conv = CNNModel().cuda()
#self, batch_size, inputs_size, img_w, hidden_size, num_layers
enc = EncoderLSTM(batch_size,
512,
46,
enc_hidden_size,
enc_layers,
gpu=True).cuda().eval()
#self, batch_size, inputs_size, vocab_size, hidden_size, max_decoder_l, dropout_p=0.01
dec = ATTNDecoder(batch_size,
enc_hidden_size,
1,
vocab_size,
dec_hidden_size,
gpu=True).cuda().eval()
conv.load_state_dict(
torch.load(
'/Users/thomasstruble/Documents/GitHub/chem-ie/omrPY/im_smiles/src/model/model_conv'
))
enc.load_state_dict(
torch.load(
'/Users/thomasstruble/Documents/GitHub/chem-ie/omrPY/im_smiles/src/model/model_enc'
))
dec.load_state_dict(
torch.load(
'/Users/thomasstruble/Documents/GitHub/chem-ie/omrPY/im_smiles/src/model/model_dec'
))
pred = test(input_tensor, target_tens, conv, enc, dec, gpu=True)
print(pred)
# return top_p
if __name__ == '__main__':
conv = CNNModel().cuda()
#self, batch_size, inputs_size, img_w, hidden_size, num_layers
enc = EncoderLSTM(BATCH_SIZE, 512, enc_hidden_size, enc_layers,
gpu=True).cuda().eval()
#self, batch_size, inputs_size, vocab_size, hidden_size, max_decoder_l, dropout_p=0.01
dec = ATTNDecoder(BATCH_SIZE,
enc_hidden_size,
vocab_size,
dec_hidden_size,
gpu=True).cuda().eval()
conv.load_state_dict(
torch.load(
'/scratch2/sophiat/chem-ie-TJS_omrPY/omrPY/im_smiles/src/model/model_conv'
))
enc.load_state_dict(
torch.load(
'/scratch2/sophiat/chem-ie-TJS_omrPY/omrPY/im_smiles/src/model/model_enc'
))
dec.load_state_dict(
torch.load(
'/scratch2/sophiat/chem-ie-TJS_omrPY/omrPY/im_smiles/src/model/model_dec'
))
pred = test_iters()
print(pred)