Merge branch 'master' into jax2 (da703c9e) · Commits · 钟慕尧 / deepchem

deepchem/models/callbacks.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -93,7 +93,7 @@ class ValidationCallback(object):
		if self._best_score is None or score < self._best_score:
		model.save_checkpoint(model_dir=self.save_dir)
		self._best_score = score
		if model.wandb or (model.wandb_logger is not None):
		if model.wandb_logger is not None:
		# Log data to Wandb
		data = {'eval/' + k: v for k, v in scores.items()}
		model.wandb_logger.log_data(data, step)
		model.wandb_logger.log_data(data, step, dataset_id=id(self.dataset))

+0 −4

Original line number	Diff line number	Diff line
		@@ -468,10 +468,6 @@ class KerasModel(Model):
		all_data = dict({'train/loss': batch_loss})
		self.wandb_logger.log_data(all_data, step=current_step)

		# Close WandbLogger
		if self.wandb_logger is not None:
		self.wandb_logger.finish()

		# Report final results.
		if averaged_batches > 0:
		avg_loss = float(avg_loss) / averaged_batches

+7 −3

Original line number	Diff line number	Diff line
		@@ -335,15 +335,19 @@ def test_wandblogger():
		tf.keras.layers.Dense(1)])
		model = dc.models.KerasModel(
		keras_model, dc.models.losses.L2Loss(), wandb_logger=wandblogger)
		vc = dc.models.ValidationCallback(valid_dataset, 1, [metric])
		model.fit(train_dataset, nb_epoch=10, callbacks=[vc])
		vc_train = dc.models.ValidationCallback(train_dataset, 1, [metric])
		vc_valid = dc.models.ValidationCallback(valid_dataset, 1, [metric])
		model.fit(train_dataset, nb_epoch=10, callbacks=[vc_train, vc_valid])
		# call model.fit again to test multiple fit() calls
		model.fit(train_dataset, nb_epoch=10, callbacks=[vc_train, vc_valid])
		wandblogger.finish()

		run_data = wandblogger.run_history
		valid_score = model.evaluate(valid_dataset, [metric], transformers)

		assert math.isclose(
		valid_score["pearson_r2_score"],
		run_data['eval/pearson_r2_score'],
		run_data['eval/pearson_r2_score_(1)'],
		abs_tol=0.0005)

+18 −0

Original line number	Diff line number	Diff line
		@@ -9,6 +9,13 @@ try:
		except:
		has_tensorflow = False

		try:
		import torch
		import deepchem.models.torch_models.layers as torch_layers
		has_torch = True
		except:
		has_torch = False


		@pytest.mark.tensorflow
		def test_cosine_dist():
		@@ -598,3 +605,14 @@ def test_DAG_gather():
		atom_features = np.random.rand(batch_size, n_atom_feat)
		membership = np.sort(np.random.randint(0, batch_size, size=(batch_size)))
		outputs = layer([atom_features, membership])


		@pytest.mark.pytorch
		def test_layer_norm():
		"""Test invoking LayerNorm."""
		input_ar = torch.tensor([[1., 99., 10000.], [0.003, 999.37, 23.]])
		layer = torch_layers.ScaleNorm(0.35)
		result1 = layer.forward(input_ar)
		output_ar = np.array([[5.9157897e-05, 5.8566318e-03, 5.9157896e-01],
		[1.7754727e-06, 5.9145141e-01, 1.3611957e-02]])
		assert np.allclose(result1, output_ar)

+7 −3

Original line number	Diff line number	Diff line
		@@ -360,15 +360,19 @@ def test_wandblogger():
		torch.nn.Linear(1000, 1))
		model = dc.models.TorchModel(
		pytorch_model, dc.models.losses.L2Loss(), wandb_logger=wandblogger)
		vc = dc.models.ValidationCallback(valid_dataset, 1, [metric])
		model.fit(train_dataset, nb_epoch=10, callbacks=[vc])
		vc_train = dc.models.ValidationCallback(train_dataset, 1, [metric])
		vc_valid = dc.models.ValidationCallback(valid_dataset, 1, [metric])
		model.fit(train_dataset, nb_epoch=10, callbacks=[vc_train, vc_valid])
		# call model.fit again to test multiple fit() calls
		model.fit(train_dataset, nb_epoch=10, callbacks=[vc_train, vc_valid])
		wandblogger.finish()

		run_data = wandblogger.run_history
		valid_score = model.evaluate(valid_dataset, [metric], transformers)

		assert math.isclose(
		valid_score["pearson_r2_score"],
		run_data['eval/pearson_r2_score'],
		run_data['eval/pearson_r2_score_(1)'],
		abs_tol=0.0005)