File size: 11,808 Bytes
375a436
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ecfea65
 
375a436
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ecfea65
 
 
 
375a436
 
 
 
 
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
# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

import os
import time
import hashlib
import pickle
import copy
import uuid
import numpy as np
import torch
import dnnlib

#----------------------------------------------------------------------------

class MetricOptions:
    def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True):
        assert 0 <= rank < num_gpus
        self.G              = G
        self.G_kwargs       = dnnlib.EasyDict(G_kwargs)
        self.dataset_kwargs = dnnlib.EasyDict(dataset_kwargs)
        self.num_gpus       = num_gpus
        self.rank           = rank
        self.device         = device if device is not None else torch.device('cuda', rank)
        self.progress       = progress.sub() if progress is not None and rank == 0 else ProgressMonitor()
        self.cache          = cache

#----------------------------------------------------------------------------

_feature_detector_cache = dict()

def get_feature_detector_name(url):
    return os.path.splitext(url.split('/')[-1])[0]

def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False):
    assert 0 <= rank < num_gpus
    key = (url, device)
    if key not in _feature_detector_cache:
        is_leader = (rank == 0)
        if not is_leader and num_gpus > 1:
            torch.distributed.barrier() # leader goes first
        with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f:
            _feature_detector_cache[key] = torch.jit.load(f).eval().to(device)
        if is_leader and num_gpus > 1:
            torch.distributed.barrier() # others follow
    return _feature_detector_cache[key]

#----------------------------------------------------------------------------

class FeatureStats:
    def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None):
        self.capture_all = capture_all
        self.capture_mean_cov = capture_mean_cov
        self.max_items = max_items
        self.num_items = 0
        self.num_features = None
        self.all_features = None
        self.raw_mean = None
        self.raw_cov = None

    def set_num_features(self, num_features):
        if self.num_features is not None:
            assert num_features == self.num_features
        else:
            self.num_features = num_features
            self.all_features = []
            self.raw_mean = np.zeros([num_features], dtype=np.float64)
            self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64)

    def is_full(self):
        return (self.max_items is not None) and (self.num_items >= self.max_items)

    def append(self, x):
        x = np.asarray(x, dtype=np.float32)
        assert x.ndim == 2
        if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items):
            if self.num_items >= self.max_items:
                return
            x = x[:self.max_items - self.num_items]

        self.set_num_features(x.shape[1])
        self.num_items += x.shape[0]
        if self.capture_all:
            self.all_features.append(x)
        if self.capture_mean_cov:
            x64 = x.astype(np.float64)
            self.raw_mean += x64.sum(axis=0)
            self.raw_cov += x64.T @ x64

    def append_torch(self, x, num_gpus=1, rank=0):
        assert isinstance(x, torch.Tensor) and x.ndim == 2
        assert 0 <= rank < num_gpus
        if num_gpus > 1:
            ys = []
            for src in range(num_gpus):
                y = x.clone()
                torch.distributed.broadcast(y, src=src)
                ys.append(y)
            x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples
        self.append(x.cpu().numpy())

    def get_all(self):
        assert self.capture_all
        return np.concatenate(self.all_features, axis=0)

    def get_all_torch(self):
        return torch.from_numpy(self.get_all())

    def get_mean_cov(self):
        assert self.capture_mean_cov
        mean = self.raw_mean / self.num_items
        cov = self.raw_cov / self.num_items
        cov = cov - np.outer(mean, mean)
        return mean, cov

    def save(self, pkl_file):
        with open(pkl_file, 'wb') as f:
            pickle.dump(self.__dict__, f)

    @staticmethod
    def load(pkl_file):
        with open(pkl_file, 'rb') as f:
            s = dnnlib.EasyDict(pickle.load(f))
        obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items)
        obj.__dict__.update(s)
        return obj

#----------------------------------------------------------------------------

class ProgressMonitor:
    def __init__(self, tag=None, num_items=None, flush_interval=1000, verbose=False, progress_fn=None, pfn_lo=0, pfn_hi=1000, pfn_total=1000):
        self.tag = tag
        self.num_items = num_items
        self.verbose = verbose
        self.flush_interval = flush_interval
        self.progress_fn = progress_fn
        self.pfn_lo = pfn_lo
        self.pfn_hi = pfn_hi
        self.pfn_total = pfn_total
        self.start_time = time.time()
        self.batch_time = self.start_time
        self.batch_items = 0
        if self.progress_fn is not None:
            self.progress_fn(self.pfn_lo, self.pfn_total)

    def update(self, cur_items):
        assert (self.num_items is None) or (cur_items <= self.num_items)
        if (cur_items < self.batch_items + self.flush_interval) and (self.num_items is None or cur_items < self.num_items):
            return
        cur_time = time.time()
        total_time = cur_time - self.start_time
        time_per_item = (cur_time - self.batch_time) / max(cur_items - self.batch_items, 1)
        if (self.verbose) and (self.tag is not None):
            print(f'{self.tag:<19s} items {cur_items:<7d} time {dnnlib.util.format_time(total_time):<12s} ms/item {time_per_item*1e3:.2f}')
        self.batch_time = cur_time
        self.batch_items = cur_items

        if (self.progress_fn is not None) and (self.num_items is not None):
            self.progress_fn(self.pfn_lo + (self.pfn_hi - self.pfn_lo) * (cur_items / self.num_items), self.pfn_total)

    def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1):
        return ProgressMonitor(
            tag             = tag,
            num_items       = num_items,
            flush_interval  = flush_interval,
            verbose         = self.verbose,
            progress_fn     = self.progress_fn,
            pfn_lo          = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_lo,
            pfn_hi          = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_hi,
            pfn_total       = self.pfn_total,
        )

#----------------------------------------------------------------------------

def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, data_loader_kwargs=None, max_items=None, **stats_kwargs):
    dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)
    if data_loader_kwargs is None:
        data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2)

    # Try to lookup from cache.
    cache_file = None
    if opts.cache:
        # Choose cache file name.
        args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs)
        md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8'))
        cache_tag = f'{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}'
        cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl')

        # Check if the file exists (all processes must agree).
        flag = os.path.isfile(cache_file) if opts.rank == 0 else False
        if opts.num_gpus > 1:
            flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device)
            torch.distributed.broadcast(tensor=flag, src=0)
            flag = (float(flag.cpu()) != 0)

        # Load.
        if flag:
            return FeatureStats.load(cache_file)

    # Initialize.
    num_items = len(dataset)
    if max_items is not None:
        num_items = min(num_items, max_items)
    stats = FeatureStats(max_items=num_items, **stats_kwargs)
    progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi)
    detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)

    # Main loop.
    item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)]
    for images, _labels in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs):
        if images.shape[1] == 1:
            images = images.repeat([1, 3, 1, 1])
        features = detector(images.to(opts.device), **detector_kwargs)
        stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
        progress.update(stats.num_items)

    # Save to cache.
    if cache_file is not None and opts.rank == 0:
        os.makedirs(os.path.dirname(cache_file), exist_ok=True)
        temp_file = cache_file + '.' + uuid.uuid4().hex
        stats.save(temp_file)
        os.replace(temp_file, cache_file) # atomic
    return stats

#----------------------------------------------------------------------------

def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, jit=False, **stats_kwargs):
    if batch_gen is None:
        batch_gen = min(batch_size, 4)
    assert batch_size % batch_gen == 0

    # Setup generator and load labels.
    G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device)
    dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)

    # Image generation func.
    def run_generator(z, c):
        img = G(z=z, c=c, **opts.G_kwargs)
        img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
        return img

    # JIT.
    if jit:
        z = torch.zeros([batch_gen, G.z_dim], device=opts.device)
        c = torch.zeros([batch_gen, G.c_dim], device=opts.device)
        run_generator = torch.jit.trace(run_generator, [z, c], check_trace=False)

    # Initialize.
    stats = FeatureStats(**stats_kwargs)
    assert stats.max_items is not None
    progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi)
    detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)

    # Main loop.
    while not stats.is_full():
        images = []
        for _i in range(batch_size // batch_gen):
            z = torch.randn([batch_gen, G.z_dim], device=opts.device)
            c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_gen)]
            c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
            images.append(run_generator(z, c))
        images = torch.cat(images)
        if images.shape[1] == 1:
            images = images.repeat([1, 3, 1, 1])
        features = detector(images, **detector_kwargs)
        stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
        progress.update(stats.num_items)
    return stats

#----------------------------------------------------------------------------