Loading deepchem/models/tensorgraph/layers.py +105 −94 Original line number Diff line number Diff line Loading @@ -39,7 +39,7 @@ class Layer(object): def set_tensors(self, tensor): self.out_tensor = tensor def _create_tensor(self): def create_tensor(self, in_layers=None): raise NotImplementedError("Subclasses must implement for themselves") def __key(self): Loading Loading @@ -70,17 +70,7 @@ class Layer(object): raise ValueError("Each Layer must implement shared for itself") def __call__(self, *in_layers): if len(in_layers) > 0: layers = [] for in_layer in in_layers: if isinstance(in_layer, Layer): layers.append(layer) elif isinstance(in_layer, tf.Tensor): layers.append(TensorWrapper(in_layer)) else: raise ValueError("Layer must be invoked on layers or tensors") self.in_layers = layers return self._create_tensor() return self.create_tensor(in_layers=in_layers) class TensorWrapper(Layer): Loading @@ -89,6 +79,22 @@ class TensorWrapper(Layer): def __init__(self, out_tensor): self.out_tensor = out_tensor def create_tensor(self, in_layers=None): """Take no actions.""" pass def convert_to_layers(in_layers): """Wrap all inputs into tensors if necessary.""" layers = [] for in_layer in in_layers: if isinstance(in_layer, Layer): layers.append(layer) elif isinstance(in_layer, tf.Tensor): layers.append(TensorWrapper(in_layer)) else: raise ValueError("convert_to_layers must be invoked on layers or tensors") return layers class Conv1DLayer(Layer): Loading @@ -98,10 +104,13 @@ class Conv1DLayer(Layer): self.out_tensor = None super(Conv1DLayer, self).__init__(**kwargs) def _create_tensor(self): if len(self.in_layers) != 1: def create_tensor(self, in_layers=None): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) if len(in_layers) != 1: raise ValueError("Only One Parent to conv1D over") parent = self.in_layers[0] parent = in_layers[0] if len(parent.out_tensor.get_shape()) != 3: raise ValueError("Parent tensor must be (batch, width, channel)") parent_shape = parent.out_tensor.get_shape() Loading Loading @@ -139,10 +148,13 @@ class Dense(Layer): scope_name = self.name self.scope_name = scope_name def _create_tensor(self): if len(self.in_layers) != 1: raise ValueError("Only One Parent to Dense over %s" % self.in_layers) parent = self.in_layers[0] def create_tensor(self, in_layers=None): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) if len(in_layers) != 1: raise ValueError("Only One Parent to Dense over %s" % in_layers) parent = in_layers[0] if not self.time_series: self.out_tensor = tf.contrib.layers.fully_connected( parent.out_tensor, Loading Loading @@ -182,10 +194,13 @@ class Flatten(Layer): def __init__(self, **kwargs): super(Flatten, self).__init__(**kwargs) def _create_tensor(self): if len(self.in_layers) != 1: raise ValueError("Only One Parent to conv1D over") parent = self.in_layers[0] def create_tensor(self, in_layers=None): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) if len(in_layers) != 1: raise ValueError("Only One Parent to Flatten") parent = in_layers[0] parent_shape = parent.out_tensor.get_shape() vector_size = 1 for i in range(1, len(parent_shape)): Loading Loading @@ -769,8 +784,8 @@ class Cutoff(Layer): return self.out_tensor class VinaNonlinearity(Layer): """Computes non-linearity used in Vina.""" class VinaFreeEnergy(Layer): """Computes free-energy as defined by Autodock Vina.""" def __init__(self, stddev=.3, Nrot=1, **kwargs): self.stddev = stddev Loading @@ -778,62 +793,91 @@ class VinaNonlinearity(Layer): # TODO(rbharath): Vina actually sets this per-molecule. See if makes # a difference. self.Nrot = Nrot super(VinaNonlinearity, self).__init__(**kwargs) super(VinaFreeEnergy, self).__init__(**kwargs) def _create_tensor(self): c = self.in_layers[0].out_tensor def nonlinearity(self, c): """Computes non-linearity used in Vina.""" w = tf.Variable(tf.random_normal((1,), stddev=self.stddev)) self.out_tensor = c / (1 + w * self.Nrot) return self.out_tensor out_tensor = c / (1 + w * self.Nrot) return out_tensor class VinaRepulsion(Layer): def repulsion(self, d): """Computes Autodock Vina's repulsion interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.where(d < 0, d**2, tf.zeros_like(d)) return self.out_tensor out_tensor = tf.where(d < 0, d**2, tf.zeros_like(d)) return out_tensor class VinaHydrophobic(Layer): def hydrophobic(self, d): """Computes Autodock Vina's hydrophobic interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.where(d < 0.5, out_tensor = tf.where(d < 0.5, tf.ones_like(d), tf.where(d < 1.5, 1.5 - d, tf.zeros_like(d))) return self.out_tensor return out_tensor class VinaHydrogenBond(Layer): def hydrogen_bond(self, d): """Computes Autodock Vina's hydrogen bond interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.where(d < -0.7, out_tensor = tf.where(d < -0.7, tf.ones_like(d), tf.where(d < 0, (1.0 / 0.7) * (0 - d), tf.zeros_like(d))) return self.out_tensor return out_tensor class VinaGaussianFirst(Layer): def gaussian_first(self, d): """Computes Autodock Vina's first Gaussian interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.exp(-(d / 0.5)**2) return self.out_tensor out_tensor = tf.exp(-(d / 0.5)**2) return out_tensor class VinaGaussianSecond(Layer): def gaussian_second(self, d): """Computes Autodock Vina's second Gaussian interaction term.""" out_tensor = tf.exp(-((d - 3) / 2)**2) return out_tensor def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.exp(-((d - 3) / 2)**2) """ Parameters ---------- X: tf.Tensor of shape (B, N, d) Coordinates/features. Z: tf.Tensor of shape (B, N) Atomic numbers of neighbor atoms. Returns ------- layer: tf.Tensor of shape (B) The free energy of each complex in batch """ X = self.in_layers[0].out_tensor Z = self.in_layers[2].out_tensor nbr_list = NeighborList( self.N_atoms, self.M_nbrs, self.ndim, self.nbr_cutoff, self.start, self.stop)(coords) # Shape (N, M) dists = InteratomicL2Distances( self.N_atoms, self.M_nbrs, self.ndim)(coords, nbr_list) repulsion = self.repulsion(dists) hydrophobic = self.hydrophobic(dists) hbond = self.hydrogen_bond(dists) gauss_1 = self.gaussian_first(dists) gauss_2 = self.gaussian_second(dists) # Shape (N, M) interactions = WeightedLinearCombo()( repulsion, hydrophobic, hbond, gauss_1, gauss_2) # Shape (N, M) thresholded = Cutoff()(dists, interactions) free_energies = self.nonlinearity(thresholded) free_energy = ReduceSum()(free_energies) self.output_tensor = free_energy return self.out_tensor Loading Loading @@ -905,37 +949,6 @@ class NeighborList(Layer): self.out_tensor = nbr_list return nbr_list #def compute_nbr_list(self, coords): # """Computes a neighbor list from atom coordinates. # Parameters # ---------- # coords: tf.Tensor # Shape (N_atoms, ndim) # Returns # ------- # nbr_list: tf.Tensor # Shape (N_atoms, M_nbrs) of atom indices # """ # N_atoms, M_nbrs, n_cells, ndim = (self.N_atoms, self.M_nbrs, self.n_cells, # self.ndim) # nbr_cutoff = self.nbr_cutoff # coords = tf.to_float(coords) # nbrs, closest_nbrs = self.get_closest_nbrs(coords) # # N_atoms elts of size (M_nbrs,) each # neighbor_list = [ # tf.gather(atom_nbrs, closest_nbr_ind) # for (atom_nbrs, closest_nbr_ind) in zip(nbrs, closest_nbrs) # ] # # Shape (N_atoms, M_nbrs) # nbr_list = tf.stack(neighbor_list) # return nbr_list def compute_nbr_list(self, coords): """Get closest neighbors for atoms. Loading Loading @@ -1207,8 +1220,6 @@ class AtomicConvolution(Layer): Nbrs_Z: tf.Tensor of shape (B, N, M) Atomic numbers of neighbor atoms. Returns ------- layer: tf.Tensor of shape (l, B, N) Loading deepchem/models/tensorgraph/tests/test_docking.py +1 −74 Original line number Diff line number Diff line Loading @@ -20,12 +20,7 @@ from deepchem.models.tensorgraph.layers import ReduceSquareDifference from deepchem.models.tensorgraph.layers import WeightedLinearCombo from deepchem.models.tensorgraph.layers import InteratomicL2Distances from deepchem.models.tensorgraph.layers import Cutoff from deepchem.models.tensorgraph.layers import VinaRepulsion from deepchem.models.tensorgraph.layers import VinaNonlinearity from deepchem.models.tensorgraph.layers import VinaHydrophobic from deepchem.models.tensorgraph.layers import VinaHydrogenBond from deepchem.models.tensorgraph.layers import VinaGaussianFirst from deepchem.models.tensorgraph.layers import VinaGaussianSecond from deepchem.models.tensorgraph.layers import VinaFreeEnergy from deepchem.models.tensorgraph.layers import L2LossLayer from deepchem.models.tensorgraph.tensor_graph import TensorGraph Loading Loading @@ -378,74 +373,6 @@ class TestDocking(test_util.TensorFlowTestCase): nbr_list = np.squeeze(nbr_list.eval()) np.testing.assert_array_almost_equal(nbr_list, np.array([-1, -1, 3, 2])) #def test_get_closest_nbrs_3D_empty_cells(self): # """Test get_closest_nbrs in 3D with empty nbrs. # Stresses the failure mode where the neighboring cells are empty # so top_k will throw a failure. # """ # N_atoms = 4 # start = 0 # stop = 10 # nbr_cutoff = 1 # ndim = 3 # M_nbrs = 1 # # 1 and 2 are nbrs. 8 and 9 are nbrs # coords = np.array( # [[1.0, 0.0, 1.0], # [2.0, 5.0, 2.0], # [8.0, 8.0, 8.0], # [9.0, 9.0, 9.0]]) # coords = np.reshape(coords, (N_atoms, ndim)) # with self.test_session() as sess: # coords = tf.convert_to_tensor(coords, dtype=tf.float32) # nbr_layer = NeighborList(N_atoms, M_nbrs, ndim, nbr_cutoff, start, # stop) # neighbor_list, padded_neighbor_list, padded_closest_nbrs, padded_dists, dists, padded_nbr_coords, nbr_coords, padded_nbrs, nbrs, closest_nbrs = nbr_layer.get_closest_nbrs(coords) # neighbor_list_eval = neighbor_list.eval() # print("neighbor_list_eval") # print(neighbor_list_eval) # padded_neighbor_list_eval = [padded_nbr.eval() for padded_nbr in padded_neighbor_list] # print("padded_neighbor_list_eval") # print(padded_neighbor_list_eval) # padded_dists_eval = [padded_dist.eval() for padded_dist in padded_dists] # print("padded_dists_eval") # print(padded_dists_eval) # dists_eval = [dist.eval() for dist in dists] # print("dists_eval") # print(dists_eval) # # nbr_coords_eval = [nbr_coord.eval() for nbr_coord in nbr_coords] # print("nbr_coords_eval") # print(nbr_coords_eval) # padded_nbr_coords_eval = [padded_nbr_coord.eval() for padded_nbr_coord in padded_nbr_coords] # print("padded_nbr_coords_eval") # print(padded_nbr_coords_eval) # nbrs_eval = [nbr.eval() for nbr in nbrs] # print("nbrs_eval") # print(nbrs_eval) # # padded_nbrs_eval = [padded_nbr.eval() for padded_nbr in padded_nbrs] # print("padded_nbrs_eval") # print(padded_nbrs_eval) # padded_closest_nbrs_eval = [padded_closest_nbr.eval() for padded_closest_nbr in padded_closest_nbrs] # print("padded_closest_nbrs_eval") # print(padded_closest_nbrs_eval) # #closest_nbrs_eval = [closest_nbr.eval() for closest_nbr in closest_nbrs] # #print("closest_nbrs_eval") # #print(closest_nbrs_eval) # assert 0 == 1 def test_neighbor_list_vina(self): """Test under conditions closer to Vina usage.""" N_atoms = 5 Loading deepchem/models/tensorgraph/tests/test_layers.py 0 → 100644 +49 −0 Original line number Diff line number Diff line import unittest import numpy as np import os import tensorflow as tf from nose.tools import assert_true from tensorflow.python.framework import test_util from deepchem.models.tensorgraph.layers import Conv1DLayer from deepchem.models.tensorgraph.layers import Dense import deepchem as dc class TestLayers(test_util.TensorFlowTestCase): """ Test that layers function as intended. """ def test_conv_1D_layer(self): """Test that Conv1D can be invoked.""" width = 5 in_channels = 2 out_channels = 3 batch_size = 10 in_tensor = np.random.rand(batch_size, width, in_channels) with self.test_session() as sess: in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32) out_tensor = Conv1DLayer(width, out_channels)(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, width, out_channels) def test_dense(self): """Test that Dense can be invoked.""" in_dim = 2 out_dim = 3 batch_size = 10 in_tensor = np.random.rand(batch_size, in_dim) with self.test_session() as sess: in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32) out_tensor = Dense(out_dim)(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, out_dim) def test_flatten(self): """Test that Flatten can be invoked.""" pass Loading
deepchem/models/tensorgraph/layers.py +105 −94 Original line number Diff line number Diff line Loading @@ -39,7 +39,7 @@ class Layer(object): def set_tensors(self, tensor): self.out_tensor = tensor def _create_tensor(self): def create_tensor(self, in_layers=None): raise NotImplementedError("Subclasses must implement for themselves") def __key(self): Loading Loading @@ -70,17 +70,7 @@ class Layer(object): raise ValueError("Each Layer must implement shared for itself") def __call__(self, *in_layers): if len(in_layers) > 0: layers = [] for in_layer in in_layers: if isinstance(in_layer, Layer): layers.append(layer) elif isinstance(in_layer, tf.Tensor): layers.append(TensorWrapper(in_layer)) else: raise ValueError("Layer must be invoked on layers or tensors") self.in_layers = layers return self._create_tensor() return self.create_tensor(in_layers=in_layers) class TensorWrapper(Layer): Loading @@ -89,6 +79,22 @@ class TensorWrapper(Layer): def __init__(self, out_tensor): self.out_tensor = out_tensor def create_tensor(self, in_layers=None): """Take no actions.""" pass def convert_to_layers(in_layers): """Wrap all inputs into tensors if necessary.""" layers = [] for in_layer in in_layers: if isinstance(in_layer, Layer): layers.append(layer) elif isinstance(in_layer, tf.Tensor): layers.append(TensorWrapper(in_layer)) else: raise ValueError("convert_to_layers must be invoked on layers or tensors") return layers class Conv1DLayer(Layer): Loading @@ -98,10 +104,13 @@ class Conv1DLayer(Layer): self.out_tensor = None super(Conv1DLayer, self).__init__(**kwargs) def _create_tensor(self): if len(self.in_layers) != 1: def create_tensor(self, in_layers=None): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) if len(in_layers) != 1: raise ValueError("Only One Parent to conv1D over") parent = self.in_layers[0] parent = in_layers[0] if len(parent.out_tensor.get_shape()) != 3: raise ValueError("Parent tensor must be (batch, width, channel)") parent_shape = parent.out_tensor.get_shape() Loading Loading @@ -139,10 +148,13 @@ class Dense(Layer): scope_name = self.name self.scope_name = scope_name def _create_tensor(self): if len(self.in_layers) != 1: raise ValueError("Only One Parent to Dense over %s" % self.in_layers) parent = self.in_layers[0] def create_tensor(self, in_layers=None): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) if len(in_layers) != 1: raise ValueError("Only One Parent to Dense over %s" % in_layers) parent = in_layers[0] if not self.time_series: self.out_tensor = tf.contrib.layers.fully_connected( parent.out_tensor, Loading Loading @@ -182,10 +194,13 @@ class Flatten(Layer): def __init__(self, **kwargs): super(Flatten, self).__init__(**kwargs) def _create_tensor(self): if len(self.in_layers) != 1: raise ValueError("Only One Parent to conv1D over") parent = self.in_layers[0] def create_tensor(self, in_layers=None): if in_layers is None: in_layers = self.in_layers in_layers = convert_to_layers(in_layers) if len(in_layers) != 1: raise ValueError("Only One Parent to Flatten") parent = in_layers[0] parent_shape = parent.out_tensor.get_shape() vector_size = 1 for i in range(1, len(parent_shape)): Loading Loading @@ -769,8 +784,8 @@ class Cutoff(Layer): return self.out_tensor class VinaNonlinearity(Layer): """Computes non-linearity used in Vina.""" class VinaFreeEnergy(Layer): """Computes free-energy as defined by Autodock Vina.""" def __init__(self, stddev=.3, Nrot=1, **kwargs): self.stddev = stddev Loading @@ -778,62 +793,91 @@ class VinaNonlinearity(Layer): # TODO(rbharath): Vina actually sets this per-molecule. See if makes # a difference. self.Nrot = Nrot super(VinaNonlinearity, self).__init__(**kwargs) super(VinaFreeEnergy, self).__init__(**kwargs) def _create_tensor(self): c = self.in_layers[0].out_tensor def nonlinearity(self, c): """Computes non-linearity used in Vina.""" w = tf.Variable(tf.random_normal((1,), stddev=self.stddev)) self.out_tensor = c / (1 + w * self.Nrot) return self.out_tensor out_tensor = c / (1 + w * self.Nrot) return out_tensor class VinaRepulsion(Layer): def repulsion(self, d): """Computes Autodock Vina's repulsion interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.where(d < 0, d**2, tf.zeros_like(d)) return self.out_tensor out_tensor = tf.where(d < 0, d**2, tf.zeros_like(d)) return out_tensor class VinaHydrophobic(Layer): def hydrophobic(self, d): """Computes Autodock Vina's hydrophobic interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.where(d < 0.5, out_tensor = tf.where(d < 0.5, tf.ones_like(d), tf.where(d < 1.5, 1.5 - d, tf.zeros_like(d))) return self.out_tensor return out_tensor class VinaHydrogenBond(Layer): def hydrogen_bond(self, d): """Computes Autodock Vina's hydrogen bond interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.where(d < -0.7, out_tensor = tf.where(d < -0.7, tf.ones_like(d), tf.where(d < 0, (1.0 / 0.7) * (0 - d), tf.zeros_like(d))) return self.out_tensor return out_tensor class VinaGaussianFirst(Layer): def gaussian_first(self, d): """Computes Autodock Vina's first Gaussian interaction term.""" def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.exp(-(d / 0.5)**2) return self.out_tensor out_tensor = tf.exp(-(d / 0.5)**2) return out_tensor class VinaGaussianSecond(Layer): def gaussian_second(self, d): """Computes Autodock Vina's second Gaussian interaction term.""" out_tensor = tf.exp(-((d - 3) / 2)**2) return out_tensor def _create_tensor(self): d = self.in_layers[0].out_tensor self.out_tensor = tf.exp(-((d - 3) / 2)**2) """ Parameters ---------- X: tf.Tensor of shape (B, N, d) Coordinates/features. Z: tf.Tensor of shape (B, N) Atomic numbers of neighbor atoms. Returns ------- layer: tf.Tensor of shape (B) The free energy of each complex in batch """ X = self.in_layers[0].out_tensor Z = self.in_layers[2].out_tensor nbr_list = NeighborList( self.N_atoms, self.M_nbrs, self.ndim, self.nbr_cutoff, self.start, self.stop)(coords) # Shape (N, M) dists = InteratomicL2Distances( self.N_atoms, self.M_nbrs, self.ndim)(coords, nbr_list) repulsion = self.repulsion(dists) hydrophobic = self.hydrophobic(dists) hbond = self.hydrogen_bond(dists) gauss_1 = self.gaussian_first(dists) gauss_2 = self.gaussian_second(dists) # Shape (N, M) interactions = WeightedLinearCombo()( repulsion, hydrophobic, hbond, gauss_1, gauss_2) # Shape (N, M) thresholded = Cutoff()(dists, interactions) free_energies = self.nonlinearity(thresholded) free_energy = ReduceSum()(free_energies) self.output_tensor = free_energy return self.out_tensor Loading Loading @@ -905,37 +949,6 @@ class NeighborList(Layer): self.out_tensor = nbr_list return nbr_list #def compute_nbr_list(self, coords): # """Computes a neighbor list from atom coordinates. # Parameters # ---------- # coords: tf.Tensor # Shape (N_atoms, ndim) # Returns # ------- # nbr_list: tf.Tensor # Shape (N_atoms, M_nbrs) of atom indices # """ # N_atoms, M_nbrs, n_cells, ndim = (self.N_atoms, self.M_nbrs, self.n_cells, # self.ndim) # nbr_cutoff = self.nbr_cutoff # coords = tf.to_float(coords) # nbrs, closest_nbrs = self.get_closest_nbrs(coords) # # N_atoms elts of size (M_nbrs,) each # neighbor_list = [ # tf.gather(atom_nbrs, closest_nbr_ind) # for (atom_nbrs, closest_nbr_ind) in zip(nbrs, closest_nbrs) # ] # # Shape (N_atoms, M_nbrs) # nbr_list = tf.stack(neighbor_list) # return nbr_list def compute_nbr_list(self, coords): """Get closest neighbors for atoms. Loading Loading @@ -1207,8 +1220,6 @@ class AtomicConvolution(Layer): Nbrs_Z: tf.Tensor of shape (B, N, M) Atomic numbers of neighbor atoms. Returns ------- layer: tf.Tensor of shape (l, B, N) Loading
deepchem/models/tensorgraph/tests/test_docking.py +1 −74 Original line number Diff line number Diff line Loading @@ -20,12 +20,7 @@ from deepchem.models.tensorgraph.layers import ReduceSquareDifference from deepchem.models.tensorgraph.layers import WeightedLinearCombo from deepchem.models.tensorgraph.layers import InteratomicL2Distances from deepchem.models.tensorgraph.layers import Cutoff from deepchem.models.tensorgraph.layers import VinaRepulsion from deepchem.models.tensorgraph.layers import VinaNonlinearity from deepchem.models.tensorgraph.layers import VinaHydrophobic from deepchem.models.tensorgraph.layers import VinaHydrogenBond from deepchem.models.tensorgraph.layers import VinaGaussianFirst from deepchem.models.tensorgraph.layers import VinaGaussianSecond from deepchem.models.tensorgraph.layers import VinaFreeEnergy from deepchem.models.tensorgraph.layers import L2LossLayer from deepchem.models.tensorgraph.tensor_graph import TensorGraph Loading Loading @@ -378,74 +373,6 @@ class TestDocking(test_util.TensorFlowTestCase): nbr_list = np.squeeze(nbr_list.eval()) np.testing.assert_array_almost_equal(nbr_list, np.array([-1, -1, 3, 2])) #def test_get_closest_nbrs_3D_empty_cells(self): # """Test get_closest_nbrs in 3D with empty nbrs. # Stresses the failure mode where the neighboring cells are empty # so top_k will throw a failure. # """ # N_atoms = 4 # start = 0 # stop = 10 # nbr_cutoff = 1 # ndim = 3 # M_nbrs = 1 # # 1 and 2 are nbrs. 8 and 9 are nbrs # coords = np.array( # [[1.0, 0.0, 1.0], # [2.0, 5.0, 2.0], # [8.0, 8.0, 8.0], # [9.0, 9.0, 9.0]]) # coords = np.reshape(coords, (N_atoms, ndim)) # with self.test_session() as sess: # coords = tf.convert_to_tensor(coords, dtype=tf.float32) # nbr_layer = NeighborList(N_atoms, M_nbrs, ndim, nbr_cutoff, start, # stop) # neighbor_list, padded_neighbor_list, padded_closest_nbrs, padded_dists, dists, padded_nbr_coords, nbr_coords, padded_nbrs, nbrs, closest_nbrs = nbr_layer.get_closest_nbrs(coords) # neighbor_list_eval = neighbor_list.eval() # print("neighbor_list_eval") # print(neighbor_list_eval) # padded_neighbor_list_eval = [padded_nbr.eval() for padded_nbr in padded_neighbor_list] # print("padded_neighbor_list_eval") # print(padded_neighbor_list_eval) # padded_dists_eval = [padded_dist.eval() for padded_dist in padded_dists] # print("padded_dists_eval") # print(padded_dists_eval) # dists_eval = [dist.eval() for dist in dists] # print("dists_eval") # print(dists_eval) # # nbr_coords_eval = [nbr_coord.eval() for nbr_coord in nbr_coords] # print("nbr_coords_eval") # print(nbr_coords_eval) # padded_nbr_coords_eval = [padded_nbr_coord.eval() for padded_nbr_coord in padded_nbr_coords] # print("padded_nbr_coords_eval") # print(padded_nbr_coords_eval) # nbrs_eval = [nbr.eval() for nbr in nbrs] # print("nbrs_eval") # print(nbrs_eval) # # padded_nbrs_eval = [padded_nbr.eval() for padded_nbr in padded_nbrs] # print("padded_nbrs_eval") # print(padded_nbrs_eval) # padded_closest_nbrs_eval = [padded_closest_nbr.eval() for padded_closest_nbr in padded_closest_nbrs] # print("padded_closest_nbrs_eval") # print(padded_closest_nbrs_eval) # #closest_nbrs_eval = [closest_nbr.eval() for closest_nbr in closest_nbrs] # #print("closest_nbrs_eval") # #print(closest_nbrs_eval) # assert 0 == 1 def test_neighbor_list_vina(self): """Test under conditions closer to Vina usage.""" N_atoms = 5 Loading
deepchem/models/tensorgraph/tests/test_layers.py 0 → 100644 +49 −0 Original line number Diff line number Diff line import unittest import numpy as np import os import tensorflow as tf from nose.tools import assert_true from tensorflow.python.framework import test_util from deepchem.models.tensorgraph.layers import Conv1DLayer from deepchem.models.tensorgraph.layers import Dense import deepchem as dc class TestLayers(test_util.TensorFlowTestCase): """ Test that layers function as intended. """ def test_conv_1D_layer(self): """Test that Conv1D can be invoked.""" width = 5 in_channels = 2 out_channels = 3 batch_size = 10 in_tensor = np.random.rand(batch_size, width, in_channels) with self.test_session() as sess: in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32) out_tensor = Conv1DLayer(width, out_channels)(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, width, out_channels) def test_dense(self): """Test that Dense can be invoked.""" in_dim = 2 out_dim = 3 batch_size = 10 in_tensor = np.random.rand(batch_size, in_dim) with self.test_session() as sess: in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32) out_tensor = Dense(out_dim)(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, out_dim) def test_flatten(self): """Test that Flatten can be invoked.""" pass
