mirror of https://github.com/Jittor/Jittor
606 lines
22 KiB
Python
606 lines
22 KiB
Python
# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Guowei Yang <471184555@qq.com>
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# Guoye Yang <498731903@qq.com>
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# Wenyang Zhou <576825820@qq.com>
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# Meng-Hao Guo <guomenghao1997@gmail.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 import init, Module
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import numpy as np
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import math
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from jittor.pool import Pool, pool, AdaptiveAvgPool2d
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from jittor.optim import *
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def matmul_transpose(a, b):
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'''
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returns a * b^T
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'''
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assert len(a.shape) >= 2 and len(b.shape) == 2
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assert a.shape[-1] == b.shape[-1]
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shape = list(a.shape)[:-1] + list(b.shape)
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a = a.broadcast(shape, [len(shape)-2])
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b = b.broadcast(shape)
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return (a*b).sum(len(shape)-1)
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def matmul(a, b):
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assert len(a.shape) >= 2 and len(b.shape) == 2
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assert a.shape[-1] == b.shape[-2]
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shape = list(a.shape) + [b.shape[-1]]
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a = a.broadcast(shape, [len(shape)-1])
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b = b.broadcast(shape)
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return (a*b).sum(len(shape)-2)
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jt.Var.matmul = jt.Var.__matmul__ = matmul
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jt.Var.__imatmul__ = lambda a,b: a.assign(matmul(a,b))
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def get_init_var_rand(shape, dtype):
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return jt.array(np.random.normal(0.0, 1.0, shape).astype(np.float32))
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@jt.var_scope('conv')
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def conv(x, in_planes, out_planes, kernel_size, padding, stride = 1, init_method=None):
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Kw = kernel_size
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Kh = kernel_size
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_C = in_planes
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Kc = out_planes
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N,C,H,W = x.shape
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assert C==_C
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if init_method==None:
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w = jt.make_var([Kc, _C, Kh, Kw], init=lambda *a: init.relu_invariant_gauss(*a, mode="fan_out"))
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else:
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w = jt.make_var([Kc, _C, Kh, Kw], init=init_method)
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xx = x.reindex([N,Kc,C,(H+padding*2-kernel_size)//stride+1,(W+padding*2-kernel_size)//stride+1,Kh,Kw], [
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'i0', # Nid
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'i2', # Cid
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f'i3*{stride}-{padding}+i5', # Hid+Khid
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f'i4*{stride}-{padding}+i6', # Wid+KWid
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])
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ww = w.broadcast(xx.shape, [0,3,4])
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yy = xx*ww
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y = yy.sum([2,5,6]) # C, Kh, Kw
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return y
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@jt.var_scope('linear')
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def linear(x, n):
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w = jt.make_var([n, x.shape[-1]], init=lambda *a: init.invariant_uniform(*a))
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w = w.reindex([w.shape[1], w.shape[0]],["i1","i0"])
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bound = 1.0/math.sqrt(w.shape[0])
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b = jt.make_var([n], init=lambda *a: init.uniform(*a,-bound,bound))
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return jt.matmul(x, w) + b
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def relu(x): return jt.maximum(x, 0)
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def leaky_relu(x, scale=0.01): return jt.ternary(x>0, x, x*scale)
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def relu6(x): return jt.minimum(jt.maximum(x, 0), 6)
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class PReLU(Module):
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def __init__(self, num_parameters=1, init_=0.25):
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self.num_parameters = num_parameters
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self.a = init.constant((num_parameters,), "float32", init_)
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def execute(self, x):
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if self.num_parameters != 1:
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assert self.num_parameters == x.size(1), f"num_parameters does not match input channels in PReLU"
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return jt.maximum(0, x) + self.a.broadcast(x, [0,2,3]) * jt.minimum(0, x)
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else:
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return jt.maximum(0, x) + self.a * jt.minimum(0, x)
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#TODO dims is 4 will cause slowly execution
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def cross_entropy_loss(output, target, ignore_index=None):
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if len(output.shape) == 4:
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c_dim = output.shape[1]
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output = output.transpose((0, 2, 3, 1))
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output = output.reshape((-1, c_dim))
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if ignore_index is not None:
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target = jt.ternary(target==ignore_index,
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jt.array(-1).broadcast(target), target)
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mask = jt.logical_and(target >= 0, target < output.shape[1])
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target = target.reshape((-1, ))
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target = target.broadcast(output, [1])
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target = target.index(1) == target
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output = output - output.max([1], keepdims=True)
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loss = output.exp().sum(1).log()
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loss = loss - (output*target).sum(1)
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if ignore_index is None:
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return loss.mean()
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else:
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return loss.sum() / jt.maximum(mask.int().sum(), 1)
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def mse_loss(output, target):
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return (output-target).sqr().mean()
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def bce_loss(output, target):
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return - (target * jt.log(jt.maximum(output, 1e-20)) + (1 - target) * jt.log(jt.maximum(1 - output, 1e-20))).mean()
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def l1_loss(output, target):
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return (output-target).abs().mean()
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class CrossEntropyLoss(Module):
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def __init__(self):
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pass
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def execute(self, output, target):
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return cross_entropy_loss(output, target)
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class MSELoss(Module):
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def __init__(self):
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pass
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def execute(self, output, target):
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return mse_loss(output, target)
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class BCELoss(Module):
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def __init__(self):
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pass
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def execute(self, output, target):
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return bce_loss(output, target)
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class L1Loss(Module):
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def __init__(self):
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pass
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def execute(self, output, target):
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return l1_loss(output, target)
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class BCEWithLogitsLoss(Module):
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def __init__(self):
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self.sigmoid = Sigmoid()
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self.bce = BCELoss()
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def execute(self, output, target):
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output = self.sigmoid(output)
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output = self.bce(output, target)
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return output
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def softmax(x, dim = None):
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if dim is None:
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x = (x - x.max()).exp()
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ret = x / x.sum()
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else:
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x = (x-x.max(dim, keepdims=True)).exp()
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ret = x / x.sum(dim, keepdims=True)
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return ret
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class Dropout(Module):
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def __init__(self, p=0.5, is_train=False):
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assert p >= 0 and p <= 1, "dropout probability has to be between 0 and 1, but got {}".format(p)
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self.p = p
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self.is_train = is_train
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#TODO: test model.train() to change self.is_train
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def execute(self, input):
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output = input
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if self.p > 0 and self.is_train:
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if self.p == 1:
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noise = jt.zeros(input.shape)
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output = output * noise
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else:
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noise = jt.random(input.shape)
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noise = (noise > self.p).int()
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output = output * noise / (1.0 - self.p) # div keep prob
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return output
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class Linear(Module):
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def __init__(self, in_features, out_features, bias=True):
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self.in_features = in_features
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self.out_features = out_features
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self.weight = init.invariant_uniform((out_features, in_features), "float32")
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bound = 1.0/math.sqrt(in_features)
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self.bias = init.uniform((out_features,), "float32",-bound,bound) if bias else None
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def execute(self, x):
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x = matmul_transpose(x, self.weight)
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if self.bias is not None:
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return x + self.bias
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return x
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class BatchNorm(Module):
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def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=None, is_train=True, sync=True):
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assert affine == None
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self.sync = sync
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self.num_features = num_features
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self.is_train = is_train
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self.eps = eps
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self.momentum = momentum
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self.weight = init.constant((num_features,), "float32", 1.0)
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self.bias = init.constant((num_features,), "float32", 0.0)
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self.running_mean = init.constant((num_features,), "float32", 0.0).stop_grad()
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self.running_var = init.constant((num_features,), "float32", 1.0).stop_grad()
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def execute(self, x):
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if self.is_train:
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xmean = jt.mean(x, dims=[0,2,3], keepdims=1)
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x2mean = jt.mean(x*x, dims=[0,2,3], keepdims=1)
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if self.sync and jt.mpi:
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xmean = xmean.mpi_all_reduce("mean")
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x2mean = x2mean.mpi_all_reduce("mean")
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xvar = x2mean-xmean*xmean
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norm_x = (x-xmean)/jt.sqrt(xvar+self.eps)
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self.running_mean += (xmean.sum([0,2,3])-self.running_mean)*self.momentum
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self.running_var += (xvar.sum([0,2,3])-self.running_var)*self.momentum
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else:
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running_mean = self.running_mean.broadcast(x, [0,2,3])
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running_var = self.running_var.broadcast(x, [0,2,3])
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norm_x = (x-running_mean)/jt.sqrt(running_var+self.eps)
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w = self.weight.broadcast(x, [0,2,3])
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b = self.bias.broadcast(x, [0,2,3])
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return norm_x * w + b
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class BatchNorm1d(Module):
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def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=None, is_train=True, sync=True):
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assert affine == None
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self.sync = sync
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self.num_features = num_features
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self.is_train = is_train
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self.eps = eps
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self.momentum = momentum
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self.weight = init.constant((num_features,), "float32", 1.0)
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self.bias = init.constant((num_features,), "float32", 0.0)
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self.running_mean = init.constant((num_features,), "float32", 0.0).stop_grad()
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self.running_var = init.constant((num_features,), "float32", 1.0).stop_grad()
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def execute(self, x):
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if self.is_train:
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xmean = jt.mean(x, dims=[0], keepdims=1)
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x2mean = jt.mean(x*x, dims=[0], keepdims=1)
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if self.sync and jt.mpi:
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xmean = xmean.mpi_all_reduce("mean")
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x2mean = x2mean.mpi_all_reduce("mean")
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xvar = x2mean-xmean*xmean
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norm_x = (x-xmean)/jt.sqrt(xvar+self.eps)
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self.running_mean += (xmean.sum([0])-self.running_mean)*self.momentum
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self.running_var += (xvar.sum([0])-self.running_var)*self.momentum
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else:
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running_mean = self.running_mean.broadcast(x, [0])
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running_var = self.running_var.broadcast(x, [0])
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norm_x = (x-running_mean)/jt.sqrt(running_var+self.eps)
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w = self.weight.broadcast(x, [0])
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b = self.bias.broadcast(x, [0])
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return norm_x * w + b
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class InstanceNorm2d(Module):
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def __init__(self, num_features, eps=1e-05, momentum=0.1, affine=None, is_train=True, sync=True):
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assert affine == None
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self.sync = sync
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self.num_features = num_features
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self.is_train = is_train
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self.eps = eps
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self.momentum = momentum
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self.weight = init.constant((num_features,), "float32", 1.0)
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self.bias = init.constant((num_features,), "float32", 0.0)
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def execute(self, x):
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xmean = jt.mean(x, dims=[2,3], keepdims=1)
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x2mean = jt.mean(x*x, dims=[2,3], keepdims=1)
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if self.sync and jt.mpi:
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xmean = xmean.mpi_all_reduce("mean")
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x2mean = x2mean.mpi_all_reduce("mean")
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xvar = jt.maximum(x2mean-xmean*xmean, 0)
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norm_x = (x-xmean)/jt.sqrt(xvar+self.eps)
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w = self.weight.broadcast(x, [0,2,3])
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b = self.bias.broadcast(x, [0,2,3])
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return norm_x * w + b
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Relu = jt.make_module(relu)
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ReLU = Relu
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Leaky_relu = jt.make_module(leaky_relu, 2)
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LeakyReLU = Leaky_relu
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ReLU6 = jt.make_module(relu6)
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Softmax = jt.make_module(softmax, 2)
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class Conv(Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size if isinstance(kernel_size, tuple) else (kernel_size, kernel_size)
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self.stride = stride if isinstance(stride, tuple) else (stride, stride)
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self.padding = padding if isinstance(padding, tuple) else (padding, padding)
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self.dilation = dilation if isinstance(dilation, tuple) else (dilation, dilation)
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self.groups = groups
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assert in_channels % groups == 0, 'in_channels must be divisible by groups'
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assert out_channels % groups == 0, 'out_channels must be divisible by groups'
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Kh, Kw = self.kernel_size
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self.groups = groups
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assert in_channels % groups == 0, 'in_channels must be divisible by groups'
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assert out_channels % groups == 0, 'out_channels must be divisible by groups'
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self.weight = init.relu_invariant_gauss([out_channels, in_channels//groups, Kh, Kw], dtype="float", mode="fan_out")
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if bias:
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self.bias = init.uniform([out_channels], dtype="float", low=-1, high=1)
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else:
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self.bias = None
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def execute(self, x):
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if self.groups == 1:
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N,C,H,W = x.shape
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Kh, Kw = self.kernel_size
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assert C==self.in_channels
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oh = (H+self.padding[0]*2-Kh*self.dilation[0]+self.dilation[0]-1)//self.stride[0]+1
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ow = (W+self.padding[1]*2-Kw*self.dilation[1]+self.dilation[1]-1)//self.stride[1]+1
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xx = x.reindex([N,self.out_channels,C,oh,ow,Kh,Kw], [
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'i0', # Nid
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'i2', # Cid
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f'i3*{self.stride[0]}-{self.padding[0]}+i5*{self.dilation[0]}', # Hid+Khid
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f'i4*{self.stride[1]}-{self.padding[1]}+i6*{self.dilation[1]}', # Wid+KWid
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])
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ww = self.weight.broadcast(xx.shape, [0,3,4])
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yy = xx*ww
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y = yy.sum([2,5,6]) # Kc, Kh, Kw
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if self.bias is not None:
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b = self.bias.broadcast(y.shape, [0,2,3])
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y = y + b
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return y
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else:
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N,C,H,W = x.shape
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Kh, Kw = self.kernel_size
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G = self.groups
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CpG = C // G # channels per group
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assert C==self.in_channels
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oc = self.out_channels
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oh = (H+self.padding[0]*2-Kh*self.dilation[0]+self.dilation[0]-1)//self.stride[0]+1
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ow = (W+self.padding[1]*2-Kw*self.dilation[1]+self.dilation[1]-1)//self.stride[1]+1
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xx = x.reindex([N,G,oc//G,CpG,oh,ow,Kh,Kw], [
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'i0', # Nid
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f'i1*{CpG}+i3', # Gid
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f'i4*{self.stride[0]}-{self.padding[0]}+i6*{self.dilation[0]}', # Hid+Khid
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f'i5*{self.stride[1]}-{self.padding[1]}+i7*{self.dilation[1]}', # Wid+KWid
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])
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# w: [oc, CpG, Kh, Kw]
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ww = self.weight.reindex([N, G, oc//G, CpG, oh, ow, Kh, Kw], [
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f'i1*{oc//G}+i2',
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'i3',
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'i6',
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'i7'
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])
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yy = xx*ww
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y = yy.reindex_reduce('add', [N, oc, oh, ow], [
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'i0',
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f'i1*{oc//G}+i2',
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'i4',
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'i5'
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])
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if self.bias is not None:
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b = self.bias.broadcast(y.shape, [0,2,3])
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y = y + b
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return y
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class ConvTranspose(Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, \
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padding=0, output_padding=0, groups=1, bias=True, dilation=1):
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self.in_channels = in_channels
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self.out_channels = out_channels
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# added
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self.dilation = dilation
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self.group = groups
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assert groups==1, "Group conv not supported yet."
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self.kernel_size = kernel_size if isinstance(kernel_size, tuple) else (kernel_size, kernel_size)
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self.stride = stride if isinstance(stride, tuple) else (stride, stride)
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self.dilation = dilation if isinstance(dilation, tuple) else (dilation, dilation)
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# added
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self.padding = padding if isinstance(padding, tuple) else (padding, padding)
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self.real_padding = (self.dilation[0] * (self.kernel_size[0] - 1) - self.padding[0],
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self.dilation[1] * (self.kernel_size[1] - 1) - self.padding[1])
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self.output_padding = output_padding if isinstance (output_padding, tuple) else (output_padding, output_padding)
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self.weight = init.relu_invariant_gauss((in_channels, out_channels) + self.kernel_size, dtype="float", mode="fan_out")
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if bias:
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self.bias = init.uniform([out_channels], dtype="float", low=-1, high=1)
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else:
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self.bias = None
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def execute(self, x):
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N,C,H,W = x.shape
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i,o,h,w = self.weight.shape
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assert C==i
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stride_h, stride_w = self.stride
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padding_h, padding_w = self.padding
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dilation_h, dilation_w = self.dilation
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h_out = (H-1) * stride_h + self.output_padding[0] - 2*padding_h + 1 + (h-1)*dilation_h
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w_out = (W-1) * stride_w + self.output_padding[1] - 2*padding_w + 1 + (w-1)*dilation_w
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out_shape = (N, o, h_out, w_out)
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shape = (N, i, o, H, W, h, w)
|
|
xx = x.broadcast(shape, (2, 5, 6)) # i,h,w
|
|
ww = self.weight.broadcast(shape, (0, 3, 4)) # N,H,W
|
|
y = (ww*xx).reindex_reduce("add", out_shape, [
|
|
'i0', # N
|
|
'i2', # o
|
|
f'i3*{stride_h}-{padding_h}+i5*{dilation_h}', # Hid+Khid
|
|
f'i4*{stride_w}-{padding_w}+i6*{dilation_w}', # Wid+KWid
|
|
])
|
|
if self.bias is not None:
|
|
b = self.bias.broadcast(y.shape, [0,2,3])
|
|
y = y + b
|
|
return y
|
|
|
|
|
|
class ReflectionPad2d(Module):
|
|
def __init__(self, padding):
|
|
self.padding = padding
|
|
if isinstance(self.padding, int):
|
|
self.pl = self.padding
|
|
self.pr = self.padding
|
|
self.pt = self.padding
|
|
self.pb = self.padding
|
|
elif isinstance(self.padding, tuple):
|
|
self.pl, self.pr, self.pt, self.pb = self.padding
|
|
else:
|
|
raise TypeError(f"ReflectionPad2d padding just support int or tuple, but found {type(padding)}")
|
|
|
|
def execute(self, x):
|
|
n,c,h,w = x.shape
|
|
assert (self.pl < w and self.pr < w), f"padding_left and padding_right should be smaller than input width"
|
|
assert (self.pt < h and self.pb < h), f"padding_top and padding_bottom should be smaller than input height"
|
|
oh=h+self.pt+self.pb
|
|
ow=w+self.pl+self.pr
|
|
l = self.pl
|
|
r = self.pl + w - 1
|
|
t = self.pt
|
|
b = self.pt + h - 1
|
|
return x.reindex([n,c,oh,ow], ["i0","i1",
|
|
f"i2<{t} ? {t}-i2 : i2 > {b} ? {h-1+b}-i2 : i2-{t}",
|
|
f"i3<{l} ? {l}-i3 : i3 > {r} ? {w-1+r}-i3 : i3-{l}",
|
|
])
|
|
|
|
class ZeroPad2d(Module):
|
|
def __init__(self, padding):
|
|
self.padding = padding
|
|
if isinstance(self.padding, int):
|
|
self.pl = self.padding
|
|
self.pr = self.padding
|
|
self.pt = self.padding
|
|
self.pb = self.padding
|
|
elif isinstance(self.padding, tuple):
|
|
self.pl, self.pr, self.pt, self.pb = self.padding
|
|
else:
|
|
raise TypeError(f"ZeroPad2d padding just support int or tuple, but found {type(padding)}")
|
|
|
|
def execute(self, x):
|
|
n,c,h,w = x.shape
|
|
return x.reindex([n,c,h+self.pt+self.pb,w+self.pl+self.pr], ["i0","i1",f"i2-{self.pt}",f"i3-{self.pl}"])
|
|
|
|
class ConstantPad2d(Module):
|
|
def __init__(self, padding, value):
|
|
self.padding = padding
|
|
if isinstance(self.padding, int):
|
|
self.pl = self.padding
|
|
self.pr = self.padding
|
|
self.pt = self.padding
|
|
self.pb = self.padding
|
|
elif isinstance(self.padding, tuple):
|
|
self.pl, self.pr, self.pt, self.pb = self.padding
|
|
else:
|
|
raise TypeError(f"ConstantPad2d padding just support int or tuple, but found {type(padding)}")
|
|
self.value = value
|
|
|
|
def execute(self, x):
|
|
n,c,h,w = x.shape
|
|
return x.reindex([n,c,h+self.pt+self.pb,w+self.pl+self.pr], ["i0","i1",f"i2-{self.pt}",f"i3-{self.pl}"], overflow_value=self.value)
|
|
|
|
class ReplicationPad2d(Module):
|
|
def __init__(self, padding):
|
|
self.padding = padding
|
|
if isinstance(self.padding, int):
|
|
self.pl = self.padding
|
|
self.pr = self.padding
|
|
self.pt = self.padding
|
|
self.pb = self.padding
|
|
elif isinstance(self.padding, tuple):
|
|
self.pl, self.pr, self.pt, self.pb = self.padding
|
|
else:
|
|
raise TypeError(f"ReplicationPad2d padding just support int or tuple, but found {type(padding)}")
|
|
|
|
def execute(self, x):
|
|
n,c,h,w = x.shape
|
|
oh=h+self.pt+self.pb
|
|
ow=w+self.pl+self.pr
|
|
l = self.pl
|
|
r = self.pl + w - 1
|
|
t = self.pt
|
|
b = self.pt + h - 1
|
|
return x.reindex([n,c,oh,ow], ["i0","i1",
|
|
f"i2<{t} ? 0 : i2 > {b} ? {h-1} : i2-{t}",
|
|
f"i3<{l} ? 0 : i3 > {r} ? {w-1} : i3-{l}"
|
|
])
|
|
|
|
class Embedding(Module):
|
|
def __init__(self, num, dim):
|
|
self.num = num
|
|
self.dim = dim
|
|
self.weight = jt.init.gauss([num,dim],'float32').stop_grad()
|
|
|
|
def execute(self, x):
|
|
res = self.weight[x].reshape([x.shape[0],self.dim])
|
|
return res
|
|
|
|
class PixelShuffle(Module):
|
|
def __init__(self, upscale_factor):
|
|
self.upscale_factor = upscale_factor
|
|
|
|
def execute(self, x):
|
|
n,c,h,w = x.shape
|
|
r = self.upscale_factor
|
|
assert c%(r**2)==0, f"input channel needs to be divided by upscale_factor's square in PixelShuffle"
|
|
return x.reindex([n,int(c/r**2),h*r,w*r], [
|
|
"i0",
|
|
f"i1*{r**2}+i2%{r}*{r}+i3%{r}",
|
|
f"i2/{r}",
|
|
f"i3/{r}"
|
|
])
|
|
|
|
class Tanh(Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
def execute(self, x) :
|
|
return x.tanh()
|
|
|
|
class Sigmoid(Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
def execute(self, x) :
|
|
return x.sigmoid()
|
|
|
|
def resize(x, size, mode="nearest"):
|
|
img = x
|
|
n,c,h,w = x.shape
|
|
H,W = size
|
|
new_size = [n,c,H,W]
|
|
nid, cid, hid, wid = jt.index(new_size)
|
|
x = hid * h / H
|
|
y = wid * w / W
|
|
if mode=="nearest":
|
|
return img.reindex([nid, cid, x.floor(), y.floor()])
|
|
if mode=="bilinear":
|
|
fx, fy = x.floor(), y.floor()
|
|
cx, cy = fx+1, fy+1
|
|
dx, dy = x-fx, y-fy
|
|
a = img.reindex_var([nid, cid, fx, fy])
|
|
b = img.reindex_var([nid, cid, cx, fy])
|
|
c = img.reindex_var([nid, cid, fx, cy])
|
|
d = img.reindex_var([nid, cid, cx, cy])
|
|
dnx, dny = 1-dx, 1-dy
|
|
ab = dx*b + dnx*a
|
|
cd = dx*d + dnx*c
|
|
o = ab*dny + cd*dy
|
|
return o
|
|
raise(f"Not support {interpolation}")
|
|
|
|
class Upsample(Module):
|
|
def __init__(self, scale_factor=None, mode='nearest'):
|
|
self.scale_factor = scale_factor if isinstance(scale_factor, tuple) else (scale_factor, scale_factor)
|
|
self.mode = mode
|
|
|
|
def execute(self, x):
|
|
return resize(x, size=(int(x.shape[2]*self.scale_factor[0]), int(x.shape[3]*self.scale_factor[1])), mode=self.mode)
|
|
|
|
class Sequential(Module):
|
|
def __init__(self, *args):
|
|
self.layers = args
|
|
def __getitem__(self, idx):
|
|
return self.layers[idx]
|
|
def execute(self, x):
|
|
for layer in self.layers:
|
|
x = layer(x)
|
|
return x
|
|
def dfs(self, parents, k, callback, callback_leave):
|
|
n_children = len(self.layers)
|
|
ret = callback(parents, k, self, n_children)
|
|
if ret == False:
|
|
return
|
|
for k,v in enumerate(self.layers):
|
|
parents.append(self)
|
|
v.dfs(parents, k, callback, callback_leave)
|
|
parents.pop()
|
|
if callback_leave:
|
|
callback_leave(parents, k, self, n_children)
|