File size: 20,006 Bytes
ecf08bc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
#    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
from nnunet.network_architecture.custom_modules.conv_blocks import StackedConvLayers
from nnunet.network_architecture.generic_UNet import Upsample
from nnunet.network_architecture.neural_network import SegmentationNetwork
from nnunet.training.loss_functions.dice_loss import DC_and_CE_loss
from torch import nn
import numpy as np
from torch.optim import SGD

"""
The idea behind this modular U-net ist that we decouple encoder and decoder and thus make things a) a lot more easy to 
combine and b) enable easy swapping between segmentation or classification mode of the same architecture
"""


def get_default_network_config(dim=2, dropout_p=None, nonlin="LeakyReLU", norm_type="bn"):
    """
    returns a dictionary that contains pointers to conv, nonlin and norm ops and the default kwargs I like to use
    :return:
    """
    props = {}
    if dim == 2:
        props['conv_op'] = nn.Conv2d
        props['dropout_op'] = nn.Dropout2d
    elif dim == 3:
        props['conv_op'] = nn.Conv3d
        props['dropout_op'] = nn.Dropout3d
    else:
        raise NotImplementedError

    if norm_type == "bn":
        if dim == 2:
            props['norm_op'] = nn.BatchNorm2d
        elif dim == 3:
            props['norm_op'] = nn.BatchNorm3d
        props['norm_op_kwargs'] = {'eps': 1e-5, 'affine': True}
    elif norm_type == "in":
        if dim == 2:
            props['norm_op'] = nn.InstanceNorm2d
        elif dim == 3:
            props['norm_op'] = nn.InstanceNorm3d
        props['norm_op_kwargs'] = {'eps': 1e-5, 'affine': True}
    else:
        raise NotImplementedError

    if dropout_p is None:
        props['dropout_op'] = None
        props['dropout_op_kwargs'] = {'p': 0, 'inplace': True}
    else:
        props['dropout_op_kwargs'] = {'p': dropout_p, 'inplace': True}

    props['conv_op_kwargs'] = {'stride': 1, 'dilation': 1, 'bias': True}  # kernel size will be set by network!

    if nonlin == "LeakyReLU":
        props['nonlin'] = nn.LeakyReLU
        props['nonlin_kwargs'] = {'negative_slope': 1e-2, 'inplace': True}
    elif nonlin == "ReLU":
        props['nonlin'] = nn.ReLU
        props['nonlin_kwargs'] = {'inplace': True}
    else:
        raise ValueError

    return props



class PlainConvUNetEncoder(nn.Module):
    def __init__(self, input_channels, base_num_features, num_blocks_per_stage, feat_map_mul_on_downscale,
                 pool_op_kernel_sizes, conv_kernel_sizes, props, default_return_skips=True,
                 max_num_features=480):
        """
        Following UNet building blocks can be added by utilizing the properties this class exposes (TODO)

        this one includes the bottleneck layer!

        :param input_channels:
        :param base_num_features:
        :param num_blocks_per_stage:
        :param feat_map_mul_on_downscale:
        :param pool_op_kernel_sizes:
        :param conv_kernel_sizes:
        :param props:
        """
        super(PlainConvUNetEncoder, self).__init__()

        self.default_return_skips = default_return_skips
        self.props = props

        self.stages = []
        self.stage_output_features = []
        self.stage_pool_kernel_size = []
        self.stage_conv_op_kernel_size = []

        assert len(pool_op_kernel_sizes) == len(conv_kernel_sizes)

        num_stages = len(conv_kernel_sizes)

        if not isinstance(num_blocks_per_stage, (list, tuple)):
            num_blocks_per_stage = [num_blocks_per_stage] * num_stages
        else:
            assert len(num_blocks_per_stage) == num_stages

        self.num_blocks_per_stage = num_blocks_per_stage  # decoder may need this

        current_input_features = input_channels
        for stage in range(num_stages):
            current_output_features = min(int(base_num_features * feat_map_mul_on_downscale ** stage), max_num_features)
            current_kernel_size = conv_kernel_sizes[stage]
            current_pool_kernel_size = pool_op_kernel_sizes[stage]

            current_stage = StackedConvLayers(current_input_features, current_output_features, current_kernel_size,
                                              props, num_blocks_per_stage[stage], current_pool_kernel_size)

            self.stages.append(current_stage)
            self.stage_output_features.append(current_output_features)
            self.stage_conv_op_kernel_size.append(current_kernel_size)
            self.stage_pool_kernel_size.append(current_pool_kernel_size)

            # update current_input_features
            current_input_features = current_output_features

        self.stages = nn.ModuleList(self.stages)
        self.output_features = current_output_features

    def forward(self, x, return_skips=None):
        """

        :param x:
        :param return_skips: if none then self.default_return_skips is used
        :return:
        """
        skips = []

        for s in self.stages:
            x = s(x)
            if self.default_return_skips:
                skips.append(x)

        if return_skips is None:
            return_skips = self.default_return_skips

        if return_skips:
            return skips
        else:
            return x

    @staticmethod
    def compute_approx_vram_consumption(patch_size, base_num_features, max_num_features,
                                        num_modalities, pool_op_kernel_sizes, num_blocks_per_stage_encoder,
                                        feat_map_mul_on_downscale, batch_size):
        npool = len(pool_op_kernel_sizes) - 1

        current_shape = np.array(patch_size)

        tmp = num_blocks_per_stage_encoder[0] * np.prod(current_shape) * base_num_features \
              + num_modalities * np.prod(current_shape)

        num_feat = base_num_features

        for p in range(1, npool + 1):
            current_shape = current_shape / np.array(pool_op_kernel_sizes[p])
            num_feat = min(num_feat * feat_map_mul_on_downscale, max_num_features)
            num_convs = num_blocks_per_stage_encoder[p]
            print(p, num_feat, num_convs, current_shape)
            tmp += num_convs * np.prod(current_shape) * num_feat
        return tmp * batch_size


class PlainConvUNetDecoder(nn.Module):
    def __init__(self, previous, num_classes, num_blocks_per_stage=None, network_props=None, deep_supervision=False,
                 upscale_logits=False):
        super(PlainConvUNetDecoder, self).__init__()
        self.num_classes = num_classes
        self.deep_supervision = deep_supervision
        """
        We assume the bottleneck is part of the encoder, so we can start with upsample -> concat here
        """
        previous_stages = previous.stages
        previous_stage_output_features = previous.stage_output_features
        previous_stage_pool_kernel_size = previous.stage_pool_kernel_size
        previous_stage_conv_op_kernel_size = previous.stage_conv_op_kernel_size

        if network_props is None:
            self.props = previous.props
        else:
            self.props = network_props

        if self.props['conv_op'] == nn.Conv2d:
            transpconv = nn.ConvTranspose2d
            upsample_mode = "bilinear"
        elif self.props['conv_op'] == nn.Conv3d:
            transpconv = nn.ConvTranspose3d
            upsample_mode = "trilinear"
        else:
            raise ValueError("unknown convolution dimensionality, conv op: %s" % str(self.props['conv_op']))

        if num_blocks_per_stage is None:
            num_blocks_per_stage = previous.num_blocks_per_stage[:-1][::-1]

        assert len(num_blocks_per_stage) == len(previous.num_blocks_per_stage) - 1

        self.stage_pool_kernel_size = previous_stage_pool_kernel_size
        self.stage_output_features = previous_stage_output_features
        self.stage_conv_op_kernel_size = previous_stage_conv_op_kernel_size

        num_stages = len(previous_stages) - 1  # we have one less as the first stage here is what comes after the
        # bottleneck

        self.tus = []
        self.stages = []
        self.deep_supervision_outputs = []

        # only used for upsample_logits
        cum_upsample = np.cumprod(np.vstack(self.stage_pool_kernel_size), axis=0).astype(int)

        for i, s in enumerate(np.arange(num_stages)[::-1]):
            features_below = previous_stage_output_features[s + 1]
            features_skip = previous_stage_output_features[s]

            self.tus.append(transpconv(features_below, features_skip, previous_stage_pool_kernel_size[s + 1],
                                       previous_stage_pool_kernel_size[s + 1], bias=False))
            # after we tu we concat features so now we have 2xfeatures_skip
            self.stages.append(StackedConvLayers(2 * features_skip, features_skip,
                                                 previous_stage_conv_op_kernel_size[s], self.props,
                                                 num_blocks_per_stage[i]))

            if deep_supervision and s != 0:
                seg_layer = self.props['conv_op'](features_skip, num_classes, 1, 1, 0, 1, 1, False)
                if upscale_logits:
                    upsample = Upsample(scale_factor=cum_upsample[s], mode=upsample_mode)
                    self.deep_supervision_outputs.append(nn.Sequential(seg_layer, upsample))
                else:
                    self.deep_supervision_outputs.append(seg_layer)

        self.segmentation_output = self.props['conv_op'](features_skip, num_classes, 1, 1, 0, 1, 1, False)

        self.tus = nn.ModuleList(self.tus)
        self.stages = nn.ModuleList(self.stages)
        self.deep_supervision_outputs = nn.ModuleList(self.deep_supervision_outputs)

    def forward(self, skips, gt=None, loss=None):
        # skips come from the encoder. They are sorted so that the bottleneck is last in the list
        # what is maybe not perfect is that the TUs and stages here are sorted the other way around
        # so let's just reverse the order of skips
        skips = skips[::-1]
        seg_outputs = []

        x = skips[0]  # this is the bottleneck

        for i in range(len(self.tus)):
            x = self.tus[i](x)
            x = torch.cat((x, skips[i + 1]), dim=1)
            x = self.stages[i](x)
            if self.deep_supervision and (i != len(self.tus) - 1):
                tmp = self.deep_supervision_outputs[i](x)
                if gt is not None:
                    tmp = loss(tmp, gt)
                seg_outputs.append(tmp)

        segmentation = self.segmentation_output(x)

        if self.deep_supervision:
            tmp = segmentation
            if gt is not None:
                tmp = loss(tmp, gt)
            seg_outputs.append(tmp)
            return seg_outputs[::-1]  # seg_outputs are ordered so that the seg from the highest layer is first, the seg from
            # the bottleneck of the UNet last
        else:
            return segmentation

    @staticmethod
    def compute_approx_vram_consumption(patch_size, base_num_features, max_num_features,
                                        num_classes, pool_op_kernel_sizes, num_blocks_per_stage_decoder,
                                        feat_map_mul_on_downscale, batch_size):
        """
        This only applies for num_blocks_per_stage and convolutional_upsampling=True
        not real vram consumption. just a constant term to which the vram consumption will be approx proportional
        (+ offset for parameter storage)
        :param patch_size:
        :param num_pool_per_axis:
        :param base_num_features:
        :param max_num_features:
        :return:
        """
        npool = len(pool_op_kernel_sizes) - 1

        current_shape = np.array(patch_size)
        tmp = (num_blocks_per_stage_decoder[-1] + 1) * np.prod(current_shape) * base_num_features + num_classes * np.prod(current_shape)

        num_feat = base_num_features

        for p in range(1, npool):
            current_shape = current_shape / np.array(pool_op_kernel_sizes[p])
            num_feat = min(num_feat * feat_map_mul_on_downscale, max_num_features)
            num_convs = num_blocks_per_stage_decoder[-(p+1)] + 1
            print(p, num_feat, num_convs, current_shape)
            tmp += num_convs * np.prod(current_shape) * num_feat

        return tmp * batch_size


class PlainConvUNet(SegmentationNetwork):
    use_this_for_batch_size_computation_2D = 1167982592.0
    use_this_for_batch_size_computation_3D = 1152286720.0

    def __init__(self, input_channels, base_num_features, num_blocks_per_stage_encoder, feat_map_mul_on_downscale,
                 pool_op_kernel_sizes, conv_kernel_sizes, props, num_classes, num_blocks_per_stage_decoder,
                 deep_supervision=False, upscale_logits=False, max_features=512, initializer=None):
        super(PlainConvUNet, self).__init__()
        self.conv_op = props['conv_op']
        self.num_classes = num_classes

        self.encoder = PlainConvUNetEncoder(input_channels, base_num_features, num_blocks_per_stage_encoder,
                                            feat_map_mul_on_downscale, pool_op_kernel_sizes, conv_kernel_sizes,
                                            props, default_return_skips=True, max_num_features=max_features)
        self.decoder = PlainConvUNetDecoder(self.encoder, num_classes, num_blocks_per_stage_decoder, props,
                                            deep_supervision, upscale_logits)
        if initializer is not None:
            self.apply(initializer)

    def forward(self, x):
        skips = self.encoder(x)
        return self.decoder(skips)

    @staticmethod
    def compute_approx_vram_consumption(patch_size, base_num_features, max_num_features,
                                        num_modalities, num_classes, pool_op_kernel_sizes, num_blocks_per_stage_encoder,
                                        num_blocks_per_stage_decoder, feat_map_mul_on_downscale, batch_size):
        enc = PlainConvUNetEncoder.compute_approx_vram_consumption(patch_size, base_num_features, max_num_features,
                                                                   num_modalities, pool_op_kernel_sizes,
                                                                   num_blocks_per_stage_encoder,
                                                                   feat_map_mul_on_downscale, batch_size)
        dec = PlainConvUNetDecoder.compute_approx_vram_consumption(patch_size, base_num_features, max_num_features,
                                                                   num_classes, pool_op_kernel_sizes,
                                                                   num_blocks_per_stage_decoder,
                                                                   feat_map_mul_on_downscale, batch_size)

        return enc + dec

    @staticmethod
    def compute_reference_for_vram_consumption_3d():
        patch_size = (160, 128, 128)
        pool_op_kernel_sizes = ((1, 1, 1),
                            (2, 2, 2),
                            (2, 2, 2),
                            (2, 2, 2),
                            (2, 2, 2),
                            (2, 2, 2))
        conv_per_stage_encoder = (2, 2, 2, 2, 2, 2)
        conv_per_stage_decoder = (2, 2, 2, 2, 2)

        return PlainConvUNet.compute_approx_vram_consumption(patch_size, 32, 512, 4, 3, pool_op_kernel_sizes,
                                                             conv_per_stage_encoder, conv_per_stage_decoder, 2, 2)

    @staticmethod
    def compute_reference_for_vram_consumption_2d():
        patch_size = (256, 256)
        pool_op_kernel_sizes = (
            (1, 1), # (256, 256)
            (2, 2), # (128, 128)
            (2, 2), # (64, 64)
            (2, 2), # (32, 32)
            (2, 2), # (16, 16)
            (2, 2), # (8, 8)
            (2, 2)  # (4, 4)
        )
        conv_per_stage_encoder = (2, 2, 2, 2, 2, 2, 2)
        conv_per_stage_decoder = (2, 2, 2, 2, 2, 2)

        return PlainConvUNet.compute_approx_vram_consumption(patch_size, 32, 512, 4, 3, pool_op_kernel_sizes,
                                                             conv_per_stage_encoder, conv_per_stage_decoder, 2, 56)


if __name__ == "__main__":
    conv_op_kernel_sizes = ((3, 3),
                            (3, 3),
                            (3, 3),
                            (3, 3),
                            (3, 3),
                            (3, 3),
                            (3, 3))
    pool_op_kernel_sizes = ((1, 1),
                            (2, 2),
                            (2, 2),
                            (2, 2),
                            (2, 2),
                            (2, 2),
                            (2, 2))
    patch_size = (256, 256)
    batch_size = 56
    unet = PlainConvUNet(4, 32, (2, 2, 2, 2, 2, 2, 2), 2, pool_op_kernel_sizes, conv_op_kernel_sizes,
                         get_default_network_config(2, dropout_p=None), 4, (2, 2, 2, 2, 2, 2), False, False, max_features=512).cuda()
    optimizer = SGD(unet.parameters(), lr=0.1, momentum=0.95)

    unet.compute_reference_for_vram_consumption_3d()
    unet.compute_reference_for_vram_consumption_2d()

    dummy_input = torch.rand((batch_size, 4, *patch_size)).cuda()
    dummy_gt = (torch.rand((batch_size, 1, *patch_size)) * 4).round().clamp_(0, 3).cuda().long()

    optimizer.zero_grad()
    skips = unet.encoder(dummy_input)
    print([i.shape for i in skips])
    loss = DC_and_CE_loss({'batch_dice': True, 'smooth': 1e-5, 'smooth_in_nom': True,
                    'do_bg': False, 'rebalance_weights': None, 'background_weight': 1}, {})
    output = unet.decoder(skips)

    l = loss(output, dummy_gt)
    l.backward()

    optimizer.step()

    import hiddenlayer as hl
    g = hl.build_graph(unet, dummy_input)
    g.save("/home/fabian/test.pdf")

    """conv_op_kernel_sizes = ((3, 3, 3),
                            (3, 3, 3),
                            (3, 3, 3),
                            (3, 3, 3),
                            (3, 3, 3),
                            (3, 3, 3))
    pool_op_kernel_sizes = ((1, 1, 1),
                            (2, 2, 2),
                            (2, 2, 2),
                            (2, 2, 2),
                            (2, 2, 2),
                            (2, 2, 2))
    patch_size = (160, 128, 128)
    unet = PlainConvUNet(4, 32, (2, 2, 2, 2, 2, 2), 2, pool_op_kernel_sizes, conv_op_kernel_sizes,
                         get_default_network_config(3, dropout_p=None), 4, (2, 2, 2, 2, 2), False, False, max_features=512).cuda()
    optimizer = SGD(unet.parameters(), lr=0.1, momentum=0.95)

    unet.compute_reference_for_vram_consumption_3d()
    unet.compute_reference_for_vram_consumption_2d()

    dummy_input = torch.rand((2, 4, *patch_size)).cuda()
    dummy_gt = (torch.rand((2, 1, *patch_size)) * 4).round().clamp_(0, 3).cuda().long()

    optimizer.zero_grad()
    skips = unet.encoder(dummy_input)
    print([i.shape for i in skips])
    loss = DC_and_CE_loss({'batch_dice': True, 'smooth': 1e-5, 'smooth_in_nom': True,
                    'do_bg': False, 'rebalance_weights': None, 'background_weight': 1}, {})
    output = unet.decoder(skips)

    l = loss(output, dummy_gt)
    l.backward()

    optimizer.step()

    import hiddenlayer as hl
    g = hl.build_graph(unet, dummy_input)
    g.save("/home/fabian/test.pdf")"""