def test_map_unmap(self):
observations = []
observations.append({
"text": "What is a painting?",
"labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
observations.append({
"text": "What is a painting?",
"labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
opt = {}
opt['no_cuda'] = True
opt['history_tokens'] = 10000
opt['history_dialog'] = 10
opt['history_replies'] = 'label_else_model'
dict = MockDict()
shared = {'opt': opt, 'dict': dict}
agent = TorchAgent(opt, shared)
vec_observations = [agent.vectorize(obs) for obs in observations]
mapped_valid = agent.map_valid(vec_observations)
text_vecs, label_vecs, labels, valid_inds = mapped_valid
self.assertTrue(text_vecs is not None, "Missing \'text_vecs\' field.")
self.assertTrue(text_vecs.numpy().tolist() == [[1, 3, 5], [1, 3, 5]],
"Incorrectly vectorized text field of obs_batch.")
self.assertTrue(label_vecs is not None, "Missing \'label_vec\' field.")
self.assertTrue(
label_vecs.numpy().tolist() == [[1, 3, 5, 2], [1, 3, 5, 2]],
"Incorrectly vectorized text field of obs_batch.")
self.assertTrue(labels == ["Paint on a canvas.", "Paint on a canvas."],
"Doesn't return correct labels.")
self.assertTrue(valid_inds == [0, 3],
"Returns incorrect indices of valid observations.")
observations = []
observations.append({
"text": "What is a painting?",
"eval_labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
observations.append({
"text": "What is a painting?",
"eval_labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
vec_observations = [agent.vectorize(obs) for obs in observations]
mapped_valid = agent.map_valid(vec_observations)
text_vecs, label_vecs, labels, valid_inds = mapped_valid
self.assertTrue(label_vecs is not None, "Missing \'label_vec\' field.")
self.assertTrue(
label_vecs.numpy().tolist() == [[1, 3, 5, 2], [1, 3, 5, 2]],
"Incorrectly vectorized text field of obs_batch.")
predictions = ["Oil on a canvas.", "Oil on a canvas."]
expected_unmapped = [
"Oil on a canvas.", None, None, "Oil on a canvas.", None, None
]
self.assertTrue(
agent.unmap_valid(predictions, valid_inds, 6) == expected_unmapped,
"Unmapped predictions do not match expected results.")
def test_map_unmap(self):
try:
from parlai.core.torch_agent import TorchAgent
except ImportError as e:
if 'pytorch' in e.msg:
print('Skipping TestTorchAgent.test_map_unmap, no pytorch.')
return
observations = []
observations.append({
"text": "What is a painting?",
"labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
observations.append({
"text": "What is a painting?",
"labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
opt = {}
opt['no_cuda'] = True
opt['truncate'] = 10000
opt['history_dialog'] = 10
opt['history_replies'] = 'label_else_model'
mdict = MockDict()
shared = {'opt': opt, 'dict': mdict}
agent = TorchAgent(opt, shared)
vec_observations = [agent.vectorize(obs) for obs in observations]
mapped_valid = agent.map_valid(vec_observations)
text_vecs, text_lengths, label_vecs, labels, valid_inds = mapped_valid
self.assertTrue(text_vecs is not None, "Missing \'text_vecs\' field.")
self.assertTrue(text_vecs.numpy().tolist() == [[7, 8, 9], [7, 8, 9]],
"Incorrectly vectorized text field of obs_batch.")
self.assertTrue(text_lengths.numpy().tolist() == [3, 3],
"Incorrect text vector lengths returned.")
self.assertTrue(label_vecs is not None, "Missing \'label_vec\' field.")
self.assertTrue(
label_vecs.numpy().tolist() == [[
mdict.START_IDX, 7, 8, 9, mdict.END_IDX
], [mdict.START_IDX, 7, 8, 9, mdict.END_IDX]],
"Incorrectly vectorized text field of obs_batch.")
self.assertTrue(labels == ["Paint on a canvas.", "Paint on a canvas."],
"Doesn't return correct labels.")
self.assertTrue(valid_inds == [0, 3],
"Returns incorrect indices of valid observations.")
observations = []
observations.append({
"text": "What is a painting?",
"eval_labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
observations.append({
"text": "What is a painting?",
"eval_labels": ["Paint on a canvas."]
})
observations.append({})
observations.append({})
vec_observations = [agent.vectorize(obs) for obs in observations]
mapped_valid = agent.map_valid(vec_observations)
text_vecs, text_lengths, label_vecs, labels, valid_inds = mapped_valid
self.assertTrue(label_vecs is not None,
"Missing \'eval_label_vec\' field.")
self.assertTrue(
label_vecs.numpy().tolist() == [[
mdict.START_IDX, 7, 8, 9, mdict.END_IDX
], [mdict.START_IDX, 7, 8, 9, mdict.END_IDX]],
"Incorrectly vectorized text field of obs_batch.")
predictions = ["Oil on a canvas.", "Oil on a canvas."]
expected_unmapped = [
"Oil on a canvas.", None, None, "Oil on a canvas.", None, None
]
self.assertTrue(
agent.unmap_valid(predictions, valid_inds, 6) == expected_unmapped,
"Unmapped predictions do not match expected results.")
