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IH Fix inference parameters #135

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Nov 14, 2022
Merged

IH Fix inference parameters #135

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31 changes: 23 additions & 8 deletions continuousflex/protocols/utilities/deep_hemnma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ def norm(imgs_path, output_path, FLAG, mode, batch_size):
random_seed = 42
validation_split = .2
shuffle_dataset = True

dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor((1-validation_split) * dataset_size))
Expand All @@ -25,8 +26,8 @@ def norm(imgs_path, output_path, FLAG, mode, batch_size):

train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
print('the train set size is: {} images'.format(len(train_sampler)))
print('the validation set size is: {} images'.format(len(valid_sampler)))
#print('the train set size is: {} images'.format(len(train_sampler)))
#print('the validation set size is: {} images'.format(len(valid_sampler)))
train_loader = DataLoader(dataset, batch_size=batch_size, sampler=train_sampler)
validation_loader = DataLoader(dataset, batch_size=batch_size, sampler=valid_sampler)
sum_, squared_sum_, num_batches = 0, 0, 0
Expand Down Expand Up @@ -60,8 +61,21 @@ def train(imgs_path, output_path, epochs=400, batch_size=2, lr=1e-4, flag=0, dev
DEVICE = 'cpu'
mean, std = norm(imgs_path, output_path, FLAG, mode, batch_size)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((mean), (std))])
dataset = cryodata(imgs_path, output_path, flag=FLAG, mode = mode, transform=transform)
dataset_size = len(dataset)
transform1 = transforms.Compose([transforms.ToTensor(),
transforms.RandomRotation((-45, 45)),
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You are augmenting the dataset for training, I am right? If so, 45 degrees for single particle images might cause severe interpolation errors since the images are too small compared to natural images. I would suggest keeping with 90 180 270 only.
Keep in mind for later if this method is to be extended to tomography that no rotation is allowed since it causes rotation of the missing wedge region.

transforms.Normalize((mean), (std))])
dataset1 = cryodata(imgs_path, output_path, flag=FLAG, mode= mode,
transform=transform)
dataset2 = cryodata(imgs_path, output_path, flag=FLAG, mode= mode,
transform=transform1)
transform2 = transforms.Compose([transforms.ToTensor(),
transforms.RandomRotation((-90, 90)),
transforms.Normalize((mean), (std))])
dataset3 = cryodata(imgs_path, output_path, flag=FLAG, mode= mode,
transform=transform2)
increased_dataset = torch.utils.data.ConcatDataset([dataset1, dataset2, dataset3])
#dataset = cryodata(imgs_path, output_path, flag=FLAG, mode = mode, transform=transform)
dataset_size = len(increased_dataset)
indices = list(range(dataset_size))
split = int(np.floor((1-validation_split) * dataset_size))

Expand All @@ -72,10 +86,10 @@ def train(imgs_path, output_path, epochs=400, batch_size=2, lr=1e-4, flag=0, dev

train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
print('the train set size is: {} images'.format(len(train_sampler)))
print('the validation set size is: {} images'.format(len(valid_sampler)))
train_loader = DataLoader(dataset, batch_size=batch_size, sampler=train_sampler)
validation_loader = DataLoader(dataset, batch_size=batch_size, sampler=valid_sampler)
print('the train set size is: {} images'.format(len(train_sampler)//3))
print('the validation set size is: {} images'.format(len(valid_sampler)//3))
train_loader = DataLoader(increased_dataset, batch_size=batch_size, sampler=train_sampler)
validation_loader = DataLoader(increased_dataset, batch_size=batch_size, sampler=valid_sampler)

im, p = next(iter(train_loader))
if FLAG=='nma':
Expand All @@ -97,6 +111,7 @@ def train(imgs_path, output_path, epochs=400, batch_size=2, lr=1e-4, flag=0, dev
running_loss = 0.0

for img, params in train_loader:
img = img/255.
optimizer.zero_grad()
pred_params = model(img.to(DEVICE), 'train')
l = criterion(params.to(DEVICE), pred_params)
Expand Down
24 changes: 17 additions & 7 deletions continuousflex/protocols/utilities/deep_hemnma_infer.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,4 @@
import torch.nn as nn
from torchvision import transforms
import torch.optim as optim
from torch.utils.data import DataLoader
from continuousflex.protocols.utilities.processing_dh.data import cryodata
from continuousflex.protocols.utilities.processing_dh.utils import quater2euler, reverse_min_max
Expand All @@ -10,7 +8,17 @@
from pathlib import Path
import sys
import pwem.emlib.metadata as md

def norm(imgs_path, weights_path, flag, mode, batch_size):
dataset = cryodata(imgs_path, weights_path, flag=flag, mode=mode, transform=transforms.ToTensor())
train_loader = DataLoader(dataset, batch_size=batch_size)
sum_, squared_sum_, num_batches = 0, 0, 0
for img, image_name in train_loader:
sum_ += torch.mean(img, dim=[0, 2, 3])
squared_sum_ += torch.mean(img**2, dim=[0, 2, 3])
num_batches += 1
mean = sum_/num_batches
std = (squared_sum_/num_batches - mean**2)**0.5
return mean, std
def infer(imgs_path, weights_path, output_path, num_modes, batch_size=2, flag=0, device=0, mode='inference'):
FLAG = ''
if flag==0:
Expand All @@ -27,8 +35,9 @@ def infer(imgs_path, weights_path, output_path, num_modes, batch_size=2, flag=0,
else:
DEVICE = 'cpu'


dataset = cryodata(imgs_path, weights_path, flag=FLAG, mode = mode, transform=transforms.ToTensor())
mean, std = norm(imgs_path, weights_path, flag=FLAG, mode=mode, batch_size=batch_size)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((mean), (std))])
dataset = cryodata(imgs_path, weights_path, flag=FLAG, mode=mode, transform=transform)

dataset_size = len(dataset)
print('the train set size is: {} images'.format(dataset_size))
Expand All @@ -45,13 +54,14 @@ def infer(imgs_path, weights_path, output_path, num_modes, batch_size=2, flag=0,
model = deephemnma(2).to(DEVICE)
predictions = np.zeros((dataset_size, 2), dtype='float32')
elif FLAG=='all':
model = deephemnma(9).to(DEVICE)
model = deephemnma(6+num_modes).to(DEVICE)
predictions = np.zeros((dataset_size, 6+num_modes), dtype='float32')

model.load_state_dict(torch.load(weights_path))
model.eval()
with torch.no_grad():
i = 0
for img, params in data_loader:
for img, image_name in data_loader:
pred_params = model(img.to(DEVICE), mode)
predictions[i * batch_size:(i + 1) * batch_size, :] = pred_params.cpu()
i+=1
Expand Down