Fix small bugs

exps/trading/workflow_tt.py

@@ -6,6 +6,7 @@
 # - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
 # python exps/trading/workflow_tt.py --gpu 1 --market csi300
 #####################################################
+import yaml
 import argparse
 
 from xautodl.procedures.q_exps import update_gpu
@@ -57,7 +58,7 @@ def main(xargs):
 
     model_config = {
         "class": "QuantTransformer",
-        "module_path": "trade_models",
+        "module_path": "xautodl.trade_models.quant_transformer",
         "kwargs": {
             "net_config": None,
             "opt_config": None,
@@ -108,6 +109,62 @@ def main(xargs):
     provider_uri = "~/.qlib/qlib_data/cn_data"
     qlib.init(provider_uri=provider_uri, region=REG_CN)
 
+    from qlib.utils import init_instance_by_config
+
+    xconfig = """
+model:
+        class: SFM
+        module_path: qlib.contrib.model.pytorch_sfm
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            output_dim: 32
+            freq_dim: 25
+            dropout_W: 0.5
+            dropout_U: 0.5
+            n_epochs: 20
+            lr: 1e-3
+            batch_size: 1600
+            early_stop: 20
+            eval_steps: 5
+            loss: mse
+            optimizer: adam
+            GPU: 0
+"""
+    xconfig = """
+model:
+        class: TabnetModel
+        module_path: qlib.contrib.model.pytorch_tabnet
+        kwargs:
+            d_feat: 360
+            pretrain: True
+"""
+    xconfig = """
+model:
+        class: GRU
+        module_path: qlib.contrib.model.pytorch_gru
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            num_layers: 4
+            dropout: 0.0
+            n_epochs: 200
+            lr: 0.001
+            early_stop: 20
+            batch_size: 800
+            metric: loss
+            loss: mse
+            GPU: 0
+"""
+    xconfig = yaml.safe_load(xconfig)
+    model = init_instance_by_config(xconfig["model"])
+    from xautodl.utils.flop_benchmark import count_parameters_in_MB
+
+    # print(count_parameters_in_MB(model.tabnet_model))
+    import pdb
+
+    pdb.set_trace()
+
     save_dir = "{:}-{:}".format(xargs.save_dir, xargs.market)
     dataset = init_instance_by_config(dataset_config)
     for irun in range(xargs.times):