806 KiB
806 KiB
import os
if not os.path.exists('cell_images'):
!pip install -U -q torch_snippets
!wget -q ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/cell_images.zip
!unzip -qq cell_images.zip
!rm cell_images.zip
from torch_snippets import *[K |████████████████████████████████| 36.7MB 82kB/s [K |████████████████████████████████| 61kB 9.7MB/s [K |████████████████████████████████| 102kB 14.5MB/s [?25h Building wheel for contextvars (setup.py) ... [?25l[?25hdone
id2int = {'Parasitized': 0, 'Uninfected': 1}from torchvision import transforms as T
trn_tfms = T.Compose([
T.ToPILImage(),
T.Resize(128),
T.CenterCrop(128),
T.ColorJitter(brightness=(0.95,1.05),
contrast=(0.95,1.05),
saturation=(0.95,1.05),
hue=0.05),
T.RandomAffine(5, translate=(0.01,0.1)),
T.ToTensor(),
T.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]),
])val_tfms = T.Compose([
T.ToPILImage(),
T.Resize(128),
T.CenterCrop(128),
T.ToTensor(),
T.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]),
])class MalariaImages(Dataset):
def __init__(self, files, transform=None):
self.files = files
self.transform = transform
logger.info(len(self))
def __len__(self):
return len(self.files)
def __getitem__(self, ix):
fpath = self.files[ix]
clss = fname(parent(fpath))
img = read(fpath, 1)
return img, clss
def choose(self):
return self[randint(len(self))]
def collate_fn(self, batch):
_imgs, classes = list(zip(*batch))
if self.transform:
imgs = [self.transform(img)[None] for img in _imgs]
classes = [torch.tensor([id2int[clss]]) for clss in classes]
imgs, classes = [torch.cat(i).to(device) for i in [imgs, classes]]
return imgs, classes, _imgsdevice = 'cuda' if torch.cuda.is_available() else 'cpu'
all_files = Glob('cell_images/*/*.png')
np.random.seed(10)
np.random.shuffle(all_files)
from sklearn.model_selection import train_test_split
trn_files, val_files = train_test_split(all_files, random_state=1)
trn_ds = MalariaImages(trn_files, transform=trn_tfms)
val_ds = MalariaImages(val_files, transform=val_tfms)
trn_dl = DataLoader(trn_ds, 32, shuffle=True, collate_fn=trn_ds.collate_fn)
val_dl = DataLoader(val_ds, 32, shuffle=False, collate_fn=val_ds.collate_fn)2020-11-08 18:09:42.461 | INFO | torch_snippets.loader:Glob:181 - 27558 files found at cell_images/*/*.png 2020-11-08 18:09:43.093 | INFO | __main__:__init__:6 - 20668 2020-11-08 18:09:43.094 | INFO | __main__:__init__:6 - 6890
def convBlock(ni, no):
return nn.Sequential(
nn.Dropout(0.2),
nn.Conv2d(ni, no, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.BatchNorm2d(no),
nn.MaxPool2d(2),
)
class MalariaClassifier(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
convBlock(3, 64),
convBlock(64, 64),
convBlock(64, 128),
convBlock(128, 256),
convBlock(256, 512),
convBlock(512, 64),
nn.Flatten(),
nn.Linear(256, 256),
nn.Dropout(0.2),
nn.ReLU(inplace=True),
nn.Linear(256, len(id2int))
)
self.loss_fn = nn.CrossEntropyLoss()
def forward(self, x):
return self.model(x)
def compute_metrics(self, preds, targets):
loss = self.loss_fn(preds, targets)
acc = (torch.max(preds, 1)[1] == targets).float().mean()
return loss, accdef train_batch(model, data, optimizer, criterion):
model.train()
ims, labels, _ = data
_preds = model(ims)
optimizer.zero_grad()
loss, acc = criterion(_preds, labels)
loss.backward()
optimizer.step()
return loss.item(), acc.item()
@torch.no_grad()
def validate_batch(model, data, criterion):
model.eval()
ims, labels, _ = data
_preds = model(ims)
loss, acc = criterion(_preds, labels)
return loss.item(), acc.item()model = MalariaClassifier().to(device)
criterion = model.compute_metrics
optimizer = optim.Adam(model.parameters(), lr=1e-3)
n_epochs = 2
log = Report(n_epochs)
for ex in range(n_epochs):
N = len(trn_dl)
for bx, data in enumerate(trn_dl):
loss, acc = train_batch(model, data, optimizer, criterion)
log.record(ex+(bx+1)/N, trn_loss=loss, trn_acc=acc, end='\r')
N = len(val_dl)
for bx, data in enumerate(val_dl):
loss, acc = validate_batch(model, data, criterion)
log.record(ex+(bx+1)/N, val_loss=loss, val_acc=acc, end='\r')
log.report_avgs(ex+1)EPOCH: 1.000 trn_loss: 0.253 trn_acc: 0.900 val_loss: 0.176 val_acc: 0.945 (142.87s - 142.87s remaining) EPOCH: 2.000 trn_loss: 0.175 trn_acc: 0.947 val_loss: 0.203 val_acc: 0.949 (285.57s - 0.00s remaining)
im2fmap = nn.Sequential(*(list(model.model[:5].children()) + list(model.model[5][:2].children())))def im2gradCAM(x):
model.eval()
logits = model(x)
heatmaps = []
activations = im2fmap(x)
print(activations.shape)
pred = logits.max(-1)[-1]
# get the model's prediction
model.zero_grad()
# compute gradients with respect to model's most confident logit
logits[0,pred].backward(retain_graph=True)
# get the gradients at the required featuremap location
# and take the avg gradient for every featuremap
pooled_grads = model.model[-6][1].weight.grad.data.mean((1,2,3))
# multiply each activation map with corresponding gradient average
for i in range(activations.shape[1]):
activations[:,i,:,:] *= pooled_grads[i]
# take the mean of all weighted activation maps
# (that has been weighted by avg. grad at each fmap)
heatmap = torch.mean(activations, dim=1)[0].cpu().detach()
return heatmap, 'Uninfected' if pred.item() else 'Parasitized'SZ = 128
def upsampleHeatmap(map, img):
m,M = map.min(), map.max()
map = 255 * ((map-m) / (M-m))
map = np.uint8(map)
map = cv2.resize(map, (SZ,SZ))
map = cv2.applyColorMap(255-map, cv2.COLORMAP_JET)
map = np.uint8(map)
map = np.uint8(map*0.7 + img*0.3)
return mapN = 20
_val_dl = DataLoader(val_ds, batch_size=N, shuffle=True, collate_fn=val_ds.collate_fn)
x,y,z = next(iter(_val_dl))
for i in range(N):
image = resize(z[i], SZ)
heatmap, pred = im2gradCAM(x[i:i+1])
if(pred=='Uninfected'):
continue
heatmap = upsampleHeatmap(heatmap, image)
subplots([image, heatmap], nc=2, figsize=(5,3), suptitle=pred)2020-11-08 18:16:46.244 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4])
2020-11-08 18:16:46.394 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4])
2020-11-08 18:16:46.547 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4])
2020-11-08 18:16:46.721 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4]) torch.Size([1, 64, 4, 4]) torch.Size([1, 64, 4, 4])
torch.Size([1, 64, 4, 4]) torch.Size([1, 64, 4, 4])
2020-11-08 18:16:46.895 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
2020-11-08 18:16:47.052 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4]) torch.Size([1, 64, 4, 4])
2020-11-08 18:16:47.208 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4])
2020-11-08 18:16:47.354 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4])
2020-11-08 18:16:47.500 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4])
2020-11-08 18:16:47.743 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4]) torch.Size([1, 64, 4, 4])
2020-11-08 18:16:47.902 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4]) torch.Size([1, 64, 4, 4])
2020-11-08 18:16:48.051 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4])
2020-11-08 18:16:48.223 | INFO | torch_snippets.loader:subplots:375 - plotting 2 images in a grid of 1x2 @ (5, 3)
torch.Size([1, 64, 4, 4]) torch.Size([1, 64, 4, 4])