File size: 20,849 Bytes
2f85de4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
# python3.7
"""Contains the VGG16 model, which is used for inference ONLY.

VGG16 is commonly used for perceptual feature extraction. The model implemented
in this file can be used for evaluation (like computing LPIPS, perceptual path
length, etc.), OR be used in training for loss computation (like perceptual
loss, etc.).

The pre-trained model is officially shared by

https://www.robots.ox.ac.uk/~vgg/research/very_deep/

and ported by

https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt

Compared to the official VGG16 model, this ported model also support evaluating
LPIPS, which is introduced in

https://github.com/richzhang/PerceptualSimilarity
"""

import warnings
import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist

from utils.misc import download_url

__all__ = ['PerceptualModel']

# pylint: disable=line-too-long
_MODEL_URL_SHA256 = {
    # This model is provided by `torchvision`, which is ported from TensorFlow.
    'torchvision_official': (
        'https://download.pytorch.org/models/vgg16-397923af.pth',
        '397923af8e79cdbb6a7127f12361acd7a2f83e06b05044ddf496e83de57a5bf0'  # hash sha256
    ),

    # This model is provided by https://github.com/NVlabs/stylegan2-ada-pytorch
    'vgg_perceptual_lpips': (
        'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt',
        'b437eb095feaeb0b83eb3fa11200ebca4548ee39a07fb944a417ddc516cc07c3'  # hash sha256
    )
}
# pylint: enable=line-too-long


class PerceptualModel(object):
    """Defines the perceptual model, which is based on VGG16 structure.

    This is a static class, which is used to avoid this model to be built
    repeatedly. Consequently, this model is particularly used for inference,
    like computing LPIPS, or for loss computation, like perceptual loss. If
    training is required, please use the model from `torchvision.models` or
    implement by yourself.

    NOTE: The pre-trained model assumes the inputs to be with `RGB` channel
    order and pixel range [-1, 1], and will NOT resize the input automatically
    if only perceptual feature is needed.
    """
    models = dict()

    @staticmethod
    def build_model(use_torchvision=False, no_top=True, enable_lpips=True):
        """Builds the model and load pre-trained weights.

        1. If `use_torchvision` is set as True, the model released by
           `torchvision` will be loaded, otherwise, the model released by
           https://www.robots.ox.ac.uk/~vgg/research/very_deep/ will be used.
           (default: False)

        2. To save computing resources, these is an option to only load the
           backbone (i.e., without the last three fully-connected layers). This
           is commonly used for perceptual loss or LPIPS loss computation.
           Please use argument `no_top` to control this. (default: True)

        3. For LPIPS loss computation, some additional weights (which is used
           for balancing the features from different resolutions) are employed
           on top of the original VGG16 backbone. Details can be found at
           https://github.com/richzhang/PerceptualSimilarity. Please use
           `enable_lpips` to enable this feature. (default: True)

        The built model supports following arguments when forwarding:

        - resize_input: Whether to resize the input image to size [224, 224]
            before forwarding. For feature-based computation (i.e., only
            convolutional layers are used), image resizing is not essential.
            (default: False)
        - return_tensor: This field resolves the model behavior. Following
            options are supported:
                `feature1`: Before the first max pooling layer.
                `pool1`: After the first max pooling layer.
                `feature2`: Before the second max pooling layer.
                `pool2`: After the second max pooling layer.
                `feature3`: Before the third max pooling layer.
                `pool3`: After the third max pooling layer.
                `feature4`: Before the fourth max pooling layer.
                `pool4`: After the fourth max pooling layer.
                `feature5`: Before the fifth max pooling layer.
                `pool5`: After the fifth max pooling layer.
                `flatten`: The flattened feature, after `adaptive_avgpool`.
                `feature`: The 4096d feature for logits computation. (default)
                `logits`: The 1000d categorical logits.
                `prediction`: The 1000d predicted probability.
                `lpips`: The LPIPS score between two input images.
        """
        if use_torchvision:
            model_source = 'torchvision_official'
            align_tf_resize = False
            is_torch_script = False
        else:
            model_source = 'vgg_perceptual_lpips'
            align_tf_resize = True
            is_torch_script = True

        if enable_lpips and model_source != 'vgg_perceptual_lpips':
            warnings.warn('The pre-trained model officially released by '
                          '`torchvision` does not support LPIPS computation! '
                          'Equal weights will be used for each resolution.')

        fingerprint = (model_source, no_top, enable_lpips)

        if fingerprint not in PerceptualModel.models:
            # Build model.
            model = VGG16(align_tf_resize=align_tf_resize,
                          no_top=no_top,
                          enable_lpips=enable_lpips)

            # Download pre-trained weights.
            if dist.is_initialized() and dist.get_rank() != 0:
                dist.barrier()  # Download by chief.

            url, sha256 = _MODEL_URL_SHA256[model_source]
            filename = f'perceptual_model_{model_source}_{sha256}.pth'
            model_path, hash_check = download_url(url,
                                                  filename=filename,
                                                  sha256=sha256)
            if is_torch_script:
                src_state_dict = torch.jit.load(model_path, map_location='cpu')
            else:
                src_state_dict = torch.load(model_path, map_location='cpu')
            if hash_check is False:
                warnings.warn(f'Hash check failed! The remote file from URL '
                              f'`{url}` may be changed, or the downloading is '
                              f'interrupted. The loaded perceptual model may '
                              f'have unexpected behavior.')

            if dist.is_initialized() and dist.get_rank() == 0:
                dist.barrier()  # Wait for other replicas.

            # Load weights.
            dst_state_dict = _convert_weights(src_state_dict, model_source)
            model.load_state_dict(dst_state_dict, strict=False)
            del src_state_dict, dst_state_dict

            # For inference only.
            model.eval().requires_grad_(False).cuda()
            PerceptualModel.models[fingerprint] = model

        return PerceptualModel.models[fingerprint]


def _convert_weights(src_state_dict, model_source):
    if model_source not in _MODEL_URL_SHA256:
        raise ValueError(f'Invalid model source `{model_source}`!\n'
                         f'Sources allowed: {list(_MODEL_URL_SHA256.keys())}.')
    if model_source == 'torchvision_official':
        dst_to_src_var_mapping = {
            'conv11.weight': 'features.0.weight',
            'conv11.bias': 'features.0.bias',
            'conv12.weight': 'features.2.weight',
            'conv12.bias': 'features.2.bias',
            'conv21.weight': 'features.5.weight',
            'conv21.bias': 'features.5.bias',
            'conv22.weight': 'features.7.weight',
            'conv22.bias': 'features.7.bias',
            'conv31.weight': 'features.10.weight',
            'conv31.bias': 'features.10.bias',
            'conv32.weight': 'features.12.weight',
            'conv32.bias': 'features.12.bias',
            'conv33.weight': 'features.14.weight',
            'conv33.bias': 'features.14.bias',
            'conv41.weight': 'features.17.weight',
            'conv41.bias': 'features.17.bias',
            'conv42.weight': 'features.19.weight',
            'conv42.bias': 'features.19.bias',
            'conv43.weight': 'features.21.weight',
            'conv43.bias': 'features.21.bias',
            'conv51.weight': 'features.24.weight',
            'conv51.bias': 'features.24.bias',
            'conv52.weight': 'features.26.weight',
            'conv52.bias': 'features.26.bias',
            'conv53.weight': 'features.28.weight',
            'conv53.bias': 'features.28.bias',
            'fc1.weight': 'classifier.0.weight',
            'fc1.bias': 'classifier.0.bias',
            'fc2.weight': 'classifier.3.weight',
            'fc2.bias': 'classifier.3.bias',
            'fc3.weight': 'classifier.6.weight',
            'fc3.bias': 'classifier.6.bias',
        }
    elif model_source == 'vgg_perceptual_lpips':
        src_state_dict = src_state_dict.state_dict()
        dst_to_src_var_mapping = {
            'conv11.weight': 'layers.conv1.weight',
            'conv11.bias': 'layers.conv1.bias',
            'conv12.weight': 'layers.conv2.weight',
            'conv12.bias': 'layers.conv2.bias',
            'conv21.weight': 'layers.conv3.weight',
            'conv21.bias': 'layers.conv3.bias',
            'conv22.weight': 'layers.conv4.weight',
            'conv22.bias': 'layers.conv4.bias',
            'conv31.weight': 'layers.conv5.weight',
            'conv31.bias': 'layers.conv5.bias',
            'conv32.weight': 'layers.conv6.weight',
            'conv32.bias': 'layers.conv6.bias',
            'conv33.weight': 'layers.conv7.weight',
            'conv33.bias': 'layers.conv7.bias',
            'conv41.weight': 'layers.conv8.weight',
            'conv41.bias': 'layers.conv8.bias',
            'conv42.weight': 'layers.conv9.weight',
            'conv42.bias': 'layers.conv9.bias',
            'conv43.weight': 'layers.conv10.weight',
            'conv43.bias': 'layers.conv10.bias',
            'conv51.weight': 'layers.conv11.weight',
            'conv51.bias': 'layers.conv11.bias',
            'conv52.weight': 'layers.conv12.weight',
            'conv52.bias': 'layers.conv12.bias',
            'conv53.weight': 'layers.conv13.weight',
            'conv53.bias': 'layers.conv13.bias',
            'fc1.weight': 'layers.fc1.weight',
            'fc1.bias': 'layers.fc1.bias',
            'fc2.weight': 'layers.fc2.weight',
            'fc2.bias': 'layers.fc2.bias',
            'fc3.weight': 'layers.fc3.weight',
            'fc3.bias': 'layers.fc3.bias',
            'lpips.0.weight': 'lpips0',
            'lpips.1.weight': 'lpips1',
            'lpips.2.weight': 'lpips2',
            'lpips.3.weight': 'lpips3',
            'lpips.4.weight': 'lpips4',
        }
    else:
        raise NotImplementedError(f'Not implemented model source '
                                  f'`{model_source}`!')

    dst_state_dict = {}
    for dst_name, src_name in dst_to_src_var_mapping.items():
        if dst_name.startswith('lpips'):
            dst_state_dict[dst_name] = src_state_dict[src_name].unsqueeze(0)
        else:
            dst_state_dict[dst_name] = src_state_dict[src_name].clone()
    return dst_state_dict


_IMG_MEAN = (0.485, 0.456, 0.406)
_IMG_STD  = (0.229, 0.224, 0.225)
_ALLOWED_RETURN = [
    'feature1', 'pool1', 'feature2', 'pool2', 'feature3', 'pool3', 'feature4',
    'pool4', 'feature5', 'pool5', 'flatten', 'feature', 'logits', 'prediction',
    'lpips'
]

# pylint: disable=missing-function-docstring

class VGG16(nn.Module):
    """Defines the VGG16 structure.

    This model takes `RGB` images with data format `NCHW` as the raw inputs. The
    pixel range are assumed to be [-1, 1].
    """

    def __init__(self, align_tf_resize=False, no_top=True, enable_lpips=True):
        """Defines the network structure."""
        super().__init__()

        self.align_tf_resize = align_tf_resize
        self.no_top = no_top
        self.enable_lpips = enable_lpips

        self.conv11 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.relu11 = nn.ReLU(inplace=True)
        self.conv12 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.relu12 = nn.ReLU(inplace=True)
        # output `feature1`, with shape [N, 64, 224, 224]

        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        # output `pool1`, with shape [N, 64, 112, 112]

        self.conv21 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.relu21 = nn.ReLU(inplace=True)
        self.conv22 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.relu22 = nn.ReLU(inplace=True)
        # output `feature2`, with shape [N, 128, 112, 112]

        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        # output `pool2`, with shape [N, 128, 56, 56]

        self.conv31 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.relu31 = nn.ReLU(inplace=True)
        self.conv32 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.relu32 = nn.ReLU(inplace=True)
        self.conv33 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.relu33 = nn.ReLU(inplace=True)
        # output `feature3`, with shape [N, 256, 56, 56]

        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        # output `pool3`, with shape [N,256, 28, 28]

        self.conv41 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.relu41 = nn.ReLU(inplace=True)
        self.conv42 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.relu42 = nn.ReLU(inplace=True)
        self.conv43 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.relu43 = nn.ReLU(inplace=True)
        # output `feature4`, with shape [N, 512, 28, 28]

        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        # output `pool4`, with shape [N, 512, 14, 14]

        self.conv51 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.relu51 = nn.ReLU(inplace=True)
        self.conv52 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.relu52 = nn.ReLU(inplace=True)
        self.conv53 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.relu53 = nn.ReLU(inplace=True)
        # output `feature5`, with shape [N, 512, 14, 14]

        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        # output `pool5`, with shape [N, 512, 7, 7]

        if self.enable_lpips:
            self.lpips = nn.ModuleList()
            for idx, ch in enumerate([64, 128, 256, 512, 512]):
                self.lpips.append(nn.Conv2d(ch, 1, kernel_size=1, bias=False))
                self.lpips[idx].weight.data.copy_(torch.ones(1, ch, 1, 1))

        if not self.no_top:
            self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
            self.flatten = nn.Flatten(start_dim=1, end_dim=-1)
            # output `flatten`, with shape [N, 25088]

            self.fc1 = nn.Linear(512 * 7 * 7, 4096)
            self.fc1_relu = nn.ReLU(inplace=True)
            self.fc1_dropout = nn.Dropout(0.5, inplace=False)
            self.fc2 = nn.Linear(4096, 4096)
            self.fc2_relu = nn.ReLU(inplace=True)
            self.fc2_dropout = nn.Dropout(0.5, inplace=False)
            # output `feature`, with shape [N, 4096]

            self.fc3 = nn.Linear(4096, 1000)
            # output `logits`, with shape [N, 1000]

            self.out = nn.Softmax(dim=1)
            # output `softmax`, with shape [N, 1000]

        img_mean = np.array(_IMG_MEAN).reshape((1, 3, 1, 1)).astype(np.float32)
        img_std = np.array(_IMG_STD).reshape((1, 3, 1, 1)).astype(np.float32)
        self.register_buffer('img_mean', torch.from_numpy(img_mean))
        self.register_buffer('img_std', torch.from_numpy(img_std))

    def forward(self,
                x,
                y=None,
                *,
                resize_input=False,
                return_tensor='feature'):
        return_tensor = return_tensor.lower()
        if return_tensor not in _ALLOWED_RETURN:
            raise ValueError(f'Invalid output tensor name `{return_tensor}` '
                             f'for perceptual model (VGG16)!\n'
                             f'Names allowed: {_ALLOWED_RETURN}.')

        if return_tensor == 'lpips' and y is None:
            raise ValueError('Two images are required for LPIPS computation, '
                             'but only one is received!')

        if return_tensor == 'lpips':
            assert x.shape == y.shape
            x = torch.cat([x, y], dim=0)
            features = []

        if resize_input:
            if self.align_tf_resize:
                theta = torch.eye(2, 3).to(x)
                theta[0, 2] += theta[0, 0] / x.shape[3] - theta[0, 0] / 224
                theta[1, 2] += theta[1, 1] / x.shape[2] - theta[1, 1] / 224
                theta = theta.unsqueeze(0).repeat(x.shape[0], 1, 1)
                grid = F.affine_grid(theta,
                                     size=(x.shape[0], x.shape[1], 224, 224),
                                     align_corners=False)
                x = F.grid_sample(x, grid,
                                  mode='bilinear',
                                  padding_mode='border',
                                  align_corners=False)
            else:
                x = F.interpolate(x,
                                  size=(224, 224),
                                  mode='bilinear',
                                  align_corners=False)
        if x.shape[1] == 1:
            x = x.repeat((1, 3, 1, 1))

        x = (x + 1) / 2
        x = (x - self.img_mean) / self.img_std

        x = self.conv11(x)
        x = self.relu11(x)
        x = self.conv12(x)
        x = self.relu12(x)
        if return_tensor == 'feature1':
            return x
        if return_tensor == 'lpips':
            features.append(x)

        x = self.pool1(x)
        if return_tensor == 'pool1':
            return x

        x = self.conv21(x)
        x = self.relu21(x)
        x = self.conv22(x)
        x = self.relu22(x)
        if return_tensor == 'feature2':
            return x
        if return_tensor == 'lpips':
            features.append(x)

        x = self.pool2(x)
        if return_tensor == 'pool2':
            return x

        x = self.conv31(x)
        x = self.relu31(x)
        x = self.conv32(x)
        x = self.relu32(x)
        x = self.conv33(x)
        x = self.relu33(x)
        if return_tensor == 'feature3':
            return x
        if return_tensor == 'lpips':
            features.append(x)

        x = self.pool3(x)
        if return_tensor == 'pool3':
            return x

        x = self.conv41(x)
        x = self.relu41(x)
        x = self.conv42(x)
        x = self.relu42(x)
        x = self.conv43(x)
        x = self.relu43(x)
        if return_tensor == 'feature4':
            return x
        if return_tensor == 'lpips':
            features.append(x)

        x = self.pool4(x)
        if return_tensor == 'pool4':
            return x

        x = self.conv51(x)
        x = self.relu51(x)
        x = self.conv52(x)
        x = self.relu52(x)
        x = self.conv53(x)
        x = self.relu53(x)
        if return_tensor == 'feature5':
            return x
        if return_tensor == 'lpips':
            features.append(x)

        x = self.pool5(x)
        if return_tensor == 'pool5':
            return x

        if return_tensor == 'lpips':
            score = 0
            assert len(features) == 5
            for idx in range(5):
                feature = features[idx]
                norm = feature.norm(dim=1, keepdim=True)
                feature = feature / (norm + 1e-10)
                feature_x, feature_y = feature.chunk(2, dim=0)
                diff = (feature_x - feature_y).square()
                score += self.lpips[idx](diff).mean(dim=(2, 3), keepdim=False)
            return score.sum(dim=1, keepdim=False)

        x = self.avgpool(x)
        x = self.flatten(x)
        if return_tensor == 'flatten':
            return x

        x = self.fc1(x)
        x = self.fc1_relu(x)
        x = self.fc1_dropout(x)
        x = self.fc2(x)
        x = self.fc2_relu(x)
        x = self.fc2_dropout(x)
        if return_tensor == 'feature':
            return x

        x = self.fc3(x)
        if return_tensor == 'logits':
            return x

        x = self.out(x)
        if return_tensor == 'prediction':
            return x

        raise NotImplementedError(f'Output tensor name `{return_tensor}` is '
                                  f'not implemented!')

# pylint: enable=missing-function-docstring