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import torch
import re
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import logging
import cv2
import math
import itertools
import collections
from torchvision.ops import nms
GlobalParams = collections.namedtuple(
"GlobalParams",
[
"batch_norm_momentum",
"batch_norm_epsilon",
"dropout_rate",
"num_classes",
"width_coefficient",
"depth_coefficient",
"depth_divisor",
"min_depth",
"drop_connect_rate",
"image_size",
],
)
# Parameters for an individual model block
BlockArgs = collections.namedtuple(
"BlockArgs",
["kernel_size", "num_repeat", "input_filters", "output_filters", "expand_ratio", "id_skip", "stride", "se_ratio"],
)
# https://stackoverflow.com/a/18348004
# Change namedtuple defaults
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
# in the old version, g_simple_padding = False, which tries to align
# tensorflow's implementation, which is not required here.
g_simple_padding = True
class MaxPool2dStaticSamePadding(nn.Module):
"""
created by Zylo117
The real keras/tensorflow MaxPool2d with same padding
"""
def __init__(self, kernel_size, stride):
super().__init__()
if g_simple_padding:
self.pool = nn.MaxPool2d(kernel_size, stride, padding=(kernel_size - 1) // 2)
else:
assert ValueError()
self.pool = nn.MaxPool2d(kernel_size, stride)
self.stride = self.pool.stride
self.kernel_size = self.pool.kernel_size
if isinstance(self.stride, int):
self.stride = [self.stride] * 2
elif len(self.stride) == 1:
self.stride = [self.stride[0]] * 2
if isinstance(self.kernel_size, int):
self.kernel_size = [self.kernel_size] * 2
elif len(self.kernel_size) == 1:
self.kernel_size = [self.kernel_size[0]] * 2
def forward(self, x):
if g_simple_padding:
return self.pool(x)
else:
assert ValueError()
h, w = x.shape[-2:]
h_step = math.ceil(w / self.stride[1])
v_step = math.ceil(h / self.stride[0])
h_cover_len = self.stride[1] * (h_step - 1) + 1 + (self.kernel_size[1] - 1)
v_cover_len = self.stride[0] * (v_step - 1) + 1 + (self.kernel_size[0] - 1)
extra_h = h_cover_len - w
extra_v = v_cover_len - h
left = extra_h // 2
right = extra_h - left
top = extra_v // 2
bottom = extra_v - top
x = F.pad(x, [left, right, top, bottom])
x = self.pool(x)
return x
class Conv2dStaticSamePadding(nn.Module):
"""
created by Zylo117
The real keras/tensorflow conv2d with same padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, groups=1, dilation=1, **kwargs):
super().__init__()
if g_simple_padding:
assert kernel_size % 2 == 1
assert dilation == 1
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride=stride,
bias=bias,
groups=groups,
padding=(kernel_size - 1) // 2,
)
self.stride = self.conv.stride
if isinstance(self.stride, int):
self.stride = [self.stride] * 2
elif len(self.stride) == 1:
self.stride = [self.stride[0]] * 2
else:
self.stride = list(self.stride)
else:
assert ValueError()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, bias=bias, groups=groups)
self.stride = self.conv.stride
self.kernel_size = self.conv.kernel_size
self.dilation = self.conv.dilation
if isinstance(self.stride, int):
self.stride = [self.stride] * 2
elif len(self.stride) == 1:
self.stride = [self.stride[0]] * 2
if isinstance(self.kernel_size, int):
self.kernel_size = [self.kernel_size] * 2
elif len(self.kernel_size) == 1:
self.kernel_size = [self.kernel_size[0]] * 2
def forward(self, x):
if g_simple_padding:
return self.conv(x)
else:
assert ValueError()
h, w = x.shape[-2:]
h_step = math.ceil(w / self.stride[1])
v_step = math.ceil(h / self.stride[0])
h_cover_len = self.stride[1] * (h_step - 1) + 1 + (self.kernel_size[1] - 1)
v_cover_len = self.stride[0] * (v_step - 1) + 1 + (self.kernel_size[0] - 1)
extra_h = h_cover_len - w
extra_v = v_cover_len - h
left = extra_h // 2
right = extra_h - left
top = extra_v // 2
bottom = extra_v - top
x = F.pad(x, [left, right, top, bottom])
x = self.conv(x)
return x
class SeparableConvBlock(nn.Module):
"""
created by Zylo117
"""
def __init__(self, in_channels, out_channels=None, norm=True, activation=False, onnx_export=False):
super(SeparableConvBlock, self).__init__()
if out_channels is None:
out_channels = in_channels
# Q: whether separate conv
# share bias between depthwise_conv and pointwise_conv
# or just pointwise_conv apply bias.
# A: Confirmed, just pointwise_conv applies bias, depthwise_conv has no bias.
self.depthwise_conv = Conv2dStaticSamePadding(
in_channels, in_channels, kernel_size=3, stride=1, groups=in_channels, bias=False
)
self.pointwise_conv = Conv2dStaticSamePadding(in_channels, out_channels, kernel_size=1, stride=1)
self.norm = norm
if self.norm:
# Warning: pytorch momentum is different from tensorflow's, momentum_pytorch = 1 - momentum_tensorflow
self.bn = nn.BatchNorm2d(num_features=out_channels, momentum=0.01, eps=1e-3)
self.activation = activation
if self.activation:
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
def forward(self, x):
x = self.depthwise_conv(x)
x = self.pointwise_conv(x)
if self.norm:
x = self.bn(x)
if self.activation:
x = self.swish(x)
return x
class BiFPN(nn.Module):
"""
modified by Zylo117
"""
def __init__(
self,
num_channels,
conv_channels,
first_time=False,
epsilon=1e-4,
onnx_export=False,
attention=True,
adaptive_up=False,
):
"""
Args:
num_channels:
conv_channels:
first_time: whether the input comes directly from the efficientnet,
if True, downchannel it first, and downsample P5 to generate P6 then P7
epsilon: epsilon of fast weighted attention sum of BiFPN, not the BN's epsilon
onnx_export: if True, use Swish instead of MemoryEfficientSwish
"""
super(BiFPN, self).__init__()
self.epsilon = epsilon
# Conv layers
self.conv6_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
self.conv5_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
self.conv4_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
self.conv3_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
self.conv4_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
self.conv5_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
self.conv6_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
self.conv7_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
# Feature scaling layers
self.p6_upsample = nn.Upsample(scale_factor=2, mode="nearest")
self.p5_upsample = nn.Upsample(scale_factor=2, mode="nearest")
self.p4_upsample = nn.Upsample(scale_factor=2, mode="nearest")
self.p3_upsample = nn.Upsample(scale_factor=2, mode="nearest")
self.adaptive_up = adaptive_up
self.p4_downsample = MaxPool2dStaticSamePadding(3, 2)
self.p5_downsample = MaxPool2dStaticSamePadding(3, 2)
self.p6_downsample = MaxPool2dStaticSamePadding(3, 2)
self.p7_downsample = MaxPool2dStaticSamePadding(3, 2)
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
self.first_time = first_time
if self.first_time:
self.p5_down_channel = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p4_down_channel = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[1], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p3_down_channel = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[0], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
if len(conv_channels) == 3:
self.p5_to_p6 = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
MaxPool2dStaticSamePadding(3, 2),
)
else:
assert len(conv_channels) == 4
self.p6_down_channel = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[3], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p6_to_p7 = nn.Sequential(MaxPool2dStaticSamePadding(3, 2))
self.p4_down_channel_2 = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[1], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p5_down_channel_2 = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
# Weight
self.p6_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p6_w1_relu = nn.ReLU()
self.p5_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p5_w1_relu = nn.ReLU()
self.p4_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p4_w1_relu = nn.ReLU()
self.p3_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p3_w1_relu = nn.ReLU()
self.p4_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
self.p4_w2_relu = nn.ReLU()
self.p5_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
self.p5_w2_relu = nn.ReLU()
self.p6_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
self.p6_w2_relu = nn.ReLU()
self.p7_w2 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p7_w2_relu = nn.ReLU()
self.attention = attention
def forward(self, inputs):
"""
illustration of a minimal bifpn unit
P7_0 -------------------------> P7_2 -------->
|-------------| ↑
↓ |
P6_0 ---------> P6_1 ---------> P6_2 -------->
|-------------|--------------↑ ↑
↓ |
P5_0 ---------> P5_1 ---------> P5_2 -------->
|-------------|--------------↑ ↑
↓ |
P4_0 ---------> P4_1 ---------> P4_2 -------->
|-------------|--------------↑ ↑
|--------------↓ |
P3_0 -------------------------> P3_2 -------->
"""
# downsample channels using same-padding conv2d to target phase's if not the same
# judge: same phase as target,
# if same, pass;
# elif earlier phase, downsample to target phase's by pooling
# elif later phase, upsample to target phase's by nearest interpolation
if self.attention:
p3_out, p4_out, p5_out, p6_out, p7_out = self._forward_fast_attention(inputs)
else:
p3_out, p4_out, p5_out, p6_out, p7_out = self._forward(inputs)
return p3_out, p4_out, p5_out, p6_out, p7_out
def _forward_fast_attention(self, inputs):
if self.first_time:
if len(inputs) == 3:
p3, p4, p5 = inputs
p6_in = self.p5_to_p6(p5)
else:
p3, p4, p5, p6 = inputs
p6_in = self.p6_down_channel(p6)
p7_in = self.p6_to_p7(p6_in)
p3_in = self.p3_down_channel(p3)
p4_in = self.p4_down_channel(p4)
p5_in = self.p5_down_channel(p5)
else:
# P3_0, P4_0, P5_0, P6_0 and P7_0
p3_in, p4_in, p5_in, p6_in, p7_in = inputs
# P7_0 to P7_2
if not self.adaptive_up:
# Weights for P6_0 and P7_0 to P6_1
p6_w1 = self.p6_w1_relu(self.p6_w1)
weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon)
# Connections for P6_0 and P7_0 to P6_1 respectively
p6_up = self.conv6_up(self.swish(weight[0] * p6_in + weight[1] * self.p6_upsample(p7_in)))
# Weights for P5_0 and P6_0 to P5_1
p5_w1 = self.p5_w1_relu(self.p5_w1)
weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon)
# Connections for P5_0 and P6_0 to P5_1 respectively
p5_up = self.conv5_up(self.swish(weight[0] * p5_in + weight[1] * self.p5_upsample(p6_up)))
# Weights for P4_0 and P5_0 to P4_1
p4_w1 = self.p4_w1_relu(self.p4_w1)
weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon)
# Connections for P4_0 and P5_0 to P4_1 respectively
p4_up = self.conv4_up(self.swish(weight[0] * p4_in + weight[1] * self.p4_upsample(p5_up)))
# Weights for P3_0 and P4_1 to P3_2
p3_w1 = self.p3_w1_relu(self.p3_w1)
weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon)
# Connections for P3_0 and P4_1 to P3_2 respectively
p3_out = self.conv3_up(self.swish(weight[0] * p3_in + weight[1] * self.p3_upsample(p4_up)))
else:
# Weights for P6_0 and P7_0 to P6_1
p6_w1 = self.p6_w1_relu(self.p6_w1)
weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon)
# Connections for P6_0 and P7_0 to P6_1 respectively
p6_upsample = nn.Upsample(size=p6_in.shape[-2:])
p6_up = self.conv6_up(self.swish(weight[0] * p6_in + weight[1] * p6_upsample(p7_in)))
# Weights for P5_0 and P6_0 to P5_1
p5_w1 = self.p5_w1_relu(self.p5_w1)
weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon)
# Connections for P5_0 and P6_0 to P5_1 respectively
p5_upsample = nn.Upsample(size=p5_in.shape[-2:])
p5_up = self.conv5_up(self.swish(weight[0] * p5_in + weight[1] * p5_upsample(p6_up)))
# Weights for P4_0 and P5_0 to P4_1
p4_w1 = self.p4_w1_relu(self.p4_w1)
weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon)
# Connections for P4_0 and P5_0 to P4_1 respectively
p4_upsample = nn.Upsample(size=p4_in.shape[-2:])
p4_up = self.conv4_up(self.swish(weight[0] * p4_in + weight[1] * p4_upsample(p5_up)))
# Weights for P3_0 and P4_1 to P3_2
p3_w1 = self.p3_w1_relu(self.p3_w1)
weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon)
p3_upsample = nn.Upsample(size=p3_in.shape[-2:])
# Connections for P3_0 and P4_1 to P3_2 respectively
p3_out = self.conv3_up(self.swish(weight[0] * p3_in + weight[1] * p3_upsample(p4_up)))
if self.first_time:
p4_in = self.p4_down_channel_2(p4)
p5_in = self.p5_down_channel_2(p5)
# Weights for P4_0, P4_1 and P3_2 to P4_2
p4_w2 = self.p4_w2_relu(self.p4_w2)
weight = p4_w2 / (torch.sum(p4_w2, dim=0) + self.epsilon)
# Connections for P4_0, P4_1 and P3_2 to P4_2 respectively
p4_out = self.conv4_down(
self.swish(weight[0] * p4_in + weight[1] * p4_up + weight[2] * self.p4_downsample(p3_out))
)
# Weights for P5_0, P5_1 and P4_2 to P5_2
p5_w2 = self.p5_w2_relu(self.p5_w2)
weight = p5_w2 / (torch.sum(p5_w2, dim=0) + self.epsilon)
# Connections for P5_0, P5_1 and P4_2 to P5_2 respectively
p5_out = self.conv5_down(
self.swish(weight[0] * p5_in + weight[1] * p5_up + weight[2] * self.p5_downsample(p4_out))
)
# Weights for P6_0, P6_1 and P5_2 to P6_2
p6_w2 = self.p6_w2_relu(self.p6_w2)
weight = p6_w2 / (torch.sum(p6_w2, dim=0) + self.epsilon)
# Connections for P6_0, P6_1 and P5_2 to P6_2 respectively
p6_out = self.conv6_down(
self.swish(weight[0] * p6_in + weight[1] * p6_up + weight[2] * self.p6_downsample(p5_out))
)
# Weights for P7_0 and P6_2 to P7_2
p7_w2 = self.p7_w2_relu(self.p7_w2)
weight = p7_w2 / (torch.sum(p7_w2, dim=0) + self.epsilon)
# Connections for P7_0 and P6_2 to P7_2
p7_out = self.conv7_down(self.swish(weight[0] * p7_in + weight[1] * self.p7_downsample(p6_out)))
return p3_out, p4_out, p5_out, p6_out, p7_out
def _forward(self, inputs):
if self.first_time:
p3, p4, p5 = inputs
p6_in = self.p5_to_p6(p5)
p7_in = self.p6_to_p7(p6_in)
p3_in = self.p3_down_channel(p3)
p4_in = self.p4_down_channel(p4)
p5_in = self.p5_down_channel(p5)
else:
# P3_0, P4_0, P5_0, P6_0 and P7_0
p3_in, p4_in, p5_in, p6_in, p7_in = inputs
# P7_0 to P7_2
# Connections for P6_0 and P7_0 to P6_1 respectively
p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_in)))
# Connections for P5_0 and P6_0 to P5_1 respectively
p5_up = self.conv5_up(self.swish(p5_in + self.p5_upsample(p6_up)))
# Connections for P4_0 and P5_0 to P4_1 respectively
p4_up = self.conv4_up(self.swish(p4_in + self.p4_upsample(p5_up)))
# Connections for P3_0 and P4_1 to P3_2 respectively
p3_out = self.conv3_up(self.swish(p3_in + self.p3_upsample(p4_up)))
if self.first_time:
p4_in = self.p4_down_channel_2(p4)
p5_in = self.p5_down_channel_2(p5)
# Connections for P4_0, P4_1 and P3_2 to P4_2 respectively
p4_out = self.conv4_down(self.swish(p4_in + p4_up + self.p4_downsample(p3_out)))
# Connections for P5_0, P5_1 and P4_2 to P5_2 respectively
p5_out = self.conv5_down(self.swish(p5_in + p5_up + self.p5_downsample(p4_out)))
# Connections for P6_0, P6_1 and P5_2 to P6_2 respectively
p6_out = self.conv6_down(self.swish(p6_in + p6_up + self.p6_downsample(p5_out)))
# Connections for P7_0 and P6_2 to P7_2
p7_out = self.conv7_down(self.swish(p7_in + self.p7_downsample(p6_out)))
return p3_out, p4_out, p5_out, p6_out, p7_out
class Regressor(nn.Module):
"""
modified by Zylo117
"""
def __init__(self, in_channels, num_anchors, num_layers, onnx_export=False):
super(Regressor, self).__init__()
self.num_layers = num_layers
self.num_layers = num_layers
self.conv_list = nn.ModuleList(
[SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)]
)
self.bn_list = nn.ModuleList(
[
nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)])
for j in range(5)
]
)
self.header = SeparableConvBlock(in_channels, num_anchors * 4, norm=False, activation=False)
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
def forward(self, inputs):
feats = []
for feat, bn_list in zip(inputs, self.bn_list):
for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list):
feat = conv(feat)
feat = bn(feat)
feat = self.swish(feat)
feat = self.header(feat)
feat = feat.permute(0, 2, 3, 1)
feat = feat.contiguous().view(feat.shape[0], -1, 4)
feats.append(feat)
feats = torch.cat(feats, dim=1)
return feats
class SwishImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = i * torch.sigmoid(i)
ctx.save_for_backward(i)
return result
@staticmethod
def backward(ctx, grad_output):
i = ctx.saved_variables[0]
sigmoid_i = torch.sigmoid(i)
return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
class MemoryEfficientSwish(nn.Module):
def forward(self, x):
if torch._C._get_tracing_state():
return x * torch.sigmoid(x)
return SwishImplementation.apply(x)
class Swish(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
class Classifier(nn.Module):
"""
modified by Zylo117
"""
def __init__(self, in_channels, num_anchors, num_classes, num_layers, onnx_export=False, prior_prob=0.01):
super(Classifier, self).__init__()
self.num_anchors = num_anchors
self.num_classes = num_classes
self.num_layers = num_layers
self.conv_list = nn.ModuleList(
[SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)]
)
self.bn_list = nn.ModuleList(
[
nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)])
for j in range(5)
]
)
self.header = SeparableConvBlock(in_channels, num_anchors * num_classes, norm=False, activation=False)
prior_prob = prior_prob
bias_value = -math.log((1 - prior_prob) / prior_prob)
torch.nn.init.normal_(self.header.pointwise_conv.conv.weight, std=0.01)
torch.nn.init.constant_(self.header.pointwise_conv.conv.bias, bias_value)
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
def forward(self, inputs):
feats = []
for feat, bn_list in zip(inputs, self.bn_list):
for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list):
feat = conv(feat)
feat = bn(feat)
feat = self.swish(feat)
feat = self.header(feat)
feat = feat.permute(0, 2, 3, 1)
feat = feat.contiguous().view(
feat.shape[0], feat.shape[1], feat.shape[2], self.num_anchors, self.num_classes
)
feat = feat.contiguous().view(feat.shape[0], -1, self.num_classes)
feats.append(feat)
feats = torch.cat(feats, dim=1)
# feats = feats.sigmoid()
return feats
class Conv2dDynamicSamePadding(nn.Conv2d):
"""2D Convolutions like TensorFlow, for a dynamic image size"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
raise ValueError("tend to be deprecated")
self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
def forward(self, x):
ih, iw = x.size()[-2:]
kh, kw = self.weight.size()[-2:]
sh, sw = self.stride
oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
if pad_h > 0 or pad_w > 0:
x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
# TODO: it seems like the standard conv layer is good enough with proper padding
# parameters.
def get_same_padding_conv2d(image_size=None):
"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.
Static padding is necessary for ONNX exporting of models."""
if image_size is None:
raise ValueError("not validated")
return Conv2dDynamicSamePadding
else:
from functools import partial
return partial(Conv2dStaticSamePadding, image_size=image_size)
def round_filters(filters, global_params):
"""Calculate and round number of filters based on depth multiplier."""
multiplier = global_params.width_coefficient
if not multiplier:
return filters
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
filters *= multiplier
min_depth = min_depth or divisor
new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
new_filters += divisor
return int(new_filters)
def round_repeats(repeats, global_params):
"""Round number of filters based on depth multiplier."""
multiplier = global_params.depth_coefficient
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats))
def drop_connect(inputs, p, training):
"""Drop connect."""
if not training:
return inputs
batch_size = inputs.shape[0]
keep_prob = 1 - p
random_tensor = keep_prob
random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
binary_tensor = torch.floor(random_tensor)
output = inputs / keep_prob * binary_tensor
return output
class MBConvBlock(nn.Module):
"""
Mobile Inverted Residual Bottleneck Block
Args:
block_args (namedtuple): BlockArgs, see above
global_params (namedtuple): GlobalParam, see above
Attributes:
has_se (bool): Whether the block contains a Squeeze and Excitation layer.
"""
def __init__(self, block_args, global_params):
super().__init__()
self._block_args = block_args
self._bn_mom = 1 - global_params.batch_norm_momentum
self._bn_eps = global_params.batch_norm_epsilon
self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1)
self.id_skip = block_args.id_skip # skip connection and drop connect
# Get static or dynamic convolution depending on image size
Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)
# Expansion phase
inp = self._block_args.input_filters # number of input channels
oup = self._block_args.input_filters * self._block_args.expand_ratio # number of output channels
if self._block_args.expand_ratio != 1:
self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)
self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)
# Depthwise convolution phase
k = self._block_args.kernel_size
s = self._block_args.stride
if isinstance(s, (tuple, list)) and all([s0 == s[0] for s0 in s]):
s = s[0]
self._depthwise_conv = Conv2d(
in_channels=oup,
out_channels=oup,
groups=oup, # groups makes it depthwise
kernel_size=k,
stride=s,
bias=False,
)
self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)
# Squeeze and Excitation layer, if desired
if self.has_se:
num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio))
self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)
self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)
# Output phase
final_oup = self._block_args.output_filters
self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)
self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps)
self._swish = MemoryEfficientSwish()
def forward(self, inputs, drop_connect_rate=None):
"""
:param inputs: input tensor
:param drop_connect_rate: drop connect rate (float, between 0 and 1)
:return: output of block
"""
# Expansion and Depthwise Convolution
x = inputs
if self._block_args.expand_ratio != 1:
x = self._expand_conv(inputs)
x = self._bn0(x)
x = self._swish(x)
x = self._depthwise_conv(x)
x = self._bn1(x)
x = self._swish(x)
# Squeeze and Excitation
if self.has_se:
x_squeezed = F.adaptive_avg_pool2d(x, 1)
x_squeezed = self._se_reduce(x_squeezed)
x_squeezed = self._swish(x_squeezed)
x_squeezed = self._se_expand(x_squeezed)
x = torch.sigmoid(x_squeezed) * x
x = self._project_conv(x)
x = self._bn2(x)
# Skip connection and drop connect
input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters
if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:
if drop_connect_rate:
x = drop_connect(x, p=drop_connect_rate, training=self.training)
x = x + inputs # skip connection
return x
def set_swish(self, memory_efficient=True):
"""Sets swish function as memory efficient (for training) or standard (for export)"""
self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
class BlockDecoder(object):
"""Block Decoder for readability, straight from the official TensorFlow repository"""
@staticmethod
def _decode_block_string(block_string):
"""Gets a block through a string notation of arguments."""
assert isinstance(block_string, str)
ops = block_string.split("_")
options = {}
for op in ops:
splits = re.split(r"(\d.*)", op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# Check stride
assert ("s" in options and len(options["s"]) == 1) or (
len(options["s"]) == 2 and options["s"][0] == options["s"][1]
)
return BlockArgs(
kernel_size=int(options["k"]),
num_repeat=int(options["r"]),
input_filters=int(options["i"]),
output_filters=int(options["o"]),
expand_ratio=int(options["e"]),
id_skip=("noskip" not in block_string),
se_ratio=float(options["se"]) if "se" in options else None,
stride=[int(options["s"][0])],
)
@staticmethod
def _encode_block_string(block):
"""Encodes a block to a string."""
args = [
"r%d" % block.num_repeat,
"k%d" % block.kernel_size,
"s%d%d" % (block.strides[0], block.strides[1]),
"e%s" % block.expand_ratio,
"i%d" % block.input_filters,
"o%d" % block.output_filters,
]
if 0 < block.se_ratio <= 1:
args.append("se%s" % block.se_ratio)
if block.id_skip is False:
args.append("noskip")
return "_".join(args)
@staticmethod
def decode(string_list):
"""
Decodes a list of string notations to specify blocks inside the network.
:param string_list: a list of strings, each string is a notation of block
:return: a list of BlockArgs namedtuples of block args
"""
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(BlockDecoder._decode_block_string(block_string))
return blocks_args
@staticmethod
def encode(blocks_args):
"""
Encodes a list of BlockArgs to a list of strings.
:param blocks_args: a list of BlockArgs namedtuples of block args
:return: a list of strings, each string is a notation of block
"""
block_strings = []
for block in blocks_args:
block_strings.append(BlockDecoder._encode_block_string(block))
return block_strings
def efficientnet(
width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2,
image_size=None,
num_classes=1000,
):
"""Creates a efficientnet model."""
blocks_args = [
"r1_k3_s11_e1_i32_o16_se0.25",
"r2_k3_s22_e6_i16_o24_se0.25",
"r2_k5_s22_e6_i24_o40_se0.25",
"r3_k3_s22_e6_i40_o80_se0.25",
"r3_k5_s11_e6_i80_o112_se0.25",
"r4_k5_s22_e6_i112_o192_se0.25",
"r1_k3_s11_e6_i192_o320_se0.25",
]
blocks_args = BlockDecoder.decode(blocks_args)
global_params = GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
# data_format='channels_last', # removed, this is always true in PyTorch
num_classes=num_classes,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
image_size=image_size,
)
return blocks_args, global_params
def efficientnet_params(model_name):
"""Map EfficientNet model name to parameter coefficients."""
params_dict = {
# Coefficients: width,depth,res,dropout
"efficientnet-b0": (1.0, 1.0, 224, 0.2),
"efficientnet-b1": (1.0, 1.1, 240, 0.2),
"efficientnet-b2": (1.1, 1.2, 260, 0.3),
"efficientnet-b3": (1.2, 1.4, 300, 0.3),
"efficientnet-b4": (1.4, 1.8, 380, 0.4),
"efficientnet-b5": (1.6, 2.2, 456, 0.4),
"efficientnet-b6": (1.8, 2.6, 528, 0.5),
"efficientnet-b7": (2.0, 3.1, 600, 0.5),
"efficientnet-b8": (2.2, 3.6, 672, 0.5),
"efficientnet-l2": (4.3, 5.3, 800, 0.5),
}
return params_dict[model_name]
def get_model_params(model_name, override_params):
"""Get the block args and global params for a given model"""
if model_name.startswith("efficientnet"):
w, d, s, p = efficientnet_params(model_name)
# note: all models have drop connect rate = 0.2
blocks_args, global_params = efficientnet(
width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s
)
else:
raise NotImplementedError("model name is not pre-defined: %s" % model_name)
if override_params:
# ValueError will be raised here if override_params has fields not included in global_params.
global_params = global_params._replace(**override_params)
return blocks_args, global_params
url_map = {
"efficientnet-b0": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b0-355c32eb.pth",
"efficientnet-b1": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b1-f1951068.pth",
"efficientnet-b2": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b2-8bb594d6.pth",
"efficientnet-b3": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b3-5fb5a3c3.pth",
"efficientnet-b4": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b4-6ed6700e.pth",
"efficientnet-b5": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b5-b6417697.pth",
"efficientnet-b6": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b6-c76e70fd.pth",
"efficientnet-b7": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b7-dcc49843.pth",
}
url_map_advprop = {
"efficientnet-b0": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b0-b64d5a18.pth",
"efficientnet-b1": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b1-0f3ce85a.pth",
"efficientnet-b2": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b2-6e9d97e5.pth",
"efficientnet-b3": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b3-cdd7c0f4.pth",
"efficientnet-b4": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b4-44fb3a87.pth",
"efficientnet-b5": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b5-86493f6b.pth",
"efficientnet-b6": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b6-ac80338e.pth",
"efficientnet-b7": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b7-4652b6dd.pth",
"efficientnet-b8": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b8-22a8fe65.pth",
}
def load_pretrained_weights(model, model_name, load_fc=True, advprop=False):
"""Loads pretrained weights, and downloads if loading for the first time."""
# AutoAugment or Advprop (different preprocessing)
url_map_ = url_map_advprop if advprop else url_map
from torch.utils import model_zoo
state_dict = model_zoo.load_url(url_map_[model_name], map_location=torch.device("cpu"))
# state_dict = torch.load('../../weights/backbone_efficientnetb0.pth')
if load_fc:
ret = model.load_state_dict(state_dict, strict=False)
print(ret)
else:
state_dict.pop("_fc.weight")
state_dict.pop("_fc.bias")
res = model.load_state_dict(state_dict, strict=False)
assert set(res.missing_keys) == set(["_fc.weight", "_fc.bias"]), "issue loading pretrained weights"
print("Loaded pretrained weights for {}".format(model_name))
class EfficientNet(nn.Module):
"""
An EfficientNet model. Most easily loaded with the .from_name or .from_pretrained methods
Args:
blocks_args (list): A list of BlockArgs to construct blocks
global_params (namedtuple): A set of GlobalParams shared between blocks
Example:
model = EfficientNet.from_pretrained('efficientnet-b0')
"""
def __init__(self, blocks_args=None, global_params=None):
super().__init__()
assert isinstance(blocks_args, list), "blocks_args should be a list"
assert len(blocks_args) > 0, "block args must be greater than 0"
self._global_params = global_params
self._blocks_args = blocks_args
# Get static or dynamic convolution depending on image size
Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)
# Batch norm parameters
bn_mom = 1 - self._global_params.batch_norm_momentum
bn_eps = self._global_params.batch_norm_epsilon
# Stem
in_channels = 3 # rgb
out_channels = round_filters(32, self._global_params) # number of output channels
self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
# Build blocks
self._blocks = nn.ModuleList([])
for block_args in self._blocks_args:
# Update block input and output filters based on depth multiplier.
block_args = block_args._replace(
input_filters=round_filters(block_args.input_filters, self._global_params),
output_filters=round_filters(block_args.output_filters, self._global_params),
num_repeat=round_repeats(block_args.num_repeat, self._global_params),
)
# The first block needs to take care of stride and filter size increase.
self._blocks.append(MBConvBlock(block_args, self._global_params))
if block_args.num_repeat > 1:
block_args = block_args._replace(input_filters=block_args.output_filters, stride=1)
for _ in range(block_args.num_repeat - 1):
self._blocks.append(MBConvBlock(block_args, self._global_params))
# Head
in_channels = block_args.output_filters # output of final block
out_channels = round_filters(1280, self._global_params)
self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
# Final linear layer
self._avg_pooling = nn.AdaptiveAvgPool2d(1)
self._dropout = nn.Dropout(self._global_params.dropout_rate)
self._fc = nn.Linear(out_channels, self._global_params.num_classes)
self._swish = MemoryEfficientSwish()
def set_swish(self, memory_efficient=True):
"""Sets swish function as memory efficient (for training) or standard (for export)"""
self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
for block in self._blocks:
block.set_swish(memory_efficient)
def extract_features(self, inputs):
"""Returns output of the final convolution layer"""
# Stem
x = self._swish(self._bn0(self._conv_stem(inputs)))
# Blocks
for idx, block in enumerate(self._blocks):
drop_connect_rate = self._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self._blocks)
x = block(x, drop_connect_rate=drop_connect_rate)
# Head
x = self._swish(self._bn1(self._conv_head(x)))
return x
def forward(self, inputs):
"""Calls extract_features to extract features, applies final linear layer, and returns logits."""
bs = inputs.size(0)
# Convolution layers
x = self.extract_features(inputs)
# Pooling and final linear layer
x = self._avg_pooling(x)
x = x.view(bs, -1)
x = self._dropout(x)
x = self._fc(x)
return x
@classmethod
def from_name(cls, model_name, override_params=None):
cls._check_model_name_is_valid(model_name)
blocks_args, global_params = get_model_params(model_name, override_params)
return cls(blocks_args, global_params)
@classmethod
def from_pretrained(cls, model_name, load_weights=True, advprop=True, num_classes=1000, in_channels=3):
model = cls.from_name(model_name, override_params={"num_classes": num_classes})
if load_weights:
load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000), advprop=advprop)
if in_channels != 3:
Conv2d = get_same_padding_conv2d(image_size=model._global_params.image_size)
out_channels = round_filters(32, model._global_params)
model._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
return model
@classmethod
def get_image_size(cls, model_name):
cls._check_model_name_is_valid(model_name)
_, _, res, _ = efficientnet_params(model_name)
return res
@classmethod
def _check_model_name_is_valid(cls, model_name):
"""Validates model name."""
valid_models = ["efficientnet-b" + str(i) for i in range(9)]
if model_name not in valid_models:
raise ValueError("model_name should be one of: " + ", ".join(valid_models))
class EfficientNetD(nn.Module):
"""
modified by Zylo117
"""
def __init__(self, compound_coef, load_weights=False):
super().__init__()
model = EfficientNet.from_pretrained(f"efficientnet-b{compound_coef}", load_weights)
del model._conv_head
del model._bn1
del model._avg_pooling
del model._dropout
del model._fc
self.model = model
def forward(self, x):
x = self.model._conv_stem(x)
x = self.model._bn0(x)
x = self.model._swish(x)
feature_maps = []
# TODO: temporarily storing extra tensor last_x and del it later might not be a good idea,
# try recording stride changing when creating efficientnet,
# and then apply it here.
last_x = None
for idx, block in enumerate(self.model._blocks):
drop_connect_rate = self.model._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self.model._blocks)
x = block(x, drop_connect_rate=drop_connect_rate)
if tuple(block._depthwise_conv.stride) == (2, 2):
feature_maps.append(last_x)
elif idx == len(self.model._blocks) - 1:
feature_maps.append(x)
last_x = x
del last_x
return feature_maps[1:]
class Anchors(nn.Module):
"""
adapted and modified from https://github.com/google/automl/blob/master/efficientdet/anchors.py by Zylo117
"""
def __init__(self, anchor_scale=4.0, pyramid_levels=None, **kwargs):
super().__init__()
from qd.qd_common import print_frame_info
print_frame_info()
self.anchor_scale = anchor_scale
if pyramid_levels is None:
self.pyramid_levels = [3, 4, 5, 6, 7]
self.strides = kwargs.get("strides", [2**x for x in self.pyramid_levels])
self.scales = np.array(kwargs.get("scales", [2**0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]))
self.ratios = kwargs.get("ratios", [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)])
self.buffer = {}
@torch.no_grad()
def forward(self, image, dtype=torch.float32, features=None):
"""Generates multiscale anchor boxes.
Args:
image_size: integer number of input image size. The input image has the
same dimension for width and height. The image_size should be divided by
the largest feature stride 2^max_level.
anchor_scale: float number representing the scale of size of the base
anchor to the feature stride 2^level.
anchor_configs: a dictionary with keys as the levels of anchors and
values as a list of anchor configuration.
Returns:
anchor_boxes: a numpy array with shape [N, 4], which stacks anchors on all
feature levels.
Raises:
ValueError: input size must be the multiple of largest feature stride.
"""
image_shape = image.shape[2:]
anchor_key = self.get_key("anchor", image_shape)
stride_idx_key = self.get_key("anchor_stride_index", image_shape)
if anchor_key in self.buffer:
return {"stride_idx": self.buffer[stride_idx_key].detach(), "anchor": self.buffer[anchor_key].detach()}
if dtype == torch.float16:
dtype = np.float16
else:
dtype = np.float32
boxes_all = []
all_idx_strides = []
for idx_stride, stride in enumerate(self.strides):
boxes_level = []
for scale, ratio in itertools.product(self.scales, self.ratios):
if features is not None:
f_h, f_w = features[idx_stride].shape[-2:]
x = np.arange(stride / 2, stride * f_w, stride)
y = np.arange(stride / 2, stride * f_h, stride)
else:
if image_shape[1] % stride != 0:
x_max = stride * ((image_shape[1] + stride - 1) // stride)
y_max = stride * ((image_shape[0] + stride - 1) // stride)
else:
x_max = image_shape[1]
y_max = image_shape[0]
x = np.arange(stride / 2, x_max, stride)
y = np.arange(stride / 2, y_max, stride)
xv, yv = np.meshgrid(x, y)
xv = xv.reshape(-1)
yv = yv.reshape(-1)
base_anchor_size = self.anchor_scale * stride * scale
anchor_size_x_2 = base_anchor_size * ratio[0] / 2.0
anchor_size_y_2 = base_anchor_size * ratio[1] / 2.0
# y1,x1,y2,x2
boxes = np.vstack(
(yv - anchor_size_y_2, xv - anchor_size_x_2, yv + anchor_size_y_2, xv + anchor_size_x_2)
)
boxes = np.swapaxes(boxes, 0, 1)
boxes_level.append(np.expand_dims(boxes, axis=1))
# concat anchors on the same level to the reshape NxAx4
boxes_level = np.concatenate(boxes_level, axis=1)
boxes_level = boxes_level.reshape([-1, 4])
idx_strides = torch.tensor([idx_stride] * len(boxes_level))
all_idx_strides.append(idx_strides)
boxes_all.append(boxes_level)
anchor_boxes = np.vstack(boxes_all)
anchor_stride_indices = torch.cat(all_idx_strides).to(image.device)
self.buffer[stride_idx_key] = anchor_stride_indices
anchor_boxes = torch.from_numpy(anchor_boxes.astype(dtype)).to(image.device)
anchor_boxes = anchor_boxes.unsqueeze(0)
# save it for later use to reduce overhead
self.buffer[anchor_key] = anchor_boxes
return {"stride_idx": self.buffer[stride_idx_key], "anchor": self.buffer[anchor_key]}
def get_key(self, hint, image_shape):
return "{}_{}".format(hint, "_".join(map(str, image_shape)))
class EffNetFPN(nn.Module):
def __init__(self, compound_coef=0, start_from=3):
super().__init__()
self.backbone_net = EfficientNetD(
EfficientDetBackbone.backbone_compound_coef[compound_coef], load_weights=False
)
if start_from == 3:
conv_channel_coef = EfficientDetBackbone.conv_channel_coef[compound_coef]
else:
conv_channel_coef = EfficientDetBackbone.conv_channel_coef2345[compound_coef]
self.bifpn = nn.Sequential(
*[
BiFPN(
EfficientDetBackbone.fpn_num_filters[compound_coef],
conv_channel_coef,
True if _ == 0 else False,
attention=True if compound_coef < 6 else False,
adaptive_up=True,
)
for _ in range(EfficientDetBackbone.fpn_cell_repeats[compound_coef])
]
)
self.out_channels = EfficientDetBackbone.fpn_num_filters[compound_coef]
self.start_from = start_from
assert self.start_from in [2, 3]
def forward(self, inputs):
if self.start_from == 3:
_, p3, p4, p5 = self.backbone_net(inputs)
features = (p3, p4, p5)
features = self.bifpn(features)
return features
else:
p2, p3, p4, p5 = self.backbone_net(inputs)
features = (p2, p3, p4, p5)
features = self.bifpn(features)
return features
class EfficientDetBackbone(nn.Module):
backbone_compound_coef = [0, 1, 2, 3, 4, 5, 6, 6]
fpn_num_filters = [64, 88, 112, 160, 224, 288, 384, 384]
conv_channel_coef = {
# the channels of P3/P4/P5.
0: [40, 112, 320],
1: [40, 112, 320],
2: [48, 120, 352],
3: [48, 136, 384],
4: [56, 160, 448],
5: [64, 176, 512],
6: [72, 200, 576],
7: [72, 200, 576],
}
conv_channel_coef2345 = {
# the channels of P2/P3/P4/P5.
0: [24, 40, 112, 320],
# to be determined for the following
1: [24, 40, 112, 320],
2: [24, 48, 120, 352],
3: [32, 48, 136, 384],
4: [32, 56, 160, 448],
5: [40, 64, 176, 512],
6: [72, 200],
7: [72, 200],
}
fpn_cell_repeats = [3, 4, 5, 6, 7, 7, 8, 8]
def __init__(self, num_classes=80, compound_coef=0, load_weights=False, prior_prob=0.01, **kwargs):
super(EfficientDetBackbone, self).__init__()
self.compound_coef = compound_coef
self.input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536]
self.box_class_repeats = [3, 3, 3, 4, 4, 4, 5, 5]
self.anchor_scale = [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0]
self.aspect_ratios = kwargs.get("ratios", [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)])
self.num_scales = len(kwargs.get("scales", [2**0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]))
num_anchors = len(self.aspect_ratios) * self.num_scales
self.bifpn = nn.Sequential(
*[
BiFPN(
self.fpn_num_filters[self.compound_coef],
self.conv_channel_coef[compound_coef],
True if _ == 0 else False,
attention=True if compound_coef < 6 else False,
adaptive_up=kwargs.get("adaptive_up"),
)
for _ in range(self.fpn_cell_repeats[compound_coef])
]
)
self.num_classes = num_classes
self.regressor = Regressor(
in_channels=self.fpn_num_filters[self.compound_coef],
num_anchors=num_anchors,
num_layers=self.box_class_repeats[self.compound_coef],
)
self.classifier = Classifier(
in_channels=self.fpn_num_filters[self.compound_coef],
num_anchors=num_anchors,
num_classes=num_classes,
num_layers=self.box_class_repeats[self.compound_coef],
prior_prob=prior_prob,
)
anchor_scale = self.anchor_scale[compound_coef]
if kwargs.get("anchor_scale"):
anchor_scale = kwargs.pop("anchor_scale")
if "anchor_scale" in kwargs:
del kwargs["anchor_scale"]
self.anchors = Anchors(anchor_scale=anchor_scale, **kwargs)
self.backbone_net = EfficientNetD(self.backbone_compound_coef[compound_coef], load_weights)
def freeze_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
def forward(self, inputs):
_, p3, p4, p5 = self.backbone_net(inputs)
features = (p3, p4, p5)
features = self.bifpn(features)
regression = self.regressor(features)
classification = self.classifier(features)
anchors = self.anchors(inputs, inputs.dtype, features=features)
return features, regression, classification, anchors
def init_backbone(self, path):
state_dict = torch.load(path)
try:
ret = self.load_state_dict(state_dict, strict=False)
print(ret)
except RuntimeError as e:
print("Ignoring " + str(e) + '"')
def init_weights(model):
for name, module in model.named_modules():
is_conv_layer = isinstance(module, nn.Conv2d)
if is_conv_layer:
nn.init.kaiming_uniform_(module.weight.data)
if module.bias is not None:
module.bias.data.zero_()
def calc_iou(a, b):
# a(anchor) [boxes, (y1, x1, y2, x2)]
# b(gt, coco-style) [boxes, (x1, y1, x2, y2)]
area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])
iw = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 0])
ih = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 1])
iw = torch.clamp(iw, min=0)
ih = torch.clamp(ih, min=0)
ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih
ua = torch.clamp(ua, min=1e-8)
intersection = iw * ih
IoU = intersection / ua
return IoU
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
Args:
anchors: [batchsize, boxes, (y1, x1, y2, x2)]
regression: [batchsize, boxes, (dy, dx, dh, dw)]
Returns:
"""
y_centers_a = (anchors[..., 0] + anchors[..., 2]) / 2
x_centers_a = (anchors[..., 1] + anchors[..., 3]) / 2
ha = anchors[..., 2] - anchors[..., 0]
wa = anchors[..., 3] - anchors[..., 1]
w = regression[..., 3].exp() * wa
h = regression[..., 2].exp() * ha
y_centers = regression[..., 0] * ha + y_centers_a
x_centers = regression[..., 1] * wa + x_centers_a
ymin = y_centers - h / 2.0
xmin = x_centers - w / 2.0
ymax = y_centers + h / 2.0
xmax = x_centers + w / 2.0
if len(anchors.shape) == 3:
return torch.stack([xmin, ymin, xmax, ymax], dim=2)
else:
return torch.stack([xmin, ymin, xmax, ymax], dim=1)
class ClipBoxes(nn.Module):
def __init__(self):
super(ClipBoxes, self).__init__()
def forward(self, boxes, img):
batch_size, num_channels, height, width = img.shape
boxes[:, :, 0] = torch.clamp(boxes[:, :, 0], min=0)
boxes[:, :, 1] = torch.clamp(boxes[:, :, 1], min=0)
boxes[:, :, 2] = torch.clamp(boxes[:, :, 2], max=width - 1)
boxes[:, :, 3] = torch.clamp(boxes[:, :, 3], max=height - 1)
return boxes
def postprocess2(x, anchors, regression, classification, transformed_anchors, threshold, iou_threshold, max_box):
anchors = anchors["anchor"]
all_above_th = classification > threshold
out = []
num_image = x.shape[0]
num_class = classification.shape[-1]
# classification = classification.cpu()
# transformed_anchors = transformed_anchors.cpu()
# all_above_th = all_above_th.cpu()
max_box_pre_nms = 1000
for i in range(num_image):
all_rois = []
all_class_ids = []
all_scores = []
for c in range(num_class):
above_th = all_above_th[i, :, c].nonzero()
if len(above_th) == 0:
continue
above_prob = classification[i, above_th, c].squeeze(1)
if len(above_th) > max_box_pre_nms:
_, idx = above_prob.topk(max_box_pre_nms)
above_th = above_th[idx]
above_prob = above_prob[idx]
transformed_anchors_per = transformed_anchors[i, above_th, :].squeeze(dim=1)
from torchvision.ops import nms
nms_idx = nms(transformed_anchors_per, above_prob, iou_threshold=iou_threshold)
if len(nms_idx) > 0:
all_rois.append(transformed_anchors_per[nms_idx])
ids = torch.tensor([c] * len(nms_idx))
all_class_ids.append(ids)
all_scores.append(above_prob[nms_idx])
if len(all_rois) > 0:
rois = torch.cat(all_rois)
class_ids = torch.cat(all_class_ids)
scores = torch.cat(all_scores)
if len(scores) > max_box:
_, idx = torch.topk(scores, max_box)
rois = rois[idx, :]
class_ids = class_ids[idx]
scores = scores[idx]
out.append(
{
"rois": rois,
"class_ids": class_ids,
"scores": scores,
}
)
else:
out.append(
{
"rois": [],
"class_ids": [],
"scores": [],
}
)
return out
def postprocess(x, anchors, regression, classification, regressBoxes, clipBoxes, threshold, iou_threshold):
anchors = anchors["anchor"]
transformed_anchors = regressBoxes(anchors, regression)
transformed_anchors = clipBoxes(transformed_anchors, x)
scores = torch.max(classification, dim=2, keepdim=True)[0]
scores_over_thresh = (scores > threshold)[:, :, 0]
out = []
for i in range(x.shape[0]):
if scores_over_thresh.sum() == 0:
out.append(
{
"rois": [],
"class_ids": [],
"scores": [],
}
)
continue
classification_per = classification[i, scores_over_thresh[i, :], ...].permute(1, 0)
transformed_anchors_per = transformed_anchors[i, scores_over_thresh[i, :], ...]
scores_per = scores[i, scores_over_thresh[i, :], ...]
from torchvision.ops import nms
anchors_nms_idx = nms(transformed_anchors_per, scores_per[:, 0], iou_threshold=iou_threshold)
if anchors_nms_idx.shape[0] != 0:
scores_, classes_ = classification_per[:, anchors_nms_idx].max(dim=0)
boxes_ = transformed_anchors_per[anchors_nms_idx, :]
out.append(
{
"rois": boxes_,
"class_ids": classes_,
"scores": scores_,
}
)
else:
out.append(
{
"rois": [],
"class_ids": [],
"scores": [],
}
)
return out
def display(preds, imgs, obj_list, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]["rois"]) == 0:
continue
for j in range(len(preds[i]["rois"])):
(x1, y1, x2, y2) = preds[i]["rois"][j].detach().cpu().numpy().astype(np.int)
logging.info((x1, y1, x2, y2))
cv2.rectangle(imgs[i], (x1, y1), (x2, y2), (255, 255, 0), 2)
# obj = obj_list[preds[i]['class_ids'][j]]
# score = float(preds[i]['scores'][j])
# cv2.putText(imgs[i], '{}, {:.3f}'.format(obj, score),
# (x1, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
# (255, 255, 0), 1)
# break
if imshow:
cv2.imshow("image", imgs[i])
cv2.waitKey(0)
def calculate_focal_loss2(classification, target_list, alpha, gamma):
from maskrcnn_benchmark.layers.sigmoid_focal_loss import sigmoid_focal_loss_cuda
cls_loss = sigmoid_focal_loss_cuda(classification, target_list.int(), gamma, alpha)
return cls_loss
def calculate_focal_loss(classification, targets, alpha, gamma):
classification = classification.sigmoid()
device = classification.device
alpha_factor = torch.ones_like(targets) * alpha
alpha_factor = alpha_factor.to(device)
alpha_factor = torch.where(torch.eq(targets, 1.0), alpha_factor, 1.0 - alpha_factor)
focal_weight = torch.where(torch.eq(targets, 1.0), 1.0 - classification, classification)
focal_weight = alpha_factor * torch.pow(focal_weight, gamma)
bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification))
cls_loss = focal_weight * bce
zeros = torch.zeros_like(cls_loss)
zeros = zeros.to(device)
cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, zeros)
return cls_loss.mean()
def calculate_giou(pred, gt):
ax1, ay1, ax2, ay2 = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
bx1, by1, bx2, by2 = gt[:, 0], gt[:, 1], gt[:, 2], gt[:, 3]
a = (ax2 - ax1) * (ay2 - ay1)
b = (bx2 - bx1) * (by2 - by1)
max_x1, _ = torch.max(torch.stack([ax1, bx1], dim=1), dim=1)
max_y1, _ = torch.max(torch.stack([ay1, by1], dim=1), dim=1)
min_x2, _ = torch.min(torch.stack([ax2, bx2], dim=1), dim=1)
min_y2, _ = torch.min(torch.stack([ay2, by2], dim=1), dim=1)
inter = (min_x2 > max_x1) * (min_y2 > max_y1)
inter = inter * (min_x2 - max_x1) * (min_y2 - max_y1)
min_x1, _ = torch.min(torch.stack([ax1, bx1], dim=1), dim=1)
min_y1, _ = torch.min(torch.stack([ay1, by1], dim=1), dim=1)
max_x2, _ = torch.max(torch.stack([ax2, bx2], dim=1), dim=1)
max_y2, _ = torch.max(torch.stack([ay2, by2], dim=1), dim=1)
cover = (max_x2 - min_x1) * (max_y2 - min_y1)
union = a + b - inter
iou = inter / (union + 1e-5)
giou = iou - (cover - union) / (cover + 1e-5)
return giou
class FocalLoss(nn.Module):
def __init__(
self,
alpha=0.25,
gamma=2.0,
cls_loss_type="FL",
smooth_bce_pos=0.99,
smooth_bce_neg=0.01,
reg_loss_type="L1",
at_least_1_assgin=False,
neg_iou_th=0.4,
pos_iou_th=0.5,
cls_weight=1.0,
reg_weight=1.0,
):
super(FocalLoss, self).__init__()
from qd.qd_common import print_frame_info
print_frame_info()
self.iter = 0
self.reg_loss_type = reg_loss_type
self.regressBoxes = BBoxTransform()
if cls_loss_type == "FL":
from qd.layers.loss import FocalLossWithLogitsNegLoss
self.cls_loss = FocalLossWithLogitsNegLoss(alpha, gamma)
elif cls_loss_type == "BCE":
from qd.qd_pytorch import BCEWithLogitsNegLoss
self.cls_loss = BCEWithLogitsNegLoss(reduction="sum")
elif cls_loss_type == "SmoothBCE":
from qd.layers.loss import SmoothBCEWithLogitsNegLoss
self.cls_loss = SmoothBCEWithLogitsNegLoss(pos=smooth_bce_pos, neg=smooth_bce_neg)
elif cls_loss_type == "SmoothFL":
from qd.layers.loss import FocalSmoothBCEWithLogitsNegLoss
self.cls_loss = FocalSmoothBCEWithLogitsNegLoss(
alpha=alpha, gamma=2.0, pos=smooth_bce_pos, neg=smooth_bce_neg
)
else:
raise NotImplementedError(cls_loss_type)
self.at_least_1_assgin = at_least_1_assgin
self.gt_total = 0
self.gt_saved_by_at_least = 0
self.neg_iou_th = neg_iou_th
self.pos_iou_th = pos_iou_th
self.cls_weight = cls_weight
self.reg_weight = reg_weight
self.buf = {}
def forward(self, classifications, regressions, anchor_info, annotations, **kwargs):
debug = (self.iter % 100) == 0
self.iter += 1
if debug:
from collections import defaultdict
debug_info = defaultdict(list)
batch_size = classifications.shape[0]
classification_losses = []
regression_losses = []
anchors = anchor_info["anchor"]
anchor = anchors[0, :, :] # assuming all image sizes are the same, which it is
dtype = anchors.dtype
anchor_widths = anchor[:, 3] - anchor[:, 1]
anchor_heights = anchor[:, 2] - anchor[:, 0]
anchor_ctr_x = anchor[:, 1] + 0.5 * anchor_widths
anchor_ctr_y = anchor[:, 0] + 0.5 * anchor_heights
# anchor_widths = anchor[:, 2] - anchor[:, 0]
# anchor_heights = anchor[:, 3] - anchor[:, 1]
# anchor_ctr_x = anchor[:, 0] + 0.5 * anchor_widths
# anchor_ctr_y = anchor[:, 1] + 0.5 * anchor_heights
device = classifications.device
for j in range(batch_size):
classification = classifications[j, :, :]
regression = regressions[j, :, :]
bbox_annotation = annotations[j]
bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1]
# classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4)
if bbox_annotation.shape[0] == 0:
# cls_loss = calculate_focal_loss2(classification,
# torch.zeros(len(classification)), alpha,
# gamma)
# cls_loss = cls_loss.mean()
cls_loss = torch.tensor(0).to(dtype).to(device)
regression_losses.append(torch.tensor(0).to(dtype).to(device))
classification_losses.append(cls_loss)
continue
IoU = calc_iou(anchor[:, :], bbox_annotation[:, :4])
IoU_max, IoU_argmax = torch.max(IoU, dim=1)
if self.at_least_1_assgin:
iou_max_gt, iou_argmax_gt = torch.max(IoU, dim=0)
curr_saved = (iou_max_gt < self.pos_iou_th).sum()
self.gt_saved_by_at_least += curr_saved
self.gt_total += len(iou_argmax_gt)
IoU_max[iou_argmax_gt] = 1.0
IoU_argmax[iou_argmax_gt] = torch.arange(len(iou_argmax_gt)).to(device)
# compute the loss for classification
targets = torch.ones_like(classification) * -1
targets = targets.to(device)
targets[torch.lt(IoU_max, self.neg_iou_th), :] = 0
positive_indices = torch.ge(IoU_max, self.pos_iou_th)
num_positive_anchors = positive_indices.sum()
assigned_annotations = bbox_annotation[IoU_argmax, :]
targets[positive_indices, :] = 0
targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1
if debug:
if num_positive_anchors > 0:
debug_info["pos_conf"].append(
classification[positive_indices, assigned_annotations[positive_indices, 4].long()].mean()
)
debug_info["neg_conf"].append(classification[targets == 0].mean())
stride_idx = anchor_info["stride_idx"]
positive_stride_idx = stride_idx[positive_indices]
pos_count_each_stride = torch.tensor([(positive_stride_idx == i).sum() for i in range(5)])
if "cum_pos_count_each_stride" not in self.buf:
self.buf["cum_pos_count_each_stride"] = pos_count_each_stride
else:
cum_pos_count_each_stride = self.buf["cum_pos_count_each_stride"]
cum_pos_count_each_stride += pos_count_each_stride
self.buf["cum_pos_count_each_stride"] = cum_pos_count_each_stride
# cls_loss = calculate_focal_loss(classification, targets, alpha,
# gamma)
cls_loss = self.cls_loss(classification, targets)
cls_loss = cls_loss.sum() / torch.clamp(num_positive_anchors.to(dtype), min=1.0)
assert cls_loss == cls_loss
classification_losses.append(cls_loss)
if positive_indices.sum() > 0:
assigned_annotations = assigned_annotations[positive_indices, :]
if self.reg_loss_type == "L1":
anchor_widths_pi = anchor_widths[positive_indices]
anchor_heights_pi = anchor_heights[positive_indices]
anchor_ctr_x_pi = anchor_ctr_x[positive_indices]
anchor_ctr_y_pi = anchor_ctr_y[positive_indices]
gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0]
gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1]
gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths
gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights
# efficientdet style
gt_widths = torch.clamp(gt_widths, min=1)
gt_heights = torch.clamp(gt_heights, min=1)
targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi
targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi
targets_dw = torch.log(gt_widths / anchor_widths_pi)
targets_dh = torch.log(gt_heights / anchor_heights_pi)
targets = torch.stack((targets_dy, targets_dx, targets_dh, targets_dw))
targets = targets.t()
regression_diff = torch.abs(targets - regression[positive_indices, :])
regression_loss = torch.where(
torch.le(regression_diff, 1.0 / 9.0),
0.5 * 9.0 * torch.pow(regression_diff, 2),
regression_diff - 0.5 / 9.0,
).mean()
elif self.reg_loss_type == "GIOU":
curr_regression = regression[positive_indices, :]
curr_anchors = anchor[positive_indices]
curr_pred_xyxy = self.regressBoxes(curr_anchors, curr_regression)
regression_loss = 1.0 - calculate_giou(curr_pred_xyxy, assigned_annotations)
regression_loss = regression_loss.mean()
assert regression_loss == regression_loss
else:
raise NotImplementedError
regression_losses.append(regression_loss)
else:
if torch.cuda.is_available():
regression_losses.append(torch.tensor(0).to(dtype).cuda())
else:
regression_losses.append(torch.tensor(0).to(dtype))
if debug:
if len(debug_info) > 0:
logging.info(
"pos = {}; neg = {}, saved_ratio = {}/{}={:.1f}, "
"stride_info = {}".format(
torch.tensor(debug_info["pos_conf"]).mean(),
torch.tensor(debug_info["neg_conf"]).mean(),
self.gt_saved_by_at_least,
self.gt_total,
1.0 * self.gt_saved_by_at_least / self.gt_total,
self.buf["cum_pos_count_each_stride"],
)
)
return self.cls_weight * torch.stack(classification_losses).mean(
dim=0, keepdim=True
), self.reg_weight * torch.stack(regression_losses).mean(dim=0, keepdim=True)
class ModelWithLoss(nn.Module):
def __init__(self, model, criterion):
super().__init__()
self.criterion = criterion
self.module = model
def forward(self, *args):
if len(args) == 2:
imgs, annotations = args
elif len(args) == 1:
imgs, annotations = args[0][:2]
_, regression, classification, anchors = self.module(imgs)
cls_loss, reg_loss = self.criterion(classification, regression, anchors, annotations)
return {"cls_loss": cls_loss, "reg_loss": reg_loss}
class TorchVisionNMS(nn.Module):
def __init__(self, iou_threshold):
super().__init__()
self.iou_threshold = iou_threshold
def forward(self, box, prob):
nms_idx = nms(box, prob, iou_threshold=self.iou_threshold)
return nms_idx
class PostProcess(nn.Module):
def __init__(self, iou_threshold):
super().__init__()
self.nms = TorchVisionNMS(iou_threshold)
def forward(self, x, anchors, regression, classification, transformed_anchors, threshold, max_box):
all_above_th = classification > threshold
out = []
num_image = x.shape[0]
num_class = classification.shape[-1]
# classification = classification.cpu()
# transformed_anchors = transformed_anchors.cpu()
# all_above_th = all_above_th.cpu()
max_box_pre_nms = 1000
for i in range(num_image):
all_rois = []
all_class_ids = []
all_scores = []
for c in range(num_class):
above_th = all_above_th[i, :, c].nonzero()
if len(above_th) == 0:
continue
above_prob = classification[i, above_th, c].squeeze(1)
if len(above_th) > max_box_pre_nms:
_, idx = above_prob.topk(max_box_pre_nms)
above_th = above_th[idx]
above_prob = above_prob[idx]
transformed_anchors_per = transformed_anchors[i, above_th, :].squeeze(dim=1)
nms_idx = self.nms(transformed_anchors_per, above_prob)
if len(nms_idx) > 0:
all_rois.append(transformed_anchors_per[nms_idx])
ids = torch.tensor([c] * len(nms_idx))
all_class_ids.append(ids)
all_scores.append(above_prob[nms_idx])
if len(all_rois) > 0:
rois = torch.cat(all_rois)
class_ids = torch.cat(all_class_ids)
scores = torch.cat(all_scores)
if len(scores) > max_box:
_, idx = torch.topk(scores, max_box)
rois = rois[idx, :]
class_ids = class_ids[idx]
scores = scores[idx]
out.append(
{
"rois": rois,
"class_ids": class_ids,
"scores": scores,
}
)
else:
out.append(
{
"rois": [],
"class_ids": [],
"scores": [],
}
)
return out
class InferenceModel(nn.Module):
def __init__(self, model):
super().__init__()
self.module = model
self.regressBoxes = BBoxTransform()
self.clipBoxes = ClipBoxes()
self.threshold = 0.01
self.nms_threshold = 0.5
self.max_box = 100
self.debug = False
self.post_process = PostProcess(self.nms_threshold)
def forward(self, sample):
features, regression, classification, anchor_info = self.module(sample["image"])
anchors = anchor_info["anchor"]
classification = classification.sigmoid()
transformed_anchors = self.regressBoxes(anchors, regression)
transformed_anchors = self.clipBoxes(transformed_anchors, sample["image"])
preds = self.post_process(
sample["image"], anchors, regression, classification, transformed_anchors, self.threshold, self.max_box
)
if self.debug:
logging.info("debugging")
imgs = sample["image"]
imgs = imgs.permute(0, 2, 3, 1).cpu().numpy()
imgs = ((imgs * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]) * 255).astype(np.uint8)
imgs = [cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for img in imgs]
display(preds, imgs, list(map(str, range(80))))
for p, s in zip(preds, sample["scale"]):
if len(p["rois"]) > 0:
p["rois"] /= s
return preds
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