['index']) | |
if int8: | |
scale, zero_point = output['quantization'] | |
x = (x.astype(np.float32) - zero_point) * scale # re-scale | |
y.append(x) | |
y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] | |
y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels | |
if isinstance(y, (list, tuple)): | |
return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] | |
else: | |
return self.from_numpy(y) | |
def from_numpy(self, x): | |
return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x | |
def warmup(self, imgsz=(1, 3, 640, 640)): | |
# Warmup model by running inference once | |
warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton | |
if any(warmup_types) and (self.device.type != 'cpu' or self.triton): | |
im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input | |
for _ in range(2 if self.jit else 1): # | |
self.forward(im) # warmup | |
def _model_type(p='path/to/model.pt'): | |
# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx | |
# types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] | |
from export import export_formats | |
from utils.downloads import is_url | |
sf = list(export_formats().Suffix) # export suffixes | |
if not is_url(p, check=False): | |
check_suffix(p, sf) # checks | |
url = urlparse(p) # if url may be Triton inference server | |
types = [s in Path(p).name for s in sf] | |
types[8] &= not types[9] # tflite &= not edgetpu | |
triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc]) | |
return types + [triton] | |
def _load_metadata(f=Path('path/to/meta.yaml')): | |
# Load metadata from meta.yaml if it exists | |
if f.exists(): | |
d = yaml_load(f) | |
return d['stride'], d['names'] # assign stride, names | |
return None, None | |
class AutoShape(nn.Module): | |
# YOLO input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS | |
conf = 0.25 # NMS confidence threshold | |
iou = 0.45 # NMS IoU threshold | |
agnostic = False # NMS class-agnostic | |
multi_label = False # NMS multiple labels per box | |
classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs | |
max_det = 1000 # maximum number of detections per image | |
amp = False # Automatic Mixed Precision (AMP) inference | |
def __init__(self, model, verbose=True): | |
super().__init__() | |
if verbose: | |
LOGGER.info('Adding AutoShape... ') | |
copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes | |
self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance | |
self.pt = not self.dmb or model.pt # PyTorch model | |
self.model = model.eval() | |
if self.pt: | |
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() | |
m.inplace = False # Detect.inplace=False for safe multithread inference | |
m.export = True # do not output loss values | |
def _apply(self, fn): | |
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers | |
self = super()._apply(fn) | |
from models.yolo import Detect, Segment | |
if self.pt: | |
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() | |
if isinstance(m, (Detect, Segment)): | |
for k in 'stride', 'anchor_grid', 'stride_grid', 'grid': | |
x = getattr(m, k) | |
setattr(m, k, list(map(fn, x))) if isinstance(x, (list, tuple)) else setattr(m, k, fn(x)) | |
return self | |
def forward(self, ims, size=640, augment=False, profile=False): | |
# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are: | |
# file: ims = 'data/images/zidane.jpg' # str or PosixPath | |
# URI: = 'https://ultralytics.com/images/zidane.jpg' | |
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) | |
# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) | |
# numpy: = np.zeros((640,1280,3)) # HWC | |
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) | |
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images | |
dt = (Profile(), Profile(), Profile()) | |
with dt[0]: | |
if isinstance(size, int): # expand | |
size = (size, size) | |
p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param | |
autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference | |
if isinstance(ims, torch.Tensor): # torch | |
with amp.autocast(autocast): | |
return self.model(ims.to(p.device).type_as(p), augment=augment) # inference | |
# Pre-process | |
n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images | |
shape0, shape1, files = [], [], [] # image and inference shapes, filenames | |
for i, im in enumerate(ims): | |
f = f'image{i}' # filename | |
if isinstance(im, (str, Path)): # filename or uri | |
im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im | |
im = np.asarray(exif_transpose(im)) | |
elif isinstance(im, Image.Image): # PIL Image | |
im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f | |
files.append(Path(f).with_suffix('.jpg').name) | |
if im.shape[0] < 5: # image in CHW | |
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) | |
im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input | |
s = im.shape[:2] # HWC | |
shape0.append(s) # image shape | |
g = max(size) / max(s) # gain | |
shape1.append([int(y * g) for y in s]) | |
ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update | |
shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape | |
x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad | |
x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW | |
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 | |
with amp.autocast(autocast): | |
# Inference | |
with dt[1]: | |
y = self.model(x, augment=augment) # forward | |
# Post-process | |
with dt[2]: | |
y = non_max_suppression(y if self.dmb else y[0], | |
self.conf, | |
self.iou, | |
self.classes, | |
self.agnostic, | |
self.multi_label, | |
max_det=self.max_det) # NMS | |
for i in range(n): | |
scale_boxes(shape1, y[i][:, :4], shape0[i]) | |
return Detections(ims, y, files, dt, self.names, x.shape) | |
class Detections: | |
# YOLO detections class for inference results | |
def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): | |
super().__init__() | |
d = pred[0].device # device | |
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations | |
self.ims = ims # list of images as numpy arrays | |
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) | |
self.names = names # class names | |
self.files = files # image filenames | |
self.times = times # profiling times | |
self.xyxy = pred # xyxy pixels | |
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels | |
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized | |
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized | |
self.n = len(self.pred) # number of images (batch size) | |
self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) | |
self.s = tuple(shape) # inference BCHW shape | |
def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): | |
s, crops = '', [] | |
for i, (im, pred) in enumerate(zip(self.ims, self.pred)): | |
s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string | |
if pred.shape[0]: | |
for c in pred[:, -1].unique(): | |
n = (pred[:, -1] == c).sum() # detections per class | |
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string | |
s = s.rstrip(', ') | |
if show or save or render or crop: | |
annotator = Annotator(im, example=str(self.names)) | |
for *box, conf, cls in reversed(pred): # xyxy, confidence, class | |
label = f'{self.names[int(cls)]} {conf:.2f}' | |
if crop: | |
file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None | |
crops.append({ | |
'box': box, | |
'conf': conf, | |
'cls': cls, | |
'label': label, | |
'im': save_one_box(box, im, file=file, save=save)}) | |
else: # all others | |
annotator.box_label(box, label if labels else '', color=colors(cls)) | |
im = annotator.im | |
else: | |
s += '(no detections)' | |
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np | |
if show: | |
display(im) if is_notebook() else im.show(self.files[i]) | |
if save: | |
f = self.files[i] | |
im.save(save_dir / f) # save | |
if i == self.n - 1: | |
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") | |
if render: | |
self.ims[i] = np.asarray(im) | |
if pprint: | |
s = s.lstrip('\n') | |
return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t | |
if crop: | |
if save: | |
LOGGER.info(f'Saved results to {save_dir}\n') | |
return crops | |
def show(self, labels=True): | |
self._run(show=True, labels=labels) # show results | |
def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): | |
save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir | |
self._run(save=True, labels=labels, save_dir=save_dir) # save results | |
def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): | |
save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None | |
return self._run(crop=True, save=save, save_dir=save_dir) # crop results | |
def render(self, labels=True): | |
self._run(render=True, labels=labels) # render results | |
return self.ims | |
def pandas(self): | |
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) | |
new = copy(self) # return copy | |
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns | |
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns | |
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): | |
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update | |
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) | |
return new | |
def tolist(self): | |
# return a list of Detections objects, i.e. 'for result in results.tolist():' | |
r = range(self.n) # iterable | |
x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] | |
# for d in x: | |
# for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: | |
# setattr(d, k, getattr(d, k)[0]) # pop out of list | |
return x | |
def print(self): | |
LOGGER.info(self.__str__()) | |
def __len__(self): # override len(results) | |
return self.n | |
def __str__(self): # override print(results) | |
return self._run(pprint=True) # print results | |
def __repr__(self): | |
return f'YOLO {self.__class__} instance\n' + self.__str__() | |
class Proto(nn.Module): | |
# YOLO mask Proto module for segmentation models | |
def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks | |
super().__init__() | |
self.cv1 = Conv(c1, c_, k=3) | |
self.upsample = nn.Upsample(scale_factor=2, mode='nearest') | |
self.cv2 = Conv(c_, c_, k=3) | |
self.cv3 = Conv(c_, c2) | |
def forward(self, x): | |
return self.cv3(self.cv2(self.upsample(self.cv1(x)))) | |
class UConv(nn.Module): | |
def __init__(self, c1, c_=256, c2=256): # ch_in, number of protos, number of masks | |
super().__init__() | |
self.cv1 = Conv(c1, c_, k=3) | |
self.cv2 = nn.Conv2d(c_, c2, 1, 1) | |
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
def forward(self, x): | |
return self.up(self.cv2(self.cv1(x))) | |
class Classify(nn.Module): | |
# YOLO classification head, i.e. x(b,c1,20,20) to x(b,c2) | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups | |
super().__init__() | |
c_ = 1280 # efficientnet_b0 size | |
self.conv = Conv(c1, c_, k, s, autopad(k, p), g) | |
self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) | |
self.drop = nn.Dropout(p=0.0, inplace=True) | |
self.linear = nn.Linear(c_, c2) # to x(b,c2) | |
def forward(self, x): | |
if isinstance(x, list): | |
x = torch.cat(x, 1) | |
return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) | |
# -------------- Deformable Convolution -------------- | |
class DCNv2(nn.Module): | |
def __init__(self, in_channels, out_channels, kernel_size, stride=1, | |
padding=1, dilation=1, groups=1, deformable_groups=1): | |
super(DCNv2, self).__init__() | |
self.in_channels = in_channels | |
self.out_channels = out_channels | |
self.kernel_size = (kernel_size, kernel_size) | |
self.stride = (stride, stride) | |
self.padding = (padding, padding) | |
self.dilation = (dilation, dilation) | |
self.groups = groups | |
self.deformable_groups = deformable_groups | |
self.weight = nn.Parameter( | |
torch.empty(out_channels, in_channels, *self.kernel_size) | |
) | |
self.bias = nn.Parameter(torch.empty(out_channels)) | |
out_channels_offset_mask = (self.deformable_groups * 3 * | |
self.kernel_size[0] * self.kernel_size[1]) | |
self.conv_offset_mask = nn.Conv2d( | |
self.in_channels, | |
out_channels_offset_mask, | |
kernel_size=self.kernel_size, | |
stride=self.stride, | |
padding=self.padding, | |
bias=True, | |
) | |
self.bn = nn.BatchNorm2d(out_channels) | |
self.act = Conv.default_act | |
self.reset_parameters() | |
def forward(self, x): | |
offset_mask = self.conv_offset_mask(x) | |
o1, o2, mask = torch.chunk(offset_mask, 3, dim=1) | |
offset = torch.cat((o1, o2), dim=1) | |
mask = torch.sigmoid(mask) | |
x = torch.ops.torchvision.deform_conv2d( | |
x, | |
self.weight, | |
offset, | |
mask, | |
self.bias, | |
self.stride[0], self.stride[1], | |
self.padding[0], self.padding[1], | |
self.dilation[0], self.dilation[1], | |
self.groups, | |
self.deformable_groups, | |
True | |
) | |
x = self.bn(x) | |
x = self.act(x) | |
return x | |
def reset_parameters(self): | |
n = self.in_channels | |
for k in self.kernel_size: | |
n *= k | |
std = 1. / math.sqrt(n) | |
self.weight.data.uniform_(-std, std) | |
self.bias.data.zero_() | |
self.conv_offset_mask.weight.data.zero_() | |
self.conv_offset_mask.bias.data.zero_() | |
class Bottleneck_DCNv2(nn.Module): | |
# Standard bottleneck with DCN | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, groups, kernels, expand | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
if k[0] == 3: | |
self.cv1 = DCNv2(c1, c_, k[0], 1) | |
else: | |
self.cv1 = Conv(c1, c_, k[0], 1) | |
if k[1] == 3: | |
self.cv2 = DCNv2(c_, c2, k[1], 1, groups=g) | |
else: | |
self.cv2 = Conv(c_, c2, k[1], 1, g=g) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
class C2f_DCNv2(nn.Module): | |
# CSP Bottleneck with 2 convolutions | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
self.c = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, 2 * self.c, 1, 1) | |
self.cv2 = Conv((2 + n) * self.c, c2, 1) # optional act=FReLU(c2) | |
self.m = nn.ModuleList(Bottleneck_DCNv2(self.c, self.c, shortcut, g, k=(3, 3), e=0.5) for _ in range(n)) | |
def forward(self, x): | |
y = list(self.cv1(x).split((self.c, self.c), 1)) | |
y.extend(m(y[-1]) for m in self.m) | |
return self.cv2(torch.cat(y, 1)) | |
class RepDCNv2Bottleneck(RepNBottleneck): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__(c1, c1, shortcut, g, k, e) | |
self.cv1 = Bottleneck_DCNv2(c1, c1, shortcut, g, k, e) | |
class RepDCNv2CSP(RepNCSP): | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) | |
self.m = nn.Sequential(*(RepDCNv2Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
class RepDCNv2LEAN4(RepNCSPELAN4): | |
def __init__(self, c1, c2, c3, c4, c5=1): | |
super().__init__(c1, c2, c3, c4, c5) | |
self.cv2 = nn.Sequential(RepDCNv2CSP(c3//2, c4, c5), Conv(c4, c4, 3, 1)) | |
self.cv3 = nn.Sequential(RepDCNv2CSP(c4, c4, c5), Conv(c4, c4, 3, 1)) | |