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inception_resnet_v1.py
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inception_resnet_v1.py
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import os
import requests
from requests.adapters import HTTPAdapter
import torch
from torch import nn
from torch.nn import functional as F
from .utils.download import download_url_to_file
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super().__init__()
self.conv = nn.Conv2d(
in_planes, out_planes,
kernel_size=kernel_size, stride=stride,
padding=padding, bias=False
) # verify bias false
self.bn = nn.BatchNorm2d(
out_planes,
eps=0.001, # value found in tensorflow
momentum=0.1, # default pytorch value
affine=True
)
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Block35(nn.Module):
def __init__(self, scale=1.0):
super().__init__()
self.scale = scale
self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(256, 32, kernel_size=1, stride=1),
BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
BasicConv2d(256, 32, kernel_size=1, stride=1),
BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
)
self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
out = self.conv2d(out)
out = out * self.scale + x
out = self.relu(out)
return out
class Block17(nn.Module):
def __init__(self, scale=1.0):
super().__init__()
self.scale = scale
self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(896, 128, kernel_size=1, stride=1),
BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)),
BasicConv2d(128, 128, kernel_size=(7,1), stride=1, padding=(3,0))
)
self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
out = self.conv2d(out)
out = out * self.scale + x
out = self.relu(out)
return out
class Block8(nn.Module):
def __init__(self, scale=1.0, noReLU=False):
super().__init__()
self.scale = scale
self.noReLU = noReLU
self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(1792, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=(1,3), stride=1, padding=(0,1)),
BasicConv2d(192, 192, kernel_size=(3,1), stride=1, padding=(1,0))
)
self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1)
if not self.noReLU:
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
out = self.conv2d(out)
out = out * self.scale + x
if not self.noReLU:
out = self.relu(out)
return out
class Mixed_6a(nn.Module):
def __init__(self):
super().__init__()
self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)
self.branch1 = nn.Sequential(
BasicConv2d(256, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1),
BasicConv2d(192, 256, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class Mixed_7a(nn.Module):
def __init__(self):
super().__init__()
self.branch0 = nn.Sequential(
BasicConv2d(896, 256, kernel_size=1, stride=1),
BasicConv2d(256, 384, kernel_size=3, stride=2)
)
self.branch1 = nn.Sequential(
BasicConv2d(896, 256, kernel_size=1, stride=1),
BasicConv2d(256, 256, kernel_size=3, stride=2)
)
self.branch2 = nn.Sequential(
BasicConv2d(896, 256, kernel_size=1, stride=1),
BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
BasicConv2d(256, 256, kernel_size=3, stride=2)
)
self.branch3 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class InceptionResnetV1(nn.Module):
"""Inception Resnet V1 model with optional loading of pretrained weights.
Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
requested and cached in the torch cache. Subsequent instantiations use the cache rather than
redownloading.
Keyword Arguments:
pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
(default: {None})
classify {bool} -- Whether the model should output classification probabilities or feature
embeddings. (default: {False})
num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
equal to that used for the pretrained model, the final linear layer will be randomly
initialized. (default: {None})
dropout_prob {float} -- Dropout probability. (default: {0.6})
"""
def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_prob=0.6, device=None):
super().__init__()
# Set simple attributes
self.pretrained = pretrained
self.classify = classify
self.num_classes = num_classes
if pretrained == 'vggface2':
tmp_classes = 8631
elif pretrained == 'casia-webface':
tmp_classes = 10575
elif pretrained is None and self.classify and self.num_classes is None:
raise Exception('If "pretrained" is not specified and "classify" is True, "num_classes" must be specified')
# Define layers
self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.maxpool_3a = nn.MaxPool2d(3, stride=2)
self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2)
self.repeat_1 = nn.Sequential(
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
)
self.mixed_6a = Mixed_6a()
self.repeat_2 = nn.Sequential(
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
)
self.mixed_7a = Mixed_7a()
self.repeat_3 = nn.Sequential(
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
)
self.block8 = Block8(noReLU=True)
self.avgpool_1a = nn.AdaptiveAvgPool2d(1)
self.dropout = nn.Dropout(dropout_prob)
self.last_linear = nn.Linear(1792, 512, bias=False)
self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True)
if pretrained is not None:
self.logits = nn.Linear(512, tmp_classes)
load_weights(self, pretrained)
if self.classify and self.num_classes is not None:
self.logits = nn.Linear(512, self.num_classes)
self.device = torch.device('cpu')
if device is not None:
self.device = device
self.to(device)
def forward(self, x):
"""Calculate embeddings or logits given a batch of input image tensors.
Arguments:
x {torch.tensor} -- Batch of image tensors representing faces.
Returns:
torch.tensor -- Batch of embedding vectors or multinomial logits.
"""
x = self.conv2d_1a(x)
x = self.conv2d_2a(x)
x = self.conv2d_2b(x)
x = self.maxpool_3a(x)
x = self.conv2d_3b(x)
x = self.conv2d_4a(x)
x = self.conv2d_4b(x)
x = self.repeat_1(x)
x = self.mixed_6a(x)
x = self.repeat_2(x)
x = self.mixed_7a(x)
x = self.repeat_3(x)
x = self.block8(x)
x = self.avgpool_1a(x)
x = self.dropout(x)
x = self.last_linear(x.view(x.shape[0], -1))
x = self.last_bn(x)
if self.classify:
x = self.logits(x)
else:
x = F.normalize(x, p=2, dim=1)
return x
def load_weights(mdl, name):
"""Download pretrained state_dict and load into model.
Arguments:
mdl {torch.nn.Module} -- Pytorch model.
name {str} -- Name of dataset that was used to generate pretrained state_dict.
Raises:
ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'.
"""
if name == 'vggface2':
path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt'
elif name == 'casia-webface':
path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt'
else:
raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"')
model_dir = os.path.join(get_torch_home(), 'checkpoints')
os.makedirs(model_dir, exist_ok=True)
cached_file = os.path.join(model_dir, os.path.basename(path))
if not os.path.exists(cached_file):
download_url_to_file(path, cached_file)
state_dict = torch.load(cached_file)
mdl.load_state_dict(state_dict)
def get_torch_home():
torch_home = os.path.expanduser(
os.getenv(
'TORCH_HOME',
os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')
)
)
return torch_home