I see a couple of such posts in forum but I have hardtime generalizing it to my own problem. Here’s the error:
torch.Size([3, 1, 224, 224])
Traceback (most recent call last):
File "test_loocv.py", line 245, in <module>
output = model_ft(test_data)
File "/scratch/sjn-p3/anaconda/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
result = self.forward(*input, **kwargs)
File "/scratch/sjn-p3/anaconda/anaconda3/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/resnet.py", line 139, in forward
File "/scratch/sjn-p3/anaconda/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
result = self.forward(*input, **kwargs)
File "/scratch/sjn-p3/anaconda/anaconda3/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 301, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[3, 1, 224, 224] to have 3 channels, but got 1 channels instead
and here is the entire code:
from __future__ import print_function, division
import torch
from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
import torch.utils.data as data_utils
from torch.utils import data
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(20),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
data_dir = "test_images"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
def train_model(model, criterion, optimizer, scheduler, dataloader, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train']:
if phase == 'train':
scheduler.step()
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
#for inputs, labels in dataloaders[phase]:
for inputs, labels in dataloader:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
# if phase == 'val' and epoch_acc > best_acc:
# best_acc = epoch_acc
# best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
# print('Best val Acc: {:4f}'.format(best_acc))
# model.load_state_dict(best_model_wts)
return model
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
#for i, (inputs, labels) in enumerate(dataloaders['test]):
for i, (inputs, labels) in enumerate(dataloaders['train']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
######################################################################
# Finetuning the convnet
# ----------------------
#
# Load a pretrained model and reset final fully connected layer.
#
#model_ft = models.resnet18(pretrained=True)
model_ft = models.resnet50(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
#model_ft = model_ft.cuda()
nb_samples = 10
nb_classes = 2
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(20),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
'''val_loader = data.DataLoader(
image_datasets['train'],
num_workers=2,
batch_size=1
)
val_loader = iter(val_loader)'''
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train']}
class_names = image_datasets['train'].classes
# LOOCV
loocv_preds = []
loocv_targets = []
for idx in range(nb_samples):
print('Using sample {} as test data'.format(idx))
# Get all indices and remove test sample
train_indices = list(range(len(image_datasets['train'])))
del train_indices[idx]
# Create new sampler
sampler = data.SubsetRandomSampler(train_indices)
dataloader = data.DataLoader(
image_datasets['train'],
num_workers=2,
batch_size=1,
sampler=sampler
)
# Train model
for batch_idx, (samples, target) in enumerate(dataloader):
print('Batch {}'.format(batch_idx))
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, dataloader, num_epochs=2) # do I add this line here?
# Test on LOO sample
model_ft.eval()
test_data, test_target = image_datasets['train'][idx]
test_data = test_data.cuda()
#test_target = test_target.cuda()
test_target = torch.tensor(test_target)
test_target = test_target.cuda()
test_data.unsqueeze_(1)
test_target.unsqueeze_(0)
print(test_data.shape)
output = model_ft(test_data)
pred = torch.argmax(output, 1)
loocv_preds.append(pred)
loocv_targets.append(test_target.item())
As shown above, test_data has the shape: torch.Size([3, 1, 224, 224])
