# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
TensorFlow, Keras and TFLite versions of YOLOv5
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
Usage:
$ python models/tf.py --weights yolov5s.pt
Export:
$ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
"""
import argparse
import sys
from copy import deepcopy
from pathlib import Path
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
# ROOT = ROOT.relative_to(Path.cwd()) # relative
import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn
from tensorflow import keras
from models.common import (
C3,
SPP,
SPPF,
Bottleneck,
BottleneckCSP,
C3x,
Concat,
Conv,
CrossConv,
DWConv,
DWConvTranspose2d,
Focus,
autopad,
)
from models.experimental import MixConv2d, attempt_load
from models.yolo import Detect, Segment
from utils.activations import SiLU
from utils.general import LOGGER, make_divisible, print_args
class TFBN(keras.layers.Layer):
# TensorFlow BatchNormalization wrapper
def __init__(self, w=None):
"""Initializes a TensorFlow BatchNormalization layer with optional pretrained weights."""
super().__init__()
self.bn = keras.layers.BatchNormalization(
beta_initializer=keras.initializers.Constant(w.bias.numpy()),
gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),
moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),
epsilon=w.eps,
)
def call(self, inputs):
"""Applies batch normalization to the inputs."""
return self.bn(inputs)
class TFPad(keras.layers.Layer):
# Pad inputs in spatial dimensions 1 and 2
def __init__(self, pad):
"""
Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple
inputs.
Inputs are
"""
super().__init__()
if isinstance(pad, int):
self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
else: # tuple/list
self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]])
def call(self, inputs):
"""Pads input tensor with zeros using specified padding, suitable for int and tuple pad dimensions."""
return tf.pad(inputs, self.pad, mode="constant", constant_values=0)
class TFConv(keras.layers.Layer):
# Standard convolution
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
"""
Initializes a standard convolution layer with optional batch normalization and activation; supports only
group=1.
Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups.
"""
super().__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
# see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
conv = keras.layers.Conv2D(
filters=c2,
kernel_size=k,
strides=s,
padding="SAME" if s == 1 else "VALID",
use_bias=not hasattr(w, "bn"),
kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()),
)
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity
self.act = activations(w.act) if act else tf.identity
def call(self, inputs):
"""Applies convolution, batch normalization, and activation function to input tensors."""
return self.act(self.bn(self.conv(inputs)))
class TFDWConv(keras.layers.Layer):
# Depthwise convolution
def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
"""
Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow
models.
Input are ch_in, ch_out, weights, kernel, stride, padding, groups.
"""
super().__init__()
assert c2 % c1 == 0, f"TFDWConv() output={c2} must be a multiple of input={c1} channels"
conv = keras.layers.DepthwiseConv2D(
kernel_size=k,
depth_multiplier=c2 // c1,
strides=s,
padding="SAME" if s == 1 else "VALID",
use_bias=not hasattr(w, "bn"),
depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()),
)
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity
self.act = activations(w.act) if act else tf.identity
def call(self, inputs):
"""Applies convolution, batch normalization, and activation function to input tensors."""
return self.act(self.bn(self.conv(inputs)))
class TFDWConvTranspose2d(keras.layers.Layer):
# Depthwise ConvTranspose2d
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
"""
Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings.
Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups.
"""
super().__init__()
assert c1 == c2, f"TFDWConv() output={c2} must be equal to input={c1} channels"
assert k == 4 and p1 == 1, "TFDWConv() only valid for k=4 and p1=1"
weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy()
self.c1 = c1
self.conv = [
keras.layers.Conv2DTranspose(
filters=1,
kernel_size=k,
strides=s,
padding="VALID",
output_padding=p2,
use_bias=True,
kernel_initializer=keras.initializers.Constant(weight[..., i : i + 1]),
bias_initializer=keras.initializers.Constant(bias[i]),
)
for i in range(c1)
]
def call(self, inputs):
"""Processes input through parallel convolutions and concatenates results, trimming border pixels."""
return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1]
class TFFocus(keras.layers.Layer):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
"""
Initializes TFFocus layer to focus width and height information into channel space with custom convolution
parameters.
Inputs are ch_in, ch_out, kernel, stride, padding, groups.
"""
super().__init__()
self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
def call(self, inputs):
"""
Performs pixel shuffling and convolution on input tensor, downsampling by 2 and expanding channels by 4.
Example x(b,w,h,c) -> y(b,w/2,h/2,4c).
"""
inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]]
return self.conv(tf.concat(inputs, 3))
class TFBottleneck(keras.layers.Layer):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with optional
shortcut.
Arguments are ch_in, ch_out, shortcut, groups, expansion.
"""
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
self.add = shortcut and c1 == c2
def call(self, inputs):
"""Performs forward pass; if shortcut is True & input/output channels match, adds input to the convolution
result.
"""
return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
class TFCrossConv(keras.layers.Layer):
# Cross Convolution
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
"""Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities."""
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1)
self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2)
self.add = shortcut and c1 == c2
def call(self, inputs):
"""Passes input through two convolutions optionally adding the input if channel dimensions match."""
return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
class TFConv2d(keras.layers.Layer):
# Substitution for PyTorch nn.Conv2D
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
"""Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter
sizes and stride.
"""
super().__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
self.conv = keras.layers.Conv2D(
filters=c2,
kernel_size=k,
strides=s,
padding="VALID",
use_bias=bias,
kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()),
bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None,
)
def call(self, inputs):
"""Applies a convolution operation to the inputs and returns the result."""
return self.conv(inputs)
class TFBottleneckCSP(keras.layers.Layer):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion
ratio.
Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
"""
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
self.bn = TFBN(w.bn)
self.act = lambda x: keras.activations.swish(x)
self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
def call(self, inputs):
"""Processes input through the model layers, concatenates, normalizes, activates, and reduces the output
dimensions.
"""
y1 = self.cv3(self.m(self.cv1(inputs)))
y2 = self.cv2(inputs)
return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))
class TFC3(keras.layers.Layer):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions.
Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
"""
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
def call(self, inputs):
"""
Processes input through a sequence of transformations for object detection (YOLOv5).
See https://github.com/ultralytics/yolov5.
"""
return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
class TFC3x(keras.layers.Layer):
# 3 module with cross-convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes layer with cross-convolutions for enhanced feature extraction in object detection models.
Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
"""
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
self.m = keras.Sequential(
[TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)]
)
def call(self, inputs):
"""Processes input through cascaded convolutions and merges features, returning the final tensor output."""
return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
class TFSPP(keras.layers.Layer):
# Spatial pyramid pooling layer used in YOLOv3-SPP
def __init__(self, c1, c2, k=(5, 9, 13), w=None):
"""Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling."""
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding="SAME") for x in k]
def call(self, inputs):
"""Processes input through two TFConv layers and concatenates with max-pooled outputs at intermediate stage."""
x = self.cv1(inputs)
return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
class TFSPPF(keras.layers.Layer):
# Spatial pyramid pooling-Fast layer
def __init__(self, c1, c2, k=5, w=None):
"""Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and
weights.
"""
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding="SAME")
def call(self, inputs):
"""Executes the model's forward pass, concatenating input features with three max-pooled versions before final
convolution.
"""
x = self.cv1(inputs)
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
class TFDetect(keras.layers.Layer):
# TF YOLOv5 Detect layer
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):
"""Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image
size.
"""
super().__init__()
self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
self.nl = len(anchors) # number of detection layers
self.na = len(anchors[0]) // 2 # number of anchors
self.grid = [tf.zeros(1)] * self.nl # init grid
self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2])
self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
self.training = False # set to False after building model
self.imgsz = imgsz
for i in range(self.nl):
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
self.grid[i] = self._make_grid(nx, ny)
def call(self, inputs):
"""Performs forward pass through the model layers to predict object bounding boxes and classifications."""
z = [] # inference output
x = []
for i in range(self.nl):
x.append(self.m[i](inputs[i]))
# x(bs,20,20,255) to x(bs,3,20,20,85)
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no])
if not self.training: # inference
y = x[i]
grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5
anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4
xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy
wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid
# Normalize xywh to 0-1 to reduce calibration error
xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
y = tf.concat([xy, wh, tf.sigmoid(y[..., 4 : 5 + self.nc]), y[..., 5 + self.nc :]], -1)
z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)
@staticmethod
def _make_grid(nx=20, ny=20):
"""Generates a 2D grid of coordinates in (x, y) format with shape [1, 1, ny*nx, 2]."""
# return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))
return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
class TFSegment(TFDetect):
# YOLOv5 Segment head for segmentation models
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
"""Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation
models.
"""
super().__init__(nc, anchors, ch, imgsz, w)
self.nm = nm # number of masks
self.npr = npr # number of protos
self.no = 5 + nc + self.nm # number of outputs per anchor
self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv
self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos
self.detect = TFDetect.call
def call(self, x):
"""Applies detection and proto layers on input, returning detections and optionally protos if training."""
p = self.proto(x[0])
# p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos
p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160)
x = self.detect(self, x)
return (x, p) if self.training else (x[0], p)
class TFProto(keras.layers.Layer):
def __init__(self, c1, c_=256, c2=32, w=None):
"""Initializes TFProto layer with convolutional and upsampling layers for feature extraction and
transformation.
"""
super().__init__()
self.cv1 = TFConv(c1, c_, k=3, w=w.cv1)
self.upsample = TFUpsample(None, scale_factor=2, mode="nearest")
self.cv2 = TFConv(c_, c_, k=3, w=w.cv2)
self.cv3 = TFConv(c_, c2, w=w.cv3)
def call(self, inputs):
"""Performs forward pass through the model, applying convolutions and upscaling on input tensor."""
return self.cv3(self.cv2(self.upsample(self.cv1(inputs))))
class TFUpsample(keras.layers.Layer):
# TF version of torch.nn.Upsample()
def __init__(self, size, scale_factor, mode, w=None):
"""
Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor is
even.
Warning: all arguments needed including 'w'
"""
super().__init__()
assert scale_factor % 2 == 0, "scale_factor must be multiple of 2"
self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode)
# self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
# with default arguments: align_corners=False, half_pixel_centers=False
# self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
# size=(x.shape[1] * 2, x.shape[2] * 2))
def call(self, inputs):
"""Applies upsample operation to inputs using nearest neighbor interpolation."""
return self.upsample(inputs)
class TFConcat(keras.layers.Layer):
# TF version of torch.concat()
def __init__(self, dimension=1, w=None):
"""Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1."""
super().__init__()
assert dimension == 1, "convert only NCHW to NHWC concat"
self.d = 3
def call(self, inputs):
"""Concatenates a list of tensors along the last dimension, used for NCHW to NHWC conversion."""
return tf.concat(inputs, self.d)
def parse_model(d, ch, model, imgsz):
"""Parses a model definition dict `d` to create YOLOv5 model layers, including dynamic channel adjustments."""
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
anchors, nc, gd, gw, ch_mul = (
d["anchors"],
d["nc"],
d["depth_multiple"],
d["width_multiple"],
d.get("channel_multiple"),
)
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
if not ch_mul:
ch_mul = 8
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
m_str = m
m = eval(m) if isinstance(m, str) else m # eval strings
for j, a in enumerate(args):
try:
args[j] = eval(a) if isinstance(a, str) else a # eval strings
except NameError:
pass
n = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in [
nn.Conv2d,
Conv,
DWConv,
DWConvTranspose2d,
Bottleneck,
SPP,
SPPF,
MixConv2d,
Focus,
CrossConv,
BottleneckCSP,
C3,
C3x,
]:
c1, c2 = ch[f], args[0]
c2 = make_divisible(c2 * gw, ch_mul) if c2 != no else c2
args = [c1, c2, *args[1:]]
if m in [BottleneckCSP, C3, C3x]:
args.insert(2, n)
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
elif m in [Detect, Segment]:
args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
if m is Segment:
args[3] = make_divisible(args[3] * gw, ch_mul)
args.append(imgsz)
else:
c2 = ch[f]
tf_m = eval("TF" + m_str.replace("nn.", ""))
m_ = (
keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)])
if n > 1
else tf_m(*args, w=model.model[i])
) # module
torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace("__main__.", "") # module type
np = sum(x.numel() for x in torch_m_.parameters()) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
LOGGER.info(f"{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}") # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
ch.append(c2)
return keras.Sequential(layers), sorted(save)
class TFModel:
# TF YOLOv5 model
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)):
"""Initializes TF YOLOv5 model with specified configuration, channels, classes, model instance, and input
size.
"""
super().__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc and nc != self.yaml["nc"]:
LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
self.yaml["nc"] = nc # override yaml value
self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
def predict(
self,
inputs,
tf_nms=False,
agnostic_nms=False,
topk_per_class=100,
topk_all=100,
iou_thres=0.45,
conf_thres=0.25,
):
y = [] # outputs
x = inputs
for m in self.model.layers:
if m.f != -1: # if not from previous layer
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
x = m(x) # run
y.append(x if m.i in self.savelist else None) # save output
# Add TensorFlow NMS
if tf_nms:
boxes = self._xywh2xyxy(x[0][..., :4])
probs = x[0][:, :, 4:5]
classes = x[0][:, :, 5:]
scores = probs * classes
if agnostic_nms:
nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
else:
boxes = tf.expand_dims(boxes, 2)
nms = tf.image.combined_non_max_suppression(
boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False
)
return (nms,)
return x # output [1,6300,85] = [xywh, conf, class0, class1, ...]
# x = x[0] # [x(1,6300,85), ...] to x(6300,85)
# xywh = x[..., :4] # x(6300,4) boxes
# conf = x[..., 4:5] # x(6300,1) confidences
# cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes
# return tf.concat([conf, cls, xywh], 1)
@staticmethod
def _xywh2xyxy(xywh):
"""Converts bounding box format from [x, y, w, h] to [x1, y1, x2, y2], where xy1=top-left and xy2=bottom-
right.
"""
x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)
class AgnosticNMS(keras.layers.Layer):
# TF Agnostic NMS
def call(self, input, topk_all, iou_thres, conf_thres):
"""Performs agnostic NMS on input tensors using given thresholds and top-K selection."""
return tf.map_fn(
lambda x: self._nms(x, topk_all, iou_thres, conf_thres),
input,
fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
name="agnostic_nms",
)
@staticmethod
def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):
"""Performs agnostic non-maximum suppression (NMS) on detected objects, filtering based on IoU and confidence
thresholds.
"""
boxes, classes, scores = x
class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
scores_inp = tf.reduce_max(scores, -1)
selected_inds = tf.image.non_max_suppression(
boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres
)
selected_boxes = tf.gather(boxes, selected_inds)
padded_boxes = tf.pad(
selected_boxes,
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
mode="CONSTANT",
constant_values=0.0,
)
selected_scores = tf.gather(scores_inp, selected_inds)
padded_scores = tf.pad(
selected_scores,
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
mode="CONSTANT",
constant_values=-1.0,
)
selected_classes = tf.gather(class_inds, selected_inds)
padded_classes = tf.pad(
selected_classes,
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
mode="CONSTANT",
constant_values=-1.0,
)
valid_detections = tf.shape(selected_inds)[0]
return padded_boxes, padded_scores, padded_classes, valid_detections
def activations(act=nn.SiLU):
"""Converts PyTorch activations to TensorFlow equivalents, supporting LeakyReLU, Hardswish, and SiLU/Swish."""
if isinstance(act, nn.LeakyReLU):
return lambda x: keras.activations.relu(x, alpha=0.1)
elif isinstance(act, nn.Hardswish):
return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667
elif isinstance(act, (nn.SiLU, SiLU)):
return lambda x: keras.activations.swish(x)
else:
raise Exception(f"no matching TensorFlow activation found for PyTorch activation {act}")
def representative_dataset_gen(dataset, ncalib=100):
"""Generates a representative dataset for calibration by yielding transformed numpy arrays from the input
dataset.
"""
for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
im = np.transpose(img, [1, 2, 0])
im = np.expand_dims(im, axis=0).astype(np.float32)
im /= 255
yield [im]
if n >= ncalib:
break
def run(
weights=ROOT / "yolov5s.pt", # weights path
imgsz=(640, 640), # inference size h,w
batch_size=1, # batch size
dynamic=False, # dynamic batch size
):
# PyTorch model
im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image
model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False)
_ = model(im) # inference
model.info()
# TensorFlow model
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
_ = tf_model.predict(im) # inference
# Keras model
im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
keras_model.summary()
LOGGER.info("PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.")
def parse_opt():
"""Parses and returns command-line options for model inference, including weights path, image size, batch size, and
dynamic batching.
"""
parser = argparse.ArgumentParser()
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
parser.add_argument("--batch-size", type=int, default=1, help="batch size")
parser.add_argument("--dynamic", action="store_true", help="dynamic batch size")
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
print_args(vars(opt))
return opt
def main(opt):
"""Executes the YOLOv5 model run function with parsed command line options."""
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)