File size: 18,131 Bytes
128757a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
'''

Copyright (C) 2019 Sovrasov V. - All Rights Reserved

 * You may use, distribute and modify this code under the

 * terms of the MIT license.

 * You should have received a copy of the MIT license with

 * this file. If not visit https://opensource.org/licenses/MIT

'''

import sys
from functools import partial

import numpy as np
import torch
import torch.nn as nn

from maskrcnn_benchmark.layers import *

def get_model_complexity_info(model, input_res,

                              print_per_layer_stat=True,

                              as_strings=True,

                              input_constructor=None, ost=sys.stdout,

                              verbose=False, ignore_modules=[],

                              custom_modules_hooks={}):
    assert type(input_res) is tuple
    assert len(input_res) >= 1
    assert isinstance(model, nn.Module)
    global CUSTOM_MODULES_MAPPING
    CUSTOM_MODULES_MAPPING = custom_modules_hooks
    flops_model = add_flops_counting_methods(model)
    flops_model.eval()
    flops_model.start_flops_count(ost=ost, verbose=verbose,
                                  ignore_list=ignore_modules)
    if input_constructor:
        input = input_constructor(input_res)
        _ = flops_model(**input)
    else:
        try:
            batch = torch.ones(()).new_empty((1, *input_res),
                                             dtype=next(flops_model.parameters()).dtype,
                                             device=next(flops_model.parameters()).device)
        except StopIteration:
            batch = torch.ones(()).new_empty((1, *input_res))

        _ = flops_model(batch)

    flops_count, params_count = flops_model.compute_average_flops_cost()
    if print_per_layer_stat:
        print_model_with_flops(flops_model, flops_count, params_count, ost=ost)
    flops_model.stop_flops_count()
    CUSTOM_MODULES_MAPPING = {}

    if as_strings:
        return flops_to_string(flops_count), params_to_string(params_count)

    return flops_count, params_count


def flops_to_string(flops, units='GMac', precision=2):
    if units is None:
        if flops // 10**9 > 0:
            return str(round(flops / 10.**9, precision)) + ' GMac'
        elif flops // 10**6 > 0:
            return str(round(flops / 10.**6, precision)) + ' MMac'
        elif flops // 10**3 > 0:
            return str(round(flops / 10.**3, precision)) + ' KMac'
        else:
            return str(flops) + ' Mac'
    else:
        if units == 'GMac':
            return str(round(flops / 10.**9, precision)) + ' ' + units
        elif units == 'MMac':
            return str(round(flops / 10.**6, precision)) + ' ' + units
        elif units == 'KMac':
            return str(round(flops / 10.**3, precision)) + ' ' + units
        else:
            return str(flops) + ' Mac'


def params_to_string(params_num, units=None, precision=2):
    if units is None:
        if params_num // 10 ** 6 > 0:
            return str(round(params_num / 10 ** 6, 2)) + ' M'
        elif params_num // 10 ** 3:
            return str(round(params_num / 10 ** 3, 2)) + ' k'
        else:
            return str(params_num)
    else:
        if units == 'M':
            return str(round(params_num / 10.**6, precision)) + ' ' + units
        elif units == 'K':
            return str(round(params_num / 10.**3, precision)) + ' ' + units
        else:
            return str(params_num)


def accumulate_flops(self):
    if is_supported_instance(self):
        return self.__flops__
    else:
        sum = 0
        for m in self.children():
            sum += m.accumulate_flops()
        return sum


def print_model_with_flops(model, total_flops, total_params, units='GMac',

                           precision=3, ost=sys.stdout):

    def accumulate_params(self):
        if is_supported_instance(self):
            return self.__params__
        else:
            sum = 0
            for m in self.children():
                sum += m.accumulate_params()
            return sum

    def flops_repr(self):
        accumulated_params_num = self.accumulate_params()
        accumulated_flops_cost = self.accumulate_flops() / model.__batch_counter__
        return ', '.join([params_to_string(accumulated_params_num,
                                           units='M', precision=precision),
                          '{:.3%} Params'.format(accumulated_params_num / total_params),
                          flops_to_string(accumulated_flops_cost,
                                          units=units, precision=precision),
                          '{:.3%} MACs'.format(accumulated_flops_cost / total_flops),
                          self.original_extra_repr()])

    def add_extra_repr(m):
        m.accumulate_flops = accumulate_flops.__get__(m)
        m.accumulate_params = accumulate_params.__get__(m)
        flops_extra_repr = flops_repr.__get__(m)
        if m.extra_repr != flops_extra_repr:
            m.original_extra_repr = m.extra_repr
            m.extra_repr = flops_extra_repr
            assert m.extra_repr != m.original_extra_repr

    def del_extra_repr(m):
        if hasattr(m, 'original_extra_repr'):
            m.extra_repr = m.original_extra_repr
            del m.original_extra_repr
        if hasattr(m, 'accumulate_flops'):
            del m.accumulate_flops

    model.apply(add_extra_repr)
    print(repr(model), file=ost)
    model.apply(del_extra_repr)


def get_model_parameters_number(model):
    params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
    return params_num


def add_flops_counting_methods(net_main_module):
    # adding additional methods to the existing module object,
    # this is done this way so that each function has access to self object
    net_main_module.start_flops_count = start_flops_count.__get__(net_main_module)
    net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module)
    net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module)
    net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(
                                                    net_main_module)

    net_main_module.reset_flops_count()

    return net_main_module


def compute_average_flops_cost(self):
    """

    A method that will be available after add_flops_counting_methods() is called

    on a desired net object.



    Returns current mean flops consumption per image.



    """

    for m in self.modules():
        m.accumulate_flops = accumulate_flops.__get__(m)

    flops_sum = self.accumulate_flops()

    for m in self.modules():
        if hasattr(m, 'accumulate_flops'):
            del m.accumulate_flops

    params_sum = get_model_parameters_number(self)
    return flops_sum / self.__batch_counter__, params_sum


def start_flops_count(self, **kwargs):
    """

    A method that will be available after add_flops_counting_methods() is called

    on a desired net object.



    Activates the computation of mean flops consumption per image.

    Call it before you run the network.



    """
    add_batch_counter_hook_function(self)

    seen_types = set()

    def add_flops_counter_hook_function(module, ost, verbose, ignore_list):
        if type(module) in ignore_list:
            seen_types.add(type(module))
            if is_supported_instance(module):
                module.__params__ = 0
        elif is_supported_instance(module):
            if hasattr(module, '__flops_handle__'):
                return
            if type(module) in CUSTOM_MODULES_MAPPING:
                handle = module.register_forward_hook(
                                        CUSTOM_MODULES_MAPPING[type(module)])
            elif getattr(module, 'compute_macs', False):
                handle = module.register_forward_hook(
                    module.compute_macs
                )
            else:
                handle = module.register_forward_hook(MODULES_MAPPING[type(module)])
            module.__flops_handle__ = handle
            seen_types.add(type(module))
        else:
            if verbose and not type(module) in (nn.Sequential, nn.ModuleList) and \
               not type(module) in seen_types:
                print('Warning: module ' + type(module).__name__ +
                      ' is treated as a zero-op.', file=ost)
            seen_types.add(type(module))

    self.apply(partial(add_flops_counter_hook_function, **kwargs))


def stop_flops_count(self):
    """

    A method that will be available after add_flops_counting_methods() is called

    on a desired net object.



    Stops computing the mean flops consumption per image.

    Call whenever you want to pause the computation.



    """
    remove_batch_counter_hook_function(self)
    self.apply(remove_flops_counter_hook_function)


def reset_flops_count(self):
    """

    A method that will be available after add_flops_counting_methods() is called

    on a desired net object.



    Resets statistics computed so far.



    """
    add_batch_counter_variables_or_reset(self)
    self.apply(add_flops_counter_variable_or_reset)


# ---- Internal functions
def empty_flops_counter_hook(module, input, output):
    module.__flops__ += 0


def upsample_flops_counter_hook(module, input, output):
    output_size = output[0]
    batch_size = output_size.shape[0]
    output_elements_count = batch_size
    for val in output_size.shape[1:]:
        output_elements_count *= val
    module.__flops__ += int(output_elements_count)


def relu_flops_counter_hook(module, input, output):
    active_elements_count = output.numel()
    module.__flops__ += int(active_elements_count)


def linear_flops_counter_hook(module, input, output):
    input = input[0]
    # pytorch checks dimensions, so here we don't care much
    output_last_dim = output.shape[-1]
    bias_flops = output_last_dim if module.bias is not None else 0
    module.__flops__ += int(np.prod(input.shape) * output_last_dim + bias_flops)


def pool_flops_counter_hook(module, input, output):
    input = input[0]
    module.__flops__ += int(np.prod(input.shape))


def bn_flops_counter_hook(module, input, output):
    input = input[0]

    batch_flops = np.prod(input.shape)
    if module.affine:
        batch_flops *= 2
    module.__flops__ += int(batch_flops)


def conv_flops_counter_hook(conv_module, input, output):
    # Can have multiple inputs, getting the first one
    input = input[0]

    batch_size = input.shape[0]
    output_dims = list(output.shape[2:])

    kernel_dims = list(conv_module.kernel_size)
    in_channels = conv_module.in_channels
    out_channels = conv_module.out_channels
    groups = conv_module.groups

    filters_per_channel = out_channels // groups
    conv_per_position_flops = int(np.prod(kernel_dims)) * \
        in_channels * filters_per_channel

    active_elements_count = batch_size * int(np.prod(output_dims))

    overall_conv_flops = conv_per_position_flops * active_elements_count

    bias_flops = 0

    if conv_module.bias is not None:

        bias_flops = out_channels * active_elements_count

    overall_flops = overall_conv_flops + bias_flops

    conv_module.__flops__ += int(overall_flops)


def batch_counter_hook(module, input, output):
    batch_size = 1
    if len(input) > 0:
        # Can have multiple inputs, getting the first one
        input = input[0]
        batch_size = len(input)
    else:
        pass
        print('Warning! No positional inputs found for a module,'
              ' assuming batch size is 1.')
    module.__batch_counter__ += batch_size


def rnn_flops(flops, rnn_module, w_ih, w_hh, input_size):
    # matrix matrix mult ih state and internal state
    flops += w_ih.shape[0]*w_ih.shape[1]
    # matrix matrix mult hh state and internal state
    flops += w_hh.shape[0]*w_hh.shape[1]
    if isinstance(rnn_module, (nn.RNN, nn.RNNCell)):
        # add both operations
        flops += rnn_module.hidden_size
    elif isinstance(rnn_module, (nn.GRU, nn.GRUCell)):
        # hadamard of r
        flops += rnn_module.hidden_size
        # adding operations from both states
        flops += rnn_module.hidden_size*3
        # last two hadamard product and add
        flops += rnn_module.hidden_size*3
    elif isinstance(rnn_module, (nn.LSTM, nn.LSTMCell)):
        # adding operations from both states
        flops += rnn_module.hidden_size*4
        # two hadamard product and add for C state
        flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
        # final hadamard
        flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
    return flops


def rnn_flops_counter_hook(rnn_module, input, output):
    """

    Takes into account batch goes at first position, contrary

    to pytorch common rule (but actually it doesn't matter).

    IF sigmoid and tanh are made hard, only a comparison FLOPS should be accurate

    """
    flops = 0
    # input is a tuple containing a sequence to process and (optionally) hidden state
    inp = input[0]
    batch_size = inp.shape[0]
    seq_length = inp.shape[1]
    num_layers = rnn_module.num_layers

    for i in range(num_layers):
        w_ih = rnn_module.__getattr__('weight_ih_l' + str(i))
        w_hh = rnn_module.__getattr__('weight_hh_l' + str(i))
        if i == 0:
            input_size = rnn_module.input_size
        else:
            input_size = rnn_module.hidden_size
        flops = rnn_flops(flops, rnn_module, w_ih, w_hh, input_size)
        if rnn_module.bias:
            b_ih = rnn_module.__getattr__('bias_ih_l' + str(i))
            b_hh = rnn_module.__getattr__('bias_hh_l' + str(i))
            flops += b_ih.shape[0] + b_hh.shape[0]

    flops *= batch_size
    flops *= seq_length
    if rnn_module.bidirectional:
        flops *= 2
    rnn_module.__flops__ += int(flops)


def rnn_cell_flops_counter_hook(rnn_cell_module, input, output):
    flops = 0
    inp = input[0]
    batch_size = inp.shape[0]
    w_ih = rnn_cell_module.__getattr__('weight_ih')
    w_hh = rnn_cell_module.__getattr__('weight_hh')
    input_size = inp.shape[1]
    flops = rnn_flops(flops, rnn_cell_module, w_ih, w_hh, input_size)
    if rnn_cell_module.bias:
        b_ih = rnn_cell_module.__getattr__('bias_ih')
        b_hh = rnn_cell_module.__getattr__('bias_hh')
        flops += b_ih.shape[0] + b_hh.shape[0]

    flops *= batch_size
    rnn_cell_module.__flops__ += int(flops)


def add_batch_counter_variables_or_reset(module):

    module.__batch_counter__ = 0


def add_batch_counter_hook_function(module):
    if hasattr(module, '__batch_counter_handle__'):
        return

    handle = module.register_forward_hook(batch_counter_hook)
    module.__batch_counter_handle__ = handle


def remove_batch_counter_hook_function(module):
    if hasattr(module, '__batch_counter_handle__'):
        module.__batch_counter_handle__.remove()
        del module.__batch_counter_handle__


def add_flops_counter_variable_or_reset(module):
    if is_supported_instance(module):
        if hasattr(module, '__flops__') or hasattr(module, '__params__'):
            print('Warning: variables __flops__ or __params__ are already '
                  'defined for the module' + type(module).__name__ +
                  ' ptflops can affect your code!')
        module.__flops__ = 0
        module.__params__ = get_model_parameters_number(module)


CUSTOM_MODULES_MAPPING = {}

MODULES_MAPPING = {
    # convolutions
    nn.Conv1d: conv_flops_counter_hook,
    nn.Conv2d: conv_flops_counter_hook,
    nn.Conv3d: conv_flops_counter_hook,
    Conv2d: conv_flops_counter_hook,
    ModulatedDeformConv: conv_flops_counter_hook,
    # activations
    nn.ReLU: relu_flops_counter_hook,
    nn.PReLU: relu_flops_counter_hook,
    nn.ELU: relu_flops_counter_hook,
    nn.LeakyReLU: relu_flops_counter_hook,
    nn.ReLU6: relu_flops_counter_hook,
    # poolings
    nn.MaxPool1d: pool_flops_counter_hook,
    nn.AvgPool1d: pool_flops_counter_hook,
    nn.AvgPool2d: pool_flops_counter_hook,
    nn.MaxPool2d: pool_flops_counter_hook,
    nn.MaxPool3d: pool_flops_counter_hook,
    nn.AvgPool3d: pool_flops_counter_hook,
    nn.AdaptiveMaxPool1d: pool_flops_counter_hook,
    nn.AdaptiveAvgPool1d: pool_flops_counter_hook,
    nn.AdaptiveMaxPool2d: pool_flops_counter_hook,
    nn.AdaptiveAvgPool2d: pool_flops_counter_hook,
    nn.AdaptiveMaxPool3d: pool_flops_counter_hook,
    nn.AdaptiveAvgPool3d: pool_flops_counter_hook,
    # BNs
    nn.BatchNorm1d: bn_flops_counter_hook,
    nn.BatchNorm2d: bn_flops_counter_hook,
    nn.BatchNorm3d: bn_flops_counter_hook,
    nn.GroupNorm : bn_flops_counter_hook,
    # FC
    nn.Linear: linear_flops_counter_hook,
    # Upscale
    nn.Upsample: upsample_flops_counter_hook,
    # Deconvolution
    nn.ConvTranspose1d: conv_flops_counter_hook,
    nn.ConvTranspose2d: conv_flops_counter_hook,
    nn.ConvTranspose3d: conv_flops_counter_hook,
    ConvTranspose2d: conv_flops_counter_hook,
    # RNN
    nn.RNN: rnn_flops_counter_hook,
    nn.GRU: rnn_flops_counter_hook,
    nn.LSTM: rnn_flops_counter_hook,
    nn.RNNCell: rnn_cell_flops_counter_hook,
    nn.LSTMCell: rnn_cell_flops_counter_hook,
    nn.GRUCell: rnn_cell_flops_counter_hook
}


def is_supported_instance(module):
    if type(module) in MODULES_MAPPING or type(module) in CUSTOM_MODULES_MAPPING \
            or getattr(module, 'compute_macs', False):
        return True
    return False


def remove_flops_counter_hook_function(module):
    if is_supported_instance(module):
        if hasattr(module, '__flops_handle__'):
            module.__flops_handle__.remove()
            del module.__flops_handle__