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deepmedic #41
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Original file line number | Diff line number | Diff line change |
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import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
import numpy as np | ||
from math import ceil | ||
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from .base import PytorchModelBase | ||
from .loss_functions import ce_minus_log_dice_with_size_mismatch | ||
from .utils import get_tensor_from_array | ||
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########################################################### | ||
# Deepmedic # | ||
# input [batch_num, in_channel, D, H, W ] # | ||
# [D1, H1, W1] = [D, H, W] // 2 # | ||
# # | ||
# output [batch_num, out_channel, D', H', W' ] # | ||
# D' = D1 - (kernel_size - 1) * conv_time * block_num # | ||
# H' = H1 - (kernel_size - 1) * conv_time * block_num # | ||
# W' = W1 - (kernel_size - 1) * conv_time * block_num # | ||
# notice that D', H', W' should > 0 # | ||
# # | ||
# for initialize # | ||
# channel_list = # | ||
# [[x0, x1], [x1, x2] ... [xn-3, xn-2], [xn-1, xn]] # | ||
# x0 = input_channel # | ||
# x1 = duplicate_num # | ||
# x2~xn-2 = block channel num # | ||
# xn-1 = final block input channel, = 2 * xn-2 # | ||
########################################################### | ||
class Deepmedic(PytorchModelBase): | ||
def __init__( | ||
self, | ||
data_format: dict, | ||
channel_list: list, | ||
kernel_size: int = 3, | ||
conv_time: int = 2, | ||
dim_in: int = 64, | ||
batch_sampler_id='center_patch3d', | ||
): | ||
super(Deepmedic, self).__init__( | ||
data_format=data_format, | ||
batch_sampler_id=batch_sampler_id, | ||
loss_fn=ce_minus_log_dice_with_size_mismatch, | ||
) | ||
if kernel_size % 2 == 0: | ||
raise AssertionError('kernel_size({}) must be odd'.format(kernel_size)) | ||
self.dim_in = dim_in | ||
self.pool = nn.AvgPool3d(kernel_size=5, stride=3, padding=1) | ||
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self.duplicate1 = Duplicate(data_format['channels'], channel_list[0][1], kernel_size) | ||
self.duplicate2 = Duplicate(data_format['channels'], channel_list[0][1], kernel_size) | ||
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self.first = Path(channel_list[1:-1], kernel_size, conv_time) | ||
self.second = Path(channel_list[1:-1], kernel_size, conv_time) | ||
self.block = Block(channel_list[-1][0], channel_list[-1][1], 1, conv_time) | ||
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self.out = Block(channel_list[-1][1], data_format['class_num'], | ||
kernel_size=1, conv_time=1, route=False) | ||
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def forward(self, inp): | ||
inp = get_tensor_from_array(inp) | ||
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# x1 : for high resolution, x2 for low resolution | ||
# c_ : cut inp to smaller piece in the middle | ||
c_d = inp.shape[2] // 4 | ||
c_h = inp.shape[3] // 4 | ||
c_w = inp.shape[4] // 4 | ||
x1 = inp[:, :, c_d:-c_d, c_h:-c_h, c_w:-c_w] | ||
x2 = self.pool(inp) | ||
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x1 = self.duplicate1(x1) | ||
x2 = self.duplicate2(x2) | ||
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x1 = self.first(x1) | ||
x2 = self.second(x2) | ||
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if x1.shape[2] < x2.shape[2]: | ||
raise AssertionError('Shape error, low resolution{} must be smaller than \ | ||
high resolution{}'.format(x2.shape, x1.shape)) | ||
""" | ||
upsampling here can be changed to convtranspose3d | ||
""" | ||
x2 = F.interpolate(x2, x1.shape[2:]) | ||
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x = torch.cat((x1, x2), 1) | ||
x = self.block(x) | ||
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x = self.out(x) | ||
x = F.softmax(x, dim=1) | ||
return x | ||
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def predict(self, test_data, **kwargs): | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 希望不要 overload 這個 method |
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test_data = test_data['volume'] | ||
self.eval() | ||
dim_in = 64 | ||
dim_out = 16 | ||
patch_list = get_patch(test_data, dim_in, dim_out) | ||
pred_list = [] | ||
for patch in patch_list: | ||
pred = self.forward(patch).cpu().data.numpy() | ||
pred_list.append(pred) | ||
assert(patch_list[0].shape[-1] == dim_in) | ||
assert(pred_list[0].shape[-1] == dim_out) | ||
return reassemble(pred_list, test_data, dim_out) | ||
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class Path(nn.Module): | ||
def __init__(self, channel_list, kernel_size, conv_time): | ||
super(Path, self).__init__() | ||
self.block_list = nn.ModuleList() | ||
channel = channel_list[0] | ||
block = Block(channel[0], channel[1], kernel_size, conv_time, route=False) | ||
self.block_list.append(block) | ||
for channel in channel_list[1:]: | ||
block = Block(channel[0], channel[1], kernel_size, conv_time) | ||
self.block_list.append(block) | ||
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def forward(self, x): | ||
for block in self.block_list: | ||
x = block(x) | ||
return x | ||
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########################################################### | ||
# Block # | ||
# input [batch_num, in_channel, D, H, W ] # | ||
# [route] True for residual connection # | ||
# # | ||
# output [batch_num, out_channel*2, D', H', W'] # | ||
# D' = D - (kernel_size - 1) * conv_time # | ||
# H' = H - (kernel_size - 1) * conv_time # | ||
# W' = W - (kernel_size - 1) * conv_time # | ||
########################################################### | ||
class Block(nn.Module): | ||
def __init__(self, input_channel, output_channel, kernel_size, conv_time=2, route=True): | ||
super(Block, self).__init__() | ||
self.route = route | ||
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self.norm_list = nn.ModuleList() | ||
self.conv_list = nn.ModuleList() | ||
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self.activation = nn.ReLU() | ||
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batch_norm = nn.BatchNorm3d(input_channel) | ||
self.norm_list.append(batch_norm) | ||
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conv = nn.Conv3d(input_channel, output_channel, kernel_size=kernel_size) | ||
self.conv_list.append(conv) | ||
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for i in range(conv_time - 1): | ||
batch_norm = nn.BatchNorm3d(output_channel) | ||
self.norm_list.append(batch_norm) | ||
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conv = nn.Conv3d(output_channel, output_channel, kernel_size=kernel_size) | ||
self.conv_list.append(conv) | ||
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def forward(self, x): | ||
x1 = x | ||
for (norm, conv) in zip(self.norm_list, self.conv_list): | ||
""" | ||
the order of norm, active, conv is weird | ||
""" | ||
x1 = norm(x1) | ||
x1 = self.activation(x1) | ||
x1 = conv(x1) | ||
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if self.route: | ||
# crop x to x1 | ||
d_ch = x1.shape[1] - x.shape[1] | ||
d_pix = x.shape[2] - x1.shape[2] | ||
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if d_pix != 0: | ||
if d_pix % 2 != 0: | ||
raise AssertionError('shape Error', x.shape, x1.shape) | ||
d = d_pix // 2 | ||
x = x[:, :, d:-d, d:-d, d:-d] | ||
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if d_ch != 0: | ||
# add channel to x | ||
pad_shape = list(x.shape) | ||
pad_shape[1] = d_ch | ||
empty = torch.zeros(pad_shape) | ||
if x.is_cuda: | ||
empty = empty.cuda() | ||
x = torch.cat((x, empty), 1) | ||
x1 = x + x1 | ||
return x1 | ||
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########################################################### | ||
# Duplication # | ||
# input [batch_num, input_channel, D, H, W] # | ||
# output [batch_num, duplication_num, D, H, W] # | ||
########################################################### | ||
class Duplicate(nn.Module): | ||
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def __init__(self, input_channel, duplication_num, kernel_size): | ||
super(Duplicate, self).__init__() | ||
self.duplicate = nn.Conv3d( | ||
input_channel, | ||
duplication_num, | ||
kernel_size=kernel_size, | ||
padding=kernel_size // 2, | ||
) | ||
self.batch_norm = nn.BatchNorm3d(duplication_num) | ||
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def forward(self, x): | ||
x = self.duplicate(x) | ||
return x | ||
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""" | ||
functions for deepmedic prediction | ||
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function description | ||
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having a function | ||
inp = [N, c_in, 64, 64, 64] | ||
out = [N, c_out, 16, 16, 16] | ||
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we want to predict array [N, c_out, d, h, w] | ||
""" | ||
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def get_patch(data, dim_in, dim_out): | ||
# first, we need to pad the array with size (dim_in - dim_out) / 2 arround | ||
# since our ouput will loss such pixels at each boundary | ||
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if (dim_in - dim_out) % 2 != 0: | ||
raise AssertionError("this should be even") | ||
pad = (dim_in - dim_out) // 2 | ||
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data_pad = np.pad(data, | ||
((0, 0), (0, 0), (pad, pad), (pad, pad), (pad, pad)), | ||
'constant', | ||
constant_values=0) | ||
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# pad data such that it can be divide by dim_out | ||
if data.shape[2] % dim_out != 0: | ||
d_p = dim_out - data.shape[2] % dim_out | ||
else: | ||
d_p = 0 | ||
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if data.shape[3] % dim_out != 0: | ||
h_p = dim_out - data.shape[3] % dim_out | ||
else: | ||
h_p = 0 | ||
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if data.shape[4] % dim_out != 0: | ||
w_p = dim_out - data.shape[4] % dim_out | ||
else: | ||
w_p = 0 | ||
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data_pad = np.pad(data_pad, | ||
((0, 0), (0, 0), (0, d_p), (0, h_p), (0, w_p)), | ||
'constant', | ||
constant_values=0) | ||
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# then we need to get the vertex of patch | ||
vertex_list = [] | ||
for d in range(data_pad.shape[2] // dim_out): | ||
for h in range(data_pad.shape[3] // dim_out): | ||
for w in range(data_pad.shape[4] // dim_out): | ||
vertex_list.append([d * dim_out, h * dim_out, w * dim_out]) | ||
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patch_list = [] | ||
shape = data_pad.shape | ||
for vertex in vertex_list: | ||
d_e = vertex[0] + dim_in | ||
h_e = vertex[1] + dim_in | ||
w_e = vertex[2] + dim_in | ||
if(d_e <= shape[2] and h_e <= shape[3] and w_e <= shape[4]): | ||
patch = data_pad[:, :, | ||
vertex[0]:vertex[0] + dim_in, | ||
vertex[1]:vertex[1] + dim_in, | ||
vertex[2]:vertex[2] + dim_in] | ||
patch_list.append(patch) | ||
return patch_list | ||
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def reassemble(pred_list, data, dim_out): | ||
d_n = ceil(data.shape[2] / dim_out) | ||
h_n = ceil(data.shape[3] / dim_out) | ||
w_n = ceil(data.shape[4] / dim_out) | ||
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# this assertion should never occur, or the logit is wrong | ||
# to fix it you might rewrite the prediction | ||
if len(pred_list) != (d_n * h_n * w_n): | ||
raise AssertionError("shape mismatch", len(pred_list), | ||
d_n * h_n * w_n) | ||
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shape = list(data.shape) | ||
shape[1] = pred_list[0].shape[1] | ||
pred_all = np.zeros(shape) | ||
idx = 0 | ||
for d in range(d_n): | ||
for h in range(h_n): | ||
for w in range(w_n): | ||
pred = pred_list[idx] | ||
# consider the situation at the boundary | ||
d_cut = dim_out | ||
if d * dim_out + dim_out > data.shape[2]: | ||
d_cut = data.shape[2] - d * dim_out | ||
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h_cut = dim_out | ||
if h * dim_out + dim_out > data.shape[3]: | ||
h_cut = data.shape[3] - h * dim_out | ||
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w_cut = dim_out | ||
if w * dim_out + dim_out > data.shape[4]: | ||
w_cut = data.shape[4] - w * dim_out | ||
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pred_all[ | ||
:, :, | ||
d * dim_out:d * dim_out + d_cut, | ||
h * dim_out:h * dim_out + h_cut, | ||
w * dim_out:w * dim_out + w_cut, | ||
] = pred[:, :, :d_cut, :h_cut, :w_cut] | ||
idx = idx + 1 | ||
return pred_all | ||
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# -------- testing functions for prediction -------- # | ||
if __name__ == "__main__": | ||
data = np.random.rand(1, 2, 200, 200, 200) | ||
patch_list = get_patch(data, 64, 32) | ||
pred_list = [] | ||
for patch in patch_list: | ||
# just test this by putting your output shape below | ||
pred = patch[:, :, 16:-16, 16:-16, 16:-16] | ||
pred_list.append(pred) | ||
pred_all = reassemble(pred_list, data, 32) | ||
print(np.all(pred_all == data)) |
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channel_list 的 shape 是固定 (N, 2) ?