Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,261 Bytes
51ce47d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
"""This file is for Inception model borrowed from torch metrics / fidelity.
This file may have been modified by Bytedance Ltd. and/or its affiliates (“Bytedance's Modifications”).
All Bytedance's Modifications are Copyright (year) Bytedance Ltd. and/or its affiliates.
Reference:
https://github.com/mseitzer/pytorch-fid/blob/master/src/pytorch_fid/inception.py
"""
# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn.functional as F
from torch_fidelity.feature_extractor_base import FeatureExtractorBase
from torch_fidelity.helpers import vassert
from torch_fidelity.feature_extractor_inceptionv3 import BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE_1, InceptionE_2
from torch_fidelity.interpolate_compat_tensorflow import interpolate_bilinear_2d_like_tensorflow1x
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# Note: Compared shasum and models should be the same.
FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
class FeatureExtractorInceptionV3(FeatureExtractorBase):
INPUT_IMAGE_SIZE = 299
def __init__(
self,
name,
features_list,
**kwargs,
):
"""
InceptionV3 feature extractor for 2D RGB 24bit images.
Args:
name (str): Unique name of the feature extractor, must be the same as used in
:func:`register_feature_extractor`.
features_list (list): A list of the requested feature names, which will be produced for each input. This
feature extractor provides the following features:
- '64'
- '192'
- '768'
- '2048'
- 'logits_unbiased'
- 'logits'
"""
super(FeatureExtractorInceptionV3, self).__init__(name, features_list)
self.feature_extractor_internal_dtype = torch.float64
self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)
self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
self.MaxPool_1 = torch.nn.MaxPool2d(kernel_size=3, stride=2)
self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
self.MaxPool_2 = torch.nn.MaxPool2d(kernel_size=3, stride=2)
self.Mixed_5b = InceptionA(192, pool_features=32)
self.Mixed_5c = InceptionA(256, pool_features=64)
self.Mixed_5d = InceptionA(288, pool_features=64)
self.Mixed_6a = InceptionB(288)
self.Mixed_6b = InceptionC(768, channels_7x7=128)
self.Mixed_6c = InceptionC(768, channels_7x7=160)
self.Mixed_6d = InceptionC(768, channels_7x7=160)
self.Mixed_6e = InceptionC(768, channels_7x7=192)
self.Mixed_7a = InceptionD(768)
self.Mixed_7b = InceptionE_1(1280)
self.Mixed_7c = InceptionE_2(2048)
self.AvgPool = torch.nn.AdaptiveAvgPool2d(output_size=(1, 1))
self.fc = torch.nn.Linear(2048, 1008)
state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
#state_dict = torch.load(FID_WEIGHTS_URL, map_location='cpu')
self.load_state_dict(state_dict)
self.to(self.feature_extractor_internal_dtype)
self.requires_grad_(False)
self.eval()
def forward(self, x):
vassert(torch.is_tensor(x) and x.dtype == torch.uint8, 'Expecting image as torch.Tensor with dtype=torch.uint8')
vassert(x.dim() == 4 and x.shape[1] == 3, f'Input is not Bx3xHxW: {x.shape}')
features = {}
remaining_features = self.features_list.copy()
x = x.to(self.feature_extractor_internal_dtype)
# N x 3 x ? x ?
x = interpolate_bilinear_2d_like_tensorflow1x(
x,
size=(self.INPUT_IMAGE_SIZE, self.INPUT_IMAGE_SIZE),
align_corners=False,
)
# N x 3 x 299 x 299
# x = (x - 128) * torch.tensor(0.0078125, dtype=torch.float32, device=x.device) # really happening in graph
x = (x - 128) / 128 # but this gives bit-exact output _of this step_ too
# N x 3 x 299 x 299
x = self.Conv2d_1a_3x3(x)
# N x 32 x 149 x 149
x = self.Conv2d_2a_3x3(x)
# N x 32 x 147 x 147
x = self.Conv2d_2b_3x3(x)
# N x 64 x 147 x 147
x = self.MaxPool_1(x)
# N x 64 x 73 x 73
if '64' in remaining_features:
features['64'] = F.adaptive_avg_pool2d(x, output_size=(1, 1)).squeeze(-1).squeeze(-1)
remaining_features.remove('64')
if len(remaining_features) == 0:
return features
x = self.Conv2d_3b_1x1(x)
# N x 80 x 73 x 73
x = self.Conv2d_4a_3x3(x)
# N x 192 x 71 x 71
x = self.MaxPool_2(x)
# N x 192 x 35 x 35
if '192' in remaining_features:
features['192'] = F.adaptive_avg_pool2d(x, output_size=(1, 1)).squeeze(-1).squeeze(-1)
remaining_features.remove('192')
if len(remaining_features) == 0:
return features
x = self.Mixed_5b(x)
# N x 256 x 35 x 35
x = self.Mixed_5c(x)
# N x 288 x 35 x 35
x = self.Mixed_5d(x)
# N x 288 x 35 x 35
x = self.Mixed_6a(x)
# N x 768 x 17 x 17
x = self.Mixed_6b(x)
# N x 768 x 17 x 17
x = self.Mixed_6c(x)
# N x 768 x 17 x 17
x = self.Mixed_6d(x)
# N x 768 x 17 x 17
x = self.Mixed_6e(x)
# N x 768 x 17 x 17
if '768' in remaining_features:
features['768'] = F.adaptive_avg_pool2d(x, output_size=(1, 1)).squeeze(-1).squeeze(-1).to(torch.float32)
remaining_features.remove('768')
if len(remaining_features) == 0:
return features
x = self.Mixed_7a(x)
# N x 1280 x 8 x 8
x = self.Mixed_7b(x)
# N x 2048 x 8 x 8
x = self.Mixed_7c(x)
# N x 2048 x 8 x 8
x = self.AvgPool(x)
# N x 2048 x 1 x 1
x = torch.flatten(x, 1)
# N x 2048
if '2048' in remaining_features:
features['2048'] = x
remaining_features.remove('2048')
if len(remaining_features) == 0:
return features
if 'logits_unbiased' in remaining_features:
x = x.mm(self.fc.weight.T)
# N x 1008 (num_classes)
features['logits_unbiased'] = x
remaining_features.remove('logits_unbiased')
if len(remaining_features) == 0:
return features
x = x + self.fc.bias.unsqueeze(0)
else:
x = self.fc(x)
# N x 1008 (num_classes)
features['logits'] = x
return features
@staticmethod
def get_provided_features_list():
return '64', '192', '768', '2048', 'logits_unbiased', 'logits'
@staticmethod
def get_default_feature_layer_for_metric(metric):
return {
'isc': 'logits_unbiased',
'fid': '2048',
'kid': '2048',
'prc': '2048',
}[metric]
@staticmethod
def can_be_compiled():
return True
@staticmethod
def get_dummy_input_for_compile():
return (torch.rand([1, 3, 4, 4]) * 255).to(torch.uint8)
def get_inception_model():
model = FeatureExtractorInceptionV3("inception_model", ["2048", "logits_unbiased"])
return model |