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p2_validation.py
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p2_validation.py
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from torch.autograd import Variable
import numpy as np
import logging
import torch.nn.functional as F
from tqdm import tqdm
import torch
from src.p_metrics import best_f2_score
## Get the same logger from main"
logger = logging.getLogger("Planet-Amazon")
##################################################
#### Validate function
def validate(epoch,valid_loader,model,loss_func,mlb):
## Volatile variables do not save intermediate results and build graphs for backprop, achieving massive memory savings.
model.eval()
total_loss = 0
predictions = []
true_labels = []
logger.info("Starting Validation")
for batch_idx, (data, target) in enumerate(tqdm(valid_loader)):
true_labels.append(target.cpu().numpy())
data, target = data.cuda(async=True), target.cuda(async=True)
data, target = Variable(data, volatile=True), Variable(target, volatile=True)
raw_pred = model(data)
# Even though we use softmax for training, it doesn't give good result here
# However activated neuro for weather will giv emuch larger response for much easier thresholding
# pred = torch.cat(
# (
# F.softmax(raw_pred[:4]),
# F.sigmoid(raw_pred[4:])
# ), 0
# )
pred = F.sigmoid(raw_pred)
predictions.append(pred.data.cpu().numpy())
total_loss += loss_func(raw_pred,target).data[0]
avg_loss = total_loss / len(valid_loader)
predictions = np.vstack(predictions)
true_labels = np.vstack(true_labels)
score, threshold = best_f2_score(true_labels, predictions)
logger.info("Corresponding tags\n{}".format(mlb.classes_))
logger.info("===> Validation - Avg. loss: {:.4f}\tF2 Score: {:.4f}".format(avg_loss,score))
return score, avg_loss, threshold