def predict(self):
try:
m = models.get_model(self.model_name)
except:
msg("%s is not available for generating responses" % self.model_name)
raise Exception
# resps = self.load('resps')
# if resps is None:
# raise ValueError('no response file found for %s' %
# self.model_name)
else:
m.load_image = load_image
preds = m.predict(self.ims, topn=5)
self.save(preds, "preds")
return preds
def predict(self):
try:
m = models.get_model(self.model_name)
except:
msg('%s is not available for generating responses' %
self.model_name)
raise Exception
# resps = self.load('resps')
# if resps is None:
# raise ValueError('no response file found for %s' %
# self.model_name)
else:
m.load_image = load_image
preds = m.predict(self.ims, topn=5)
self.save(preds, 'preds')
return preds
def classify(self):
try:
m = models.get_model(self.model_name)
except:
msg("%s is not available for generating responses" % self.model_name)
resps = self.load("resps")
if resps is None:
raise ValueError("no response file found for %s" % self.model_name)
else:
m.load_image = load_image
output = m.run(self.ims, layers=self.layers, return_dict=True)
resps = OrderedDict()
for layer, out in output.items():
resps[layer] = out.reshape((out.shape[0], -1))
if self.model_name in ["hmax_hmin", "hmax_pnas"]:
self.save(resps, "resps")
return resps
def classify(self):
try:
m = models.get_model(self.model_name)
except:
msg('%s is not available for generating responses' %
self.model_name)
resps = self.load('resps')
if resps is None:
raise ValueError('no response file found for %s' %
self.model_name)
else:
m.load_image = load_image
output = m.run(self.ims, layers=self.layers, return_dict=True)
resps = OrderedDict()
for layer, out in output.items():
resps[layer] = out.reshape((out.shape[0], -1))
if self.model_name in ['hmax_hmin', 'hmax_pnas']:
self.save(resps, 'resps')
return resps
def predict(self):
_, sel = self.filter_synset_ids()
try:
m = models.get_model(self.model_name)
except:
base.msg('%s is not available for generating responses' %self.model_name)
raise Exception
else:
m.load_image = base.load_image
preds = m.predict(self.ims, topn=1000)
# limit to top 5 guesses that are in Snodgrass
top5 = []
for pred in preds:
tmp = []
for p in pred:
if p['synset'] in sel: tmp.append(p)
if len(tmp) == 5:
top5.append(tmp)
break
self.save(top5, 'preds')
return preds
def predict(self):
_, sel = self.filter_synset_ids()
try:
m = models.get_model(self.model_name)
except:
base.msg('%s is not available for generating responses' %
self.model_name)
raise Exception
else:
m.load_image = base.load_image
preds = m.predict(self.ims, topn=1000)
# limit to top 5 guesses that are in Snodgrass
top5 = []
for pred in preds:
tmp = []
for p in pred:
if p['synset'] in sel: tmp.append(p)
if len(tmp) == 5:
top5.append(tmp)
break
self.save(top5, 'preds')
return preds
