def runTest():
global model
aug = Augmentation.Augmentation(sys.argv[2])
data_transforms = aug.applyTransforms()
imageLink = request.form['link']
timestr = ''.join(random.choice(string.lowercase) for x in range(8))
dst = "static/augdata/" + timestr + '.jpg'
float_formatter = lambda x: "\t %.3f" % x
try:
urllib.urlretrieve(imageLink, dst)
except:
print('Cannot find %s' % (dst))
return render_template('index.html')
#time.sleep(3);
#model = torch.load("/home/koustav/Desktop/IPA/intelligent_photograph_assesment/CNN/models/net_DenseNet161_crop_True_lr_0.001.model");
t = TestModule.TestModule(model, data_transforms['val'], '', '', '', '')
namevaluepairs = t.PredictSortedLabels(dst)
for count, (style, prob) in enumerate(namevaluepairs):
namevaluepairs[count] = (style, float_formatter(prob))
#styleandvalues = "\n".join('{}: {}'.format(val,key) for key, val in namevaluepairs.items())
return render_template('index.html',
testImageName=dst,
styleLabels=namevaluepairs)
print('Loading CNN from %s' % (model_path))
model_ft = torch.load(model_path)
print('Training Started...')
train_model(model_ft,
criterion,
optimizer_ft,
scheduler_ft,
num_epochs=args.epochs)
#Testing
if args.withTesting:
DAug = ag.Augmentation(args.aug_test)
data_transforms = DAug.applyTransforms()
t = TestModule.TestModule(model_ft, data_transforms['val'],
args.testDataPath, args.testLabels, args.testIds,
args.fiftyPatch)
CM, AP, mAP_macro, mAP_weighted, mAP_micro, PerClassP = t.MAPTracker()
printed_results = '\n'.join([
"AP : " + str(AP), "MAP_MACRO : " + str(mAP_macro),
"MAP_MICRO : " + str(mAP_micro), "MAP_WEIGHTED : " + str(mAP_weighted)
])
PCP = '\n'.join([
cname + ':' + str(pcp)
for cname, pcp in zip(dset_classes, PerClassP.tolist())
])
#print ("\nAP = %f \nmAP_macro = %f \nmAP_weighted = %f \nmAP_micro = %f \n"%(AP,mAP_macro,mAP_weighted,mAP_micro));
print("\nPrecision\n######################")
print(colored(printed_results, 'white'))
