mirror of https://github.com/Jittor/Jittor
densenet & lr_scheduler
This commit is contained in:
parent
03aefb620a
commit
f32bc4ae49
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@ -777,6 +777,8 @@ double = float64
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Var.double = Var.float64
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from . import nn
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from . import attention
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from . import lr_scheduler
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from . import linalg
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from .nn import matmul
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from . import contrib
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@ -0,0 +1,86 @@
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# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Guowei Yang <471184555@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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#
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# All Rights Reserved.
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import jittor as jt
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from jittor.optim import Optimizer
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import math
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class ReduceLROnPlateau(object):
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def __init__(self, optimizer, mode='min', factor=0.1, patience=10, verbose=False, threshold=1e-4, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-8):
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assert factor < 1.0, "factor should be < 1.0."
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assert isinstance(optimizer, Optimizer), '{} is not an Optimizer'.format(type(optimizer).__name__)
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assert mode in {'min', 'max'}, 'mode ' + mode + ' is unknown!'
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assert threshold_mode in {'rel', 'abs'}, 'threshold mode ' + threshold_mode + ' is unknown!'
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if isinstance(min_lr, list) or isinstance(min_lr, tuple):
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assert len(min_lr) == len(optimizer.param_groups), "expected {} min_lrs, got {}".format(len(optimizer.param_groups), len(min_lr))
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self.min_lrs = list(min_lr)
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else:
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self.min_lrs = [min_lr] * len(optimizer.param_groups)
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self.factor = factor
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self.optimizer = optimizer
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self.patience = patience
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self.verbose = verbose
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self.cooldown = cooldown
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self.n_cd = 0
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self.mode = mode
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self.threshold = threshold
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self.threshold_mode = threshold_mode
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self.loss_best = None
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self.n_bad = 0
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self.eps = eps
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self.last_epoch = 0
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self.loss_best = math.inf if mode=="min" else -math.inf
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def step(self, loss, epoch=None):
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# convert `metrics` to float, in case it's a zero-dim Tensor
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loss_now = float(loss)
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if epoch is None:
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epoch = self.last_epoch + 1
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self.last_epoch = epoch
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if self.better(loss_now, self.loss_best):
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self.loss_best = loss_now
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self.n_bad = 0
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else:
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self.n_bad += 1
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if self.n_cd > 0:
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self.n_cd -= 1
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self.n_bad = 0
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if self.n_bad > self.patience:
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self.update_lr(epoch)
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self.n_cd = self.cooldown
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self.n_bad = 0
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def update_lr(self, epoch):
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for i, param_group in enumerate(self.optimizer.param_groups):
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old_lr = float(param_group.get("lr", self.optimizer.lr))
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new_lr = max(old_lr * self.factor, self.min_lrs[i])
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if old_lr - new_lr > self.eps:
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if param_group.get("lr")!=None:
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param_group["lr"] = new_lr
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else:
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self.optimizer.lr = new_lr
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if self.verbose:
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print('Epoch {:5d}: reducing learning rate of group {} from {:.4e} to {:.4e}.'.format(epoch, i, old_lr, new_lr))
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def better(self, a, b):
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if self.mode == 'min' and self.threshold_mode == 'rel':
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save = 1.0 - self.threshold
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return a < b * save
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elif self.mode == 'min' and self.threshold_mode == 'abs':
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return a < b - self.threshold
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elif self.mode == 'max' and self.threshold_mode == 'rel':
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save = self.threshold + 1.0
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return a > b * save
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else:
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return a > b + self.threshold
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@ -17,3 +17,5 @@ from .mnasnet import *
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from . import shufflenetv2
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from .shufflenetv2 import *
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from .res2net import res2net50, res2net101
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from . import densenet
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from .densenet import *
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@ -0,0 +1,97 @@
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# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Guowei Yang <471184555@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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# All Rights Reserved.
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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# This model is generated by pytorch converter.
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import jittor as jt
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from jittor import nn
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from jittor import init
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from collections import OrderedDict
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def Densenet169(pretrained=False, **kwargs):
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'Densenet-169 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32), **kwargs)
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assert not pretrained, "pretrained doesn't support now"
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# if pretrained:
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# save=torch.load('parameter.pkl')
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# params=model.parameters()
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# model.load_parameters(save)
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return model
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class _DenseLayer(nn.Sequential):
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def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
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super(_DenseLayer, self).__init__()
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self.add_module('norm1', nn.BatchNorm(num_input_features))
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self.add_module('relu1', nn.ReLU())
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self.add_module('conv1', nn.Conv(num_input_features, (bn_size * growth_rate), 1, stride=1, bias=False))
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self.add_module('norm2', nn.BatchNorm((bn_size * growth_rate)))
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self.add_module('relu2', nn.ReLU())
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self.add_module('conv2', nn.Conv((bn_size * growth_rate), growth_rate, 3, stride=1, padding=1, bias=False))
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self.drop_rate = drop_rate
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self.drop = nn.Dropout(self.drop_rate)
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def execute(self, x):
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new_features = super(_DenseLayer, self).execute(x)
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if (self.drop_rate > 0):
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new_features = self.drop(new_features)
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return jt.contrib.concat([x, new_features], dim=1)
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class _DenseBlock(nn.Sequential):
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def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
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super(_DenseBlock, self).__init__()
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for i in range(num_layers):
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layer = _DenseLayer((num_input_features + (i * growth_rate)), growth_rate, bn_size, drop_rate)
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self.add_module('denselayer%d' % (i + 1), layer)
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class _Transition(nn.Sequential):
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def __init__(self, num_input_features, num_output_features):
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super(_Transition, self).__init__()
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self.add_module('norm', nn.BatchNorm(num_input_features))
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self.add_module('relu', nn.ReLU())
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self.add_module('conv', nn.Conv(num_input_features, num_output_features, 1, stride=1, bias=False))
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self.add_module('pool', nn.Pool(2, stride=2, op='mean'))
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class DenseNet(nn.Module):
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'Densenet-BC model class, based on\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_\n\n Args:\n growth_rate (int) - how many filters to add each layer (`k` in paper)\n block_config (list of 4 ints) - how many layers in each pooling block\n num_init_features (int) - the number of filters to learn in the first convolution layer\n bn_size (int) - multiplicative factor for number of bottle neck layers\n (i.e. bn_size * k features in the bottleneck layer)\n drop_rate (float) - dropout rate after each dense layer\n num_classes (int) - number of classification classes\n '
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def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000):
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super(DenseNet, self).__init__()
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self.features = nn.Sequential(OrderedDict([
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('conv0', nn.Conv(3, num_init_features, 7, stride=2, padding=3, bias=False)),
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('norm0', nn.BatchNorm(num_init_features)),
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('relu0', nn.ReLU()),
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('pool0', nn.Pool(3, stride=2, padding=1, op='maximum')),
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]))
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num_features = num_init_features
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for (i, num_layers) in enumerate(block_config):
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block = _DenseBlock(num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
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self.features.add_module('denseblock%d' % (i + 1), block)
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num_features = (num_features + (num_layers * growth_rate))
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if (i != (len(block_config) - 1)):
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trans = _Transition(num_input_features=num_features, num_output_features=(num_features // 2))
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self.features.add_module('transition%d' % (i + 1), trans)
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num_features = (num_features // 2)
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self.features.add_module('norm5', nn.BatchNorm(num_features))
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self.fc = nn.Linear(num_features, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv):
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nn.init.invariant_uniform_(m.weight)
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elif isinstance(m, nn.BatchNorm):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.constant_(m.bias, 0)
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def execute(self, x):
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features = self.features(x)
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out = nn.relu(features)
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out = jt.pool.pool(out, kernel_size=7, op="mean",stride=1).reshape([features.shape[0], -1])
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out = jt.sigmoid(self.fc(out))
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return out
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@ -0,0 +1,102 @@
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# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Guowei Yang <471184555@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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# All Rights Reserved.
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import jittor as jt
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from jittor import nn, Module
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from jittor.models import densenet
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import numpy as np
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import sys, os
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import random
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import math
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import unittest
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from jittor.test.test_reorder_tuner import simple_parser
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from jittor.test.test_log import find_log_with_re
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from jittor.dataset.mnist import MNIST
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import jittor.transform as trans
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import time
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skip_this_test = False
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class MnistNet(Module):
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def __init__(self):
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self.model = densenet.Densenet169()
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self.layer = nn.Linear(1000,10)
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def execute(self, x):
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x = self.model(x)
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x = self.layer(x)
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return x
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@unittest.skipIf(skip_this_test, "skip_this_test")
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class TestDensenet(unittest.TestCase):
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@classmethod
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def setUpClass(self):
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# hyper-parameters
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self.batch_size = 100
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self.weight_decay = 0.0001
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self.momentum = 0.9
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self.learning_rate = 0.1
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# mnist dataset
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self.train_loader = MNIST(train=True, transform=trans.Resize(224)) \
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.set_attrs(batch_size=self.batch_size, shuffle=True)
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self.train_loader.num_workers = 4
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# setup random seed
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def setup_seed(self, seed):
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np.random.seed(seed)
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random.seed(seed)
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jt.seed(seed)
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@unittest.skipIf(not jt.has_cuda, "Cuda not found")
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@jt.flag_scope(use_cuda=1, use_stat_allocator=1)
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def test_densenet(self):
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self.setup_seed(1)
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loss_list=[]
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acc_list=[]
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mnist_net = MnistNet()
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global prev
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prev = time.time()
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SGD = nn.SGD(mnist_net.parameters(), self.learning_rate, self.momentum, self.weight_decay)
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# SGD = jt.optim.Adam(mnist_net.parameters(), lr=0.0001)
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for batch_idx, (data, target) in enumerate(self.train_loader):
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output = mnist_net(data)
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loss = nn.cross_entropy_loss(output, target)
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SGD.step(loss)
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def callback(batch_idx, loss, output, target):
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# print train info
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global prev
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pred = np.argmax(output, axis=1)
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acc = np.mean(target==pred)
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loss_list.append(loss[0])
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acc_list.append(acc)
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAcc: {:.6f} \tTime:{:.3f}'
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.format(0, batch_idx, 600,1. * batch_idx / 6.0, loss[0], acc, time.time()-prev))
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# prev = time.time()
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jt.fetch(batch_idx, loss, output, target, callback)
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# Train Epoch: 0 [0/600 (0%)] Loss: 2.402650 Acc: 0.060000
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# Train Epoch: 0 [1/600 (0%)] Loss: 2.770145 Acc: 0.100000
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# Train Epoch: 0 [2/600 (0%)] Loss: 3.528072 Acc: 0.100000
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# Train Epoch: 0 [3/600 (0%)] Loss: 2.992042 Acc: 0.100000
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# Train Epoch: 0 [4/600 (1%)] Loss: 4.672772 Acc: 0.060000
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# Train Epoch: 0 [5/600 (1%)] Loss: 5.003410 Acc: 0.080000
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# Train Epoch: 0 [6/600 (1%)] Loss: 5.417546 Acc: 0.100000
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# Train Epoch: 0 [7/600 (1%)] Loss: 5.137665 Acc: 0.100000
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# Train Epoch: 0 [8/600 (1%)] Loss: 5.241075 Acc: 0.070000
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# Train Epoch: 0 [9/600 (2%)] Loss: 4.515363 Acc: 0.100000
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# Train Epoch: 0 [10/600 (2%)] Loss: 3.357187 Acc: 0.170000
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# Train Epoch: 0 [20/600 (3%)] Loss: 2.265879 Acc: 0.100000
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# Train Epoch: 0 [30/600 (5%)] Loss: 2.107000 Acc: 0.250000
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# Train Epoch: 0 [40/600 (7%)] Loss: 1.918214 Acc: 0.290000
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# Train Epoch: 0 [50/600 (8%)] Loss: 1.645694 Acc: 0.400000
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jt.sync_all(True)
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assert np.mean(loss_list[-50:])<0.3
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assert np.mean(acc_list[-50:])>0.9
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if __name__ == "__main__":
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unittest.main()
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@ -0,0 +1,52 @@
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# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Guowei Yang <471184555@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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# All Rights Reserved.
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import unittest
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import jittor as jt
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import numpy as np
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import random
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skip_this_test = False
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try:
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jt.dirty_fix_pytorch_runtime_error()
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import torch
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except:
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torch = None
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skip_this_test = True
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def check_equal(q,k,v,tatt,jatt):
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tq=torch.from_numpy(q)
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jq=jt.array(q)
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tk=torch.from_numpy(k)
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jk=jt.array(k)
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tv=torch.from_numpy(v)
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jv=jt.array(v)
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jatt.load_parameters(tatt.state_dict())
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ty, tw = tatt(tq, tk, tv)
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jy, jw = jatt(jq, jk, jv)
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assert np.allclose(ty.detach().numpy(), jy.numpy(), rtol=1e-3)
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assert np.allclose(tw.detach().numpy(), jw.numpy(), rtol=1e-3)
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@unittest.skipIf(skip_this_test, "No Torch found")
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class TestAttention(unittest.TestCase):
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def test_attention(self):
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j_opt = jt.optim.SGD([jt.array([1])], 1.0)
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t_opt = torch.optim.SGD([torch.ones([1])], 1.0)
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j_scheduler = jt.lr_scheduler.ReduceLROnPlateau(j_opt)
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t_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(t_opt)
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for i in range(100):
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loss=random.random()
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j_scheduler.step(loss)
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t_scheduler.step(loss)
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assert j_opt.lr == t_opt.state_dict()['param_groups'][0]['lr']
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if __name__ == "__main__":
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unittest.main()
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