stuck debug

deepsocflow/py/hardware.py

@@ -223,7 +223,7 @@ def simulate(self, SIM='verilator', SIM_PATH=''):
             assert subprocess.run(cmd).returncode == 0
 
         if SIM == "verilator":
-            cmd = f'{SIM_PATH}verilator --binary -j 0 -O3 --relative-includes --top {self.TB_MODULE} -I../ -F ../sources.txt -CFLAGS -DSIM -CFLAGS -I../ {self.MODULE_DIR}/c/sim.c -CFLAGS -g --Mdir ./'
+            cmd = f'{SIM_PATH}verilator --binary -j 0 -O3 --relative-includes --trace --trace-depth 0 --top {self.TB_MODULE} -I../ -F ../sources.txt -CFLAGS -DSIM -CFLAGS -I../ {self.MODULE_DIR}/c/sim.c -CFLAGS -g --Mdir ./'
             print(cmd)
             assert subprocess.run(cmd.split(' '), cwd='build').returncode == 0
         print("\n\nSIMULATING...\n\n")

deepsocflow/test/sv/axi_sys_tb.sv

@@ -91,8 +91,8 @@ module axi_sys_tb;
   initial begin
     $dumpfile("axi_tb_sys.vcd");
     $dumpvars();
-    // #200us;
-    // $finish;
+    #2000us;
+    $finish;
   end
 
   chandle mpv, cp;

run/resnet50.py

@@ -447,7 +447,7 @@ def call (self, x):
         x = x_skip15 = self.sk15(x, x_skip14) # 39
         x = self.b26(x) # 40
         x = self.b27(x) # 41
-        x = x_skip16 = self.sk16(x, x_skip15) # 42
+        x = self.sk16(x, x_skip15) # 42
         x_skip17 = self.sk17(x) # 43
         x = self.b28(x) # 44
         x = self.b29(x) # 45
@@ -459,7 +459,6 @@ def call (self, x):
         x = self.b33(x) # 51
         x = x_skip20 = self.sk20(x, x_skip19) # 52
         x = self.b34(x) # 53
-        exit()
         return x
 
 x = x_in =  Input(input_shape, name="input")

run/stuck.py

@@ -0,0 +1,135 @@
+import os
+import pytest
+import itertools
+import sys
+sys.path.append("../../")
+from tensorflow import keras
+from keras.layers import Input
+from keras.models import Model, save_model
+from keras.datasets import mnist
+from keras.optimizers import Adam
+from keras.utils import to_categorical
+from qkeras.utils import load_qmodel
+import numpy as np
+import pprint
+# import tensorflow as tf
+#tf.keras.utils.set_random_seed(0)
+
+from deepsocflow import *
+
+(SIM, SIM_PATH) = ('xsim', "F:/Xilinx/Vivado/2022.2/bin/") if os.name=='nt' else ('verilator', '')
+
+
+input_shape = (14,14,256)
+sys_bits = SYS_BITS(x=4, k=4, b=16)
+
+@keras.saving.register_keras_serializable()
+class UserModel(XModel):
+    def __init__(self, sys_bits, x_int_bits, *args, **kwargs):
+        super().__init__(sys_bits, x_int_bits, *args, **kwargs)
+
+        self.b0 = XBundle( 
+            core=XConvBN( 
+                k_int_bits=0, b_int_bits=0, filters=1024, kernel_size=1,strides=1,
+                act=XActivation(sys_bits=sys_bits, o_int_bits=0, type=None),),
+            # add_act=XActivation(sys_bits=sys_bits, o_int_bits=0, type='relu', slope=0)
+        )
+
+        self.b1 = XBundle( 
+            core=XConvBN(
+                k_int_bits=0, b_int_bits=0, filters=256, kernel_size=1,strides=1,
+                act=XActivation(sys_bits=sys_bits, o_int_bits=0, type='relu', slope=0),),
+        )
+
+        self.b2 = XBundle( 
+            core=XConvBN(
+                k_int_bits=0, b_int_bits=0, filters=256, kernel_size=3,strides=1,
+                act=XActivation(sys_bits=sys_bits, o_int_bits=0, type='relu', slope=0),),
+        )
+
+        self.b3 = XBundle( 
+            core=XConvBN( 
+                k_int_bits=0, b_int_bits=0, filters=1024, kernel_size=1,strides=1,
+                act=XActivation(sys_bits=sys_bits, o_int_bits=0, type=None),),
+            add_act=XActivation(sys_bits=sys_bits, o_int_bits=0, type='relu', slope=0)
+        )
+
+        self.b4 = XBundle( 
+            core=XConvBN( 
+                k_int_bits=0, b_int_bits=0, filters=2048, kernel_size=1,strides=2,
+                act=XActivation(sys_bits=sys_bits, o_int_bits=0, type=None),),
+            # add_act=XActivation(sys_bits=sys_bits, o_int_bits=0, type='relu', slope=0)
+        )
+
+
+    def call (self, x):
+        x = self.input_quant_layer(x)
+
+        x = x_skip15 = self.b0(x) # 39
+        x = self.b1(x) # 40
+        x = self.b2(x) # 41
+        x = self.b3(x, x_skip15) # 42
+        x = self.b4(x) # 43
+
+        return x
+
+x = x_in =  Input(input_shape, name="input")
+user_model = UserModel(sys_bits=sys_bits, x_int_bits=0)
+x = user_model(x_in)
+
+model = Model(inputs=[x_in], outputs=[x])
+model.compile(loss="categorical_crossentropy", optimizer=Adam(learning_rate=0.0001), metrics=["accuracy"])
+
+'''
+Save & Reload
+'''
+save_model(model, "resnet50.h5")
+loaded_model = load_qmodel("resnet50.h5")
+
+def product_dict(**kwargs):
+    for instance in itertools.product(*(kwargs.values())):
+        yield dict(zip(kwargs.keys(), instance))
+
+@pytest.mark.parametrize("PARAMS", list(product_dict(
+                                        processing_elements  = [(7,96)   ],
+                                        frequency_mhz        = [ 250     ],
+                                        bits_input           = [ 4       ],
+                                        bits_weights         = [ 4       ],
+                                        bits_sum             = [ 20      ],
+                                        bits_bias            = [ 16      ],
+                                        max_batch_size       = [ 64      ], 
+                                        max_channels_in      = [ 512     ],
+                                        max_kernel_size      = [ 9       ],
+                                        max_image_size       = [ 512     ],
+                                        max_n_bundles        = [ 64      ],
+                                        ram_weights_depth    = [ 512     ],
+                                        ram_edges_depth      = [ 3584    ],
+                                        axi_width            = [ 128      ],
+                                        config_baseaddr      = ["B0000000"],
+                                        target_cpu_int_bits  = [ 32       ],
+                                        valid_prob           = [ 1       ],
+                                        ready_prob           = [ 1       ],
+                                        data_dir             = ['vectors'],
+                                    )))
+def test_dnn_engine(PARAMS):
+
+    '''
+    SPECIFY HARDWARE
+    '''
+    hw = Hardware (**PARAMS)
+    hw.export_json()
+    hw = Hardware.from_json('hardware.json')
+    hw.export() # Generates: config_hw.svh, config_hw.tcl
+    hw.export_vivado_tcl(board='zcu104')
+
+
+    '''
+    VERIFY & EXPORT
+    '''
+    export_inference(loaded_model, hw, batch_size=1)
+    verify_inference(loaded_model, hw, SIM=SIM, SIM_PATH=SIM_PATH)
+
+    d_perf = predict_model_performance(hw)
+    pp = pprint.PrettyPrinter(indent=4)
+    print(f"Predicted Performance")
+    pp.pprint(d_perf)