From be71b85833287dcf98c775ce473850080f1054ac Mon Sep 17 00:00:00 2001 From: Ryan Date: Tue, 16 Apr 2024 16:42:56 -0500 Subject: [PATCH] another fix for cpu vs. cuda --- metapredict/backend/predictor.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/metapredict/backend/predictor.py b/metapredict/backend/predictor.py index 2dbc2f8..4322cad 100644 --- a/metapredict/backend/predictor.py +++ b/metapredict/backend/predictor.py @@ -487,7 +487,7 @@ def predict(inputs, # get output values from the seq_vector based on the network (brnn_network) with torch.no_grad(): - outputs = model(seq_vector.float()).detach().numpy()[0] + outputs = model(seq_vector.float()).detach().cpu().numpy()[0] # Take care of rounding and normalization if normalized == True and round_values==True: @@ -576,7 +576,7 @@ def predict(inputs, # get output values from the seq_vector based on the network (brnn_network) with torch.no_grad(): - outputs = model(seq_vector.float()).detach().numpy()[0].flatten() + outputs = model(seq_vector.float()).detach().cpu().numpy()[0].flatten() # Take care of rounding and normalization if normalized == True and round_values==True: @@ -1091,11 +1091,12 @@ def predict_pLDDT(inputs, # encode the sequence seq_vector = encode_sequence.one_hot(inputs) + seq_vector = seq_vector.to(device) seq_vector = seq_vector.view(1, len(seq_vector), -1) # get output values from the seq_vector based on the network (brnn_network) with torch.no_grad(): - outputs = model(seq_vector.float()).detach().numpy()[0]*multiplier + outputs = model(seq_vector.float()).detach().cpu().numpy()[0]*multiplier # convert to disorder score if needed. if return_as_disorder_score==True: @@ -1179,11 +1180,12 @@ def predict_pLDDT(inputs, for cur_seq_num, seq in enumerate(sequence_list): # encode the sequence seq_vector = encode_sequence.one_hot(seq) + seq_vector = seq_vector.to(device) seq_vector = seq_vector.view(1, len(seq_vector), -1) # get output values from the seq_vector based on the network (brnn_network) with torch.no_grad(): - outputs = model(seq_vector.float()).detach().numpy()[0].flatten()*multiplier + outputs = model(seq_vector.float()).detach().cpu().numpy()[0].flatten()*multiplier # convert to disorder score if needed. if return_as_disorder_score==True: