added small 1M paramter model

train.py

@@ -44,7 +44,7 @@ SUM_FREQ = 100
 VAL_FREQ = 5000
 
 
-def sequence_loss(flow_preds, flow_gt, valid, max_flow=MAX_FLOW):
+def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8, max_flow=MAX_FLOW):
     """ Loss function defined over sequence of flow predictions """
 
     n_predictions = len(flow_preds)    
@@ -55,7 +55,7 @@ def sequence_loss(flow_preds, flow_gt, valid, max_flow=MAX_FLOW):
     valid = (valid >= 0.5) & (mag < max_flow)
 
     for i in range(n_predictions):
-        i_weight = 0.8**(n_predictions - i - 1)
+        i_weight = gamma**(n_predictions - i - 1)
         i_loss = (flow_preds[i] - flow_gt).abs()
         flow_loss += i_weight * (valid[:, None] * i_loss).mean()
 
@@ -71,16 +71,11 @@ def sequence_loss(flow_preds, flow_gt, valid, max_flow=MAX_FLOW):
 
     return flow_loss, metrics
 
-def show_image(img):
-    img = img.permute(1,2,0).cpu().numpy()
-    plt.imshow(img/255.0)
-    plt.show()
-    # cv2.imshow('image', img/255.0)
-    # cv2.waitKey()
 
 def count_parameters(model):
     return sum(p.numel() for p in model.parameters() if p.requires_grad)
 
+
 def fetch_optimizer(args, model):
     """ Create the optimizer and learning rate scheduler """
     optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)
@@ -169,9 +164,6 @@ def train(args):
             optimizer.zero_grad()
             image1, image2, flow, valid = [x.cuda() for x in data_blob]
 
-            # show_image(image1[0])
-            # show_image(image2[0])
-
             if args.add_noise:
                 stdv = np.random.uniform(0.0, 5.0)
                 image1 = (image1 + stdv * torch.randn(*image1.shape).cuda()).clamp(0.0, 255.0)
@@ -179,7 +171,7 @@ def train(args):
 
             flow_predictions = model(image1, image2, iters=args.iters)            
 
-            loss, metrics = sequence_loss(flow_predictions, flow, valid)
+            loss, metrics = sequence_loss(flow_predictions, flow, valid, args.gamma)
             scaler.scale(loss).backward()
             scaler.unscale_(optimizer)                
             torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
@@ -188,7 +180,6 @@ def train(args):
             scheduler.step()
             scaler.update()
 
-
             logger.push(metrics)
 
             if total_steps % VAL_FREQ == VAL_FREQ - 1:
@@ -243,6 +234,7 @@ if __name__ == '__main__':
     parser.add_argument('--epsilon', type=float, default=1e-8)
     parser.add_argument('--clip', type=float, default=1.0)
     parser.add_argument('--dropout', type=float, default=0.0)
+    parser.add_argument('--gamma', type=float, default=0.8, help='exponential weighting')
     parser.add_argument('--add_noise', action='store_true')
     args = parser.parse_args()