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#!/usr/bin/env python | ||
"""`nested_dict` provides dictionaries with multiple levels of nested-ness.""" | ||
from __future__ import print_function | ||
from __future__ import division | ||
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################################################################################ | ||
# | ||
# nested_dict.py | ||
# | ||
# Copyright (c) 2009, 2015 Leo Goodstadt | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in | ||
# all copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN | ||
# THE SOFTWARE. | ||
# | ||
################################################################################# | ||
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from collections import defaultdict | ||
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import sys | ||
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def flatten_nested_items(dictionary): | ||
""" | ||
Flatten a nested_dict. | ||
iterate through nested dictionary (with iterkeys() method) | ||
and return with nested keys flattened into a tuple | ||
""" | ||
if sys.hexversion < 0x03000000: | ||
keys = dictionary.iterkeys | ||
keystr = "iterkeys" | ||
else: | ||
keys = dictionary.keys | ||
keystr = "keys" | ||
for key in keys(): | ||
value = dictionary[key] | ||
if hasattr(value, keystr): | ||
for keykey, value in flatten_nested_items(value): | ||
yield (key,) + keykey, value | ||
else: | ||
yield (key,), value | ||
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class _recursive_dict(defaultdict): | ||
""" | ||
Parent class of nested_dict. | ||
Defined separately for _nested_levels to work | ||
transparently, so dictionaries with a specified (and constant) degree of nestedness | ||
can be created easily. | ||
The "_flat" functions are defined here rather than in nested_dict because they work | ||
recursively. | ||
""" | ||
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def iteritems_flat(self): | ||
"""Iterate through items with nested keys flattened into a tuple.""" | ||
for key, value in flatten_nested_items(self): | ||
yield key, value | ||
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def iterkeys_flat(self): | ||
"""Iterate through keys with nested keys flattened into a tuple.""" | ||
for key, value in flatten_nested_items(self): | ||
yield key | ||
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def itervalues_flat(self): | ||
"""Iterate through values with nested keys flattened into a tuple.""" | ||
for key, value in flatten_nested_items(self): | ||
yield value | ||
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items_flat = iteritems_flat | ||
keys_flat = iterkeys_flat | ||
values_flat = itervalues_flat | ||
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def to_dict(self, input_dict=None): | ||
"""Convert the nested dictionary to a nested series of standard ``dict`` objects.""" | ||
# | ||
# Calls itself recursively to unwind the dictionary. | ||
# Use to_dict() to start at the top level of nesting | ||
plain_dict = dict() | ||
if input_dict is None: | ||
input_dict = self | ||
for key in input_dict.keys(): | ||
value = input_dict[key] | ||
if isinstance(value, _recursive_dict): | ||
# print "recurse", value | ||
plain_dict[key] = self.to_dict(value) | ||
else: | ||
# print "plain", value | ||
plain_dict[key] = value | ||
return plain_dict | ||
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def __str__(self, indent=None): | ||
"""Representation of self as a string.""" | ||
import json | ||
return json.dumps(self.to_dict(), indent=indent) | ||
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class _any_type(object): | ||
pass | ||
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def _nested_levels(level, nested_type): | ||
"""Helper function to create a specified degree of nested dictionaries.""" | ||
if level > 2: | ||
return lambda: _recursive_dict(_nested_levels(level - 1, nested_type)) | ||
if level == 2: | ||
if isinstance(nested_type, _any_type): | ||
return lambda: _recursive_dict() | ||
else: | ||
return lambda: _recursive_dict(_nested_levels(level - 1, nested_type)) | ||
return nested_type | ||
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if sys.hexversion < 0x03000000: | ||
iteritems = dict.iteritems | ||
else: | ||
iteritems = dict.items | ||
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# _________________________________________________________________________________________ | ||
# | ||
# nested_dict | ||
# | ||
# _________________________________________________________________________________________ | ||
def nested_dict_from_dict(orig_dict, nd): | ||
"""Helper to build nested_dict from a dict.""" | ||
for key, value in iteritems(orig_dict): | ||
if isinstance(value, (dict,)): | ||
nd[key] = nested_dict_from_dict(value, nested_dict()) | ||
else: | ||
nd[key] = value | ||
return nd | ||
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def _recursive_update(nd, other): | ||
for key, value in iteritems(other): | ||
#print ("key=", key) | ||
if isinstance(value, (dict,)): | ||
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# recursive update if my item is nested_dict | ||
if isinstance(nd[key], (_recursive_dict,)): | ||
#print ("recursive update", key, type(nd[key])) | ||
_recursive_update(nd[key], other[key]) | ||
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# update if my item is dict | ||
elif isinstance(nd[key], (dict,)): | ||
#print ("update", key, type(nd[key])) | ||
nd[key].update(other[key]) | ||
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# overwrite | ||
else: | ||
#print ("self not nested dict or dict: overwrite", key) | ||
nd[key] = value | ||
# other not dict: overwrite | ||
else: | ||
#print ("other not dict: overwrite", key) | ||
nd[key] = value | ||
return nd | ||
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# _________________________________________________________________________________________ | ||
# | ||
# nested_dict | ||
# | ||
# _________________________________________________________________________________________ | ||
class nested_dict(_recursive_dict): | ||
""" | ||
Nested dict. | ||
Uses defaultdict to automatically add levels of nested dicts and other types. | ||
""" | ||
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def update(self, other): | ||
"""Update recursively.""" | ||
_recursive_update(self, other) | ||
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def __init__(self, *param, **named_param): | ||
""" | ||
Constructor. | ||
Takes one or two parameters | ||
1) int, [TYPE] | ||
1) dict | ||
""" | ||
if not len(param): | ||
self.factory = nested_dict | ||
defaultdict.__init__(self, self.factory) | ||
return | ||
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if len(param) == 1: | ||
# int = level | ||
if isinstance(param[0], int): | ||
self.factory = _nested_levels(param[0], _any_type()) | ||
defaultdict.__init__(self, self.factory) | ||
return | ||
# existing dict | ||
if isinstance(param[0], dict): | ||
self.factory = nested_dict | ||
defaultdict.__init__(self, self.factory) | ||
nested_dict_from_dict(param[0], self) | ||
return | ||
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if len(param) == 2: | ||
if isinstance(param[0], int): | ||
self.factory = _nested_levels(*param) | ||
defaultdict.__init__(self, self.factory) | ||
return | ||
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raise Exception("nested_dict should be initialised with either " | ||
"1) the number of nested levels and an optional type, or " | ||
"2) an existing dict to be converted into a nested dict " | ||
"(factory = %s. len(param) = %d, param = %s" | ||
% (self.factory, len(param), param)) |
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import torch | ||
from torch.nn.init import kaiming_normal_ | ||
import torch.nn.functional as F | ||
from torch.nn.parallel._functions import Broadcast | ||
from torch.nn.parallel import scatter, parallel_apply, gather | ||
from functools import partial | ||
from mixmatch.models.nested_dict import nested_dict | ||
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def cast(params, dtype='float'): | ||
if isinstance(params, dict): | ||
return {k: cast(v, dtype) for k, v in params.items()} | ||
else: | ||
return getattr(params.cuda() if torch.cuda.is_available() else params, dtype)() | ||
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def conv_params(ni, no, k=1): | ||
return kaiming_normal_(torch.Tensor(no, ni, k, k)) | ||
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def linear_params(ni, no): | ||
return {'weight': kaiming_normal_(torch.Tensor(no, ni)), 'bias': torch.zeros(no)} | ||
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def bnparams(n): | ||
return {'weight': torch.rand(n), | ||
'bias': torch.zeros(n), | ||
'running_mean': torch.zeros(n), | ||
'running_var': torch.ones(n)} | ||
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def data_parallel(f, input, params, device_ids, output_device=None): | ||
assert isinstance(device_ids, list) | ||
if output_device is None: | ||
output_device = device_ids[0] | ||
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if len(device_ids) == 1: | ||
return f(input, params) | ||
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params_all = Broadcast.apply(device_ids, *params.values()) | ||
params_replicas = [{k: params_all[i + j * len(params)] for i, k in enumerate(params.keys())} | ||
for j in range(len(device_ids))] | ||
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replicas = [partial(f, params=p) | ||
for p in params_replicas] | ||
inputs = scatter([input], device_ids) | ||
outputs = parallel_apply(replicas, inputs) | ||
return gather(outputs, output_device) | ||
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def flatten(params): | ||
return {'.'.join(k): v for k, v in nested_dict(params).items_flat() if v is not None} | ||
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def batch_norm(x, params, base): | ||
return F.batch_norm(x, weight=params[base + '.weight'], | ||
bias=params[base + '.bias'], | ||
running_mean=params[base + '.running_mean'], | ||
running_var=params[base + '.running_var'],) | ||
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def print_tensor_dict(params): | ||
kmax = max(len(key) for key in params.keys()) | ||
for i, (key, v) in enumerate(params.items()): | ||
print(str(i).ljust(5), key.ljust(kmax + 3), str(tuple(v.shape)).ljust(23), torch.typename(v), v.requires_grad) | ||
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def set_requires_grad_except_bn_(params): | ||
for k, v in params.items(): | ||
if not k.endswith('running_mean') and not k.endswith('running_var'): | ||
v.requires_grad = True |
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import torch.nn.functional as F | ||
import mixmatch.models.utils as utils | ||
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def resnet(depth, width, num_classes): | ||
assert (depth - 4) % 6 == 0, 'depth should be 6n+4' | ||
n = (depth - 4) // 6 | ||
widths = [int(v * width) for v in (16, 32, 64)] | ||
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def gen_block_params(ni, no): | ||
return { | ||
'conv0': utils.conv_params(ni, no, 3), | ||
'conv1': utils.conv_params(no, no, 3), | ||
'bn0': utils.bnparams(ni), | ||
'bn1': utils.bnparams(no), | ||
'convdim': utils.conv_params(ni, no, 1) if ni != no else None, | ||
} | ||
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def gen_group_params(ni, no, count): | ||
return {'block%d' % i: gen_block_params(ni if i == 0 else no, no) | ||
for i in range(count)} | ||
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flat_params = utils.cast(utils.flatten({ | ||
'conv0': utils.conv_params(3, 16, 3), | ||
'group0': gen_group_params(16, widths[0], n), | ||
'group1': gen_group_params(widths[0], widths[1], n), | ||
'group2': gen_group_params(widths[1], widths[2], n), | ||
'bn': utils.bnparams(widths[2]), | ||
'fc': utils.linear_params(widths[2], num_classes), | ||
})) | ||
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utils.set_requires_grad_except_bn_(flat_params) | ||
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def block(x, params, base, stride): | ||
o1 = F.relu(utils.batch_norm(x, params, base + '.bn0'), inplace=True) | ||
y = F.conv2d(o1, params[base + '.conv0'], stride=stride, padding=1) | ||
o2 = F.relu(utils.batch_norm(y, params, base + '.bn1'), inplace=True) | ||
z = F.conv2d(o2, params[base + '.conv1'], stride=1, padding=1) | ||
if base + '.convdim' in params: | ||
return z + F.conv2d(o1, params[base + '.convdim'], stride=stride) | ||
else: | ||
return z + x | ||
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def group(o, params, base, stride): | ||
for i in range(n): | ||
o = block(o, params, '%s.block%d' % (base, i), stride if i == 0 else 1) | ||
return o | ||
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def f(input, params): | ||
x = F.conv2d(input, params['conv0'], padding=1) | ||
g0 = group(x, params, 'group0', 1) | ||
g1 = group(g0, params, 'group1', 2) | ||
g2 = group(g1, params, 'group2', 2) | ||
o = F.relu(utils.batch_norm(g2, params, 'bn')) | ||
o = F.avg_pool2d(o, 8, 1, 0) | ||
o = o.view(o.size(0), -1) | ||
o = F.linear(o, params['fc.weight'], params['fc.bias']) | ||
return o | ||
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return f, flat_params | ||
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f, p = resnet(28, 2, 10) | ||
import torch | ||
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f(torch.rand(16, 3, 100, 100), p, ) |