From 4bc103f504e0d4f36d36c728aedb32966a7806cc Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Feb 2021 13:15:52 -0800
Subject: [PATCH 01/13] Fix CUDA crash w/ channels-last + CSP models. Remove
 use of chunk()

---
 timm/models/cspnet.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/timm/models/cspnet.py b/timm/models/cspnet.py
index ca9eaf16..afd1dcd7 100644
--- a/timm/models/cspnet.py
+++ b/timm/models/cspnet.py
@@ -264,9 +264,11 @@ class CrossStage(nn.Module):
         if self.conv_down is not None:
             x = self.conv_down(x)
         x = self.conv_exp(x)
-        xs, xb = x.chunk(2, dim=1)
+        split = x.shape[1] // 2
+        xs, xb = x[:, :split], x[:, split:]
         xb = self.blocks(xb)
-        out = self.conv_transition(torch.cat([xs, self.conv_transition_b(xb)], dim=1))
+        xb = self.conv_transition_b(xb).contiguous()
+        out = self.conv_transition(torch.cat([xs, xb], dim=1))
         return out
 
 

From 0e16d4e9fb15a7a51cfe2d01d69c7a23dce713c3 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Feb 2021 13:26:42 -0800
Subject: [PATCH 02/13] Add benchmark.py script, and update optimizer factory
 to be more friendly to use outside of argparse interface.

---
 benchmark.py                | 459 ++++++++++++++++++++++++++++++++++++
 timm/optim/__init__.py      |   2 +-
 timm/optim/optim_factory.py |  79 +++++--
 train.py                    |   4 +-
 4 files changed, 519 insertions(+), 25 deletions(-)
 create mode 100755 benchmark.py

diff --git a/benchmark.py b/benchmark.py
new file mode 100755
index 00000000..c745e278
--- /dev/null
+++ b/benchmark.py
@@ -0,0 +1,459 @@
+#!/usr/bin/env python3
+""" Model Benchmark Script
+
+An inference and train step benchmark script for timm models.
+
+Hacked together by Ross Wightman (https://github.com/rwightman)
+"""
+import argparse
+import os
+import csv
+import json
+import time
+import logging
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+from collections import OrderedDict
+from contextlib import suppress
+from functools import partial
+
+from timm.models import create_model, is_model, list_models
+from timm.optim import create_optimizer
+from timm.data import resolve_data_config
+from timm.utils import AverageMeter, setup_default_logging
+
+
+has_apex = False
+try:
+    from apex import amp
+    has_apex = True
+except ImportError:
+    pass
+
+has_native_amp = False
+try:
+    if getattr(torch.cuda.amp, 'autocast') is not None:
+        has_native_amp = True
+except AttributeError:
+    pass
+
+torch.backends.cudnn.benchmark = True
+_logger = logging.getLogger('validate')
+
+
+parser = argparse.ArgumentParser(description='PyTorch Benchmark')
+
+# benchmark specific args
+parser.add_argument('--bench', default='both', type=str,
+                    help="Benchmark mode. One of 'inference', 'train', 'both'. Defaults to 'inference'")
+parser.add_argument('--detail', action='store_true', default=False,
+                    help='Provide train fwd/bwd/opt breakdown detail if True. Defaults to False')
+parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
+                    help='Output csv file for validation results (summary)')
+
+# common inference / train args
+parser.add_argument('--model', '-m', metavar='NAME', default='resnet50',
+                    help='model architecture (default: resnet50)')
+parser.add_argument('-b', '--batch-size', default=256, type=int,
+                    metavar='N', help='mini-batch size (default: 256)')
+parser.add_argument('--img-size', default=None, type=int,
+                    metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--input-size', default=None, nargs=3, type=int,
+                    metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
+parser.add_argument('--num-classes', type=int, default=None,
+                    help='Number classes in dataset')
+parser.add_argument('--gp', default=None, type=str, metavar='POOL',
+                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
+parser.add_argument('--channels-last', action='store_true', default=False,
+                    help='Use channels_last memory layout')
+parser.add_argument('--amp', action='store_true', default=False,
+                    help='Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.')
+parser.add_argument('--apex-amp', action='store_true', default=False,
+                    help='Use NVIDIA Apex AMP mixed precision')
+parser.add_argument('--native-amp', action='store_true', default=False,
+                    help='Use Native Torch AMP mixed precision')
+parser.add_argument('--torchscript', dest='torchscript', action='store_true',
+                    help='convert model torchscript for inference')
+
+
+# train optimizer parameters
+parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
+                    help='Optimizer (default: "sgd"')
+parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
+                    help='Optimizer Epsilon (default: None, use opt default)')
+parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
+                    help='Optimizer Betas (default: None, use opt default)')
+parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
+                    help='Optimizer momentum (default: 0.9)')
+parser.add_argument('--weight-decay', type=float, default=0.0001,
+                    help='weight decay (default: 0.0001)')
+parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
+                    help='Clip gradient norm (default: None, no clipping)')
+parser.add_argument('--clip-mode', type=str, default='norm',
+                    help='Gradient clipping mode. One of ("norm", "value", "agc")')
+
+
+# model regularization / loss params that impact model or loss fn
+parser.add_argument('--smoothing', type=float, default=0.1,
+                    help='Label smoothing (default: 0.1)')
+parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
+                    help='Dropout rate (default: 0.)')
+parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
+                    help='Drop path rate (default: None)')
+parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
+                    help='Drop block rate (default: None)')
+
+
+def timestamp(sync=False):
+    return time.perf_counter()
+
+
+def cuda_timestamp(sync=False, device=None):
+    if sync:
+        torch.cuda.synchronize(device=device)
+    return time.perf_counter()
+
+
+def count_params(model):
+    return sum([m.numel() for m in model.parameters()])
+
+
+class BenchmarkRunner:
+    def __init__(self, model_name, detail=False, device='cuda', torchscript=False, **kwargs):
+        self.model_name = model_name
+        self.detail = detail
+        self.device = device
+        self.model = create_model(
+            model_name,
+            num_classes=kwargs.pop('num_classes', None),
+            in_chans=3,
+            global_pool=kwargs.pop('gp', 'fast'),
+            scriptable=torchscript).to(device=self.device)
+        self.num_classes = self.model.num_classes
+        self.param_count = count_params(self.model)
+        _logger.info('Model %s created, param count: %d' % (model_name, self.param_count))
+
+        self.channels_last = kwargs.pop('channels_last', False)
+        self.use_amp = kwargs.pop('use_amp', '')
+        self.amp_autocast = torch.cuda.amp.autocast if self.use_amp == 'native' else suppress
+        if torchscript:
+            self.model = torch.jit.script(self.model)
+
+        data_config = resolve_data_config(kwargs, model=self.model, use_test_size=True)
+        self.input_size = data_config['input_size']
+        self.batch_size = kwargs.pop('batch_size', 256)
+
+        self.example_inputs = None
+        self.num_warm_iter = 10
+        self.num_bench_iter = 50
+        self.log_freq = 10
+        if 'cuda' in self.device:
+            self.time_fn = partial(cuda_timestamp, device=self.device)
+        else:
+            self.time_fn = timestamp
+
+    def _init_input(self):
+        self.example_inputs = torch.randn((self.batch_size,) + self.input_size, device=self.device)
+        if self.channels_last:
+            self.example_inputs = self.example_inputs.contiguous(memory_format=torch.channels_last)
+
+
+class InferenceBenchmarkRunner(BenchmarkRunner):
+
+    def __init__(self, model_name, device='cuda', torchscript=False, **kwargs):
+        super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
+        self.model.eval()
+        if self.use_amp == 'apex':
+            self.model = amp.initialize(self.model, opt_level='O1')
+        if self.channels_last:
+            self.model = self.model.to(memory_format=torch.channels_last)
+
+    def run(self):
+        def _step():
+            t_step_start = self.time_fn()
+            with self.amp_autocast():
+                output = self.model(self.example_inputs)
+            t_step_end = self.time_fn(True)
+            return t_step_end - t_step_start
+
+        _logger.info(
+            f'Running inference benchmark on {self.model_name} for {self.num_bench_iter} steps w/ '
+            f'input size {self.input_size} and batch size {self.batch_size}.')
+
+        with torch.no_grad():
+            self._init_input()
+
+            for _ in range(self.num_warm_iter):
+                _step()
+
+            total_step = 0.
+            num_samples = 0
+            t_run_start = self.time_fn()
+            for i in range(self.num_bench_iter):
+                delta_fwd = _step()
+                total_step += delta_fwd
+                num_samples += self.batch_size
+                if (i + 1) % self.log_freq == 0:
+                    _logger.info(
+                        f"Infer [{i + 1}/{self.num_bench_iter}]."
+                        f" {num_samples / total_step:0.2f} samples/sec."
+                        f" {1000 * total_step / num_samples:0.3f} ms/sample.")
+            t_run_end = self.time_fn(True)
+            t_run_elapsed = t_run_end - t_run_start
+
+        results = dict(
+            samples_per_sec=round(num_samples / t_run_elapsed, 2),
+            step_time=round(1000 * total_step / num_samples, 3),
+            batch_size=self.batch_size,
+            param_count=round(self.param_count / 1e6, 2),
+        )
+
+        _logger.info(
+            f"Inference benchmark of {self.model_name} done. "
+            f"{results['samples_per_sec']:.2f} samples/sec, {results['step_time']:.2f} ms/sample")
+
+        return results
+
+
+class TrainBenchmarkRunner(BenchmarkRunner):
+
+    def __init__(self, model_name, device='cuda', torchscript=False, **kwargs):
+        super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
+        self.model.train()
+
+        if kwargs.pop('smoothing', 0) > 0:
+            self.loss = nn.CrossEntropyLoss().to(self.device)
+        else:
+            self.loss = nn.CrossEntropyLoss().to(self.device)
+        self.target_shape = tuple()
+
+        self.optimizer = create_optimizer(
+            self.model,
+            opt_name=kwargs.pop('opt', 'sgd'),
+            lr=kwargs.pop('lr', 1e-4))
+
+        if self.use_amp == 'apex':
+            self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level='O1')
+        if self.channels_last:
+            self.model = self.model.to(memory_format=torch.channels_last)
+
+    def _gen_target(self, batch_size):
+        return torch.empty(
+            (batch_size,) + self.target_shape, device=self.device, dtype=torch.long).random_(self.num_classes)
+
+    def run(self):
+        def _step(detail=False):
+            self.optimizer.zero_grad()  # can this be ignored?
+            t_start = self.time_fn()
+            t_fwd_end = t_start
+            t_bwd_end = t_start
+            with self.amp_autocast():
+                output = self.model(self.example_inputs)
+                if isinstance(output, tuple):
+                    output = output[0]
+                if detail:
+                    t_fwd_end = self.time_fn(True)
+                target = self._gen_target(output.shape[0])
+                self.loss(output, target).backward()
+                if detail:
+                    t_bwd_end = self.time_fn(True)
+            self.optimizer.step()
+            t_end = self.time_fn(True)
+            if detail:
+                delta_fwd = t_fwd_end - t_start
+                delta_bwd = t_bwd_end - t_fwd_end
+                delta_opt = t_end - t_bwd_end
+                return delta_fwd, delta_bwd, delta_opt
+            else:
+                delta_step = t_end - t_start
+                return delta_step
+
+        _logger.info(
+            f'Running train benchmark on {self.model_name} for {self.num_bench_iter} steps w/ '
+            f'input size {self.input_size} and batch size {self.batch_size}.')
+
+        self._init_input()
+
+        for _ in range(self.num_warm_iter):
+            _step()
+
+        t_run_start = self.time_fn()
+        if self.detail:
+            total_fwd = 0.
+            total_bwd = 0.
+            total_opt = 0.
+            num_samples = 0
+            for i in range(self.num_bench_iter):
+                delta_fwd, delta_bwd, delta_opt = _step(True)
+                num_samples += self.batch_size
+                total_fwd += delta_fwd
+                total_bwd += delta_bwd
+                total_opt += delta_opt
+                if (i + 1) % self.log_freq == 0:
+                    total_step = total_fwd + total_bwd + total_opt
+                    _logger.info(
+                        f"Train [{i + 1}/{self.num_bench_iter}]."
+                        f" {num_samples / total_step:0.2f} samples/sec."
+                        f" {1000 * total_fwd / num_samples:0.3f} ms/sample fwd,"
+                        f" {1000 * total_bwd / num_samples:0.3f} ms/sample bwd,"
+                        f" {1000 * total_opt / num_samples:0.3f} ms/sample opt."
+                    )
+            total_step = total_fwd + total_bwd + total_opt
+            t_run_elapsed = self.time_fn() - t_run_start
+            results = dict(
+                samples_per_sec=round(num_samples / t_run_elapsed, 2),
+                step_time=round(1000 * total_step / num_samples, 3),
+                fwd_time=round(1000 * total_fwd / num_samples, 3),
+                bwd_time=round(1000 * total_bwd / num_samples, 3),
+                opt_time=round(1000 * total_opt / num_samples, 3),
+                batch_size=self.batch_size,
+                param_count=round(self.param_count / 1e6, 2),
+            )
+        else:
+            total_step = 0.
+            num_samples = 0
+            for i in range(self.num_bench_iter):
+                delta_step = _step(False)
+                num_samples += self.batch_size
+                total_step += delta_step
+                if (i + 1) % self.log_freq == 0:
+                    _logger.info(
+                        f"Train [{i + 1}/{self.num_bench_iter}]."
+                        f" {num_samples / total_step:0.2f} samples/sec."
+                        f" {1000 * total_step / num_samples:0.3f} ms/sample.")
+            t_run_elapsed = self.time_fn() - t_run_start
+            results = dict(
+                samples_per_sec=round(num_samples / t_run_elapsed, 2),
+                step_time=round(1000 * total_step / num_samples, 3),
+                batch_size=self.batch_size,
+                param_count=round(self.param_count / 1e6, 2),
+            )
+
+        _logger.info(
+            f"Train benchmark of {self.model_name} done. "
+            f"{results['samples_per_sec']:.2f} samples/sec, {results['step_time']:.2f} ms/sample")
+
+        return results
+
+
+def decay_batch_exp(batch_size, factor=0.5, divisor=16):
+    out_batch_size = batch_size * factor
+    if out_batch_size > divisor:
+        out_batch_size = (out_batch_size + 1) // divisor * divisor
+    else:
+        out_batch_size = batch_size - 1
+    return max(0, int(out_batch_size))
+
+
+def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
+    batch_size = initial_batch_size
+    results = dict()
+    while batch_size >= 1:
+        try:
+            bench = bench_fn(model_name=model_name, batch_size=batch_size, **bench_kwargs)
+            results = bench.run()
+            return results
+        except RuntimeError as e:
+            torch.cuda.empty_cache()
+            batch_size = decay_batch_exp(batch_size)
+            print(f'Reducing batch size to {batch_size}')
+    return results
+
+
+def benchmark(args):
+    if args.amp:
+        if has_native_amp:
+            args.native_amp = True
+        elif has_apex:
+            args.apex_amp = True
+        else:
+            _logger.warning("Neither APEX or Native Torch AMP is available.")
+    if args.native_amp:
+        args.use_amp = 'native'
+        _logger.info('Benchmarking in mixed precision with native PyTorch AMP.')
+    elif args.apex_amp:
+        args.use_amp = 'apex'
+        _logger.info('Benchmarking in mixed precision with NVIDIA APEX AMP.')
+    else:
+        args.use_amp = ''
+        _logger.info('Benchmarking in float32. AMP not enabled.')
+
+    bench_kwargs = vars(args).copy()
+    model = bench_kwargs.pop('model')
+    batch_size = bench_kwargs.pop('batch_size')
+
+    bench_fns = (InferenceBenchmarkRunner,)
+    prefixes = ('infer',)
+    if args.bench == 'both':
+        bench_fns = (
+            InferenceBenchmarkRunner,
+            TrainBenchmarkRunner
+        )
+        prefixes = ('infer', 'train')
+    elif args.bench == 'train':
+        bench_fns = TrainBenchmarkRunner,
+        prefixes = 'train',
+
+    model_results = OrderedDict(model=model)
+    for prefix, bench_fn in zip(prefixes, bench_fns):
+        run_results = _try_run(model, bench_fn, initial_batch_size=batch_size, bench_kwargs=bench_kwargs)
+        if prefix:
+            run_results = {'_'.join([prefix, k]): v for k, v in run_results.items()}
+        model_results.update(run_results)
+    param_count = model_results.pop('infer_param_count', model_results.pop('train_param_count', 0))
+    model_results.setdefault('param_count', param_count)
+    model_results.pop('train_param_count', 0)
+    return model_results
+
+
+def main():
+    setup_default_logging()
+    args = parser.parse_args()
+    model_cfgs = []
+    model_names = []
+
+    if args.model == 'all':
+        # validate all models in a list of names with pretrained checkpoints
+        args.pretrained = True
+        model_names = list_models(pretrained=True, exclude_filters=['*in21k'])
+        model_cfgs = [(n, None) for n in model_names]
+    elif not is_model(args.model):
+        # model name doesn't exist, try as wildcard filter
+        model_names = list_models(args.model)
+        model_cfgs = [(n, None) for n in model_names]
+
+    if len(model_cfgs):
+        results_file = args.results_file or './benchmark.csv'
+        _logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
+        results = []
+        try:
+            for m, _ in model_cfgs:
+                args.model = m
+                r = benchmark(args)
+                results.append(r)
+        except KeyboardInterrupt as e:
+            pass
+        sort_key = 'train_samples_per_sec' if 'train' in args.bench else 'infer_samples_per_sec'
+        results = sorted(results, key=lambda x: x[sort_key], reverse=True)
+        if len(results):
+            write_results(results_file, results)
+
+        import json
+        json_str = json.dumps(results, indent=4)
+        print(json_str)
+    else:
+        benchmark(args)
+
+
+def write_results(results_file, results):
+    with open(results_file, mode='w') as cf:
+        dw = csv.DictWriter(cf, fieldnames=results[0].keys())
+        dw.writeheader()
+        for r in results:
+            dw.writerow(r)
+        cf.flush()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/timm/optim/__init__.py b/timm/optim/__init__.py
index 33e4907f..8bb21abb 100644
--- a/timm/optim/__init__.py
+++ b/timm/optim/__init__.py
@@ -10,4 +10,4 @@ from .radam import RAdam
 from .rmsprop_tf import RMSpropTF
 from .sgdp import SGDP
 
-from .optim_factory import create_optimizer
\ No newline at end of file
+from .optim_factory import create_optimizer, optimizer_kwargs
\ No newline at end of file
diff --git a/timm/optim/optim_factory.py b/timm/optim/optim_factory.py
index 4c0aaca0..c3abdb76 100644
--- a/timm/optim/optim_factory.py
+++ b/timm/optim/optim_factory.py
@@ -1,8 +1,11 @@
 """ Optimizer Factory w/ Custom Weight Decay
 Hacked together by / Copyright 2020 Ross Wightman
 """
+from typing import Optional
+
 import torch
-from torch import optim as optim
+import torch.nn as nn
+import torch.optim as optim
 
 from .adafactor import Adafactor
 from .adahessian import Adahessian
@@ -37,9 +40,49 @@ def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
         {'params': decay, 'weight_decay': weight_decay}]
 
 
-def create_optimizer(args, model, filter_bias_and_bn=True):
-    opt_lower = args.opt.lower()
-    weight_decay = args.weight_decay
+def optimizer_kwargs(cfg):
+    """ cfg/argparse to kwargs helper
+    Convert optimizer args in argparse args or cfg like object to keyword args for updated create fn.
+    """
+    kwargs = dict(opt_name=cfg.opt, lr=cfg.lr, weight_decay=cfg.weight_decay)
+    if getattr(cfg, 'opt_eps', None) is not None:
+        kwargs['eps'] = cfg.opt_eps
+    if getattr(cfg, 'opt_betas', None) is not None:
+        kwargs['betas'] = cfg.opt_betas
+    if getattr(cfg, 'opt_args', None) is not None:
+        kwargs.update(cfg.opt_args)
+    kwargs['momentum'] = cfg.momentum
+    return kwargs
+
+
+def create_optimizer(
+        model: nn.Module,
+        opt_name: str = 'sgd',
+        lr: Optional[float] = None,
+        weight_decay: float = 0.,
+        momentum: float = 0.9,
+        filter_bias_and_bn: bool = True,
+        **kwargs):
+    """ Create an optimizer.
+
+    TODO currently the model is passed in and all parameters are selected for optimization.
+    For more general use an interface that allows selection of parameters to optimize and lr groups, one of:
+      * a filter fn interface that further breaks params into groups in a weight_decay compatible fashion
+      * expose the parameters interface and leave it up to caller
+
+    Args:
+        model (nn.Module): model containing parameters to optimize
+        opt_name: name of optimizer to create
+        lr: initial learning rate
+        weight_decay: weight decay to apply in optimizer
+        momentum:  momentum for momentum based optimizers (others may use betas via kwargs)
+        filter_bias_and_bn:  filter out bias, bn and other 1d params from weight decay
+        **kwargs: extra optimizer specific kwargs to pass through
+
+    Returns:
+        Optimizer
+    """
+    opt_lower = opt_name.lower()
     if weight_decay and filter_bias_and_bn:
         skip = {}
         if hasattr(model, 'no_weight_decay'):
@@ -48,26 +91,18 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
         weight_decay = 0.
     else:
         parameters = model.parameters()
-
     if 'fused' in opt_lower:
         assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
 
-    opt_args = dict(lr=args.lr, weight_decay=weight_decay)
-    if hasattr(args, 'opt_eps') and args.opt_eps is not None:
-        opt_args['eps'] = args.opt_eps
-    if hasattr(args, 'opt_betas') and args.opt_betas is not None:
-        opt_args['betas'] = args.opt_betas
-    if hasattr(args, 'opt_args') and args.opt_args is not None:
-        opt_args.update(args.opt_args)
-
+    opt_args = dict(lr=lr, weight_decay=weight_decay, **kwargs)
     opt_split = opt_lower.split('_')
     opt_lower = opt_split[-1]
     if opt_lower == 'sgd' or opt_lower == 'nesterov':
         opt_args.pop('eps', None)
-        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
+        optimizer = optim.SGD(parameters, momentum=momentum, nesterov=True, **opt_args)
     elif opt_lower == 'momentum':
         opt_args.pop('eps', None)
-        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
+        optimizer = optim.SGD(parameters, momentum=momentum, nesterov=False, **opt_args)
     elif opt_lower == 'adam':
         optimizer = optim.Adam(parameters, **opt_args)
     elif opt_lower == 'adamw':
@@ -78,30 +113,30 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
         optimizer = RAdam(parameters, **opt_args)
     elif opt_lower == 'adamp':        
         optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args)
-    elif opt_lower == 'sgdp':        
-        optimizer = SGDP(parameters, momentum=args.momentum, nesterov=True, **opt_args)
+    elif opt_lower == 'sgdp':
+        optimizer = SGDP(parameters, momentum=momentum, nesterov=True, **opt_args)
     elif opt_lower == 'adadelta':
         optimizer = optim.Adadelta(parameters, **opt_args)
     elif opt_lower == 'adafactor':
-        if not args.lr:
+        if not lr:
             opt_args['lr'] = None
         optimizer = Adafactor(parameters, **opt_args)
     elif opt_lower == 'adahessian':
         optimizer = Adahessian(parameters, **opt_args)
     elif opt_lower == 'rmsprop':
-        optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
+        optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=momentum, **opt_args)
     elif opt_lower == 'rmsproptf':
-        optimizer = RMSpropTF(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
+        optimizer = RMSpropTF(parameters, alpha=0.9, momentum=momentum, **opt_args)
     elif opt_lower == 'novograd':
         optimizer = NovoGrad(parameters, **opt_args)
     elif opt_lower == 'nvnovograd':
         optimizer = NvNovoGrad(parameters, **opt_args)
     elif opt_lower == 'fusedsgd':
         opt_args.pop('eps', None)
-        optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
+        optimizer = FusedSGD(parameters, momentum=momentum, nesterov=True, **opt_args)
     elif opt_lower == 'fusedmomentum':
         opt_args.pop('eps', None)
-        optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
+        optimizer = FusedSGD(parameters, momentum=momentum, nesterov=False, **opt_args)
     elif opt_lower == 'fusedadam':
         optimizer = FusedAdam(parameters, adam_w_mode=False, **opt_args)
     elif opt_lower == 'fusedadamw':
diff --git a/train.py b/train.py
index 9abcfed3..9db5175b 100755
--- a/train.py
+++ b/train.py
@@ -32,7 +32,7 @@ from timm.data import create_dataset, create_loader, resolve_data_config, Mixup,
 from timm.models import create_model, resume_checkpoint, load_checkpoint, convert_splitbn_model, model_parameters
 from timm.utils import *
 from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
-from timm.optim import create_optimizer
+from timm.optim import create_optimizer, optimizer_kwargs
 from timm.scheduler import create_scheduler
 from timm.utils import ApexScaler, NativeScaler
 
@@ -384,7 +384,7 @@ def main():
         assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
         model = torch.jit.script(model)
 
-    optimizer = create_optimizer(args, model)
+    optimizer = create_optimizer(model, **optimizer_kwargs(cfg=args))
 
     # setup automatic mixed-precision (AMP) loss scaling and op casting
     amp_autocast = suppress  # do nothing

From f0ffdf89b308b192be6390657110e44cf9cecf26 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Feb 2021 15:54:55 -0800
Subject: [PATCH 03/13] Add numerous experimental ViT Hybrid models w/ ResNetV2
 base. Update the ViT naming for hybrids. Fix #426 for pretrained vit
 resizing.

---
 timm/models/resnetv2.py           |   6 +-
 timm/models/vision_transformer.py | 276 ++++++++++++++++++++++++++----
 2 files changed, 247 insertions(+), 35 deletions(-)

diff --git a/timm/models/resnetv2.py b/timm/models/resnetv2.py
index 73c2e42c..2df02f49 100644
--- a/timm/models/resnetv2.py
+++ b/timm/models/resnetv2.py
@@ -274,7 +274,9 @@ class ResNetStage(nn.Module):
         return x
 
 
-def create_stem(in_chs, out_chs, stem_type='', preact=True, conv_layer=None, norm_layer=None):
+def create_resnetv2_stem(
+        in_chs, out_chs=64, stem_type='', preact=True,
+        conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32)):
     stem = OrderedDict()
     assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same')
 
@@ -322,7 +324,7 @@ class ResNetV2(nn.Module):
 
         self.feature_info = []
         stem_chs = make_div(stem_chs * wf)
-        self.stem = create_stem(in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
+        self.stem = create_resnetv2_stem(in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
         # NOTE no, reduction 2 feature if preact
         self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module='' if preact else 'stem.norm'))
 
diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index acd4d18d..02c32cb7 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -28,9 +28,9 @@ import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import load_pretrained
-from .layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_
+from .layers import StdConv2dSame, StdConv2d, DropPath, to_2tuple, trunc_normal_
 from .resnet import resnet26d, resnet50d
-from .resnetv2 import ResNetV2
+from .resnetv2 import ResNetV2, create_resnetv2_stem
 from .registry import register_model
 
 _logger = logging.getLogger(__name__)
@@ -97,17 +97,62 @@ default_cfgs = {
         url='',  # FIXME I have weights for this but > 2GB limit for github release binaries
         num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
 
-    # hybrid models (weights ported from official Google JAX impl)
-    'vit_base_resnet50_224_in21k': _cfg(
+    # hybrid in-21k models (weights ported from official Google JAX impl where they exist)
+    'vit_base_r50_s16_224_in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
         num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'),
-    'vit_base_resnet50_384': _cfg(
+
+    # hybrid in-1k models (weights ported from official Google JAX impl where they exist)
+    'vit_small_r_s16_p8_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r20_s16_p2_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r20_s16_p2_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r20_s16_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r20_s16_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r26_s32_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r26_s32_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r20_s16_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r20_s16_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r26_s32_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r26_s32_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r50_s16_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r50_s16_384': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
-        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'),
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_large_r50_s32_224': _cfg(
+        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_large_r50_s32_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
 
     # hybrid models (my experiments)
     'vit_small_resnet26d_224': _cfg(),
-    'vit_small_resnet50d_s3_224': _cfg(),
+    'vit_small_resnet50d_s16_224': _cfg(),
     'vit_base_resnet26d_224': _cfg(),
     'vit_base_resnet50d_224': _cfg(),
 
@@ -227,11 +272,13 @@ class HybridEmbed(nn.Module):
     """ CNN Feature Map Embedding
     Extract feature map from CNN, flatten, project to embedding dim.
     """
-    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
+    def __init__(self, backbone, img_size=224, patch_size=1, feature_size=None, in_chans=3, embed_dim=768):
         super().__init__()
         assert isinstance(backbone, nn.Module)
         img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
         self.img_size = img_size
+        self.patch_size = patch_size
         self.backbone = backbone
         if feature_size is None:
             with torch.no_grad():
@@ -253,8 +300,9 @@ class HybridEmbed(nn.Module):
                 feature_dim = self.backbone.feature_info.channels()[-1]
             else:
                 feature_dim = self.backbone.num_features
-        self.num_patches = feature_size[0] * feature_size[1]
-        self.proj = nn.Conv2d(feature_dim, embed_dim, 1)
+        assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
+        self.num_patches = feature_size[0] // patch_size[0] * feature_size[1] // patch_size[1]
+        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size)
 
     def forward(self, x):
         x = self.backbone(x)
@@ -270,9 +318,10 @@ class VisionTransformer(nn.Module):
     A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
         https://arxiv.org/abs/2010.11929
     """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+    def __init__(self, img_size=224, patch_size=None, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                  num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None):
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None,
+                 act_layer=None):
         """
         Args:
             img_size (int, tuple): input image size
@@ -296,10 +345,12 @@ class VisionTransformer(nn.Module):
         self.num_classes = num_classes
         self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
         norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        act_layer = act_layer or nn.GELU
+        patch_size = patch_size or 1 if hybrid_backbone is not None else 16
 
         if hybrid_backbone is not None:
             self.patch_embed = HybridEmbed(
-                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
+                hybrid_backbone, img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
         else:
             self.patch_embed = PatchEmbed(
                 img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
@@ -313,7 +364,7 @@ class VisionTransformer(nn.Module):
         self.blocks = nn.ModuleList([
             Block(
                 dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
             for i in range(depth)])
         self.norm = norm_layer(embed_dim)
 
@@ -423,13 +474,15 @@ class DistilledVisionTransformer(VisionTransformer):
             return (x + x_dist) / 2
 
 
-def resize_pos_embed(posemb, posemb_new):
+def resize_pos_embed(posemb, posemb_new, token='class'):
     # Rescale the grid of position embeddings when loading from state_dict. Adapted from
     # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
     _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
     ntok_new = posemb_new.shape[1]
-    if True:
-        posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
+    if token:
+        assert token in ('class', 'distill')
+        token_idx = 2 if token == 'distill' else 1
+        posemb_tok, posemb_grid = posemb[:, :token_idx], posemb[0, token_idx:]
         ntok_new -= 1
     else:
         posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
@@ -633,33 +686,190 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
     return model
 
 
+def _resnetv2(layers=(3, 4, 9), **kwargs):
+    """ ResNet-V2 backbone helper"""
+    padding_same = kwargs.get('padding_same', True)
+    if padding_same:
+        stem_type = 'same'
+        conv_layer = StdConv2dSame
+    else:
+        stem_type = ''
+        conv_layer = StdConv2d
+    if len(layers):
+        backbone = ResNetV2(
+            layers=layers, num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
+            preact=False, stem_type=stem_type, conv_layer=conv_layer)
+    else:
+        backbone = create_resnetv2_stem(
+            kwargs.get('in_chans', 3), stem_type=stem_type, preact=False, conv_layer=conv_layer)
+    return backbone
+
+
 @register_model
-def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
+def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
     """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
     """
-    # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
-    backbone = ResNetV2(
-        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
-        preact=False, stem_type='same', conv_layer=StdConv2dSame)
+    backbone = _resnetv2(layers=(3, 4, 9), **kwargs)
+    model_kwargs = dict(
+        embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, representation_size=768, **kwargs)
+    model = _create_vision_transformer('vit_base_r50_s16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
+    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=3, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_p2_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r_s16_p8_224(pretrained=False, **kwargs):
+    """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r_s16_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r20_s16_p2_224(pretrained=False, **kwargs):
+    """ R52+ViT-S/S16 w/ 2x2 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2((2, 4), **kwargs)
     model_kwargs = dict(
-        embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone,
-        representation_size=768, **kwargs)
-    model = _create_vision_transformer('vit_base_resnet50_224_in21k', pretrained=pretrained, **model_kwargs)
+        patch_size=2, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_p2_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r20_s16_p2_384(pretrained=False, **kwargs):
+    """ R20+ViT-S/S16 w/ 2x2 Patch hybrid @ 384x384.
+    """
+    backbone = _resnetv2((2, 4), **kwargs)
+    model_kwargs = dict(
+        embed_dim=384, patch_size=2, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_p2_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r20_s16_224(pretrained=False, **kwargs):
+    """ R20+ViT-S/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_base_resnet50_384(pretrained=False, **kwargs):
+def vit_small_r20_s16_384(pretrained=False, **kwargs):
+    """ R20+ViT-S/S16 hybrid @ 384x384.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r26_s32_224(pretrained=False, **kwargs):
+    """ R26+ViT-S/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r26_s32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r26_s32_384(pretrained=False, **kwargs):
+    """ R26+ViT-S/S32 hybrid @ 384x384.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r26_s32_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r20_s16_224(pretrained=False, **kwargs):
+    """ R20+ViT-B/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(
+        embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, act_layer=nn.SiLU, **kwargs)
+    model = _create_vision_transformer('vit_base_r20_s16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r20_s16_384(pretrained=False, **kwargs):
+    """ R20+ViT-B/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r20_s16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r26_s32_224(pretrained=False, **kwargs):
+    """ R26+ViT-B/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r26_s32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r50_s16_224(pretrained=False, **kwargs):
+    """ R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
+    """
+    backbone = _resnetv2((3, 4, 9), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r50_s16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r50_s16_384(pretrained=False, **kwargs):
     """ R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
     """
-    # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
-    backbone = ResNetV2(
-        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
-        preact=False, stem_type='same', conv_layer=StdConv2dSame)
+    backbone = _resnetv2((3, 4, 9), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r50_s16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_large_r50_s32_224(pretrained=False, **kwargs):
+    """ R50+ViT-L/S32 hybrid.
+    """
+    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_large_r50_s32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_large_r50_s32_384(pretrained=False, **kwargs):
+    """ R50+ViT-L/S32 hybrid.
+    """
+    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
     model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_base_resnet50_384', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_large_r50_s32_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
@@ -674,12 +884,12 @@ def vit_small_resnet26d_224(pretrained=False, **kwargs):
 
 
 @register_model
-def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
+def vit_small_resnet50d_s16_224(pretrained=False, **kwargs):
     """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
     """
     backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
     model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_small_resnet50d_s3_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_small_resnet50d_s16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 

From de97be9146435c896a62e017700a124183e9db0c Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Feb 2021 16:22:55 -0800
Subject: [PATCH 04/13] Spell out diff between my small and deit small vit
 models.

---
 timm/models/vision_transformer.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index 02c32cb7..f6a09ac2 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -541,7 +541,11 @@ def _create_vision_transformer(variant, pretrained=False, distilled=False, **kwa
 
 @register_model
 def vit_small_patch16_224(pretrained=False, **kwargs):
-    """ My custom 'small' ViT model. Depth=8, heads=8= mlp_ratio=3."""
+    """ My custom 'small' ViT model. embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.
+    NOTE:
+        * this differs from the DeiT based 'small' definitions with embed_dim=384, depth=12, num_heads=6
+        * this model does not have a bias for QKV (unlike the official ViT and DeiT models)
+    """
     model_kwargs = dict(
         patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,
         qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs)
@@ -994,4 +998,4 @@ def vit_deit_base_distilled_patch16_384(pretrained=False, **kwargs):
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
     model = _create_vision_transformer(
         'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
-    return model
\ No newline at end of file
+    return model

From 2db2d87ff7e78f8326d37cace94315167d409c29 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Tue, 23 Feb 2021 17:31:42 -0800
Subject: [PATCH 05/13] Add epoch-repeats arg to multiply the number of dataset
 passes per epoch. Currently for iterable datasets (read TFDS wrapper) only.

---
 timm/data/config.py              | 2 +-
 timm/data/dataset.py             | 3 ++-
 timm/data/dataset_factory.py     | 1 +
 timm/data/parsers/parser_tfds.py | 9 +++++----
 train.py                         | 6 +++++-
 validate.py                      | 2 +-
 6 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/timm/data/config.py b/timm/data/config.py
index dad8eb13..38f5689a 100644
--- a/timm/data/config.py
+++ b/timm/data/config.py
@@ -5,7 +5,7 @@ from .constants import *
 _logger = logging.getLogger(__name__)
 
 
-def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=True):
+def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=False):
     new_config = {}
     default_cfg = default_cfg
     if not default_cfg and model is not None and hasattr(model, 'default_cfg'):
diff --git a/timm/data/dataset.py b/timm/data/dataset.py
index a7c5ebed..e719f3f6 100644
--- a/timm/data/dataset.py
+++ b/timm/data/dataset.py
@@ -73,12 +73,13 @@ class IterableImageDataset(data.IterableDataset):
             batch_size=None,
             class_map='',
             load_bytes=False,
+            repeats=0,
             transform=None,
     ):
         assert parser is not None
         if isinstance(parser, str):
             self.parser = create_parser(
-                parser, root=root, split=split, is_training=is_training, batch_size=batch_size)
+                parser, root=root, split=split, is_training=is_training, batch_size=batch_size, repeats=repeats)
         else:
             self.parser = parser
         self.transform = transform
diff --git a/timm/data/dataset_factory.py b/timm/data/dataset_factory.py
index b2c9688f..ccc99d5c 100644
--- a/timm/data/dataset_factory.py
+++ b/timm/data/dataset_factory.py
@@ -23,6 +23,7 @@ def create_dataset(name, root, split='validation', search_split=True, is_trainin
             root, parser=name, split=split, is_training=is_training, batch_size=batch_size, **kwargs)
     else:
         # FIXME support more advance split cfg for ImageFolder/Tar datasets in the future
+        kwargs.pop('repeats', 0)  # FIXME currently only Iterable dataset support the repeat multiplier
         if search_split and os.path.isdir(root):
             root = _search_split(root, split)
         ds = ImageDataset(root, parser=name, **kwargs)
diff --git a/timm/data/parsers/parser_tfds.py b/timm/data/parsers/parser_tfds.py
index 15361cb5..0c2e10c0 100644
--- a/timm/data/parsers/parser_tfds.py
+++ b/timm/data/parsers/parser_tfds.py
@@ -52,7 +52,7 @@ class ParserTfds(Parser):
         components.
 
     """
-    def __init__(self, root, name, split='train', shuffle=False, is_training=False, batch_size=None):
+    def __init__(self, root, name, split='train', shuffle=False, is_training=False, batch_size=None, repeats=0):
         super().__init__()
         self.root = root
         self.split = split
@@ -62,6 +62,7 @@ class ParserTfds(Parser):
             assert batch_size is not None,\
                 "Must specify batch_size in training mode for reasonable behaviour w/ TFDS wrapper"
         self.batch_size = batch_size
+        self.repeats = repeats
 
         self.builder = tfds.builder(name, data_dir=root)
         # NOTE: please use tfds command line app to download & prepare datasets, I don't want to call
@@ -126,7 +127,7 @@ class ParserTfds(Parser):
         # avoid overloading threading w/ combo fo TF ds threads + PyTorch workers
         ds.options().experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
         ds.options().experimental_threading.max_intra_op_parallelism = 1
-        if self.is_training:
+        if self.is_training or self.repeats > 1:
             # to prevent excessive drop_last batch behaviour w/ IterableDatasets
             # see warnings at https://pytorch.org/docs/stable/data.html#multi-process-data-loading
             ds = ds.repeat()  # allow wrap around and break iteration manually
@@ -143,7 +144,7 @@ class ParserTfds(Parser):
         #     This adds extra samples and will slightly alter validation results.
         #   2. determine loop ending condition in training w/ repeat enabled so that only full batch_size
         #     batches are produced (underlying tfds iter wraps around)
-        target_sample_count = math.ceil(self.num_samples / self._num_pipelines)
+        target_sample_count = math.ceil(max(1, self.repeats) * self.num_samples / self._num_pipelines)
         if self.is_training:
             # round up to nearest batch_size per worker-replica
             target_sample_count = math.ceil(target_sample_count / self.batch_size) * self.batch_size
@@ -176,7 +177,7 @@ class ParserTfds(Parser):
     def __len__(self):
         # this is just an estimate and does not factor in extra samples added to pad batches based on
         # complete worker & replica info (not available until init in dataloader).
-        return math.ceil(self.num_samples / self.dist_num_replicas)
+        return math.ceil(max(1, self.repeats) * self.num_samples / self.dist_num_replicas)
 
     def _filename(self, index, basename=False, absolute=False):
         assert False, "Not supported" # no random access to samples
diff --git a/train.py b/train.py
index 9db5175b..2fdf68d8 100755
--- a/train.py
+++ b/train.py
@@ -141,6 +141,8 @@ parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                     help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
 parser.add_argument('--epochs', type=int, default=200, metavar='N',
                     help='number of epochs to train (default: 2)')
+parser.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
+                    help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
 parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                     help='manual epoch number (useful on restarts)')
 parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
@@ -450,7 +452,9 @@ def main():
 
     # create the train and eval datasets
     dataset_train = create_dataset(
-        args.dataset, root=args.data_dir, split=args.train_split, is_training=True, batch_size=args.batch_size)
+        args.dataset,
+        root=args.data_dir, split=args.train_split, is_training=True,
+        batch_size=args.batch_size, repeats=args.epoch_repeats)
     dataset_eval = create_dataset(
         args.dataset, root=args.data_dir, split=args.val_split, is_training=False, batch_size=args.batch_size)
 
diff --git a/validate.py b/validate.py
index a311112d..6df71aab 100755
--- a/validate.py
+++ b/validate.py
@@ -152,7 +152,7 @@ def validate(args):
     param_count = sum([m.numel() for m in model.parameters()])
     _logger.info('Model %s created, param count: %d' % (args.model, param_count))
 
-    data_config = resolve_data_config(vars(args), model=model, use_test_size=True)
+    data_config = resolve_data_config(vars(args), model=model, use_test_size=True, verbose=True)
     test_time_pool = False
     if not args.no_test_pool:
         model, test_time_pool = apply_test_time_pool(model, data_config, use_test_size=True)

From 0706d05d525e67753137a38057b513d793701fe0 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Sun, 28 Feb 2021 16:00:33 -0800
Subject: [PATCH 06/13] Benchmark models listed in txt file. Add more hybrid
 vit variants for testing

---
 benchmark.py                      |  13 +-
 timm/models/vision_transformer.py | 261 ++++++++++++++++++++++++++++--
 2 files changed, 261 insertions(+), 13 deletions(-)

diff --git a/benchmark.py b/benchmark.py
index c745e278..faac1fc5 100755
--- a/benchmark.py
+++ b/benchmark.py
@@ -45,6 +45,8 @@ _logger = logging.getLogger('validate')
 parser = argparse.ArgumentParser(description='PyTorch Benchmark')
 
 # benchmark specific args
+parser.add_argument('--model-list', metavar='NAME', default='',
+                    help='txt file based list of model names to benchmark')
 parser.add_argument('--bench', default='both', type=str,
                     help="Benchmark mode. One of 'inference', 'train', 'both'. Defaults to 'inference'")
 parser.add_argument('--detail', action='store_true', default=False,
@@ -357,7 +359,7 @@ def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
         except RuntimeError as e:
             torch.cuda.empty_cache()
             batch_size = decay_batch_exp(batch_size)
-            print(f'Reducing batch size to {batch_size}')
+            print(f'Error: {str(e)} while running benchmark. Reducing batch size to {batch_size} for retry.')
     return results
 
 
@@ -413,7 +415,12 @@ def main():
     model_cfgs = []
     model_names = []
 
-    if args.model == 'all':
+    if args.model_list:
+        args.model = ''
+        with open(args.model_list) as f:
+            model_names = [line.rstrip() for line in f]
+        model_cfgs = [(n, None) for n in model_names]
+    elif args.model == 'all':
         # validate all models in a list of names with pretrained checkpoints
         args.pretrained = True
         model_names = list_models(pretrained=True, exclude_filters=['*in21k'])
@@ -429,6 +436,8 @@ def main():
         results = []
         try:
             for m, _ in model_cfgs:
+                if not m:
+                    continue
                 args.model = m
                 r = benchmark(args)
                 results.append(r)
diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index f6a09ac2..f834d8e1 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -103,48 +103,90 @@ default_cfgs = {
         num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'),
 
     # hybrid in-1k models (weights ported from official Google JAX impl where they exist)
+    'vit_tiny_r_s16_p8_224': _cfg(
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_tiny_r_s16_p8_224_in21k': _cfg(
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_tiny_r_s16_p8_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+
     'vit_small_r_s16_p8_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r_s16_p8_224_in21k': _cfg(
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r_s16_p8_384': _cfg(
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+
     'vit_small_r20_s16_p2_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r20_s16_p2_224_in21k': _cfg(
+        inum_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
     'vit_small_r20_s16_p2_384': _cfg(
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
+
+
     'vit_small_r20_s16_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r20_s16_224_in21k': _cfg(
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
     'vit_small_r20_s16_384': _cfg(
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
+
     'vit_small_r26_s32_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_small_r26_s32_224_in21k': _cfg(
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
     'vit_small_r26_s32_384': _cfg(
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
+
     'vit_base_r20_s16_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r20_s16_224_in21k': _cfg(
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
     'vit_base_r20_s16_384': _cfg(
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
+
     'vit_base_r26_s32_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_base_r26_s32_224_in21k': _cfg(
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
     'vit_base_r26_s32_384': _cfg(
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
+
     'vit_base_r50_s16_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
     'vit_base_r50_s16_384': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
+
     'vit_large_r50_s32_224': _cfg(
-        input_size=(3, 224, 224), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
+        first_conv='patch_embed.backbone.stem.conv'),
+    'vit_large_r50_s32_224_in21k': _cfg(
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
         first_conv='patch_embed.backbone.stem.conv'),
     'vit_large_r50_s32_384': _cfg(
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0,
@@ -159,8 +201,19 @@ default_cfgs = {
     # deit models (FB weights)
     'vit_deit_tiny_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'),
+    'vit_deit_tiny_patch16_224_in21k': _cfg(num_classes=21843),
+    'vit_deit_tiny_patch16_224_in21k_norep': _cfg(num_classes=21843),
+    'vit_deit_tiny_patch16_384': _cfg(input_size=(3, 384, 384)),
+
     'vit_deit_small_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'),
+    'vit_deit_small_patch16_224_in21k': _cfg(num_classes=21843),
+    'vit_deit_small_patch16_384': _cfg(input_size=(3, 384, 384)),
+
+    'vit_deit_small_patch32_224': _cfg(),
+    'vit_deit_small_patch32_224_in21k': _cfg(num_classes=21843),
+    'vit_deit_small_patch32_384': _cfg(input_size=(3, 384, 384)),
+
     'vit_deit_base_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',),
     'vit_deit_base_patch16_384': _cfg(
@@ -728,7 +781,29 @@ def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
     backbone = _resnetv2(layers=(), **kwargs)
     model_kwargs = dict(
         patch_size=8, embed_dim=192, depth=12, num_heads=3, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_small_r20_s16_p2_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_tiny_r_s16_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_tiny_r_s16_p8_224_in21k(pretrained=False, **kwargs):
+    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=3, representation_size=192, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_tiny_r_s16_p8_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_tiny_r_s16_p8_384(pretrained=False, **kwargs):
+    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=3, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_tiny_r_s16_p8_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
@@ -740,6 +815,29 @@ def vit_small_r_s16_p8_224(pretrained=False, **kwargs):
     model_kwargs = dict(
         patch_size=8, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
     model = _create_vision_transformer('vit_small_r_s16_p8_224', pretrained=pretrained, **model_kwargs)
+
+    return model
+
+
+@register_model
+def vit_small_r_s16_p8_224_in21k(pretrained=False, **kwargs):
+    """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r_s16_p8_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r_s16_p8_384(pretrained=False, **kwargs):
+    """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r_s16_p8_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
@@ -754,6 +852,17 @@ def vit_small_r20_s16_p2_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_small_r20_s16_p2_224_in21k(pretrained=False, **kwargs):
+    """ R52+ViT-S/S16 w/ 2x2 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2((2, 4), **kwargs)
+    model_kwargs = dict(
+        patch_size=2, embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_p2_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_small_r20_s16_p2_384(pretrained=False, **kwargs):
     """ R20+ViT-S/S16 w/ 2x2 Patch hybrid @ 384x384.
@@ -775,6 +884,16 @@ def vit_small_r20_s16_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_small_r20_s16_224_in21k(pretrained=False, **kwargs):
+    """ R20+ViT-S/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_small_r20_s16_384(pretrained=False, **kwargs):
     """ R20+ViT-S/S16 hybrid @ 384x384.
@@ -795,6 +914,17 @@ def vit_small_r26_s32_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_small_r26_s32_224_in21k(pretrained=False, **kwargs):
+    """ R26+ViT-S/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(
+        embed_dim=384, depth=12, num_heads=6, representation_size=384, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r26_s32_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_small_r26_s32_384(pretrained=False, **kwargs):
     """ R26+ViT-S/S32 hybrid @ 384x384.
@@ -810,12 +940,22 @@ def vit_base_r20_s16_224(pretrained=False, **kwargs):
     """ R20+ViT-B/S16 hybrid.
     """
     backbone = _resnetv2((2, 2, 2), **kwargs)
-    model_kwargs = dict(
-        embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, act_layer=nn.SiLU, **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
     model = _create_vision_transformer('vit_base_r20_s16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
+@register_model
+def vit_base_r20_s16_224_in21k(pretrained=False, **kwargs):
+    """ R20+ViT-B/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(
+        embed_dim=768, depth=12, num_heads=12, representation_size=768, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r20_s16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_base_r20_s16_384(pretrained=False, **kwargs):
     """ R20+ViT-B/S16 hybrid.
@@ -836,6 +976,27 @@ def vit_base_r26_s32_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_base_r26_s32_224_in21k(pretrained=False, **kwargs):
+    """ R26+ViT-B/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(
+        embed_dim=768, depth=12, num_heads=12, representation_size=768, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r26_s32_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r26_s32_384(pretrained=False, **kwargs):
+    """ R26+ViT-B/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r26_s32_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_base_r50_s16_224(pretrained=False, **kwargs):
     """ R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
@@ -867,6 +1028,17 @@ def vit_large_r50_s32_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_large_r50_s32_224_in21k(pretrained=False, **kwargs):
+    """ R50+ViT-L/S32 hybrid.
+    """
+    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
+    model_kwargs = dict(
+        embed_dim=768, depth=12, num_heads=12, representation_size=768, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_large_r50_s32_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_large_r50_s32_384(pretrained=False, **kwargs):
     """ R50+ViT-L/S32 hybrid.
@@ -927,6 +1099,31 @@ def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_deit_tiny_patch16_224_in21k_norep(pretrained=False, **kwargs):
+    """ DeiT-tiny model"""
+    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
+    model = _create_vision_transformer('vit_deit_tiny_patch16_224_in21k_norep', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
+    """ DeiT-tiny model"""
+    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, representation_size=192, **kwargs)
+    model = _create_vision_transformer('vit_deit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_tiny_patch16_384(pretrained=False, **kwargs):
+    """ DeiT-tiny model"""
+    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
+    model = _create_vision_transformer('vit_deit_tiny_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+
 @register_model
 def vit_deit_small_patch16_224(pretrained=False, **kwargs):
     """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
@@ -937,6 +1134,48 @@ def vit_deit_small_patch16_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_deit_small_patch16_224_in21k(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch16_384(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch32_224(pretrained=False, **kwargs):
+    """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch32_384(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch32_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_deit_base_patch16_224(pretrained=False, **kwargs):
     """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).

From 17cdee7354d70ab8874583cd3f2868840aeb8f05 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Mon, 1 Mar 2021 16:53:32 -0800
Subject: [PATCH 07/13] Fix C&P patch_size error, and order of op patch_size
 arg resolution bug. Remove a test vit model.

---
 timm/models/vision_transformer.py | 17 ++++-------------
 1 file changed, 4 insertions(+), 13 deletions(-)

diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index f834d8e1..aed295ec 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -202,7 +202,6 @@ default_cfgs = {
     'vit_deit_tiny_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'),
     'vit_deit_tiny_patch16_224_in21k': _cfg(num_classes=21843),
-    'vit_deit_tiny_patch16_224_in21k_norep': _cfg(num_classes=21843),
     'vit_deit_tiny_patch16_384': _cfg(input_size=(3, 384, 384)),
 
     'vit_deit_small_patch16_224': _cfg(
@@ -399,7 +398,7 @@ class VisionTransformer(nn.Module):
         self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
         norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
         act_layer = act_layer or nn.GELU
-        patch_size = patch_size or 1 if hybrid_backbone is not None else 16
+        patch_size = patch_size or (1 if hybrid_backbone is not None else 16)
 
         if hybrid_backbone is not None:
             self.patch_embed = HybridEmbed(
@@ -1099,14 +1098,6 @@ def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
     return model
 
 
-@register_model
-def vit_deit_tiny_patch16_224_in21k_norep(pretrained=False, **kwargs):
-    """ DeiT-tiny model"""
-    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
-    model = _create_vision_transformer('vit_deit_tiny_patch16_224_in21k_norep', pretrained=pretrained, **model_kwargs)
-    return model
-
-
 @register_model
 def vit_deit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
     """ DeiT-tiny model"""
@@ -1155,7 +1146,7 @@ def vit_deit_small_patch32_224(pretrained=False, **kwargs):
     """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
     ImageNet-1k weights from https://github.com/facebookresearch/deit.
     """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
     model = _create_vision_transformer('vit_deit_small_patch32_224', pretrained=pretrained, **model_kwargs)
     return model
 
@@ -1163,7 +1154,7 @@ def vit_deit_small_patch32_224(pretrained=False, **kwargs):
 @register_model
 def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs):
     """ DeiT-small """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs)
     model = _create_vision_transformer('vit_deit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
     return model
 
@@ -1171,7 +1162,7 @@ def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs):
 @register_model
 def vit_deit_small_patch32_384(pretrained=False, **kwargs):
     """ DeiT-small """
-    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
     model = _create_vision_transformer('vit_deit_small_patch32_384', pretrained=pretrained, **model_kwargs)
     return model
 

From 4445eaa470ff321413b114c258de1be64d7156a6 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Fri, 5 Mar 2021 16:48:31 -0800
Subject: [PATCH 08/13] Add img_size to benchmark output

---
 benchmark.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/benchmark.py b/benchmark.py
index faac1fc5..e692eacc 100755
--- a/benchmark.py
+++ b/benchmark.py
@@ -208,6 +208,7 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
             samples_per_sec=round(num_samples / t_run_elapsed, 2),
             step_time=round(1000 * total_step / num_samples, 3),
             batch_size=self.batch_size,
+            img_size=self.input_size[-1],
             param_count=round(self.param_count / 1e6, 2),
         )
 
@@ -310,6 +311,7 @@ class TrainBenchmarkRunner(BenchmarkRunner):
                 bwd_time=round(1000 * total_bwd / num_samples, 3),
                 opt_time=round(1000 * total_opt / num_samples, 3),
                 batch_size=self.batch_size,
+                img_size=self.input_size[-1],
                 param_count=round(self.param_count / 1e6, 2),
             )
         else:

From 4de57ccf0123650bf759960d9ac64dca6263da7c Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Thu, 18 Mar 2021 15:35:22 -0700
Subject: [PATCH 09/13] Add weight init scheme that's closer to JAX impl

---
 timm/models/vision_transformer.py | 74 +++++++++++++++++++++++++------
 1 file changed, 61 insertions(+), 13 deletions(-)

diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index aed295ec..5fb5c7c7 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -373,7 +373,7 @@ class VisionTransformer(nn.Module):
     def __init__(self, img_size=224, patch_size=None, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                  num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None,
-                 act_layer=None):
+                 act_layer=None, weight_init=''):
         """
         Args:
             img_size (int, tuple): input image size
@@ -434,17 +434,13 @@ class VisionTransformer(nn.Module):
         self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
         trunc_normal_(self.pos_embed, std=.02)
-        trunc_normal_(self.cls_token, std=.02)
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
+        if weight_init != 'jax':  # leave as zeros to match JAX impl
+            trunc_normal_(self.cls_token, std=.02)
+        for n, m in self.named_modules():
+            if weight_init == 'jax':
+                _init_weights_jax(m, n)
+            else:
+                _init_weights_original(m, n)
 
     @torch.jit.ignore
     def no_weight_decay(self):
@@ -479,6 +475,58 @@ class VisionTransformer(nn.Module):
         return x
 
 
+def _init_weights_original(m: nn.Module, n: str = ''):
+    if isinstance(m, nn.Linear):
+        trunc_normal_(m.weight, std=.02)
+        if isinstance(m, nn.Linear) and m.bias is not None:
+            nn.init.constant_(m.bias, 0)
+    elif isinstance(m, nn.LayerNorm):
+        nn.init.zeros_(m.bias)
+        nn.init.ones_(m.weight)
+
+
+def _init_weights_jax(m: nn.Module, n: str):
+    """ Weight init scheme closer to the official JAX impl than my original init"""
+
+    def _fan_in(tensor):
+        dimensions = tensor.dim()
+        if dimensions < 2:
+            raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
+
+        num_input_fmaps = tensor.size(1)
+        receptive_field_size = 1
+        if tensor.dim() > 2:
+            receptive_field_size = tensor[0][0].numel()
+        fan_in = num_input_fmaps * receptive_field_size
+        return fan_in
+
+    def _lecun_normal(w):
+        stddev = (1.0 / _fan_in(w)) ** 0.5 / .87962566103423978
+        trunc_normal_(w, 0, stddev)
+
+    if isinstance(m, nn.Linear):
+        if 'head' in n:
+            nn.init.zeros_(m.weight)
+            nn.init.zeros_(m.bias)
+        elif 'pre_logits' in n:
+            _lecun_normal(m.weight)
+            nn.init.zeros_(m.bias)
+        else:
+            nn.init.xavier_uniform_(m.weight)
+            if m.bias is not None:
+                if 'mlp' in n:
+                    nn.init.normal_(m.bias, 0, 1e-6)
+                else:
+                    nn.init.zeros_(m.bias)
+    elif isinstance(m, nn.Conv2d):
+        _lecun_normal(m.weight)
+        if m.bias is not None:
+            nn.init.zeros_(m.bias)
+    elif isinstance(m, nn.LayerNorm):
+        nn.init.constant_(m.bias, 0.)
+        nn.init.constant_(m.weight, 1.)
+
+
 class DistilledVisionTransformer(VisionTransformer):
     """ Vision Transformer with distillation token.
 
@@ -496,7 +544,7 @@ class DistilledVisionTransformer(VisionTransformer):
 
         trunc_normal_(self.dist_token, std=.02)
         trunc_normal_(self.pos_embed, std=.02)
-        self.head_dist.apply(self._init_weights)
+        self.head_dist.apply(_init_weights_original)
 
     def forward_features(self, x):
         B = x.shape[0]

From cbcb76d72c74cab6d0ab12915e1cf851605c6f59 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Thu, 18 Mar 2021 23:15:48 -0700
Subject: [PATCH 10/13] Should have included Conv2d layers in original weight
 init. Lets see what the impact is...

---
 timm/models/vision_transformer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index 5fb5c7c7..42943fab 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -476,7 +476,7 @@ class VisionTransformer(nn.Module):
 
 
 def _init_weights_original(m: nn.Module, n: str = ''):
-    if isinstance(m, nn.Linear):
+    if isinstance(m, (nn.Conv2d, nn.Linear)):
         trunc_normal_(m.weight, std=.02)
         if isinstance(m, nn.Linear) and m.bias is not None:
             nn.init.constant_(m.bias, 0)

From f42f1df26c5b44321a9cc65aca3f728a89d7479d Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Thu, 18 Mar 2021 23:16:14 -0700
Subject: [PATCH 11/13] Improve evenness of per-worker split for validation set
 with TFDS

---
 timm/data/parsers/parser_tfds.py | 41 +++++++++++++++++++++++---------
 1 file changed, 30 insertions(+), 11 deletions(-)

diff --git a/timm/data/parsers/parser_tfds.py b/timm/data/parsers/parser_tfds.py
index 0c2e10c0..0b12a4db 100644
--- a/timm/data/parsers/parser_tfds.py
+++ b/timm/data/parsers/parser_tfds.py
@@ -29,6 +29,11 @@ SHUFFLE_SIZE = 16834  # samples to shuffle in DS queue
 PREFETCH_SIZE = 4096  # samples to prefetch
 
 
+def even_split_indices(split, n, num_samples):
+    partitions = [round(i * num_samples / n) for i in range(n + 1)]
+    return [f"{split}[{partitions[i]}:{partitions[i+1]}]" for i in range(n)]
+
+
 class ParserTfds(Parser):
     """ Wrap Tensorflow Datasets for use in PyTorch
 
@@ -63,6 +68,7 @@ class ParserTfds(Parser):
                 "Must specify batch_size in training mode for reasonable behaviour w/ TFDS wrapper"
         self.batch_size = batch_size
         self.repeats = repeats
+        self.subsplit = None
 
         self.builder = tfds.builder(name, data_dir=root)
         # NOTE: please use tfds command line app to download & prepare datasets, I don't want to call
@@ -96,6 +102,7 @@ class ParserTfds(Parser):
         if worker_info is not None:
             self.worker_info = worker_info
             num_workers = worker_info.num_workers
+            global_num_workers = self.dist_num_replicas * num_workers
             worker_id = worker_info.id
 
             # FIXME I need to spend more time figuring out the best way to distribute/split data across
@@ -115,15 +122,27 @@ class ParserTfds(Parser):
             #     split = split + '[{}:]'.format(start)
             # else:
             #     split = split + '[{}:{}]'.format(start, start + split_size)
-
-        input_context = tf.distribute.InputContext(
-            num_input_pipelines=self.dist_num_replicas * num_workers,
-            input_pipeline_id=self.dist_rank * num_workers + worker_id,
-            num_replicas_in_sync=self.dist_num_replicas  # FIXME does this have any impact?
-        )
-
-        read_config = tfds.ReadConfig(input_context=input_context)
-        ds = self.builder.as_dataset(split=split, shuffle_files=self.shuffle, read_config=read_config)
+            if not self.is_training and '[' not in self.split:
+                # If not training, and split doesn't define a subsplit, manually split the dataset
+                # for more even samples / worker
+                self.subsplit = even_split_indices(self.split, global_num_workers, self.num_samples)[
+                    self.dist_rank * num_workers + worker_id]
+
+        if self.subsplit is None:
+            input_context = tf.distribute.InputContext(
+                num_input_pipelines=self.dist_num_replicas * num_workers,
+                input_pipeline_id=self.dist_rank * num_workers + worker_id,
+                num_replicas_in_sync=self.dist_num_replicas  # FIXME does this arg have any impact?
+            )
+        else:
+            input_context = None
+
+        read_config = tfds.ReadConfig(
+            shuffle_seed=42,
+            shuffle_reshuffle_each_iteration=True,
+            input_context=input_context)
+        ds = self.builder.as_dataset(
+            split=self.subsplit or self.split, shuffle_files=self.shuffle, read_config=read_config)
         # avoid overloading threading w/ combo fo TF ds threads + PyTorch workers
         ds.options().experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
         ds.options().experimental_threading.max_intra_op_parallelism = 1
@@ -161,8 +180,8 @@ class ParserTfds(Parser):
         if not self.is_training and self.dist_num_replicas and 0 < sample_count < target_sample_count:
             # Validation batch padding only done for distributed training where results are reduced across nodes.
             # For single process case, it won't matter if workers return different batch sizes.
-            # FIXME this needs more testing, possible for sharding / split api to cause differences of > 1?
-            assert target_sample_count - sample_count == 1  # should only be off by 1 or sharding is not optimal
+            # FIXME if using input_context or % based subsplits, sample count can vary by more than +/- 1 and this
+            # approach is not optimal
             yield img, sample['label']  # yield prev sample again
             sample_count += 1
 

From cf5fec504754ecd56b2d4307b521a1d7d2eeaa8a Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Sat, 20 Mar 2021 09:44:24 -0700
Subject: [PATCH 12/13] Cleanup experimental vit weight init a bit

---
 timm/models/layers/__init__.py    |  2 +-
 timm/models/layers/weight_init.py | 29 +++++++++++++
 timm/models/vision_transformer.py | 69 +++++++++++++++++--------------
 3 files changed, 68 insertions(+), 32 deletions(-)

diff --git a/timm/models/layers/__init__.py b/timm/models/layers/__init__.py
index f8d8d8c0..89fb859c 100644
--- a/timm/models/layers/__init__.py
+++ b/timm/models/layers/__init__.py
@@ -31,4 +31,4 @@ from .split_attn import SplitAttnConv2d
 from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
 from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
 from .test_time_pool import TestTimePoolHead, apply_test_time_pool
-from .weight_init import trunc_normal_
+from .weight_init import trunc_normal_, variance_scaling_, lecun_normal_
diff --git a/timm/models/layers/weight_init.py b/timm/models/layers/weight_init.py
index d731029f..305a2fd0 100644
--- a/timm/models/layers/weight_init.py
+++ b/timm/models/layers/weight_init.py
@@ -2,6 +2,8 @@ import torch
 import math
 import warnings
 
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
 
 def _no_grad_trunc_normal_(tensor, mean, std, a, b):
     # Cut & paste from PyTorch official master until it's in a few official releases - RW
@@ -58,3 +60,30 @@ def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
         >>> nn.init.trunc_normal_(w)
     """
     return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == 'fan_in':
+        denom = fan_in
+    elif mode == 'fan_out':
+        denom = fan_out
+    elif mode == 'fan_avg':
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
+    elif distribution == "normal":
+        tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index 42943fab..45c1eddb 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -28,7 +28,7 @@ import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import load_pretrained
-from .layers import StdConv2dSame, StdConv2d, DropPath, to_2tuple, trunc_normal_
+from .layers import StdConv2dSame, StdConv2d, DropPath, to_2tuple, trunc_normal_, lecun_normal_
 from .resnet import resnet26d, resnet50d
 from .resnetv2 import ResNetV2, create_resnetv2_stem
 from .registry import register_model
@@ -373,7 +373,7 @@ class VisionTransformer(nn.Module):
     def __init__(self, img_size=224, patch_size=None, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                  num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None,
-                 act_layer=None, weight_init=''):
+                 act_layer=None, weight_init='new_nlhb'):
         """
         Args:
             img_size (int, tuple): input image size
@@ -433,14 +433,20 @@ class VisionTransformer(nn.Module):
         # Classifier head
         self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
+        self._init_weights(weight_init)
+
+    def _init_weights(self, weight_init: str):
         trunc_normal_(self.pos_embed, std=.02)
-        if weight_init != 'jax':  # leave as zeros to match JAX impl
+        if weight_init.startswith('jax'):
+            init_fn = _init_weights_jax
+            # leave cls token as zeros to match jax impl
+        else:
             trunc_normal_(self.cls_token, std=.02)
+            init_fn = _init_weights_new if weight_init.startswith('new') else _init_weights_old
+        hb = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
+        init_fn = partial(init_fn, head_bias=hb)
         for n, m in self.named_modules():
-            if weight_init == 'jax':
-                _init_weights_jax(m, n)
-            else:
-                _init_weights_original(m, n)
+            init_fn(m, n)
 
     @torch.jit.ignore
     def no_weight_decay(self):
@@ -475,41 +481,42 @@ class VisionTransformer(nn.Module):
         return x
 
 
-def _init_weights_original(m: nn.Module, n: str = ''):
-    if isinstance(m, (nn.Conv2d, nn.Linear)):
+def _init_weights_old(m: nn.Module, n: str = '', head_bias: float = 0.):
+    if isinstance(m, nn.Linear):
         trunc_normal_(m.weight, std=.02)
-        if isinstance(m, nn.Linear) and m.bias is not None:
-            nn.init.constant_(m.bias, 0)
+        if m.bias is not None:
+            if 'head' in n:
+                nn.init.constant_(m.bias, head_bias)
+            else:
+                nn.init.zeros_(m.bias)
     elif isinstance(m, nn.LayerNorm):
         nn.init.zeros_(m.bias)
         nn.init.ones_(m.weight)
 
 
-def _init_weights_jax(m: nn.Module, n: str):
-    """ Weight init scheme closer to the official JAX impl than my original init"""
-
-    def _fan_in(tensor):
-        dimensions = tensor.dim()
-        if dimensions < 2:
-            raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
+def _init_weights_new(m: nn.Module, n: str = '', head_bias: float = 0.):
+    if isinstance(m, (nn.Conv2d, nn.Linear)):
+        #trunc_normal_(m.weight, std=.02)
+        lecun_normal_(m.weight)
+        if m.bias is not None:
+            if 'head' in n:
+                nn.init.constant_(m.bias, head_bias)
+            else:
+                nn.init.zeros_(m.bias)
+    elif isinstance(m, nn.LayerNorm):
+        nn.init.zeros_(m.bias)
+        nn.init.ones_(m.weight)
 
-        num_input_fmaps = tensor.size(1)
-        receptive_field_size = 1
-        if tensor.dim() > 2:
-            receptive_field_size = tensor[0][0].numel()
-        fan_in = num_input_fmaps * receptive_field_size
-        return fan_in
 
-    def _lecun_normal(w):
-        stddev = (1.0 / _fan_in(w)) ** 0.5 / .87962566103423978
-        trunc_normal_(w, 0, stddev)
+def _init_weights_jax(m: nn.Module, n: str, head_bias: float = 0.):
+    """ Attempt at weight init scheme closer to the official JAX impl than my original init"""
 
     if isinstance(m, nn.Linear):
         if 'head' in n:
             nn.init.zeros_(m.weight)
-            nn.init.zeros_(m.bias)
+            nn.init.constant_(m.bias, head_bias)
         elif 'pre_logits' in n:
-            _lecun_normal(m.weight)
+            lecun_normal_(m.weight)
             nn.init.zeros_(m.bias)
         else:
             nn.init.xavier_uniform_(m.weight)
@@ -519,7 +526,7 @@ def _init_weights_jax(m: nn.Module, n: str):
                 else:
                     nn.init.zeros_(m.bias)
     elif isinstance(m, nn.Conv2d):
-        _lecun_normal(m.weight)
+        lecun_normal_(m.weight)
         if m.bias is not None:
             nn.init.zeros_(m.bias)
     elif isinstance(m, nn.LayerNorm):
@@ -544,7 +551,7 @@ class DistilledVisionTransformer(VisionTransformer):
 
         trunc_normal_(self.dist_token, std=.02)
         trunc_normal_(self.pos_embed, std=.02)
-        self.head_dist.apply(_init_weights_original)
+        self.head_dist.apply(_init_weights_new)
 
     def forward_features(self, x):
         B = x.shape[0]

From e2e3290fbfdd6dc9b6f4f8ba206317faac20d956 Mon Sep 17 00:00:00 2001
From: Ross Wightman <rwightman@gmail.com>
Date: Sat, 20 Mar 2021 12:02:17 -0700
Subject: [PATCH 13/13] Add '--experiment' to train args for fixed exp name if
 desired, 'train' not added to output folder if specified.

---
 train.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/train.py b/train.py
index 2fdf68d8..d0886c08 100755
--- a/train.py
+++ b/train.py
@@ -259,6 +259,8 @@ parser.add_argument('--no-prefetcher', action='store_true', default=False,
                     help='disable fast prefetcher')
 parser.add_argument('--output', default='', type=str, metavar='PATH',
                     help='path to output folder (default: none, current dir)')
+parser.add_argument('--experiment', default='', type=str, metavar='NAME',
+                    help='name of train experiment, name of sub-folder for output')
 parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
                     help='Best metric (default: "top1"')
 parser.add_argument('--tta', type=int, default=0, metavar='N',
@@ -544,13 +546,15 @@ def main():
     saver = None
     output_dir = ''
     if args.local_rank == 0:
-        output_base = args.output if args.output else './output'
-        exp_name = '-'.join([
-            datetime.now().strftime("%Y%m%d-%H%M%S"),
-            args.model,
-            str(data_config['input_size'][-1])
-        ])
-        output_dir = get_outdir(output_base, 'train', exp_name)
+        if args.experiment:
+            exp_name = args.experiment
+        else:
+            exp_name = '-'.join([
+                datetime.now().strftime("%Y%m%d-%H%M%S"),
+                args.model,
+                str(data_config['input_size'][-1])
+            ])
+        output_dir = get_outdir(args.output if args.output else './output/train', exp_name)
         decreasing = True if eval_metric == 'loss' else False
         saver = CheckpointSaver(
             model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler,